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BAHAGIAN A – Pengesahan Kerjasama *
Adalah disahkan bahawa projek penyelidikan tesis ini telah dilaksanakan melalui
kerjasama
antara_________________________dengan____________________________
Disahkan oleh :
Tandatangan : ………………………………….. Tarikh : ………………….
Nama : …………………………………..
Jawatan : …………………………………..
(Cop Rasmi)
* Jika penyelidikan tesis/projek melibatkan kerjasama
Bahagian B – Untuk Kegunaan Pejabat Sekolah Pengajian Siswazah
Tesis ini telah diperiksa dan diakui oleh:
Nama dan Alamat Pemeriksa Luar : Prof. Dr. Hamidi bin Abdul Aziz Pusat Pengajian Kejuruteraan Awam, Kampus Kejuruteraan, Universiti Sains Malaysia,
14300 Nibong Tebal, Seberang Prai Selatan,
Pulau Pinang.
Nama dan Alamat Pemeriksa Dalam : Prof. Ir. Dr. Mohd Azraai bin Kassim Timbalan Naib Canselor (Akademik), Pejabat Timbalan Naib Canselor, (Akademik), UTM Skudai. Prof. Madya Dr. Johan Sohaili Fakulti Kejuruteraan Awam, UTM Skudai.
Nama Pemeriksa Lain (jika ada) : -
Disahkan oleh Timbalan Pendaftar di Sekolah Pengajian Siswazah:
Tandatangan : ………………………………….. Tarikh : ……………………….
Nama : ZAINUL RASHID BIN ABU BAKAR
FACULTATIVE ANAEROBIC GRANULAR SLUDGE FOR TEXTILE DYEING
WASTEWATER TREATMENT
KHALIDA MUDA
A thesis submitted in fulfilment of the
requirements for the award of the degree of
Doctor of Philosophy (Civil Engineering)
Faculty of Civil Engineering
Universiti Teknologi Malaysia
JANUARY 2010
ii
I declare that this thesis entitled “Facultative Anaerobic Granular Sludge for Textile
Dyeing Wastewater Treatment” is the result of my own research except as cited in
the references. The thesis has not been accepted for any degree and is not
concurrently submitted in candidature of any other degree.
Signature : ………………………………….
Name : KHALIDA MUDA
Date : 5 January 2010
iii
Dedicated to my precious love
AKMAL, AIMAN & AMMAR
iv
ACKNOWLEDGEMENT
In the name of Allah the Most Gracious, the Most Merciful. First and foremost I am truly grateful for the blessings of Allah that gives me the strength to complete this thesis.
I would like to convey my highest gratitude to all my supervisors; Professor Dr. Mohd Razman Salim, Associate Professor Dr. Zaharah Ibrahim and Dr. Azmi Aris for their excellent supervision, encouragement, understanding and patience throughout my study. May Allah bless and reward all of them. Without them, my PhD experience would be a very difficult one.
A special thanks to Dr Adibah for allowing me to use all the equipment and facilities at the Faculty of Bioscience and Bioengineering laboratories. To Dr. Arifah and Dr. Robiah of the Mathematics Department, Faculty of Science, thank you very much for assisting me on the statistical analysis. I would also like to express my appreciation to all the laboratory staff; Pak Usop, Ramli, Muzafar and Kak Ros of the Environmental Engineering Laboratory; Kak Fatimah, Awang and Yus of the Faculty Bioscience and Bioengineering laboratories, Encik Jefry of Mechanical Faculty Laboratory. I would also like to extend my thanks to all my seniors; Dr. Ismid, Kak Tim, Kak Farid, Kak Mala, Kak Fauziah, Kak Ngah, Kak Su for the continuous support, advice and encouragement during my study. To my colleagues and juniors, Shamila, Nana, Yati, Isal, Zana, Linda, Muzafar, Norly, Azlan, Zaini, Rosnani, Rosnita, Fairuzah, An and others from IPASA and MP2, thank you very much for their immeasurable friendship, motivation and support. To my dear friend, Aloes, thank you very much for all the support that you have given to me.
I am sincerely indebted to my sisters, brothers and other family members especially Kak Hasmah for all the love, support and du'a. Lastly to my precious heroes, Akmal, Aiman and Ammar, thank you very much for all the unconditional love, support, sacrifice and du'a during the hard times. All of you are my reasons to continue striving and overcome all obstacles.
Special thanks to the Ministry of Science, Technology and Innovation (ScienceFund-79137), Ministry of Higher Education (FRGS-78122) and UTM (IRGS-75221) for funding this research.
v
ABSTRACT
Dye residuals found in textile dyeing wastewater contribute to the difficulty
in treating such wastewater. Conventional biological processes failed to treat the wastewater while physico-chemical processes, although successful, are often costly in practice. Sequential anaerobic-aerobic process has been found to succeed in treating the textile wastewater. This study looks at the possibility of developing and applying facultative anaerobic granular sludge (FAnGS) in treating the wastewater in a single reactor under intermittent anaerobic and aerobic conditions. Synthetic textile wastewater which comprised of a mixture of Sumifix Navy Blue EXF, Synozol Red K-4B and Sumifix Black EXA, were used throughout the study. Different sludge and anaerobic granule mixture with the addition of specialized dye degrader microbes customized to treat dyeing wastewater were used at the initial stage. The initial development took place using a 4 L column reactor. Some of the studies were conducted in the same reactor while the remaining was conducted in a smaller scale, all under intermittent anaerobic and aerobic phases. After about 70 days of development, mature FAnGS were developed possessing excellent granules quality. The average size of the FAnGS was 2.3 ± 1.0 mm with average settling velocity of 80 ± 8 m/h resulting in settling velocity index (SVI) of 69 mL/g. Such properties have caused a significant increase in the biomass concentration to 7.3 ± 0.9 g/L, which was observed to be beneficial to the performance of the system. At the end of the development process, the biogranules were able to achieve 94% of COD, 95% of ammonia and 62% of color removal. The oxygen uptake rate (OUR) /specific oxygen uptake rate (SOUR) and specific methanogenic activity (SMA) indicate the presence of facultative, anaerobic and aerobic bacteria within the granules. Six bacteria were identified within the FAnGS which include Bacillus cereus, Pseudomonas veronii, three species of Pseudomonas genus and Enterobacter sp., all are considered in the literature as dye degrader microbes. With the aid of statistical experimental design, subsequent studies showed that the microbial activity of the FAnGS and their performance in removal of organics (in terms of COD) and color were affected by several factors which include substrate concentration, pH, temperature, hydraulic retention time (HRT) and concentration of redox mediator. Interaction effects between the factors were also observed. The magnitude and the direction (positive or negative) of the effects are however dependent on the reacting conditions. Several statistical models describing the relationship between some of the variables were developed. From the study, the highest removal of color (87%) and COD (94%) were achieved by the FAnGS biomass in IFAnGSBioRec system operated with 24 hours HRT with an intermittent of anaerobic (18 hours) and aerobic (6 hours) reactions.
vi
ABSTRAK
Lebihan baki pewarna dalam air sisa tekstil menyumbang kepada kesukaran
dalam olahan airsisa tersebut. Olahan konvensional biologi gagal mengolah airsisa ini manakala olahan kimia-fizikal, walaupun berhasil, melibatkan kos yang tinggi. Olahan berselang seli anaerobik-aerobik telah didapati berjaya mengolah airsisa tekstil. Dalam kajian ini, keupayaan menghasil dan menggunakan enapcemar granul fakultatif anaerobik (FAnGS) bagi mengolah airsisa tekstil dalam satu reaktor dengan fasa anaerobik dan aerobik secara berselang seli diterokai. Air sisa tekstil yg mengandungi campuran pewarna Sumifix Navy Blue EXF, Synozol Red K-4B dan Sumifix Black EXA digunakan sepanjang kajian. Di awal kajian, enapcemar yang berbeza, granul anaerobik dan beberapa mikrob pengurai pewarna dicampurkan dan digunakan dalam mengolah airsisa pewarna. Pembentukan granul dilakukan dalam reaktor berisipadu 4 L. Kesemua kajian yang dijalankan adalah secara berselang seli bagi fasa anaerobik dan aerobik samaada dengan menggunakan reaktor yang sama atau dalam skala yang lebih kecil. Setelah lebih kurang 70 hari, pembentukan FAnGS matang yang memiliki ciri granul yang baik berjaya dihasilkan. FAnGS yang terbentuk mempunyai saiz purata 2.3 ± 1.0 mm dengan halaju enapan 80 ± 8 m/j dan menghasilkan index halaju enapan (SVI) 69 mL/g. Dengan memiliki ciri-ciri tersebut, kepekatan biomas telah meningkat dengan signifikan kepada 7.3 ± 0.9 g/L. Di akhir pembentukan granul, peratus penyingkiran terhadap permintaan oksigen biokimia (COD), ammonia dan warna adalah masing-masing 94%, 95% dan 62%. Analisis bagi kadar pengambilan oksigen (OUR)/ kadar pengambilan oksigen spesifik (SOUR) mengesahkan kehadiran bakteria fakultatif, anaerobik dan aerobik dalam granul yang dihasilkan. Enam bakteria daripada FAnGS dikenal pasti sebagai Bacillus cereus, Pseudomonas veronii, tiga spesis Pseudomonas genus dan Enterobacter sp. Dengan menggunakan kaedah rekabentuk eksperimen, kajian menunjukkan aktiviti mikrob dari FAnGS dan keupayaan menyingkirkan bahan organik (berdasarkan kepada COD) dan warna adalah dipengaruhi oleh beberapa faktor termasuk kepekatan substrat, pH, suhu, masa tahanan hidraul dan kepekatan perantara redox. Kesan interaksi yang wujud antara faktor juga telah diperhatikan. Magnitud dan arah (positif dan negatif) sesuatu kesan adalah bergantung kepada keadaan tindakbalas yang berlaku. Beberapa model statistik telah dibina menghubungkan beberapa faktor yang dikaji. Daripada kajian ini, penyingkiran tertinggi terhadap warna (87%) dan COD (94%) telah dicapai oleh biojisim FAnGS dalam system IFAnGSBioRec yang beroperasi secara tindakbalas olahan berselang seli anaerobik (18 jam) dan aerobik (6 jam) dengan masa tahanan hidraul (HRT) selama 24 jam.
vii
TABLE OF CONTENTS
CHAPTER TITLE
PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xvi
LIST OF FIGURES xx
LIST OF ABBREVIATIONS xxx
LIST OF SYMBOLS xxxiii
LIST OF APPENDICES
xxxv
1 INTRODUCTION
1
1.1 Preamble 1
1.2 Objectives of the Study 3
1.3 Scope of the Study 4
1.4 Significance of the Study 5
1.5 Organization of Thesis
7
2 BIOGRANULATION TECHNOLOGY IN
WASTEWATER TREATMENT
9
2.1 Introduction 9
viii
2.2 Biogranulation 10
2.3 Development of Aerobic Granules 12
2.3.1 Aerobic Granules from Aerobic Activated
Sludge
14
2.3.2 Aerobic Granules Seeded with Anaerobic
Granular Sludge
15
2.4 Microbial Structure and Diversity of
Microorganisms
18
2.4.1 Microbial Structure 18
2.4.2 Microbial Diversity 20
2.5 Characteristics of Aerobic Granules 21
2.5.1 Size and Morphology 21
2.5.2 Settleability 23
2.5.3 Density and Strength 24
2.5.4 Cell Surface Hydrophobicity 25
2.5.5 Specific Oxygen Utilization Rate 26
2.5.6 Storage Stability 27
2.5.7 Exopolysaccharides 28
2.6 Factors Affecting the Formation of Aerobic Granules 29
2.6.1 Substrate Composition 29
2.6.2 Organic Loading Rate 30
2.6.3 Hydrodynamic Shear Force 31
2.6.4 Feast and Famine Regime 33
2.6.5 Hydraulic Retention Time 34
2.6.6 Presence of Inorganic Composition 34
2.6.7 Concentration of Dissolved Oxygen 35
2.6.8 Slow Growing Organisms 36
2.6.9 Settling Time 37
2.6.10 Reactor Configuration 38
2.6.11 Volumetric Exchange Ratio 38
2.7 Applications of Aerobic Granule in
Wastewater Treatment Systems
39
2.7.1 High Strength Organic Wastewater 39
ix
Treatment
2.7.2 Simultaneous Organic and Nitrogen
Removal
40
2.7.3 Phosphorus Removal 42
2.7.4 Phenol Wastewater Treatment 43
2.7.5 Biosorption of Heavy Metals and Nuclear
Waste
44
3 DYE DEGRADATION PROCESS
46
3.1 Textile Industry 46
3.2 Characteristics of Textile Wastewater 48
3.2.1 Quantity 49
3.2.2 Quality 50
3.3 Dye and Environmental Problems 52
3.4 Treatment of Dyes 54
3.4.1 Biodegradation of Dyes 57
3.4.2 Bacterial Degradation of Dyes 59
3.4.3 Mechanisms of Biodegradation of Azo Dyes 60
3.4.3.1 Aerobic Dye Degradation Process 60
3.4.3.2 Anaerobic Dye Degradation
Process
62
3.4.3.3 Anoxic Dye Degradation Process 63
3.4.4 Mineralization of Aromatic Amines 65
3.5 Treatment System for Biodegradation of Azo Dyes 67
3.5.1 The Sequential Anaerobic/Aerobic Reactor
System
68
3.5.2 The Integrated Anaerobic/Aerobic Reactor
System
70
4 DEVELOPMENT OF FACULTATIVE ANAEROBIC
GRANULES
75
4.1 Introduction 75
x
4.2 Materials 76
4.2.1 Wastewater Composition 79
4.2.2 Granules Precursor 81
4.2.3 Reactor Set-up 83
4.3 Analytical Methods 86
4.3.1 Biological Characteristics 86
4.3.1.1 Morphological and Structural
Observation
86
4.3.1.2 Microbial Activity 89
4.3.2 Physical Characteristics 91
4.3.2.1 Settling Velocity 91
4.3.2.2 Sludge Volume Index 91
4.3.2.3 Granular Strength 92
4.3.2.4 Biomass Concentration 92
4.3.2.5 Sludge Retention Time 93
4.3.3 Chemical Characteristics 94
4.3.4 Removal Performance 95
4.3.4.1 Color 95
4.3.4.2 Chemical Oxygen Demand 95
4.3.4.3 Ammonia 96
4.4 Experimental Procedures 96
4.5 Results and Discussion 99
4.5.1 Morphology of Facultative Anaerobic
Granular Sludge
99
4.5.2 Cellular Characterization of Facultative
Anaerobic Granular Sludge
103
4.5.3 Microbial Activity 106
4.5.4 Size of the Facultative Anaerobic Granular
Sludge
108
4.5.5 Settling Velocity of the Facultative
Anaerobic Granular Sludge
109
4.5.6 Granular Strength of the Facultative
Anaerobic Granular Sludge
110
xi
4.5.7 Biomass Concentration and Sludge
Retention Time
113
4.5.8 Mineral and Metal Content 114
4.5.9 Removal Performance 117
5
4.6 Conclusions
EFFECT OF AGGREGATION AND SURFACE
HYDROPHOBICITY BY SELECTED MICROBES
FROM FACULTATIVE ANAEROBIC GRANULAR
SLUDGE
121
123
5.1 Introduction 123
5.2 Materials 124
5.3 Analytical Methods
5.3.1 Chemical Oxygen Demand and Color
Removal
126
126
5.4 Experimental Procedures 127
5.4.1 Isolation Procedure of Bacteria Strain 127
5.4.2 Morphological Characterization 128
5.4.3 Identification of Microorganisms Isolated
from Facultative Anaerobic Granular Sludge
131
5.4.4 Specific Growth and Screening for Dye-
Degrading Bacteria
131
5.4.5 Autoaggregation Assay 132
5.4.6 Surface Hydrophobicity Assay 133
5.4.7 Effect of Substrate Concentration, pH and
Temperature on Coaggregation and Surface
Hydrophobicity
134
5.4.8 2-Level Factorial Experimental Design 135
5.4.9 Response Surface Methodology (Central
Composite Design)
137
5.5 Results and Discussion 139
5.5.1 Morphological and Cellular Characterization 139
xii
of Bacteria Isolation from Facultative
Anaerobic Granular Sludge
5.5.2 Screening for Dye Degrader and
Autoaggregator From Bacteria Strain
Isolated from Facultative Anaerobic
Granular Sludge
139
5.5.3 Analysis of the Isolates from Facultative
Anaerobic Granular Sludge
141
5.5.4 Effect of Substrate, pH and Temperature on
Coaggregation and Surface Hydrophobicity
147
5.5.4.1 Factorial Analysis: The Main
Effect of Substrate on
Coaggregation
152
5.5.4.2 Factorial Analysis: The Main
Effect of pH on Coaggregation
156
5.5.4.3 Factorial Analysis: The Main
Effect of Temperature on
Coaggregation
157
5.5.4.4 Factorial Analysis: The Interaction
Effect on Coaggregation
157
5.5.4.5 Factorial Analysis: The Main
Effect of Substrate on Surface
Hydrophobicity
159
5.5.4.6 Factorial Analysis: The Main
Effect of pH on Surface
Hydrophobicity
161
5.5.4.7 Factorial Analysis: The Main
Effect of Temperature on Surface
Hydrophobicity
162
5.5.4.8 Factorial Analysis: The Interaction
Effect on Surface Hydrophobicity
164
5.5.5 Response Surface Analysis 167
5.6 Conclusions 176
xiii
6 THE EFFECT OF HYDRAULIC RETENTION TIME
ON FACULTATIVE ANAEROBIC GRANULAR
SLUDGE
180
6.1 Introduction 180
6.2 Materials 182
6.3 Analytical Methods 182
6.3.1 Microbial Activity 186
6.3.2 Physical Characteristics 186
6.3.3 Removal Performances 186
6.4 Experimental Procedures 187
6.5 Results and Discussion 188
6.5.1 Microbial Activity 188
6.5.2 Physical Profile of the Reactor System 189
6.5.3 Effect of Hydraulic Retention Time on
Physical Properties of the Granular Biomass
196
6.5.4 Effect of Hydraulic Retention Times on
Chemical Oxygen Demand Removal
202
6.5.5 Effect of Hydraulic Retention Time on
Color Removal
204
6.5.6 Effect of Hydraulic Retention Time on the
Biokinetics of Facultative Anaerobic
Granular Sludge during Biodegradation of
Dye
207
6.6 Conclusions
212
7 EFFECT OF SUBSTRATE AND RIBOFLAVIN ON
FACULTATIVE ANAEROBIC GRANULAR
SLUDGE
214
7.1 Introduction 214
7.2 Materials 215
xiv
7.2.1 Granular Precursor 216
7.3 Analytical Methods 216
7.3.1 Chemical Oxygen Demand and Color
Removal
216
7.4 Experimental Procedures 217
7.4.1 Screening for Concentration of Redox
Mediator
218
7.4.2 Batch Experiment for Chemical Oxygen
Demand and Color Removal Using
Facultative Anaerobic Granular Sludge
218
7.4.3 2-Level Factorial and Central Composite
Design Composite Experiment
218
7.5 Results and Discussion 221
7.5.1 Screening for Redox Concentration 221
7.5.2 Factorial Design Analysis of Chemical
Oxygen Demand Removal
223
7.5.2.1 Factorial Analysis: The Main
Effect of Substrate Chemical
Oxygen Demand Removal
226
7.5.2.2 Factorial Analysis: The Main
Effect of Riboflavin on Chemical
Oxygen Demand Removal
228
7.5.2.3 Factorial Analysis: The
Interaction Effect of Substrate and
Riboflavin on Chemical Oxygen
Demand Removal
230
7.5.3 Central Composite Design Analysis of
Chemical Oxygen Demand Removal
232
7.5.4 Factorial Design Analysis of Color
Removal
234
7.5.4.1 Factorial Analysis: Main Effect of
Substrate on Color Removal
237
7.5.4.2 Factorial Design Analysis: Main 241
xv
Effect of Riboflavin on Color
Removal
7.5.4.3 Factorial Analysis: Interaction
Effect
242
7.5.5 Central Composite Design Analysis of
Color Removal
246
7.6 Conclusions
257
8 CONCLUSIONS AND RECOMMENDATIONS
259
8.1 Conclusions 260
8.2 Recommendations
264
REFERENCES
267
Appendices A-G 301-350
xvi
LIST OF TABLES
TABLE NO. TITLE PAGE
3.1 Characteristics of textile wastewater (Bisschops and
Spanjers, 2003 and Dos Santos et al., 2006a)
52
3.2 Release of typical pollutants associated with various
textile manufacturing processes (Crini, 2006 and Dos
Santos et al., 2006a)
53
3.3 Advantages and disadvantages of the current methods of
dye removal from industrial effluents (Robinson et al.,
2000 and Crini, 2006)
56
3.4 Sequential anaerobic-aerobic treatment system for dye
degradation
71-72
3.5
Integrated anaerobic-aerobic sequential treatment
system for dye degradation
73-74
4.1
4.2
Sequential batch reactor system with intermittent
anaerobic/aerobic/anoxic reaction phase treating
different types of wastewater
List of reagents used in the experiment
77-78
79
xvii
4.3 List of equipment used in the experiment
80
4.4 One complete cycle of the IFAnGSBioRec
99
4.5 The OUR levels during the aerobic reaction phase of
one complete cycle
108
4.6 Characteristics of seed sludge and FAnGS
112
4.7 Comparison of mineral content at different stages
during the development of FAnGS
115
5.1 List of reagents used in the experiment
125
5.2 List of equipment used in the experiment
126
5.3 The variables and their range of high and low values
used in the factorial experiment
136
5.4 Two-level fractional factorial design with three
variables (in coded levels) conducted in duplicate (not
in randomized order)
136
5.5 Two-level of CCD experimental run in coded units
138
5.6 Morphological and cellular characterization of the
twelve isolated bacteria from FAnGS
140
5.7 Characteristics and performance of the isolated bacterial
from the FAnGS
142
5.8 Taxonomic and phylogenetic characteristic of the
isolates from FAnGS
148
xviii
5.9 Characteristics of identified selected bacteria strains
from FAnGS
149
5.10 Experimental results for 2-level factorial design analysis
151
5.11 The P-values of the estimated main and interaction
effects of variables substrates, pH and temperature on to
the percentage of coaggregation and surface
hydrophobicity after six hours aeration phase
152
5.12 Experimental results for CCD analysis
168
5.13 Summary of the P-value of the response surface
modeling analysis
169
5.14 Mathematical models in terms of actual factors
170
6.1 Dye degradation process using integrated reactor system
183-184
6.2 Operation steps during single cycle operation
188
6.3 Details of experimental condition of the IFAnGSBioRec
192
6.4 Oxidation Reduction Potential
192
6.5 Biomass concentrations at different stages of the
experiment
193
6.6 Physical properties of the granular biomass at different
stage of experiment
197
6.7 Profile of COD and color removal percentage at
different stages of experiment
207
xix
6.8 Coefficient of biokinetic parameters
209
6.9 Kinetic coefficients of FAnGS at different stages of the
experiment
210
7.1 Experimental runs of factorial design and CCD in actual
and coded values (not in random order)
220
7.2 Experimental results for factorial design analysis
224
7.3 The P-values of the estimated main and interaction
effects of substrates and riboflavin for the percentage of
COD removal
224
7.4 Experimental results for CCD analysis
233
7.5 Summary of the P-value of the response surface
modeling analysis
234
7.6 Experimental results for factorial design analysis
236
7.7 The P-values of the estimated main and interaction
effects of variables substrates and redox mediator for
the percentage of color removal
237
7.8 Experimental results for CCD analysis
246
7.9 Summary of the P-value of the response surface
modeling analysis
247
7.10 Mathematical models in terms of actual values
249
xx
LIST OF FIGURES
FIGURES NO. TITLE
PAGE
1.1 Outline of the study 8
2.1 Design principles of sequencing batch reactor
(Jern, 1989)
13
2.2 Schematic diagram of aerobic granulation
developed without any carrier material (Beun et al.,
1999)
16
2.3 Granulation development supported by ciliates. A:
Formation of floc where the ciliates settle on other
organisms or particles (Phase 1). B: Arrow shows
the colonization of bacteria on the ciliate stalks
(Phase 2). C: Granules grown into bigger sizes with
dense core. Zooids of the ciliates stalks completely
overgrown by bacterial, die and act as the
“backbone” structure (Phase 3). D: Unstalked free
swimming ciliates detach from the biofilm to
escape death. Smooth and compact granules are
formed. E: The surviving swarming ciliate cells
get attached to the matured surface granules
(shown by arrow) (Weber et al., 2007)
17
xxi
2.4 The flowchart of the morphological and physical
changes of the anaerobic granules in the process of
aerobic granule formation in SBR system (Linlin et
al., 2005)
18
4.1 Location of textile industry; Ramatex Industry Sdn.
Bhd., Sri Gading Industrial Park, Batu Pahat and
sewage treatment plant; Indah Water Konsortium
Treatment Plant System, Taman Sutera, Skudai.
82
4.2 Schematic layout of the IFAnGSBioRec system (Wang et al. (2004) and Zheng et al. (2005)
84
4.3 The IFAnGSBioRec system used in the study
85
4.4 Preparation frame work for granule development 87
4.5 Characterizations of FAnGS 88
4.6 The morphological development of facultative
anaerobic granular sludge (scale bar at steady-state
equals to 1mm) Pictures were taken using stereo
microscope with magnification of 6.3X (a)
Granules developed from the activated sludge (b)
Granules developed from anaerobic granules
patches
100
4.7 Pictures of sludge particles during the initial stage
of the experiment (a) and matured FAnGS granules
at the 66 days of the experiment (b). Pictures were
taken using stereo microscope with magnification
of 6.3X (scale bar equals to 1 mm)
102
xxii
4.8 FESEM microstructure observations on mature
facultative anaerobic granular sludge under the
magnification of 10,000K. (a) Coccoid bacteria
tightly linked to one another. (b) Cavities that
appear between bacteria clumped inside the
granules
104
4.9 The changes on the microbial population during the
process development of the FAnGS observed by
gram staining procedures under microscopic
magnification of 1000K (a) The sludge being
dominated by the filamentous organisms. (b)
Changes in the domination species within the
FAnGS
105
4.10 The profile of dissolved oxygen and oxygen uptake
rate in one complete cycle of the IFAnGSBioRec
system (♦) Dissolve oxygen, (□) Oxygen uptake
rate (PI and PIII-Anaerobic phase; PII and PIV-
Aerobic phase)
107
4.11 The relationship between the biomass
concentrations retained in the reactor with the
settling velocity of the FAnGS (■) Settling
velocity; (○) Biomass concentration
110
4.12 The relationship between the SVI values and
settling velocity of the FAnGS (○) SVI, (■)
Settling velocity
111
4.13 The profile of integrity coefficient representing the
granular strength of the FAnGS
112
xxiii
4.14 The profile of biomass concentration in the SBR.
(●) MLSS, (□) MLVSS
114
4.15 The settling velocity profile in relation to mean cell
residence time (SRT). (○) SVI, (■) SRT
115
4.16 Profile of COD removal during FAnGS
development in IFAnGSBioRec system. (▲)
Influent COD, (■) Effluent COD, (○) COD
removal
118
4.17 Profile of Ammonia removal during FAnGS
development in IFAnGSBioRec system. (▲)
Influent ammonia, (■) Effluent ammonia, (○)
Ammonia removal
119
4.18 Profile of color removal during FAnGS
development in IFAnGSBioRec system. (100
ADMI ≈ 1 Platimun-Cobalt). (▲) Influent color,
(■) Effluent color, (○) Color removal
119
4.19 The removal for COD, ammonia and color in one
complete cycle of the SBR system (■) Color, (○)
COD, (▲) Ammonia
120
5.1 Characterization of microbes isolated from the
FAnGS granules
129
5.2 Experimental work for the investigation on the
effect of substrate concentration, pH and
temperature on the percentage of coaggregation
and surface hydrophobicity og the mixed culture
130
xxiv
5.3 Agarose gel electrophoresis of DNA extraction
144
5.4 Agarose gel electrophoresis of purified PCR
amplification product
145
5.5 The pareto chart of the percentage of (a)
coaggregation and (b) surface hydrophobicity after
six hours of aeration phase (A: substrate; B: pH; C:
temperature; α: 0.1)
155
5.6 Main effects plot on the coaggregation
158
5.7 Interaction effects plot on the coaggregation
process (• Centre point)
159
5.8 Main effects plot of variables for the percentage of
SHb
161
5.9 Interaction effect plots for the percentage of SHb
(• Centre point)
165
5.10 Predicted versus actual data for (a) coaggregation
and (b) surface hydrophobicity
171
5.11 (a) Contour and (b) 3D response surface plots
representing relationship between pH, temperature
and percentage of coaggregation
172
5.12 (a) Contour and (b) 3D response surface plots
representing relationship between the concentration
of substrate, pH and percentage of surface
hydrophobicity
174
xxv
5.13 (a) Contour and (b) 3D response surface plots
representing relationship between the concentration
of substrate, temperature and percentage of surface
hydrophobicity
175
5.14 (a) Contour and (b) 3D response surface plots
representing relationship between pH, temperature
and percentage of surface hydrophobicity
177
6.1 Experimental analyses on the effect of HRT on
granular biomass in treating synthetic textile
dyeing wastewater
185
6.2 OUR profile of (a) Stage I (Aerobic phase 2.84
hours), (b) Stage II (Aerobic phase 5.84 hours) and
(c) Stage III (Aerobic phase 11.84 hours)
190
6.3 OUR profile of (a) Stage IV (Aerobic phase 11.84
hours), (b) Stage V (Aerobic phase 5.84 hours), (c)
Stage VI (Aerobic phase 17.84 hours)
191
6.4 Profile of biomass concentration at different stages
of the experiment. Stage I: anaerobic (2.8 h):
aerobic (2.8 h); Stage II: anaerobic (5.8 h): aerobic
(5.8 h); Stage III and Stage IV: anaerobic (11.8 h):
aerobic (11.8 h); Stage V: anaerobic (17.8 h):
aerobic (5.8 h); Stage V: anaerobic (5.8 h): aerobic
(17.8 h)
195
6.5 Distribution of size particles at different stages of
the experiment. Stage I: anaerobic (2.8 h): aerobic
(2.8 h); Stage II: anaerobic (5.8 h): aerobic (5.8 h);
Stage III and Stage IV: anaerobic (11.8 h): aerobic
200
xxvi
(11.8 h); Stage V: anaerobic (17.8 h): aerobic (5.8
h); Stage V: anaerobic (5.8 h): aerobic (17.8 h)
6.6 Profile of sludge volume index throughout the
experiment. Stage I: anaerobic (2.8 h): aerobic (2.8
h); Stage II: anaerobic (5.8 h): aerobic (5.8 h);
Stage III and Stage IV: anaerobic (11.8 h): aerobic
(11.8 h); Stage V: anaerobic (17.8 h): aerobic (5.8
h); Stage V: anaerobic (5.8 h): aerobic (17.8 h)
201
6.6 Profile of COD removal performance of the reactor
system at different stages of the experiment. Stage
I: anaerobic (2.8 h): aerobic (2.8 h); Stage II:
anaerobic (5.8 h): aerobic (5.8 h); Stage III and
Stage IV: anaerobic (11.8 h): aerobic (11.8 h);
Stage V: anaerobic (17.8 h): aerobic (5.8 h); Stage
V: anaerobic (5.8 h): aerobic (17.8 h)
191
6.7 Profile of COD removal performance of the reactor
system at different stages of the experiment. (○)
Influent COD; (■) Effluent COD, (▲) COD
removal. Stage I: anaerobic (2.8 h): aerobic (2.8
h); Stage II: anaerobic (5.8 h): aerobic (5.8 h);
Stage III and Stage IV: anaerobic (11.8 h): aerobic
(11.8 h); Stage V: anaerobic (17.8 h): aerobic (5.8
h); Stage V: anaerobic (5.8 h): aerobic (17.8 h)
203
6.8 Profile of color removal performance of the reactor
system at different stages of the experiment. (♦)
Influent color, (■) Effluent color, (○) Color
removal. (100 ADMI ≈ 1 Pt-Co). Stage I:
anaerobic (2.8 h): aerobic (2.8 h); Stage II:
anaerobic (5.8 h): aerobic (5.8 h); Stage III and
205
xxvii
Stage IV: anaerobic (11.8 h): aerobic (11.8 h);
Stage V: anaerobic (17.8 h): aerobic (5.8 h); Stage
V: anaerobic (5.8 h): aerobic (17.8 h)
7.1 Experimental works for the investigation on the
effect of substrate concentration and redox
mediator on COD and color removal via the aid of
experimental design
217
7.2 Color removal at different concentrations of
riboflavin. Absorbance at 600 nm (♦), absorbance
at 542 nm (□)
222
7.3 The Pareto chart of COD removal for (a) anaerobic,
(b) aerobic and (c) total removal (A: substrate; B:
riboflavin; α: 0.1)
225
7.4 Main effect plot of substrate and riboflavin for (a)
anaerobic, (b) aerobic and (c) total COD removal
229
7.5 Interaction plot for the percentage of COD removal
for (a) anaerobic, (b) aerobic and (c) total removal
(Substrate: ____ 2633.88 m/L; ____ 866.12 mg/L; ●
Centre point)
231
7.6 The relationship between substrate, riboflavin and
percentage of total COD removal after 24 hours of
experimental run, (a) Contour plot and (b)
Responses surface plot
235
7.7 Pareto chart of Sumifix Navy Blue EXF removal at
(a) 5 and (b) 12 hours (α: 0.1; A: Substrate; B:
Riboflavin)
238
xxviii
7.8 Pareto chart of Synozol Red K-4B removal at (a) 5
and (b) 12 hours (α: 0.1; A: Substrate; B:
Riboflavin)
239
7.9 Main effect plot of substrate and riboflavin on the
color removal of Sumifix Navy Blue EXF at (a) 5
and (b) 12 hours of experiment under anaerobic
condition
243
7.10 Main effect plot of substrate and riboflavin on
color removal of Synozol Red K-4B at (a) 5 and (b)
12 hours of experiment under anaerobic condition
244
7.11 Interaction of variables substrate and riboflavin for
Sumifix Navy Blue EXF at (a) 5 and (b) 12 hours
of the experimental conditions (Substrate: ____
2366.88 m/L ; ____ 866.12 mg/L; ● Centre point)
245
7.12 Interaction of variables substrate and riboflavin for
Synozol Red K-4B at (a) 5 and (b) 12 hours of the
experimental conditions (Substrate: ____ 2366.88
m/L; ____ 866.12 mg/L; • Centre point)
245
7.13 Predicted versus actual data for Sumifix Navy Blue
EXF removal at (a) 5 hours and (b) 12 hours
250
7.14 Predicted versus actual data for Synozol Red K-4B
removal at (a) 5 hours and (b) 12 hours
251
7.15 (a) Contour and (b) 3D response surface plots
representing relationship between the concentration
of substrate, riboflavin and color removal of
Sumifix Navy Blue EXF removal at 5 hours
253
xxix
(Reduced Quadratic Model)
7.16 (a) Contour and (b) 3D response surface plots
representing relationship between the
concentrations of substrate, riboflavin and color
removal of Sumifix Navy Blue EXF removal at 12
hours
254
7.17 (a) Contour and (b) 3D response surface plots
representing relationship between the
concentrations of substrate, riboflavin and color
removal of Synozol Red K-4B removal at 5 hours
255
7.18 (a) Contour and (b) 3D response surface plots
representing relationship between the
concentrations of substrate, riboflavin and color
removal of Synozol Red K-4B removal at 12 hours
256
xxx
LIST OF ABBREVIATIONS
16S rRNA - 16 subunit ribosomal ribonucleic acid
ADMI - American Dye Manufacturing Index
AnAHR - Anaerobic-aerobic hybrid reactor
AnFBR - Anaerobic fluidized bed reactor
ANOVA - Analysis of variance
AO7 - Acid orange 7
AR151 - Acid red 151
BLASTn - Basic local alignment search tool
CAg - Coaggregation
CCD - Central Composite Design
COD - Chemical oxygen demand (C-mmoL or mg/L or g/L)
CR - Congo red
CSTR - Continuous stirring tank reactor
DB79 - Direct Blue 79
DGGE - Denaturing gradient gel electrophoresis
DNA - Deoxyribonucleic acid
DNT - Dinitrotoluene
DO - Dissolved oxygen (mg/L)
EBPR - Enhanced biological phosphorus removal
EPA - Environmental Protection Act
EPS - Extracellular polymeric substances
FAD - Flavin adenine dinucleotide
FESEM - Field-Emission Scanning Electron Microscope
FAnGS - Facultative anaerobic granular sludge
FLAA - Flame Atomic Absorption Spectrophotometer
xxxi
FMN - Flavin mononucleotide
FSIH - Fluorescent in situ hybridization
GAO - Glycogen-accumulating organism
GDP - Gross domestic product
HRT - Hydraulic retention time (h or day)
IC - Integrity coefficient
IFAnGSBioRec - Intermittent facultative anaerobic granular sludge
biological reactor
IPC - Integrated pollution control
IPPC - Integrated Pollution and Prevention Control
LOFT - Lack of fit test
MD - Mixed dye
MG - Malachite green
MIDA - Malaysian Industrial Department Authority
MLSS - Mixed liquor suspended solid (mg/L or g/L)
MLVSS - Mixed liquor volatile suspended solid (mg/L or g/L)
N & P - Nitrogen & Phosphorus
N/COD - Nitrogen/Chemical oxygen demand
NA - Nutrient agar
NAD - Nicotinamide adenine dinucleotide
NCBI - National Center of Biotechnology Information
OLR - Organic loading rate (mg/L·day or kg/m3·day)
ORP - Oxidation reduction potential
OUR - Oxygen uptake rate (mg/L.h)
P/COD - Phosphorus/Chemical oxygen demand
PAO - Polyphosphate-accumulating organism
PCR - Polymerase chain reaction
Pt-Co - Platinum Cobalt
PN - Exoprotein
POVH - Poly(vinyl alcohol)
PS - Polysaccharide
RB - Reactive black
RDR - Rotating disc reactor
RG - Residual granules (mg)
xxxii
RSM - Response surface method
SBCR - Sequencing biofilm configured reactor
SBR - Sequencing batch reactor
SDS - Sodium dodecylsulfate
SG - Settled granules (mg)
SHb - Surface hydrophobicity
SMA - Specific methanogenic activity
SOUR - Specific oxygen uptake rate (mg DO/g VSS.h)
SRB - Sulfate reducing bacteria
SRT - Sludge retention time (day)
STDW - Synthetic textile dyeing wastewater
STDW - Synthetic textile dyeing wastewater (mL or L)
SVI - Sludge volume index (mL/g)
TAA - Total aromatic amines
TOC - Total organic carbon (C-mmoL or mg/L or g/L)
UAFB - Upflow anaerobic fixed bed
UASB - Up-flow anaerobic sludge blanket
VER - Volumetric exchange rate
xxxiii
LIST OF SYMBOLS
Ce - COD concentration in the effluent (C-mmoL or mg/L or g/L)
Ci - COD concentration in the influent (C-mmoL or mg/L or g/L)
kd - Endogenenous decay rate
M - Biomass concentration (mg VSS/L)
Ox - Theoretical chemical oxygen demand which is assume as 1.42
mg O2/ mg biomass
Qe - Effluent flowrate (L/d)
tc - Cycle time of SBR operation (d)
tc - Cycle time of the SBR operation (d)
Vd - Manually discharge mixture volume (L)
Ve - Effluent volume in SBR operating cycle (L)
Ve - Effluent volume of the SBR operating cycle (L)
Ve - Working volume of the SBR system (mL or L)
Vr - Working volume of the SBR system (L)
VT - Total working volume in reactor (L)
Xd - Biomass concentration of manually discharged mixture
(g VSS/L)
Xe - Effluent volatile solid concentration (g VSS/L)
Xe - Effluent volatile solid concentration (g VSS/ L)
Xr - Mixed liquor volatile suspended solid in reactor (mg/L)
Xvss - Volatile solid concentration in the reactor system (g VSS/L)
XVSS1 - Volatile solid concentration at the beginning of
operation in SBR reactor (g VSS/L)
XVSS2 - Volatile solid concentration at the end of cycle operation in
SBR reactor (g VSS/L)
xxxiv
Y - Theoretical biomass yield
Yobs - Observed biomass yield
- Solid retention time (d)
μ - Biomass growth rate
xxxv
LIST OF APPENDICES
APPENDIX TITLE
PAGE
A Data and Calculation
301
B Molecular Procedure of 16S Sequence Analysis
314
C Morphology of Bacteria
319
D Molecular Data Analysis
320
E Factorial Design and Response Surface Methodology
Data Analysis for Coaggregation and Surface
Hydrophobicity Assay
335
F Factorial Design and Response Surface Methodology
Data Analysis for COD Removal
341
G Factorial Design and Response Surface
Methodology Data Analysis for Color Removal
344
1
CHAPTER 1
INTRODUCTION
1.1 Preamble
Dyes have been one of the most demanding compounds in many industrial
sectors with textile industries as the leading and biggest consumers. According to
Dos Santos et al. (2003), nearly one million metric tonnes of dyes are annually
produced throughout the world with azo dyes representing about 70% of the total
production. Dyes are manufactured in such a way to provide long lasting attractive
color design to suit a variety of customer needs. Dyes are designed with high
stability towards light, heat, and sweat, and resistance to oxidizing agents (Ravi
Kumar et al., 1998 and Sun and Yang, 2003). These criteria make the dyes very
recalcitrant to degradation, and impose threats to the environment.
With huge consumptions and demand, treatment of textile industrial effluents
presents an arduous task. There have been a number of techniques used in treating
textile industrial effluent. At present, the main methods in textile wastewater
treatment involve physical and chemical processes. However, such methods are
often costly. Some treatments, even though capable of removing color, just merely
transfer the contaminants from one form to another. The generation of concentrated
2
sludge from coagulation process, for example, would generate another environmental
issue which is the disposal of sludge (Pearce et al., 2003). Excessive chemical usage
in the textile wastewater treatment process might create secondary pollution
problems to the environment. Due to the high cost on operation, maintenance and
disposal problems, the present treatment technologies are not favored to be applied at
large scale for textile industries (Ghoreishi and Haghighi, 2003). Furthermore,
according to the Integrated Pollution Control (IPC) regulations, any decoloration
systems involving destruction technologies that prevail as transferal of pollution
from one part of the environment to another is prohibited (Willmott et.al., 1998).
The biological treatment process has been a major unit in wastewater
treatment plants. However, a variety of chemicals are used in the textile industry and
due to stringent effluent requirements by the authority, conventional biological
treatment seems to be ineffective in treating wastewater. Furthermore, dyes are
known for their complex chemical structure and mostly are of synthetic origin. Due
to the recalcitrant nature of these compounds, the conventional treatment system fails
to remove sufficient color and other pollutants that are present in the textile
wastewaters (Stolz, 2001; Pandey et al., 2007; van der Zee and Cervantes, 2009).
Studies have shown that complete mineralization of dye compounds requires
both anaerobic and aerobic biological treatment approaches (Melgoza et al., 2004).
The former will cause the cleavage of the azo bond. The latter performs complete
mineralization of the dye compounds to form harmless and stable byproducts.
Nowadays, there is a trend to use microorganisms in the form of aggregates
as compared to suspended cells. These aggregates perform degradation process
either through cell-to-cell interaction or in combination with other particulates,
forming biofloc known as granules. The granular system is endowed with some
characteristics of good settling ability, high concentration of microorganisms with
strong and compact structure and high biomass retention that could withstand
significantly higher organic loading (Morgenroth et al., 1997 and Moy et al., 2002).
3
Having such characteristics, the granular system has great advantages over
conventional activated sludge.
Granules consist of millions of microorganisms that clump together with
anaerobic microorganisms occupying the inner layer of the granules and with aerobic
microbes at the outer layer. With the presence of both types of microorganisms
within the granules, the granular system can be used for complete degradation of
textile wastewater. However, studies on the applications of granular systems treating
textile wastewater are apparently lacking. Hence, more research needs to be
conducted in this area to provide a better understanding on the mechanisms and
capability of the treatment system.
The aim of this study is to develop an effective bioprocess that is able to treat
textile wastewater. The study is focused on the use of facultative anaerobic granular
sludge as the treatment process. The development of facultative anaerobic granular
sludge and identification of factors affecting their effectiveness in degradation
process is the emphasis in this study.
1.2 Objectives of the Study
The specific objectives of this study are:
i. To develop facultative anaerobic granular sludge (FAnGS) under
intermittent anaerobic and aerobic reaction mode in a sequential batch
reactor system with the use of synthetic textile dyeing wastewater.
4
ii. To characterize the physical, chemical and biological properties of the
developed FAnGS and to identify the most suitable mixed bacteria
consortia isolated from the FAnGS that are capable of being an
aggregator and dye degrader.
iii. To characterize the aggregation and surface hydrophobicity properties
of the selected mixed bacteria consortia as a function of substrate
concentration, pH and temperature.
iv. To study the effect of hydraulic retention time with variation of
intermittent reaction mode on the performance of FAnGS in terms of
chemical oxygen demand (COD) and color removal.
v. To investigate the effect of substrate concentration and riboflavin as
the redox mediator on the performance of FAnGS in terms of COD
and color removal.
1.3 Scope of the Study
This study covers the design and application of a laboratory-scale reactor
system identified as Intermittent Facultative Anaerobic Granular Sludge Biological
Reactor (IFAnGSBioRec). The design and operation of the reactor system are based
on the sequential batch reactor system. The FAnGS is developed using synthetic
textile dyeing wastewater containing a mixture of three dyes namely Sumifix Black
EXA, Sumifix Navy Blue EXF and Synozol Red K-4B.
5
The matured FAnGS is characterized for its physical, chemical and biological
properties. The microorganisms present in the FAnGS are identified through the
application of a molecular technique and are used for further detailed investigation
with respect to the aggregation and surface hydrophobicity, the important aspects of
the initial mechanism that takes place in the development of FAnGS. The
performance of the FAnGS in treating textile dyeing wastewater are investigated
based on COD and color removal. The effects of different substrate and redox
mediator concentration as well as variation of hydraulic retention times on the COD
and color removal are also explored. The performance of the FAnGS in COD and
color removal has also been studied with the variation on substrate and redox
mediator concentration in synthetic textile dyeing wastewater (STDW). In addition
to the IFAnGSBioRec, some of the experiments are conducted in serum bottles.
Some of these experiments also involve the use of statistical experimental design.
1.4 Significance of the Study
Biogranular systems either anaerobic or aerobic granules have been studied
for the degradation of different types of wastewater (Beun et al. 1999; Moy et al.,
2002; Arrojo et al., 2004; Lemaire et al., 2007 and Chen et al. 2008a). The
applications of granular system for the treatment of textile wastewater have been
reported by many researchers (Razo-Flores et al., 1997; Tan et al., 2000; van der
Zee, 2001a; and Dos Santos et al., 2003). However, most of the research on the
degradation of dye stuff in textile wastewater is focused on the applications of
anaerobic granular system. Apparently, the use of FAnGS for the dye degradation
process appears to be missing. The importance of this study is therefore listed as
follows;
i. The study provides the design and procedural input of a compact
laboratory-scale reactor system known as IFAnGSBioRec, fabricated
6
specifically for the formation of facultative anaerobic granular sludge
for treating textile wastewater.
ii. The study provides the procedures for the formation of FAnGS and its
physico-chemical and biological characteristics.
iii. The study provides details in relation to the effect of substrate, pH and
temperature on aggregation and surface hydrophobicity of the mixed
bacteria culture selected from FAnGS. The findings would provide
knowledge on suitable conditions for the development of the FAnGS,
customized for degradation of textile wastewater.
iv. The study provides information regarding the most suitable
combination time for anaerobic and aerobic reaction phases in the
IFAnGSBioRec cycle tailored for degradation of textile dyeing
wastewater.
v. The study also provides the biokinetic parameters including biomass
growth rate (μ), endogenenous decay rate (kd), observed biomass yield
(Yobs) and theoretical biomass yield (Y) in relation to changes of HRT
during textile dyeing degradation by the FAnGS.
vi. The study also provides the effect of using different concentrations of
substrate and redox mediator in relation to dye degradation by the
FAnGS.
7
1.5 Organization of Thesis
The thesis is presented in eight chapters. Chapter 1 provides the overview of
problems generated from the textile wastewater contamination and setbacks in
effluent treatment. The chapter also points out the importance of biological
treatment in degrading the dyes from the textile wastewater. The literature review is
divided into two parts i.e. Chapter 2 and 3. Chapter 2 mainly discusses on the outline
of the granulation process including the theoretical features of granules development,
factors affecting the granulation process and also the applications of granular
systems. Chapter 3 highlights the mechanisms involved in dye degradation process
and the biological treatment system used for textile wastewater treatment.
Chapters 4, 5, 6 and 7 present the works that have been conducted in this
study. Chapter 4 presents the study on the development and characterization of the
FAnGS and also the performance of the FAnGS reactor system on COD and color
removal. Chapter 5 focuses on the initial stage of the granulation process by looking
into the aggregation and surface hydrophobicity of the selected mixed bacteria
isolated from the FAnGS. Chapter 6 presents the effect of hydraulic retention time
on the performance of COD and color removal. Chapter 7 focuses on the applications
of redox mediator to enhance color removal by the FAnGS. Lastly, Chapter 8
presents the conclusions of this study. Figure 1.1 gives the flowchart illustrating the
overall outline of the experimental work for this study. This chapter also provides
recommendation for future research exploration in relation to the findings of this
study.
8
Study on Facultative Anaerobic Granular Sludge for Textile Wastewater
Treatment
Development of facultative anaerobic granular sludge for textile wastewater
treatment
(Refer Chapter 4)
Characterization of Facultative
Anaerobic Granular Sludge
Study on the effect of substrate concentration, pH and temperature on
coaggregation and surface hydrophobicity
(Refer Chapter 5)
Study on the effect of hydraulic retention time on facultative anaerobic granular
sludge
(Refer Chapter 6)
Study on the effect of substrate and
riboflavin on facultative anaerobic
granular sludge
(Refer Chapter 7)
Coaggregation (Refer 5.5.4.2-
5.5.4.4 and 5.5.5)
Factorial Design
(Refer 5.4.8)
Central Composite
Design (Refer 5.4.9)
Central Composite
Design (Refer 7.4.3)
Factorial Design
(Refer 7.4.3)
Data Analysis
Surface Hydrophobicity (Refer 5.5.4.6-
5.5.4.8 and 5.5.5)
Removal Performance
Biokinetic parameter
(Refer 6.5.6)
Biological Characteristic
Physical Characteristic
Chemical Characteristic
Removal Performance
Morphological & Structural (Refer 4.5.1-
4.5.2)
COD Removal (Refer 5.5.9 &
6.5.4)
Granular Biomass &
SRT (Refer 4.5.7 &
6.5.2-6.5.3)
Microbial Activities
(Refer 4.5.3 and 6.5.1)
Characterization of Microbes (Refer 5.5.1-
5.5.3)
Settling Velocity & SVI
(Refer 4.5.5)
Mineral & Metal Content (Refer 4.5.8)
Granular Strength
(Refer 4.5.6)
Ammonia Removal
(Refer 5.5.9)
Color Removal (Refer 5.5.9 and 6.5.5)
COD Removal
(Refer 7.5.2-7.5.3)
Color Removal
(Refer 7.5.4-7.5.5)
Conclusions
Figure 1.1 Outline of the study
9
CHAPTER 2
BIOGRANULATION TECHNOLOGY IN WASTEWATER TREATMENT
2.1 Introduction
The technology of cell immobilization has been used for decades in the
environmental engineering and bioengineering fields (Liu and Tay, 2002). Microbial
immobilization can be classified into three different categories which are biofilm,
entrapped microorganisms and microbial aggregates. Biofilms are formed when
bacteria adhere to surfaces in aqueous environments and begin to excrete a slimy,
glue-like substance that can anchor them to all kinds of material such as plastics,
polymers, ceramics, rocks, basalts, activated carbon or any other solid surface
(Costerton et al., 1995 and Kwok et al., 1998).
Entrapped or encapsulation of the microorganisms is another form of
microbial agglomeration where microbes can be trapped in hydrophobic gels or other
types of gels such as polyacrylamide, chitosan, alginate, agar cellulose acetate and
polyvinyl-alcohol (Kim et al., 2000). These gel substances confine the migration and
maintain high concentration of microorganisms in the reactor system. The
performance of the reactor system would be expected to reach a higher removal
efficiency due to the presence of high cell concentration.
10
Granular sludge, also regarded as the special case of biofilm formation, has
been successfully developed either through anaerobic conditions (Lettinga et al.,
1980; Schmidt and Ahring, 1996; Zhang et al., 2007a) or aerobic conditions
(Morgenroth et al., 1997, Bao et al., 2009; Shi et al., 2009). Microbial granulation is
a self-immobilization community that can be formed with or without support
material. It is estimated that about 900 anaerobic granular sludge systems have been
successfully operated all over the world (Alves et al., 2000).
2.2 Biogranulation
Granules, defined as discrete macroscopic aggregates consist of dense
microbial consortia packed with different bacterial species amounting to millions of
organisms per gram of biomass (Weber et al., 2007). They are formed through self-
immobilization microorganisms which involves cell to cell interactions inclusive of
biological, physical and chemical processes. According to Calleja (1984), microbial
granulation is gathering of cells to form a fairly stable, contiguous, multicellular
association under physiological condition.
The granulation system is first recognized in an up-flow anaerobic sludge
blanket (UASB) system as anaerobic granular sludge. Since then, extensive
investigations have been carried out by many researchers for the past two decades via
the innovative upflow sludge bed (USB) type reactor (Bachman et al., 1985 and
Lettinga et al., 1997). The application of anaerobic granulation system is relatively
well known through the successful demonstration particularly in removing
biodegradable organic matter from municipal and industrial wastewater (Lettinga et
al., 1980; Fang and Chui, 1993; Schmidt and Ahring, 1996).
11
The success of granulation systems is related to their capacity of good settling
property of the biomass without the need of a biomass carrier. This would allow
high solids retention time and process stability with simple and low-cost equipment
(Ahn and Richard, 2003). Large size and high density of microorganisms have led to
rapid settling capacity which simplifies the separation of treated effluent from the
biomass (Liu and Tay, 2004). Such characteristics enable the granular sludge to
handle high liquid flows with long biomass residence times and minimal suspended
solids released in the effluent (Wirtz and Dague, 1996).
Since the microorganisms within the granules are closely clumped together,
this generates syntrophic associations which occur due to optimum distances
between microbial associates at appropriate substrate levels. Such condition enables
high and stable performance of metabolism activities (Batstone et al., 2004).
Granules also consist of extracellular polysaccharides substances (EPS)
produced by the microorganisms within the granules that help to strengthen the
granular structure. The presence of EPS covers the granular structure and acts as
protection shield to the microbes against shock loading and toxic compound that may
be present in the wastewater (Tay et al., 2005a). With such characteristics,
granulation system can also be regarded as an efficient device in the removal process
of xenobiotic from wastewater (Bathe et al., 2004 and Wuertz et al., 2004).
Despite the successful performance of anaerobic granular sludge systems,
attention is later diverted to the development and application of aerobic granulation.
This is due to several drawbacks that have been observed in the application of the
anaerobic, including long start-up period, operations at relatively high temperatures
and are not suitable for nutrient removal and low strength organic wastewater (Liu
and Tay, 2004).
12
2.3 Development of Aerobic Granules
Development of aerobic granules has been first reported in a continuous
aerobic up-flow sludge blanket reactor by Mishima and Nakamura (1991). The
granules are claimed to exhibit very good settling property. Aerobic granules are
compact, regular and of smooth rounded shape with an apparent outer surface which
can easily be differentiated from the loose, fluffy and irregular flocs of conventional
suspended sludge. Some researchers have also claimed that aerobic granules are an
extension and a special case of biofilm formation (El-Mamouni et al., 1995).
Aerobic granulation system has been used for organics, nitrogen, phosphorus
and toxic substances removal, especially of the high strength wastewater (Moy et al.,
2002; Arrojo et al., 2004; Qin and Liu, 2006; Yi et al., 2008; Kishida et al., 2009).
Bacteria normally do not aggregate naturally to each other due to repulsive
electrostatic forces via the presence of negatively-charged protein compounds of the
cell wall (Voet and Voet, 2004). However, under selective environmental condition,
microorganisms are capable of attaching to one another and thus form aggregates.
These aggregates, consisting millions of microorganisms with different functioning
roles are responsible for degrading a mixture of organic compounds within the
wastewater as well as removing the nutrients.
Most research and reports on aerobic granulation are developed in sequencing
batch reactor (SBR) systems (Morgenroth et al., 1997; Beun et al., 1999; Hailei et
al., 2006; Chen et al., 2008a; Chen et al., 2008b; Kim et al., 2008). The reaction
phase can be in the condition of anaerobic, aerobic or anoxic with or without mixing
depending on the purpose of the treatment process. Figure 2.1 shows the steps
involved in one complete cycle of the SBR system.
13
Figure 2.1 Design principles of sequencing batch reactor (Jern, 1989)
Aerobic granulation involves multiple-step processes engaged with both
physicochemical and biological forces that make significant contributions in the
granules development (Calleja, 1984; Linlin et al., 2005; Weber et al., 2007).
Aerobic granules can be developed purely from activated sludge, as described by
Beun et al. (1999) and they can also be developed using anaerobic granules as the
seed sludge (Linlin et al., 2005). The mechanisms for development of aerobic
granules from activated sludge are slightly different from the development of
granules seeded with anaerobic granules. This is discussed in the following sections.
Aeration/mixing
Fill
React
Settle Draw
Idle
Decanting
14
2.3.1 Aerobic Granules from Aerobic Activated Sludge
Aerobic granulation is not a standalone process but resulted from the
integration of many aspects such as physical, chemical and biological processes and
interaction with the surrounding environment. Liu and Tay (2002) explain the
involvement of different types of physicochemical and biological forces that are
responsible for the development of aerobic granules. The first stage of the
granulation process is the cell-to-cell or cell-to-solid surface interaction initiated by
diffusion of mass transfer, hydrodynamic forces of the surrounding areas,
thermodynamic effects, gravitational force, as well as the competency of cells to
move towards one another (Pratt and Kolter, 1998). In the second step several
physical (for instance, the Van der Waals forces, surface tension, hydrophobicity,
opposite charge attractions, thermodynamic of surface free energy, bridges by
filamentous bacteria), chemical and biochemical (cell surface dehydration, cell
membrane fussions and signals among microbial communities) attractive forces are
involved in stabilizing the multicell links that are formed in the earlier step (Bossier
and Verstraete, 1996 and Tchobanoglous et al., 2004). The third step is the maturing
stage which involves the production of substances that facilitate the interaction of
cell-to-cell and results in the development of highly organized microbial structure.
During this stage there are changes in the mechanisms of metabolite production such
as higher production of extracellular polymer, growth of cellular cluster, metabolite
change and environmental-induced genetic effects. The final step in aerobic
granulation involves shaping of the three dimensional aerobic granules by
hydrodynamic shear forces (Chisti, 1999a).
Beun et al. (1999) has also described the path of aerobic granules formation.
Immediately after inoculation, the reactor system is found to be dominated by
bacteria and fungi. Mycelial pellets that are formed by the dominating fungi manage
to retain in the reactor due to the good settling ability. Bacteria which do not hold
this characteristic are discarded with the effluent. With the shear force imposed by
aeration, the filaments are detached from the surface of the pellets. The pellets grow
bigger and bigger until they manage to reach up to 5-6 mm in diameter. With time,
15
when the pellets have grown too big, they will be defragmented. The matured pellet
start to rupture into smaller pieces when there is limitation of oxygen to penetrate
into the inner parts of the pellets. The fragment of mycelial pellet acts as the
immobilized matrix for the bacteria to grow and form new colonies. At this stage,
the bacteria can be considered big enough to settle at faster speed and able to escape
washout. The bacterial colonies grew larger and developed granules. When the
granules are formed, the whole system is governed by bacterial growth. Figure 2.2
illustrates the steps in the development of aerobic granules, as explained by Beun et
al. (1999).
Weber et al. (2007) have illustrated three consecutive phases of granular
mechanism development with the involvement of several eukaryotic organisms.
Microscopic analysis has revealed that eukaryotic organisms play a key role in
aerobic granule formation seeded with sludge from municipal wastewater treatment
plants. Most frequently seen, stalked ciliates of the subclass Peritrichia and
occasionally, the fungi, are found to be involved in the granulation process
development. Figure 2.3 shows the development of aerobic granules with the ciliates
as the main foundation (Weber et al., 2007).
2.3.2 Aerobic Granules Seeded with Anaerobic Granular Sludge
Development of aerobic granules using anaerobic granular sludge as seeding
material has been demonstrated by Linlin et al. (2005). Through microscopic
observation, the mechanisms involving morphological and physical changes of the
anaerobic granular sludge into the formation of aerobic granules in the SBR system
is demonstrated in a flow chart shown in Figure 2.4. During the initial stage, the
anaerobic granular seeds disintegrate into irregular smaller flocs and debris when
exposed to hydrodynamic shear force during aerobic conditions. Some of these flocs
and debris are washed out. The remaining flocs and debris act as a precursor that
16
initiates the growth of new aerobic granules. The hydrodynamic shear force also
help in shaping the formation of the structural community of the microbial
aggregates during the maturing stage (Di Iaconi et al., 2006). The optimal
combination of the shear force and the growth of the microorganisms within the
aggregates govern the stable structural formation of the granules (Chen et al., 2008a).
The morphology of these aerobic granules is slightly different as compared to the
aerobic granules developed without the presence of anaerobic granular seed sludge.
Small patches of defragmented anaerobic granular seeds are clearly observed within
the developed aerobic granules.
● ●
● ● ●
Figure 2.2 Schematic diagram of aerobic granulation developed without any carrier
material (Beun et al., 1999)
Inoculation Pellet
formation Shear force
Lysis Granules of bacteria coloni
Oxygen limitation
Colonisation of bacteria
17
Figure 2.3 Granulation development supported by ciliates. A: Formation of floc
where the ciliates settle on other organisms or particles (Phase 1). B: Arrow shows
the colonization of bacteria on ciliate stalks (Phase 2). C: Granules grow into bigger
sizes with dense core. Zooids of the ciliate stalks completely overgrown by bacteria,
die and act as the “backbone” structure (Phase 3). D: Unstalked free swimming
ciliates detach from the biofilm to escape death. Smooth and compact granules area
formed. E: The surviving swarming ciliate cells get attached to the matured surface
granules (shown by arrow) (Weber et al., 2007)
18
Figure***: The
Figure 2.4 The flowchart of the morphological and physical changes of the
anaerobic granules in the process of aerobic granule formation in SBR systems
(Linlin et al., 2005)
2.4 Microbial Structure and Diversity of Microorganisms
The structural and diversity of microorganisms have been one of the main
focuses in a granulation study. A variety of micro-scale techniques and instrument
together with molecular biotechnological approaches have been applied by
researchers in order to obtain a better understanding on the interaction and
mechanisms involved in the process and development of granulation systems.
2.4.1 Microbial Structure
The microscopic structure of aerobic granules have been examined using a
wide range of micro-scale techniques including scanning and transmission electron
Anaerobic granular
seeding; regular shape, black color; D=1.1mm
“Steady stage” granules
D=1.2 mm
Small aerobic granules appear with filamentous
dominancy
Settling time
decreased to 5 min; most SS were washed
out
Yellow colored granules; dominancy by
aerobic microbes, SS increased and recombined
Anaerobic granular seed shrunk and disintegrated due to aerobic condition Day 7 Day 21
Day 35
Day 42Day 50
19
microscopy, confocal laser scanning microscopy combined with fluorescent in situ
hybridization (FISH) and specific fluorochromes. Different arrangement either by
reactor configuration or substrate utilized as the sole carbon source in media
preparation or even different microorganisms specifically used in the granulation
process reveal different microbial structures of the aerobic granules (Toh et al., 2003;
Weber et al., 2007; Lemaire et al., 2008a).
The structure of an aerobic granule consists of different layers occupied by
different types of microorganisms or substances depending on its individual function
in the granulation development (Tsuneda et al., 2004; de Kreuk et al., 2005; Abreu et
al., 2007). Usually the outer layer of the aerobic granule will be conquered by
aerobic or obligate aerobic microbes. For example, ammonium-oxidizing bacterium
Nitrosomonas spp. has been found mainly at a depth of 70 to 100 µm from the
granule surface (Tay et al., 2002a). At the deeper area of the aerobic granules where
oxygen could not penetrate, anaerobic bacteria Bacteroides spp. is found 800 to 900
µm below the granules surface (Tay et al., 2002b).
Most of the structures of the aerobic granules contain channels and cavities
covering thickness areas of 900 µm from the surface of the granules. The pore
structures assist and create pathways for the exchange of nutrient, metabolites and
oxygen moving into and out of the inner parts of the granules to the surrounding
areas. However, at the depth of 300 to 500 µm from the granules surface, the pores
are denser (Tay et al., 2003). The pores at the depth of 400 µm below the granule
surface are filled with polysaccarides. Due to the dense structure, the movement of
oxygen and nutrient are obstructed and has resulted in death to the microorganisms at
the core of the aerobic granules. The layer of the dead microbes is located at 800 to
1000 µm (Toh et al., 2003).
The optimal diameter for aerobic granules is less than 1600 µm in order to
obtain full utilization of the aerobic microbes. This is twice the distance from the
granule surface area before reaching the anaerobic region within the aerobic granules
20
(Tay et al., 2002c). Mushroom-like structures are observed to be present in the
development of aerobic granules mediated with high substrate N/COD ratios (Liu et
al. 2004a). The observation of thick layers of differential mushroom-like structure
has been reported earlier by Costerton et al. (1995) in biofilms of mixed bacterial
communities. Complex microbial community distributes themselves to increase
accessibility towards nutrients, which increases survivality and stability of its micro-
structure (Watnick and Kolter, 2000). A matured granule comprises of two separate
layers which are a dense core zone and a fringe zone. The core zone is consisted of a
mixture of dense rods and cocci bacterial cells and EPS. While, the fringe zone is
represented with a loose structure that comprises of bacteria and stalked ciliates of
fungal filaments (Weber et al., 2007).
2.4.2 Microbial Diversity
Molecular biotechnology has been used in the investigation of microbial
diversity developed within granular biomass (Jang et al., 2003; Lemaire et al.,
2008a; Zhu et al., 2008). Nitrifying, denitrifying, glycogen-accumulating bacteria
and phosphorus-accumulating bacteria are among the identified bacteria that can be
present in the aerobic granules operating under different experimental conditions
(Tsuneda et al., 2003a; Meyer et al., 2006; Lemaire et al., 2008a).
Different types of microbes are observed to dominate within granules which
are closely related to the composition of the culture media. Jiang et al. (2004a) has
used denaturing gradient gel electrophoresis (DGGE) analysis techniques to study
the microbial diversity of the aerobic granules. There was a major shift in the
microbial community as the seed sludge developed into granules. Culture isolation
and DGGE assays confirmed the dominance of beta-Proteobacteria and high-G+C
gram-positive bacteria in the phenol-degrading aerobic granules. By using
Fluorescence in situ hybridization identified by Lemaire et al. (2008a),
21
Accumulibacter spp. (a polyphosphate-accumulating organism, PAO) localized at the
outermost 200 μm region of the granule while Competibacter spp. (a glycogen-
accumulating organism, GAO) dominated in the granule central zone, the area that
could not be penetrated by the oxygen molecules.
2.5 Characteristics of Aerobic Granules
Aerobic granules are known for their characteristics that represent their
outstanding features required for excellent stability and high efficiency performance
of a reactor system making it an innovative modern technology for the wastewater
treatment industry.
2.5.1 Size and Morphology
The size of a granule is an important parameter that can influence the
performance and stability of the reactor system. Granules with bigger diameter can
easily be defragmented under high shear force resulting in high biomass washout.
Meanwhile, granules that are too small cannot develop good settling property which
may end up with higher suspended substances in the effluent. Granules with bigger
sizes will be developed in the SBR system supplied with low superficial air velocity
while smaller granular sizes will be observed formed in systems aerated at higher
superficial air velocity (Chen et al., 2007). Different granular sizes ranging from as
small as 0.3 mm to as big as 8.8 mm in diameter possessing different granular
characteristics were reported by various researchers (Dangcong et al., 1999; Tay et
al., 2003; Zheng et al., 2005).
22
According to Chisti (1999a), the size of the suspended biosolids is controlled
by the hydrodynamic shear force of the reactor system. The size of the aerobic
granule varies depending on the balance between the growth and shear force imposed
by superficial air velocity that give the hydrodynamic shear force on the newly
developed granules. The observed growth of microbial aggregates is the net result of
the interaction between growth and shear forces (Yang et al., 2004a). The usual
reported average diameter of an aerobic granule is in the range of 0.2 mm to 5 mm
(Liu et al., 2003a). Bigger size of aerobic granules has been reported with size of 7-
10 mm (Morgenroth et al., 1997 and Wang et al., 2004). Based on the biological
viability and physical properties, Toh et al. (2003), suggested that for the optimal
performance and economic purposes, the operational size range for effective aerobic
SBR granular sludge should be in the diameter of 1.0-3.0 mm.
The usual structure of the aerobic granule is normally spherical in shape with
smooth surface areas (Peng et al., 1999; Zhu and Wilderer, 2003; Adav and Lee,
2008a). The morphology of the granules can be influenced by the type and
concentration of substrate used in the media compositions. Based on the electron
microscope (SEM) observations, glucose-fed granules appeared with fluffy outer
surface due to the predominance of filamentous bacteria growth. On the other hand,
the acetate-fed granules showed compact microstructure and smooth outer surface.
The non-filamentous and rodlike bacteria were observed dominating the acetate-fed
granules that are tightly linked together (Tay et al., 2001a).
Since difference type of microorganisms may predominate at different
substrate concentration levels, using different concentrations of substrate in granules
development may influence the structural and morphology of the developed granules.
The growth rate of filamentous organisms is shown to be higher at lower substrate
concentrations as compared to floc forming organisms (Schwarzenbeck et al., 2005).
Fluffy and loose morphology, mainly occupied by filamentous bacteria are observed
in granules cultivated at low organic loading rate (OLR). However, the granular
structures evolved into smooth irregular shapes with fold, crevices and depressions at
higher loading rate. The irregular structures are thought to allow better diffusion and
23
penetration of nutrients into the internal part of the granules and the same goes for
the metabolites excreted out from the granules to the surrounding areas (Moy et al.,
2002).
2.5.2 Settleability
Settleability of a granular sludge shows the aptitude of the granule to settle
down within a specified period of time. Good settleability properties are indicated
by fast and clear separation between the sludge biomass and the effluent. It can be
represented by the settling velocity and sludge volume index (SVI) of a particular
granule. The settling velocity of aerobic granules is in the range of 30 to 70 m/h
depending on the size and structure of the granules. The settling velocity of the
aerobic granules is comparable to the anaerobic granules. Settling velocity of
activated sludge flocs is in the range of 8 to 10 m/h which is three times lower than
to those of aerobic granules. Good settleability profile of aerobic granules is
desirable in wastewater treatment plants as good settling properties facilitate a high
percentage of sludge retention in the reactor system. Superior characteristic of
settleability assist to maintain the stability performance of the reactor system, show
high removal efficiency and can be used for wastewater with high hydraulic loading
(Beun et al., 2000 and Tay et al., 2001b).
Sludge volume index represents the volume of 1 g of sludge that can settle
within 30 min (Tchobanoglous et al., 2004). The SVI of conventional bioflocs is
very much higher as compared to the SVI of the aerobic granules indicating very
poor settling property. The bioflocs with average diameter around 70 µm have the
SVI value of 280 ml/g which is mainly dominated by filamentous bacteria (Tay et
al., 2001b). The SVI value of flocs in an activated sludge system is observed to be
above 150 mL/g (Crites and Tchnobanoglous, 1998). Granular sludge, on the other
hand, has SVI of lower than 100 mL/g (Peng et al., 1999, Liu et al., 2003b and Qin et
24
al., 2004a). Most of the sludge biomass will be retained in the clarifier and can avoid
washout.
2.5.3 Density and Strength
In environmental engineering, the density of microbial aggregates is
frequently used to describe the strength and compactness of the microbial interaction.
The observed density of microbial aggregates is the consequence of balance
interaction between cells (Liu and Tay, 2004). The density of the aerobic granule is
reported to be in the range of 32.2 to 110 g VSS/L (Beun et al., 2002; Arrojo et al.,
2006; Di laconi et al., 2006). The biomass density of detached bioflim particles from
the biofilm airlift suspension reactor is 15 g/L particles, lower compared to 48 g/L of
the biomass density of aerobic granules developed in the sequential batch airlift
reactor. Both of the reactor systems are operated at the same organic loading rate
and same superficial air velocity (Beun et al., 1999).
The specific gravity of aerobic granules is in the range of 1.004 to 1.065
(Etterer and Wilderer, 2001, Liu et al., 2004a; Yang et al., 2004a). It is observed that
when the aerobic granules grow bigger the compactness of the granules decreases
revealing less solid and loose architectural assembly. In other words, granules with
smaller sizes are more compact as compared to larger aerobic granules (Toh et al.,
2003). Liu et al. (2004a) reported that, as the specific growth rate reduce from
0.1/day to 0.04/day, smaller aerobic granular size but with higher specific gravity
(1.065 to 1.015) are formed. This indicates a compact and stronger formation of
microbial structure.
Granules which can withstand high abrasion and shear force are considered as
granules that possess high physical strength. The physical strength of the granules is
25
expressed as the integrity coefficient, an indirect quantitative measurement on the
ability of the granules to endure the hydrodynamic shear force often imposed on to
the granules during the reactor operations (Ghangrekar et al., 2005). This is
measured by placing the granules in a conical flask subjected to 200 rpm agitation
speed for 5 minutes. The parts that are loosely attached within the granules will be
detached and known as the residual of the granules. The ratio of residual granules to
the total weight of the granular sludge represents the integrity coefficient of the
granules. A good granular strength is indicated with the integrity coefficient of
lower than 20.
2.5.4 Cell Surface Hydrophobicity
It has been reported that the formation of biofilm and anaerobic granules are
very much affected by the changes in the physico-chemical properties of cell surface
(Zita and Hermansson, 1997 and Kos et al., 2003). When the bacterium approaches
another bacterium, there will be energy involved as the crucial force (hydrophobic
interaction) in the formation of the adhesive connection (Liu et al., 2004b).
Cell adhesion process is governed by three important forces which are
electrostatic force, Van der Waals force and polymeric interaction (Azeredo and
Oliveira, 2000). Since all bacteria cells have negative surface potential, electrostatic
force caused repulsion between cells. Meanwhile, the Van der Waals force and
polymeric interaction are the attractive forces. However, the Van der Waals force is
considered as an independent environmental factor so the adhesion of cell is
governed more by the electrostatic force and polymeric interaction. The polymeric
interaction is enhanced by the presence of the EPS. Any changes in the EPS
production and composition will be reflected by alteration of the physicochemical
characteristic of the cellular surface including surface charges, hydrophobicity and
other properties (Wang et al., 2006a).
26
Aerobic granulation can be regarded as a microorganism-to-microorganism
self-immobilization process, in which cell hydrophobicity could be used as a decisive
parameter in determining the microbial interaction and the structural compactness of
aerobic granules (Liu et al., 2004b). Cell hydrophobicity is an important affinity
force in cell self-immobilization which governed the mechanisms of cell adhesion
(Daffonchio et al., 1995 and Kos et al., 2003). The cell hydrophobicity is believed to
be the main triggering force in the initial stage of the biogranulation process and
strengthen the cell-to-cell interaction (Liu et al., 2003b). Lin et al. (2003) reported
that the formation of heterotrophic and nitrifying granules show nearly two fold
higher of cell surface hydrophobicity as compared to the bioflocs.
2.5.5 Specific Oxygen Utilization Rate
Measurements on the activity of certain enzymes or specific products of the
bacterial metabolism are among methods available to evaluate the activity of the
activated sludge (Lazarova and Manem., 1995). Specific oxygen utilization rate
(SOUR) is a useful parameter that can be used as an indicator of microbial activity of
the microorganisms. The effect of any alteration on the physical and chemical
conditions of the reactor system on microbial activity can be represented by
measuring the SOUR. The value can be regarded as an important parameter that can
be used to assist the permissibility of substance loading rate most importantly onto
treatment of toxic chemicals such as phenol or any petrochemical substances. The
SOUR values measured for aerobic granules have been reported by many
researchers.
The values vary depending on various aspects such as biomass density of the
microbes involved, types and concentration of substrates used as well as the
conditions of experiment (Zhu and Wilderer, 2003; Ergurder and Demirer, 2005a;
Liu and Tay, 2007a; Chen et al., 2008b). Granules that contain high concentrations
27
of Ca2+ seem to give lower SOUR values as compared to the non-Ca2+-accumulated
granule. It has been suggested that the presence of too much of Ca2+ might give a
negative effect on the bioactivity of the granules (Ren et al., 2008). Increase in the
hydrodynamic shear force in terms of superficial air velocity will significantly
increase the SOUR level by increasing the respiration activities of the
microorganisms (Tay et al., 2001a). This may be due to the fact that the high
hydrodynamic shear force causes increment on rate of the oxygen transfer between
the granules and the liquid interface (Chisti, 1999b). Linear correlation is observed
on the biochemical reaction between the oxygen consumption that represents the
bioactivity of microbial metabolisms with the production of carbon dioxide. At high
metabolism rates where high oxygen utilization occurred, less cell mass is produced
and more of the substrate is converted into carbon dioxide (Tay et al., 2004).
The SOUR values are inversely related to the settling time imposed by
hydraulic selection pressure onto the microorganisms in the reactor system (Qin et
al., 2004b). Changes in the hydraulic selection pressure are able to regulate the
respiratory activity of the microorganisms. The SOUR of microorganisms is also
affected by the long storage of aerobic granules under anaerobic conditions. The
SOUR value of granular sludge decreased after a long storage (Zhu and Wilderer,
2003).
2.5.6 Storage Stability
The condition during the storage of granules is another important aspect that
needs to be considered. Without proper storage, granules may lose its stability and
microbial activities within the granules may deteriorate. These may affect the
characteristics and performances of the granules. The obvious changes that could be
observed after a long storage in tap water are the changes in color of the aerobic
granules which turn from brownish-yellowish (fresh aerobic granules) to gray and
28
black. However, aerobic granules stored in phosphate buffer saline solution
experienced less color changes. It is expected that the color changes are due to the
anaerobic metabolisms generated from stored aerobic granules (Ng, 2002 and Tay et
al., 2002c).
Granules may lose its microbial activity and stability when stored for an
extended period and are also closely related to the storage temperature. The granules
experience an endogenous respiration and disintegration of the granules during
storage at high temperatures and without any supplement of external carbon sources.
Long storage periods at cool temperatures were reported to cause decrease in the
granular strength as compared to fresh aerobic granules. The strength of glucose-fed
and acetate-fed granules both reduced by 7-8% after four months stored at 4oC (Tay
et al., 2002c and Liu and Tay, 2004).
2.5.7 Exopolysaccharides
The extracellular polysaccharides substances (EPS) are metabolic products
secreted by microorganisms in the form of sticky material (Liu et al, 2004c). EPS
consists of a variety of organic substances such as polysaccharide (PS), exoprotein
(PN), deoxyribonucleic acid (DNA), humic acid, uronic acid and other materials
(Matthew and John, 1997 and Wang et al., 2005a). They act as a buffering stratum
for cells against a harsh exterior environment. Under nourished conditions, EPS
would serve as carbon and energy source (Liu et al., 2002 and Zhang and Bishop,
2003). EPS are thought to act as the glue that holds the bioflocs together to form
bigger aggregates (Matthew and John, 1997). They are responsible to mediate both
the cells cohesion and adhesion, and play a crucial role in maintaining structural
integrity of the biofilm matrix. The networking between cell and EPS would form
aggregates that led to the formation of biofilm (Nielsen et al., 1997 and Zhang et al.,
2007b). The aggregates combine through several binding interaction such as specific
29
protein–polysaccharide interactions, hydrophobic interactions, hydrogen bonding,
and ionic interactions (Zhang et al., 2007a).
2.6 Factors Affecting the Formation of Aerobic Granules
The formation of aerobic granules can be affected by many factors and
conditions. Factors that have been identified to influence the granular formation
include substrate composition, OLR, hydrodynamic shear force, feast and famine
regime, feeding strategy, SRT, concentration of dissolved oxygen, reactor
configuration, settling time and volumetric exchange ratio. Of all the listed factors,
the major selection pressures responsible for the successful aerobic granular
formations are the settling time and volumetric exchange ratio (Liu et al. 2005a).
Unsuitable adjustment on the values for the settling time and the volumetric
exchange ratio will lead to the failure of granules formation.
2.6.1 Substrate Composition
Different substrate composition used as the source of energy in the aerobic
granules development resulted with different granular structures and microbial
diversity found within the granules. The microstructure and species diversity of
aerobic granules are closely related to the type of carbon source used. Glucose-fed
aerobic granules exhibit a filamentous structure, while acetate-fed aerobic granules
are dominated by the rodlike bacteria with very compact structure. Formation of
aerobic granules is a process independent of the characteristics of the feed
wastewater (Beun et al., 1999; Moy et al., 2002; Jiang et al., 2002; Arrojo et al.,
2004).
30
The size of developed granules is also affected by the type of substrate
presence in the media used as the feed solution. Amongst the four substrates (i.e
glucose, glucose with acetate acid, acetate acid and ethanol), ethanol-fed granules
appeared to be the largest and most stable granules as compared to others (Erguder
and Demirer, 2005b).
Aerobic granules developed with the presence of nitrogen and carbon sources
have resulted with the co-existing of heterotrophic, nitrifying and denitrifying
microniches within the granules. The activities exhibited by the different
microniches are found governed by the substrate N/COD ratio. The nitrifying
activity is significantly enhanced with the increase of the substrate N/COD ratio,
while the heterotrophic activity is decreasing (Yang et al., 2004b). More compact
granular structure is developed with high substrate N/COD ratio. The Kagg value that
represents the equilibrium position of a microbial aggregation process and ρeq, the
density of aerobic at equilibrium, shows an increasing trend as the substrate N/COD
ratio increases (Liu and Tay, 2004).
2.6.2 Organic Loading Rate
Aerobic granules can be cultivated in a wide range of organic loading rates
(2.5 -15 kg COD/m3·day) demonstrating that the level of organic loading rates have
insignificant effect on the formation of the aerobic granules (Moy et al., 2002; Liu et
al., 2003d; Yang et al., 2004b). However, different concentrations of the OLR
greatly influenced the characteristic of the formed granules.
At OLR of 1.68 kg COD /m3·day, smaller granules are developed containing
mainly bacteria microcolonies with minimal settling velocity of 9.6 m/h. When the
OLR is increased to 4.2 kg COD/m3·day, denser and more compact granular structure
31
with improved settling velocity are observed. The granular structure of higher OLR
is dominated by bacteria with different types of morphotypes (Li et al., 2006a). Liu
et al. (2003b) reported, increase in organic loading from 3 to 9 kg COD/m3·day
resulted in an increase of the mean granular size from 1.6 to 1.9 mm. However, the
physical strength of aerobic granules decreased as the organic loading rate is
increased. This is associated with the increased in the biomass growth rate that has
caused reduction in the strength of the three-dimensional structure of the microbial
community (Liu et al., 2003a). Tay et al. (2004) observed when the OLR was lower
than 1-2 kg COD /m3·day, the development of granules would be a failure.
However, at too high OLR (more the 8 kg COD /m3·day), unstable granules with
destruction on granular strength and structural integrity would appear. Having OLR
set at about 4 kg COD /m3·day, stable granules are developed, characterized by high
removal performance (99% of soluble COD removal) and good settleability
properties with SVI of 24 mL/g MLVSS (Tay et al., 2004).
From the perspective of microbiological surface properties, a higher ratio of
extracellular protein to polysaccharides showed more percentage of surfaces
hydrophobic and less negative surface charge. This condition is suitable for
granulation development. At higher OLR (4-12 g COD/L·day), decrease of the
protein secretion and increase in the polysaccharides concentration in the sludge EPS
have been observed indicating a low extracellular protein to polysaccharides ratio.
This condition is inappropriate for granules formation. This explains why the
disintegration of granules occurred when the OLR increases between 10-12 g
COD/L·day (Zhang et al., 2007b).
2.6.3 Hydrodynamic Shear Force
Through observation, diverse characteristic with respect to the physical
changes on the granular structure is developed under different pressure imposed by
32
the hydrodynamic shear force. Aerobic granules can be formed at hydrodynamic
shear force in terms of superficial upflow air velocity of above 1.2cm/s in a SBR
column. When the system is operated with superficial air velocity of less than 0.3
cm/s, it is only filled with bioflocs (Tay et al., 2001c).
Stable and robust granular structure for long-term reactor operation could be
achieved in the system operated with high hydraulic shear force (2.4-3.2 cm/s).
Granules developed at higher hydraulic shear force will be smaller in size but more
regular, rounded and compact. However, large-sized filamentous granules with
irregular shape and loose structure can lead to poor performance, and operation
instability can occur in systems run with low hydraulic shear force (0.8-1.6 cm/s)
(Chen et al., 2007). In terms of equilibrium size and size-dependent growth rate, the
growth of aerobic granules are inversely related to shear force imposed to microbial
community, while a high organic loading favors the growth of aerobic granules,
leading to large size granules (Yang et al. 2004b). The compactness of the granular
structure formed under high superficial air velocity is due to the excretion of the EPS
and reduction of surface free energy (Beun et al., 1999 and Liu et al., 2004d).
The hydraulic shear force not only influences the physical structure of the
formed granules but also affect the metabolic behavior of the microbes within the
granules. When the superficial gas velocity increases, the respiration activity
(SOUR) and the ratio of sludge polysaccharides to sludge-proteins are also increased
(Tay et al., 2001c). The changes on the bioactivity among the microorganisms under
high shear force are directed with the formation of larger, compact and stable
granules. Mild shear force at agitation rates of 400-600 rpm exerts biological floc
with higher surface hydrophobicity, larger floc size and lower sludge volume index,
indicating a favorable condition for settleable floc growth (Liu et al., 2005b). From
an engineering point of view, hydrodynamic shear force can be manipulated, as a
control parameter, to enhance the microbial granulation process (Liu and Tay, 2002).
However, Liu and Tay (2002) have added further that the hydrodynamic shear force
is not a primary inducer for the aerobic granulation in the SBR.
33
2.6.4 Feast and Famine Regime
Sequential batch reactor which operates with intermittent feeding strategy can
cause the microorganisms within the reactor to experience periodic starvation
through feast and famine regimes. Under periodic starvation conditions, the
microorganisms become more hydrophobic and high cell hydrophobicity that
facilitates microbial aggregation (Bossier and Verstraete, 1996 and Liu et al., 2004b).
Periodic feast and famine regimes can be regarded as a kind of selection
pressure for the microbes that could cause alteration of the cell surface properties and
lead to more of cell aggregation. According to Liu et al. (2005b), high feast-famine
ratio feeding applied to SBR systems may influence the characteristic of the
developed granules that lead to the formation of dense and compact aerobic granules.
Prolonging famine regime means an increase in the starvation period. Under
starvation conditions, bacteria become more hydrophobic and facilitate more
microbial adhesion and aggregation. Since aggregation is an effective strategy of
cells against starvation, utilizing prolong starvation treatment would improve the
efficiency of bioaugmentation. The starvation phase has caused a decrease in surface
negative charge from 0.203 to 0.023 meq/ g VSS and an increase in the relative
hydrophobicity from 28.8 to 60.3% of aerobic granules. The EPS, especially protein
concentrations, are well correlated with surface charge and relative hydrophobicity.
It is concluded that a reasonable amount of EPS should be controlled to form and
maintain aerobic granules and starvation is important in initiating the aerobic
granulation (Li et al., 2006b). However, according to Liu et al. (2007b), the
starvation phase in aerobic granulation is not a prerequisite since granules are
developed in SBR systems operated with 1 hour cycle time operation. However,
prolonged starvation times exhibited more stable granule formation. The starvation
time of a system may need to be controlled in a reasonable range.
34
2.6.5 Hydraulic Retention Time
Hydraulic retention times of 4 to 6 hours have resulted in a good granulation
process while higher HRTs (i.e 24 hours) could not support granulation. HRTs in the
range between 2 to 12 hours are favorable for formation of stable aerobic granules
with good settling properties and activity (Pan et al., 2004). At shorter HRTs (i.e 1.5
hours), the granulation process speeds up due to strong hydraulic selective pressure.
However, the structures of the granules are fluffy and exhibit poor settling ability
that demonstrated very unstable granules (Liu and Tay, 2008). Granules cultivated at
HRT ranging between 6 to 12 hours, possess high percentage of cell hydrophobicity
as compared to the granules developed with HRT of 24 hours (Liu et al., 2003c).
2.6.6 Presence of Inorganic Composition
The presence of divalent ions are reported to enhance microbial aggregation.
Ca2+ probably acts as a constituent of extracellular polysaccharides or proteins used
as linking materials (Grotenhuis et al., 1991a).
The additional Ca2+ has accelerated the aerobic granulation process and
produced better settling and strength of aerobic granular sludge properties, and also
exhibited higher polysaccharide contents (Bruus et al., 1992). Augmentation with
100 mg of Ca2+/L, speed up the granulation development from 32 days to 16 days.
The Ca2+ binds to the negatively-charged groups present on the bacterial surface and
the EPS to form a strong and sticky non deformable polymeric gel-like matrix (Jiang
at el., 2003).
35
Ca2+-rich granules have successfully developed after 3 months operation in
SBR systems supplied with media-rich Ca2+ (40 mg/L). The granules exhibited
higher granular strength with increasing granular size. Calcium-fed granules with
calcium content from 89.8 to 151 mg/g SS show compressive strength of 0.16-0.42
N/mm2. However, high accumulation of Ca2+ has reduced microbial activity (Ren et
al., 2008).
Aerobic granules are also capable of absorbing inorganic compounds. The
increased absorption of Ni2+ is observed with the increase of pH from pH 2 to pH 6
with the maximum absorption occurring at pH 6. Large quantities of K+, Mg2+ and
Ca2+ are released when Ni+ is being absorbed into the granules indicating an ion
exchange mechanism that take place (Xu et al., 2006).
2.6.7 Concentration of Dissolved Oxygen
The concentrations of DO in the reactor for aerobic granules development is
not considered as the decisive parameter. This is because aerobic granules can be
developed at DO concentrations as low as 0.7-1.0 mg/L and as high as 6 mg/L (Yang
et al., 2003; Qin et al., 2004a; Tsuneda et al., 2004).
Aerobic granules developed under low DO concentrations (0.5 -2.0 mg/L)
produce sludge with poor settling properties and high turbidity in the effluent.
Deterioration on settling properties of the sludge is associated with excessive growth
of filamentous bacteria and the formation of porous flocs (Martins et al., 2003 and
Liu and Liu, 2006). High oxygen concentrations are required to obtain stable
granules (de Kreuk and van Loosdrecht, 2004).
36
The percentage of dissolved oxygen that enables penetration into the granules
depends on the size of the granules. The diffusivity of dissolved oxygen becomes a
major limiting factor for metabolic activity when the size of the granules is more
than 0.5 mm (Li and Liu, 2005).
Different types of substrate used to feed the granules formation exhibit
different properties with respect to oxygen diffusivity. Acetate-fed granule sizes
between 1.28-2.5 mm show oxygen diffusivity coefficients of 1.24-2.28×10-9 m2/s
while phenol-fed granule sizes between 0.42-0.78 mm exhibit oxygen diffusivity
coefficients of 2.50-7.65×10-10 m2/s. Oxygen diffusivity declines as the diameter of
the granules increase. The diffusivity of oxygen for acetate-fed granules is
proportional to the granular size. Meanwhile, for phenol-fed granules the diffusivity
is proportional to the square of the granule diameter. The different patterns of oxygen
diffusivity among the two types of granules is due to higher secretion of extracellular
polymer content by phenol-fed granules, yielding lower oxygen diffusivity (Chiu et
al., 2006).
2.6.8 Slow Growing Organisms
Slow growing organisms can be used to improve the stability and removal
efficiency of the aerobic granular sludge as seen in the SBR system supplemented
with low oxygen concentrations (Beun et al., 2002). Slow growing bacteria with a
low growth yield are more capable to grow as granules than fast growing aerobic
heterotrophic bacteria. Selection of slow growing bacteria can be achieved by having
a long anaerobic feeding period followed by an anaerobic reaction phase (Brdjanovic
et al., 1998 and de Kreuk and van Loosdrecth, 2004).
37
By having substrate concentrations with a high ratio of N/COD, the nitrifying
population will be enriched. This would enhance the slow growth rate of the aerobic
granules. At this condition, a smaller size of matured granules are observed with
strong structure and good settleability as compared to the granules generated with
high growth microbial rates (Liu et al., 2004a).
2.6.9 Settling Time
Settling time is considered as one of the most decisive factor in the formation
of aerobic granules in a SBR system (Liu et al., 2005a). For a successful formation
of aerobic granules, short settling time is compulsory. If the settling time is not short
enough, the dominancy of granular biomass will not happen.
Aerobic granules are successfully cultivated and become dominant in SBR
systems operated with 5 minutes settling time. Whereas, a mixture of aerobic
granules and suspended sludge is observed in a reactor system operated at settling
times longer than 10 minutes (Qin et al., 2004b). When the SBR system is operated
with 2 to 5 minutes settling time, complete granular biomass is formed in the reactor.
These granules contain higher EPS protein with improved cell surface
hydrophobicity (McSwain et al., 2005 and Qin et al., 2004b).
A short setting time exerts a major hydraulic selection pressure that would
select good settling bioparticles for granulation. Short settling time allows more
sludge floc to retain in the reactor and overcome the presence of bioparticles. This
will result in a successful development of the granulation process (Lin et al., 2003;
Wang et al., 2004; Hu et al., 2005).
38
2.6.10 Reactor Configuration
The configuration of the reactor gives an impact on the liquid flow pattern
and able to cause microbial aggregates in the reactor. In a column type reactor,
allowing the air or liquid upflow, creates a relatively homogenous circulation flow
and localized vortex along the reactor height (Beun et al., 2002 and Liu and Tay,
2002). This pattern forces the microbial aggregation to adopt a regular granular shape
that has minimum free surface energy. The SBR should have a high H/D ratio to
improve selection of granules by the difference in settling velocity (Beun et al.,
1999).
Successful development of stable aerobic granules is observed in an airlift
reactor operated at superficial air velocity of 1.2 cm/s. The reactor is designed with
an additional draft tube placed in the reactor column. Such configuration is believed
to be able to increase the shear forces built up in the reactor system compared to a
bubble column reactor configuration (Liu et al., 2007b).
2.6.11 Volumetric Exchange Ratio
The volumetric exchange rate (VER) and settling time are the most important
factors that determine the successful development of the aerobic granules (Liu et al.,
2005c). The fraction of aerobic granules in the total biomass is proportional to the
VER. The reactor is dominated by aerobic granules when the VER of the reactor
system is designed with higher percentage of about 60-80%. However, when the
VER is 40% or less, a mixture of aerobic granules and suspended sludge will be
present. The production of EPS is stimulated significantly by high VER (Wang et
al., 2006b and Zhu and Wilderer, 2003).
39
2.7 Applications of Aerobic Granules in Wastewater Treatment Systems
Aerobic granules are a rapidly emerging technology in biological wastewater
treatment. The applications of aerobic granules improve process efficiency by
allowing high and stable biodegradation conversion rates, efficient biomass
separation and high microorganism accumulation within the aggregates. Aerobic
granular sludge systems have been reported capable of treating high-strength organic
wastewater and could be used to remove a wide range of pollutants. Biogranulation
is capable of accommodating a wide range of treatment capacities with varying
loading rates, wastewater composition and treatment goals. Biogranules could be
used for a specific treatment target by developing specific granules for specific
treatment.
.
2.7.1 High Strength Organic Wastewater Treatment
One of the prominent characteristics of aerobic granular sludge is the ability
to retain high concentrations of biomass. Such ability enables the aerobic granular
sludge system to withstand and treat high strength organic loading wastewater. The
feasibility on treating high strength organic loading by using this system has been
demonstrated by several researchers such as Moy et al. (2002), Jiang et al. (2004b),
Eckenfelder et al. (2006) and Chen et al. (2008a). An average of 3.3 mm of aerobic
granules was developed with biomass density of 11.9 gVSS/L granule in systems
supplied with organic loading substrate of 7.5 kg COD/m3·day. When higher organic
loading is applied, a balance between the hydrodynamic shear force and the level of
organic loading rate used is very important for the production of more compact
granules and stability of the reactor system (Buen et al., 1999).
40
Moy et al. (2002) has successfully cultivated glucose-fed aerobic granules at
higher organic loading rates (6–15 kg COD/m3·day). At OLR of 15 kg COD/m3·day,
the performance of the reactor system showed more than 92% of COD removal. The
COD removal rate was observed high at various organic loading rates (6 to 12 kg
COD/m3·day) indicating high granular bioactivity with good reactor performance
(Chen et al., 2008a).
Jiang et al. (2004) reported that as the concentration of phenol loading
increases from 1.0 to 2.5 kg phenol/m3·day, an obvious influence on the structure,
activity and the metabolism of the aerobic granules was observed. The granular
system shows complete phenol degradation at all different phenol concentration
levels used in the study. The structure, activity and the rate of metabolisms of the
granules, increased and at a peak when the concentration of phenol loading is
increased from 1.0 to 2.0 kg phenol/m3·day. Compact granules with good setteability
properties are sustained at the loading rate until 2.0 kg phenol/m3·day. However,
those characteristics start to decrease when the loading is increased to more than 2.0
kg phenol/m3·day. The granular structure starts to weaken when the phenol loading
is increased to 2.5 kg phenol/m3·day, due to the toxic effect of the phenol compound.
2.7.2 Simultaneous Organics and Nitrogen Removal
Nitrification and denitrification for complete mineralization of nitrogen
compound is observed to be carried out by aerobic granules. Arrojo et al. (2004) had
successfully showed the simultaneous removal of organics and nitrogen with
removal efficiencies of 80% and 70% respectively. This has been conducted at high
organic and nitrogen loading rates of 7 g COD/ (L·d) and 0.7g NH4+-N/(L·d),
respectively, of a dairy wastewater. Beun et al. (2001) had also reported on the
application of aerobic granules in the nitrification and denitrification processes. The
ammonia oxidizing bacteria responsible for the nitrification process has been found
41
localized at the upper layer of the granules and down to 300 μm into the granular
thickness. The denitrification process takes place at the inner core of the granules
where the oxygen could not penetrate. The coexistence of heterotrophic and
nitrifying populations in aerobic granules has also been reported by Jang et al.
(2003).
Qin and Liu (2006) have successfully used microbial granules cultivated
under alternating anaerobic and aerobic reaction phases in the SBR system. The
system shows an effective percentage removal for organic carbon (95-97%) and
complete conversion of ammonia to nitrogen gas (99-100%). The results give an
indication on the coexistence of heterotrophic, nitrifying and denitrifying populations
in the microbial granules.
The activities of the nitrifying and denitrifying populations are very much
affected by the N/COD ratio and the levels of dissolved oxygen. High N/COD ratios
enhance the performance of the nitrifiers. However, the activities of the heterotropes
population within the granules are reduced with the increase in the N/COD ratio. A
sufficient level of dissolved oxygen concentration is required for a sufficient mass
transfer between the liquid and granules during denitrification (Yang et al. 2003).
The successful cultivation and performance of nitrifying organisms within the
granular structure show that the aggregates are able to act as an effective protection
for the sensitive nitrifying population. This shows that granulation systems are
adaptable to treatment for different types of wastewater compositions.
2.7.3 Phosphorus Removal
Removal of phosphorus by aerobic granules has been studied by several
researchers including Cassidy and Belia (2005), De Kreuk et al. (2005) and Lemaire
42
et al. (2007). Aerobic granules can be designed to treat different types of pollutants.
This could be achieved through incorporating specific degrader microbes during the
development of the aerobic granules. Lin et al. (2003) had successfully developed
aerobic granules containing phosphorus-accumulating microbes by applying
substrate P/COD ratio ranging from 1/100 to 10/100 in a SBR system. Phosphorus-
accumulating microbial granules are developed with an attempt to improve the
problems associated with phosphorus removal by conventional biological treatment.
Phosphorus-accumulating microbial granules are reported capable of adsorbing
phosphorus in the range of 1.9% to 9.3% by weight of the granules. There would be
an uptake of soluble organic carbon and release of phosphate during the anaerobic
stage followed by rapid phosphate uptake in the aerobic stage. This typical profile of
soluble COD and PO4-P could be an indication for the typical P-accumulating
characteristics. The accumulated phosphorus decreased with an increase in the
substrate P/COD ratio. A 2.5% of influent P/COD ratio resulted in an accumulation
of 6% of P in the granules. The same percentage of accumulated P has been reported
by Cassidy and Belia (2005) with the use of influent P/COD ratio of 2.8%. Over
98% of COD and P removal and over 97% removal of N and VSS are reported in
treating abattoir wastewater by using aerobic sludge granules.
De Kreuk et al. (2005) has claimed that at low oxygen concentrations (20%),
simultaneous COD, N and P removal could occur since heterotrophic growth was
able to develop inside the granules. Accumulibacter spp (a polyphosphate-
accumulating organism, PAO) and Campetibacter spp (a glycogen non-
polyphosphate-accumulating organism, GAO) are incorporated in the development
of aerobic granules in a SBR system with alternating aerobic and anaerobic reaction
periods by Lemaire et al. (2008b). The PAO spp. dominated the 200 μm of the outer
region of the granule while the Campetibacter spp. dominated in the core zone of the
granule. This aerobic granule is able to demonstrate a good phosphorus and nitrogen
removal.
43
2.7.4 Phenol Wastewater Treatment
Biological degradation of phenolic wastewater is generally preferred due to
lower cost and the possibility of having complete mineralization process.
Degradation at low concentrations of phenol was successful but dealing with high
concentrations of phenol-containing wastewater exerted toxicity effect by the
substrate itself. High concentrations and fluctuations of phenol load cause
breakdown of the activated sludge processes (Watanabe et al., 1999) and death to the
phenol–degrading bacteria. However, phenol degradation by using aerobic granules
displays an excellent degradation performance (Chou et al., 2004, Chou and Huang,
2005; Tay et al., 2005a).
Chou et al., (2004) has reported the percentage of COD removal of phenol-
containing wastewater was high with an average removal of 93.9% when the system
operated at 25oC. The COD removal is higher and reached 97.9 to 98.2% when the
temperature is increased from 30 to 40oC. Tay et al., (2005b), reported the phenol
degradation rate of aerobic granular biomass was not affected by the increase of
phenol loading rate from 0 to 2.4 kg/ m3·day.
The microbial aggregation matrix within the compact granules is likely to
serve as an effective protection barrier against high phenol concentrations. Due to
the diffusion limitation, a substrate concentration gradient is developed at the surface
of the granular matrix. This condition seems to be able to protect the
microorganisms from toxicity effect by means of diluting the chemical compound
below some threshold value and avoid substrate inhibition (Rittmann and McCarty,
2001 and Liu and Tay, 2004). Adav et al. (2007) reported that aerobic granules are
capable of degrading phenol at 1.18 g phenol/g VSS/d. An addition of co-substrate
such as glucose and ethanol is capable of treating phenol-containing wastewater
(Wang et al., 2007 and Zhang et al., 2008).
44
2.7.5 Biosorption of Heavy Metals and Nuclear Waste
Aerobic granules seem to have the ability as a biosorbent towards some
heavy metals that are often found in a wide variety and range of industrial
wastewaters. Aerobic granules have shown capability in absorbing the heavy metals
as revealed by other biomaterials such as fungus (Kumari and Abraham, 2007 and
Patel and Suresh, 2007), marine algae (Daneshyar et al., 2007), waste activated
sludge (Otero et al., 2003) and biosludge (Wang et al., 2006c). Granules have large
surface area, high porosity and good settling properties that can be responsible for
the performance as a good biosorbent.
Concentration gradient has become the driving force for the absorption of
metals onto aerobic granules. The maximum biosorption capacities of individual
Cu2+ and Zn2+ by aerobic granules are closely related to the initial concentrations of
the metals in the reactor i.e. 246.1 mg/g and 180 mg/g respectively (Xu et al., 2006).
Sun et al., (2008a) revealed that in the adsorption mechanisms, the functional groups
such as alcoholic and carboxylate of the aerobic granules would be the active binding
sites for the biosorption of Co2+ and Zn2+. The maximal adsorption capacity of the
granules was 55.25 mg/g of Co2+ at pH 7 and 62.50 mg/g of Zn2+ at pH 5.
The ability as novel biomaterials for nuclear waste (soluble uranium) removal
by the aerobic granular sludge has been demonstrated by Nancharajah et al. (2006).
The effect of different pH levels (pH 1 to 8) and initial uranium concentrations (6 to
750 mg/L) are among the main focus of the study. In less than one hour, almost
complete removal of uranium at concentrations ranging between 6-100 mg/L is
reported. Rapid biosorption occurred in a pH range of 1 to 6 as compared to pH 7
and above. In the biosorption of uranium, an ion-exchange mechanism is observed
to take place. Light metal ions such as Na2+, K2+, Ca2+ and Mg2+ are simultaneously
expelled from the granules during the absorption of the uranium. The maximum
biosorption capacity of uranium is reported to be at 218 ± 2 mg/g dry granular
biomass.
45
As a conclusion, granulation systems offer good removal performance of
various types of pollutant. However, the behavior with respect to the removal
performance, changes in the physical characteristics of the granules as well as the
stability of the reactor systems varies in treating different types of pollutants. The
biological activity and microbial diversity within the granules may also differ with
the granules used in treating different types of wastewater. Even though there are
many studies being reported on the use of anaerobic granular biomass in treating
textile wastewater, the knowledge of the use of aerobic or facultative anaerobic
granular biomass particularly with the application of intermittent anaerobic and
aerobic reaction phase is still lacking. Therefore, this study is conducted with the
objective of filling the insufficient knowledge with regard to the use of facultative
anaerobic granular biomass in treating textile wastewater.
CHAPTER 3
DYE DEGRADATION PROCESS
3.1 Textile Industry
The textile industry, which is known as one of the main industrial trades,
established its first textile processing factory way back in the 1500s (Neefus, 1982).
The world production of natural and chemical fibres in 2003, have reached almost 63
million tonnes, 1.8 million tonnes or 2.4% more as compared to the production in
2002 which provided huge advantages for world economic values (Aizenshtein,
2004). In social terms, it gives benefit to more than 2.2 million workers through
114,000 textile-related companies with a turnover of about 198 billion Euros. In
2001, the European textile and clothing industries have contributed to about 3.4% of
the EU manufacturing industry’s revenue and granted 6.9% work opportunity to the
citizens (IPPC, 2003).
Malaysia is also known for its textile and apparel, recognized around the
world for quality and reliability. It has become one of the important industrial
activities of this country. When the country started to embark on a path of export-
oriented in the early 1970s, the growth on Malaysian’s textile and apparel industry
shot up very high and accelerated with an export valued at RM 10.3 billion. This has
47
listed the textile industrial activities as the ninth largest contributor to total earnings
from manufactured exports in 2007. The industry has provided more than 67,000
work opportunities through 637 licensed companies in textile production with
investments of RM7.8 billion (MIDA, 2007).
Apart from running textile manufacturing as a large scale activity, quite a
number of Malaysian fabric productions are conducted on small scales. One
common practice from conventional cottage textile industries is that the textile
effluents are mainly discharged directly into the drainage system without proper
treatment. Even though textile manufacturing has added great value in terms of its
economic and social aspects, it has also been identified as one of the significant
environmental polluters. One of the greatest contributors to Malaysia’s GDP, textile
industry activities have also been listed as the fourth industrial wastewater polluter
discharging significant quantities of high level pollutants amounting up to 7.4% into
streams. Fiber manufacturing and dyeing textile sectors are predominant for its
contribution both to the economy and environmental emissions (Haroun and Azni,
2009).
The textile industry is also known for its longest and most complicated
industrial chains in the manufacturing industry. It has diverse sectors in its
production with respect to the raw material, processes and products equipment. The
textile industry can be divided into different fragmented groups that produce and
process textile-related products such as fiber, yarn or fabric for further processing
into apparel, home furnishings and industrial goods. The most important stage that
has been identified to contribute significant adverse impacts to environmental water
pollution problems is the dyeing and finishing stage. This stage covers the
bleaching, dyeing, printing and stiffening of textile products conducted in various
processing steps. The purpose of the dyeing and finishing stage is to improve the
serviceability and increase the durability of products to suit the demands of fashion
and function (IPPC, 2000 and Savin and Butnaru, 2008). The finishing stage is also
known as the “wet processing”. The term is given as “wet” due to the huge amount
of water usage in most of the processes. In order to achieve the desired effect, a wide
48
range of chemicals, dyes and chemical auxiliaries are used. Textile processing
employs a variety of chemicals depending on the nature of the raw material and the
desired product (Aslam et al., 2004). Any impurity from each of the processing
stages will be discarded into wastewater treatment systems. The dyeing and
finishing process of the textile industry has been recognized as the main contributor
with respect to the amount of water usage and its quality (Savin and Butnaru, 2008).
3.2 Characteristics of Textile Wastewater
Dyes are used in many manufacturing activities as coloring agents to produce
many types of goods such as textile, plastic, paper printing, leather, food and in
specialized applications such as drugs, cosmetics and photochemical products
(Zollinger, 1987). Among these, the textile industry is the largest consumer. Due to
the high consumer demand, there are over 100,000 commercially obtainable dyes
existing with more than 700,000 tonnes of dyes produced annually (McMullan et al.,
2001 and Pearce et al., 2003). This scenario has resulted with the high generation of
colored wastewater. The characteristics of textile wastewater for both its quantity
and quality vary greatly depending on the type of raw materials, chemicals,
techniques or specific process operations at the mill, equipment used and production
design of the textile processes (Bisschops and Spanjers, 2003 and Dos Santos et al.,
2006a). The prevailing management philosophy of a company also influences the
amount of water usage.
Lacasse and Baumann (2006) reported that the textile industry gave adverse
impact to the environment through its high pollutant discharge. In textile processing
activities, about 10% of the chemicals in the pre-treatment and dyeing operation will
remain, giving the desired design and color on the fabric. Meanwhile, the other 90%
of chemicals will be discharged as textile effluent (IPPC, 2003). Due to the
inefficiency of the treatment system, the textile industry has been recognized as one
49
of the main pollutant discharge. Apart from that, textile industrial activities involve
large amount of water in its processes. Due to these factors, textile industrial
processes have been listed as one of the top pollution contributor to the environment.
3.2.1 Quantity
One of the prominent profiles of the textile industry is its high water usage. It
has been estimated that nearly one million metric tonnes of dyes is produced
annually (Dos Santos et al., 2003). The average wastewater generation from a
dyeing facility is estimated at between 3785-7570 million m3 per day. The dyeing
and rising processes for disperse dyeing generate about 100 to 142 L of wastewater
per kilogram of product. The textile industry presents its biggest impact on the
environment through its primary water consumption that could reach up to 80-100
m3/ton of finishing textile (Savin and Butnaru, 2008).
Dye and pigments from printing and dyeing operations are the principal
sources of color in textile effluent. Dyes and pigments are highly colored materials
used in relatively small quantities (a few percent or less of the weight of the
substrate) to impart color to textile materials for aesthetic or functional purposes.
Desizing, scouring, bleaching, mercerizing and dyeing are the common cotton wet
textile processing. Among these processes, the mercerizing and dyeing processes
consume large volumes of water with a water usage of 232-308 L and 8-300 L for
every kilogram of textile processed, respectively (Dos Santos et al. 2007). In typical
dyeing and printing processes, 50 to 100% of the color is fixed on the fiber and the
remainder is discarded in the form of spent dye baths or in the wastewater from
subsequent textile-washing operations (EPA, 1997). The amount of dye lost into the
wastewater depends upon the type of dyestuff used, methods and application route in
the textile processing operation. It also depends on the intended color intensity that
is required for each particular design (Willmott et al., 1998). During the dyeing
50
process, a range of 5-20% of acid dyes is lost in the effluent and in numerous cases
these dyes are directly flushed into the receiving water body (Trovaslet et al., 2007).
The release of dyes into the effluent may vary greatly leading to a wide range of total
annual discharge between 30,000 and 150,000 tonnes (Faraco et al., 2009).
High content of salts in textile dyeing wastewater has been identified as a
potential environmental problem. Many types of salt are either used as raw materials
or produced as by-products of neutralization or other reactions in textile wet
processes. Typical cotton batch dyeing operations use quantities of salt that range
from 20 to 80% of the weight of goods dyed, with usual concentrations between
2,000 mg/L to 3,000 mg/L. Sodium chloride and sodium sulfate constitute the
majority of the total salts used, while other salts such as magnesium chloride and
potassium chloride are used as raw materials in lower concentrations (EPA, 1997).
3.2.2 Quality
Dye industrialized wastewater are normally characterized by high chemical
and biological oxygen demand, suspended solids, high values of conductivity and
turbidity and intense color owing to the presence of dye intermediates or residues and
auxiliary chemicals added in many stages in textile processing (Mohan et al., 2007a
and Miranda et al., 2009). Pollution from this manufacturing is very much related to
the type and origin of the fiber involved. Textile processes with natural fibers
generate higher pollution loads as compared to synthetic fibers. The usage of
pesticide as preservation of natural fibers contributes to high COD concentrations in
natural fibers textile processing wastewater. These pesticides are released into the
wastewater during washing and scouring operations (Correia et al., 1994). Finishing
processes generate wastewater containing natural and synthetic polymers and a range
of other potentially toxic substances (Snowden-Swan, 1995).
51
Common characteristics of textile wastewater for cotton textile wet
processing for different processing categories are shown in Table 3.1. The highest
organic loading is generated from the scouring process with 8 g COD/L followed by
bleaching and desizing processes. The desizing process produces the highest
concentration of total solids which may come from impurity of the previous
processes. The primary sources of biological oxygen demand (BOD) of the desizing
process include waste chemicals or batch dumps, starch sizing agents, knitting oils,
and degradable surfactants. Desizing, which is the process of removing size
chemicals from textiles, is one of the industry’s largest sources of wastewater
pollutants in the U.S textile industry. More than 90% of the size used is disposed of
in the effluent streams. The remaining 10% is recycled (EPA, 1997). Desizing
processes often contribute up to 50% of BOD loading in wastewater from wet
processing (Snowden-Swan, 1995). The dyeing process is a process where color is
added to the fibres which normally require a large amount of water usage. Table 3.1
shows the dyeing process releasing the colored effluent with very high dye
concentration of 1450-4750 of ADMI units (Bisschops and Spanjers, 2003; Dos
Santos et al., 2006a; Dos Santos et al., 2007).
Table 3.2 summarizes the typical pollutant released by various associated
textile manufacturing processes. Desizing, scouring and dyeing are among the
processing steps that contribute to the most pollutant in textile waste stream. Source
of metals such as copper, cadmium, chromium, nickel and zinc found in textile mill
effluents include fiber, dyes, plumbing, and chemical impurities (IPPC, 2003). In
some dyes, metals are the functional group which forms an integral part of the dye
molecule. However, in most textile effluents, the metals present are simply from
impurities generated during dye manufacturing.
52
Table 3.1: Characteristics of textile wastewater (Bisschops and Spanjers, 2003; Dos
Santos et al., 2006a)
Process COD (g/L) BOD (g/L) TS (g/L) TDS (g/L) pH Color (ADMI)
Desizing 4.6-5.9 1.7-5.2 16.0-32.0 - - -
Scouring 8 0.1-2.9 7.6-17.4 - 10--13 694
Bleaching 6.7-13.5 0.1-1.7 2.3-14.4 4.8-19.5 8.5-9.6 153
Mercerising 1.6 0.05-0.10 0.6-1.9 4.3-4.6 5.5-9.5 -
Dyeing 1.1-4.6 0.01-1.80 0.5-14.1 0.05 5-10 1450-4750
Bleaching and
Dyeing* 0.2-5.5 2.0-3.0 0.1-5.0 - 2-10 280-2000
*Characterization of textile wastewater in Malaysia (Ahmed et al., 2005; Lau and Ismail, 2009; Ibrahim et al.,
2009; Ibrahim et al. (in review))
3.3 Dye and Environmental Problems
In textile dyeing processes, dyes are lost into the effluent due to the
incomplete exhaustion of dyes on the fibres. Approximately 2% of dyes are
discharged directly into the aqueous effluent and 10 % are subsequently lost during
the dyeing process of textile (Easton, 1995 and Pearce et al., 2003). The problem
related to the dye lost is very much accelerated when the annual market for dye was
reported to be more than 109 kg (Zollinger, 1987 and Dos Santos et al., 2007).
Reactive dyes are among the popular type of dyes used especially in textile
dyeing processes of cellulose fibres and make up approximately 30 % of the total dye
market (Kamilaki, 2000). Dyeing with reactive dyes contribute more problems due
53
to its low degree of fixation ability which causes as much as 50% of the dye lost into
the wastewater stream. Losses of dye are due to the relatively low levels of dye-fiber
fixation degree and to the presence of unreactive hydrolyzed dye in the dyebath. Dye
hydrolysis takes place when the dye molecule reacts with water rather than with the
hydroxyl groups of the cellulose. These problems become more complex with high
water solubility and characteristics of the dyes involved (Pearce et al., 2003).
Table 3.2: Release of typical pollutants associated with various textile manufacturing
processes (Crini, 2006 and Dos Santos et al., 2006a)
Steps in Textile Processing Main pollutants
Sizing BOD, COD, metals, cleaning waste, size
Desizing BOD from water-soluble sizes, lubricants, biocides, antistatic compounds, size agents, enzymes, starches, waxes, ammonia,
Scouring/ washing
Disinfectants and insecticides residues, NaOH, surfactants, soaps, fats, waxes, pectin, oils, sizes and antistatic agents, detergents, knitting lublicants, spin finishes, spent solvents
Bleaching Adsorbable organic halogens, sodium silicate or organic stabilizers, Hydrogen peroxide,high pH
Mercerizing NaOH and other salts, high pH
Dyeing Color, metals, salts, surfactants, sulphide, formaldehyde, toxics, organic processing aids, cationic materials, BOD, COD, sulfide, acidity/alkalinity, spent solvents
Finishing BOD, COD, suspended solids, toxics, spent solvents
The presence of very small amount of dyes in water even less than 1 mg/L for
certain types of dyes is highly visible and undesirable (Robinson et al., 2001 and
54
Crini et al., 2007). The release of dyes into the environment has become a public
concern since its presence gives adverse effects on aesthetic merit, water
transparency and gas solubility in lakes, rivers and other water bodies (Banat et al.,
1996). Since textile processing wastewaters typically contain dye concentration in
the range of 10-200 mg/L (Pandey et al., 2007) these can be considered as highly
colored and could impose aesthetic problems of the environment. Dyes that are
released in the water body in the form of colored wastewater could lead to several
adverse effects to the environment. Aquatic organisms are exposed to acute effects
due to toxicity of the dyes. The intensity of light that penetrate into the water body
will be reduced which will end up with reduction in the photosynthesis process by
the plants in the aquatic ecosystem. Additionally, the dyes are chemically complex
and photolytically stable which will make the dyes highly persistent in the
environment for a long while. As the dye components are very difficult to be
degraded, they persist in the environment. These conditions are even worse with the
fact that many of the dyes are made from known carcinogens such as aromatic
compounds and benzidines (Clarke and Anliker, 1980 and Brown and DeVito, 1993).
Based on the European criteria for the classification of dangerous substances,
the acute toxicity effect of azo dyes is considered rather low with the LD50 values of
250-2000 mg/kg of body weight (Clarke and Anliker, 1980). However, the
degradation products of the azo dyes include the aromatic amines and the impurities
of the textile wastewater, are the compounds of concern due to their potential
carcinogenicity (Novotny et al., 2006). Due to these circumstances, the release of
dye-contained wastewater has become a source of public concern.
3.4 Treatment of Dyes
There are a number of approaches that have been used in treating textile
industrial effluents. At present, the major techniques in textile wastewater treatment
55
mainly involve physical and/or chemical processes. However, such methods are
often costly. Some treatments may remove color by just transferring one problem to
another when those treatments produce accumulation of concentrated sludge which
creates disposal problems (Pearce et al., 2003). Excessive use of chemicals in dye
treatment creates secondary pollution problems to the environment.
Some of the physico-chemical techniques that have been applied for textile
wastewater treatment are coagulation and flocculation (Harrelkas et al., 2009),
electrokinetics coagulation (Kobya et al., 2003 and Alinsafi et al., 2005),
precipitation (Solmaz et al., 2007), adsorption (Ong et al., 2008a and Sayed and
Ashtoukhy, 2009), membrane filtration and nanofiltration (Miranda et al., 2009 and
Unlu et al., 2009), ion exchange (Wu et al., 2008), ultrasonic mineralization
(Maezawa et al., 2007) and electrolysis (De Jonge et al., 1996). Treatment using
ozonation, Fenton’s reagent, electrochemical destruction and photocatalysis are some
of the emerging techniques reported to have potential use for decolorization (Tang
and Chen, 2004; Faouzi et al., 2006; Papadopoulos et al., 2007; Ay et al., 2009; Ma
and Zhou, 2009). However, such technologies usually involve complicated
procedures and are economically unfeasible (Chang and Lin, 2000). Table 3.3 shows
some of the advantages and disadvantages of using physical and chemical
technologies in color removal (Robinson et al, 2001 and Crini 2006).
The technique finally chosen for a particular textile wastewater treatment
usually depends on types of dyes used in the textile processing, quantity and quality
of the textile effluent, operational cost from energy consumptions, equipment,
chemical requirement and as well as cost of handling the generated waste products.
Environmental fate over the chosen treatment for textile wastewater is one of the
significant factors that need to be taken into account. Treatment systems that can
offer effective dye removal from large volumes of wastewater at low cost is a more
preferable alternative in solving the textile wastewater problem and this can be
achieved through biological and/or combination treatment processes.
56
Table 3.3: Advantages and disadvantages of the current methods of dye removal from industrial effluents (Robinson et al. 2001 and Crini , 2006)
Categories Technology Advantages Disadvantages
Physical treatment
Membranes filtration
Removes all types of dyes, produce a high quality treated effluent
High pressure, expensive, incapable of treating large volumes.
Adsorption on activated carbon
The most effective adsorbent, produce a high-quality treated effluent
Ineffective for disperse and vat dyes, require regeneration that increase in the expenses, loss of adsorbent, non-destructive process
Chemical treatment
Coagulation Simple, economically feasible High sludge generation, handling and disposal problems
Ozonation Applied in gaseous state: no alteration of volume Short half-life (20 min)
Irradiation Effective oxidation at lab scale Requires a lot of dissolved O2
Ion exchange Regeneration; no adsorbent loss Not effective for all dyes
Photochemical No sludge production Formation of byproduct
Electrochemical destruction
Breakdown compounds are non-hazardous Very expensive
Fenton reagent Effective decolonization of both soluble and insoluble dyes
Sludge generation
Biological treatment
Biomass Low operating cost, good efficiency and selectivity, no toxic effect on microorganisms
Slow process, performance depends on some environmental conditions such as temperature, pH, salts
Biosorbents Peat Good adsorbent due to cellular structure Requires long retention time
Chitin and chitosan
Low cost, abundant, renewable, biodegradable resources Resulted with pressure drop in sorption columns, cannot be used as insoluble sorbent under acidic conditions
57
Bioremediation using microbial biocatalysts to reduce the dyes present in the
effluent offer potential advantages over physico-chemical processes. Such a system
has become the main focus of recent research. However, many aspects have to be
investigated in order to really gain advantages over the system especially when it
comes to applying the treatment system in-situ. Each of the technique mentioned has
its own limitations. Complete dye degradation seems to be difficult to achieve if the
treatment only depends on a single treatment process. At present, a combination of
different treatment techniques is being practiced in order to achieve complete
mineralization of dyes.
3.4.1 Biodegradation of Dyes
Biodegradation of dyes means using a biological approach in decolorizing the
dyes. It can be carried out using bacteria, algae or fungi. Advantages achieved by
using biological approaches have been claimed by many researchers either by having
partial or complete degradation of dyes (Kudlich et al., 1999; Chen et al., 2005;
Frijters et al., 2006; Dos Santos et al., 2007; Mohan et al., 2007a; Ertugrul et al.,
2008). Effective dye removal from large volumes of wastewater at a considerable
low cost as compared to other techniques can be obtained through biological
approaches. Furthermore, biodegradation does not contain complicated procedures
(Pearce et al., 2003).
Microalgae which acted as the primary producers to the aquatic food chains
were found to be competent as an ideal biosorbent for color removal from textile
wastewater (Daneshvar et al., 2007). However, its application was limited due to
low chemical and heat resistance (Ertugrul et al., 2008). Due to the advantages of
using bacterial in dye degradation, extensive research have been conducted for the
58
past two decades involving the isolation and identification of bacteria capable of
degrading various types of dyes (Rai et al., 2005).
The application of fungi is influenced by the bioadsorption capacity of the
biomass. The absorbed dye compounds are degraded by the powerful extracellular
ligninolytic enzymatic system (Dias et al., 2007). However, fungi are ineffective for
the removal of reactive dyes. Reactive dyes are usually changed into a hydrolyzed
form, losing its aptitude to bind to cellulose. In this condition, these dyes may no
longer be successfully absorbed by the fungal biomass. The regeneration process by
extraction with methanol is required to regain back the absorption capacity.
However, the regeneration process may slightly reduce the percentage of absorption
(Carliell et al., 1994).
Pretreatment process is required in order to increase the biomass absorption
capacity. The pretreatment methods may include autoclaving, contacting with
chemicals, either organic chemicals (formaldehyde) or inorganic chemicals (natrium
oxide, calcium chloride, hydrogen sulfide) (Fu and Viraraghavan, 2001). The
pretreatment process may increase the cost of the operation set-up and increase the
operating time. The application of fungi in a large scale system has become a
limitation since high growth of fungi may inhibit the growth of other useful
microorganisms. Furthermore, fungi require low pH levels for optimum activity and
need long hydraulic retention times for high dye removal (Chen et al., 2003). The
main principle of dye removal using algae and fungi depends on the adsorption rather
than the degradation process. As a result, the dyes still remain in the environment
(Wang et al., 2009a). Due to the drawbacks in using algae and fungi as the
biodegradation agent, many studies have focused on the biodegradation process by
bacteria.
In recent reports, a number of studies have focused on the immobilized
microorganisms able to decolorize textile wastewater (Kornaros and Lyberatos,
2006; Sirianuntapiboon et al., 2007; Somasiri et al., 2008; Sun et al., 2008b; Zhu et
59
al., 2008). The immobilization of dye-removing microorganisms provides important
advantages including degradation at higher dye concentrations without lost of cell
viability, protective environment for the dye degradation activities against changes in
temperature, pH and effect of toxic compounds that co-exists in the wastewater
(Ertugrul et al., 2008).
3.4.2 Bacterial Degradation of Dyes
Isolation of decolorizing bacteria started in the 1970s with cultured bacteria
Bacillus subtilis able to degrade azo dyes (Horitsu et al., 1977), followed by isolation
of Aeromonas hydrophila (Idaka et al., 1978) and Bacillus cereus (Wuhrmann et al,
1980). Nowadays, numerous capable dye degrader bacteria have been reported
(Chen et al., 2003; Xu et al., 2007; Hsueh and Chen, 2007; Moosvi and Madamwar,
2007; Dave and Dave, 2009). A bacterial strain, Citrobacter sp. CK3, isolated from
activated sludge from a textile paper mill was found capable of degrading Reactive
Red 180 with 95% color removal within 36 hour incubation under anaerobic
condition. It also exhibited high tolerance to dye concentration up to 1000 mg/L
(Wang et al., 2009a). Some of the isolates are capable of degrading a broad
spectrum of dyes which include azo, anthraquinone and triphenylmethane dyes such
as Aeromonas hydrophila (Ren et al., 2006), Bacillus cereus strain DC11 (Deng et
al., 2008) and Bacillus thuringiensis (Dave and Dave, 2009).
Most of the researches on color degradation mechanisms have been
conducted and focused on the biotransformation of azo dyes, as, among the 10,000
dyes applied in textile processing industries, 60-70% are azo compounds (van der
Zee, 2003a). Hence, most study in relation to dye degradation has been focused
more on the azo dyes. Azo dyes are molecules with one or more azo (N=N) bridges
linking substituted aromatic structures (Carliell et al., 1995). However, there have
been several reports focusing on other types of dyes such as degradation of
60
anthraquinone dyes (Lee and Pavlostathis, 2004 and Trovaslet et al., 2007),
phtalocyanine dyes (Nilsson et al., 2006) and triphenylmethane dyes (Shedbalkar et
al., 2008).
3.4.3 Mechanisms of Biodegradation of Azo Dyes
Biodegradation of azo dyes can occur under aerobic, anaerobic
(methanogenic), and anoxic conditions by many different trophic groups of bacteria
(Pandey et al., 2007). Biodegradation of azo dyes can occur as a direct degradation
process through the presence of enzymes or as an indirect mechanism process. The
indirect mechanism process involves the presence of other substances that could aid
the degradation process. Many research studies have been concentrated under
anaerobic degradation process due to the higher removal percentage of dye
degradation as compared to aerobic condition.
3.4.3.1 Aerobic Dye Degradation Process
Aerobic biodegradation of azo dyes involve enzymatic reduction process. In
this mechanism, enzymes play an important role in transferring reducing equivalents
from the oxidation of organic substances or from the coenzyme to the azo dyes. Azo
reductase is a specialized azo dye reducing enzyme found in some aerobic and
facultative bacteria that are able to degrade simple molecular structure of the azo dye
as sole carbon and energy sources (Zimmermann et al., 1984). Since the enzymes
react specifically to the dye molecule, the reaction is known as specific enzymatic
reaction (Blumel and Stolz, 2003 and Chen et al., 2004).
61
Pseudomonas aeroginosa can decolorize a few types of azo dyes under
aerobic condition with the presence of external carbon sources (Nachiyar and
Rajkumar, 2003). An obligate aerobic strain, Shingomonas 1CX, is able to grow on
Acid Orange 7 (AO7) and used it as the source of energy, carbon and nitrogen
(Coughlin et al, 2003). However, these bacterial strains could only cleave the N=N
bond of a simple dye structure and utilize the amines as the source of carbon and
energy for bacterial growth but not on complex azo dye structure. Xenophilus
azovorans KF46 could utilize azo dye carboxy-orange I (Zimmermann et al., 1982)
but this strain could not grow on more complex dye structure such as analogous
sulfonated dyes, Acid Orange 20 and Acid Orange 7.
The azo dye, Acid red 151 (AR151) was successfully degraded under aerobic
condition when this dye acted as the sole carbon source for microorganisms in a
sequencing batch biofilter with a porous volcanic rock. The system showed a high
percentage of color removal (99%) with initial concentration of 50 mg/L of AR151
(Bruiton et al., 2004). Arora et al. (2007) reported extensive degradation (95-98%)
of monoazo disperse dye under shaking aerobic condition by bacterial strain Bacillus
firmus isolated from local sewage.
Azo dye degradation has also been observed to occur under microaerophilic
conditions (Sandhya et al., 2005; Xu et al., 2007; Franciscon et al., 2009; Khalid et
al., 2008, Elisangela et al., 2009). Microaerophilic condition is where the percentage
of oxygen is only 5% (Engelkirk et al., 1992). Several azo dyes were degraded in
sequential microaerophilic-aerobic treatment condition by a facultative Klebsiella sp.
Strain VN-31 with 94% of color removal (Franciscon et al., 2009). Another
facultative Staphylococcus arlettae bacterium isolated from an activated sludge
process of the textile industry, has successfully decolorized four different azo dyes
under micraerophilic condition with percentage dye removal of more the 97%
(Elisangela et al., 2009). However, since the aerobic biodegradation of azo dyes is
restricted to the presence of azoreductase and only suitable for the degradation of
simple molecule dye structures. This has led many researchers to conclude that azo
62
dyes are persistent under aerobic conditions (Pagga and Taegar, 1994 and Ong et al.,
2008).
3.4.3.2 Anaerobic Dye Degradation Process
Dye degradation under methanogenic (anaerobic) condition could be
participated by various types of bacteria trophic groups including acidogenic,
acetogenic and methanogenic bacteria (Talarposhti et al., 2001; Bras et al., 2005;
Ong et al., 2005a; Somasiri et al., 2008). Extensive studies on dye degradation have
been carried out by many researchers using diverse groups of bacteria. Based on the
molecular characterizations of microbial populations in anaerobic baffled reactors
treating industrial textile wastes, sulfate reducing bacteria (SRB), γ-proteobacteria
and Mehanosaeta species and Methanome donthylovorans hollandica of the
methanogenic populations were among the prominent bacteria groups identified in
the treatment of dye waste (Plumb et al., 2001). Dos Santos et al. (2006b) confirmed
the ability of fermentative bacteria to use humic acid as electron acceptor in the
reduction of azo dyes. The anaerobic treatment of wool dyeing effluents which
contained predominantly methanogenic cultures have shown higher performance for
color removal of more than 88% at the HRT of 24 hours (Bras et al., 2005). Ong et
al. (2008b) had reported 100% degradation of 625 mg/L of initial concentration of
Acid Orange 7 under limited oxygen supply (DO below 0.25 mg/L) without any
addition of external carbon sources in granular activated carbon-biofilm configured
sequencing batch reactor system. Study on the methanogenic consortia on
decolorization has been studied earlier on by Carliell et al. (1996), Razo-Flores et al.
(1997) and many others.
Based on many documented research reports, various types of azo dyes could
be degraded by anaerobic bacteria. This gives an indication that azo dye reduction
mechanisms are non-specific reactions (Moutaouakkil et al., 2003 and Hong et al.,
63
2007). Under this condition, in order for dye degradation to occur, organic carbon
or energy source from simple substrates such as glucose, starch, acetate or ethanol or
complex substrates such as whey and tapioca are required (Chinwetkitvanich et al.,
2000, Isik and Sponza, 2005a, van der Zee and Villaverde, 2005). The rate of
degradation defers depends on the addition of co-substrate and the dye structure itself
(Pandey et al., 2007).
First-order kinetics with respect to dye concentrations has been reported by
many researchers for the mechanisms of dye decolorisation (Isik and Sponza, 2004a;
Ong et al., 2005b; Lourenco et al., 2006). According to van der Zee (2001a), the
biological reduction of mono-azo dyes by anaerobic bioreactors followed the first
order kinetics without any lag phase. Multiphase kinetics was observed for biological
reduction of dyes containing azo linkages such as diazo and polyazo dyes.
Meanwhile, Wijetunga et al., (2007) reported biodegradation of Acid Red 131 and
Acid Yellow 79 under anaerobic condition using mixed anaerobic granular sludge
followed by the first order and second order kinetics, respectively. Other researchers
found dye decolorization occurring according to zero-order kinetics (Brown, 1981
and Dos Santos et al., 2004). Degradation of azo dyes (Acid Orange 7 and Reactive
Red 2) by anaerobic granular sludge demonstrates zero order kinetics for biological
dye reduction and second order kinetics for chemical dye reduction as a function of
sulfide and dye concentrations (van der Zee, 2003b). The contradictory findings may
be due to the different experimental conditions that imposed different rate-limiting
step in the reduction of azo dye.
3.4.3.3 Anoxic Dye Degradation Process
Degradation of azo dyes could also occur under anoxic condition by
maintaining the oxygen concentration levels in the range of 2.5-3.0 mg/L (Mohan et
al., 2007b). Dye degradation under anoxic condition is considered as a non-specific
64
enzymatic reaction (Pearce et al., 2003 and Silveira et al., 2009). Azo dye
decolorizations by mixed aerobic or facultative anaerobic microbes are reported to
occur under anoxic conditions (Kapdan et al., 2000, Moosvi et al., 2005, Ren et al.,
2006; Dafale et al., 2008). Aeromonas hydrophila strain, new isolated species found
that could decolorize triphenylmethane, azo and anthraquinone dyes with more than
85% decolorization for azo and anthraquinone dyes within 36 hours under anoxic
condition (Ren et al., 2006).
Decolorization of Remavol Black-B under anoxic condition was found to fit
the first order kinetics with respect to dye concentration and electron donor (carbon
source) as well as operational parameters, pH and temperature (Dafale et al., 2008).
Significant degradation of azo dyes by a specific bacterial consortium was achieved
in a two stage anoxic-oxic reactor system with 84% and 80% for color and COD
removal in raw textile wastewater (Dafale et al., 2008). A novel bacterial strain
isolated from soil samples contaminated with textile wastewater, identified as
Pseudomonas sp. SUK.1, is capable of decolorizing Red BLI (50 mg/L) up to
99.28% within 1 hour under static anoxic condition at a pH range of between 6.5 to
7.0 and temperature at 30oC (Kalyani et al., 2008).
Dye degradation under anoxic conditions by facultative, anaerobic and
fermentative bacteria seems to be affected by the type of substrate used as the
external carbon sources (Pandey et al., 2007). The azo dye decolorization (Reactive
Red 22) under anoxic condition was significantly decreased when glucose was used
as the main carbon source for Pseudomonas luteola (Chang et al., 2001).
65
3.4.4 Mineralization of Aromatic Amines
The mineralization of aromatic amines can be considered as the second stage
of dye degradation process after the cleavage of the azo bond. At this stage the
amine compounds will be converted into harmless end products under aerobic
condition. This degradation stage is very important since the persistent occurrence
of these substances may impose significant adverse effects on the ecosystem.
Many of the aromatic amines released from the anaerobic degradation of azo
dye are further mineralized in the aerobic conditions. Under aerobic treatment
conditions, aromatic amines can be mineralized by non-specific enzymes through the
hydroxylation and ring opening of the aromatic compounds by incorporating two
oxygen atoms (Zissi and Lyberatos, 1996 and Pandey et al., 2007). The aerobic
degradation of aromatic amines has been extensively studied such as the degradation
of aniline (Konopka, 1993), chlorinated aromatic amines (Loidle et al., 1990),
carboxylated aromatic amines (Russ et al., 1994), sulfonated aromatic amines (Sen
and Demirer, 2003), benzene-based aromatic amines (Cinar et al., 2008) and
nitroaniline (Khalid et al., 2009). Biodegradation of sulfanilic acid and 1-amino-2-
napthol has been successfully demonstrated by a bacterial culture in an aerobic
rotating biological contactor (Coughlin et al., 2002). However, there are certain
types of aromatic compounds that cannot be further degraded even in aerobic
condition especially byproducts from the cleavage of reactive azo dye Reactive
Black 5, Reactive Violet 5 and Direct Black 8 (Panswad and Luangdilok, 2000; Libra
et al., 2004; Sponza and Isik, 2005).
The conversion of the aromatic amines is generally carried out by specialized
aerobes. Under microaerophilic conditions where DO concentrations are in the range
of 0.2 to 0.5 mg/l, certain aromatic amines including aniline, 1,4-diaminobenzene, 1-
amino-2-naphthol; catechol and 4-amonobenzoic acid can be completely degraded by
Shewanella decolorationis S12 via the oxidative cleavage of the aromatic ring (Xu et
al., 2007). Mixed bacterial culture identified as Acinetobacter sp., Citrobacter
66
freundii and Klebsiella oxytoca, showed complete degradation of 100 mg/L of
nitroaniline within 72 hours under aerobic conditions (Khalid et al., 2009).
Aromatic amines are considered as stable biotransformation byproduct of azo
dye degradation under anaerobic conditions due to the resistance for further
degradation under anaerobic conditions (Stolz, 2001 and Sponza and Isik, 2005).
However, amines with simple structure were reported able to be degraded under
anaerobic condition such aniline and nitroaromatic compounds (De et al., 1994 and
Razo-Flores et al., 1999). Complete degradation of aniline by methanogenic granular
sludge was reported by Kato et al. (1993) and Tan et al. (1999). Amines that could
not be further degraded will remain and contribute to the untreated COD level in the
effluent.
The amine compounds can be autoxidized when exposed to air. This reaction
will cause the colorless anaerobic dye degradation byproduct to become colored with
difference color intensity. The increase of color during autoxidation of aromatic
amines was confirmed by several researchers (Cruz and Buitron, 2001; Libra et al.,
2004; Sponza and Isik, 2005). This reaction may reduce the overall percentage of
color removal and further investigations are required to overcome this problem.
As discussed in the earlier sections, dye degradation mainly occurred under
anaerobic or anoxic conditions while further degradation of the aromatic amines
mainly takes place under aerobic conditions. Hence, many researchers are focusing
more on having both anaerobic and aerobic conditions in textile wastewater
treatment processes for complete and effective dye degradation either as integrated or
sequential treatment systems.
67
3.5 Treatment System for Biodegradation of Azo Dyes
The requirement of azo dye cleavage prior to degradation through oxidative
reaction of the aromatic amines allow the process of anaerobic and aerobic reaction
phase as the logical prerequisite requirement for complete biodegradation for most
colored substances through biological treatment (Knackmuss, 1996 and van der Zee
and Villaverde, 2005). The applications of both anaerobic and aerobic conditions are
indeed required for complete degradation of azo dyes (Melgoza et al., 2004). These
conditions have become important features that need to be considered in designing
the treatment system for textile wastewater.
The treatment condition that would be best for complete mineralization of the
azo dye could be appropriate in two approaches. It uses two separate compartments
or a reactor that separates the sludge in sequential anaerobic/aerobic treatment
system (Khelifi et al., 2008) or integrated anaerobic/aerobic treatment in a single
reactor system (Frijters et al., 2006 and Cinar et al., 2008). The study on the dye
degradation process has been conducted by many researchers either in sequential
treatment system such as a series of reactor systems or integrated treatment in a
single reactor using sequential batch reactor. The study on dye biodegradation is
also conducted under limited oxygen supply such as anoxic or microaerophilic
condition, either in sequential or integrated reactor system. Different types of media
have also been used as the degradation agents such as suspended biomass, biofilms
and granules in different types of reactor systems to achieve the most effective
treatment system for dye degradation of textile wastewater.
68
3.5.1 The Sequential Anaerobic/Aerobic Reactor System
In sequential anaerobic and aerobic reactor system, two separate reactors are
used. The wastewater is first treated under anaerobic condition in an anaerobic
reactor followed by an aerobic reactor for the aerobic treatment phase. The studies
on the conversion of azo dyes using a sequential anaerobic and aerobic reactor
system have been extensively studied by many researchers (Brown and Hamburger,
1987; Rajaguru et al., 2000; Kapdan et al. 2003; Libra et al. 2004; Mohanty et al.,
2006). Materials such as charcoal and calcium alginate beads have been used in dye
degradation process as the support materials for the immobilization of
microorganisms in the reactor system. Activated sludge and anaerobic granular
sludge are some of the common biomass used as the source of the microorganisms
for biodegradation of textile wastewater. Table 3.4 summarizes some of the studies
conducted on dye degradation using a sequential anaerobic and aerobic reactor
system.
In this sequential treatment process, the main substrate source that represents
the organic loading are consumed anaerobically and at the same time the cleavage of
the azo dye takes place producing the aromatic amines as the byproduct. During the
aerobic reaction phase, the amines are used as the additional carbon and energy
sources for the microbes in the aerobic reactor. However, in the case of recalcitrant
amines such as the sulphonated aromatic amines (Tan and Field, 2000 and Tan et al.,
2005), these amines either remain in the reactor system or escape with the released
effluents.
The first anaerobic–aerobic full-scale treatment of textile wastewater was
applied in the Netherlands in 1999 by a textile company known as Ten Cate Protect
dealing with vat, disperse and reactive dyes. The company managed to remove 80-
95% of the dyes in the anaerobic reactor system. The removal of reactive dyes took
place in the anaerobic treatment while the absorption of vat and disperse dye and also
69
the mineralization of the aromatic amines occurred in the aerobic reactor (Frijters et
al., 2006).
Overall observation shows that high color removal is achieved under
anaerobic condition while higher aromatic amines and COD removal can be
accomplished under aerobic condition. A coupled system with anaerobic and aerobic
reaction mode has confirmed to be a successful strategy in achieving complete
biodegradation of azo dyes. Isik and Sponza (2006) suggested that having post
aerobic treatment step would provide even more complete mineralization of textile
wastewater. Increase in the retention time under anaerobic condition would increase
the COD and color removal (Ong et al., 2005a). Most of the degradation of organic
compound occurred at the early stage of aerobic reaction, so increase in the HRT of
the aerobic system does not really effect the COD removal.
The addition of organic loading rate would improve the percentage of color
removal (Ong et al., 2005b). This is because an increase in the organic loading rate
means more of substrate is added into the treatment system. The presence of
substrates such as glucose, sucrose, acetic acid acted as the electron donor. Increase
of these substances multiplies the amount of electron donor transferred to the N=N
bond and results with increase in color removal. Increase in organic loading rate
showed a slight increase in COD removal due to the increase in the biomass
production under aerobic condition. However, the COD removal deteriorates in the
anaerobic treatment system when the organic loading rate increases. The reduction
effect on COD removal is more obvious when there is increase in loading rate due to
increase in the concentration of dyes. When comparing between static and shaking
conditions, the static condition would exhibit more percentage color removal
(Moosvi and Madamwar, 2007).
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3.5.2 The Integrated Anaerobic/Aerobic Reactor System
Complete degradation and mineralization of dye-containing wastewater
require both anaerobic and aerobic conditions. As discussed in the earlier section,
many researches have been conducted with both anaerobic and aerobic treatment
conditions for textile wastewater treatment. However, using two or more separate
reactor systems may not be economical as it requires more area for reactor set-up.
In order to achieve complete dye degradation for textile wastewater within a suitable
working area, an economical budget integrated system has been introduced. In the
integrated treatment system, the most important part is the development of different
microniches occupied by different types of microorganisms. Control on the oxygen
level is very important in order to develop different microniches in the reactor
column. Type and concentration of co-substrate present in the wastewater may also
influence the oxygen level within the reactor system during the degradation process
(Tan et al., 2000). This means choosing the appropriate type and amount of substrate
for use in the treatment system are also important aspects that need to be considered
to achieve the desired removal and stability performance of the reactor system.
The integration of different microniches will be useful for the degradation of
several types of pollutants in a single compartment or reactor system. Table 3.5
shows the summary of the integrated system of anaerobic and aerobic dye
degradation process in a sequential batch reactor system. Based on the results of the
integrated treatment system, most of the cleavage of the azo bond takes place under
anaerobic condition while mineralization of the aromatic amines occurs mainly under
aerobic condition. The results are comparable to the treatment systems that used
separate reactor system.
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Table 3.4: Sequential anaerobic-aerobic treatment system for dye degradation
Treatment system Dye/Wastewater Biomass/ Microorganisms Removal Performance Reference
AnAHR-UASB Azo dyes (Siriusgeib and Siriuslichtbraun).
Mesophilic anaerobic granular sludge and activated sludge
56-90% (color); Methane production rate 22 mg COD/L/day
Kalyuzhnyl and Sklyar (2000)
UASB (HRT:24h) → Aerobic tank (HRT:16 h)
Simulated textile effluent; Reactive azo dye + modified starch
Granules UASB: 73% (COD); 84% (BOD); 64% (Color); Aerobic tank: 12% (COD); 11% (BOD); 11% (Color). Overall: 84% (COD); 96% (BOD); 75% (Color)
O'Neill et al., (2000a)
UASB→CSTR Remazol Black-5 (100 mg/L)+ glucose
Anaerobic granule UASB: 92-87% (COD); 50-76% (Methane Gas); CSTR: 28%, 42%, 90% (SRT: 1.7, 5.7 and 11d)(COD); 90-95% (Color)
Sponza and Isik (2002)
UASB (HRT: 0.5d) → CSTR(HRT: 2d)
2,4 dinitrotoluene (DNT) (2-500 mg/L) + Molasses
Partially granulated anaerobic sludge; activated sludge
85% (COD); 90% (Color) Sponza and Atalay (2003)
RDR (HRT: 15h) Reactive Black 5 (530 mg/L) + acetic acid
- 65% (Color); 85% (methane gas) Libra et al. (2004)
UASB (HRT: 30h) → CSTR (HRT: 4.5 d)
Raw cotton textile wastewater + glucose
Partially granulated anaerobic sludge; activated sludge
UASB: 9-15% (COD); 46-55% (Color); Overall: 40-85% (COD); 39-81% (Color)
Isik and Sponza, (2004a)
UASB:→CSTR Reactive Black 5 (RB 5) and Congo Red (CR)
Partially granulated anaerobic sludge; activated sludge
RB5: 94.1-45.4% (COD); 79-73% (Color); CR: 92.3% (COD); 95.3-92.2% (Color) (HRT 3.5-0.5d)
Isik and Sponza, (2004b)
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Table 3.4: Sequential anaerobic-aerobic treatment system for dye degradation (Continued)
Treatment system Dye/Wastewater Biomass/ Microorganisms Removal Performance Reference
UASB (HRT: 16.5-15 h) → SBR (HRT: 2.5-2.3 h)
Direct Black 38 (6.1-213 g/m3·h) + glucose
Partially granulated anaerobic sludge; activated sludge
UASB: 49% (COD); 80% (Color)(HRT: 15 d) CSTR: 67%(COD) (HRT:2.3d); Overall: 84% (COD); 52% (Color) (HRT: 2.9d).
Sponza and Isik (2005)
UASB (HRT: 24 h) → SBR (HRT:24 h)
Orange II (0-100 mg/L); STWW + sucrose
Activated sludge UASB: 25-45% (COD); >95% (Color); SBR: 90% (COD)
Ong et al. (2005a)
SBR1 (aerobic)→ SBR2 (anaerobic); HRT(24 h)
Orange II (0-100 mg/L); STWW + sucrose
Activated sludge 15% (COD-SBR1); 80% (COD-SBR2) Ong et al. (2005b)
Full scale: AnFBR (anaerobic) → Aerobic basin
Mixture of vat, disperse, reactive, anthraquinone and indigoids (40 mg/L)
- 80-90% (COD); 80-95% (Color) Frijters et al. (2006)
Fermenter (anaerobic) → Aerobic tank
Reactive Black 5(100-3000 mg/L)
Activated sludge >90% (COD); 46% (amines);HRT: 2 days Mohanty et al. (2006)
UAFB (HRT: 5-15 days)→Aerobic tank
Effluent from manufacturing textile and pharmaceuticals
UAFB (charcoal support material); Aeration tank + P. aeroginosa ;5%, (v/v)
UAFB: 37-70% (COD); 7-58%(color); Aerobic Tank: 40-45% (COD); 35-40% (color) ; Overall: 94% (COD); 89% (color).
Moosvi et al. (2007)
UASB (HRT: 6-100 days)→CSTR (HRT: 15-1days)
Mixed dyes (Reactive Black 5, Direct Red 28, Direct Black 38, Direct Brown 2 and Direct Yellow) (250 mg/L)
UASB: Partially granulated anaerobic sludge; CSTR: activated sludge from dye industry
Overall removal: 97% (COD) and 91% (Color). CSTR: TAA (70-85%); HRT: 9-6 days.
Isik and Sponza (2008)
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Table 3.5: Integrated anaerobic-aerobic sequential treatment system for dye degradation
Treatment system Dye/Wastewater Biomass/ Microorganisms Removal Performance Reference
SBR: Fill: <5min; React: Anaerobic (18/6 h); Aerobic (5 h); Settle: 55 min; Decant: 5 min.
Remazol Black B (10 mg/L) + Nutrient broth + Sodium acetate + glucose
Sewage treatment plant
73-77% (Color-PAOs); 59-64% (Color-GAOs). 66% (18h HRT); 59% (6h HRT) of anaerobic contact time.
Panswad et al., (2001a)
SBR: Fill: 15min:React: 18.5 h; 0.5 h (aerobic); Settle: 4 h; Decant: 0.25 h; Idle: 0.5 h.
Remazol Black. STWW (+ starch,+ polyvinyl- alcohol (POVH),+ carboxymethyl cellulose)
Anaerobic granules from UASB
66% (TOC); 94% (Color) Shaw et al. (2002)
SBR: VER: 75%. Intermittent agitation: 180 rpm
STWW (propionate : DB79 [1:50]); Dye (50% dye + 50% dispersing agent)
Activated sludge 65% (Color); 96% (amines); Overall removal: 96% (Color)
Melgoza et al. (2004)
SBR: Fill: 50 min; React: Anaerobic (10.5 h) / Aerobic (10 h) ; Settle: 1.5 h; Draw: 35 min; Idle: 10 min
Azo dye: Remozol Brilliant Violet 5 and Acid Orange; STWW + starch derivative
Activated sludge 80% (COD); 90-99% (Color) removal and 80% of COD removal
Albuquerque et al. (2005)
SBR: Fill: 48 h (static or mixed); Anaerobic: Aerobic (8-12h); Settle: 2 h; Draw: 48 min; Idle: 24 min
Jane Sandolane MF-RL & Rouge Sandolane MF-2BL dyes + antraquinone
Raw wastewater + aerobic sludge.
85% (soluble COD); 95% (BOD5) Goncalves et al. (2005)
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Table 3.5: Integrated anaerobic-aerobic sequential treatment system for dye degradation (Continued)
Treatment system Dye/Wastewater Biomass/ Microorganisms Removal Performance Reference
SBR: Different anaerobic/aerobic residence time (2-19 h)
Vinylsulphonyl, monochlortriazine, Remazol Rot RR.
Alcaligenes faecalis and Comamonas acidovorans
90% (Color-4-6 h of anaerobic cond. At 60 mg/L of dye conc.); >85% (COD); >90% (Color) at 500 mg/L dye conc.
Kapdan and Oztekin (2006)
SBCR: Fill: 3 h; React: 20 h; Draw: 0.45 h; Idle: 0.15 h.
Acid Orange 7; STWW + sucrose
Granular activated carbon; dye degrading microbes
88% (COD); 100% (Color) Ong et al. (2008b)
SBR: Fill: 3 min; Anaerobic: Aerobic reaction 48 hrs(24:23.9); 12 hrs(12: 11.9); 12 hrs(6:5.9); Draw (3 min)
Remazol Brilliant Violet 5R; STWW + glucose
Facultative mixed bacterial culture
Anaerobic: 75% (COD); 72%, 89% and 86% (color); Aerobic:64%, 92% and 89% (amines)
Cinar et al. (2008)
SBR: HRT: 8 h; Working vol.: 2 L; VER: 50%,
Malachite green (MG) as main carbon source
Aerobic digested sludge (+nutrient + micronutrient);
92.3% (Color) Mondal and Ahmad (2009)
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CHAPTER 4
DEVELOPMENT OF FACULTATIVE ANAEROBIC GRANULES
4.1 Introduction
In recent years, the ability of biodegradation process for treating different
types of wastewater involving both anaerobic and aerobic processes has been widely
reported in the literature (Jang et al., 2003; Arrojo et al., 2004; Su and Yu, 2005;
Yilmaz et al., 2007; Wang et al., 2009b). Table 4.1 summarizes some of the studies
conducted on anaerobic and aerobic/anoxic treatment process in SBR system
treating different types of wastewater. The intermittent anaerobic and aerobic
treatment approach showed high percentage of COD and nutrient removal. The
intermittent anaerobic and aerobic treatment process in wastewater treatment is
important in obtaining complete degradation process particularly for treating
recalcitrant compounds.
Since color removal and complete mineralization of the dyestuff could be
achieved through the combination of both processes as discussed in Chapter 3, many
studies have been conducted on the textile wastewater using the intermittent
anaerobic and aerobic treatment approach (Kapdan et al., 2003; Goncalves et al.,
2005; Albuquerque et al., 2005; Franciscon et al., 2009). Nevertheless, the
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integrated process used so far, requires the use of two separate reactors to suit the
different oxygen requirements of the degradation processes.
An attempt was therefore made to develop FAnGS which are capable to live
and work under both anaerobic and aerobic conditions. While achieving both
conditions fulfill the requirements to treat textile wastewater, the use of microbial
granules enhances the treatment system as discussed in the previous chapter.
Furthermore, the application of FAnGS under anaerobic and aerobic conditions
requires the use of a single reactor.
In this study, the progress of the FAnGS development was monitored and
their properties were determined. The effectiveness of the FAnGS in treating
synthetic wastewater during the development process was also assessed. This
chapter presents the results of the work conducted.
4.2 Materials
All of the chemicals/reagents used in this experimental work are listed in
Table 4.2 while the analytical equipments are listed in Table 4.3. Distilled water
generated from a water distiller was used throughout the experiment. Trace
elements and mixed dye were prepared as stock solutions and were kept at room
temperature for daily use. Synthetic textile dyeing wastewater (STDW) was
prepared three times a week for influent supply to the reactor system. In order to
avoid contamination, the mixture of the synthetic wastewater, trace elements and
dye were carried out in a laminar flow.
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Table 4.1 Sequential batch reactor system with intermittent anaerobic/aerobic/anoxic reaction phase treating variety types of wastewater Reference Biomass Wastewater Reaction phase/HRT Performance/Remark
Jang et al. (2003)
Activated sludge of MWTP
SWW (Carbon source: glucose)
HRT: 6 hours; Feed: 15 min; Aerobic: Anoxic: 4.75 hours (2:1); Settling: 45 min; Decant: 15 min
Removal: Nitrification (97%); COD (95%)
Lin et al. (2003)
Sludge from MWTP
SWW (Carbon source: acetate)
HRT: 6 hours; Feed: 5 min; Anaerobic: 120 min; Aerobic: 226; Settling: 5 min; Decant: 4 min.
P-accumulating organisms were enriched in the granule as the substrate P/COD ratio was increased.
Meyer et al.(2003)
Sludge enriched with GAO
SWW (Carbon source: acetate)
HRT: 8 hours; Feed: 5 min; Anaerobic 1.5 hours; Aerobic: 2 hours; Settling: 25 min; Decant: 5 min
Dense and highly active aggregates of microbes can lead to mass transport limitation
Zhu and Wilderer (2003)
Activated sludge
SWW (Carbon source: glucose & pepton)
HRT: 6 hours; Feed: 15 min; Anaerobic: 1 hour 40 min; Aerobic: 3.5 hours; Settling: 20 min; Decant: 15 min
Removal: COD (78-94%)
Arrojo et al. (2004)
Sludge from industrial wastewater
Industrial WW and SWW (Carbon source: acetate)
HRT: 3 hours; Feed: 3 min; Anoxic/Aerobic: 171 min; Settling: 1 min; Decant: 30 min; Idle: 3 min
Removal: Nitrification (70%); COD (85-95%)
Schwarzenbeck et al. (2005)
Sludge biomass
Dairy WW HRT: 8 hours; Feed: 5 min; Anaerobic: 0-60 min; Aerobic: 335-345 min; Settling: 5 min; Decant: 4 min; Idle: 5 min.
Removal:90% (CODtotal); 80% Ntotal; 67% Ptotal
Su and Yu (2005)
Activated sludge
Soy-bean WW HRT: 4 hours; Feed: 5 min; Aerobic: 220 min; Settling: 5 min; Decant: 10 min
Removal: COD (98-99%- after 7 days)
Cassidy and Belia (2006)
Flocculating sludge
Abattoir WW HRT: 7.2-6.2 hours; Fill: 120 min (anaerobic); Aerobic : 220 min; Settle: 2-60 min; Decant/Idle: 15 min.
Removal: COD and Phosphates (>98%); Nitrogen (>97%)
Qin and Liu (2006)
Activated sludge
SWW (Carbon source: ethanol)
HRT: 6 hours; Feed: 5 min; Aerobic: 230; Anaerobic: 119 min;Settling: 2 min; Decant: 4 min.
Removal: COD (95-97); Nitrogen (99-100%)
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Table 4.1 Sequential batch reactor system with intermittent anaerobic/aerobic/anoxic reaction phase treating variety types of wastewater (Continued)
Reference Biomass Wastewater Reaction phase/HRT Performance/Remark Zhang et al.
(2006) Activated sludge of MWTP
Raw swine manure WW
HRT: 3.3 days; Cycle time: 8 hours; Anaerobic: 1hour 15 min; Anoxic/aerobic: 2 hour 45 min; Anaerobic: 1 hour 30 min; Anoxic/aerobic: 2 hours; Settling: 30 min
Removal: Total Nitrogen (97.5%); Total Phosphorus (95%); COD (96%); BOD5 (100%); Turbidity (95%)
Wang et al. (2007)
Activated sludge of MWTP
SWW (Carbon source: glucose & 2,4-dichlorophenol)
HRT: 8 hours; Cycle time: 4 hours; Feed: 4 min; Anoxic (no stirring): 30 min; Aerobic: 200-210 min: Settle: 1-11 min; Decant: 5 min
Removal: COD (95%); 2,4-dicholophenol effluent (94%)
Yilmaz et al. (2007)
Aerobic granule
Abattoir WW/SWW (0-100% ratio)
HRT: 6.7-13.3 hours; Feed: 18 min; Anaerobic (50-60 min); Aerobic (160-400 min); Post Aerobic (80 min)
Removal: Soluble Nitrogen (93%); Soluble COD (85%); Soluble Phosphorus (89%)
Kim et al. (2008)
Activated sludge of MWTP
SWW (Carbon source: glucose & acetate)
HRT: 6H; Aerobic: 4.75 hours; Anaerobic: 1.25 hours; Feed: 0.25 hours; Settle: 0.75 hours; Decant: 0.25 hours
Removal: COD (95-98%); NH4+-N (47-99%)
depending on the NH4+-N loading rate
Lemaira et al. (2008a)
Aerobic granules
SWW; different ratio of SWW and abattoir WW
HRT: 13.3 hours; 8 hour cycle; Feed: 18 min; Anaerobic/anoxic: 60 min; Aerobic: 315 min; mixed anoxic: 80 min; Settle: 2 min; Decant: 5 min
Total removal: Soluble COD (85%); Ammonia (99%); Phosphates (98%)
Wichern et al. (2008)
Flocculating sludge
Dairy WW HRT: 8 hrs; Feed: 60 min; Anaerobic: 60-0 min; Aerobic: 334-405 min; Settling: 15-4 min; Decant: 5 min; Idle: 5 min
Nitrogen removal rate: 4.5-9.0 kgCOD/m3·d.
This Study (2009)
Mixed sludge with anaerobic granule
Synthetic textile dyeing WW
HRT: 6 hours: Feed: 5 min; Anaerobic: 80 min; Aerobic: 260 min; Settling/Decant/Idle: 5 min
Removal: COD (93%); Ammonia (95%); Color: (62%)
*MWTP-municipal wastewater treatment plant; WW-wastewater; SWW-synthetic wastewater; GAO-glycogen accumulating organisms.
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Table 4.2: List of reagents used in the experiment
Chemical Solutions/Reagents Applications
Synthetic wastewater Wastewater model compounds (Section 4.2.1) Trace element
Mixed dyes
Sludge sewage Granular precursor (Section 4.2.2) Anaerobic granules
Dye degrader microbes
COD reagent COD measurement (Section 4.2.4.4(b))
Nessler reagent Ammonia measurement (Section 4.2.4.4.(c))
Concentrated H2SO4 Digestion of mineral (Section 4.3.3) Nitric Acid
Gold sputter Sample coating (FESEM)
Crystal violet
Gram Staining (Section 4.2.4.5(a)) Iodine
Ethyl alcohol
Safranin
Nitrogen gas Anaerobic condition (Section 4.2.4.5 (b))
Oil emulsion Observation under 100x microscope magnification
Sodium hydroxide, NaOH pH adjustment
Hydrogen cloride, HCl
4.2.1 Wastewater Composition
Synthetic wastewater with the following composition was used: NH4Cl 0.16
g/L, KH2PO4 0.23 g/L, K2HPO4 0.58 g/L, CaCl2⋅2H20 0.07 g/L, MgSO4⋅7H2O 0.09
g/L, EDTA 0.02 g/L and trace solution 1 ml/L. The carbon sources used in this
experiment were glucose (0.5 g/L), ethanol (0.125 g/L) and sodium acetate (0.5 g/L).
The trace elements used were based on the composition recommended by Smolders
et al. (1995). The composition of the trace element was H3BO3 (0.15 g/L),
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FeCl3⋅4H2O (1.5 g/L), ZnCl2 (0.12 g/L), MnCl2⋅4H2O (0.12 g/L), CuCl2⋅2H2O (0.03
g/L), NaMoO4⋅2H2O (0.06 g/L), CoCl2⋅6H2O (0.15 g/L), and KI 0.03 g/L. Mixed
azo dyes consisted of Sumifix Black EXA, Sumifix Navy Blue EXF and Synozol
Red K-4B with total concentrations of 50 mg/L was used in this study. The mixture
gave an initial COD of 1270 mg/L; 1020 ADMI (≈102 Pt-Co) and average ammonia
concentration of 38 mg/L. The pH of the synthetic wastewater was adjusted to 7.0 ±
0.5 before feeding.
Table 4.3: List of equipment used in the experiment
Equipments Manufacturer/Product
IFAnGSBioRec UTM/Fabricated
Filter paper Wartmann/125 mm diameter
Filter apparatus Sartorius/DOA-P504-BN Incubator ELBA/EMO-1706 Furnace Interscience Sdn. Bhd./Carbolite
Glass column UTM/Fabricated Orbital shaker Protech/Orbital Shaker Model 720
Spectrophotometer HACH/DR/4000U COD reflux HACH/DRB 200
Flame Atomic Absorption Spectrophotometer Perkin Elmer/Analyst 400
Stereo microscope Leica Mycrosystems Wetzlar GmbH/Leica DMLS
Digital image management and analyzer ARC PAX-CAM/PAX-ITv6
Scanning electronic microscope Carl Zeiss/Zeiss Supra 35 VPFESEM
Coating system Biorad/Polaron Division SEM Coating System
DO meter & Data acquisition software ISTEK®/PH/ISE/DO Meter Model 125 PD
Water bath Memmert / Julabo PT30511 Autoclave Hirayama/Hiclave HV-50 Air pump RESUN/LP-100 air-pump
Water distiller Apex/Water Distiller
Laminar flow ERLA/CFM Series Air Cabinet Laminar Flow
Sieve WYKEHAM FARRANCE SLOUGH ENGLAND/British Standard Test Sieve (0.1-2.0 mm)
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As discussed in Chapter 3, the most commonly used dyes in the textile
processing are the azo dye. Hence, a mixed azo dye was applied in this study in order
to provide a closer resemblance to the real textile wastewater that usually consist of
several mixtures of dyes. Furthermore, synthetic textile wastewater was used in this
study in order to ensure the consistency of the wastewater quality with respect to the
compositions and concentrations. High variation in the wastewater compositions
may result in difficulty in analyzing and interpreting the results of the study.
4.2.2 Granules Precursor
The development of the FAnGS involved the mixture of sludge sewage
treatment plant (Taman Sutera, Indah Water Konsortium Treatment Plant System)
and textile wastewater treatment plant (Ramatex Industry Sdn. Bhd., Sri Gading
Industrial Park). Sludge from sewage treatment plant was used since the sludge
contains very high concentration of microbial populations that may be useful for
granule development. The sludge from textile wastewater was used since the
microorganisms that are present in the textile sludge may have already acclimatized
to the textile wastewater and would be easier to grow in the synthetic textile
wastewater used in the study. The acclimatized microbes in the textile sludge may
also have high capability in dye degradation process. This is important for the
development of granules that is going to be used for treating textile wastewater. An
equal volume of sludge from a municipal sewage treatment plant and a textile mill
wastewater treatment plant were mixed. The sludge inoculums were sieved with a
mesh of 1.0 mm to remove large debris and inert impurities. Figure 4.1 shows the
location of the sewage treatment plant (Indah Water Konsortium Treatment Plant
System, Taman Sutera) and textile wastewater treatment plant (Ramatex Industry
Sdn. Bhd., Sri Gading Industrial Park) where the sludge samples were taken.
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Figure 4.1 Location of textile industry; Ramatex Industry Sdn. Bhd., Sri Gading
Industrial Park, Batu Pahat and sewage treatment plant; Indah Water Konsortium
Treatment Plant System, Taman Sutera, Skudai.
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Anaerobic granules collected from a UASB treating paper mill industrial
effluent (Denmark) were used as the seed sludge. For every 1 L of sludge mixture,
about 100 mL of anaerobic granules of sizes less than 1 mm diameter were used as
seed for the granulation process. The MLSS of the anaerobic granules were 3.3 g/L.
Dye degrader microbes were also added into the sludge mixture. These microbes
were obtained from previous research that has successfully isolated the microbes
from textile wastewater treatment plants (Nawahwi, 2009 and Ibrahim et al., 2009).
The sludge mixtures were acclimatized with synthetic textile dyeing wastewater for
two months prior to the experimental start-up.
4.2.3 Reactor Set-up
The schematic representation of the IFAnGSBioRec set-up is given in Figure
4.2. The design of the reactor used in this study was based on several studies
conducted by previous researchers such as Wang et al. (2004) and Zheng et al.
(2005) with several modifications. A water jacketed column reactor was used in the
study. The column was designed for a working volume of 4 L with internal diameter
of 8 cm and a total height of 100 cm. The water jacketed column was designed to
provide temperature controlled conditions by allowing the circulation of hot water
from a water heating circulation system (Julabo PT30511) to the water jacketed
column of the reactor system. The temperature of the heating system was set at 30oC.
The wastewater was fed into the reactor from the bottom of the reactor. Air was
supplied into the reactor by a fine air bubble diffuser also located at the bottom of the
reactor column. The decanting of the wastewater took place via an outlet sampling
port located at 40 cm height from the bottom of the reactor. The reactor system was
equipped with dissolved oxygen meter and pH meter (Istek ®, Korean Model 125
PD) for the continuous monitoring of the DO and pH level throughout the
experiment. The 4-L laboratory scale of IFAnGSBioRec used in this study is shown
in Figure 4.3. Figure 4.4 shows the preparation carried out in the development of
FAnGS for textile wastewater treatment.
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1. Influent tank 2-5. Peristaltic pumps
6. Mass-flow controller 7. Air pump 8. Timer controller 9. Effluent tank Figure 4.2 Schematic layout of the IFAnGSBioRec system (Wang et al. (2004)
and Zheng et al. (2005)
Sampling point
DO probe
pH probe
Effluent
Influent
o o o o o
o o o o
o o
o o o o o
o o o o o
o o
1
7
3
9
5
8
6
2
4
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Figure 4.3 The IFAnGSBioRec system used in this study
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4.3 Analytical Methods
The FAnGS was analyzed for biological, physical and chemical
characteristics. The biological characteristic of the FAnGS was investigated in terms
of morphological, structure as well as cellular observation of the microbes within the
FAnGS. The microbial activities of the granules were investigated with respect to
their specific oxygen uptake rate (SOUR) and specific methanogenic activity (SMA).
The physical characteristics of the FGS tested include settling velocity, granular
strength and sludge volume index (SVI). The chemical aspect of the FAnGS
analyzed includes their mineral content. Profiles of the reactor system were
evaluated on the biomass concentration retained in the reactor as well as the sludge
retention time. Figure 4.5 shows the experimental analysis conducted for
characterization of the FAnGS.
4.3.1 Biological Characteristics
4.3.1.1 Morphological and Structural Observation
The morphological and structural observations of the granules were carried
out by using a stereo microscope equipped with digital image management and
analyzer (PAX-ITv6, ARC PAX-CAM). The microbial compositions of the granules
were observed qualitatively with a scanning electronic microscope (FESEM-Zeiss
Supra 35 VPFESEM). The granules were left dried at room temperature prior to
gold sputter coating (Bio Rad Polaron Division SEM Coating System) with coating
current of 20 mM for 45 s.
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Figure 4.4 Preparation frame work for granule development
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88
Figure 4.5 Characterizations of FAnGS
89
The cellular observation of the microorganisms present within the FAnGS
was carried out using gram staining. The gram staining procedures were carried out
by preparing a smear onto a glass slide. The slide was heated in order to fix the
smear onto the slide. The fixed smear was covered with one or two drops of crystal
violet for one minute. The stained smear was poured off and carefully washed with
distilled water. Then, iodine was added and left for 1 minute before once again
washed off with distilled water. The preparation was then washed with ethyl alcohol
of 95%. The slide was counterstained with safranin for 1 to 2 minutes prior to
rinsing with distilled water. A drop of oil was placed on the slide and was examined
under 100x magnification of stereo microscope (Leica DMLS). Gram-positive cells
appear violet and gram negative cells appear red when observed under the light
microscope.
4.3.1.2 Microbial Activity
The microbial activity of the FAnGS was conducted by measuring the
oxygen utilization rate (OUR), specific oxygen utilization rate (SOUR) and specific
methanogenic activity (SMA). The OUR and SOUR measurements were performed
by following Standard Methods (APHA, 2005). The OUR value was measured
during both the first and second stage of the aerobic reaction phase. The profile of
dissolved oxygen (DO) concentration in the reactor was measured continuously
online using a DO electrode (Istek®, Model 125 PD). The data were electronically
recorded using data acquisition software (Istek®, Model 125 PD).
The OUR measurement was conducted as soon as the aeration phase started.
Sample for the reactor system was taken from the sampling port and used to fill the
300 mL BOD bottle. Then, the DO probe was immediately inserted into the BOD
bottle. The sample in the BOD bottle was mixed using a magnetic stirrer at 20 rpm.
The initial DO was measured as DOa. The time taken for the DO to reduce to 2
90
mg/L was measured as T. DOb was measured at the end of each OUR measurement
(i.e. when the DO level in the BOD bottle nearly reaching to 2 mg/L). Then the
sample in the BOD bottle was returned back into the reactor. The DO measurement
is repeated with 10 minutes time interval with new samples from the reactor until the
aeration phase completed. For the SOUR measurement, the biomass concentrations
collected in the BOD bottle were quantified and measured as M. The measurement
for OUR and SOUR were conducted at room temperature. The calculations for OUR
and SOUR are given in the equations below:
where
OUR = Oxygen uptake rate (mg/L.h)
SOUR = Specific oxygen uptake rate (mL CH4/g VSS.h)
DOa = Initial dissolved oxygen (mg/L)
DOb = End dissolved oxygen (mg/L)
T = Time (min)
M = Granular biomass (mg/L)
The SMA analysis was conducted according to Erguder and Demirer (2008) with
several modifications where a 500 mL BOD bottle seeded with FAnGS with final
concentrations of 1-2 g VSS/L and basal medium (250 mL effective volume). The
bottle was purged with N2 gas mixture for 5 minutes to obtain an anaerobic
condition. The bottle was then sealed with a rubber septum. Acetate acid (HAc) was
fed into the serum bottle at the concentration of 3000 mg/L. The experiments were
conducted at room temperature (28 ± 2oC). The production of methane gas (CH4)
was determined daily for 5-7 days by using liquid displacement methods containing
concentrated KOH stock solution (20 g/L) (Erguder and Demirer, 2005a). After
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each gas measurement, the bottle was manually shaken. At the end of the SMA
assay, the VSS content in the bottle was measured. The SMA was calculated as the
maximum CH4 produced per gram of VSS per hour (mL CH4 g/VSS.h) (Zitomer and
Shrout 1998).
4.3.2 Physical Characteristics
4.3.2.1 Settling Velocity
The settling velocity was determined by recording the average time taken for
the individual granule to settle at a certain height in a glass column filled with tap
water (Linlin et al., 2005).
4.3.2.2 Sludge Volume Index
The SVI value could be calculated by measuring the bedvolume of the sludge
biomass in the reactor divided with the dry weight of the biomass in the reactor. The
bedvolume can be obtained by measuring the bed height of the sludge biomass that
settled in the reactor 5 minutes after the aeration phase stopped. The bedvolume is
obtained by multiplying the bedheight with the surface area of the bedcolumn (Beun
et al., 1999).
92
4.3.2.3 Granular Strength
Determination of the granules’ strength was based on Ghangrekar et al.
(1996). Shear force on the granules was introduced through agitation using an
orbital shaker (Protech Orbital Shaker Model 720) at 200 rpm for 5 minutes. At
certain degree of the shear force, parts of the granules that were not strongly attached
within the granules will detach. The ruptured granules were separated by allowing
the fractions to settle for 1 minute in a 150 ml measuring cylinder. The dry weight
of the settled granules (SG) and the residual granules in the supernatant (RG) were
measured. The ratio of the solids in the supernatant (RG) to the total weight of the
granular sludge (SG+RG) used for granular strength measurement was expressed as
the integrity coefficient (IC) in percent. This percentage indirectly represents the
strength of the granules. The higher the IC value the lesser the strength of the
granules and vice versa. The calculation for the IC value is given in the equation
below.
where
IC = Integrity coefficient
RG = Residual granules (mg)
SG = Settled granules (mg)
4.3.2.4 Biomass Concentration
The biomass concentration in term of mixed liquor suspended solid (MLSS)
and mixed liquor volatile suspended solid (MLVSS) were measured according to
93
Standard Methods (APHA, 2005). Ten (10) mL of samples were filtered using 45μm
filter paper (Wartmann) using a filter apparatus (DOA-P504-BN). The filter papers
with samples were then weighed (Ma) before heated at 150oC for one hour. After the
samples were allowed to cool in the desiccators, the filter papers were weighed again
(Mb). Then the filter papers were heated at 550oC for 15 min and were weighed (Mc)
after the filter papers were allowed to cool in the desiccators. The MLSS and
MLVSS were measured using Eq. 4.4 and Eq. 4.5, respectively.
where,
MLSS = Mixed liquor suspended solid (mg/L)
MLVSS = Mixed liquor volatile suspended solid (mg/L)
Ma = Weight of filter paper with sample before heating at
150oC (mg)
Mb = Weight of filter paper with sample after heating at
150oC (mg)
Mc = Weight of filter paper with sample after heating at
550oC (mg)
V = Sample volume (mL)
4.3.2.5 Sludge Retention Time
Sludge retention time (SRT) is determined as follows (Beun et al., 1999).
94
where,
Xr = Mixed liquor volatile suspended solid in reactor (mg/L)
VT = Total working volume in reactor (L)
Qe = Effluent flowrate (L/d)
Xe = Mixed liquor volatile suspended solid in effluent (waste
sludge) (mg/L)
4.3.3 Chemical Characteristics
The granules were analyzed chemically for their mineral content which
includes Ca2+, Mg2+, Na+, K+, Fe2+, Ni2+ and Co2+. The procedures for acid digestion
of the granular sample were based on Ghangrekar et al. (2005) with some
modification. The granular sample was evaporated to dryness in an incubator
(EMO-1706) at 105oC. About 5 g of the dry sludge were dissolved in a minimum
quantity of concentrated sulfuric acid giving a brownish solution. Then, concentrated
nitric acid was added into the solution until it turned colorless. The solution was
diluted with distilled water to a total volume of 25 mL for mineral and metal
determination. The mineral contents were determined by using a Perkin Elmer
Analyst 400 Flame Atomic Absorption Spectrophotometer (FLAA).
95
4.3.4 Removal Performance
Synthetic wastewater samples of the influent and effluent from the
IFAnGSBioRec were used for the quantification of removal performance of the
Chemical Oxygen Demand (COD), color and ammonia. All samples were
centrifuged prior to measurement. This step is carried out to prevent any
interference that may caused by the presence of suspended particles in the samples.
4.3.4.1 Color
A quantitative estimation of the color intensity was carried out by
calorimetric approach. Color was analyzed using a HACH Spectrophotometer
(DR/4000U) according to Procedure No.1660. Using distilled water as blank, the
method gives color value in terms of American Dye Manufacturing Index (ADMI)
4.3.4.2 Chemical Oxygen Demand
Chemical oxygen demand was quantified using a HACH Spectrophotometer
(DR/4000U) according to Procedure No. 2720. Each sample was added to the COD
reagent (High Range Digestion for COD, Cat. 21259-15) and was digested at 150oC
for 2 hours in COD reactor (Model DRB 200). After the digestion was completed,
the sample was allowed to cool at room temperature before the COD levels were
measured using the spectrophotometer.
96
4.3.4.3 Ammonia
The determination of ammonia was according to the Nessler Method (APHA
2005). One (1) mL of sample was diluted using 25 mL deionized water. Then three
drops of mineral stabilizer was added into the samples and mixed. This was followed
by adding another three drops of dispersing agent and mixed. Lastly, 1 mL of
Nessler reagent was added into the sample and mixed again before the sample was
left to react for 1 minute. The sample was then measured using a HACH
Spectrophotometer (DR4000/U) according to Method No 2400.
4.4 Experimental Procedures
During the start up period, 2 L of mixed sludge and 2 L of synthetic textile
wastewater were added into the reactor system making the final volume of 4 L with a
total sludge concentration after inoculation of 5.5 g/L. The system was supplied with
external carbon sources consisting of glucose, sodium acetate and ethanol which
gave a substrate loading rate of 2.54 kg COD/m3·d. The calculation for the OLR is
given in Appendix A. The hydraulic retention time (HRT) of the reactor was 6 hours
and was divided into several phases.
The reactor was operated in successive cycles of 6 hours, each one with an
intermittent anaerobic and aerobic reaction phase. All of the operation of peristaltic
pumps, circulation of influent, air diffuser and decanting process were controlled by
means of a timer. The reaction phase was started with an anaerobic phase for 40
min, followed by an aerobic reaction phase for 130 min. The reaction phase was
repeated with another 40 min of anaerobic phase and 130 min for a second aerobic
reaction phase. During the anaerobic reaction phase, the wastewater in the reactor
system was allowed to circulate. The wastewater from the upper level of the reactor
97
system was pumped out of the reactor column and returned back through the valve
located at the bottom of the reactor. The circulation process was carried out by using
a peristaltic pump (Cole-Parmer System Model; 6-600 rpm). The wastewater was
circulated at a rate of 18 L/h. The circulation system was stopped when the
anaerobic phase ended. The circulation process is required to achieve a
homogeneous distribution of substrate as well as a uniform distribution of the
granular biomass and restricts the concentration gradient. Each of the cycle
comprised of 5 min filling, 340 min reaction, 5 min settling, 5 min decanting and 5
min idle.
Samples of the synthetic wastewater from the IFAnGSBioRec were taken
twice a week. Fifteen (15) mL of influent sample as the initial value was taken from
the influent tank before the new cycle operation started, while another 15 mL of the
effluent sample was taken from the effluent tank after the effluent was released
during the decanting phase as the final values. The samples were filled in a separate
15 mL centrifuge tube. Samples were centrifuged for 5 min at 4000 rpm at 4oC in
order to pellet down all of the suspended particles from the samples. The
supernatant was used to measure the removal performance of the Chemical Oxygen
Demand (COD), color and ammonia removal.
Ten (10) mL of sample was taken into 15 mL of centrifuge tube from the top
portion of the reactor about 10 minutes after the filling stage ended. The sample was
measured as the initial concentration of the suspended solids. Another 10 mL of
sample was taken from the effluent after the decanting stage. When conducting
sampling for the measurement of the suspended solids, the effluent from the reactor
was collected in a 5L beaker. The effluent in the beaker was mixed before the
sample taken with the purpose to get a correct representative value of the suspended
particles in the effluent. These samples were used for the measurement of the
suspended particles in the influent and in the effluent. Lastly, another 10 mL of
sample volume was taken during the aeration phase. In order to get a representative
value, the samples were taken at two sampling points i.e. the upper and lower
98
sampling ports. Both samples were then mixed in a beaker and analyzed for the
MLSS and MLVSS.
The bedheight of the biomass in the reactor was also measured twice a week
for the calculation of the SVI. The bedheight was measured immediately after the
settling time ended and before the wastewater was drained out during the decanting
time. The measurement of the OUR and the SMA were conducted a few days before
the experiment ended which is after the FGS has reached the maturation stage. The
granular sample was taken almost once in two weeks for the measurements of the
physical properties of the FAnGS.
Table 4.4 shows the successive phase for one complete cycle of the
IFAnGSBioRec. The dissolved oxygen (DO) concentrations remained low during
the anaerobic condition (0.2 mg/L) and reached saturation concentrations during the
aerobic phase. The superficial air velocity during the aerobic phase was 1.6 cm/s.
The calculation of the superficial air velocity is given in Appendix A. The pH
during the reaction process varied in the range of 6.0 to 7.8 and the temperature of
the experiment was at 30oC. The reactor system was operated for a period of 66
days. Two litres of the wastewater remained in the reactor after the decanting stage
yielding a volumetric exchange rate (VER) of 50%. At this settling time (5 min),
only particles with settling velocity larger than 4.8 m/h remained in the column. Any
particles having smaller settling velocity would be washed out in the effluent.
99
Table 4.4: One complete cycle of the IFAnGSBioRec
Successive Phase One complete cycle (6 hours)
Filling 5 min
React
Anaerobic Aerobic
1st phase 40 130
2nd phase 40 130
Settling 5 min
Decant 5 min
Idle 5 min
Total cycle length 360 min
4.5 Results and Discussion
4.5.1 Morphology of Facultative Anaerobic Granular Sludge
A week after inoculation in the reactor, visual and microscopic observations
of granules formation were made. At this stage, the developed granules were
composed more of loosely clumped sludge which could easily break up into pieces if
placed under vigorous shaking. Within a week, the anaerobic seed granules
underwent morphological changes from spherical in shape and black in color with
average diameter of 1 mm into smaller grey granules due to exposure to the aeration
as mentioned earlier. On day 30, two different types of granules were clearly
observed in the reactor as shown in Figure 4.6.
100
Figure 4.6 The morphological development of facultative anaerobic granular
sludge (scale bar at steady-state equals to 1mm). Pictures were taken using a stereo
microscope with magnification of 6.3X. (a) Granules developed from the activated
sludge. (b) Granules developed from anaerobic granules patches.
a
b
101
Figure 4.6a shows mainly irregular-shaped with yellow colored granules
(Type A) that are solely developed from the activated sludge. In Figure 4.6b, the
anaerobic granules that have fragmented into smaller pieces have formed different
sizes of granules (Type B) that contained pieces of anaerobic granules. The outer
layer of the latter were yellow in color indicating the domination of aerobic or
facultative microorganisms while the darker spots within the granules indicate the
presence of anaerobic fragments originated from the anaerobic granules. The
formation of Type A granules could be elucidated by the mechanisms explained by
Beun et al. (1999). The development was initiated from the mycelial pellets that
were retained in the reactor due to high settling velocity. These mycelial pellets
eventually become the support matrix for the bacteria growth. Bacteria that were
able to attach to this matrix were retained and suppressed the growth of filamentous
microorganisms and became the dominant species in the reactor.
The formation of Type B granules has been discussed by Linlin et al. (2005).
These granules were formed through a series of physical and morphological changes.
The anaerobic granules initially disintegrated into smaller size flocs and debris when
exposed to aeration forces in the SBR column. Some of the granules and debris that
were too small were washed out with the effluent while the heavier ones were
retained in the column and acted as nuclei for the formation of new granules. Having
these types of granules that consisted of the combination of aerobic and anaerobic
portions within the granules could increase the possibility of degradation process that
requires both aerobic and anaerobic conditions for complete degradation particularly
for textile wastewater. Figure 4.7 shows the obvious morphological differences
between sludge particles during the initial stage of the experiment and matured
FAnGS at the final stage (day 66) of the experiment. The average sludge particles
are 0.02 ± 0.01 mm (Figure 4.7a), while the FAnGS developed with the average
particle diameter size of 2.3 ± 1.0 mm with maximum size reaching up to 4 mm
(Figure 4.7b).
102
Figure 4.7 Pictures of sludge particles during the initial stage of the experiment (a)
and matured FAnGS granules at the 66 days of the experiment (b). Pictures were
taken using a stereo microscope with magnification of 6.3X (scale bar equals to 1
mm)
a
b
103
The microstructure of the FAnGS that was examined using SEM is shown in
Figure 4.8. The SEM observation of the mature granules shows the domination of
non-filamentous coccoid bacteria that is tightly linked and embedded to one another
and form a rounded shape on the surface of the granule and covered with
extracellular polysaccharides substances (EPS) (Figure 4.8a). The absence of
filamentous bacteria in the developed granules may be due to the experimental
conditions that did not favor their growth such as high concentrations of DO during
the aerobic phase (i.e. 7.0 ± 0.5 mg/L) and considerably high organic loading rates
(2.4 kg COD/ m3·d) (Chudoba, 1985; Eckenfelder, 2000; Zheng et al., 2006). Figure
4.8b shows the presence of cavities between the clumped bacteria. These cavities are
anticipated to be responsible in allowing a smooth mass transfer of substrates or
metabolite products in and out of the granules (Tay et al., 2003 and Toh et al., 2003).
4.5.2 Cellular Characterization of Facultative Anaerobic Granular Sludge
The seeding sludge under microscopic observation showed a typical
morphological structure of conventional activated sludge that contained the
filamentous bacteria as the typical dominant species. The gram staining of the
seeding sludge in Figure 4.9a shows the presence of filamentous bacteria as the main
dominating species and mostly resulted with negative gram staining. The mature
FAnGS reveal more of fat-rod and coccal-types bacterial morphotypes. The
microbial presence resulted with both positive and negative gram stains (Figure
4.9b). This shows that there are changes in the dominancy of microorganisms in the
development of the FAnGS.
104
Figure 4.8 FESEM microstructure observations on mature facultative anaerobic
granular sludge under the magnification of 10,000K. (a) Coccoid bacteria tightly
linked to one another. (b) Cavities that appear between bacteria clumped inside the
granules
b
a
Cavities
105
Figure 4.9 The changes on the microbial population during the process development
of the FAnGS observed by gram staining procedures under microscopic
magnification of 1000K (a) The sludge being dominated by the filamentous
organisms. (b) Changes in the domination species within the FAnGS
a
b
106
4.5.3 Microbial Activity
A typical DO concentration profile for one complete cycle and the OUR
profile during both of the aerobic reaction phases are shown in Figure 4.10. OUR
gives an indication of the performance of biological activity of microorganisms in
the reactor in terms of oxygen utilization. Higher OUR values designate of high
biological activity and vice versa.
PI and PIII show the first and second stages of anaerobic reaction phase,
respectively. At these anaerobic stages, most of the dye degradation process
occurred where amines as the byproduct, were released (Sponza and Isik, 2005),
while PII and PIV represent the first and second stage of aerobic reaction phase,
respectively. Most of the co-substrates provided to the reactor system were
consumed within few minutes of the first aerobic reaction phase (PII) and is known
as the feast period. During the feast period, the DO concentrations in the reactor
were low (about 4 mg/L). The high utilization of DO during the feast period was
also indicated by the high OUR which was 281 mg/L.h. The amines, which were
produced during the anaerobic reaction phase (PI), were mineralized under this
aerobic condition (PII) as they cannot be further degraded under anaerobic phases
(Stolz, 2001 and Sponza and Isik, 2005).
When all the carbon sources (substrate and amines) in the wastewater have
being utilized, an endogenous respiration process took place, referred as the famine
period. The DO concentration immediately increased to around 7.0 mg/L which was
closed to the DO saturation level. The OUR also reduced to 14 mg/L.h indicating
low utilization of DO. The transition from the feast to famine phase was clearly
observed with the drastic increase of the dissolved oxygen and the extreme drop of
the OUR within few minutes of the aerobic reaction phase (PII).
107
Figure 4.10 The profile of dissolved oxygen and oxygen uptake rate in one
complete cycle of the IFAnGSBioRec system (♦) Dissolve oxygen, (□) Oxygen
uptake rate (PI and PIII-Anaerobic phase; PII and PIV- Aerobic phase)
Since there was no addition of substrate during the second aerobic reaction
phase (PIV), the consumption of DO during this phase was also low. This is shown
by high DO levels reaching saturation values of 7.6 mg/L. A sharp increase in the
OUR was also observed at the beginning of this phase. Apparently, the residual dyes
which were not degraded in Stage PI and PII were transformed into smaller
molecules (e.g. amines) during the second stage of the anaerobic phase (PIII). These
smaller molecules were further mineralized in Stage IV which resulted in a sharp
increase in the OUR. As the concentration of these molecules were reduced, the
OUR also became lower until it reached a minimum of 11 mg/L.h. Table 4.5 shows
the OUR value during both of the aerobic reaction phases.
PI PII PIII PIV
108
Table 4.5: The OUR levels during the aerobic reaction phase of one complete cycle
Aerobic reaction phase OUR (mg/L.h)
1st stage (PII) 2nd stage (PIV)
Begin react 281 ± 39 167 ± 51
End react 14 ± 2 11 ± 2
The SOUR of the FAnGS was determined before the termination of the
experiment. The SOUR was 51.1 ± 6.8 mg DO/g VSS.h. This value was slightly
lower than those of the aerobic granules reported by Tay et al. (2001a) which ranged
from 55.9- 69.4 mg DO/g VSS.h and higher than the coupled granules reported by
Erguder and Demirer (2005a) (6-47 mg DO/g VSS.h). The SMA of the FAnGS is
lower (10.3 mL CH4/g VSS.h) than the one reported by Erguder and Demirer
(2005a) (14-42 mL CH4/g VSS.h). However, despite the low SMA emission, it
provides the evidence of the existence of methanogens within the FAnGS.
4.5.4 Size of the Facultative Anaerobic Granular Sludge
The shear force imposed in the development of granules in this experiment,
in terms of superficial upflow air velocity (i.e. 1.6 cm/s), resulted in the development
of FAnGS with average diameter of 2.25 mm. This is in the range normally observed
for anaerobic and anoxic granules. According to Peng et al. (1999), the diameter of
the developed aerobic granule is in the range of 0.3 to 0.5 mm which is much smaller
as compared to anaerobic and anoxic granules that could develop up to 2 to 3 mm.
The strong shearing force produced by mixing and aeration during the aerobic phase
prevents the development of bigger aerobic granules. However, reduction in famine
period may also lead to the formation of bigger aerobic granular’ sizes (Liu and Tay,
2006). Other factors and conditions of the experimental set-up could also resulted
to much bigger sizes (eg. 5 mm) as reported by Liu and Tay (2004).
109
4.5.5 Settling Velocity of the Facultative Anaerobic Granular Sludge
The average settling velocity of the sludge and anaerobic granular sludge
used as the seeding in this experiment were 9.9 ± 0.7 m/h and 42 ± 8 m/h
respectively. The average settling velocity of the anaerobic granular seed is in
accordance with those reported by Schmidt and Ahring (1996) which was in the
range of 18-100 m/h. The settling velocity of the FAnGS increased from 17.8 ± 2.6
m/h to 83.6 ± 2.6 m/h at the end of experiment. The average settling velocity of the
mature FAnGS at the end of the experiment reached almost 80 ± 7.6 m/h. The
settling velocity obtained from this study was almost three times greater than the
settling velocity of the aerobic granules reported by Zheng et al. (2005).
The increase in settling velocity has given significant impact on the biomass
concentration in the reactor. The relationship between the concentration of the
MLSS and settling velocity of the granules is shown in Figure 4.11. Despite the
short settling time (5 min in this experiment), the high settling velocity possessed by
the developed FAnGS enabled the granules to escape from being flushed out during
the decanting phase. Such conditions have caused more FAnGS to retain in the
reactor and resulted in the increase of biomass concentration.
The SVI value has also improved from 276.6 mL/g at the initial stage of the
experiment to 69 mL/g at the end of the experiment indicating the good settling
properties of the granules which is favorable in wastewater treatment plant operation.
Figure 4.12 demonstrates the SVI profile along with settling velocity. As the SVI
value improved, the granular settling properties increased from 50 m/h to about 80
m/h.
The SVI value achieved in this experiment is in agreement with the result
reported by McSwain et al. (2004) with SVI values of 115 ± 36 ml/g (settling time
10 min) and 47 ± 6 ml/g (settling time 2 min). The higher settling velocity and lower
110
SVI value of the mature FAnGS as compared to previous reports by other
researchers indicate that the formation of granules seeded with anaerobic granules
would develop better settling properties of the granules.
Figure 4.11 The relationship between the biomass concentrations retained in the
reactor with the settling velocity of the FAnGS (■) Settling velocity; (○) Biomass
concentration
4.5.6 Granular Strength of the Facultative Anaerobic Granular Sludge
The granular strength of the granules was measured based on the integrity
coefficient (IC) as mentioned earlier. The smaller the value of IC, the higher the
strength and ability of the granules to clump themselves from being broken due to
shear force of the aeration. Figure 4.13 shows the profile of IC of the developed
FAnGS as a function of time. The IC reduced as the granules developed. With an
initial value of 30, the IC was reduced to about 9 at the termination of the
111
experiment. A sharp reduction of IC was observed after 40 days of the experimental
run. According to Ghangrekar et al. (2005), granules with integrity coefficient of
less than 20 were considered high strength granules. The reduction in IC value
indicates the increase in the strength of the bond that holds the microorganisms
together within the developed granules.
Figure 4.12 The relationship between the SVI values and settling velocity of the
FAnGS (○) SVI, (■) Settling velocity
During the early stage of the granule development, the microbes within the
granules were loosely bounded to each other. At this stage, the granules may consist
of more cavities which make the granules less dense, as manifested by low settling
velocity. As more microbes are linked together, the granules increase in size. Under
certain selective pressures (i.e. short settling time, hydrodynamic shear force,
starvation of the microbial cell) within the reactor, microbes may produce more
extrapolysaccarides (EPS) (Lin et al., 2003 and Qin et al., 2004a). As reported by
Zhang et al. (2007b) and Adav and Lee, (2008a), the EPS contribute greatly to the
strength and the stability of aerobic granules. When more EPS are being produced
112
by the microbial cells, they form a cross-linked network and further strengthen the
structural integrity of the granules. The cavities within the granules will be filled
with the EPS as it is a major component of the biogranule matrix material. This
caused the granules to become denser and stronger as shown by their high settling
velocity and low IC value at the end of the experiment.
Figure 4.13 The profile of integrity coefficient representing the granular strength of
the FAnGS
The physical characteristics of the seed sludge and the matured FAnGS are
summarized in Table 4.6. The developed FAnGS possess the biomass characteristics
that are desirable in the biological wastewater treatment system.
113
Table 4.6: Characteristics of seed sludge and FAnGS
Characteristics Seed Sludge FAnGS
SVI (mL/g) 276.6 69
Average diameter (mm) 0.02 ± 0.01 2.3 ± 1.0
Average settling velocity (m/h) 9.9 ± 0.7 80 ± 8
IC 92 ± 6 9.4 ± 0.5
MLSS (g/L) 2.9 ± 0.8 7.3 ± 0.9
MLVSS (g/L) 1.9 ± 0.5 5.6 ± 0.8
4.5.7 Biomass Concentration and Sludge Retention Time
The profile of the biomass concentration (i.e. MLSS) after seeding with the
anaerobic granules is shown in Figure 4.14. During the first few days of the
experiment, almost half of the sludge was washed out from the reactor causing a
rapid decrease in the biomass concentration. The MLSS reduced from initial
concentrations of 5.5 g/L to 2.9 g/L mainly due to the short settling time used in the
cycle (i.e 5 min). During this initial stage, the anaerobic granules were also observed
to disintegrate into smaller fragmented granules and small debris resulted from shear
force caused by the aeration during the aerobic stage. These small fragments have
poor settling ability and were washed out from the reactor. This caused an increase
in the suspended solids concentration in the effluent. However, as the experiment
continued, the concentration of the biomass increased and finally reached 7.3 g
MLSS/L when the experiment was discontinued on the 66th day. The profile of
MLVSS follows the same trend of MLSS, ranging from 1.9 g/L to 5.6 g/L.
114
Figure 4.14 The profile of biomass concentration in the SBR. (●) MLSS, (□) MLVSS
The mean cell residence time (SRT) also increased from 1.4 days at the initial
stage to 8.3 days on the 66th day, indicating the accumulation of the biomass in the
reactor. As less biomass was washed out during the decanting period, the increase in
SRT is another manifestation of good settling characteristics resulting from the high
settling velocity. Nonetheless, the benefit of high SRT will depend on the goal of the
treatment process (Tchobanoglous et al., 2004). The SRT is affected by the settling
velocity. The profiles of the settling velocity and the SRT as a function of time are
given in Figure 4.15.
4.5.8 Mineral and Metal Content
The concentration of mineral and metal contents in sludge, newly developed
and matured FGS were determined in mg/g of dry sludge and presented in Table 4.7.
The trend of the concentration of the metal differs according to specific metals.
115
Figure 4.15 The settling velocity profile in relation to mean cell residence time
(SRT). (○) SVI, (■) SRT
The concentration of Na+ and K+ shows not much difference in the newly
developed and matured granules as compared to the content in the sludge. However,
there is an obvious increment on the concentration of Ca2+ and Mg2+ within the
matured granule. The concentration of Fe2+ is slightly reduced in newly-developed
and matured FAnGS as compared to the concentration in the sludge. As for the
concentration of Ni2+ and Co2+, there is not much difference when compared
between the newly-developed and matured granules. The concentration for both of
these metals is lower as compared to the concentration in the sludge.
Stable concentrations of Na+ and slight decrease of K+ concentrations in the
sludge and the matured granules may indicate that these monovalent cations are not
involved in the granulation process. However, it was reported that at high
concentrations, Na+ and K+ may cause adverse effect on the granules formation. It
could cause reduction in sludge concentration, settling velocity of the sludge,
granular strength and treatment efficiency (Ghangrekar et al., 2005). The
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monovalent cation, Na+ could become the reason for detrimental impact on the
flocculation system (Sobeck and Higgins, 2002).
Table 4.7: Comparison of mineral content at different stages during the development of FAnGS
Mineral contents (mg/g of dry sludge)
Mineral / Metal Sludge Newly developed FAnGS (1 week)
Matured FAnGS (10 weeks)
Ca2+ 1.53 ± 0.02 2.01 ± 0.58 4.65 ± 0.04
Mg2+ 0.13 ± 0.01 0.322 ± 0.003 1.75 ± 0.08
Na+ 0.22 ± 0.06 0.25 ± 0.49 0.24 ± 0.05
K+ 1.31 ± 0.06 1.15 ± 0.03 0.93 ± 0.05
Fe2+ 2.32 ± 0.02 1.90 ± 0.04 1.98 ± 0.08
Ni2+ 0.60 ± 0.02 0.22 ± 0.01 0.23 ± 0.01
Co2+ 0.149 ± 0.003 1.021 ± 0.002 0.02 ± 0.01
The developed FAnGS in this study showed higher accumulation of Ca2+ and
Mg2+ as compared to the newly-developed and the seed sludge. This may indicate
the involvement of these elements in the granulation process. Based on the divalent
cation bridging theory, the presence of Ca2+ and Mg2+ promotes equivalent floc
properties (Soberck and Higgins, 2002). The presence of Ca2+ was reported to
intensify the granular strength and enhance the granulation process (Grotenhuis et
al., 1991b; Jiang et al., 2003; Ghangrekar et al., 2005). These divalent cations are
postulated to be able to stimulate granulation by neutralizing the negative charges on
the bacterial cell surfaces that were developed due to the strong “van der vaal”
attraction forces (Grotenhuis et al., 1991a and Tay et al., 2000). The Ca2+ acts as a
cation bridge that interconnects between the EPS molecules and bacterial surfaces.
The connections form as stiff polymeric gel-like matrix that could augment the
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granulation development (Costerton et al., 1987; Sutherland, 2001; Jiang et al.
2003).
The presence of Ca2+ in the form of calcium carbonate and calcium
phosphate produced an inert support for the bacteria to grow in the granulation
process (Yu et al., 2001). Calcium uptake around 60 mg/g of Ca2+ in the granule
has been reported to be the ideal concentration for good granule characterization
with high strength and good settling property (Ghangrekar et al., 2005). Anything
higher than this concentration was not recommended since it could cause increase in
the ash content in the sludge due to chemical precipitation. Too much of Ca2+ could
also give a detrimental effect on the performance and stability of the reactor systems
and decrease in specific activity of the sludge system (Yu et al., 2001). Ca2+ at
concentrations higher than 780 mg/L could caused 60-90% reduction in COD
removal and serious cementation in the sludge bed through the high precipitation of
Ca2+ (Langerak et al., 1998).
The Ni2+ and Co2+ uptake into the granules might be influenced by the metal
requirement of the variety of microorganisms present in the reactor system
(Ghangrekar et al., 2005). However, at higher concentration, these metals could
inhibit some of the biological mechanisms of organisms (Bae et al., 2000).
4.5.9 Removal Performance
The performance of the FAnGS (after acclimatization stage) based on the
removal of COD, color and ammonia is given in Figure 4.16 to 4.18. Figure 4.16
and 4.17 shows that, at the initial stage of the operation, the percentage removal for
COD and ammonia were 71% and 67%, respectively. The removal efficiency has
increased to 94% for COD and 95% for ammonia at the end of the experiment. The
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increase in removal efficiency indicates the occurrence of high biological activity in
the reactor system. During the first month, the removal efficiency for COD and
ammonia was fluctuating. However, the removal became more stable for the
remaining period.
Figure 4.16 Profile of COD removal during FAnGS development in
IFAnGSBioRec system. (▲) Influent COD, (■) Effluent COD, (○) COD removal
The removal efficiency for color as shown in Figure 4.18 was fluctuating
almost throughout the experiment. The percentage of color removal was about 25%
during the start up and increased to 62% at the end of the experiment. The average
color removal was 55%. This low percentage of the color removal may be due to
insufficient HRT. As dye substances are recalcitrant and difficult to be degraded,
more time is required by the organisms to degrade the dyes. The inconsistent
percentage for color removal may be also contributed by the unstable condition of
the aromatic amines, the byproduct of dye degradation which easily oxidized when
exposed to oxygen during the aerobic phase. As will be discussed in Chapter 6, the
removal of color becomes better at higher HRT.
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Figure 4.17 Profile of Ammonia removal during FAnGS development in
IFAnGSBioRec system. (▲) Influent ammonia, (■) Effluent ammonia, (○)
Ammonia removal
Figure 4.18 Profile of color removal during FAnGS development in
IFAnGSBioRec system. (100 ADMI ≈ 1 Platimun-Cobalt). (▲) Influent color, (■)
Effluent color, (○) Color removal
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Figure 4.19 shows the percentage of removal of COD, ammonia and color in
a complete 340-minute reaction mode of the SBR system recorded on the 66th days
of experiment. The profile and the percentage of removal for COD and ammonia
were almost the same while the removal of color was much lower. After 340
minutes of intermittent anaerobic and aerobic modes, about 93%, 95% and 62% of
COD, ammonia, and color respectively were removed.
Figure 4.19 The removal for COD, ammonia and color in one complete cycle of
the SBR system (■) Color, (○) COD, (▲) Ammonia
During the first anaerobic phase (PI) (0-40 min), approximately 15% and 4%
of COD and ammonia respectively, were removed. In the first aerobic phase (PII)
(40-170 min), about 69% of the COD was removed while 80% of the ammonia was
oxidized. In the second anaerobic phase (PIII) (170-210 min), only about 5% of
COD and ammonia were removed while the remaining (about 4% for COD and 6%
for ammonia removal) were removed in the second aerobic phase (PIV) (210 to 340
min). As for color, about 46% and 16% were removed during the anaerobic and
aerobic phases respectively.
PIII PI PII PIV
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The degradation and decolorization of dye during the anaerobic condition has
been widely reported in the literatures (van der Zee et al., 2001a and Dos Santos et
al., 2007). The electrons from the electron donor are transferred to the N=N bond of
the azo dye causing the cleavage of the bond forming aromatic amines in anaerobic
condition. The amines are then degraded under aerobic condition reducing the COD
value of the wastewater. In addition to the degradation mechanism, dye removal
may also occur via adsorption onto the biomass. The removal of ammonia which
mainly takes place during the first aerobic stage is expected to be caused by the
nitrification process.
Based on the removal performance of the system, it has been proven that the
developed FAnGS is capable of performing the degradation process during the
anaerobic and aerobic phases. This indicates the presence of facultative and
anaerobic microorganisms in the FAnGS. According to Li and Liu (2005), when the
granules grew to a size larger than 0.5 mm, the diffusion of oxygen into the inner
part of the granules became a limitation. This may give an indication of the presence
of anaerobic microorganisms within the centre of the FAnGS since the average size
of FAnGS developed in this study was 2.3 ± 1.0 mm. Aerobic microorganisms may
be present at the outer layer of the granules which easily access the oxygen molecule
and mainly responsible for the COD removal. Meanwhile, the facultative
microorganisms may be present in any part of the FAnGS due to their capability to
live under both anaerobic and aerobic conditions.
4.6 Conclusions
Several conclusions could be drawn from the study and they are as follows:
i. Stable FAnGS could be cultivated in the SBR system with the application of
intermittent anaerobic and aerobic reaction modes during the reaction
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phase. The matured granules showed the domination of non-filamentous
bacteria that were tightly linked and embedded to one another and
covered with EPS. The morphology of the developed granules is affected
by the seed used in the development process.
ii. After 66 days of operation, matured FAnGS has reached an average diameter
of 2.3 ± 1.0 mm with settling velocity of 80 ± 8 m/h. The SVI value of
the biomass has decreased from 276.6 mL/g to 69 mL/g at the end of 66
days, also indicating the excellent settling properties of the granules. The
SRT increased from 1.4 days during the initial stage to about 9 days at the
end of the experiment demonstrating the accumulation of biomass in the
reactor system. The FAnGS is also structurally strong as shown by a low
IC value of 9.4 ± 0.5. By comparing the settling property of the FAnGSs’
with the granules developed by other researchers, the cultivation of
granules seeded with anaerobic granules resulted in better granules.
iii. The development of FAnGS is positively-correlated with the accumulation of
divalent cationic Ca2+ and Mg2+ in the granules suggesting the role played
by the cations in the granulation process.
iv. The developed FAnGS was able to remove COD and ammonia in the
wastewater up to more than 93%. Although the average removal of color
was only about 55%, the results indicate the viability of the granulation
system in treating textile wastewater under intermittent anaerobic and
aerobic phase strategy.
v. OUR/SOUR and SMA analysis proved the presence of anaerobic and aerobic
microorganisms activities in the FAnGS and capable of performing the
degradation process both in anaerobic and aerobic conditions.
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CHAPTER 5
EFFECT OF AGGREGATION AND SURFACE HYDROPHOBICITY BY
SELECTED MICROBES FROM FACULTATIVE ANAEROBIC GRANULAR
SLUDGE
5.1 Introduction
Granulation is a complex process. Development of compact aerobic granules
is initialized by the formation of small aggregates (Adav et al., 2008a) that does not
rely on the need for carriers or artificial surfaces for cell attachment (Liu and Tay,
2002). The mechanisms involved in granulation are subjected to a multiple-step
process that involves many aspects of physicochemical and microbiological features.
These steps may be affected by many factors including types and concentrations of
substrate in the influent, nature of the seed sludge, availability of essential nutrients,
presence of extracellular polymeric substrates (EPS), composition of the media that
may contain different concentrations of divalent cations, pH and temperature of the
experiments and also the operational set-up of the reactor system (Dignac et al.,
1998; Linlin et al., 2005; Liu and Tay, 2007c; Yi et al., 2008; Wang et al., 2009b).
The interaction of microorganisms during the initial stage of the granulation process
may play a significant role in the assurance of successful development of the
granulation process. Investigation on factors that may affect the mechanisms during
124
the initial stage of the granulation process should be considered as a crucial aspect to
be explored.
Aggregation ability and the surface hydrophobicity (SHb) of bacteria are two
independent traits that can be used as an indirect method for evaluating the adhesion
ability of bacteria (Marin et al., 1997; Ibrahim et al., 2005; Rahman et al., 2008).
Since the adhesion ability is postulated to be involved in the granulation process,
study on autoaggregation or coaggregation (CAg) and SHb of the granules and/or
among the bacteria found within the granules have become one of the main focuses
in the foundation for a better understanding on the granulation mechanisms. Few
attempts have been made by researchers concentrating on the effect of aggregation
and SHb (Dabert et al., 2005; Wang et al., 2005a; Ivanov et al., 2006). However,
the knowledge on this aspect particularly on the development of dye degrading
granule is very limited. Not much research has been carried out to study the factors
affecting aggregation and SHb among the microbes involved in the granulation
process.
This study was conducted to investigate the response on CAg and SHb of
selected mixed bacteria which have been isolated from FAnGS. Deeper
investigation at microscopic level was carried out to determine the effect of substrate
concentration, pH and temperature on the CAg and the SHb of the microbes isolated
from the FAnGS developed in the IFAnGSBioRec system described in Chapter 4.
5.2 Materials
Some of the chemicals/reagents and equipments used in this study are listed
in Section 4.2 and are described in Section 4.3. In addition, another list of chemicals
125
and reagents and analytical equipment specified used in this experiment of this
chapter are given in Tables 5.1 and 5.2, respectively.
Table 5.1: List of reagents used in the experiment
Chemical/Reagent Applications
Nutrient agar Spread plate (Section 5.3.1)
Potassium phosphate, K2HPO4 Washing buffer (Section 5.3.6)
Xylene (C6H4(CH3)2) Surface hydrophobicity assay (Section
5.3.6)
Nutrient broth Bacteria culture (Section 5.3.7)
Promega DNA extraction kits
DNA extraction and purification (Appendix C-1) Promega DNA purification kits
Ethanol
Agarose gel Gel eletrophoresis (Appendix C-2)
TAE buffer Buffering system for electrophoresis (Appendix C-2)
Bromophenol blue
Loading dye (Appendix C-2) SDS
Glycerol
Gene Ruler Ladder Mix Marker
Reverse and Forward primer PCR amplification (Appendix C-3)
PCR reaction solution
Promega Wizard®SV gel PCR product purification (Appendix C-4)
PCR clean-up system
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Table 5.2: List of equipment used in the experiment
Equipment Manufacturer/Product
Microcentrifuge Sartorius/Sigma 1-14
Turbidity Meter HANNA Instrument/ H 93703 Microprocessor
Optical Density Meter BUCK Scientific/100 VIS Spectrophometer
Vortex IKA/MS 1 Minishaker
0.2 μm Filter Sartorius (M) Sdn. Bhd./Minisart-Ny25
Syringe filter Sartorius (M) Sdn. Bhd./Minisart-Ny25
Microwave ELBA/EMO-1706
Eletrophoresis Apparatus BIO-RAD Laboratores/MiniSub-Cell
Power pack BIO-RAD Laboratores/PowerPac Basic
UV Transilluminator Vilber Lourmat/ TFX-35 Vilber Lourmat
Thermocycler Perkin Elmer/GeneAmp PCR System 9700
5.3 Analytical Methods
5.3.1 Chemical Oxygen Demand and Color Removal
In this study, the removal efficiency of COD and color of each of the isolated
bacteria from the FGS were conducted by batch experiment. The COD and color
removal were measured according to the analytical methods described in Section
127
4.3.4. The specific COD and color degradation rate were obtained by dividing the
percentage of COD or color removal with the biomass concentration for each of the
individual bacteria and time taken for the removal to take place. The biomass
concentrations for each of the bacteria were measured according to the analytical
methods as described in Section 4.3.2.4.
5.4 Experimental Procedures
The experiments in this study were conducted in two stages. The first stage
involved the isolation of microorganisms from the FAnGS and screening of
microorganisms based on their ability to degrade COD and dyes. Figure 5.1 shows
the experimental work conducted on the characterization of the microbes isolated
from the FAnGS. The degradation of dyes was measured as the percentage of color
removal. In the second stage the effect of substrate concentration, pH and
temperature on CAg and SHb of selected bacteria as a mixed culture were
investigated using factorial and response surface methods. The summary of the
experimental work for this second stage is shown in Figure 5.2.
5.4.1 Isolation Procedure of Bacteria Strain
The synthetic textile dyeing wastewater (STDW) was prepared as explained
in Section 4.2.1. For the bacteria isolation, the synthetic dyeing wastewater which
was used as the growth medium was sterilized by autoclaving the media for 20 min
at 121oC. The mixed dyes, glucose and minerals were filtered through 0.2 μm
membrane filter for sterilization purposes. Then, these materials were added
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separately into the autoclaved medium. Using aseptic techniques, a small amount of
mature granules were added to synthetic dyeing medium (15 mL) and mixed in a
sterilized beaker in order to disintegrate the granules. The supernatant was serially
diluted with medium (101 to 108 fold dilutions) before inoculation on to nutrient agar
(NA). Eight dilution bottles filled with 9 mL of sterilized distilled water were
prepared for sample dilution process. The samples were serially diluted by
transferring 1 mL of samples from the lower serial dilution (10-1) to the next serial
dilution bottle (10-2) until the eighth serial dilution bottle (10-8). Finally, about 1 mL
of each dilution was spread onto nutrient agar using a glass spreader. The isolation of
microorganisms was carried out by the spread plate method (Madigan et al., 2000).
The plate was inverted and incubated at room temperature and were monitored over
several weeks. Pure bacterial cultures were obtained by repeatedly subculturing onto
new nutrient agar plates until single pure colonies were obtained. The single
bacterial colonies were investigated for morphological and cellular characteristics.
5.4.2 Morphological Characterization
The pure culture bacteria isolated from FAnGS were characterized based on
the colony and cellular morphology of single colonies obtained. The colony
morphology on the nutrient agar was characterized for their size, shape, color,
margin and elevation. The colonies were examined using a stereo microscope (Leica
Zoom 2000) and the Pax-it Image Analyzing System. The analytical method of
gram-staining for the cellular morphology examination was according to Section
4.3.1.1.
129
Figure 5.1 Characterization of microbes isolated from the FAnGS granules
130
Figure 5.2 Experimental work for the investigation on the effect of substrate concentration, pH and temperature on the percentage of coaggregation and surface hydrophobicity of the mixed culture
131
5.4.3 Identification of Microorganisms Isolated from Facultative Anaerobic
Granular Sludge
Bacteria isolated from the FAnGS were further investigated for identification
of the microorganisms. The identification of the microorganisms involved several
stages beginning with the DNA extraction by using the DNA extraction kits
(Promega) in order to obtain the genomic DNA of the microbes. The successful
isolations of the genomic DNA were identified by running the gel electrophoresis. In
order to increase the magnitude of the isolated DNA, the genomics DNA were then
amplified by using the polymerase chain reaction (PCR) amplification process. In
the PCR process, two universal primers were used to amplify the 16S rRNA gene of
the selected bacteria. The amplified DNA was purified using PCR purification kits
before the sequencing of the genomic DNA was performed. The genomic DNAs
were sent to Vivantis Sdn. Bhd. for the sequencing purposes. Through the result of
the sequencing process, the identifications of the bacteria were obtained through the
BLASTn search which was carried out at the National Center of Biotechnology
Information (NCBI). The detailed procedure of microbial identification by using 16S
rDNA sequence analysis is provided in Appendix B.
5.4.4 Specific Growth and Screening for Dye-Degrading Bacteria
The cells used in the specific growth observation were at the exponential
growth in mixed dye (MD) medium. Each bacterium was inoculated in separate
bottles with 10% v/v of pure culture into the sterilized synthetic textile dyeing
wastewater. The experiments were performed in duplicate in 1 L schott bottle.
Each bottle contained 1L of MD medium with an initial mixed dye concentration of
100 mg/L. A mixture of substrate consisted of glucose; acetate and ethanol were
added at the concentration of 1500 mg/L. Individual strains were introduced into the
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medium and were cap immediately and incubated at room temperature. The
decolorizations of the mixed dye were measured at regular intervals during
incubation. The cell growth was monitored by optical density (OD) measurement
with an Optical Density Meter (100 VIS Spectrophometer) at wavelength of 600 nm.
The specific growth rate and dye degradation rate were calculated by performing a
linear regression on initial curves of cell growth and dye decolorization, respectively
against time.
5.4.5 Autoaggregation Assay
Samples which were used for the growth and dye degradation rate were used
for CAg and hydrophobicity assay. The synthetic textile dyeing wastewater in each
of the schott bottle was allowed to reach complete decolorization (more than 90%)
before autoaggregation assay was carried out. The aggregation assay was conducted
based on the procedure presented by Rahman et al. (2008) with several
modifications. Each of the samples was aerated using an air diffuser which was
connected to an air pump at a rate of 5 L/min. The aeration was stopped 15 min after
the growth of the bacteria reached the stationary phase. Fifteen (15) mL of samples
from each bottle was taken for the autoaggregation assay.
The initial turbidity of each sample was measured using a turbidity meter (H
93703 Microprocessor) as the initial reading. Then, the samples were centrifuged at
a slow centrifugation speed of 650 g for 2 min as described by Malik et al. (2003)
before the turbidity measurements were taken again. The CAg ability was expressed
as coaggregation percentage and calculated by using the equation below:
133
(5.1)
where,
CAg% = Percentage of coaggregation
CA0 = The absorbance of cultured media at 0 h
CAi = The absorbance of cultured media after centrifugation
From the CAg%, the mixed culture could be classified into three groups: high
coaggregation (HCAg: >70% CAg), medium coaggregation (MCAg: 20-70% CAg)
and low coaggregation (LCAg:<20% CAg) cultures. A high aggregation index
denotes a strong tendency of the cells to agglomerate into an aggregate (Adav and
Lee, 2008b).
5.4.6 Surface Hydrophobicity Assay
The surface hydrophobicity (SHb) of the bacterial strains either in the form of
single or as mixed cultures were based on the microbial adhesion to hydrocarbon
assay. SHb of the mixed bacteria was determined according to the methods
described by Zavaglia et al. (2002) and Canzi et al. (2005). Fifteen (15) mL of
samples were taken from the sample bottles used for growth and dye degradation rate
tests in the previous experiments. The bacterial cells were harvested by
centrifugation at 14,000xg for 5 min. The samples were washed twice with 50 mM
K2HPO4 (pH 7.0) and then resuspended in the same buffer to obtain an absorbance of
about 0.5 at 660nm. Five (5) mL of bacterial suspension was mixed with 1 mL of
xylene (C6H4(CH3)2) by vortexing for 120s and then allowed to stand for 1 hour at
room temperature. The absorbances of the bacterial suspension in the aqueous phase
after mixing (Ai) were compared to the absorbance taken at the initial stage of the
experiment (A0). Changes in absorbance due to the bacterial adhesion to the
hydrocarbons were measured as 660 nm by using an Optical Density Meter (OD)
134
(Jenwai-6300 Spectrophometer). The surface hydrophobicity was expressed as
surface hydrophobicity percentage (SHb%) and calculated by using the following
equation:
(5.2)
where,
SHb% = Percentage of surface hydrophobicity
A0 = The absorbance of sample before mixing with xylene
Ai = The absorbance of sample after extraction with xylene.
5.4.7 Effect of Substrate Concentration, pH and Temperature on
Coaggregation and Surface Hydrophobicity
Based on the results of the previous analysis which includes dye degradation
tests, autoaggregation and SHb assay, six out of twelve isolated bacteria were
selected for further study. These selected bacteria were labels as bacteria BS1FAnGS,
BS6FAnGS, BS7FAnGS, BS10FAnGS, BS11FAnGS and BS12FAnGS.
These bacteria were used as a mixed culture to determine the effect of
substrate concentration, pH and temperature on the CAg of and SHb of the mixed
culture. Each of the selected single bacteria cell were cultured separately in nutrient
broth until the measurement of the OD was near to 1. Then, the cell cultures were
harvested and re-suspended in saline water. The individual bacteria were then mixed
together and about 25 mL (10%v/v) of sample culture were inoculated in a separate
500 mL schott bottle containing STWW with the concentration of dye at 10 ppm.
135
The substrates containing glucose, acetate and ethanol were used as the
mixed external carbon sources. The final pH of the culture media, the temperature
for sample incubation and the concentration of substrate used in the experiment were
varied depending on the experimental design. The synthetic textile dyeing
wastewater which has been inoculated with the selected mixed culture was allowed
to decolorize before the CAg and SHb assay were carried out. After the synthetic
textile dyeing wastewater was decolorized more than 90%, the sample were aerated
by supplying air at a flow rate of 5 L/min. The samples were aerated continuously
for 5 hours. Ten (10) mL samples were taken hourly throughout the aeration phase
and used for the CAg and SHb assay.
5.4.8 2-Level Factorial Experimental Design
The effects of substrate concentration, pH and temperature on CAg and SHb
of the selected mixed culture in the synthetic textile dyeing wastewater were
investigated using a 2-level factorial experimental design. The range values of the
factors considered in the experiments are listed in Table 5.3. For a three variables
factorial design, a complete matrix would have been 23 which are equal to 8
experimental runs. Since the experiments were conducted in duplicate, a total of 16
experiments were carried out. Table 5.4 shows the factorial design of the study.
Each of the independent variable was investigated at a high (+1) and a low (-1) level.
The experimental design and the analysis were carried out using MINITAB®
statistical software.
136
Table 5.3: The variables and their range of high and low values used in the factorial
experiment
Variables Unit Low Value High Value
A: Substrate mg/L 500 3000
B: pH 5 9
C: Temperature oC 20 40
Table 5.4: Two-level fractional factorial design with three variables (in coded
levels) conducted in duplicate (not in randomized order)
Run No. Factor 1 Factor 2 Factor 3
A: Substrate B: pH C: Temperature
CASE01 -1 -1 -1
CASE02 -1 -1 -1
CASE03 +1 -1 -1
CASE04 +1 -1 -1
CASE05 -1 +1 -1
CASE06 -1 +1 -1
CASE07 +1 +1 -1
CASE08 +1 +1 -1
CASE09 -1 -1 +1
CASE10 -1 -1 +1
CASE11 +1 -1 +1
CASE12 +1 -1 +1
CASE13 -1 +1 +1
CASE14 -1 +1 +1
CASE15 +1 +1 +1
CASE16 +1 +1 +1
137
5.4.9 Response Surface Methodology (Central Composite Design)
The Response Surface Method (RSM), namely Central Composite Design
(CCD) was used to investigate the possibility of non-linear effect of the selected
variables. In addition to the factorial trials, the CCD was run with five replicate at
the central point together with +α and –α points. This was employed to fit the
second–order polynomial models and to obtain an experimental error of the
experiment. The range and levels of experimental variables investigated in this study
are the same used in the factorial design process as presented in Table 5.3. For CCD,
a total of twenty experimental runs were carried out. Table 5.5 shows the design
matrix for substrate, pH and temperature as the variables in coded units for the CCD
experimental run.
Based on the CCD design matrix, the experiments were divided into three
parts; a 23 factorial design point run (CASE01 to CASE08), the star point run
(CASE09 to CASE14) and the centre point runs (CASE15 to CASE20). In this
study, the responses were the percentage of CAg and SHb of the mixed
microorganisms selected from the FAnGS. The main and synergistic effects of the
factors were determined based on the factorial points and centre point runs. The non-
linear response behavior was analyzed using the star points and centre points runs.
The centre point runs were repeated six times in order to achieve a better estimation
of the experimental error (pure error). The experimental runs were conducted in
randomized order. The main reason of randomization is to reduce bias of unexpected
element during experimental runs.
138
Table 5.5: Two-level of CCD experimental run in coded units
Run No. Factor 1 Factor 2 Factor 3
A: Substrate B: pH C: Temperature
CASE01 -1 -1 -1
CASE02 1 -1 -1
CASE03 -1 1 -1
CASE04 1 1 -1
CASE05 -1 -1 1
CASE06 1 -1 1
CASE07 -1 1 1
CASE08 1 1 1
CASE09 -1.682 0 0
CASE 10 1.682 0 0
CASE 11 0 -1.682 0
CASE 12 0 1.682 0
CASE 13 0 0 -1.682
CASE 14 0 0 1.682
CASE 15 0 0 0
CASE 16 0 0 0
CASE 17 0 0 0
CASE 18 0 0 0
CASE 19 0 0 0
CASE 20 0 0 0
139
5.5 Results and Discussion
5.5.1 Morphological and Cellular Characterization of Bacteria Isolation from
Facultative Anaerobic Granular Sludge
A total of twelve microorganisms have been successfully isolated as pure
culture bacteria from the FAnGS developed in synthetic textile wastewater in the
IFAnGSBioRec system. All of the 12 bacteria were characterized in terms of form,
shape, edges and colony surface for their colony characteristics. Gram staining
procedure was carried out for each bacterium in order to characterize the cellular
morphology of the isolated bacteria. The pure bacteria isolates were named
accordingly as BS1FAnGS to BS12FAnGS. Almost all of the isolated pure culture showed
the presence of EPS indicated by slimy and gleaming emergence. Table 5.6 shows
the results of the cellular and colony morphology of the isolated pure culture from
FAnGS. The examples that illustrate the colony and cell morphology used to
describe the characteristics of the isolated bacteria are given in Appendices C-2 and
C-3.
5.5.2 Screening for Dye Degrader and Autoaggregator from Bacteria Strain
Isolated from Facultative Anaerobic Granular Sludge
All of the 12 pure bacteria cultures isolated from FAnGS were screened for
the ability of dye degradation and autoaggregation. The specific COD and dye
degradation rates were also determined to assist the selection of the isolated bacteria.
Table 5.7 shows the bacterial growth rate, percentage of color and COD removal,
specific COD and dye degradation rate, percentage of aggregation and
hydrophobicity of the individual bacteria. The percentage of COD removal was
140
measured during the decolorization of the mixed dye medium where most of the
decolorization took place after seven days of incubation period. Not much of the
COD was removed during the decolorization that occurred under anaerobic
condition. Most of the individual bacteria isolate showed a slight increase in the
COD during the anaerobic phase. COD reduction was higher during the aerobic
reaction phase.
Table 5.6: Morphological and cellular characterization of the twelve isolated
bacteria from FAnGS
Bacteria
strain
Gram
staining
Cellular
morphology
Colony morphology
Form Edge Surface Elevation
BS1 FAnGS - Palisades Circular Entire Smooth Convex
BS2 FAnGS + Streptobacilli Punctiform Entire Smooth Convex
BS3 FAnGS + Coccobacilli Punctiform Entire Smooth Convex
BS4 FAnGS ₋ Long rod
streptobacilli Irregular Undulate
Dry,
powdery Raised
BS5 FAnGS + Palisades Circular Entire Dry Flat
BS6 FAnGS
+ Rod Irregular Undulate Dry Flat
BS7 FAnGS
₋ Rod Irregular Undulate
Dry,
powdery Raised
BS8 FAnGS + Streptobacilli Punctiform Entire Smooth Convex
BS9FAnGS ₋ Palisades Circular Entire Smooth Convex
BS10FAnGS - Long rod
streptobacilli Irregular Undulate
Dry,
powdery Raised
BS11 FAnGS ₋
Coccobacilli Irregular Undulate Dry,
powdery Raised
BS12 FAnGS ₋ Coccobacilli Circular Entire Smooth Convex
141
Based on the ability to degrade dye and the competency of the individual
bacteria to form aggregates, the six bacteria i.e. BS1FAnGS, BS6FAnGS, BS7FAnGS,
BS10FAnGS, BS11FAnGS and BS12FAnGS are selected for further analysis. These six
selected bacteria show high capability to degrade the dye with the percentage of
more than 85% and a moderate range of COD removal with the percentage of 15-
56%. Based on the percentage of autoaggregation and SHb, all the selected bacteria
are of high CAg and high SHb with more than 70% for both CAg and SHb. The
identity of the selected bacteria was investigated to classify the genus and species of
the bacteria through 16S rDNA sequence analysis.
5.5.3 Analysis of the Isolates from Facultative Anaerobic Granular Sludge
Molecular techniques of bacteria identification involved several steps which
include bacteria isolation, PCR amplification process either by using specific or
universal primer, characterization and finally determination of taxonomic and
phylogenetic class of bacteria isolates via 16S rDNA sequence analysis (Margarita et
al., 2001). PCR is a powerful tool with its ability to exponentially amplify specific
nucleic acid sequences in a short period of time. The amplification of
deoxyribonucleic acid (DNA) via PCR is achieved through multiple cycles of in vitro
DNA replication (Kuslich et al., 2008). The result of nucleic acid sequence analysis
will complement and confirmed the conventional bacteria identification assay. In
this study, the corresponding 16S ribosomal DNA (rDNA) sequence analysis was
applied rather than using the ribosomal RNA (rRNA) since the former approach
would grant stable and more informative analysis as compared to the latter.
Furthermore, the analysis of 16S ribosomal DNA (rDNA) would be able to provide
information of bacteria identification up to the genus and species level via its
sequence polymorphisms (Fox et al., 1993).
142
Table 5.7: Characteristics and performance of the isolated bacterial from the FAnGS
Bacteria strain
Bacterial growth rate Max biomass (mg /L)
Color removal (ADMI)
(%)
Specific dye degradation
rate (mg/g/h)
COD removal
(%)
Specific COD
degradation rate
(mg/g/h)
Autoaggregation (%)
Surface Hydrophobicity
(%) Anaerobic phase
Aerobic phase
BS1FGS 0.026 0.229 2.28 86.5 0.287 47.3 1.80 97.3 ± 1.4 82.8 ± 1.6
BS2FGS 0.024 0.233 2.05 83.0 0.278 10.3 4.40 40.2 ± 0.9 39.6 ± 1.8
BS3FGS 0.027 0.173 1.73 77.9 0.040 10.2 0.29 20.4 ± 1.5 61.4 ± 2.5
BS4FGS 0.027 0.147 1.42 82.6 0.221 40.6 2.06 35.7 ± 1.1 35.5 ± 1.3
BS5FGS 0.030 0.179 1.74 82.7 0.045 27.3 0.77 18.5 ± 1.8 90.2 ± 2.8
BS6FGS 0.044 0.072 1.48 91.5 0.229 15.2 0.73 77.4 ± 1.2 97.3 ± 1.4
BS7FGS 0.030 0.195 2.28 87.1 0.285 37.6 1.52 77.9 ± 1.5 87.4 ± 1.1
BS8FGS 0.077 0.141 1.40 82.1 0.150 12.1 0.32 31.4 ± 1.1 12.5 ± 2.7
BS9FGS 0.027 0.206 1.60 79.3 0.136 40.0 1.94 12.5 ± 1.4 21.5 ± 3.2
BS10FGS 0.035 0.143 2.07 86.7 0.150 54.5 2.29 85.5 ± 1.4 85.7 ± 1.8
BS11FGS 0.032 0.174 2.19 88.0 0.425 33.3 1.32 82.8 ± 1.8 90.2 ± 4.2
BS12FGS 0.030 0.159 2.05 82.7 0.196 40.9 1.69 61.4 ± 1.5 88.4 ± 3.3
143
As discussed earlier, six bacteria have been selected for further analysis for
their taxonomic and phylogenetic status. The bacteria were extracted for their
ribosomal DNA and were amplified through the PCR amplification process by using
universal primers with forward and reverse primer. In this study, all the DNA of the
selected bacteria was successfully extracted individually. These were shown by the
clear formed band on the agarose gel electrophoresis. The amplified 16S rDNA
sequences were purified using protein purification kits (Promega PCR Clean-up
System). The purification kits were used to remove impurities that may interfere the
recovery of clear DNA band. Figure 5.3 shows the qualitative analysis of the PCR
products. The observation of visible and clear bands from Lane II to VII indicates
the successful isolation of high concentration and purity of the genomic DNA.
Based on the comparison with the DNA marker Ladder, the obtained genomic DNA
extraction was more than 10kbp indicating that the DNA samples are pure enough to
be used as a template in the PCR amplification process for further analysis.
The amplification of the PCR products after purification by using Vivantis
GF-1 Gel DNA Recovery Kit is shown in Figure 5.2. Lane II to VII shown in
Figure 5.4 represents the genomic DNA PCR product of bacteria strain BS1FAnGS,
BS6FAnGS, BS7FAnGS, BS10FAnGS, BS11FAnGS and BS12FAnGS, respectively. Lane I
represent the DNA ladder marker. The PCR product of the selected bacteria strain
were successfully amplified and purified based on the comparison with the DNA
ladder. The results indicated that all PCR products could be observed at
approximately 1.5 kbp.
The amplified 16S rDNA of the PCR products were sent to Vivantis
Technologies Sdn. Bhd. for the sequencing process. The 16s rDNA sequencing
results were then compared with the Genbank database at the National Center for
Biotechnology Information (NCBI) using the nucleotide-Basic Local Alignmentt
Search Tool (BLASTn) program for the identification of genus and species of the
isolated bacteria (Altschul et al., 1990). Through the BLASTn analysis, the
alignment scores based on forward and reverse sequence of partial 16S rDNA of the
144
selected bacteria strains were determined. BLASTn is a nucleotide-matching
program with improved search speed and firm statistical establishment to support the
database searching. A reliable justification of a homology is obtained through high
percentage of DNA nucleotide similarity, an E-value of less than one and a high
score of more than 80 bits (Altschul et al., 1990 and Bergeron, 2002).
Figure 5.3 Agarose gel electrophoresis of DNA extraction
Lane I: DNA ladder marker
Lane II: Genomic DNA extraction of BS1FAnGS
Lane III: Genomic DNA extraction of BS6FAnGS
Lane IV: Genomic DNA extraction of BS7FAnGS
Lane V: Genomic DNA extraction of BS10FAnGS
Lane VI: Genomic DNA extraction of BS11FAnGS
Lane VII: Genomic DNA extraction of BS12FAnGS
10 kbp
145
Figure 5.4 Agarose gel electrophoresis of purified PCR amplification product
Lane I: DNA ladder marker
Lane II: Amplified 16S rDNA of BS1FAnGS
Lane III: Amplified 16S rDNA of BS6FAnGS
Lane IV: Amplified 16S rDNA of BS7FAnGS
Lane V: Amplified 16S rDNA of BS10FAnGS
Lane VI: Amplified 16S rDNA of BS11FAnGS
Lane VII: Amplified 16S rDNA of BS12FAnGS
The sequencing result analysis reveals that BS1FAnGS could be identified as
Pseudomonas veronii. A full length of sequencing containing 1394 nucleotides base
pair was obtained from the NBCI showing 100% similarity with strain Pseudomonas
veronii UFZ-B547. The results showed that the E value was zero with a very high
total score of 2555 indicating a reliable justification of a homology. Based on the
alignment scores of sequence generated from the forward primer, 338 sequence
nucleotides were obtained. From this sequence, it shows that BS6FAnGS has 94%
similarity with Bacillus cereus strain S10 with a total score of 525 and E-value of 5e-
145 generated from forward primer. Meanwhile, based on the 595 nucleotides
1.5 kbp
I II III IV V VI VII VIII
146
sequence result generated from the reverse primer has 98% similarity to Bacillus
cereus with a total score of 1066 and E-value of zero. Based on the forward and
reverse sequence analysis that show high percentage of similarity, high total score
and less than one of E-value, BS6FAnGS is identified as Bacillus cereus.
Strains BS7FAnGS, BS10FAnGS and BS12FAnGS were identified as Pseudomonas
sp. However, based on the alignment score of the sequence generated by the forward
and reverse primers were not from the same species since there were differences in
the arrangement of nucleotides bases. Based on the obtained sequences, these three
strains can be grouped under the same type of Pseudomonas genus. The analysis
sequence for strain BS7FAnGS showed the forward and reverse sequences with 383 and
620 nucleotides based pair, respectively. Based on the 383 nucleotides forward
sequence, strain BS7FAnGS demonstrate 98% similarity, total score of 686 and zero E-
value with Pseudomonas citronellolis strain NK 2.C2-1. Meanwhile, the reverse
primer generated 620 nucleotides sequence that showed 99% similarity with
Pseudomonas sp.J9(2007). The total score was high (1110) and the E-value was
zero. Based on the obtained result, therefore strain BS7FAnGS was identified as
Pseudomonas sp. Strain BS10FAnGS was identified as Pseudomonas sp. based on the
99% similarity and zero E-value of the forward (538 nucleotides) and reverse (787
nucleotides) sequences with high total score of 966 and 1443, respectively. The
closest relative to the strain identified from the forward nucleotides sequences was
Pseudomonas trivials strain BIHB 745. Meanwhile, the Pseudomonas sp. mandelli
was identified as the closest relative to strain BS10FAnGS based on the reverse
nucleotides sequence. BS12FAnGS was also identified as Pseudomonas species based
on the full length sequences with 1403 based pair nucleotides. The generated full
sequences exhibit 99% similarity to strain Pseudomonas species with a very high
total score of 2536 and zero E-value.
BS11FAnGS is identified as Enterobacter sp. This was also based on the full
length sequencing result analysis that show 99% similarity, high total score of 2603
and zero E-value with Enterobacter sp. VET-7, Enterobacter sp. L3R3-1 and
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Enterobacter asburiae strain J2S4. The detailed results on the DNA sequencing
analysis and the BLASTn analysis for selected bacteria strain are provided in
Appendix D. Table 5.8 shows the result of the alignment scores of sequences
generated as the percentage of similarity and the confirmed identification of the
bacteria strains BS1FAnGS, BS6FAnGS, BS7FAnGS, BS10FAnGS, BS11FAnGS and BS12FAnGS.
The detailed characterizations of the identified bacteria strain describing the physical
and chemical distinctiveness is given in Table 5.9 (John et al., 1994).
5.5.4 Effect of Substrate, pH and Temperature on Coaggregation and Surface
Hydrophobicity
In this study, aeration was applied to generate shear force effect rather than
using physical shaking as commonly reported in previous aggregation research
papers (Rahman et al., 2008; Nishiyama et al., 2007; Adav and Lee, 2009). The
reason of using aeration is to allow a close resemblance of a real condition that took
place in the granulation process. As discussed in Chapter Four, the aeration was
used to introduce the shear effect onto the microorganisms in the reactor to allow the
initialization of the granulation process.
In this chapter, focus is made on investigating the effect of substrate, pH and
temperature (terms as variables) on CAg and SHb (terms as responses) of the
selected mixed culture from FAnGS in synthetic textile dyeing wastewater.
MINITAB™ statistical software is used as the analytical tool for factorial design
analysis. The effect of these variables was presented by the response produced by a
change in the level of the factors investigated. When the effect of one variable is
affecting the responses of other variables, there is an interaction effect between the
variables studied.
148
Table 5.8: Taxonomic and phylogenetic characteristic of the isolates from FAnGS
Isolates No. of bases used
to establish identity
16S rRNA gene sequence identity (%)
E value Total score Closest relative Taxonomic affliation(s)
BS1FGS 1394 99 0 2555 Pseudomonas veronii 6S rRNA Pseudomonas veronii
BS6FGS Forward seq. 338 94
5.00E-146 525 Bacillus cereus strain S10
Bacillus cereus Reverse seq. 595 98 1066 Bacillus cereus partial
BS7FGS Forward seq. 383 98
0 686 Pseudomonas citronellolis
strain NK 2.C2-1 Pseudomonas sp.
Reverse seq. 620 98 1110 Pseudomonas sp.J9(2007)
BS10FGS Forward seq. 538 99
0 966 Psedumonas trivialis strain
BIHB 745 Pseudomonas sp.
Reverse seq. 787 99 1443 Pseudomonas sp. mandelii
BS11FGS 1429 99 0 2603 Enterobacter sp. VET-7 Enterobacter sp.
BS12FGS 1403 99 0 2536 Pseudomonas veronii strain INA06 Pseudomonas sp.
149
Table 5.9: Characteristics of identified selected bacteria strains from FAnGS
Bacteria strain
Characteristics
Bacillus cereus (BS1FAnGS)
• gram positive or facultative anaerobic spore forming rod • well grown in anaerobic condition; usually found in soil, air and water• frequently found in pasteurized milk, causing spoilage because of the production lipases and proteases • grew and produced a protease using wool as sole of carbon and nitrogen • associated with rice based food poisoning • denitrifying bacteria; associated with the formation of "clumping" in secondary clarifiers and forming in anaerobic digester. • identified to have capability in degrading the azo dye compounds (Khehra et al., 2005; Pourbabaee et al., 2005; Deng et al., 2008)
Pseudomonas veronii (BS6FAnGS)
• gram positive • non-spore forming • facultative anaerobic • capable in degrading ketones (Onaca et al., 2007)
Pseudomonass sp. (BS7FAnGS, BS10FAnGS, BS12FAnGS)
• gram negative with non-spore forming • obligated aerobic; facultative anaerobic • floc forming bacteria that could initiate floc formation in the activated sludge process • used as bioaugmentation in sanitary sewer system and biological treatment plan • have the capability to degrade phenol and phenolic compounds • easily degrade sulfur containing compounds that are associated with malodor production • capable as denitrifying species • many species from genus Psuedomonas were identified capable in degrading dye compounds (El-Naggar et al., 2004; Khehra et al., 2005; Barragan et al., 2007) • widely distributed in nature • some species are pathogenic for humans, animals or plants
Enterobacter sp. (BS11FAnGS)
• gram negative with non-spore forming • aerobic, facultative anaerobic, optimal temperature 30-37oC • capable as denitrifying species and involve in fermentation process • acted as phosphorus accumulating organisms (PAO) in enhance biological phosphorus removal system • many species from genus Enterobacter sp. capable in degrading dye compounds (Moutaouakkil et al., 2003; Barragan et al., 2007) • commonly distributed in fresh water, soil sewage, plants vegetables, animal and human faces
150
The results from the factorial design are presented in the form of an ANOVA
table and table that provides information on the estimated effects with coefficients.
The ANOVA table gives a summary of the significance of the main and interaction
effects by observation on the P-value. Table of the estimated effects with
coefficients shows the P-value associated with each individual model term. In this
factorial design, the responses obtained were statistically evaluated with the
confidence levels of above 90% (P-value less than 0.1).
The experimental results for factorial design analysis are given in Table 5.10.
The summary table of the ANOVA that shows the main, two and three ways
interaction effect is given in Table 5.11. Detailed discussions for both responses of
this study are based on the results obtained after five hours exposure to shear force
through the aeration process. With respect to the experimental conditions exploited
in the study, the P-value (at 0.1 level of significant) indicates that all factors, i.e.
substrate, pH and temperature show significant effect on CAg. The significant
interaction effects are only observed between pH and temperature (pH ×
Temperature) while the three way interaction (Substrate × pH × Temperature) is not
significant. As for the responses of SHb, all model terms (main, 2-way and 3-way
interaction) except for the 2-way interaction between substrate and temperature
(Substrate × Temperature), are all significant. Figure 5.5 shows the Pareto chart
generated by MINITAB™ for CAg and SHb of the mixed culture. The vertical
disconnected red line represents the significance level determined by the statistical
software. The horizontal column bar that does not reach the disconnected red line
would imply the insignificance of the model terms. The detailed results of the
analysis constructed by the software are given in Appendices E1 to E6.
151
Table 5.10: Experimental results for 2-level factorial design analysis
Run No. Coaggregation (%) Surface Hydrophobicity (%)
CASE01 49.6 19.0
CASE02 44.7 21.7
CASE03 67.2 33.8
CASE04 64.1 29.4
CASE05 46.8 37.3
CASE06 44.1 41.2
CASE07 65.6 39.6
CASE08 62.9 36.9
CASE09 65.2 32.1
CASE10 61.9 31.5
CASE 11 84.5 53.9
CASE 12 85.4 52.0
CASE 13 38.8 20.4
CASE 14 38.7 18.2
CASE 15 57.4 8.6
CASE 16 57.6 6.8
152
Table 5.11: The P-values of the estimated main and interaction effects of variables
substrates, pH and temperature on to the percentage of coaggregation and surface
hydrophobicity after six hours aeration phase
Effects Coaggregation (%) Significanta
Surface Hydrophobicity
(%) Significanta
The Main
Substrate < 0.0001 Yes 0.001 Yes
pH < 0.0001 Yes < 0.0001 Yes
Temperature 0.0004 Yes 0.0019 Yes
The 2-way interaction
Substrate × pH 0.5607 No < 0.0001 Yes
Substrate × Temperature 0.4827 No 0.8624 No
pH × Temperature < 0.0001 Yes < 0.0001 Yes
The 3-way interaction
Substrate × pH × Temperature 0.4679 No 0.0008 Yes
a significant at α = 0.1
5.5.4.1 Factorial Analysis: The Main Effect of Substrate on Coaggregation
With respect to the main effect on the percentage of CAg, the substrate shows
a significant effect with P-value of less than 0.0001. The substrate gave a positive
effect with estimated main effect of 19.36. This means that increase in the
153
concentration of substrate will cause an increase in the CAg process. Such
phenomenon may due to the increase in the cell growth of the mixed culture when
the substrate is increased. The presence of more cell biomass increases the collision
among the cells and may cause more CAg to take place. Liu and Tay (2002)
reported that the collision between particles is one of the key factors that influences
the formation and stabilization of biofilms, anaerobic and aerobic granules.
The cell surfaces and their characteristics change with the alteration of the
surrounding environmental condition (van Loosdrecht et al. 1989). van Loosdrecht
et al. (1987) reported that increase in the substrate flux which cause increase in the
bacterial growth is capable in changing the cell SHb. Hence, in order to accelerate
the granulation start-up process, it was suggested that high OLR should be applied
during the granulation process. Moy et al. (2002) reported that there was no negative
effect on the granulation process when the OLR was increased as high as 15
kg/m3·day.
The effect of collision among the microorganisms which was due to high
biomass production at high substrate concentration was prominent during the initial
stage of the aeration phase where the amount of substrate was still high. During the
first hour of aeration phase, the percentage of CAg was about 57%, with the
estimated significant effect of +7.775 and the P-value of 0.005 (Detailed results for
one to four hours of CAg assay are given in the Appendices E1 to E4). As the
aeration phase was prolonged up to six hours, the effect of substrate has increased
(estimated effect +19.36) with the percentage of CAg at high substrate
concentrations increased to 68%. The difference may due to the production of EPS
during the starvation the phase after long aeration times. Microorganisms will
produce more EPS when undergoing starvation conditions when most of the
substrate in the medium is being utilized (Wang et al., 2006a). The formation of EPS
that covers the cell surface from physicochemical point of view could be regarded as
polyelectrolyte adsorbed onto a colloidal particle. The presence of EPS on the cell
surface could alter the characteristics of the physicochemical properties of the cell
154
surface which includes the surface charge, surface hydrophobicity and others. Varon
and Choder (2000) reported that there is physical change of the bacterial surface
during starvation condition. Bacteria were observed to produce connecting fibrils
that emerged on the surface of the cell. The connecting fibrils acted as a form of
microbial communication which is believed to be involved in the initial stage of cell
aggregation. The changes in the bacterial surface properties are important aspects
with regard to the flocculation, adhesion and granulation process (Veiga et al., 1997
and Liu et al., 2004d).
Under the condition where cells are poor with EPS, the cell surface will be
dominated by electrostatic interactions resulting with the repulsion among the cells
and cause separation between cells. Cells with poor EPS will follow the Derjaguin-
Landau-Verwey-Overbeek (DLVO) theory which indicated that increase in surface
charge will lead to increase in the repulsion electrostatic interactions between
approaching surfaces, resulting with a weakening of the bonding within cell. This
condition may occur in the flocculation process that is poor with EPS. On the other
hand, cells which are rich with EPS would be dominated with the polymeric
interaction that resulted with enhancement of cell adhesion (Tsuneda et al., 2003b).
Furthermore, the interaction effect between polymers is much greater as compared to
the repulsion effect due to the increase of the surface charge. The amino of
exoprotein (PN) which is one of the components of the EPS that are able to
neutralize the surface charge. With the presence of EPS, the effect of surface charge
will be too weak to inhibit sludge flocculation. Since increase in substrate
concentration is associated with increase in cell biomass, the effect of substrate
concentration could indirectly affect the aggregation of cells through the changes on
the production of EPS particularly during the starvation phase.
155
Figure 5.5 The pareto chart of the percentage of (a) coaggregation and (b) surface
hydrophobicity after six hours of aeration phase (A: substrate; B: pH; C:
temperature;
α: 0.1)
156
5.5.4.2 Factorial Analysis: The Main Effect of pH on Coaggregation
The effect of pH is also significant on the percentage of CAg with the P-
value of less than 0.0001. The pH gave an opposite effect onto the CAg process as
compared to the effect of substrate. It was observed that the pH increase caused a
decrease in CAg. The pH variables gave a -13.84 of estimated main effect onto the
CAg process.
Under neutral conditions, microorganisms would be negatively-charged due
to the ionization of carboxyl, sulphate and phosphate that acted as functional groups
in the cell’s surface (Sutherland, 1982 and Wiley et al., 2008). According to the
DLVO theory, when two surfaces possess the same charges, there will be a repulsive
force between the two surfaces resulting with prohibition of cell aggregation. This
repulsive force is known as Gibbs Free Energy. In acidic conditions, there will be an
excess of ions H+ which will cause neutralization of the cell surface charge. Such
condition will reduce the free Gibbs energy. Reduction in the electrical repulsion in
turn favors cell-to-cell approach and would initiate the formation of cell aggregation
(Derjaugin and Landau, 1941). When the pH is higher, the excess of OH- would
enhance the surface charges of the bacteria cell and increased the free Gibbs energy
and caused the cell to be driven even further apart.
Nonetheless, aggregation was also observed to occur under neutral pH
conditions. This may suggest that beside the electrostatic repulsion, other
mechanisms such as SHb may also be involved in cell aggregation (Adav and Lee,
2008b). There is evidence that cell hydrophobicity is inversely correlated to the
quantity of the surface charge of the microorganisms (Liao et al., 2001). An increase
of cell hydrophobicity reflects reduced negative charges on the bacterial surface.
157
5.5.4.3 Factorial Analysis: The Main Effect of Temperature on Coaggregation
Temperature was also found to have a mainly positive significant effect on
the CAg (P-value less than 0.001). However, the estimated main effect of
temperature was only +5.56 suggesting that the effect can be considered weaker as
compared to the other two variables. Increase in temperature within the range of this
experimental condition would increase the microbial activities which include
metabolisms and mobility of the microorganisms. In other words, increase in
temperature may increase the specific growth rate of the microorganisms. According
to Liu et al. (2004a), the microbial aggregation process is faster at higher specific
growth rates of the microorganisms. Increase in cell growth rate will lead to increase
in cell biomass and the percentage of collisions between the cells resulting in
increase in the probability of cell aggregation.
Increase in temperature will also result in decrease of the free Gibbs energy.
As mentioned earlier, reduction of the Gibbs free energy will favor the aggregation
of cell. The same observation was reported by Ibrahim et al. (2005) who said that as
the temperature of the incubation increased, the ability of the Bifidobacteria to
perform aggregation also increased. Figure 5.6 showed the main effect of variables
substrate, pH and temperature on the CAg process.
5.5.4.4 Factorial Analysis: The Interaction Effect on Coaggregation
As shown in Figure 5.7, among the three variables, the interaction effect was
only observed to be significant between variable pH and temperature with a P-value
less than 0.0001. At low temperatures, the percentage of CAg was almost the same
at low and high values of pH (pH 5.81 and 8.19) which was about 53%. However,
158
when the temperature increased, the percentage of CAg in acidic conditions
increased up to more than 70% while the percentage of CAg in alkaline conditions
reduced to less than 50%. The main effects of acidic and high temperature
conditions have caused the percentage of CAg to increase to 66% and 62%,
respectively. The interaction between pH and temperature has increased CAg up to
about 75%.
Figure 5.6 Main effects plot on the coaggregation
When the pH of the experimental condition was alkaline, the increase in the
temperature has reduced the percentage of CAg. As discussed earlier, when the
temperature increases, the free Gibbs energy between two same surfaces charge
particles decreases and eventually would enhance CAg. However, in alkaline
conditions, there are possibly high concentrations of OH- ions which cause the
repulsive force between cells to become even greater and could have overshadowed
the effect of high temperatures. Increase in temperature would also cause the
movement of the particles to increase based on the theory of Brownian movement
(Tchobanoglous et al. 2004) causing the cell particles driven even more apart. This
could be the possible reason of why during high temperature and high pH conditions,
the CAg among the cell reduced.
159
Figure 5.7 Interaction effects plot on the coaggregation process (• Centre point)
The relationship between substrate and pH and between substrate and
temperature shows no significant interaction effect since the lines are almost parallel
to each other. The 3-way interaction effect between variable substrate, pH and
temperature were insignificant with a P-value of 0.4679.
5.5.4.5 Factorial Analysis: The Main Effect of Substrate on Surface
Hydrophobicity
Surface hydrophobicity represents one of the physicochemical characteristics
of cellular surface of the microorganisms. Hydrophobicity of sludge is believed to
play a crucial role and acted as the triggering force towards aggregation (Mahoney et
al., 1987; Liu et al., 2004b, Wang et al., 2005a). According to Liu et al. (2004b), the
••
160
surface cell hydrophobicity could be induced by the conditions of the cultures and
capable of initiating the cell-to-cell aggregation. It is believed that the
hydrophobicity of the cell is one of the most important attraction forces in the
microbial aggregation and high cell hydrophobicity seems to be a prerequisite for
biogranulation to take place.
Previous studies indicate that changes in cell SHb is affected by many factors
that cause stress to the culture condition such as starvation, growth rate, growth
substrate, pH and temperature (Liu et al., 2004b). However, different types of
bacteria, as single or as mixed culture may response differently especially when the
bacteria is found in different types of solution or wastewater. According to Liu et
al., (2004b), the knowledge regarding the role of cell hydrophobicity in the
biogranulation process is far from complete. Under stressful culture conditions,
bacterial cells would change its cell hydrophobicity (Bossier et al., 1996 and
Mattarelli et al., 1999). The bacteria would become more hydrophobic and lead to
the strengthening of the cell to cell interaction of a microbial structure. It is a form
of protective mechanisms of the cells against unfavorable environmental conditions.
The result of the 2-level factorial design experimental run shows that
substrate has significant effect on SHb with the P-value of 0.001 and estimated effect
of +4.95. Figure 5.8 shows the main effect of the variables on the SHb of the mixed
culture used in this study. The substrate has caused positive effect on the SHb where
as the increase in substrate has caused increase in SHb. Increase in the substrate
concentration means more food supplied to the microorganisms which will increase
the bacterial growth. When more bacteria are present, more EPS will be produced
when faced with the starvation stage. Increase in substrate may also induce the
bacterial growth rate which would increase the production of the EPS. Increase in
the EPS would cause increase in the SHb and the bacteria would become more
hydrophobic which may facilitate adhesion or aggregation process (Kjelleberg et al.,
1987; Liu et al., 2003c; Jiang et al., 2004b). The effect of substrate has the same
pattern to the percentage of CAg and SHb with the presence of EPS playing a
161
prominent role for both responses. The EPS is known to be capable in mediating
both the cohesion and adhesion of cells and play a fundamental role in sustaining the
structural integrity in the development of biofilm, anaerobic granules and aerobic
granules (Tsuneda et al., 2001 and Flemming et al., 2007).
Figure 5.8: Main effects plot of variables for the percentage of SHb
5.5.4.6 Factorial Analysis: The Main Effect of pH on Surface Hydrophobicity
The pH caused a highly significant effect on SHb with P-value less than
0.0001 and an estimated effect of -8.05. Increase in pH from pH 5.81 to pH 8.19 has
caused a decrease in the percentage of SHb from 34% to 27.5%. During acidic
condition, the presence of H+ ions in the media solution would cause neutralization
of the surface charge of the bacterial cell and reduced the electrostatic force. Since
the cell hydrophobicity is inversely correlated to the quantity of the surface charge of
microorganisms (Liao et al. 2001), the percentage of SHb increased and would
enhance the aggregation of the cells as the surface charge of the microorganisms is
reduced. When the pH of the media solution increases (alkaline), there will be more
162
of OH- presence in the solution. This will enhance the repulsive force of the
bacterial surface charge and reduced the chances of the bacteria cell forming
aggregates subsequently reducing the percentage of SHb.
Difference in pH of the media can also be associated with the type of growth
substrate used in the growth media. It was reported that the formation of anaerobic
granules in the UASB was highly affected by the substrate composition of the media
solution (Liu et al., 2003e). This is because different types of growth substrate may
cause acidic or alkaline conditions when being hydrolyzed. The concentration of H+
and OH- will have an effect on the surface tension of the liquid media and eventually
affecting the SHb of the bacteria cell.
The effect of pH on the SHb may basically depend on the net surface charge
of the exoprotein of the bacteria cell which eventually depends on the type of protein
of the bacteria cell wall. However, further investigations are required for a better
understanding on the effect of pH on the SHb particularly on the association of
different proteins of the bacteria cell wall.
5.5.4.7 Factorial Analysis: The Main Effect of Temperature on Surface
Hydrophobicity
The P-value of 0.002 indicates that the temperature of the experimental
conditions has a highly significant effect on the SHb. The estimated effect of -4.43
showed that temperature has caused a negative effect on the SHb. In this
experiment, when the temperature was increased from about 24oC to 36oC, there was
a significant reduction of the percentage of SHb from 32.5% to 28%.
163
As temperature increases, the liquid surface tension decreases (Moraes et al.,
2008), allowing the adhesion of hydrophilic cells and resulted with reduction in the
percentage of cell SHb. The same observation was reported in the study carried out
by Blanco et al. (1997), on 42 strains of Candida albicans. The majority of the
strains become hydrophobic at lower temperature (20oC) as more cell-to-cell
aggregation takes place. When the temperature was increased to 37oC, the strains
may undergo changes in their surface property and become hydrophilic with less
aggregation taking place. In the development of anaerobic granules in the UASB
reactor system, the bacteria cells become more hydrophilic and grow in rather loose
association when the liquid surface tension in the UASB is lesser than 50 mN/m.
When the liquid surface tension is larger than 56 mN/m, the adhesion of more
hydrophobic bacteria cells will take place and form bigger aggromeration (Thaveesri
et al., 1995 and Grootaerd et al., 1997).
The effect of temperature has given an opposite result for the percentage of
aggregation and SHb eventhough it was claimed that the SHb is the triggering force
for cell aggregation (Liu et al., 2004b). Rahman et al. (2007) has categorized
bacteria (Bifidobacteria) into high, medium and low autoaggregator groups. Among
the high autoaggregator, increase in temperature has caused a decrease in the
percentage of autoaggregator. As for the medium and low autoaggregators, increase
in temperature has caused an increase in the percentage of autoaggregator. In
addition to temperature, other factors such as pH of the media, types of protein
bound on cell surface of the different bacteria strains may give a different effect on
the ability to autoaggregate. Further investigation is required in order to investigate
the effect of temperature onto the autoaggregation and SHb among different bacteria
strains under different experimental conditions.
Furthermore, different types of bacteria may response differently when
exposed to different environmental conditions with regard to the ability to adhere
either among cells or on to a solid surface. Different types of protein may also be
secreted or produced at different temperature conditions resulting with a different
164
degree of the cell hydrophobicity (Maclagan and Old, 1980). Mattarelli et al. (1999)
reported the production of lipoteichoic acid and other mechanisms have caused high
hydrophobicity of cells incubated at low temperature (25oC) as compared to 37oC
incubation temperature condition. This study involved the use of a mixed selected
bacteria culture which individually may give different responses towards the
temperature changes. The overall effect may be affected by the most dominant
species in the group and may differ from the expected outcome.
5.5.4.8 Factorial Analysis: The Interaction Effect on Surface Hydrophobicity
The interaction effect between variables is given in Figure 5.9. The
interaction effect between substrate with pH and pH with temperature were highly
significant with the P-value of less than 0.0001. The values of the estimated effects
show a strong relationship for both the above significant interactions. The
interaction effect between substrate and temperature is insignificant with the P-value
of 0.862. The insignificant interaction is shown by the parallel line representing the
SHb responses of SHb during high and low levels of substrate and temperature
variables. The plot in Figure 5.9 describes the interaction between substrate and pH,
showing at high concentrations of substrate, change in pH from a lower value to a
higher value has caused the SHb to reduce. When the concentration of substrate is
low, the increase in pH has caused a slight increase in the SHb of the mixed
microorganisms.
The phenomenon observed is believed to be caused by the media solution
used in preparing the synthetic textile dyeing wastewater which consists of ethanol,
glucose and sodium acetate. During the aeration phase, when the degradation of
substrate took place, hydrolysis of acetate released more of OH- that could cause the
media to become more alkaline (Voet and Voet, 2004). At high pH conditions,
165
where there will be more of OH- in the solution, hydrolysis of acetate will further
increase the concentration of OH- and make the media solution more alkaline.
Increase in the OH- will increase the surface tension due to the repulsive electrostatic
force. This condition will make the cell to be driven apart and reduce the changes to
form aggregation and that probably could elucidate on why the percentage of
aggregation reduce even more when the substrate increase in the alkaline condition
(high pH value). In this condition, it is believed that the electrostatic force is much
stronger as compared to the polymeric interaction eventhough during high substrate
concentrations.
Figure 5.9: Interaction effect plots for the percentage of SHb (• Centre point)
At low pH, hydrolysis of acetate that produces more OH- will neutralize the
existing H+. In this situation, the electrostatic force is reduced and when the
substrate concentration is increased, the production of EPS may contribute to cells
hydrophobicity and make the polymeric interaction force become predominant and
able to promote cell adhesion. This explanation is based on the report of Tsuneda et
al. (2003b) who claim that if the amount of EPS is relatively small, the cell adhesion
will be inhibited by the electrostatic interaction (referring to the alkaline condition of
• •
166
this experiment) and if the EPS is relatively large, cell adhesion will be relatively
enhanced by the polymeric interaction.
For the interaction effect of pH and temperature, the plot in Figure 5.9 shows
that when the pH of the media (synthetic textile dyeing wastewater) was acidic,
increase in temperature caused the SHb to increase from about 25% up to more than
40%. However, when the pH of the media was alkaline, increase in temperature has
caused reduction on SHb from almost 40% to less than 10% of SHb. Increase in
temperature has enhanced the effect of pH either in acidic or basic conditions. In the
acidic media where the ion H+ is abundant, most of the negative surface charges of
the bacteria cell are neutralized. This will reduce the free Gibbs energy and reduce
the repulsive effect between the cells. Increase in temperature will cause an increase
in the collision between the cells (increase in the Brownian movement) which will
encourage the interaction between cells and enhances the aggregation process. In the
alkaline media condition, abundance of ion OH- may increase the repulsive effect of
surface charge. Increase in temperature which increased the cell movement may
cause the cell with high free Gibbs energy (due to increase in the surface charges) to
be driven further apart from one another and reduce the changes to form aggregates.
A significant 3-way interaction effect of substrate, pH and temperature is also
within the experimental condition with the P-value of 0.001. The curvature effect of
the significant interaction was further investigated by using the response surface
experimental design and will be discussed in the next section.
167
5.5.5 Response Surface Analysis
The results of the CCD analysis are shown in Table 5.12. The analysis was
carried out using full quadratic terms including linear, square and interaction with the
aid of Design-Expert 7P statistical software. The summarized results of the analysis
of variance (ANOVA) for the percentage of CAg and SHb are shown in Table 5.13.
The detailed results consisting of estimated regression coefficient and ANOVA table
are given in Appendices E7 to E9.
For CAg, based on the P-value, all the linear terms show significant values.
The model exhibits significant non-linearity for substrate and pH with the P-values
of 0.0042 and 0.0427, respectively. However, temperature shows non-curvature
effect with the P-value of 0.2397. The interaction between pH and temperature are
found to be highly significant with the P-value of 0.02 while the other two
interactions are not significant (P-values more than 0.2). The R-squared value of the
model is acceptable (86.63%) but the P-value for the Lack of Fit Test (LOFT) is
significant (0.0029). This implies that the analytical understanding of the model is
not statistically accurate. It may indicate that the process appears to be too complex
to model.
In order to improve the model, another attempt was carried out by omitting
two interaction terms (Substrate × pH, Substrate × Temperature) and one square term
(Temperature × Temperature). All the omitted terms were selected based on the
insignificant result obtained from the full quadratic terms analysis. The results of
this analysis with omitted terms are given in Appendix E8. Omitting the selected
insignificant terms lowered the P-value of all the included terms (Reduced quadratic
terms) compared to the previous analysis (Full quadratic terms). The R-squared
term reduced from 86.63% to 84.06% and the LOFT increased from 0.0029 to
0.048%. The drop of the R-squared value points out that the omitted terms only cost
2.5% in goodness of fit and this is acceptable. Eventhough the LOFT P-value has
168
improved by more than sixteen times higher compared to the analysis of the full
quadratic terms, the value still implies the inadequacy of the fitted model.
Table 5.12: Experimental results for CCD analysis
Run Coaggregation (%) Surface Hydrophobicity (%)
CASE01 49.6 19.0
CASE02 67.2 33.8
CASE03 46.8 37.3
CASE04 65.6 39.6
CASE05 65.2 32.1
CASE06 84.5 53.9
CASE07 38.8 20.4
CASE08 57.4 8.6
CASE09 68.8 50.9
CASE10 73.0 70.7
CASE11 54.2 62.4
CASE12 5.50 7.8
CASE13 29.3 71.2
CASE14 58.3 83.9
CASE15 59.4 75.2
CASE16 56.4 77.2
CASE17 55.1 72.7
CASE18 60.3 74.0
CASE19 57.5 76.2
CASE20 60.2 72.2
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Table 5.13: Summary of the P-value of the response surface modeling analysis
Term
Coaggregation (%) Surface Hydrophobicity
(SHb) (%) Full
Quadratic Terms
Linear + Square + pH ×
Temperature
The P-valuea
Substrate 0.0067 0.0037 0.3582
pH 0.0022 0.0009 0.075
Temperature 0.0369 0.0262 0.9194
Substrate*pH 0.6329 - 0.3596
Substrate*Temperature 0.8147 - 0.8854
pH*Temperature 0.02 0.0133 0.1221
Substrate*Substrate 0.0042 0.0016 0.0423
pH*pH 0.0427 0.0352 0.0014
Temperature*Temperature 0.2397 - 0.3393
R-squared value 86.63% 84.06% 75.89%
Lack of Fit (LOFT) 0.0029 0.048 <0.0001 0.01 – 0.04: Highly significant; 0.05 – 0.1: significant; 0.1 – 0.2: less significant; < 0.2: insignificant (Vecchio,
1997)
The response surface analysis of SHb shows that among the investigated
variables, only pH shows a significant effect with the P-value of 0.0750. The model
also shows significant non-linearity for both substrate and pH with the P-value of
0.0423 and 0.0014. Temperature shows the same response for the square term as in
the CAg assay with insignificant effect with P-value of 0.3393. The R-squared value
of this model eventhough slightly lower as compared to the value for responses of
coaggregation is still considered within acceptable range with a percentage of
75.88%. However, the P-value for the Lack of Fit Test (LOFT) is significant (less
than 0.0001) for this model indicating the inaccuracy of the statistical model.
Nonetheless the best statistical models that can be used to represent the responses
170
(CAg and SHb) within the range of the experimental conditions in this study as
accomplished from the CCD design analysis are given in Table 5.14.
Table 5.14: Mathematical models in terms of actual factors
Responses Statistical Model
(Coagggregation)2 (%) = -30449.2 - 4.02×A + 7114.5×B + 843.1×C – 108.1×BC + 1.4×10-3×A2 –329.1×B2
Surface Hydrophobicity (%)
= -1122.2 + 0.12×A + 239.7×B + 18.1×C – 6.5×10-3×AB – 2.01×10-4×AC – 1.4×BC – 1.9×10-5×A2 – 13.8×B2 – 0.1×C2
A: Substrate (mg/L); B: pH C: Temperature (oC)
The predicted versus actual plots for the responses of CAg and SHb are
shown in Figure 5.10. The plots reveal that the actual values are distributed
relatively near to the straight line in both cases. The predicted and actual values
obtained from this experiment are considered to be statistically acceptable indicated
by the high value of the R-squared calculated from the analysis of variance of this
study conditions.
The response surface and contour plots which illustrate the relationship
between the percentage of CAg and independent variables of pH and temperature is
shown in Figure 5.11. The figure shows the surface plots that represent the effect of
varying pH and temperature at fixed substrate concentrations (1750 mg/L). It can be
seen that increasing the temperature value at low pH levels, has caused an increase in
the CAg process with a high percentage of 69.5%. However, at high pH levels, the
percentage of CAg was slightly decreased with increase in the temperature. Having a
condition at high temperature levels and low pH conditions would be the best
condition for achieving the maximum percentage of CAg.
171
Actual
Pred
icte
d
29.75
1808.33
3586.91
5365.49
7144.08
29.75 1808.33 3586.91 5365.49 7144.08
Actual
Pred
icte
d
4.97
24.69
44.41
64.13
83.85
4.97 24.69 44.41 64.13 83.85
Figure 5.10: Predicted versus actual data for (a) coaggregation and (b) surface
hydrophobicity
(a)
(b)
172
(a)
(b) Figure 5.11: (a) Contour and (b) 3D response surface plots representing relationship between pH, temperature and percentage of coaggregation
173
Figures 5.12 to 5.13 show the relationship between SHb and independent
variables of substrate, pH and temperature, respectively. The contour plots of the
RSM are drawn as a function of two variables at a time, holding other remaining
factors or variables at a fixed level.
The surface plots of the interactions between variables for the percentage of
SHb as the observed response was found to be a symmetrical mound shape shown in
Figures 5.12 and 5.13. Meanwhile, the contour plot in Figure 5.14 appears to be of
elliptical shape. As shown in Figure 5.12 where the variable temperature was held at
30oC, at any substrate concentration level, the percentage of SHb was low both at
high and low levels of pH (8.19 and 5.81). However, the response increased as the
pH level approached the neutral pH condition (pH 7). The percentage of SHb was
the highest (77.34%) when the pH and substrate level were at 6.7 and 1967.9 mg/L,
respectively.
Figure 5.13 illustrates the percentage of SHb as a response in a contour and
response surface plots for the interaction between varying concentrations of substrate
and temperature. The interaction was observed at constant pH level (pH 7) as the
hold value. The symmetrical mound shape plot shows that at any temperature level,
the percentage of SHb increases as the substrate concentration increased from
1006.75 mg/L to1750 mg/L. However, as the concentration of substrate increases
from 1750 to 2493.25 mg/L, the response started to reduce. The highest percentage
of response is 75.91% obtained at substrate concentration of 1909.63 mg/L and
temperature of 30.05oC. The point that shows the highest percentage of SHb was
positioned at the middle of the symmetrical mound shape plots.
174
(a)
(b) Figure 5.12: (a) Contour and (b) 3D response surface plots representing relationship between the concentration of substrate, pH and percentage of surface hydrophobicity
175
(a)
(b)
Figure 5.13: (a) Contour and (b) 3D response surface plots representing relationship between the concentration of substrate, temperature and percentage of surface hydrophobicity
176
Having substrate variables at constant concentration of 1750 mg/L, Figure
5.14 demonstrates the contour and response surface plots of surface hydrophobicity
at varying pH and temperature values. The percentage of response reduces from
71.34 to 50.17% when the temperature reduced from 35.95 to 24.05oC at a pH level
of 5.81. However, at a high pH level (8.19) the response increases from 32.96 to
52.02% when the temperature of the experiment reduced from 35.95 to 24.05oC. The
graph also shows that at a high temperature (35.95oC), the percentage of response
was doubled when pH reduced from 8.19 to 5.81 at a temperature of 35.95oC. As the
temperature of the experiment was set at 24.05oC, there was not much change on the
percentage of response as the pH level reduced in the same manner. When the
temperature and pH levels were at 32.68 and 6.6, respectively, the predicted
percentage of SHb shows the highest value of 77.14. The response illustrated in
Figures 5.12 to 5.14 shows that the maximum predicted percentage of SHb is
indicated by the surface confined in the smallest curve of the contour diagram.
5.6 Conclusions
i. The FAnGS is consisted of different types of bacteria where among the
twelve bacteria successful isolates demonstrated different morphological and
cellular characteristics.
177
(a)
(b)
Figure 5.14: (a) Contour and (b) 3D response surface plots representing relationship between pH, temperature and percentage of surface hydrophobicity
178
ii. Among the twelve bacteria isolated from FAnGS, all of the isolates show the
same growth pattern under anaerobic (slow growth) and aerobic condition
(fast growth). However, they show different capability in degrading the COD
and dye compound with different degradation rate. The twelve isolated
bacteria also demonstrate different SHb and ability in performing aggregation
when imposed under high shear force. Most of the isolated bacteria that
exhibit high percentage of SHb also show high aptitude to form aggregate.
iii. With the aid of molecular technology via PCR application, the six selected
bacteria that show considerable high capacity in degrading dye and COD and
at the same time also able to perform aggregation and high SHb, have been
identified as Bacillus cereus, Pseudomonas veronii, three species of
Pseudomonas genus and Enterobacter sp. Based on the literature, all of the
six selected isolates are capable to grow both under anaerobic and aerobic
conditions. This ability may become as one of the reasons for the survival of
these bacteria to grow within the FAnGS.
iv. Within the experimental condition of this study, all the selected variables
investigated imposed a significant linear effect on the CAg process. The
substrate concentration and temperature show a positive effect on CAg as the
concentration of substrate and the degree of the temperature increased, while
pH imposed negative effect on CAg when the pH level change from acidic to
alkaline condition. However, the interaction effect onto CAg was only
significant between pH and temperature. Substrate concentration and pH
showed significant non-linear effect on CAg, while the effect of temperature
was not significant. The three way interaction between the three variables
was not significant.
v. All of the three variables, substrate, pH and temperature demonstrate a
significant effect on SHb. Variable substrate shows an increase in percentage
179
of SHb as the concentration of substrate increased. However, the percentage
of SHb reduced as the degree of temperature reduced and the pH change from
acidic to alkaline condition. The interactive effects were observed to occur
between pH and substrate and between pH and temperature. All of the
variables exhibit significant three way interaction effect. With respect to the
non-linearity effect of variables, pH and substrate indicated significant effect
but the result for temperature was opposite.
CHAPTER 6
THE EFFECT OF HYDRAULIC RETENTION TIME ON FACULTATIVE
ANAEROBIC GRANULAR SLUDGE
6.1 Introduction
The applications of granulation techniques for dye degradation have been
reported by many researchers. Most of the studies were focused on using anaerobic
granules under anaerobic condition since major decolorization process occurs under
this condition (Bras et al., 2005; Isik and Sponza, 2005b; Somasiri et al., 2008). The
UASB reactor system containing anaerobic granules was capable in treating raw
textile wastewater with 90% and 92% of COD and color removal, respectively with
24 hours of HRT (Somasiri et al., 2008). Color removal higher than 88% was
reported by Bras et al. (2005) treating mixed monoazo and diazo in a methanogenic
laboratory-scale UASB system at 24 hours HRT.
There are also several reports on the application of aerobic condition for color
removal. Most of the studies use activated sludge, suspended cells or biofilms as the
biomass compositions (Vives et al., 2003; Buitron et al., 2004; Sandhya et al., 2005;
Sirianuntapiboon and Srisornsak, 2007; Sirianuntapiboon and Sansak, 2008). Almost
100% of color removal was reported for the degradation of Direct Blue and Direct
181
Red in a series of granular activated carbon system and sequential batch reactor
system operated under aerobic condition at HRT of 7.5 days (Sirianuntapiboon and
Sansak, 2008). Buiton et al. (2004) reported an average of 80% color removal was
observed for the degradation of Acid Red 151 in a sequential biofilter packed with
porous volcanic rock-pozolane under aerobic condition.
Since complete mineralization of dye containing wastewater requires both
anaerobic and aerobic conditions, several attempts have been conducted to
investigate the removal efficiency under both operating conditions using anaerobic
granules with a series of anaerobic and aerobic reactor systems (Sponza and Atalay,
2003; Isik and Sponza, 2004a; Isik and Sponza, 2004b). However, the application of
granular system in an integrated textile wastewater treatment system has not been
much reported (Shaw et al., 2002).
Table 6.1 shows previous research studies on dye degradation process in
integrated reactor systems using different forms of biomass under different reaction
phase conditions. The table shows that the overall color removal percentage is very
much affected by the HRT particularly during the anaerobic phase. Color removal
increased as the retention time of anaerobic phase increased (Panswad et al., 2001a;
Buitron et al., 2004; Goncalves et al., 2005). However, different types of biomass
either in the form of suspended cells, activated sludge or granules, used in the
treatment system for treating different types of dyes (single or mixed) may result in
different removal efficiencies.
Successful cultivation of FAnGS has been achieved using synthetic textile
dyeing wastewater in an integrated reactor system under intermittent anaerobic and
aerobic reaction phases as reported in Chapter 4. The FAnGS consisting of
anaerobic, aerobic and facultative microorganisms may become the most suitable
biomass for treating the textile wastewater. However, knowledge on the
performance of color removal using FAnGS under different HRT is lacking. The
changes in terms of the granular properties, performance of COD and color removal
182
as well as the performance of the reactor system towards the effect of different HRT
are the main focus of this chapter. Biokinetic parameters such as biomass growth
rate (μ), endogenous decay rate (kd), observed biomass yield (Yobs) and theoretical
biomass yield (Y) were also investigated in relation to the changes of HRT of the
anaerobic and aerobic reaction phases.
6.2 Materials
All of the chemical or reagents and equipments used in this study were given
in Section 4.2. In addition, an Orion 2 Star pH-Benchtop meter (SN-016655) was
used to measure the redox potential of the wastewater while conducting the
experiment. However, in this experiment the concentration of the carbon sources
(glucose, acetate and ethanol) was increased giving an initial OLR of 2.5 kg
COD/m3·day.
6.3 Analytical Methods
The effect of HRT was investigated with respect to the changes in the
microbial activity, physical characteristics and removal performance of the granular
biomass in the IFGSBioRec. Figure 6.1 shows the experimental analysis conducted
for this study.
183
Table 6.1: Dye degradation process using integrated reactor system
Reactor system Biomass Dye Reaction phase/HRT Performance Reference
SBR Activated sludge
Raw wastewater containing disperse, sulfur & reactive dyes
Conventional SBR (Aerobic, 10 hrs); Anoxic+ anaerobic (2 hrs) / Aerobic (8 hrs)
Color reduction was not so good due to less contact hour during anaerobic condition
Pansuwan et al. (1999)
SBR Sludge Reactive Black 5 & Reactive Blue 5,19, 198
Anoxic/Anaerobic (18 hrs)/ Aerobic (5hrs)
Color removal: at 20 mg/L of dyes (63-66%); at 100 mg/L of dyes (32-58%)
Luangdilok and Panswad (2000)
SBR Activated sludge
Reactive azo dye Procion Red H-E7B
Anaerobic (24 hrs ) /Aerobic(16 hrs)
63.9% (anaerobic stage) and 11.1% (aerobic stage)
O’Neil et al. (2000b)
SBR Activated sludge
Remazol Black B Anoxic+Anaerobic/ Aerobic: (0/11; 2/9; 4/7; and 8/3 hrs)
Color removal: 26.9-60.5% (anaerobic); 1.9-16.7% (aerobic)
Panswad et al. (2001a)
SBR Activated sludge
Remazol Black B Anaerobic (18/6 hrs) /Aerobic (5hrs)
73-77% (enriched with PAOs); 59-64% (enriched with GAOs) of color removal
Panswad et al. (2001b)
SBR Activated sludge
Remazol Brilliant Violet 5R & Remazol Black B
Anaerobic (9-11 hrs) /Aerobic (8-12 hrs)
90% removal for RBV; 75% removal for RBB
Lourenco et al. (2001)
SBR Anaerobic granules
Remazol Black reactive dye
Anaerobic (18.5 hrs) /Aerobic (30 min)
94% (color removal); 66% (TOC removal)
Shaw et al. (2002)
RDBR Aerobic biofilm
Acid Orange 7 HRT: 1.5 hrs 90% of color removal Coughlin et al. (2002)
184
Table 6.1: Dye degradation process using integrated reactor system (Continued)
Reactor system Biomass Dye Reaction phase/HRT Performance Reference
SBR Activated carbon
Reactive dyes Anoxic (14;17.5 hrs)/ Oxic (6; 2.5 hrs)
>80% (COD removal); 18-25% (color removal).
Pasukphun and Vinitnantharat (2003)
SBBR Porous volcanic rock
Azo dyes Acid Red 151
HRT: 4-24 hrs 99% color removal (14-16% contributed by porous material )
Buitron et al. (2004)
SBR Aerobic sludge
Azo dyes Anaerobic (12;8 hrs)/ Aerobic (8;12;12 hrs)
85% (COD removal); 95% (BOD5 removal)
Goncalves et al. (2005)
SBR Bio-sludge Vat dye Aerobic (19 hrs) /Anaerobic (3 hrs) /Anoxic (0.5 hrs); HRT : 3 d
Color; COD; BOD5; TKN removal: STIWW (98.5%; 96.9%; 98.6%; 93.4%); RWW (75%; 71; 96.7;63%)
Sirianuntapiboon et al. (2006)
SBR Activated sludge
Acid Black Azo dye Anoxic (30 min)/ Aerobic (23 hrs)/Anoxic (30 min)
100% (color removal); 92% (COD removal)
Mohan et al. (2007b)
Reaction vessel
Sludge Reactive azo dyes Anoxic (8 hrs) / Aerobic (16 hrs)
12-85% (color removal ); 95% (COD removal )
Smith et al. (2007)
SBR Granular activated carbon
Acid Orange 7 Anaerobic (20 hrs) 100% (color removal); 88% (COD removal)
Ong et al. (2008b)
SBR Facultative anaerobic granule
Mixed azo dyes Anaerobic/Aerobic HRT=3:3;6:6;12:12; 18:6; 6:18 hrs
Highest color removal (86.5%), Highest COD removal (94.1%)
This study (2009)
185
Figure 6.1 Experimental analyses on the effect of HRT on granular biomass in treating synthetic textile dyeing wastewater
186
6.3.1 Microbial Activity
The microbial activities as the observation of the biological characteristics
were measured based on the OUR of the granular biomass used in this experiment.
The procedure for OUR measurement is as described in Section 4.3.1.2.
6.3.2 Physical Characteristics
The physical changes were investigated in terms of the concentration of the
MLSS and MLVSS, SRT, settling velocity and the sizes of the granular biomass.
The settling velocity of the granules was measured as described in Section 4.3.2.1.
The measurement of MLSS and MLVSS concentrations were according to Standard
Methods (APHA, 2005) as given in Section 4.3.2.4. The granular sizes were
measured by sieving 250 mL of suspended granular biomass using different sizes of
sieve mesh. The amount of the sieved granules were divided with the total granular
biomass and measured as a percentage of volume fractions.
6.3.3 Removal Performances
The removal performances of the reactor system with respect to color and
COD removal were analyzed according to the description in Section 4.3.4.1 and
Section 4.3.4.2, respectively. The redox potential levels were measured throughout
the experiment using an Orion 2 Star pH-Benchtop meter (SN-016655).
187
6.4 Experimental Procedures
The FAnGS which was developed in the IFAnGSBioRec as discussed in
Chapter 4 was used as the granular biomass in this study. The size of FAnGS
selected for this experiment was in the range of 0.3-2.5 mm. The FAnGS was
inoculated into the bioreactor at a ratio of 1:4 of the working volume of the reactor
system. One (1) L of acclimated mixed sludge as described in Section 4.2.2 was also
added into the reactor system. During the start-up of the experiment, 2 L of synthetic
textile dyeing wastewater was filled into the reactor. This has made the
concentration of MLSS and MLVSS during the start up of the experiment as 23.2 g/L
and 18.4 g/L respectively. During the first two month of the start-up, 10% (v/v) of
selected dye degrader microbes were added twice a week into the reactor.
The operation steps of one complete cycle of the IFAnGSBioRec are shown
in Table 6.2. The HRT were varied between 6 to 24 hours in order to study the effect
of HRT towards the removal of COD and color by the FAnGS in the continuous
operation of IFAnGSBioRec. During the anaerobic react phase, circulation of
wastewater was carried out using peristaltic pump (Cole-Parmer System Model, 6-
600 rpm), pumping out the wastewater from the upper part of the reactor system and
entering back into the system at the bottom of the reactor. The circulation was
conducted at a flow rate of 18 L/h. Throughout the aerobic react phase, air pump
was used to provide the oxygen to the system. The air was supplied at a superficial
air velocity of 2.5 cm/s. The reaction phases were operated intermittently starting
with anaerobic and followed by aerobic. The reaction phase was then continued with
a second anaerobic phase followed by a second aerobic phase. Then, the biomass
was allowed to settle during the settling phase. The detailed descriptions on the
reaction phase variation are discussed in Section 6.5.2.
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Table 6.2: Operation steps during single cycle operation
Sequence phase Phase period Air supply Recirculation
Fill 15 min Off Off
Reaction
Anaerobic Varies* Off On
Aerobic Varies* On Off
Settle 5 min Off Off
Decant 5 min Off Off
Idle 5 min Off Off
6.5 Results and Discussion
6.5.1 Microbial Activity
The microbial activity was measured based on the oxygen uptake rate of a
complete one cycle operation. The OUR of a complete cycle was measured several
times before each of the stages ended. The OUR measurement of each stage of the
experiment showed that most of the external substrate was consumed more or less
within the first 30 minutes of each aerobic reaction phase. Figures 6.2 to 6.3 show
the profiles of the OUR throughout the experiment from Stage I to Stage VI. The
OUR profile (Figure 6.2) shows that the initial measurement of the OUR reduced as
the HRT increased (Stage I to Stage III). This is due to the reduction in the OLR as
the HRT increased. Less oxygen is required as the concentration of the organic
loading reduced. After a sharp increase of OUR at the beginning of each cycle in all
stages, the OUR measurement was constantly low until the end of the cycle. The low
measurement of OUR gives an indication that most of the external substrates have
been consumed. This also means that the microorganisms in the reactor system were
under starvation phase. At this phase, no further degradation was observed
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eventhough the HRT was extended. During the starvation phase, endogenous
respiration will take place, except at the beginning of the second phase of aerobic
reaction where there was a short increase in the OUR. In this case, the short increase
on the OUR measurement was due to the mineralization of amines, the byproduct of
dye degradation during the second anaerobic reaction phase. As the duration of
anaerobic reaction phase increased, the short pulse increased as shown in Figure 6.3
(a and b) of Stage IV and V, respectively. Stage IV and Stage V were operated with
the same HRT and organic loading but different in the anaerobic and aerobic reaction
phase ratio.
6.5.2 Physical Profile of the Reactor System
The details of the experimental conditions of the reactor system are shown in
Table 6.3. The HRT of the experiment was increased from 6 hours in Stage I to 24
hours in Stage III. The increase in the HRT resulted with a reduction of the OLR
supplemented into the reactor system from 2.5 to 0.6 kg COD/m3·day. The HRT for
Stage III to VI was kept constant i.e. 24 hours, but the duration of anaerobic and
aerobic reaction phases was varied. From Stage III onwards, the OLR was increased
to 0.8 kg COD/m3·day by increasing the concentration of the carbon sources in the
synthetic textile dyeing wastewater. The temperature of the treatment system was
kept constant at 30.0 ± 2.0oC while the pH throughout the experiment was between
6.3 and 8.0.
Table 6.4 shows the oxidation reduction potential (ORP) values measured
during the second phase of the anaerobic and aerobic reactions during the
experiments. The ORP profile of all the stage corresponded very well with the
dissolved oxygen. The ORPs were recorded with more negative values when the
anaerobic reaction phase increased while during the aerobic phase the ORP varies
between +98 to +177 mV.
190
Figure 6.2 OUR profile of (a) Stage I (Aerobic phase 2.84 hours), (b) Stage II
(Aerobic phase 5.84 hours) and (c) Stage III (Aerobic phase 11.84 hours)
(c)
191
Figure 6.3 OUR profile of (a) Stage IV (Aerobic phase 11.84 hours), (b) Stage V
(Aerobic phase 5.84 hours), (c) Stage VI (Aerobic phase 17.84 hours)
(b)
(c)
192
Table 6.3: Details of experimental condition of the IFAnGSBioRec
Stage Days covered
Phase (hours) HRT (hrs)
OLR (kg COD/ m3·day)
1st 2nd
Anaerobic Aerobic Anaerobic Aerobic
I 49 1.42 1.42 1.42 1.42 6 2.5
II 43 2.92 2.92 2.92 2.92 12 1.3
III 51 5.92 5.92 5.92 5.92 24 0.6
IV 43 5.92 5.92 5.92 5.92 24 0.8
V 46 8.92 2.92 8.92 2.92 24 0.8
VI 46 2.92 8.92 2.92 8.92 24 0.8
, where X = COD concentration of the influent (mg/L); Vadd= Volume of influent added in
each cycle operation (mL); Vtotal = Total working volume of the experiment (mL); T = Hydraulic retention time (hour).
Table 6.4: Oxidation Reduction Potential
Stage Anaerobic Reaction Phase Aerobic Reaction Phase
I -124 ± 27 125 ± 19
II -219 ± 33 129 ± 24
III -358 ± 29 174 ± 34
IV -355 ± 51 151 ± 17
V -407 ± 21 112 ± 21
VI -225 ± 28 177 ± 15
The biomass profile at steady state with stepwise increment of HRT (Stage I
to III) and variation of reaction phases (Stage IV to VI) are shown in Table 6.5. As
shown in Table 6.5, it is apparent that the biomass concentration (MLSS) in the
reactor decreased and the VSS in the effluent was also reduced with the increase in
the HRT (Stage I to III). The reduction of the biomass concentration in the reactor
193
may be due to the lower value of OLR applied in the reactor system as the HRT
increased.
Table 6.5: Biomass concentrations at different stages of the experiment
Reaction Phase
Stage
I II III IV V VI
Anaerobic (hours) 2.8 5.8 11.8 11.8 17.8 5.8
Aerobic (hours) 2.8 5.8 11.8 11.8 5.8 17.8
MLSS (g/L) 35.3 ± 1.6 28.7 ± 0.6 25.2 ± 1.8 30.5 ± 3.4 31.6 ± 3.7 23.3 ±0.8
MLVSS (g/L) 31.9 ± 1.8 24.5 ± 2.2 18.5 ± 2.2 26.0 ± 3.4 22.4 ± 2.0 20.2 ± 0.8
VSS/SS 0.90 0.85 0.73 0.85 0.71 0.87
Effluent (VSS g/L) 0.34 ± 0.16 0.31 ± 0.11 0.26 ± 0.19 0.34 ± 0.11 0.33 ± 0.10 0.55 ± 0.22
SRT (day) 27.6 ± 13.4 42.4 ± 10.2 78.9 ± 23.9 70.1 ± 23.9 72.5 ± 23.3 41.6 ± 18.4
When the OLR was increased to 0.8 kg COD/m3·day, there was an
improvement in the biomass concentration where the biomass concentration have
increased to 30.5 ± 3.4 g/L and 31.6 ± 3.7 g/L in Stage IV and Stage V as compared
to 25.2 ± 1.8 g/L of biomass concentration in Stage III which run at the same HRT
(24 hours) but with OLR 0.6 kg COD/m3·day. The increase in OLR has caused an
increment in the biomass concentration in the reactor. A slight increase in the
biomass concentration was also observed along with the longer period of the
anaerobic phase (Stage V), i.e. 18 hours.
The ratio of the volatile biomass (MLVSS) to total biomass (MLSS) reduced
from Stage I to Stage III mainly due to decrease in the OLR as the HRT increased
from 6 to 24 hours, whereas the MLVSS/MLSS ratio of the Stage III and Stage IV
194
with 12 hours aerobic reaction phase was observed higher with the ratio of 0.73 and
0.85, respectively. The increment may be due to the increase of the OLR from 0.6 to
0.8 kg COD/m3·day (Stage III to Stage IV). Increase in the OLR means more carbon
sources were supplied to the microorganisms in the reactor. When more food is
available, more growth will take place and this is indicated by the increase in the
MLVSS/MLSS ratio.
However, when the anaerobic period of the HRT is extended, the
MLVSS/MLSS ratio decreased (0.71). Decrease in MLVSS/MLSS ratio may
indicate an increase of inorganic accumulation within the granulation biomass. The
same observation was reported by Panswad et al. (2001a) that increase of inert solids
in the biomass was observed when the system was exposed to high anoxic/anaerobic
condition in the SBR cycle. When the duration of aeration phase was increased up to
18 hours, the biomass started to reduce again (Stage VI) and increase of VSS in the
effluent was once again observed. This may give an indication that too long of
aerobic reaction phase is not suitable for granular biomass system. Prolong of
aeration time may result in instability of the reactor performance. The profile of
biomass concentration of the reactor system throughout the experimental process is
given in Figure 6.4.
The sludge retention time (SRT) of the biomass in the SBR system can be
calculated by using Equation 6.1.
where,
= Solid retention time (d)
= Volatile solid concentration in the reactor system
(g VSS/L),
195
= Working volume of the SBR system (L),
= Biomass concentration of manually discharged mixture
(g VSS/L)
= Manually discharge mixture volume (L),
= Effluent volatile solid concentration (g VSS/L)
= Effluent volume of the SBR operating cycle (L)
= Cycle time of the SBR operation (d)
Figure 6.4 Profile of biomass concentration at different stages of the experiment.
(●) MLSS, (□) MLVSS. Stage I: anaerobic (2.8 h): aerobic (2.8 h); Stage II:
anaerobic (5.8 h): aerobic (5.8 h); Stage III and Stage IV: anaerobic (11.8 h): aerobic
(11.8 h); Stage V: anaerobic (17.8 h): aerobic (5.8 h); Stage V: anaerobic (5.8 h):
aerobic (17.8 h)
196
Since in the operating system there was no physical sludge discharging at any
of the operating time, Eq. 6.1 can be simplified as Eq. 6.2 (Liu and Tay, 2007b).
cee
rvss
/tVXVX
θ = (6.2)
Based on Eq. 6.2, the SRT of the reactor system increased from 27.6 ± 13.4
to 78.9 ± 30.8 d when the length of the HRT increased from 6 to 24 hours (Stage I to
Stage III). With HRT of 24 hours, increase of anaerobic reaction phase up to 18
hours (Stage IV to Stage V) has slightly increased the SRT from 70.1 ± 23.9 to 72.5
± 23.3 d. The SRT value changes in each stage of the experiment. According to
Wijffels and Tramper (1995), the favorable sludge age for high removal efficiency
for COD and nitrification process is more than 4 days. Based on the SRT obtained,
this granular system is capable of the simultaneous degradation of nitrification
process and COD removal. Since the treatment goal is to remove recalcitrant dyeing
compound, the SRT value of all stages evaluated in this experiment was in the
acceptable range from degradation of xenobiotic compounds (Grady et al. 1999).
6.5.3 Effect of Hydraulic Retention Time on Physical Properties of the
Granular Biomass
In this experiment, the HRTs were between 6 to 24 hours with variation time
for anaerobic and aerobic conditions during the reaction phase. The effects of this
variation on the physical properties of the granules are given in Table 6.6.
197
The mean granular size in the reactor was the largest (i.e 843 ± 44 μm) during
Stage I which was at the shortest HRT and highest OLR. When the HRT increases
from 6 to 24 hours, the OLR was reduced from 2.5 to 0.6 kg COD/m3·day. This
condition may contribute to the smaller granules formation due to the reduction in
the food supply. Furthermore, as the HRT increased from 6 to 24 hours, the mean
granular size was reduced may also be due to the long exposure of the granules to the
shear force imposed by high superficial air velocity during the increasing aerobic
reaction phase. When the OLR were increased from 0.6 to 0.8 kg COD/m3·day from
Stage III to Stage IV, the mean granular size slightly increased due to increase in
food supply.
Table 6.6: Physical properties of the granular biomass at different stages of
experiment
Reaction Phase
Stage
I II III IV V VI
Anaerobic (hours) 2.8 5.8 11.8 11.8 17.8 5.8
Aerobic (hours) 2.8 5.8 11.8 11.8 5.8 17.8
Mean size (μm) 843 ± 44 590 ± 55 440 ± 40 567 ± 79 575 ± 46 385 ± 22
SVI (mL/g) 13.1 ± 0.4 18.8 ± 1.5 21.4 ± 1.6 16.8 ± 1.3 15.5 ± 1.3 24.8 ± 0.9
SV (m/h) 41.3 ± 3.1 35.1 ± 0.8 24.5 ± 1.1 28.4 ± 1.3 33.4 ± 2.5 21.3 ± 0.5
At Stage V, even though the HRT was 24 hours, the mean granular size was
observed to increase. This is due to the short aerobic reaction phase (i.e. 6 hours) as
anaerobic reaction phase was prolonged up to 18 hours and with increase in the OLR.
This shows that bigger granules could still be maintained in the reactor system at
higher HRT provided that aerobic reaction phase is reduced by increasing the
198
anaerobic reaction phase. This condition seems to be suitable for treating textile
wastewater that requires both anaerobic and aerobic phases.
With respect to the development of the granulation process, increase in the
anaerobic reaction phase would cause changes in the EPS component within the
granular sludge enhancing the granulation process. Aerobic granules may consist of
layers of aerobic and facultative anaerobic granules (Jang et al., 2003 and Tay et al.,
2002a). The aerobic microorganisms are able to produce more EPS as compared to
the anaerobic microorganisms (Foster, 1991). As the aerobic microorganisms are
responsible in producing the EPS, under anaerobic condition the facultative
microorganisms suppressed the EPS production and encourage the consumption of
EPS. Fermentation of EPS and disruption of microorganisms would take place
inside the granules during anaerobic condition leading to reduction of the EPS in the
granule. Reduction of the EPS in the inner part of the granules resulted in the
reduction of the surface negative charges, the steric interaction and entanglement of
the EPS and increase in hydrophobicity which means less water trapped (Foster,
1991). With the effect of shear force, the compacting process onto the granule would
have taken place and the granules continue to grow. The increase of the granular
size is stabilized when the balance between growth and detachment due to shear
force effect is reached. The interaction between the production of EPS by the
aerobic microorganisms during aerobic reaction phase may has been balanced with
the consumption of the EPS by the anaerobic or facultative microbes during the
anaerobic reaction phase that caused increase in the granular size during Stage V.
This means having an intermittent reaction phase of anaerobic and aerobic process
would be a good strategy for the application of granulation system for textile
wastewater treatment. Balance in bioactivity of the production and consumption of
EPS could maintain a reasonable amount of EPS within the granules (Li et al.,
2006b) which are important for the successful development and growth of the
granular biomass.
The SVI value of the granular sludge was used to evaluate the granular
settling ability. It is anticipated that bigger granules would have higher settling
velocity and hence, reduce the SVI value, indicating good settling ability. The SVI
199
value changes with the same pattern as the granular biomass concentration as well as
the mean particle size of the granules. As the granular particles decreased in size, the
SVI value increased. The SVI value improved when the anaerobic reaction phase
was prolonged in Stage V indicating such reaction pattern would help to develop
granules with better settling profile. As stated earlier, according to Panswad et al.
(2001a), inert biomass increased as the anoxic/anaerobic condition was prolonged. It
could be possible that the accumulation of inert particles within the granules
increased and resulted with improved SVI properties of the granular biomass.
Figure 6.5 shows the particle size distribution of granular biomass in the
reactor at each stage of the experiment. The figure shows that the particle size
distribution was clearly affected by the HRT and aeration time which imposed shear
force to the granules. As shown earlier in Table 6.4, when the HRT increased,
without increasing the concentration of substrate in the influent, will cause reduction
in the OLR. This means less food is supplied into the reactor. The granular biomass
in the reactor will be exposed to a longer starvation period when the HRT is
increased. The starvation effect become more obvious when the aeration time is
longer as the HRT increased. When there was no more food to be consumed
(starvation phase), the microorganisms will undergo endogenous respiration where
the EPS within the granules will be used as the alternative of the energy sources (Liu
et al., 2005a). The granular biomass, then, would experience microbial decay and
lyses. This would lead to increase in the granules porosity and weaken the granular
structure. Empty holes in the granules would be observed with fragile granule
structure. In these circumstances, the granules would easily defragment into smaller
sizes under operational condition. Control over the granular sizes and the length of
starvation time are among the important factors to be considered for maintaining
performance stability of the granular reactor system.
Hydraulic retention time is an important parameter that control the contact
time between the biomass and the wastewater in a reactor system. The HRT of a
system must be long enough for the degradation process by the microorganisms to
take place. However, in the application of granular biomass in the treatment system,
200
the HRT should not be too long as it may cause the disintegration of the granules.
According to Tay et al. (2002b) and Wang et al. (2005b), a short HRT is favorable
for rapid granulation process, while too long HRTs may lead to granulation system
failure due to high biomass lost (Pan et al., 2004). An optimum HRT of
biogranulation systems would be able to stabilize the reactor performance with good
biomass retention and high removal performance. According to Pan et al. (2004),
the optimum HRT for aerobic granulation systems ranging from 2 to 12 hours
enabled the formation and maintenance of stable aerobic granules with good
settleability and microbial activities. However, the optimum HRT for the treatment
of different types of wastewater may vary depending on the type of wastewater and
the targeted degradation compound.
Figure 6.5 Distribution of size particles at different stages of the experiment. Stage
I: anaerobic (2.8 h): aerobic (2.8 h); Stage II: anaerobic (5.8 h): aerobic (5.8 h); Stage
III and Stage IV: anaerobic (11.8 h): aerobic (11.8 h); Stage V: anaerobic (17.8 h):
aerobic (5.8 h); Stage V: anaerobic (5.8 h): aerobic (17.8 h)
201
Figure 6.6 shows the profile of SVI throughout the experiments. The SVI
value in Stage V was reduced from 16.8 ± 1.3 mL/g (in Stage IV) to 15.5 ± 1.3 mL/g.
This is expected to be due to the accumulation of more inert solids within the
granules as shown with low levels of MLVSS/MLSS ratio in Stage V (0.71). Despite
changes in HRT that caused decrease in the granular sizes, the SVI values of the
whole experiments were good except for Stage VI. During Stage VI, the prolonged
duration of the aerobic phase (i.e. 17.8 hours) which was operated at high superficial
air velocity (2.5 cm/s), cause the granular biomass to rupture. At this stage, size of
the granular biomass becomes smaller causing the settleability of the particles to
reduce and was demonstrated with increase in SVI value.
Figure 6.6 Profile of sludge volume index throughout the experiment. Stage I:
anaerobic (2.8 h): aerobic (2.8 h); Stage II: anaerobic (5.8 h): aerobic (5.8 h); Stage
I II III IV V
202
III and Stage IV: anaerobic (11.8 h): aerobic (11.8 h); Stage V: anaerobic (17.8 h):
aerobic (5.8 h); Stage V: anaerobic (5.8 h): aerobic (17.8 h)
6.5.4 Effect of Hydraulic Retention Times on Chemical Oxygen Demand
Removal
The profile for COD concentration in the influent, effluent and removal
performance for all six stages of experiment is given in Figure 6.7. The biogranular
system showed consistent COD degradation performance with 84.2 ± 0.9% removals
after about 50 days of start-up period (acclimatization phase). The overall
performance was almost consistent despite the fact that the duration of the
experimental process was increased from 6 hours to 24 hours. This phenomenon
may be due to the decreasing biomass concentration and also due to the decrease in
the OLR as mentioned earlier. When the OLR was increased from 0.6 kg
COD/m3·day to 0.8 kg COD/m3·day on the 194th day of the experiment (Stage III to
Stage IV), the COD removal efficiency increased from about 84.4 ± 0.4% at the end
of Stage III (day 193) to 90.7 ± 0.2% at the end Stage IV(day 236).
Mohan et al. (2007b) reported that the performance efficiency of the system
was found to be affected by the operating OLR. The SBR system operating at higher
OLR resulted with a high substrate uptake rate at the end of the cycle period. This
was also observed by Ong et al. (2005b).
An increase in the percentage of COD removal efficiency was also observed
when the period of anaerobic phase was increased from 12 hours to 18 hours. The
removal increased from 90.7 ± 0.2 % in Stage IV to 94.1 ± 0.6 % in Stage V.
Psukphun and Vinitnantharat (2003) claimed that the increase in the non-aeration
phase in the SBR system would cause an alteration in the population of anaerobic
microorganisms in the system which is expected to produce good COD and color
removal for textile wastewater. However, according to Kapdan and Oztekin (2006),
when the duration of anaerobic phase is too long, the contribution of aerobic reaction
203
phase might be decreased. This is possibly due to the toxic effect of aromatic amines
produced during dye degradation.
Figure 6.7 Profile of COD removal performance of the reactor system at different
stages of the experiment. (○) Influent COD; (■) Effluent COD, (▲) COD removal.
Stage I: anaerobic (2.8 h): aerobic (2.8 h); Stage II: anaerobic (5.8 h): aerobic (5.8 h);
Stage III and Stage IV: anaerobic (11.8 h): aerobic (11.8 h); Stage V: anaerobic (17.8
h): aerobic (5.8 h); Stage V: anaerobic (5.8 h): aerobic (17.8 h)
Owing to the condition in the SBR system where different reaction phases
occur in the same column, too long anaerobic reaction periods will cause high
accumulation of aromatic amine in the same compartment. High concentrations of
aromatic amines may inhibit the activity of aerobic microorganisms during the
aerobic phase. In this study, eventhough the anaerobic reaction phase was extended
up to 18 hours, there was no reduction in COD removal. This shows that there was
204
no inhibition on the activity of aerobic microorganisms by the long accumulation of
the byproduct produced from anaerobic degradation of the dye compound. It might
be that the concentration of dye used during this experiment was not that high to
produce enough concentration of the aromatic amines that may cause toxic effect
towards the microorganisms within the biogranules. Furthermore, the biogranules
might not be affected by the dyestuff degradation byproducts due to the structural
form of the biogranules. The biogranules structure which consisted of EPS acts as a
shield for microorganisms within the granules against any shock loading or toxic
compound.
At the final stage (Stage VI) of the experiment, a surge drop of COD removal
efficiency was observed. As the aeration time was increased from 6 to 18 hours, the
COD removal reduced from 94.1 ± 0.6% to 82.6 ± 0.8%. The drop in the COD
removal efficiency was due to the increase in biomass loss into the effluent. The
MLSS in Stage VI was 23.3 ± 0.8 g/L as compared to 31.6 ± 3.7 g/L observed in the
previous stages.
6.5.5 Effect of Hydraulic Retention Time on Color Removal
Color removal was observed to increase from 66.7 ± 1.6 % to 76.5 ± 0.8 % as
the HRT increased from Stage I to Stage III. Increase in the HRT allows longer
contact time between the granules and the wastewater resulting in better color
removal. Furthermore, when the OLR was increased from 0.6 kg COD/m3·day
(Stage III) to 0.8 kg COD/m3·day (Stage IV), a significant improvement in color
removal from 76.5 ± 0.8 % to 83.1 ± 1.4 % was observed. This may be caused by the
increase in the microbial population. Ong et al. (2005b) reported that the percentage
of color removal efficiency increased by 16% in anaerobic and 50% in aerobic SBR
reactor systems when the OLR rate was increased from 2.66 to 5.32 g COD/L·day.
An increase from 82% to 90% of color removal efficiency was observed by
205
Talarposhiti et al. (2001) when the COD loading was increased in a two-phase
anaerobic packed bed reactor from 0.25 to 1 kg COD/m3·day.
Since more color removal took place in anaerobic condition (Banat et al.,
1996; van der Zee et al., 2001a and Dos Santos et al., 2007), the percentage of color
removal was once again increased from Stage IV (83.1 ± 1.4%) to Stage V (86.5 ±
0.5%) when the anaerobic reaction phase was extended from 12 to 18 hours of the 24
hours reaction cycle. Improved decolorization process that occurs during the
anaerobic stage enhances the overall wastewater biodegradation since more readily
biodegradable substances could be degraded in the following aerobic treatment
(Stolz, 2001). Figure 6.8 shows the profile of the color removal performance.
Figure 6.8 Profile of color removal performance of the reactor system at different
stages of the experiment. (♦) Influent color, (■) Effluent color, (○) Color removal.
(100 ADMI ≈ 1 Pt-Co). Stage I: anaerobic (2.8 h): aerobic (2.8 h); Stage II:
anaerobic (5.8 h): aerobic (5.8 h); Stage III and Stage IV: anaerobic (11.8 h): aerobic
I II III IV V
206
(11.8 h); Stage V: anaerobic (17.8 h): aerobic (5.8 h); Stage V: anaerobic (5.8 h):
aerobic (17.8 h)
With respect to the mechanisms that are involved in color degradation, the
addition of electron–donating substrate could considerably improve the
decolorization reductive rate (Bras et al., 2001, Dos Santos et al., 2005). Their
studies using anaerobic and aerobic sequential wastewater treatment system indicated
that the anaerobic stage was the main step for color degradation while the aerobic
phase acted as the polishing step and enhancement in COD removal. Higher initial
COD concentration did not improve color removal but caused deterioration in COD
removal in the anaerobic-aerobic SBR system (Kapdan and Oztekin, 2006).
Psukphun and Vinitnantharat (2003) reported that the duration of the
anaerobic phase should be long enough to obtain better COD and color removal.
Increase in the HRT would provide enough time of the COD and intermetabolites of
simulated textile wastewater in anaerobic or/and anaerobic/aerobic systems (Isik and
Sponza, 2008). This means biodegradation of the azo bonds may require a certain
contact time in order to achieve high removal efficiency. Depending only on the
filling stage to provide anaerobic condition for the cleavage of azo bond compounds
may not be adequate for textile wastewater treatment. However, the time requires for
the cleavage of the azo bond may be affected by the complexity of the dye molecule
structures. Higher contact time for the anaerobic reaction phase can be provided by
having the anaerobic reaction stage during the react phase in the SBR cycle as
proposed in this experimental study. The suitable contact time of anaerobic and
aerobic reaction phase may provide high removal performance for the cleavage of the
N=N bond (anaerobic condition) and mineralization of aromatic amines (aerobic
phase). Furthermore, the reduction of COD is more effective during the aerobic
stage as compared to the anaerobic reaction condition (Smith et al., 2007). From this
study, it shows that having longer anaerobic (18 hours) and shorter aerobic (6 hours)
reaction phase resulted with the highest removal for color and slight improvement in
the efficiency of COD removal. The effect of HRT on the COD and color removal
performance by the FGS biomass at different stages of the experiment is given in
Table 6.7.
207
Table 6.7: Profile of COD and color removal percentage at different stages of
experiment
Reaction Phase
Stage
I II III IV V VI
Anaerobic (hours) 2.8 5.8 11.8 11.8 17.8 5.8
Aerobic (hours) 2.8 5.8 11.8 11.8 5.8 17.8
COD (%) 84.2 ± 0.9 84.6 ± 1.1 84.4 ± 0.4 90.7 ± 0.2 94.1 ± 0.6 82.6 ± 0.8
Color (%) 66.7 ± 1.6 74.3 ± 0.4 76.5 ± 0.8 83.1 ± 1.4 86.5 ± 0.5 75.4 ± 0.3
6.5.6 Effect of Hydraulic Retention Time on the Biokinetics of Facultative
Anaerobic Granular Sludge during Biodegradation of Dye
The total solid biomass concentration in a biological reactor system is
governed by the rate of substrates utilization and biomass production by the
microorganisms. The rates of such processes which are known as the biokinetic
parameters would give prediction on the performance of the biological process in
wastewater treatment. The understanding and information on the rate of biological
reactions and basic principles governing the growth of microorganisms are very
important in developing an effective design and operation of the biological reactor
system (Tchobanoglous et al., 2004).
208
In this study, the biokinetic parameters of the FAnGS were also investigated
in relation to the effect of different HRTs. The biokinetic parameters that were
investigated are the overall specific biomass growth rate (μoverall), endogenous decay
rate , observed biomass yield , and theoretical biomass yield All of
the calculations for the biokinetic parameters are according to the equations listed in
Table 6.8 and are based on the reports by Liu and Tay (2007a) and Chen et al.
(2008b). The results of biokinetic parameters for all stages in this experiment are
given in Table 6.9.
When the experiment moved from Stage I to Stage III, the SRT was increased
from 27.6 ± 13.4 to 78.9 ± 30.8 d, these have caused the μoverall to reduce from 0.036
to 0.013/d. The results are in accordance with the report stated by Li et al. (2006b)
that sludge biomass will loose their bioactivity when the SRT is increased. The
reduction of the μoverall as the HRT increased was also observed by Liu and Tay
(2007a). As mentioned earlier, the OLR was reduced when the HRT was increased
from 6 to 24 hours (from Stage I to Stage III). The reduction in the OLR may also
contribute to the reduction of μoverall from Stage I to Stage III. The μoverall of Stage IV
and Stage V was the same when the SRT of these two stages slightly increased from
70.1 ± 23.9 to 72.5 ± 23.3 d, respectively. The μoverall of Stage VI increased as the
SRT was reduced to 41.6 ± 18.4 d eventhough Stage VI was operated with the same
HRT as Stage IV and V. The reduction of the SRT in Stage VI may be contributed
by the increase in the sludge washout that was shown by the increase in the
suspended solids concentration in the effluent discharge.
The rate of biomass lost due to endogenous respiration is represented by
endogenous decay rate kd, as given in Equation 6.4. The OUR that was measured
during the last 10 min or before the second aeration phase stop of one cycle operation
was used to calculate the kd. As the HRT increased from 6 to 24 hours (Stage I to
Stage III), the kd values reduced. However, since the reduction was also very small,
the kd can be considered as constant when the HRT was increased. Furthermore, the
kd value during 24 hours HRT of Stage III to V can also be considered constant
(0.0075 to 0.0076/d). It can thus be concluded that the kd is considered constant
209
throughout the experiment. The kd values calculated from this study were very small
as compared to the kd values of aerobic granules (Chen et al., 2008b) and of the
activated sludge (Tchobanoglous, 2004).
Table 6.8: Coefficient of biokinetic parameters
Biokinetic Coefficient Units Formula Equation
Overall specific biomass growth rate
Per day
θ = sludge retention time
(6.3)
Endogenous decay rate
Per day
= oxygen uptake rate (mg/L.h)
= theoretical chemical oxygen
demand which is assume as 1.42 mg O2/ mg biomass M= biomass concentration (mg VSS/L)
(6.4)
Observed biomass yield
mg VSS/mg COD
= Effluent volatile solid concentration (g VSS/L) Ci = COD concentration in the influent (mg/L) Ce = COD concentration in the effluent (mg/L)
(6.5)
210
Theoretical biomass yield
mg VSS/mg COD
(6.6)
Table 6.9: Kinetic coefficients of FAnGS at different stages of the experiment
Kinetic coefficients of facultative granules
Stage I Stage II Stage III Stage IV Stage V Stage VI
Observed specific biomass growth rate (μoverall) (per day)
0.036 0.024 0.013 0.014 0.014 0.024
Endogenous decay rate kd (per day) 0.0096 0.0086 0.0075 0.0075 0.0076 0.0060
Observed biomass yield (Yobs) (mg VSS/ mg COD)
0.316 0.298 0.242 0.269 0.217 0.412
Theoretical biomass yield Y (mg VSS/ mg COD)
0.399 0.395 0.385 0.410 0.338 0.515
The observed biomass yield Yobs is the ratio of the biomass production rate to
the substrate removal rate and is calculated according to Equation 6.5. The Yobs is
one of the most important parameter used in biological kinetic models. Equation 6.5
is derived from the equation below (Liu and Tay, 2007a):
where,
211
XVSS1 = Volatile solid concentration at the beginning of cycle
operation in SBR reactor (g VSS/L)
XVSS2 = Volatile solid concentration at the end of cycle operation in
SBR reactor (g VSS/L)
Ve = Working volume of the SBR system
tc = Cycle time of SBR operation (d)
Xe = Effluent volatile solid concentration (g VSS/ L)
Ve = Effluent volume in SBR operating cycle (L)
Equation 6.5 is simplified from Equation 6.7 when the reactor system reached
steady state and the biomass was maintained at a constant value (Chen et al., 2008b).
The Yobs can be used to describe the sludge productivity which relates to the net
sludge production.
The results in Table 6.9 show that the sludge production is inversely related
to the value of SRT as shown in Stage I to III. As SRT increased, the Yobs, value
decreased. Since the biomass activity is reduced when the SRT increased, this has
caused the reduction of the biomass yield. The results obtained from this experiment
are in accordance with the ones reported by van Loosdrecht and Hence (1999). It is
well known that the net sludge production in an activated sludge system decreases
with increasing sludge age. The biokinetic parameters could give a good indication
for the system performance. It can be used as a basis for the design and product
optimization of a system reactor. The Yobs value of Stage IV to Stage V, decreased
from 0.269 to 0.217 mg VSS/ mg COD as the SRT of those stages was increased
from 70.1 ± 23.9 to 72.5 ± 23.3 d, respectively. Eventhough Stage IV and Stage V
were operated with the same HRT, the ratio of anaerobic/aerobic reaction phase was
different. It shows that when the ratio of anaerobic/aerobic time was increased, the
Yobs decreased.
The theoretical value is calculated using Equation 6.6. It is expected that
the theoretical Y value will be higher as compared to Yobs. The difference between
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the and theoretical Y value is contributed by endogenous metabolism, predation,
death and lysis process. The theoretical Y value obtained in this study shows the
same pattern as given by the Yobs. The results for the Yobs and theoretical Y value
obtained in this experiment are within the typical reported range of conventional
activated sludge system (Al-Malack, 2006 and Tchobanoglous et al., 2004).
6.6 Conclusions
i. The granular biomass concentration of the reactor system reduced as the HRT
increased which is mainly due to the reduction in the OLR. However, with
HRT of 24 hours, the biomass slightly increased when the period of anaerobic
reaction phase was longer then the aerobic reaction. The ratio of
MLVSS/MLSS increased during Stage V with anaerobic/aerobic reaction
phase was set with 17.8/5.8 hour which may be due to the increased
accumulation of inert particles within the granules. Eventhough with increase
in the HRT, the concentration of granular biomass can be improved with
increase in the anaerobic reaction time and reduction in the aerobic reaction
time as shown in Stage V.
ii. The size and the SVI of the FAnGS reduced as the HRT of the system
increased (Stage I to Stage III) due to increase in the aeration time that
resulted with the disintegration of the FAnGS. Too long aerobic reaction
times exposed the granules under prolonged starvation condition causing
instability of the granular structure that lead to disruption of the granules.
The size and the SVI value were improved with the increase in the OLR
(Stage IV). The size and the SVI were also improved with increase in the
anaerobic and reduction in the aerobic reaction phases (Stage V).
iii. The percentage of COD removal in this study was not likely affected by the
increase in the HRT which was mainly due to the decrease in the granular
biomass and OLR (Stage I to Stage III). However, the COD removal was
improved with the increase in the anaerobic reaction phase shown in Stage V
213
as compared to Stage IV. The percentage of color removal has improved
with the increase in the HRT.
iv. Stage V (17.8 and 5.8 hours of anaerobic and aerobic reaction phase,
respectively) can be considered as the best condition for the removal of color
and the organic compound since the percentage of color and COD removal
are the highest.
v. Increase in the HRT resulted with an increase in the SRT. Since the SRT is
inversely related to the μoverall, increase in the HRT will cause a reduction in
the μoverall. Increase in the HRT has caused a reduction in the bioactivity of
the granular sludge shown by the reduction of the μoverall, Yobs and Y values.
A slight increase in the SRT was observed with increase in the
anaerobic/aerobic time ratio. This has caused a reduction in the Yobs and Y
values but the μoverall is considered constant. The kd is also considered
constant throughout the experiment.
CHAPTER 7
EFFECT OF SUBSTRATE AND RIBOFLAVIN ON FACULTATIVE
ANAEROBIC GRANULAR SLUDGE
7.1 Introduction
The biodegradation process of azo dyes can be influenced by many factors.
Among them, the presence of redox mediator and the use of different compositions
as the primary substrate were identified as factors that may give effect on the rate of
dye degradation process (van der Zee et al., 2001b; Keck et al., 2002; van der Zee
and Cervantes, 2009). Despite many studies conducted on biodegradation of azo
dyes, the effectiveness of these two factors in enhancing the decolorization of textile
wastewaters is still ambiguous. Most of the study on the effect of redox mediator
focused on either single or mixed bacteria cultures in degrading the dye. Anaerobic
granular sludge has been frequently used as the source of biomass in dye degradation
under anaerobic condition while only few studies were conducted by using aerobic
biomass (Keck et al., 1997 and Kudlich et al., 1997).
Furthermore, knowledge on the effect of the redox mediator and primary
substrate concentration on the use of facultative granules for decolorization of the
azo dye through the application of experimental design is still lacking. The presence
215
of substrate in the textile wastewater will release electrons during its degradation
process. These electrons are required for the degradation of dye which will be
mediated by the redox mediator. Redox mediators are responsible in transferring the
electrons to the dye compounds. However, the understanding on the interactions
between these factors is still indistinct and need to be explored further. In this study,
the impacts of the redox mediator and primary substrate concentration were
investigated. Mixed azo dye consisted of Sumifix Black EXA, Sumifix Navy Blue
EXF and Synozol Red K-4B were again used as the model compound. A similar
substrate which has been used in studies presented in Chapters 4 through 6 was used
and riboflavin was used as the redox mediator. Riboflavin was chosen as the redox
mediator since it represents the redox active moiety in ubiquitous enzyme cofactors
such as flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD), which
have been implicated in the azo dye reduction. FMN and FAD are not practical to be
used since they are expensive biochemical materials. Riboflavin is an affordable
vitamin which could be added into the bioreactor to stimulate azo dye reduction
during anaerobic treatment (Field and Brady, 2003). This chapter presents the
findings on the study.
7.2 Materials
Most of the chemical or reagents and equipment used in this study are as
described in Section 4.2. In addition, a 150 mL serum bottle with rubber stopper was
used as a batch test in this experiment. A sealer (E-Z Crimper-20 mm) was used to
seal the serum bottle with a metal cap. A 30 mL syringe with needle was used for
sampling purposes. Synthetic textile dyeing wastewater as described in Section 4.2.1
was used as the media solution of the experiment. The concentration of the mixed
dyes in the wastewater was prepared at 100 mg/L. Riboflavin was obtained from
Sigma (St Louis, USA). Substrate and riboflavin concentration were varied
according to the experimental design.
216
7.2.1 Granular Precursor
A range of 0.3 to 0.6 mm of FAnGS biomass with an average granular size of
0.45 mm was used in this experiment. The FAnGS used in this study was developed
as presented in Chapter 4. About 10% (v/v) of granules were inoculated into the
serum bottle containing 130 mL of synthetic medium which give about 1.94 g/L of
volatile suspended solids (VSS). The FAnGS was always kept in the synthetic
dyeing wastewater prior to conducting experiments for acclimatization purposes.
7.3 Analytical Methods
7.3.1 Chemical Oxygen Demand and Color Removal
In this study, the removal efficiency of COD through the batch experiment
using FAnGS was conducted according to the analytical methods described in
Section 4.3.4.2. The percentage of color removal was estimated quantitatively by
measuring the absorbance reduction at the maximum absorbance wavelength of the
dyes used in the experiment. The individual dye was measured at 600 and 542 nm
wavelength using a UV-visible spectrophotometer (Shimadzdu Model UV-2450).
Distilled water was used as the blank.
217
7.4 Experimental Procedures
Screening of redox mediator was conducted as the preliminary test in order to
determine the suitable range of redox concentration to be used in the experimental
design study. Figure 7.1 show the experimental work carried out in this study.
Figure 7.1 Experimental works for the investigation on the effect of substrate concentration and redox mediator on COD and color removal via the aid of experimental design
218
7.4.1 Screening for Concentration of Redox Mediator
The screening process was conducted using batch experiment. Forty-five
(45) mL of synthetic dye wastewater with the organic substrate concentration at 1500
mg/L and dye concentration of 100 mg/L were filled in a 50 mL centrifuge tube. Ten
(10) % (v/v) of granular biomass was added into the synthetic media. Different
concentrations of riboflavin ranging from 0.001 to 0.01 mM were added in each of
the centrifuge tube. A half (0.5) mL of sample was taken hourly for an interval of 24
hours and measured for the color reduction. The experiments were conducted at
room temperature with the samples kept under static condition. The samples were
centrifuged at 5,000 rpm for five minutes to pellet down any suspended particles
prior to color measurement.
7.4.2 Batch Experiment for Chemical Oxygen Demand and Color Removal
Using Facultative Anaerobic Granular Sludge
Batch experiments were conducted to study the effects of substrate and redox
mediator concentration on COD and color removal under anaerobic and aerobic
conditions. One hundred and thirty (130) mL of synthetic wastewater was added to
the serum bottle. Then, 10% (v/v) of granular biomass was added to the serum
bottle. The bottles were capped with rubber stoppers and then were sealed with
metal caps using a sealer (E-Z Crimper).
The anaerobic condition was established by purging the headspace with
N2/CO2 (80%/20%) for 2 min into the serum bottle. By doing this, the concentration
of DO was kept lower than 0.2 mg/L. After purging, the redox mediator was added
into the wastewater sample in the serum bottle. The concentration of redox mediator
219
and substrate in the synthetic wastewater were based on the concentration set by the
experimental design as will be explained in the next section. During the anaerobic
reaction phase, the serum bottles were placed on an orbital shaker and were shaken at
a speed of 100 rpm for continuous contact of granules with the synthetic wastewater.
The experiment under anaerobic condition was conducted for twelve hours. After
the anaerobic reaction phase was completed, the samples were then exposed to the
aeration phase by supplying air bubbles at an air flow rate of 10 ml/h for another
twelve hours.
Samples were taken every two hours during the 24 hours of the reaction
period. During the anaerobic phase, the samples were taken using a 30 mL syringe
with needle. To avoid introducing oxygen into the reactor during the anaerobic
phase, the needle was plugged through the rubber cap after removing the top part of
the metal cap.
7.4.3 2-Level Factorial and Central Composite Design Experiment
Two-level factorial and Central Composite Experimental design was used in
this study. The concentrations of substrates and redox mediator were used as the
variables while the removal of COD and color were the responses. The
concentrations of the substrate and redox mediator were in the range of 500 to 3000
mg/L and 1 to 150 μM, respectively. MinitabTM Statistical Software was used for the
design and analysis of the factorial experiment while Design Expert Statistical
Software was used for the CCD experiment.
Table 7.1 shows the experimental runs used in the factorial design and CCD
in actual and coded values. The two variables of 2-level factorial design comprised
220
of four experimental runs (CC01 to CC04). Since the experiments were conducted in
duplicate, a total of eight runs were carried out. The results provide the linear effect
as well as the interaction effects of the variables. Star point (CC05 to CC08) and
centre point (CC09 to CC013) values were added for the CCD experiments that
present any non-linearity effect of the variables in the reaction process.
Table 7.1: Experimental runs of factorial design and CCD in actual and coded
values (not in random order)
Run Factor 1 Factor 2
A: Substrate B: Riboflavin
CC01 -1 (866.1) -1 (22.8)
CC02 1 (2633.8) -1 (22.8)
CC03 -1 (866.1) 1 (128.2)
CC04 1 (2633.8) 1 (128.2)
CC05 -1.414 (500) 0 (75.5)
CC06 1.414 (3000) 0 (75.5)
CC07 0 (1750) -1.414 (1)
CC08 0 (1750) 1.414 (150)
CC09 0 (1750) 0 (75.5)
CC10 0 (1750) 0 (75.5)
CC11 0 (1750) 0 (75.5)
CC12 0 (1750) 0 (75.5)
CC13 0 (1750) 0 (75.5)
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7.5 Results and Discussion
7.5.1 Screening for Redox Concentration
The percentage of color removal from the mixed dye at different
concentrations of riboflavin is shown in Figure 7.2. As indicated earlier, the mixed
dye consisted of Sumifix Black EXA, Synozol Red K-4B and Sumifix Navy Blue
EXF with the highest peak measured at wavelength of 480, 542 and 600 nm,
respectively. Since riboflavin is a colored compound that has a peak wavelength
absorbance at 475 nm, the reading for Sumifix Black EXA dye at 480 nm
wavelength has been interfered by the color of the riboflavin. Hence, only the results
of Sumifix Navy Blue EXF and Synozol Red K-4B are shown in the figure.
As shown in the figure, a very low concentration of riboflavin is capable of
increasing the color removal of the mixed dye. Up to riboflavin concentration of 0.1
mM, increase in its concentration, increased the extent of color removal. The highest
color removal (80%) was achieved when the concentration of redox mediator was at
0.1 mM. The color removal was then reduced when the concentration of the redox
mediator was further increased. A minimum percentage of color removal was
observed at the redox mediator dose of 1 mM. In the control sample where there was
no addition of riboflavin, the color reduction can be considered as reasonably high
with about 60% removal. This may be due to the presence of other accelerating dye
degradation agent that may naturally have been produced in the sludge or in the
granules (Dos Santos et al., 2006b).
Different concentrations of redox mediator have been used to accelerate the
dye degradation process in previous studies. For example, 0.012 -0.024 mM and 1
mM of AQDS has been used by Dos Santos et al. (2004) in batch experiments. van
der Zee et al. (2001a) has used AQDS at the concentration of 19 μM to 155 μM in a
222
continuous experiment for the degradation of the Reactive Red 2 by anaerobic
granular sludge in an UASB system. Riboflavin in the range between 0.091 to 0.546
mM was used by Field and Brady (2003) in enhancing the reduction of Mordant
Yellow 10 by anaerobic granular sludge in batch experiments.
Figure 7.2: Color removal at different concentrations of riboflavin. Absorbance at
600 nm (♦), absorbance at 542 nm (□)
Most of the previous experiments were conducted at very low concentrations
of redox mediator. A small amount of redox mediator is capable of achieving high
color removal. Furthermore, the results for color removal would be interfered when
high concentrations of redox mediator are applied since most of the redox mediators
are colored chemical compounds. Only few types of redox mediator are in the form
of colorless compounds such as methyl vilogen and NAD. However, as mentioned
earlier, the application of NAD is limited due to its economical constraint.
223
As a result of the screening study, a further study on the effect of redox
mediator on dye degradation was conducted at low concentrations of 1 to 150 μM of
riboflavin.
7.5.2 Factorial Design Analysis of Chemical Oxygen Demand Removal
The experimental results for the factorial runs are given in Table 7.2. The
overall COD removal was higher under the anaerobic reaction phase reaching almost
80% as compared to 68% of the highest COD removal for the aerobic reaction phase.
The lowest percentage of COD removal of anaerobic and aerobic reaction conditions
was about 24% and 29%, respectively. The percentage of total COD removal varied
between 66 to 86%.
The summary table of the ANOVA that shows the main and two-way
interaction effect is given in Table 7.3. Figure 7.3 shows the Pareto chart generated
by MINITAB™ for anaerobic, aerobic and total COD removal. The detailed results
of the analysis constructed by the software are given in Appendices F1 to F3. The
analyses show that at 90% confidence level (P-value less than 0.1), substrate and
riboflavin have significant main and interactive effects on aerobic COD removal and
total COD removal. As for COD removal during the anaerobic stage, only substrate
was found to have a significant effect.
224
Table 7.2: Experimental results for factorial design analysis
Run Anaerobic COD removal
Aerobic COD removal
Total COD removal
CC01 32.9 67.8 78.4
CC02 79.4 29.7 85.5
CC03 28.8 52.2 66.0
CC04 78.4 27.9 84.4
CC05 25.9 68.3 76.5
CC06 78.9 28.8 85.0
CC07 24.4 55.3 66.2
CC08 77.1 37.5 85.7
Table 7.3: The P-values of the estimated main and interaction effects of substrates
and riboflavin for the percentage of COD removal
Effect Anaerobic
COD removal
SignificantaAerobic
COD removal
SignificantaTotal COD
removal Significanta
Main
Substrate < 0.0001 Yes < 0.0001 Yes < 0.0001 Yes
Riboflavin 0.373 No 0.001 Yes < 0.0001 Yes
2-way interaction
Substrate× Riboflavin 0.755 No 0.002 Yes 0.001 Yes
a significant at α = 0.1
225
Figure 7.3 The Pareto chart of COD removal for (a) anaerobic, (b) aerobic and (c)
total removal (A: substrate; B: riboflavin; α: 0.1)
226
7.5.2.1 Factorial Analysis: The Main Effect of Substrate on Chemical Oxygen
Demand Removal
With respect to the experimental conditions used in this study, the substrate
concentration shows highly significant effects for both the anaerobic and aerobic
reaction phases with P-value of less than 0.0001. The effect was also significant for
total COD removal where the P-value was also less than 0.0001. However, the
direction of the effect was opposite when compared between the anaerobic and
aerobic reaction phases.
During the anaerobic reaction phase, the estimated effect was +50.45 while
during the aerobic reaction phase the value was -32.45. This means during the
anaerobic reaction phase, the percentage of COD removal increased as the substrate
was increased. Under the aerobic reaction phase, the percentage of COD removal
decreased with increase in substrate concentration. Substrate concentration was
found to cause a positive effect on total COD removal with an estimated effect of
13.4.
When the substrate concentrations were increased, more food was supplied to
the microorganisms. Such condition was postulated to cause increment in the
concentration of the suspended biomass in the serum bottle which leads to increase in
the percentage of COD removal under anaerobic condition. Increase in COD
removal from 73% to 88% has been also reported by Siman et al. (2004) when the
OLR was increased from 1.5 to 5.4 g COD/L·day in an anaerobic sequencing biofilm
batch reactor system.
The negative effect of substrate concentration on aerobic COD removal can
be due to several reasons. Since the experiment for aerobic reaction phase was a
continuation from the anaerobic reaction phase, the same sample was used for the
227
aerobic phase after the anaerobic reaction was completed. Since most of the
substrate has been degraded during anaerobic reaction, so the amount of substrate left
for aerobic reaction was lesser. In other words, there was only a little amount of
substrate left to be used during the aerobic phase eventhough the initial concentration
of substrate at the start of the experiment was high.
Another possible reason for the lower percentage of COD removal as the
substrate increased under aerobic reaction phase was insufficient oxygen supplied.
As the substrate increased, the demand for oxygen is expected to be greater in order
to degrade more substrate. Since the rate of the aeration was not increased
throughout the experiment, this might contribute to the decreasing COD removal
since the supply of oxygen is not enough to oxidize the increasing concentration of
substrate.
Another possibility might be due to the uncleavage dye compounds and the
accumulation of non-mineralized or slow degradation of amines compounds as
reported by Tan and Field (2000). It can be due to the toxic effect of the
accumulation of aromatic amines, the byproduct produced during the increasing
anaerobic degradation of the mixed dye presence in the synthetic media. Aromatic
amines are known as toxic compounds (Weisburger, 2002) which may affect the
microbial degradation activity of the aerobic microorganisms localized at the outer
layer of the granules.
228
7.5.2.2 Factorial Analysis: The Main Effect of Riboflavin on Chemical Oxygen
Demand Removal
The concentration of riboflavin did not have a significant effect on the
percentage of COD removal for the anaerobic reaction phase since the P-value was
0.373. As the concentration of redox mediator increased, more of the N=N bond are
expected to be cleaved (Field and Brady, 2003 and Mendez-Paz et al., 2005) and
more amines were released during the anaerobic reaction phase. Since amine could
not be further degraded under anaerobic condition, the concentration of COD did not
reduce significantly.
However, for the aerobic reaction phase, the redox mediator has a significant
effect on the percentage of COD removal with the P-value of 0.001. The percentage
of COD removal during the aerobic reaction phase was reduced when the
concentration of redox mediator was increased with the estimated effect of -7.95. As
stated earlier, under anaerobic condition, when the redox mediator was increased,
more cleavage of the azo bond will take place and hence increased the release of the
amines. Since the aeration rate was kept constant throughout the experiment, as the
concentration of redox mediator increased, the oxygen supply was not enough to
degrade the increasing concentration of the amines thus causing reduction in COD
removal. The presence of high concentrations of amines may also impose a toxicity
effect to the microorganisms in the serum bottle that lead to reduction in aerobic
COD removal.
The effect of increasing redox mediator on the percentage of total COD
removal was also significant where the P-value is less than 0.0001 and the estimated
effect was -5.78. This means, as the concentration of the redox mediator increased,
the percentage of total COD removal was reduced. Based on the value of the
estimated effect, it shows that the significant effect of redox mediator in total COD
removal was mainly contributed by the aerobic reaction phase. The reasoning for the
229
reduction of the total COD removal was the same as explained for the reduced COD
removal under aerobic condition. Figure 7.4 shows the main effect of substrate and
riboflavin during the anaerobic and aerobic reaction phases and also for the total
COD removal.
Figure 7.4 Main effect plot of substrate and riboflavin for (a) anaerobic, (b) aerobic
and (c) total COD removal
230
7.5.2.3 Factorial Analysis: The Interaction Effect of Substrate and Riboflavin
on Chemical Oxygen Demand Removal
The interaction effect between substrate and riboflavin was insignificant for
percentage of COD removal in the anaerobic reaction phase but was significant for
the aerobic reaction phase and the total COD removal with P-values of 0.02 and
0.001, respectively.
At low substrate concentration, increase in riboflavin is expected to result in
increase in the production of amines. Amines that can be further degraded under
aerobic condition contributed to the increased concentration of COD. Since there
was no increment on the aeration rate, the amount of oxygen may not be enough for
the microorganisms to degrade the increasing COD level resulting in decreasing
percentage of COD removal as the riboflavin increased. Due to the same reason,
increase in the concentration of riboflavin has also caused a reduction but only with a
slight decrease in the percentage of COD removal at high concentrations of substrate.
Higher percentages of total COD removal were observed at higher
concentrations of substrate (2633.88 mg COD/L) as compared to the lower
concentrations of substrate (866.12 mg COD/L) at low concentrations of riboflavin.
However, increase of riboflavin at higher substrate levels has barely caused any
changes on the percentage of total COD removal as the riboflavin itself has caused
an increase in the COD value (through the formation of amines). The high removal
percentage during high substrate concentrations was due to the anaerobic degradation
process.
The reduction of the total COD removal was observed at low substrate
concentration as the riboflavin increased, mainly contributed by the increase in the
concentration of the aromatic amines and the insufficiency of oxygen supply. Figure
231
7.5 shows the interaction plot of substrate and riboflavin for anaerobic, aerobic and
total COD removal.
Figure 7.5 Interaction plot for the percentage of COD removal for (a) anaerobic, (b) aerobic and (c) total removal (Substrate: ---- 2633.88 m/L; ___ 866.12 mg/L; ● Centre point)
•
232
7.5.3 Central Composite Design Analysis of Chemical Oxygen Demand
Removal
Table 7.4 shows the experimental results of the CCD for COD removal. The
analysis was carried out using full quadratic terms including linear, square and
interaction. The results of the ANOVA for COD percentage removal at different
reaction conditions are shown in Table 7.5. The detailed results for estimated
regression coefficient and ANOVA table are given in Appendices F4 to F6.
Based on the P-value, the results showed that only the linear term of substrate
(P-value 0.094) of the anaerobic reaction phase and the square term of substrate (P-
value of 0.045) of the aerobic reaction phase are significant. This shows that
substrate concentration only caused a linear effect on the COD removal during the
anaerobic reaction phase and has a non-linear effect during the aerobic reaction
phase. Both the models of COD removal under anaerobic and aerobic reaction phases
are insignificant with R-squared values of 46% and 47.9%, respectively.
Figure 7.6 shows the contour and surface plot of the significance defined
model for total COD removal. The figure clearly shows that as the substrate
concentration increases, the percentage of COD removal also increases. With respect
to the effect of riboflavin, at low substrate concentrations, increase in the
concentration of riboflavin has caused a reduction in the total COD removal from
71.2% to 61.8%. At high concentrations of substrate, increase in the concentration of
riboflavin have caused a slight increase in the total COD removal from 82.3 to
84.1%. The highest percentage removal of total COD was observed at substrate and
riboflavin concentrations of 2165.8 mg/L and 23.4 μM, respectively with 84.5%
removal.
233
Table 7.4: Experimental results for CCD analysis
Run Anaerobic reaction phase
Aerobic reaction phase Total COD removal
CC01 32.9 67.8 78.4
CC02 79.4 29.7 85.5
CC03 28.8 52.2 66.0
CC04 78.4 27.9 84.4
CC05 23.9 32.0 48.2
CC06 31.6 66.8 77.3
CC07 24.4 73.7 80.1
CC08 21.3 73.1 78.8
CC09 22.1 75.4 80.8
CC10 25.6 72.5 79.5
CC11 24.3 73.7 80.1
CC12 23.9 72.6 79.2
CC13 24.0 73.6 79.9
The best statistical model that can be used to represent the total COD removal
for this process based on the experimental conditions in this study obtained from the
response surface analysis is:
Total COD removal = +51.26 + 0.04 × A - 0.23 × B + 6.07×10-5 × AB (7.1)
- 8.58×10-6 × A2 + 5.92×10-4 × B2
where:
A = Substrate in mg/L
B = Ribofalvin in uM
234
Table 7.5: Summary of the P-value of the response surface modeling analysis
Term
Anaerobic COD removal
Aerobic COD removal
Total COD removal
The P-valuea
Substrate 0.094 0.799 0.002
Riboflavin 0.868 0.725 0.295
Substrate×Substrate 0.249 0.045 0.008
Riboflavin×Riboflavin 0.385 0.538 0.396
Substrate×Riboflavin 0.939 0.707 0.277
R-squared value 46% 47.90% 85.80%
Lack of Fit <0.0001 <0.0001 <0.0001
0.01 – 0.04: Highly significant; 0.05 – 0.1: significant; 0.1 – 0.2: less significant; < 0.2: insignificant (Vecchio,
1997)
7.5.4 Factorial Design Analysis of Color Removal
In the investigation of color removal, focus was made on the anaerobic
reaction phase only. This is because when the measurements for color removal were
made during the aerobic reaction phase, negative responses were obtained. The
negative response was due to the resurgence of color in the samples under the
aerobic reaction phase. As mentioned earlier, since the absorbance for Sumifix
Black dye is at 480 nm which is near to the absorbance of the riboflavin which is at
475 nm, the measurement for Sumifix Black dye was interfered by the color of the
riboflavin. Hence, further analysis and discussion only focused on Sumifix Navy
Blue EXE and Synozol Red K-4B only.
235
(b)
Figure 7.6 The relationship between substrate, riboflavin and percentage of total
COD removal after 24 hours of experimental run, (a) Contour plot and (b) Responses
surface plot
(a)
236
Table 7.6 shows the experimental results for factorial runs for the percentage
of color removal measured at absorbance 600 nm and 542 nm for Sumifix Navy Blue
EXF and Synozol Red K-4B, respectively. Table 7.7 shows the summary of the
ANOVA of the factorial runs. The detailed results for the table of estimated effect
and coefficient and ANOVA table are given in Appendices G1 to G4. The Pareto
chart that shows the effects of substrate and riboflavin concentration on the
percentage of color removal of Sumifix Navy Blue EXF and Synozol Red K-4B at
five and twelve hours of the experimental runs are given in Figures 7.7 and 7.8,
respectively. The results of color removal were investigated at the early and end
stages of the experiment in order to observe any time dependent effect among these
variables. Figure 7.8 shows both variables give significant effect on color removal
except for the interaction effect at twelve hours experiment for Sumifix Navy Blue
EXF. Figure 7.8 demonstrates that both variables are significant at both five and
twelve hour experiments for Synozol Red K-4B.
Table 7.6: Experimental results for factorial design analysis
Run Sumifix Navy Blue EXF Synozol Red K-4B
5 hours 12 hours 5 hours 12 hours
CC01 74.7 77.0 68.5 75.2
CC02 80.0 76.7 77.5 75.8
CC03 77.2 83.9 62.3 82.3
CC04 83.5 81.0 78.0 80.0
CC05 75.0 78.8 69.3 76.0
CC06 79.7 76.4 76.1 76.0
CC07 76.5 84.0 62.8 82.5
CC08 84.0 82.0 79.4 81.0
237
Table 7.7: The P-values of the estimated main and interaction effects of variables
substrates and redox mediator for the percentage of color removal
Effect
Sumifix Navy Blue EXF Synozol Red K-4B
5 hours Significanta 12 hours Significanta 5 hours Significanta 12
hours Significanta
Main effect
Substrate <0.0001 Yes 0.022 Yes <0.0001 Yes 0.071 Yes
Riboflavin <0.0001 Yes <0.0001 Yes 0.015 Yes <0.0001 Yes
Interaction effect
Substrate× Riboflavin 0.017 Yes 0.351 No 0.002 Yes 0.028 Yes
asignificant at α = 0.1
7.5.4.1 Factorial Analysis: Main Effect of Substrate on Color Removal
The results of the factorial design analysis showed that at five hours after the
experiment started, the substrate was observed to give a significant main effect for
both dyes; both with P-values of less than 0.0001 and an estimated effect of +5.95
and +12.02 for Sumifix Navy Blue EXF and Synozol Red K-4B, respectively. The
percentage of color removal increased as the concentration of substrate was increased
from 866.1 mg/L to 2633.9 mg/L. The percentage of color removal was a bit higher
by about 4% for Sumifix Navy Blue EXF as compared to Synozol Red K-4B.
238
Figure 7.7 Pareto chart of Sumifix Navy Blue EXF removal at (a) 5 and (b) 12
hours (α: 0.1; A: Substrate; B: Riboflavin)
239
Figure 7.8 Pareto chart of Synozol Red K-4B removal at (a) 5 and (b) 12 hours
(α: 0.1; A: Substrate; B: Riboflavin)
240
The effect of substrate concentration in this study is in accordance with the
results reported by other researchers. It has been reported that the percentage of
color removal increased as the substrate supplement to the system was increased (Fu
et al., 2002 and Sirianuntapiboon and Srisornsak, 2007). Dafale et al. (2008)
measured the kinetic constant of dye degradation of azo dyes with and without the
presence of substrate glucose and reported that the kinetic constant was increased
fivefold for the decolorization of Remazol Black B with the addition of 2 g/L of
glucose. The use of substrate is essential in obtaining a good percentage of dye
degradation (Delee et al., 1998 and Ozsoy et al., 2005). It was reported that the
azoreductase enzyme system is responsible for the decolorization of dyes by bacteria
with the presence of substrate under anaerobic conditions (Yoo, 2002).
The presence of external carbon sources will donate or produce electrons
after being oxidized under the catabolism process. The released electrons will be
used for the formation of the reducing equivalent or the reduced co-factor (Carliell et
al., 1995). These reducing equivalent or reduced co-factor will involve in the
electron transfer to the N=N bond of dye chemical structure and resulted with the
cleavage of the double bond. Therefore, when the concentration of substrate is
increased, more electrons will be donated to the azo bond and this improved the
percentage of color removal.
The effect of substrate concentration on color removal was different at twelve
hours for both dyes. As the effect of substrate was found to cause a positive effect at
five hours reaction, the effect was negative at twelve hours reaction (i.e -1.9 for
Sumifix Navy Blue EXF and -0.8 for Synozol Red K-4B).
The negative effect is probably due to the accumulation of amines as they are
colored compounds which can cause different color intensity. The presence of these
colored amines may not affect the color removal at the early stage of the experiment
since the intensity of color due to the presence of dye was still high and the amines
241
were still relatively low in concentration. However, as the degradation of the dyes
continued and reached the maximum removal at the later stage of the experiment
(twelve hours), more amines are being produced and accumulated. Hence, the color
of the wastewater was no longer due to the dye compounds alone. At twelve hours,
the wastewater in the serum bottle did not become colorless but has turned greenish.
This greenish color may be due to the accumulation of the aromatic amines and
resulted with a slight increase in color intensity of the wastewater.
7.5.4.2 Factorial Design Analysis: Main Effect of Riboflavin on Color Removal
In this study, the results showed that the concentration of riboflavin has a
significant effect on the color removal of the mixed azo dyes. The P-values for the
effect of riboflavin on the color removal of Sumifix Navy Blue EXF is less than
0.0001 for both five and twelve hours of the experimental period. The color removal
of Synozol Red K-4B is also significant at five and twelve hours of the experiment
with the P-values of 0.015 and less than 0.0001, respectively.
At twelve hours of the experiment, the effect of riboflavin shows nearly the
same positive magnitude for both types of dyes with an estimated effect of +5.5 and
+5.7 for Sumifix Navy Blue EXF and Synozol Red K-4B, respectively. This mean,
at twelve hours of the experimental run, as the concentration of riboflavin increased,
the percentage of color removal for both types of dyes were also increased.
The results obtained at five hours of the experiment showed that the
estimated effect for Sumifix Navy Blue EXF removal is positive with magnitude of
2.95. However, the result obtained for the removal of Synozol Red K-4B shows a
negative value of the estimated effect (-2.225). It shows that the addition of
242
riboflavin has caused a reduction in the percentage of Synozol Red K-4B removal at
the earlier stage of the degradation process. It is possible that Synozol Red K-4B has
a more complex chemical structure as compared to Sumifix Navy Blue EXF. Due to
the complexity, the degradation of Synozol Red K-4B at five hours of the experiment
was lesser than Sumifix Navy Blue. As discussed earlier in Section 7.4.1, riboflavin
is a colored chemical compound; so, increase in the concentration of riboflavin will
add color to the synthetic media. While the color of Synozol Red K-4B is not being
sufficiently removed at five hours of the experiment, the addition of riboflavin has
caused the overall color reduction for Synozol Red K-4B to decrease.
The addition of redox mediator has been reported to accelerate the transfer of
electron from a primary electron donor to the azo dye bond that acted as the terminal
electron acceptor (Kudlich et al., 1997 and Keck et al., 2002). Besides, the presence
of redox mediator is also capable of minimizing the steric hindrance due to high
density of the electrons in the azo bond dye molecule structure (Moir et al., 2001)
and cause decreasing energy of the chemical reaction (Dos Santos et al, 2007). This
would help to increase the dye degradation process.
Figures 7.9 and 7.10 show the main effect of substrate and riboflavin for both
dyes at both 5 and 12 hours of anaerobic experimental condition.
7.5.4.3 Factorial Analysis: Interaction Effect
The concentration of substrate and riboflavin show a significant interaction
effect for both dyes. However, for the removal of Sumifix Navy Blue EXF, the
effect was not significant where the P-value is 0.351 at twelve hours of the
experimental runs. The interaction effect at five hours for the removal of Sumifix
243
Navy Blue EXF gave the P-value of 0.017. Since Sumifix Navy Blue EXF is
presumed to have a simpler molecular structure, the transfer of the reducing
equivalence is not a limiting factor. The transfer of electron to the N=N bond of the
Sumifix Navy Blue EXF was not so much dependent on the presence of the electron
transfer. Furthermore, the amount of reducing equivalence produced due to the
degradation of substrate could be high and easily being transferred to the dyes. Such
condition may result with non-interaction effect between the two variables at twelve
hours of the experimental conditions. The biodegradation of dye is a time dependent
degradation process. This has been proven in the previous chapter (Chapter 6) where
the percentage of color removal increase at the HRT increased. This could be
explained by the increasing magnitude effect on the color removal between five and
twelve hours of the reaction process.
Figure 7.9 Main effect plot of substrate and riboflavin on the color removal of
Sumifix Navy Blue EXF at (a) 5 and (b) 12 hours of experiment under anaerobic
condition
244
Figure 7.10 Main effect plot of substrate and riboflavin on color removal of
Synozol Red K-4B at (a) 5 and (b) 12 hours of experiment under anaerobic condition
The P-values of the interaction effect at five hours and twelve hours are 0.002
and 0.028, respectively, indicating significant interaction results. As for the Synozol
Red K-4B, the dye is assumed to have more complex molecule structure as compared
to Sumifix Navy Blue EXF. This has caused a reduction in color removal with the
addition of riboflavin at the early stage (i.e five hours). At higher substrate
concentration, more reducing equivalence would be released and this could be seen
with a slight increase in the percentage of color removal for Synozol Red K-4B. At
twelve hours, the presence of riboflavin has significantly accelerated the percentage
of Synozol Red K-4B and Sumific Navy Bleu EXF removals at both low and high
substrate concentrations.
245
The interaction effect for Sumifix Navy Blue EXF and Synozol Red K-4B at
five and twelve hours of the experimental conditions are given in Figures 7.11 and
7.12, respectively.
Figure 7.11 Interaction of variables substrate and riboflavin for Sumifix Navy Blue EXF at (a) 5 and (b) 12 hours of the experimental conditions (Substrate: ---- 2366.88 m/L; ____ 866.12 mg/L; ● Centre point)
Figure 7.12 Interaction of variables substrate and riboflavin for Synozol Red K-4B
at (a) 5 and (b) 12 hours of the experimental conditions (Substrate: ---- 2366.88 m/L;
____ 866.12 mg/L; • Centre point)
246
7.5.5 Central Composite Design Analysis of Color Removal
The color removal was measured at five and twelve hours of the experimental
conditions for both type of dyes and the results are given in Table 7.8. The analysis
was carried out using full quadratic terms including linear, square and interaction.
The results of the ANOVA for color removal at different reaction conditions are
shown in Table 7.9. The detailed results for estimated regression coefficient and
ANOVA table are given in Appendices G5 to G9.
Table 7.8: Experimental results for CCD analysis
Run Sumifix Navy Blue EXF Synozol Red K-4B
5 hours 12 hours 5 hours 12 hours
CC01 74.7 77 68.5 75.2
CC02 80.0 76.7 77.5 75.8
CC03 77.2 83.9 62.3 82.3
CC04 83.5 81.0 78.0 80.0
CC05 79.8 86.9 66.5 85.5
CC06 82.3 81.8 73.9 81.1
CC07 78.9 71.8 68.8 71.3
CC08 83.3 81.4 82.3 79.8
CC09 82.8 79.5 76.2 78.1
CC10 82.4 80.9 79.7 80.1
CC11 87.3 85.9 82.5 83.9
CC12 83.2 81.5 79.1 80.5
CC13 85.8 79.8 79.8 79.1
247
Table 7.9: Summary of the P-value of the response surface modeling analysis
Term
Sumifix Navy Blue EXF Synozol Red K-4B
The P-value
5 hours
12 hours 5 hours 12 hours Full Quadratic
terms
Linear + Square
Substrate 0.0512 0.0369 0.1221 0.0219 0.1769
Riboflavin 0.0998 0.078 0.0041 0.3008 0.0031
Substrate2 0.0396 0.0275 0.1717 0.0177 0.1322
Riboflavin2 0.0413 0.0289 0.0121 0.1988 0.0069
Substrate× Riboflavin 0.8326 - 0.5543 0.4552 0.4624
R-squared value 74.36% 74.19% 83.49% 75.04% 85.59%
Lack of Fit (LOFT) 0.3865 0.5007 0.8885 0.0456 0.7938
a0.01 – 0.04: Highly significant; 0.05 – 0.1: significant; 0.1 – 0.2: less significant; < 0.2: insignificant (Vecchio,
1997)
Statistical models were developed to relate the concentration of substrate and
redox mediator with color removal. Since the effects of both variables are time
dependent, the models were developed at five and twelve hours of the experimental
condition. The modeling attempts were developed with the aid of Design Expert 7P.
Based on the P-value, the results associated with substrate concentration were
significant at five hours of the experiment and became trivial at twelve hours. The
results showed the same patterns for both linear and square terms. With regard to the
248
effect of redox mediator (riboflavin), the variable shows a significant effect for the
color removal of Sumifix Navy Blue EXF at both five and twelve hours of the
experimental conditions for both linear and square terms.
The color removal of Synozol Red K-4B for the effect of riboflavin was
opposite as compared to the effect of substrate concentrations. The effect of the
redox mediator was insignificant at five hours but became significant at twelve hours
for both linear and square terms. The effect of substrate and redox mediator with
respect to the interaction terms for Sumifix Navy Blue EXF and Synozol Red K-4B
were all insignificant for both five and twelve hours. The R-squared values for all
models were in the acceptable ranges (74-86%). For the color removal of Sumifix
Navy Blue EXF at both five and twelve hours of the experiment, the P-values for the
LOFT was insignificant with the value of 0.3865 and 0.8885, respectively.
For the color removal of Synozol Red K-4B, the P-value of the LOFT is only
insignificant at twelve hours (P-value 0.7938) and significant (P-value 0.0456) at
five hours of the experimental conditions. The significant P-value for the LOFT
implies that the predictive understanding of the model is not statistically accurate and
that the process appears to be too complex to model.
An attempt was made in order to improve the statistical model by removing
all the insignificant terms for the full quadratic terms. However, the insignificant
terms were only removed for the five hours reaction phase of Sumifix Navy Blue
EXF. Removing the interaction term has improved the statistical model since the P-
value of the LOFT has increased to 0.5007 as compared to 0.3865 for the full
quadratic term model. All the insignificant terms for Synozol Red K-4B and the
twelve hours reaction phase of Sumifix Navy Blue EXF were unchanged since
removing those insignificant terms will cause reduction for the R-squared and P-
value of the LOFT.
249
The best statistical models that can be used to represent the color removal
process based on the experimental conditions in this study as attained from the CCD
experimental design analysis are given in Table 7.10. The mathematical model of
Sumifix Navy Blue EXF for five hour reaction phase was generated based on the
reduced quadratic model.
Table 7.10: Mathematical models in terms of actual values
Dye Time (hours) Statistical model
Sumifix Navy
Blue EXF
5
= 65.38 + 0.01×A + 0.15×B - 2.79 × 10-6×A2
– 7.77× 10-4×B2
12
= 77.1 – 5.8 × 10-3×A + 0.23×B + 1.54 × 10-6×A2
– 9.61× 10-4×B2 – 1.4×A.B
Synozol Red K-
4B
5
= 49.0 + 0.02×A + 0.09×B – 6.34 × 10-6×A2
– 8.21× 10-4×B2 + 3.6×A.B
12
= 75.3 – 5.34 × 10-3×A + 0.22×B + 1.54 × 10-6×A2
– 9.62×B2 – 1.56 × 10-5×A.B
A: Substrate (mg/L); B: Riboflavin(μM)
The predicted versus actual plots for the COD removal for Sumifix Navy
Blue EXF and Synozol Red K-4B are shown in Figures 7.13 to 7.14. The observed
points of the plots reveal that the actual values can be considered distributed
relatively near to the straight line for the color removal of dyes at both five and
twelve hours of the experimental conditions. The predicted and actual values
obtained from this experiment are considered to be fit.
250
Actual
Pred
icte
d
74.70
77.85
81.00
84.15
87.30
74.70 77.85 81.00 84.15 87.30
Actual
Pred
icte
d
71.80
75.58
79.35
83.13
86.90
71.80 75.58 79.35 83.13 86.90
Figure 7.13: Predicted versus actual data for Sumifix Navy Blue EXF removal at (a)
5 hours and (b) 12 hours
(b)
(a)
251
Actual
Pred
icte
d
62.30
67.35
72.40
77.45
82.50
62.30 67.35 72.40 77.45 82.50
Actual
Pred
icte
d
70.88
74.53
78.19
81.84
85.50
70.88 74.53 78.19 81.84 85.50
Figure 7.14 Predicted versus actual data for Synozol Red K-4B removal at (a) 5
hours and (b) 12 hours
(a)
(b)
252
The response surface and contour plots based on the significant model are
given in Figures 7.15 to 7.18. The response surface and contour plots show that the
reactions that took placed in the experiment changed with time with respect to the
observed variables for both dyes. The reactions were also different as the structure
of the dyes differed. Figure 7.15 shows the surface plot and response surface plots of
substrate and riboflavin for the percentage of Sumifix Navy Blue EXF at five hours
reaction time with a reduced quadratic model. The response was found to be a
symmetrical mound shape. The maximum prediction of the percentage of color
removal was indicated by the surface confined in the smallest curve of the contour
diagram. The maximum percentage of color removal was 85% that occurred when
the concentration of substrate and riboflavin were at 2111.8 mg/L and 96.1 μM,
respectively.
Based on Figure 7.16, the surface plot indicating the best predicted
decolorization for Sumifix Navy Blue EXF (85.3%) at twelve hours reaction phase
was obtained with substrate and riboflavin concentrations of 866.1 mg/L and 128.2
μM, respectively. The predicted value agrees with the actual removal (82.9) which
deferred by only 3% obtained experimentally under the same condition.
The pattern of the contour and response surface plot for the color removal of
Synozol Red K-4B as shown in Figures 7.17 to 7.18 are almost the same as shown
for Sumifix Navy Blue EXF except at five hours of the reaction period. The pattern
of the plots for Synozol Red K-4B at five hours appears as an elliptical shape (Figure
7.17). At low substrate concentration, increase in the concentration of riboflavin did
not cause significant change on the color removal. At the lowest concentration of
riboflavin (22.8 mg/L), the percentage of color removal was about 67.83%. The
percentage of color removal slightly increased to 70.22% when the concentration of
riboflavin reached the centre point (75.5 μm) before reducing again to 67.83% when
the concentration increased to the highest value of riboflavin (128.2 μm). The
highest percentage of color removal was 81.12% that occurred at substrate and
riboflavin concentrations of 2238.4 mg/L and 104.07 μm, respectively.
253
(a)
866.117 1308.06 1750 2191.94 2633.8
22.8205
49.1603
75.5
101.84
128.179
Substrate
Rib
ofla
vin
77.949
79.3552
80.7615
80.7615
82.1678
83.574
55555
(b) 866.117
1308.06
1750
2191.94
2633.88
22.8205
49.1603
75.5
101.84
128.179
74.7
77.85
81
84.15
87.3
Substrate Riboflavin
Figure 7.15 (a) Contour and (b) 3D response surface plots representing relationship
between the concentrations of substrate, riboflavin and color removal of Sumifix
Navy Blue EXF removal at 5 hours (Reduced Quadratic Model)
Riboflavin (μM)
Substrate (mg/L)
Col
or R
emov
al (%
)
Rib
ofla
vin
(μM
)
Substrate (mg/L)
254
(a) 866.117 1308.06 1750 2191.94 2633.8
22.8205
49.1603
75.5
101.84
128.179
77.2823
78.8952
80.5081
82.121
83.7339
55555
(b) 866.117
1308.06
1750
2191.94
2633.88
22.8205
49.1603
75.5
101.84
128.179
71.8
75.575
79.35
83.125
86.9
A: Substrate B: Riboflavin
Figure 7.16: (a) Contour and (b) 3D response surface plots representing relationship between the concentrations of substrate, riboflavin and color removal of Sumifix Navy Blue EXF removal at 12 hours
Rib
ofla
vin
(μM
)
Substrate (mg/L)
Substrate (mg/L)
Riboflavin (μM)
Col
or R
emov
al (%
)
255
(a)
866.117 1308.06 1750 2191.94 2633.8
22.8205
49.1603
75.5
101.84
128.179
A S b t t
B: R
ibof
lavi
n
70.043
70.043
72.257774.4724
74.4724
76.687
78.9017
55555
866.117
1308.06
1750
2191.94
2633.88
22.8205
49.1603
75.5
101.84
128.179
62.3
67.35
72.4
77.45
82.5
A: Substrate B: Riboflavin
(b)
Figure 7.17: (a) Contour and (b) 3D response surface plots representing relationship between the concentrations of substrate, riboflavin and color removal of Synozol Red K-4B removal at 5 hours
Col
or r
emov
al (%
)
Rib
ofla
vin
(μM
)
Substrate (mg/L)
Substrate (mg/L)
Riboflavin (μM)
256
866.117 1308.06 1750 2191.94 2633.8
22.8205
49.1603
75.5
101.84
128.179
A: Substrate
B: R
ibof
lavi
n
76.2462
77.752
79.2578
80.7635
80.763582.2693
55555
(a)
866.117
1308.06
1750
2191.94
2633.88
22.8205
49.1603
75.5
101.84
128.179
71.3
74.85
78.4
81.95
85.5
A: Substrate B: Riboflavin
(b)
Figure 7.18: (a) Contour and (b) 3D response surface plots representing relationship between the concentrations of substrate, riboflavin and color removal of Synozol Red K-4B removal at 12 hours
Col
or R
emov
al (%
)
Rib
ofla
vin
(μM
)
Substrate (mg/L)
Substrate (mg/L) Riboflavin
(μM)
257
Figure 7.18 shows the contour and response surface plots for color removal of
Synozol Red K-4B at 12 hours reaction. At this reaction time, the plots have
changed to more of a saddler plot. The plot shows that as the concentration of redox
mediator increased, the percentages of color removal were also increased. The
highest and lowest removal percentages at this hour were predicted to be 83.8% and
74.7%. The highest color removal was observed when keeping the substrate and
riboflavin concentrations at 866.1 mg/L and 128.18 μm, respectively. The lowest
color removal occurred at riboflavin concentration of 22.8 μm but with the same
substrate concentration. This may give an indication that at this hour the
concentration of riboflavin affects the color removal as compared to the
concentration of substrate. The percentages of color removal obtained from the
actual experiment were 82.3% and 75.2% at the same substrate and riboflavin
concentrations. The percentages of color removal given by the statistical model were
only deferred by less than 1% indicating high prediction efficiency of the developed
model. Both results indicate the accuracy of the model developed.
7.6 Conclusions
i. Only a small amount of riboflavin as the redox mediator is required to
accelerate a high percentage of color removal. Too much of the compound
may overshadow the color removal since riboflavin itself is a colored
compound.
ii. The effect of substrate concentration was significant for COD removal at both
anaerobic and aerobic reaction phases as well as for total COD removal. The
magnitude and direction of the effect were different among the reaction
phases. The effect of substrate was positive for the COD removal under
anaerobic condition and also for total COD removal. However, the effect
was negative under aerobic condition.
258
iii. The interaction effect was insignificant for COD removal under anaerobic
condition but significant for aerobic reaction and total COD removal.
iv. The effect of riboflavin was insignificant for COD removal under anaerobic
condition but was significant for the reaction under aerobic condition and
total COD removal. The direction of the effect was negative for COD
removal under all experimental conditions.
v. The effect of substrate on the color removal of Sumifix Navy Blue EXF and
Synozol Red K-4B was significant for both five and twelve hours of the
reaction phase. However, the magnitude and direction of the effect were
opposite when compared between the two reactions. The five hour reaction
demonstrates a positive effect while negative for twelve hours reaction.
vi. The effect of riboflavin was also significant at both five and twelve hours
reaction phases for color removal of both dyes. All responses were positive
with increasing magnitude as the reaction time move from five to twelve
hours of reactions. Except for the five hours reaction phase for Synozol Red
K-4B, riboflavin was negatively affecting the percentage of color removal.
vii. The interaction effect of substrate and riboflavin were significant for both 5
and twelve hours of the reaction for Synozol Red K-4B. As for Sumifix Navy
Blue EXF, the interaction effect was only significant for five hours of the
reaction. The direction of the interaction effect was positive for five hours
reaction but negative at twelve hours reaction phase. This applied for both
dyes.
CHAPTER 8
CONCLUSIONS AND RECOMENDATIONS
Granulation system is a feasible treatment process that can be used to treat
recalcitrant pollutant containing wastewater such as those found in the textile dyeing
wastewater. This study was aimed at developing granular biomass that is capable of
treating such wastewater. Synthetic wastewater comprising of a mixed dye of
Sumifix Navy Blue EXF and Synozol Red K-4B and Sumifix Black EXA was used
in this study. The biogranules were developed using a mixture of sludge from a
sewage treatment plant and a textile mill wastewater treatment plant, anaerobic
granules from a paper mill, and with the addition of a specialized dye degrader
microbes customized to treat dye containing wastewater. Different types of study
were conducted in several types of reactor. However, the different reactors shared a
common feature, i.e. intermittent anaerobic and aerobic conditions in which the
FAnGS were developed. With the aid of statistical experimental design, the effects of
some variables were assessed.
The following are the conclusions that can be derived from this study. The
recommendations to improve the findings of the study are given in the following
section.
260
8.1 Conclusions
Several conclusions that could be derived from the experimental results of this study
are given below:
a. Successful development of FAnGS has been demonstrated in the
IFAnGSBioRec system with specialized features of the intermittent anaerobic
and aerobic reaction mode during the reaction process. The FAnGS
developed from the mixture of sludge and anaerobic granules were compact,
strong in structure and possessed good settling properties. Such properties
have increased the concentration of the biomass in the reactor which was
observed to improve the performance of the system. The anaerobic granules
seeding have created a noticeable different on the morphological features of
the granules by having fragmented anaerobic granules within the FAnGS as
compared to granules developed without the addition of anaerobic granular
seeding.
b. Under intermittent anaerobic and aerobic reaction phase strategy, the FAnGS
was capable of eliminating pollutants that required both anaerobic and
aerobic treatment conditions. Within 6 hours of HRT, the FAnGS was able to
remove more than 95% ammonia, 62% color and 94% COD. Based on
OUR/SOUR and SMA analyses, the granules seem to have facultative,
anaerobic and aerobic microorganisms that degrade under both anaerobic and
aerobic conditions.
c. Several aerobic and facultative anaerobic types of microorganisms were
identified within the granules and they are Bacillus cereus, Pseudomonas
veronii, three species of Pseudomonas genus and Enterobacter sp. They are
among those considered in the literature as dye degrader microbes.
261
d. Further investigation on the isolated microorganisms within the FAnGS
showed that they are capable of forming aggregates and exhibit reasonably
high to moderate range of surface hydrophobicity. The percentage of
aggregation and surface hydrophobicity of the mixed culture are higher when
compared to individual microorganisms.
e. Through the aid of factorial design and response surface modeling, substrate
concentration, pH and temperature imposed significant effect on the
coaggregation and surface hydrophobicity. Substrate concentration shows a
positive significant effect on coaggregation and surface hydrophobicity while
pH caused a negative effect. As the substrate concentration increased, the
percentage of coaggregation and surface hydrophobicity also increased. As
the pH value increased from acidic to alkaline, the percentage of
coaggregation and surface hydrophobicity decreased. The temperature
caused a positive effect on the coaggregation process but a negative effect on
the surface hydrophibicity. Among the three variables, the significant
interaction was only observed between pH and temperature for coaggregation
process. While for surface hydrophobicity, the interaction effect was
significant between pH and substrate concentration and between pH and
temperature. The 3-way interaction effect of substrate concentration, pH and
temperature was only significant for surface hydrophobicity. Based on the
central composite analyses, substrate concentration and pH have a non-linear
effect on coaggregation process and surface hydrophobicity.
f. Increase in the HRT has affected the biomass profile and the physical
properties of the granules. Not only the concentration of the granular
biomass has reduced, the size and settling properties of the FAnGS have also
reduced with the increase in the HRT. Due to the reduction of the biomass
profile, the removal performance for COD was also reduced. However, the
removal percentage of color improved as the HRT increased.
g. In the application of FAnGS with intermittent reaction phase, increase in the
anaerobic reaction time with minimum aerobic reaction time can be
262
considered as a good strategy to maintain and improve the performance of the
granular reactor system particularly for the removal of color for textile
wastewater treatment.
h. Changes in the HRT have also affected the microbial activity of the granular
biomass. Increase in the HRT has caused a reduction in the microbial activity
with the reduction in the μoverall, Yobs and Y values. More stable condition of
the granular biomass can be achieved through the increase in ratio of
anaerobic reaction time to aerobic reaction time. The kd value also remains
unchanged.
i. Substrate concentration imposed a significant effect for COD removal. In the
anaerobic condition, more COD is being removed as the substrate
concentration increased. However, the result was opposite for COD removal
under aerobic condition. The direction of total COD removal followed the
removal under anaerobic condition but with lesser magnitude. Substrate
concentration has a linear effect on COD removal under anaerobic condition
and non-linear under aerobic condition. The effects of substrate
concentration on total COD removal were found to be non-linear.
j. The amount of redox mediator that is required to accelerate the color removal
is very small. Within the range used in the experiment, the concentration of
redox mediator did not give any significant effect for anaerobic COD
removal. However, the effect was highly significant for aerobic and total
COD removal. The concentration of riboflavin was negatively affecting the
removal of COD under aerobic condition and total COD removal. The
concentration of substrate and riboflavin also response interactively for
aerobic and total COD removal.
263
k. With respect to color removal, the effect of substrate caused a positive
significant effect at the early stage of the experiment but the opposite
direction was observed at the later stage. Hence, the effect of substrate
concentration on color removal was also considered as time dependent. The
non-linear effect of substrate on color removal was only significant during the
early stage of the experiment.
l. The riboflavin has a significant positive effect on the color removal of
Sumifix Navy Blue EXF throughout the experiment. The negative significant
effect of riboflavin was found on Synozol Red K-4B at the early stage of the
experiment and change to positive at the later stage of the experiment. The
effect of riboflavin was found to be non-linear for the color removal of
Sumifix Navy Blue EXF throughout the experiment. As for Synozol Red K-
4B, the non-linear effect of riboflavin was only significant at the later stage of
the experiment.
m. The application of experimental design (i.e. factorial design and response
surface) could provide more reliable results where a significant effect either
as the main or/and interaction can be obtained and quantitatively identified.
The use of experimental design is a more effective approach that could
provide more information on the association of the variables towards the
responses through less experimental work as compared to one-factor-at-a-
time approach. However, the selection of values and the variables that will
be included in the statistical model may affect the result of the experiment. A
misleading conclusion may occur when the selection of values and variables
are not carefully made. The development of the statistical model from the
experimental design is also dependent on the complexity of the process. A
process can become difficult to model when the outcomes of the mechanisms
involved in the process are not directly associated with the investigated
variables.
264
8.2 Recommendations
In order to improve the performance of the FAnGS in treating textile wastewater,
further studies are needed and are given as follows:
a. Since the size of the granules is an important aspect in the degradation of the
dye, a study focusing on the effect of the granule’s size on dye degradation is
needed in order to obtain maximum removal rate.
b. Knowledge on the kinetics of decolorization and COD removal with the
effect of the environmental factors such as temperature, pH, co-substrate,
with and without the presence of redox mediator are severely lacking. The
results of kinetic studies would be able to assist on the design and operation
of the reactor system. This information is important in order to develop an
efficient biodegradation process for the dye containing wastewater.
c. In this study, the presence of aromatic amines with its autoxidation effect has
caused interference in the quantification of the color removal. A study
approach on the fate of the aromatic amines with respect to the detection,
degree of mineralization and autoxidation as well as the toxicity effect to the
microorganisms and the surrounding environment are important aspects that
need to be investigated. Further investigations are needed to overcome the
problem of recolorization so that true measurement on the color removal
under aerobic condition could be precisely determined.
d. The application of biogranules treatment approach is affected by many
factors. Cost effect analyses with the exploitation on the affecting variables
are required in order to obtain the most economical application of biogranular
treatment system at the most optimal condition.
APPENDIX A: DATA AND EXAMPLES OF CALCULATIONS
A-1: Organic Loading Rate
,
where X = COD concentration of the influent (mg/L) Vadd= Volume of influent added in each cycle operation (mL) Vtotal = Total working volume of the experiment (mL) T = Hydraulic retention time (hour).
X Vadd Vtotal T OLR (kg/m3·d)
FAnGS development
1270 2 4 6 2.54
Stage X Vadd Vtotal T OLR
(kg/m3·d) I 1270 2 4 6 2.54 II 1270 2 4 12 1.27 III 1270 2 4 24 0.635 IV 1604 2 4 24 0.802 V 1604 2 4 24 0.802 VI 1604 2 4 24 0.802
A-2: Superficial Air Velocity
Air flow rate (L/min)
Diameter of column (m)
Surface area (m2) Superficial air velocity (cm/s)
5 0.08 5.024 x10-3 1.66
Air flow rate (L/min)
Diameter of column (m)
Surface area (m2) Superficial air velocity (cm/s)
7.5 0.08 5.024 x10-3 2.5
302
A-3: Oxygen Uptake Rate
where OUR = Oxygen uptake rate (mg/L.h) DOa = Initial dissolved oxygen (mg/L) DOb = End dissolved oxygen (mg/L) T = Time (min)
Stage I Stage II
Time DOa DOb OUR Time DOa DOb OUR 32 6.79 2.00 538.65 210 7.85 3.30 78.00 57 7.28 1.97 335.37 91 5.85 2.33 139.25 90 7.67 1.98 227.60 71 8.18 2.99 263.15 77 7.12 1.99 239.84 101 7.72 2.96 169.66103 7.48 1.99 191.88 119 8.00 2.97 152.17 108 7.59 2.00 186.33 172 7.75 2.98 99.84 180 7.80 2.00 116.00 210 8.14 2.99 88.29 230 7.81 1.99 91.10 298 7.51 3.00 54.48 250 7.40 1.97 78.19 385 8.13 2.98 48.16 280 7.47 1.99 70.46 498 8.13 2.97 37.30 340 7.55 1.99 58.87 617 7.78 3.00 27.89 500 7.78 1.98 41.76 698 8.56 3.00 28.68 530 7.80 2.00 39.40 818 8.29 2.99 23.33 600 7.57 1.99 33.48 926 7.74 3.00 18.43 640 7.40 2.00 30.38 936 8.15 2.98 19.88 680 7.20 2.00 27.53 1110 8.17 2.68 17.81720 7.30 1.96 26.70 1129 9.16 3.00 19.64
1136 8.48 2.69 18.35 75 6.10 4.92 56.64 1186 8.38 3.00 16.33 109 6.38 2.00 144.66 450 7.60 2.00 44.80 190 8.19 2.92 99.85 540 7.50 1.98 36.80 470 8.25 2.98 40.37 740 7.40 1.99 26.32 850 8.12 2.33 24.52780 7.20 1.98 24.09 930 7.94 3.00 19.12 830 7.50 2.00 23.86 1200 8.49 3.00 16.47 870 7.40 2.00 22.34 1300 8.59 3.00 15.48 900 7.10 2.40 18.80 1350 8.41 2.56 15.60
1623 8.70 3.00 12.64 1500 8.70 3.00 13.68 1240 7.30 3.00 12.48
303
Stage III Stage IV
Time DOa DOb OUR Time DOa DOb OUR 500 7.50 3.00 32.40 85 7.00 2.00 211.76 150 7.50 1.80 136.80 169 6.90 2.00 104.38 185 7.40 2.41 97.10 240 7.20 1.98 78.30 400 7.50 3.30 37.80 173 7.60 2.00 116.53 207 7.30 2.00 92.17 293 7.30 2.00 65.12 370 7.80 2.00 56.43 415 7.30 1.98 46.15 400 7.40 2.00 48.60 500 7.30 2.00 38.16 400 7.50 2.00 49.50 660 7.40 2.00 29.45 500 7.50 2.00 39.60 690 7.30 2.00 27.65 600 7.50 2.00 33.00 890 7.30 2.00 21.44 550 7.40 2.41 32.66 835 7.40 1.98 23.37 640 7.60 2.41 29.19 880 7.40 2.00 22.09 760 7.70 2.41 25.06 940 7.40 1.98 20.76 800 7.70 2.41 23.81 970 7.40 1.98 20.12 1200 7.80 2.41 16.17 990 7.50 1.98 20.07 900 7.70 2.41 21.16 1000 7.50 2.00 19.80 1100 7.80 2.41 17.64 1020 7.30 2.00 18.71 1220 7.50 2.42 14.99 1090 7.40 2.00 17.83 1920 7.80 2.42 10.09 1100 7.30 1.98 17.41 2200 7.70 2.42 8.64 1180 7.50 2.00 16.78 2300 7.80 2.44 8.39 1190 7.40 2.00 16.34 2400 7.80 2.48 7.98 1200 7.60 2.00 16.80 2300 7.80 3.00 7.51 1400 7.50 1.98 14.19
1500 7.50 2.00 13.20 430 7.50 2.58 41.19 1650 7.60 1.98 12.26 1800 7.76 2.58 10.36 1900 8.06 3.00 9.59 235 7.71 1.98 87.78 2200 7.74 3.00 7.76 375 7.82 2.00 55.87 2000 7.98 3.00 8.96 420 7.30 1.98 45.60 2300 7.85 3.00 7.59 420 7.50 2.00 47.14 2500 7.81 3.00 6.93 800 7.70 2.00 25.65 2000 8.00 3.00 9.00 1000 7.60 2.00 20.16 2750 8.20 3.00 6.95 640 7.60 1.98 31.61 2800 8.00 3.00 7.07 840 7.40 2.00 23.14
900 7.30 2.00 21.20 650 7.50 2.00 30.46 690 7.40 2.00 28.17 870 7.80 1.98 24.08 910 7.60 1.98 22.23 980 7.70 1.98 21.01 990 7.50 1.98 20.07 930 7.50 2.00 21.29 950 7.30 2.00 20.08 1300 7.50 1.98 15.29 1440 7.80 2.00 14.50 1600 7.60 2.00 12.60 1700 7.40 2.00 11.44 1800 7.70 2.00 11.40
304
Stage V Stage VI
Time DOa DOb OUR Time DOa DOb OUR 129 6.83 2.00 134.79 500 7.91 3.00 35.4 107 8.11 2.00 205.57 720 8.11 3.00 25.6 125 7.01 1.98 144.86 166 8.90 2.99 128.2 190 7.81 2.00 110.08 678 8.70 3.00 30.3 170 8.32 2.00 133.84 820 8.80 2.99 25.5 160 8.05 2.00 136.13 932 8.90 2.92 23.1 165 7.59 2.00 121.96 1179 9.20 2.31 21.0180 7.45 2.00 109.00 1100 8.90 2.99 19.3 260 7.67 2.00 78.51 1070 8.70 2.84 19.7 290 7.41 2.00 67.16 1110 9.30 3.00 20.4 350 7.37 2.00 55.23 1500 9.00 2.99 14.4 390 7.22 2.00 48.18 1950 9.22 2.99 11.5 480 7.60 2.00 42.00 1700 9.14 2.36 14.4 580 7.60 2.00 34.76 1620 9.60 2.99 14.7 630 7.44 2.00 31.09 1890 9.44 2.98 12.3 780 7.30 2.00 24.46 1650 9.10 2.89 13.5 860 7.60 2.00 23.44 1780 8.98 2.99 12.1 900 7.40 2.00 21.60 1600 8.50 2.99 12.4 860 7.20 2.00 21.77 1680 9.40 2.98 13.8
1000 7.41 2.00 19.48 1640 9.20 2.97 13.7 1100 7.37 2.00 17.57 1570 8.90 2.80 14.0 1050 7.17 2.00 17.73 1690 9.10 2.90 13.2
1640 9.20 2.97 13.7 124 6.50 2.00 130.65 1690 9.30 2.80 13.8 170 7.20 2.00 110.12 300 7.10 2.00 61.20 194 7.60 3.50 76.1 350 7.10 2.00 52.46 480 7.40 3.00 33.0 450 7.30 2.00 42.40 300 6.01 2.98 36.4 430 7.40 2.00 45.21 600 8.59 2.91 34.1 600 7.30 2.00 31.80 720 8.41 3.00 27.1800 7.40 2.00 24.30 740 8.20 2.98 25.4 900 7.20 2.00 20.80 1260 8.37 2.99 15.4 980 7.60 2.00 20.57 1300 8.22 3.00 14.5
1080 7.30 2.00 17.67 900 8.30 2.49 23.2 1100 7.60 2.00 18.33 1050 8.30 2.33 20.5 1600 7.70 2.00 12.83 2100 8.41 3.00 9.3 1900 7.40 2.00 10.23 2100 8.20 2.98 8.9
2000 8.23 2.99 9.4 2100 8.12 3.00 8.8 2200 8.10 2.98 8.4 2200 8.00 2.99 8.2 2300 8.00 2.98 7.9 2200 8.10 2.98 8.4 2400 8.20 3.00 7.8 2500 8.20 3.00 7.5 2590 8.20 3.00 7.2
305
A-4: Sludge Retention Time
cee
rvss
/tVXVX
θ =
where,
= Solids retention time (d) = Volatile solids concentration in the reactor system
(g VSS/L) = Working volume of the SBR system (L) = Effluent volatile solids concentration (g VSS/L) = Effluent volume of the SBR operating cycle (L) = Cycle time of the SBR operation (d)
Stage SRT average sd
I
32.9 4 0.30 2 3 27.4
27.6 13.4 29.8 4 0.52 2 3 14.3
32.9 4 0.20 2 3 41.1
II
24.5 4 0.27 2 6 45.4
42.4 10.2 26.7 4 0.43 2 6 31.0
22.3 4 0.22 2 6 50.7
III
20.9 4 0.20 2 12 104.5
78.9 30.8 17.9 4 0.40 2 12 44.8
16.6 4 0.19 2 12 87.4
IV
26.7 4 0.52 2 12 51.3
70.1 23.9 22.3 4 0.36 2 12 61.9
29.1 4 0.30 2 12 97.0
V
24.3 4 0.30 2 12 81.0
72.5 23.3 20.3 4 0.44 2 12 46.1
22.6 4 0.25 2 12 90.4
VI
20.2 4 0.59 2 12 34.2
41.6 18.4 19.4 4 0.31 2 12 62.6
21 4 0.75 2 12 28.0
306
A-5: Sludge Volume Index
where,
SVI = Sludge volume index (mL/g SS) Bedvolume = Volume of settled biomass in reactor (L) d.w = Dry weight of biomass in reactor (g SS/L ) 4 = Working volume (L) 1000 = Conversion factor (L to mL)
Stage Average
dry weight
Bedheight (cm) at 5 min
settling
A (cm2)
Bedvolume (cm3)
Bedvolume (L)
SVI (mL/g MLSS)
SVI (average) SD
I
34.7 37.0 50.14 1855.17 1.855 13.4
13.1 0.4 36.7 37.0 50.14 1855.17 1.855 12.6
34.5 37.0 50.14 1855.17 1.855 13.4
II
26.3 43.0 50.14 2156.01 2.156 20.5
18.8 1.5 29.5 43.0 50.14 2156.01 2.156 18.3
30.4 43.0 50.14 2156.01 2.156 17.7
III
23.6 43.0 50.14 2156.01 2.156 22.8
21.4 1.6 27.3 43.0 50.14 2156.01 2.156 19.7
24.8 43.0 50.14 2156.01 2.156 21.7
IV
30.1 41.0 50.14 2055.73 2.056 17.1
16.9 1.3 28.3 41.0 50.14 2055.73 2.056 18.2
33.1 41.0 50.14 2055.73 2.056 15.5
V
30.4 39.0 50.14 1955.45 1.955 16.1
15.5 1.3 34.8 39.0 50.14 1955.45 1.955 14.0
29.6 39.0 50.14 1955.45 1.955 16.5
VI
23.9 46.0 50.14 2306.43 2.306 24.1
24.8 0.9 22.4 46.0 50.14 2306.43 2.306 25.7
23.6 46.0 50.14 2306.43 2.306 24.4
307
Day Stage Average dry weight
Bedheight (cm) at 5 min
settling A (cm2) Bedvolume
(cm3) Bedvolume
(L) SVI
(mL/g MLSS)
0
23.0 39.5 50.14 1980.52 1.981 21.53 3 15.4 38.0 50.14 1905.31 1.905 30.91 5 17.5 38.0 50.14 1905.31 1.905 27.19 7 14.1 46.0 50.14 2306.43 2.306 40.78
14 15.7 42.0 50.14 2105.87 2.106 33.45 17 19.2 41.0 50.14 2055.73 2.056 26.77 21 23.1 43.0 50.14 2156.01 2.156 23.35 24 25.9 40.0 50.14 2005.59 2.006 19.36 28 26.5 40.1 50.14 2010.61 2.011 18.97 30 27.3 30.5 50.14 1529.26 1.529 14.00 32 28.1 24.0 50.14 1203.36 1.203 10.72 33 26.5 25.0 50.14 1253.50 1.253 11.83 36 28.8 27.5 50.14 1378.85 1.379 11.97 37 28.9 37.0 50.14 1855.17 1.855 16.05 38 29.8 37.5 50.14 1880.24 1.880 15.77 39 31.3 38.5 50.14 1930.38 1.930 15.42 43 30.8 42.0 50.14 2105.87 2.106 17.08 46 32.6 42.0 50.14 2105.87 2.106 16.15 47 33.3 34.5 50.14 1729.82 1.730 13.00 50
I
32.1 35.5 50.14 1779.96 1.780 13.86 56 33.3 35.0 50.14 1754.89 1.755 13.17 72 33.7 35.0 50.14 1754.89 1.755 13.02 78 34.8 29.0 50.14 1454.05 1.454 10.45 84 35.3 31.0 50.14 1554.33 1.554 11.01 88 35.5 33.0 50.14 1654.61 1.655 11.65 92 35.4 36.5 50.14 1830.10 1.830 12.92 95 35.3 38.0 50.14 1905.31 1.905 13.49 99 35.3 37.0 50.14 1855.17 1.855 13.14 103
II
35.1 41.5 50.14 2080.80 2.081 14.82 106 33.3 41.0 50.14 2055.73 2.056 15.44 111 33.2 41.0 50.14 2055.73 2.056 15.47 119 30.8 44.0 50.14 2206.15 2.206 17.91 124 29.7 42.0 50.14 2105.87 2.106 17.73 129 29.9 42.0 50.14 2105.87 2.106 17.61 135 28.7 44.0 50.14 2206.15 2.206 19.21 142 28.7 43.0 50.14 2156.01 2.156 18.78 148
III
27.9 44.0 50.14 2206.15 2.206 19.74 155 27.2 44.0 50.14 2206.15 2.206 20.28 161 25.6 42.0 50.14 2105.87 2.106 20.57 169 24.8 44.0 50.14 2206.15 2.206 22.24 175 25.3 44.0 50.14 2206.15 2.206 21.79 187 25.2 43.0 50.14 2156.01 2.156 21.39 193
IV
25.4 39.0 50.14 1955.45 1.955 19.25 201 26.6 39.0 50.14 1955.45 1.955 18.38 209 28.4 38.0 50.14 1905.31 1.905 16.77 215 27.7 38.0 50.14 1905.31 1.905 17.19 224 30.5 40.0 50.14 2005.59 2.006 16.44 230 30.5 40.0 50.14 2005.59 2.006 16.44 236 30.5 41.0 50.14 2055.73 2.056 16.83
308
Day Stage Average dry weight
Bedheight (cm) at 5 min
settling A (cm2) Bedvolume
(cm3) Bedvolume
(L) SVI
(mL/g MLSS)
241
V
29.6 41.0 50.14 2055.73 2.056 17.36 248 30.4 40.0 50.14 2005.59 2.006 16.49 255 30.5 38.0 50.14 1905.31 1.905 15.64 262 30.8 38.0 50.14 1905.31 1.905 15.47 268 30.5 40.0 50.14 2005.59 2.006 16.44 276 31.4 39.0 50.14 1955.45 1.955 15.57 282 31.6 39.0 50.14 1955.45 1.955 15.47 286
VI
29.4 42.0 50.14 2105.87 2.106 17.92 292 24.8 43.0 50.14 2156.01 2.156 21.73 299 23.7 43.0 50.14 2156.01 2.156 22.74 307 20.3 42.0 50.14 2105.87 2.106 25.93 314 22.7 44.0 50.14 2206.15 2.206 24.25 320 23.0 45.0 50.14 2256.29 2.256 24.52 328 23.3 46.0 50.14 2306.43 2.306 24.75
A-6: Removal Performance (COD, Ammonia and Color Removal)
Day COD (mg/L) Color(ADMI) Ammonia (mg/L)
Influent Effluent %
Removal Influent Effluent %
Removal Influent Effluent %
Removal 1 1270 390 69.3 1010 761 24.6 35.0 11.6 66.8 3 1260 278 78.0 1010 703 30.4 37.2 15.1 59.5 8 1275 241 81.1 1005 495 50.7 34.7 2.2 93.7
11 1265 187 85.2 1020 458 55.1 33.3 3.2 90.5 14 1255 237 81.1 1050 426 59.4 29.4 1.7 94.3 17 1260 258 79.5 1010 403 60.1 37.5 1.5 95.9 21 1270 160 87.4 1020 473 53.6 35.9 1.4 96.0 24 1255 136 89.1 1020 370 63.8 39.5 0.9 97.7 28 1280 161 87.4 1030 373 63.8 40.9 1.3 96.931 1275 181 85.8 1020 429 58.0 30.2 2.0 93.334 1250 89 92.9 1020 407 60.1 31.9 1.1 96.4 52 1265 104 91.8 1040 339 67.4 41.2 1.3 96.7 55 1260 89 92.9 1030 433 58.0 49.8 3.7 92.6 59 1270 120 90.6 1020 418 59.0 40.7 2.5 93.9 66 1270 80 93.7 1010 388 61.6 32.3 1.5 95.2
309
Day Influent COD
Effluent COD Average COD
(Effluent)
COD removal Average COD
removal sd
a b c a b c
0 1270 345 357 348 350 72.8 71.9 72.6 72.4 0.5 3 1265 325 331 329 328 74.3 73.8 74.0 74.0 0.3 5 1268 290 287 316 298 77.1 77.4 75.1 76.5 1.3
14 1260 261 258 256 258 79.3 79.5 79.7 79.5 0.2 17 1270 268 262 236 255 78.9 79.4 81.4 79.9 1.3 21 1275 240 231 223 231 81.2 81.9 82.5 81.9 0.7 24 1277 222 226 221 223 82.6 82.3 82.7 82.5 0.2 28 1270 226 234 231 230 82.2 81.6 81.8 81.9 0.3 30 1272 212 212 215 213 83.3 83.3 83.1 83.2 0.1 32 1266 211 197 214 208 83.3 84.4 83.1 83.6 0.7 36 1270 210 204 199 204 83.5 83.9 84.3 83.9 0.4 38 1269 209 207 226 214 83.5 83.7 82.2 83.1 0.8 39 1270 192 208 217 206 84.9 83.6 82.9 83.8 1.0 43 1271 206 215 208 210 83.8 83.1 83.6 83.5 0.4 46 1268 207 209 202 206 83.7 83.5 84.1 83.8 0.3 47 1273 211 201 205 206 83.4 84.2 83.9 83.8 0.4 50 1275 203 205 194 201 84.1 83.9 84.8 84.3 0.556 1280 198 224 229 217 84.5 82.5 82.1 83.0 1.372 1274 199 214 186 200 84.4 83.2 85.4 84.3 1.1 78 1272 198 216 197 204 84.4 83.0 84.5 84.0 0.8 84 1269 193 212 193 199 84.8 83.3 84.8 84.3 0.9 88 1266 186 189 195 190 85.3 85.1 84.6 85.0 0.4 92 1270 199 192 187 193 84.3 84.9 85.3 84.8 0.5 95 1270 199 194 203 199 84.3 84.7 84.0 84.3 0.4 99 1281 191 199 214 201 85.1 84.5 83.3 84.3 0.9 103 1268 193 188 209 197 84.8 85.2 83.5 84.5 0.9 106 1271 193 184 194 191 84.8 85.5 84.7 85.0 0.4 111 1274 200 203 187 197 84.3 84.1 85.3 84.6 0.6 119 1277 203 194 202 200 84.1 84.8 84.2 84.4 0.4 124 1270 217 206 202 208 82.9 83.8 84.1 83.6 0.6 129 1270 202 202 196 200 84.1 84.1 84.6 84.3 0.3 135 1275 201 196 193 197 84.2 84.6 84.9 84.6 0.4 142 1275 189 207 215 204 85.2 83.8 83.1 84.0 1.1 148 1270 198 202 197 199 84.4 84.1 84.5 84.3 0.2 155 1270 212 215 230 219 83.3 83.1 81.9 82.8 0.8161 1270 199 216 231 215 84.3 83.0 81.8 83.0 1.3 171 1264 193 211 202 202 84.7 83.3 84.0 84.0 0.7 180 1276 202 198 193 197 84.2 84.5 84.9 84.5 0.4 190 1275 185 196 207 196 85.5 84.6 83.8 84.6 0.9 195 1605 116 169 149 144 92.8 89.5 90.7 91.0 1.7 202 1600 149 155 166 157 90.7 90.3 89.6 90.2 0.6 209 1608 125 159 174 153 92.2 90.1 89.2 90.5 1.5 215 1608 172 145 148 155 89.3 91.0 90.8 90.4 0.9 224 1611 153 150 147 150 90.5 90.7 90.9 90.7 0.2 230 1605 162 141 135 146 89.9 91.2 91.6 90.9 0.9 236 1600 146 152 149 149 90.9 90.5 90.7 90.7 0.2 241 1608 127 137 125 130 92.1 91.5 92.2 91.9 0.4248 1606 132 117 114 121 91.8 92.7 92.9 92.5 0.6255 1608 119 93 116 109 92.6 94.2 92.8 93.2 0.9
310
262 1608 108 122 117 116 93.3 92.4 92.7 92.8 0.5 268 1607 117 87 125 110 92.7 94.6 92.2 93.2 1.3 276 1603 99 83 99 94 93.8 94.8 93.8 94.1 0.6282 1600 106 90 88 94 93.4 94.4 94.5 94.1 0.6 286 1605 124 141 138 134 92.3 91.2 91.4 91.6 0.6 292 1608 146 169 150 155 90.9 89.5 90.7 90.4 0.8 299 1605 218 209 220 216 86.4 87.0 86.3 86.6 0.4 307 1606 210 226 222 219 86.9 85.9 86.2 86.3 0.5 314 1606 236 234 239 237 85.3 85.4 85.1 85.3 0.2 320 1600 290 280 261 277 81.9 82.5 83.7 82.7 0.9 328 1608 296 275 270 280 81.6 82.9 83.2 82.6 0.9
Day Influent Color
Effluent Color Average Color
(Effluent)
Color removal Average color
removal sd a b c a b c
0 1051 796 802 790 796 24.3 23.7 24.8 24.3 0.6 3 1054 736 729 740 735 30.2 30.8 29.8 30.3 0.5 5 1047 620 601 640 620 40.8 42.6 38.9 40.8 1.9
14 1050 562 569 548 560 46.5 45.8 47.8 46.7 1.0 17 1049 543 541 536 540 48.2 48.4 48.9 48.5 0.4 21 1055 567 572 586 575 46.3 45.8 44.5 45.5 0.9 24 1050 617 629 637 628 41.2 40.1 39.3 40.2 1.0 28 1055 544 554 552 550 48.4 47.5 47.7 47.9 0.5 30 1052 482 498 485 488 54.2 52.7 53.9 53.6 0.8 32 1052 465 472 472 470 55.8 55.1 55.1 55.3 0.4 36 1050 547 562 571 560 47.9 46.5 45.6 46.7 1.2 38 1051 490 479 496 488 53.4 54.4 52.8 53.5 0.8 39 1050 558 562 563 561 46.9 46.5 46.4 46.6 0.3 43 1055 554 560 536 550 47.5 46.9 49.2 47.9 1.2 46 1050 446 467 453 455 57.5 55.5 56.9 56.6 1.0 47 1049 461 471 473 468 56.1 55.1 54.9 55.4 0.6 50 1055 383 392 407 394 63.7 62.8 61.4 62.6 1.2 56 1050 377 386 395 386 64.1 63.2 62.4 63.2 0.9 72 1048 327 321 319 322 68.8 69.4 69.6 69.3 0.4 78 1052 294 322 315 310 72.1 69.4 70.1 70.5 1.4 84 1052 311 306 313 310 70.4 70.9 70.2 70.5 0.4 88 1051 327 343 322 330 68.9 67.4 69.4 68.6 1.0 92 1050 344 349 334 342 67.2 66.8 68.2 67.4 0.7 95 1050 321 326 289 312 69.4 69 72.5 70.3 1.9 99 1054 344 370 337 350 67.4 64.9 68 66.8 1.6 103 1055 350 344 322 339 66.8 67.4 69.5 67.9 1.4 106 1059 325 331 309 322 69.3 68.7 70.8 69.6 1.1 111 1055 319 318 291 309 69.8 69.9 72.4 70.7 1.5 119 1052 307 309 290 302 70.8 70.6 72.4 71.3 1.0 124 1055 332 329 310 324 68.5 68.8 70.6 69.3 1.1 129 1050 282 288 266 279 73.1 72.6 74.7 73.5 1.1 135 1050 273 270 291 278 74 74.3 72.3 73.5 1.1 142 1052 270 266 275 270 74.3 74.7 73.9 74.3 0.4 148 1050 247 254 271 257 76.5 75.8 74.2 75.5 1.2 155 1054 284 277 310 290 73.1 73.7 70.6 72.5 1.6
311
161 1053 249 266 284 266 76.4 74.7 73 74.7 1.7 171 1050 268 277 282 276 74.5 73.6 73.1 73.7 0.7 180 1050 257 248 265 257 75.5 76.4 74.8 75.6 0.8 190 1056 259 242 250 250 75.5 77.1 76.3 76.3 0.8 195 1053 250 237 258 248 76.3 77.5 75.5 76.4 1.0 202 1050 181 190 180 183 82.8 81.9 82.9 82.5 0.6 209 1050 160 161 171 164 84.8 84.7 83.7 84.4 0.6 215 1057 186 189 194 190 82.4 82.1 81.6 82.0 0.4 224 1050 159 155 171 162 84.9 85.2 83.7 84.6 0.8 230 1054 174 194 180 183 83.5 81.6 82.9 82.7 1.0 236 1050 191 179 162 177 81.8 83 84.6 83.1 1.4 241 1052 157 161 183 167 85.1 84.7 82.6 84.1 1.3 248 1050 162 155 172 163 84.6 85.2 83.6 84.5 0.8 255 1048 175 176 159 170 83.3 83.2 84.8 83.8 0.9 262 1047 146 150 160 152 86.1 85.7 84.7 85.5 0.7 268 1050 143 154 150 149 86.4 85.3 85.7 85.8 0.6 276 1050 140 135 158 144 86.7 87.1 85 86.3 1.1 282 1050 141 145 134 140 86.6 86.2 87.2 86.7 0.5 286 1049 232 215 234 227 77.9 79.5 77.7 78.4 1.0 292 1050 222 214 212 216 78.9 79.6 79.8 79.4 0.5 299 1050 232 235 246 238 77.9 77.6 76.6 77.4 0.7 307 1059 268 264 254 262 74.7 75.1 76 75.3 0.7 314 1055 236 248 263 249 77.6 76.5 75.1 76.4 1.3 320 1050 270 254 279 268 74.3 75.8 73.4 74.5 1.2 328 1050 258 260 255 258 75.4 75.2 75.7 75.4 0.3
A-7: Coaggregation
where,
CAg% = Percentage of coaggregation CA0 = The absorbance of cultured media at 0 h CAi = The absorbance of cultured media after centrifugation
312
CA0 = The absorbance of cultured media at 0 h Cai = The absorbance of cultured media after centrifugation
No 1 hour 2 hour 3 hour 4 hour 5 hour 1 hour 2 hour 3 hour 4 hour 5 hour
1 117 113 115 127 135 71 59 63 64 68
2 69 72 68 67 72 40 38 33 37 40
3 115 131 135 165 189 50 42 52 44 62
4 98 114 128 147 128 38 39 40 37 46
5 68 68 69 65 74 39 42 51 42 39
6 62 70 85 79 71 39 42 32 28 40
7 85 80 84 83 80 49 40 34 30 28
8 81 80 83 80 76 48 37 32 27 28
9 86 107 97 110 111 29 30 34 33 39
10 85 102 99 100 108 38 43 40 44 41
11 115 207 210 234 243 35 33 35 37 38
12 126 178 211 188 244 34 31 33 35 36
13 83 81 82 85 85 37 31 41 39 52
14 97 94 88 97 93 43 45 49 53 57
15 117 111 123 118 113 48 47 46 47 48
16 93 89 96 94 92 45 48 37 32 39
% of AGG 1 hour 2 hour 3 hour 4 hour 5 hour
1 39.3 47.8 45.2 49.6 49.6 2 42.6 47.0 52.0 45.3 44.7 3 56.9 67.6 61.5 73.1 67.2 4 60.8 65.4 69.0 75.0 64.1 5 43.1 37.7 26.1 36.0 46.8 6 36.5 40.5 62.6 64.5 44.1 7 42.4 50.6 59.8 64.3 65.6 8 41.3 53.9 61.9 65.9 62.9 9 65.9 71.7 64.6 69.8 65.2
10 55.0 57.7 59.8 55.8 61.9 11 69.5 84.2 83.1 84.0 84.5 12 73.1 82.3 84.6 81.5 85.4 13 55.1 62.2 50.3 54.6 38.8 14 55.6 52.2 44.8 45.4 38.7 15 59.4 57.3 62.7 59.9 57.4 16 51.9 46.3 61.8 66.4 57.6
313
A-8: Surface Hydrophobicity
where, SHb% = Percentage of surface hydrophobicity A0 = The absorbance of sample before mixing with xylene Ai = The absorbance of sample after extraction with xylene.
No A0 Ai % of Hydrophobicity
1 0.625 0.506 19.0
2 0.456 0.357 21.7
3 0.647 0.428 33.8
4 0.868 0.613 29.4
5 0.217 0.136 37.3
6 0.233 0.137 41.2
7 0.366 0.221 39.6
8 0.393 0.248 36.9
9 0.629 0.427 32.1
10 0.61 0.418 31.5
11 1.255 0.579 53.9
12 1.128 0.541 52.0
13 0.426 0.339 20.4
14 0.413 0.338 18.2
15 0.771 0.705 8.6
16 0.673 0.627 6.8
314
APPENDIX B: MOLECULAR PROCEDURE OF 16S SEQUENCE
ANALYSIS
B-1: Genomic DNA Isolation
Each of the selected bacteria (BS1FAnGS, BS6FAnGS, BS7FAnGS, BS8FAnGS,
BS10FAnGS, BS11FAnGS and BS12FAnGS) was extracted separately in order to obtain the
DNA genomic of the bacteria. The genomic DNA was extracted from the cell pellets
by using genomic DNA extraction and purification kit (Promega). Each of the
bacteria was culture in nutrient broth for 24 hours before 1 ml of each bacterium
cultures with optical density (OD at 660 nm) of more than 0.5 was taken and
centrifuged at 13,000-16,000xg for 2 minutes in order to obtain the cell pellet. The
supernatant was removed before 600 μl of Nuclei Lysis Solution was added to the
pelleted cell. The cells were gently resuspended using pipetters. The solution was
incubated at 80oC for 5 minutes in order to lyses the cells. The solution was then left
to cool at room temperature.
Following this, 3 μl of RNase solution was added to the cell lysate before the
tube was inverted for 2-5 times to well mix the solution. The solution was incubated
at 37oC for 30 minutes. Then the sample was to cool again at room temperature.
After that, 200 μl of Protein Precipitation solution was added to the RNase-treated
cell lysate. The solution was vortex vigorously at high speed for 20 second to mix the
Protein Precipitation solution with the cell lysate. The sample was then incubated in
ice for 5 minutes before centrifugation at 13,000-16,000 g for 3 minutes.
The supernatant containing the DNA was transferred to a clean 1.5 mL
microcentrifuge, which contain 600 μl of room temperature isopropanol. The
solution was gently mixed by inversion until the thread-like strands of DNA form
appeared as a visible mass. The sample was then centrifugation at 13000-16,000 g
for 2 minutes. The supernatant was carefully poured off after centrifugation. Then
the tube was drained on clean adsorbent paper before 600 μl of 70% ethanol was
added into the tube. Then the tube was gently inverted for several times for washing
the DNA pellet. Then the sample was centrifuged again at 13,000-16,000 g for 2
minutes. The ethanol was aspirate carefully. The tube was drained on clean
315
absorbent paper and the pellet was air-dry for 15 minutes. Then 100 μl of DNA
Rehydration solution was added to the tube. The DNA was rehydrated by
incubating at 65oC for 1 hour. The successful isolated DNA solution was stored at
4oC.
B-2: Analysis of Genomic DNA
The analysis of the genomic DNA of each of the bacteria was measured
qualitatively via agarose gel electrophoresis. 1% (w/v) of agarose was dissolved in 1
x TAE buffer (Table B-1) in microwave oven until there were no solid particles in
the solution and the solution is completely homogenous. Table B-1 showed the TAE
buffer that was prepared as concentrated stock solution (50x). The solution was
allowed to cool to approximately 50oC and then was poured into the gel base sealed
at both with masking tapes. A suitable comb was placed vertically at one end of gel
base. The comb was removed after the gel agar was left solidified for 30 minutes.
The gel was then submerged using 1 x TAE running buffer in the electrophoresis
tank. The sample was mixed with 1 μL of loading dye which consisted of
bromophenol blue 0.25% (w/v), SDS 1% (w/v) and glycerol 80 % (v/v). The sample
and 5 μl of universal DNA marker (Gene Ruler Ladder Mix Marker) were loading
into the performed wells. The gel was run at 80 V and 45 W for 60 minutes. Lastly,
the gel was stained for 30 minutes in running buffer containing 0.5 μg/mL ethidium
bromide (EtBr) before the extracted DNA was examined and photographed on a UV
transilluminator (TFX-35 Vilber Lourmat).
B-3: Polymerase Chain Reaction (PCR)
Polymerase chain reaction (PCR) is a method for amplification of DNA in
vitro where through polymerase chain reaction the DNA can be multiply up to a
billion fold (Madigan et al., 2000). The PCR copies the DNA using basic elements
of natural DNA synthesis and replication processes (McPherson and Moller, 2006).
316
Table B-1: Composition of the TAE buffer (50x)
Components Volume
Tris-Base 242.0 g Glacial acetic acid 57.1 ml
0.5 M EDTA (pH 8.0) 1000 mL
Deionized water Top up until 1000 ml
The extracted DNAs were then amplified via PCR. In the PCR amplification
process, universal primers that normally consisted of at least 17 to 25 nucleotides
were used since they are homologous to broadly conserved sequences. Primers are
needed that act as sites for initiation of DNA synthesis by the DNA polymerase.
These primers define the region of the template DNA that need to be copied.
Primers or also known amplimers are complementary to the regions of known
sequence on opposite strands of the DNA template (McPherson and Moller, 2006).
The universal primer used in this amplification is listed in Table B-2.
The PCR process consisted of three distinct steps that are governed by
temperature. The first step is known as the denaturation. At this stage, the double-
stranded template DNA is denatured by heating typically at 94oC. Here the double-
stranded DNA is separated into two complementary single strands. The second step
is the annealing process where the oligonucleotide primers will be hybridized to the
DNA template. The temperature used during this stage normally is between 40-72oC.
The last step is the DNA synthesis process where the thermostable DNA polymerase
will be effectively synthesis the DNA at reaction temperature of 72oC (McPherson
and Moller, 2006).
Table B-2: Reverse and forward of universal primers
Primer Sequence
16S-Reverse primer 5’- cgg cta cct tgt tac gac tt - 3’
16S-Forward primer 5’- gag ttt gat cct ggc tca g - 3’
100 μl of the reaction solution was prepared in PCR reaction tube. The
reaction solution used for the amplification process was shown in Table B-3. The
317
preparation of the reaction solutions were carried out on ice. The samples were
resuspended gently to homogenize the mixtures well. Then samples were placed in a
thermocycler (Perkin Elmer GeneAmp PCR System 9700) where polymerase chain
reaction took place. Parameters for PCR cycle were shown in Table B-4.
Table A-B: Composition of PCR reaction solution
Components Volume (μl )
Genomic DNA 5
16S-Reverse primer 3
16S-Forward primer 3
Buffer 10
MgCl2 8
dNTP mix 2
Taq polymerase 1
dH2O 68 Total 100
B-4: PCR Product Purification
PCR product was purified using Promega Wizard®SV gel and PCR clean-up
system. According to this procedure an equal volume of Membrane Binding Solution
and PCR reaction were mixed homogenously. The prepared PCR product was
poured to SV minicolumn assembled to a clean 2 mL collection tube. After
incubation at room temperature for 1 minute, the sample was centrifuged at 16,000 x
g for 1 minute. The flow-through liquid was discarded and the minicolumn was
reinserted back into the collection tube.
Then 700 μL of Membrane Wash Solution that has been diluted with ethanol
for washing the column were added into the minicolumn. The mixed solution was
centrifuge at 16,000 x g for 1 minute. The washing procedure was repeated once
again with 500 μL of Membrane Wash Solution and centrifuged at 16,000 x g for 5
minutes. All the flow-through was discarded. The column assembly was
318
recentrifuged for another 1 minute with the microcentrifuge lid opened for
evaporation of any ethanol residual. Then the minicolumn was transferred to a clean
1.5 mL microcentrifuge tube and was added with 50 μL of Nuclease-Free Water.
The solution was incubated for 1 minute at room temperature before centrifuge at
16,000 x g for 1 minute. Finally the minicolumn was discarded and the eluted
purified DNA was store 4oC or -20oC.
Table B-4: Parameter of PCR cycle
Parameter Temperature (oC) Time
Pre-denaturation 94 4 min Denaturation 94 1 min
Annealing 55 45 s Extension 72 1 min
Final Extension 72 7 min Preservation 4 Infinite
B-5: Purified DNA Estimation
Yields of the purified DNA were determined using agarose electrophoresis
analysis. Agarose with 1% (w/v) was used and was run at 80 V and 45 W for 60
minutes. Then, the gel was stained in running buffer plus 0.5 μg/ml ethidium
bromide (EtBr) for 30 minutes. The result of the DNA electrophoresis was examined
using UV transilluminator (TFX-35 Vilber Lourmat) and photographs were taken
using digital camera. Gene Ruler Ladder Mix Marker was used as the universal
marker.
B-6: Sequencing of the 16S rRNA Gene and Homology Analysis
The purified DNA (PCR product) was sent to Vivantis Laboratories Sdn Bhd
for sequencing purposes. The identification of selected bacteria were determined via
comparing the 16S rDNA sequences obtained in this study to the GenBank database
of National Center for Biotechnology Information (NCBI) websites
319
(http://www.ncbi.nlm.nihgov/BLAST). The nucleotides were compared through
Basic Local Alignment Search Tool (BLASTn) search program.
APPENDIX C: MORPHOLOGY OF BACTERIA Appendix C-1: Morphology of Bacteria Colony
Figure C-1 Example of classification of bacteria colony morphology (Wiley et al.,
2008)
Appendix C-2: Morphology of Bacteria Cell
Figure C-2 Example of classification of bacteria cellular morphology (Lester and
Birkett, 1999)
320
APPENDIX D: MOLECULAR DATA ANALYSIS
D1: BLASTn Analysis Result for the Determination of the Alignment Scores
of the Full Sequence of 16S rDNA for BS1FAnGS
BS1FAnGS– Full length sequence (1394 nucleotides) CGAGCGGTAGAGAGAAGCTTGCTTCTCTTGAGAGCGGCGGACGGGTGAGTAATGCCTAGGAATCTGCCTGGTAGTGGGGGATAACGTTCGGAAACGGACGCTAATACCGCATACGTCCTACGGGAGAAAGCAGGGGACCTTCGGGCCTTGCGCTATCAGATGAGCCTAGGTCGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCGTAACTGGTCTGAGAGGATGATCAGTCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGCAGTTACCTAATACGTGATTGTTTTGACGTTACCGACAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGGTTAGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCAAAACTGACTGACTAGAGTATGGTAGAGGGTGGTGGAAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGGAACACCAGTGGCGAAGGCGAACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCAACTAGCCGTTGGGAGCCTTGAGCTCTTAGTGGCGCAGCTAACGCATTAAGTTGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGAAGAACCTTACCAGGCCTTGACATCCAATGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACATTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGTAACGAGCGCAACCCTTGTCCTTAGTTACCAGCACGTAATGGTGGGCACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACGGCCTGGGCTACACACGTGCTACAATGGTCGGTACAGAGGGTTGCCAAGCCGCGAGGTGGAGCTAATCCCAGAAAACCGATCGTAGTCCGGATCGCAGTCTGCAACTCGACTGCGTGAAGTCGGAATCGCTAGTAATCGCGAATCAGAATGTCGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGTTGCACCAGAAGTAGCTAGTCTAACCTTCGGGGGG Nucleotides marked in RED had been edited for sequence analysis. Top 10 Blast hits sequence on the full length sequence from NCBI Accession Description
Max score
Total score
Query coverage
E value
Max ident
Links
AY144583.1 Pseudomonas veronii strain UFZ-B547 16S ribosomal RNA gene, partial sequence
2555 2555 100% 0.0 99%
AF064460.1 Pseudomonas veronii 16S ribosomal RNA gene, complete sequence
2555 2555 100% 0.0 99%
FJ594447.1 Pseudomonas sp. BS2(2009) 16S ribosomal RNA gene, partial sequence
2551 2551 100% 0.0 99%
AB365063.1 Pseudomonas sp. Pi 3-62 gene for 16S rRNA, partial sequence
2549 2549 100% 0.0 99%
EU099375.1 Pseudomonas sp. J7 16S ribosomal RNA gene, partial sequence
2549 2549 100% 0.0 99%
321
Accession Description Max score
Total score
Query coverage
E value
Max ident
Links
AY179328.1 Pseudomonas veronii 16S ribosomal RNA gene, partial sequence
2549 2549 100% 0.0 99%
AF539745.1 Pseudomonas veronii UFZ-B549 16S ribosomal RNA gene, partial sequence
2549 2549 100% 0.0 99%
AF195777.1 Pseudomonas sp. H1 16S ribosomal RNA gene, partial sequence
2547 2547 100% 0.0 99%
FM162562.1 Pseudomonas veronii partial 16S rRNA gene, strain MT4
2543 2543 100% 0.0 99%
FJ184354.1 Uncultured soil bacterium clone T7_3 16S ribosomal RNA gene, partial sequence
2543 2543 100% 0.0 99%
Phylogenetic Analysis
The phylogenetic tree showed the interrelationship between BS1FAnGS and top 10 Blast hits from NCBI. D2: BLASTn Analysis Result for the Determination of the Alignment Scores of Forward and Reverse Sequences of Partial 16S rDNA for BS6FAnGS
BS6FAnGS - Forward sequence (338 nucleotides) CCTGCCCATAAGACTGGCATAACTCCGGGAAACCGGGGCTAATACCGGATAAAATTTTGAACCGCATGGTTCGAAATTGAAAGGCGTATTCGATTGTCACTTATGGATGGACCCGCGTCGCGTTAGCTAGTTGGTGAGGTAACGGCTCACCAAGGGAACGATGCGTAGCCGACCTGAGAGGGTGATCGGCCACACTGGCACTGAGACTCGGCCCAGACTCCTACGGGAGGCATCAACAGGGAATCTTCCGCAATGGACGAAAGTCTGACGGAGCAACGCCGCGCGAGTGATTGAGGCTTTCTGGTCGTAAAACTCTGGTGTTATGGAAGAACAAGTGC
Nucleotides marked in RED had been edited for sequence analysis.
AF064460 Pseudomonas veronii
AF539745 Pseudomonas veronii UFZ-B549
FJ594447 Pseudomonas sp. BS2
BS1FAnGS
FM162562 Pseudomonas veronii
AF195777 Pseudomonas sp. H1
AB365063 Pseudomonas sp. Pi 3-62 gene
AY144583 Pseudomonas veronii strain U...
AY179328 Pseudomonas veronii
EU099375 Pseudomonas sp. J7
FJ184354 Uncultured soil bacterium cl...
13
5
4
2
4
11
6330
322
Top 10 Blast hits sequence on the forward sequence from NCBI Accession Description
Max score
Total score
Query coverage
E value
Max ident
Links
FM873953.1 Uncultured bacterium partial 16S rRNA gene, clone MB04A04
525 525 100% 5e-146
94%
EU558974.1 Bacillus sp. cp-h31 16S ribosomal RNA gene, partial sequence
525 525 100% 5e-146
94%
EF422070.1 Bacillus cereus strain S10 16S ribosomal RNA gene, partial sequence
525 525 100% 5e-146
94%
EF113618.1 Bacillus thuringiensis strain IYM2 16S ribosomal RNA gene, partial sequence
521 521 100% 6e-145
94%
FJ598018.1 Bacillus cereus strain Bc6301 16S ribosomal RNA gene, partial sequence
520 520 100% 2e-144
94%
FJ528077.1 Bacillus sp. BM1-4 16S ribosomal RNA gene, partial sequence
520 520 100% 2e-144
94%
FJ598437.1 Bacillus sp. PM-3 16S ribosomal RNA gene, partial sequence
520 520 100% 2e-144
94%
FJ598436.1 Bacillus cereus strain PM-2 16S ribosomal RNA gene, partial sequence
520 520 100% 2e-144
94%
EU429664.1 Bacillus thuringiensis serovar ostriniae 16S ribosomal RNA gene, partial sequence
520 520 100% 2e-144
94%
FJ581461.1 Bacillus cereus strain HWB1 16S ribosomal RNA gene, partial sequence
520 520 100% 2e-144
94%
Phylogenetic Analysis
The phylogenetic tree showed the interrelationship between BS6FAnGS _For and top 10 Blast hits from NCBI.
BS6FAnGS -For
FM873953 Uncultured bacterium
EF422070 Bacillus cereus strain S10
FJ598436 Bacillus cereus strain PM-2
FJ598437 Bacillus sp. PM-3
FJ581461 Bacillus cereus strain HWB1
FJ528077 Bacillus sp. BM1-4
EF113618 Bacillus thuringiensis strai...
EU558974 Bacillus sp. cp-h31
FJ598018 Bacillus cereus strain Bc6301
EU429664 Bacillus thuringiensis serov...
FM162562 Pseudomonas veronii (outgroup)
3358
10
13
4
2
3
3
4
323
BS6FAnGS- Reverse sequence (595 nucleotides) CCGCGATTACTAGCGATTCCAGTTTCATGTAGGCGAGTTGCAGCCTACAATCCAAACTGAAAACGGTTTTATGAGATTAGCTCCACCTCGCGGTCTTGCACCTCTTTGTACCGTCCATTGTAGCACGTGTGTAGCCCAGGTCATAAGGGGCATGATGATTTGACGTCATCCCCACCTTCCTCCGGTTTGTCACCGGCAGTCACCTTAGAGTGCCCAACTTAATGATGGCAACTAAGATCAAGGGTTGCGCTCGTTGCGGGACTTAACCCAACATCTCACGACACGAGCTGACGACAACCATGCACCACCTGTCACTCTGCTCCCGAAGGAGAAGCCCTATCTCTAGGGTTTTCAGAGGATGTCAAGACCTGGTAAGGTTCTTCGCGTTGCTTCGAATTAAACCACATGCTCCACCGCTTGTGCGGGCCCCCGTCAATTCCTTTGAGTTTCAGCCTTGCGGCCGGACTCCCCAGGCGGAGTGCTTAATGCGTTAACTTCAGCACTAAAGGGCGGAAACCCTCTAACACTTAACACTCATCGTTTACGGCGTGGACTACCAGGGTATCTAATCCCTGTTTGCTCCCCACGCTTTCGC Nucleotides marked in RED had been edited for sequence analysis. BS6FAnGS- Reverse complementary sequence GCGAAAGCGTGGGGAGCAAACAGGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGAGTGTTAAGTGTTAGAGGGTTTCCGCCCTTTAGTGCTGAAGTTAACGCATTAAGCACTCCGCCTGGGGAGTCCGGCCGCAAGGCTGAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGAAGAACCTTACCAGGTCTTGACATCCTCTGAAAACCCTAGAGATAGGGCTTCTCCTTCGGGAGCAGAGTGACAGGTGGTGCATGGTTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGATCTTAGTTGCCATCATTAAGTTGGGCACTCTAAGGTGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAATCATCATGCCCCTTATGACCTGGGCTACACACGTGCTACAATGGACGGTACAAAGAGGTGCAAGACCGCGAGGTGGAGCTAATCTCATAAAACCGTTTTCAGTTTGGATTGTAGGCTGCAACTCGCCTACATGAAACTGGAATCGCTAGTAATCGCGG Top 10 Blast hits sequence on the reverse sequence from NCBI
Accession Description Max score
Total score
Query coverage
E value
Max ident
Links
FM209283.1 Bacillus cereus partial 16S rRNA gene, strain TC4
1066 1066 100% 0.0 98%
EU239120.1 Bacillus cereus strain KNUC260 16S ribosomal RNA gene, partial sequence
1061 1061 100% 0.0 98%
AY461756.2 Bacillus sp. H-15 16S ribosomal RNA gene, partial sequence
1061 1061 100% 0.0 98%
AY461752.2 Bacillus sp. H-11 16S ribosomal RNA gene, partial sequence
1061 1061 100% 0.0 98%
DQ067207.1 Bacillus sp. A36 16S ribosomal RNA gene, partial sequence
1061 1061 99% 0.0 98%
EU429669.1 Bacillus thuringiensis serovar toumanoffi 16S ribosomal RNA gene, partial sequence
1059 1059 100% 0.0 98%
EU429668.1 Bacillus thuringiensis serovar thuringiensis 16S ribosomal RNA gene, partial sequence
1059 1059 100% 0.0 98%
EU429666.1 Bacillus thuringiensis serovar cameroun 16S ribosomal RNA gene, partial sequence
1059 1059 100% 0.0 98%
EU429665.1 Bacillus thuringiensis serovar berliner 16S 1059 1059 100% 0.0 98%
324
Accession Description Max score
Total score
Query coverage
E value
Max ident
Links
ribosomal RNA gene, partial sequence
EU429662.1 Bacillus thuringiensis serovar galleriae 16S ribosomal RNA gene, partial sequence
1059 1059 100% 0.0 98%
Phylogenetic Analysis
The phylogenetic tree showed the interrelationship between BS6FAnGS_Rev and top 10 Blast hits from NCBI.
D3: BLASTn Analysis Result for the Determination of the Alignment Scores of Forward and Reverse Sequences of Partial 16S rDNA for BS7FAnGS
BS7FAnGS - Forward sequence (383 nucleotides) TAACACATGCGAGTCGAGCGGATGAAGGGAGCTTGCTCTCTGATTCAGCGGCGGACGGGTGAGTAATGCCTAGGAATCTGCCTGGTAGTGGGGGACAACGTTCCGAAAGGGACGCTAATACCGCATACGTCCTACGGGAGAAAGTGGGGGATCTTCGGACCTCACGCTATCAGATGAGCCTAGGTCGGATTAGCTAGTAGGTGGGGTAATGGCTCACCTAGGCGACGATCCGTAACTGGTCTGAGAGGATGATCAGTCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAATGCACT Nucleotides marked in RED had been edited for sequence analysis.
EU239120 Bacillus cereus strain KNUC260
AY461752 Bacillus sp. H-11
EU429666 Bacillus thuringiensis serov...
EU429662 Bacillus thuringiensis serov...
AY461756 Bacillus sp. H-15
EU429665 Bacillus thuringiensis serov...
FM209283 Bacillus cereus
EU429669 Bacillus thuringiensis serov...
EU429668 Bacillus thuringiensis serov...
BS6FAnGS-Rev
DQ067207 Bacillus sp. A36
FM162562 Pseudomonas veronii (outgroup)
63
11
2
1
1
2
0
0
2
325
Top 10 Blast hits sequence on the forward sequence from NCBI Accession Description
Max score
Total score
Query coverage
E value
Max ident
Links
FJ393104.1 Uncultured proteobacterium clone MFC-B162-E02 16S ribosomal RNA gene gene, partial sequence
697 697 100% 0.0 99%
EU352759.1 Pseudomonas citronellolis strain NK 2.C2-1 16S ribosomal RNA gene, partial sequence
686 686 100% 0.0 98%
EU170480.1 Pseudomonas aeruginosa strain L-4 16S ribosomal RNA gene, partial sequence
686 686 100% 0.0 98%
EU170479.1 Pseudomonas sp. LF-1 16S ribosomal RNA gene, partial sequence
686 686 100% 0.0 98%
EF593111.1 Pseudomonas citronellolis 16S ribosomal RNA gene, partial sequence
682 682 99% 0.0 98%
EU312076.1 Pseudomonas sp. Pds-5 16S ribosomal RNA gene, partial sequence
680 680 98% 0.0 99%
EU287480.1 Pseudomonas sp. J9(2007) 16S ribosomal RNA gene, partial sequence
680 680 100% 0.0 98%
EF660333.1 Pseudomonas sp. LFJS3-9 16S ribosomal RNA gene, partial sequence
680 680 100% 0.0 98%
AB007999.1 Pseudomonas sp. WAS2 gene for 16S rRNA, partial sequence
680 680 100% 0.0 98%
EF379150.1 Uncultured bacterium clone AA4 32 16S ribosomal RNA gene, partial sequence
676 676 98% 0.0 98%
Phylogenetic Analysis
The phylogenetic tree showed the interrelationship between BS7FAnGS_For and top 10 Blast hits from NCBI.
EU170480 Pseudomonas aeruginosa strai...
EF379150 Uncultured bacterium
EU170479 Pseudomonas sp. LF-1 16S
EF593111 Pseudomonas citronellolis
AB007999 Pseudomonas sp. WAS2
EU352759 Pseudomonas citronellolis st...
EU287480 Pseudomonas sp. J9
EF660333 Pseudomonas sp. LFJS3-9
BS7FAnGS-For
FJ393104 Uncultured proteobacterium c...
EU312076 Pseudomonas sp. Pds-5
8865
50
62
6022
62
19
326
BS7FAnGS -Reverse sequence (620 nucleotides) TGGTGACCGTCCCCCCGAAGGTTAGACTAGCTACTTCTGGAGCAACCCACTCCCATGGGGTGACGGGCGGTGTGTACAAGGCCCGGGAACGTATTCACCGTGACATTCTGATTCACGATTACTAGCGATTCCGACTTCACGCAGTCGAGTTGCAGACTGCGATCCGGACTACGATCGGTTTTGTGGGATTAGCTCCACCTCGCGGCTTGGCAACCCTCTGTACCGACCATTGTAGCACGTGTGTAGCCCTGGCCGTAAGGGCCATGATGACTTGACGTCATCCCCACCTTCCTCCGGTTTGTCACCGGCAGTCTCCTTAGAGTGCCCACCTTAACGCGCTGGTAACTAAGGACAAGGGTTGCGCTCGTTACGGGACTTAACCCAACATCTCACGACACGAGCTGACGACAGCCATGCAGCACCTGTGTTCCGATTCCCGAAGGCACTCCCACATCTCTGCAGGATTCCGGACATGTCAAGGCCAGGTAAGGTTCTTCGCGTTGCTTCAAATTAAACCACATGCTCCACCGCTTGTGCGGGCCCCCGTCAATTCATTTGAGTTTTAACCTTGCGGGCCGTACTCCCCAGGCGGTCGACTTATCGCGTTAGCTGCGCCACTA Nucleotides marked in RED had been edited for sequence analysis. BS7FAnGS -Reverse complementary sequence TAGTGGCGCAGCTAACGCGATAAGTCGACCGCCTGGGGAGTACGGCCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTTGAAGCAACGCGAAGAACCTTACCTGGCCTTGACATGTCCGGAATCCTGCAGAGATGTGGGAGTGCCTTCGGGAATCGGAACACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGTAACGAGCGCAACCCTTGTCCTTAGTTACCAGCGCGTTAAGGTGGGCACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACGGCCAGGGCTACACACGTGCTACAATGGTCGGTACAGAGGGTTGCCAAGCCGCGAGGTGGAGCTAATCCCACAAAACCGATCGTAGTCCGGATCGCAGTCTGCAACTCGACTGCGTGAAGTCGGAATCGCTAGTAATCGTGAATCAGAATGTCACGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGTTGCTCCAGAAGTAGCTAGTCTAACCTTCGGGGGGACGGTCACCA Top 10 Blast hits sequence on the reverse sequence from NCBI
Accession Description Max score
Total score
Query coverage
E value
Max ident
Links
EU287480.1 Pseudomonas sp. J9(2007) 16S ribosomal RNA gene, partial sequence
1110 1110 100% 0.0 99%
AF039488.1 Pseudomonas sp. 273 small subunit ribosomal RNA gene, partial sequence
1105 1105 100% 0.0 98%
DQ339153.1 Pseudomonas sp. RLD-1 16S ribosomal RNA gene, partial sequence
1099 1099 100% 0.0 98%
EU043324.1 Pseudomonas sp. SBR3-tpnb 16S ribosomal RNA gene, partial sequence
1096 1096 98% 0.0 99%
DQ926686.1 Uncultured bacterium clone N3 16S ribosomal RNA gene, partial sequence
1094 1094 100% 0.0 98%
327
Accession Description Max score
Total score
Query coverage
E value
Max ident
Links
EU312076.1 Pseudomonas sp. Pds-5 16S ribosomal RNA gene, partial sequence
1092 1092 99% 0.0 98%
FJ470323.1 Uncultured bacterium clone SLB16 16S ribosomal RNA gene, partial sequence
1088 1088 100% 0.0 98%
DQ136054.2 Bacterium PT09 16S ribosomal RNA gene, partial sequence
1088 1088 100% 0.0 98%
EU170480.1 Pseudomonas aeruginosa strain L-4 16S ribosomal RNA gene, partial sequence
1088 1088 100% 0.0 98%
EU170479.1 Pseudomonas sp. LF-1 16S ribosomal RNA gene, partial sequence
1088 1088 100% 0.0 98%
Phylogenetic Analysis
The phylogenetic tree showed the interrelationship between BS7FAnGS_Rev and top 10 Blast hits from NCBI.
D4: BLASTn Analysis Result for the Determination of the Alignment Scores of Forward and Reverse Sequences of Partial 16S rDNA for BS10FAnGS
BS10FAnGS– Forward sequence (538 nucleotides) GGCCCTACACATGCGAGTCGAGCGGTAGAGAGAAGCTTGCTTCTCTTGAGAGCGGCGGACGGGTGAGTAATGCCTAGGAATCTGCCTGGTAGTGGGGGATAACGTTCGGAAACGGACGCTAATACCGCATACGTCCTACGGGAGAAAGCAGGGGACCTTCGGGCCTTGCGCTATCAGATGAGCCTAGGTCGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCGTAACTGGTCTGAGAGGATGATCAGTCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGCAGTTACCTAATACGAGATTGTTTTGACGTTACCGACAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCCCAATTACTGGGCGTAAAGCGCGCGTAGGTGG
AF039488 Pseudomonas sp. 273
DQ926686 Uncultured bacterium clone N3
DQ339153 Pseudomonas sp. RLD-1
EU312076 Pseudomonas sp. Pds-5
EU287480 Pseudomonas sp. J9
BS7FAnGS- Rev EU043324 Pseudomonas sp. SBR3-tpnb
FJ470323 Uncultured bacterium clone S...
DQ136054 Bacterium PT09
EU170480 Pseudomonas aeruginosa strai...
EU170479 Pseudomonas sp. LF-1
74
50
78
67
58
79
98
69
328
Nucleotides marked in RED had been edited for sequence analysis. Top 10 Blast hits sequence on the forward sequence from NCBI
Accession Description Max score
Total score
Query coverage
E value
Max ident
Links
DQ536516.1 Pseudomonas trivialis strain BIHB 745 16S ribosomal RNA gene, partial sequence
966 966 100% 0.0 99%
EF379138.1 Uncultured bacterium clone Mat Z4 19 16S ribosomal RNA gene, partial sequence
965 965 99% 0.0 99%
DQ264528.1 Uncultured bacterium clone BANW559 16S ribosomal RNA gene, partial sequence
965 965 99% 0.0 99%
EU086550.1 Bacterium THCL4 16S ribosomal RNA gene, partial sequence
963 963 99% 0.0 99%
FM162562.1 Pseudomonas veronii partial 16S rRNA gene, strain MT4
961 961 99% 0.0 99%
FJ434132.1 Pseudomonas sp. IMER-A2-24 16S ribosomal RNA gene, partial sequence
961 961 99% 0.0 99%
FJ184354.1 Uncultured soil bacterium clone T7_3 16S ribosomal RNA gene, partial sequence
961 961 99% 0.0 99%
FJ184352.1 Uncultured soil bacterium clone T7_7 16S ribosomal RNA gene, partial sequence
961 961 99% 0.0 99%
FJ184350.1 Uncultured soil bacterium clone T7_14 16S ribosomal RNA gene, partial sequence
961 961 99% 0.0 99%
FJ184346.1 Uncultured soil bacterium clone T8_5 16S ribosomal RNA gene, partial sequence
961 961 99% 0.0 99%
Phylogenetic Analysis
The phylogenetic tree showed the interrelationship between BS10FAnGS_For and top 10 Blast hits from NCBI.
EF379138 Uncultured bacterium clone M...
FJ184350 Uncultured soil bacterium cl...
EU086550 Bacterium THCL4
FJ184354 Uncultured soil bacterium cl...
DQ536516 Pseudomonas trivialis strain...
DQ264528 Uncultured bacterium clone B...
FM162562 Pseudomonas veronii
FJ184352 Uncultured soil bacterium cl...
FJ434132 Pseudomonas sp. IMER-A2-24
FJ184346 Uncultured soil bacterium cl...
BS10FAnGS
AE005177 Escherichia coli (outgroup)
10
6
6
3
0
1
1
48
8
329
BS10FAnGS -Reverse sequence (787 nucleotides) GTCCCCCCGAAGGTTAGACTAGCTACTTCTGGTGCAACCCACTCCCATGGTGTGACGGGCGGTGTGTACAAGGCCCGGGAACGTATTCACCGCGACATTCTGATTCGCGATTACTAGCGATTCCGACTTCACGCAGTCGAGTTGCAGACTGCGATCCGGACTACGATCGGTTTTCTGGGATTAGCTCCACCTCGCGGCTTGGCAACCCTCTGTACCGACCATTGTAGCACGTGTGTAGCCCAGGCCGTAAGGGCCATGATGACTTGACGTCATCCCCACCTTCCTCCGGTTTGTCACCGGCAGTCTCCTTAGAGTGCCCACCATTACGTGCTGGTAACTAAGGACAAGGGTTGCGCTCGTTACGGGACTTAACCCAACATCTCACGACACGAGCTGACGACAGCCATGCAGCACCTGTCTCAATGTTCCCGAAGGCACCAATCCATCTCTGGAAAGTTCATTGGATGTCAAGGCCTGGTAAGGTTCTTCGCGTTGCTTCGAATTAAACCACATGCTCCACCGCTTGTGCGGGCCCCCGTCAATTCATTTGAGTTTTAACCTTGCGGCCGTACTCCCCAGGCGGTCAACTTAATGCGTTAGCTGCGCCACTAAAGAGCTCAAGGCTCCCAACGGCTAGTTGACATCGTTTACGGCGTGGACTACCAGGGTATCTAATCCTGTTTGCTCCCCACGCTTTCGCACCTCAGTGTCAGTATCAGTCAGGTGGTCGCCTTCGCCACTGGTGTTCCTTCCTATATCTACGCATTTCACCGCTACACAGGAAATT Nucleotides marked in RED had been edited for sequence analysis BS10FAnGS -Reverse complementary sequence AATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCAACTAGCCGTTGGGAGCCTTGAGCTCTTTAGTGGCGCAGCTAACGCATTAAGTTGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGAAGAACCTTACCAGGCCTTGACATCCAATGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACATTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGTAACGAGCGCAACCCTTGTCCTTAGTTACCAGCACGTAATGGTGGGCACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACGGCCTGGGCTACACACGTGCTACAATGGTCGGTACAGAGGGTTGCCAAGCCGCGAGGTGGAGCTAATCCCAGAAAACCGATCGTAGTCCGGATCGCAGTCTGCAACTCGACTGCGTGAAGTCGGAATCGCTAGTAATCGCGAATCAGAATGTCGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGTTGCACCAGAAGTAGCTAGTCTAACCTTCGGGGGGAC Top 10 Blast hits sequence on the reverse sequence from NCBI
Accession Description Max score
Total score
Query coverage
E value
Max ident
Links
EU934227.1 Pseudomonas sp. LaGso27g, 16S ribosomal RNA gene, partial sequence
1443 1443 100% 0.0 99%
AY512607.1 Pseudomonas sp. A3YXyl2-4 16S ribosomal RNA gene, partial sequence
1443 1443 100% 0.0 99%
AY263479.1 Pseudomonas sp. R1enr 16S ribosomal RNA gene, partial sequence
1443 1443 100% 0.0 99%
AY144583.1 Pseudomonas veronii strain UFZ-B547 16S ribosomal RNA gene, partial sequence
1443 1443 100% 0.0 99%
330
Accession Description Max score
Total score
Query coverage
E value
Max ident
Links
AY882021.1 Pseudomonas sp. GD100 16S ribosomal RNA gene, partial sequence
1443 1443 100% 0.0 99%
AF058286.1 Pseudomonas mandelii 16S ribosomal RNA gene, complete sequence
1443 1443 100% 0.0 99%
DQ339583.1 Pseudomonas sp. Enf22 16S ribosomal RNA gene, partial sequence
1443 1443 100% 0.0 99%
AF064460.1 Pseudomonas veronii 16S ribosomal RNA gene, complete sequence
1443 1443 100% 0.0 99%
FJ594447.1 Pseudomonas sp. BS2(2009) 16S ribosomal RNA gene, partial sequence
1437 1437 100% 0.0 99%
FJ517635.1 Pseudomonas sp. DM2 16S ribosomal RNA gene, partial sequence
1437 1437 100% 0.0 99%
Phylogenetic Analysis
The phylogenetic tree showed the interrelationship between BS10FAnGS_Rev and top 10 Blast hits from NCBI.
D5: BLASTn Analysis Result for the Determination of the Alignment Scores of the Full Sequence of 16S rDNA for BS11FAnGS
BS11FAnGS -Full sequence (1429 nucleotides) TGCGGCAGGGCTACACATGCAGTCGAGCGGTAGCACAGAGAGCTTGCTCTCGGGTGACGAGCGGCGGACGGGTGAGTAATGTCTGGGAAACTGCCTGATGGAGGGGGATAACTACTGGAAACGGTAGCTAATACCGCATAACGTCGCAAGACCAAAGAGGGGGACCTTCGGGCCTCTTGCCATCAGATGTGCCCAGATGGGATTAGCTAGTAGGTGGGGTAACGGCTCACCTAGGCGACGATCCCTAGCTGGTCTGAGAGGATGACCAGCCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTATGAAGAAGGCCTTCGGGTTGTAAAGTACTTTCAGCGGGGAGGAAGGTGCTGAGGTTAATAAC
BS10FAnGS Rev
FJ594447 Pseudomonas sp. BS
AF058286 Pseudomonas mandelii
DQ339583 Pseudomonas sp. Enf22
AY263479 Pseudomonas sp. R1enr
EU934227 Pseudomonas sp. LaGso27g
AY512607 Pseudomonas sp. A3YXyl2-4
AY144583 Pseudomonas veronii strain U...
AF064460 Pseudomonas veronii
AY882021 Pseudomonas sp. GD100
FJ517635 Pseudomonas sp. DM2
13
10
9
8
3
0
2
11
331
CTCAGCAATTGACGTTACCCGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCGACTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTAACGCGTTAAGTCGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGCAACGCGAAGAACCTTACCTACTCTTGACATCCAGAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTCTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTTGTGAAATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTATCCTTTGTTGCCAGCGGTTCGGCCGGGAACTCAAAGGAGACTGCCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACGAGTAGGGCTACACACGTGCTACAATGGCGCATACAAAGAGAAGCGACCTCGCGAGAGCAAGCGGACCTCATAAAGTGCGTCGTAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTACGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGTTGCAAAAGAAGTAGGTAGCTTAACCTTCGGGAGGGCGCTCACCATC Nucleotides marked in RED had been edited for sequence analysis. Top 10 Blast hits sequence on the full length sequence from NCBI
Accession Description Max score
Total score
Query coverage
E value
Max ident
Links
EU781735.1 Enterobacter sp. VET-7 16S ribosomal RNA gene, partial sequence
2603 2603 99% 0.0 99%
EU331414.1 Enterobacter sp. L3R3-1 16S ribosomal RNA gene, partial sequence
2603 2603 99% 0.0 99%
EU221358.1 Enterobacter asburiae strain J2S4 16S ribosomal RNA gene, partial sequence
2603 2603 99% 0.0 99%
DQ068845.1 Uncultured bacterium clone 5s2 16S ribosomal RNA gene, partial sequence
2591 2591 99% 0.0 99%
DQ068880.1 Uncultured bacterium clone bb2s4 16S ribosomal RNA gene, partial sequence
2588 2588 99% 0.0 99%
AB244469.1 Enterobacter cloacae gene for 16S rRNA, partial sequence, strain: NC1111
2586 2586 99% 0.0 99%
AB114621.1 Uncultured Enterobacteriaceae bacterium gene for 16S rRNA, partial sequence, clone:ER-9
2586 2586 99% 0.0 99%
AM184307.1 Pantoea agglomerans partial 16S rRNA gene, strain WAB1969
2582 2582 99% 0.0 99%
FJ445214.1 Pantoea sp. NIIST-186 16S ribosomal RNA, partial sequence
2580 2580 99% 0.0 99%
EF655641.1 Uncultured bacterium clone B12 16S ribosomal RNA gene, partial sequence
2580 2580 99% 0.0 99%
332
Phylogenetic Analysis
The phylogenetic tree showed the interrelationship between BS11FAnGS and top 10 Blast hits from NCBI. D6: BLASTn analysis result for the determination of the alignment scores of the full sequence of 16S rDNA for BS12FAnGS
BS12FAnGS -Full Length sequence (1403 nucleotides) GTCGAGCGGTAGAGAGAAGCTTGCTTCTCTTGAGAGCGGCGGACGGGTGAGTAATGCCTAGGAATCTGCCTGGTAGTGGGGGATAACGTTCGGAAACGGACGCTAATACCGCATACGTCCTACGGGAGAAAGCAGGGGACCTTCGGGCCTTGCGCTATCAGATGAGCCTAGGTCGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCGTAACTGGTCTGAGAGGATGATCAGTCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGCAGTTACCTAATACGTGATTGCTTTGACGTTACCGACACAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCTTAATTACTGGTCATAAAGCGCGCGTAGGTGGGTTTGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCAAAACTGACTGACTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCAACTAGCCGTTGGGAGCCTTGAGCTTTTAGTGGCGCAGCTAACGCATTAAGTTGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGAAGAACCTTACCAGGCCTTGACATCCAATGAACTTTCTAGAGATAGATTGGTGCCTTCGGGAACATTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGTAACGAGCGCAACCCTTGTCCTTAGTTACCAGCACGTAATGGTGGGCACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACGGCCTGGGCTACACACGTGCTACAATGGTCGGTACAGAGGGTTGCCAAGCCGCGAGGTGGAGCTAATCCCAGAAAACCGATCGTAGTCCGGATCGCAGTCTGCAACTCGACTGCGTGAAGTCGGAATCGCTAGTAATCGCGAATCA
DQ068845 Uncultured bacterium clone 5s2
FJ445214 Pantoea sp. NIIST-186
EF655641 Uncultured bacterium clone B12
EU221358 Enterobacter asburiae strain...
AB244469 Enterobacter cloacae gene
DQ068880 Uncultured bacterium clone b...
EU331414 Enterobacter sp. L3R3-1
AM184307 Pantoea agglomerans
BS11FAnGS
EU781735 Enterobacter sp. VET-7
AB114621 Uncultured Enterobacteriacea...
60
51
50
4421
12
27
50
333
GAATGTCGCGGTGAATACGTTCCCGGGCCTTGTACACCCCGCCCGTCACACCATGGGAGTGGGTTGCACCAGAAGTAGCTAGTCTAACCCTCGGGAGGACGGTACCA Nucleotides marked in RED had been edited for sequence analysis. Top 10 Blast hits sequence on the full length sequence from NCBI
Accession Description Max score
Total score
Query coverage
E value
Max ident
Links
AJ583501.3 Pseudomonas extremaustralis 16S rRNA gene, strain CT14-3
2536 2536 100% 0.0 99%
DQ136048.2 Bacterium PT03 16S ribosomal RNA gene, partial sequence
2536 2536 100% 0.0 99%
AB056120.1 Pseudomonas veronii gene for 16S rRNA, strain:INA06
2536 2536 100% 0.0 99%
AM421982.1 Pseudomonas sp. NJ-61 16S rRNA gene, strain NJ-61
2531 2531 100% 0.0 99%
AY014806.1 Pseudomonas sp. NZ024 16S ribosomal RNA gene, partial sequence
2531 2531 100% 0.0 99%
FJ179366.1 Pseudomonas trivialis strain BIHB 750 16S ribosomal RNA gene, partial sequence
2525 2525 100% 0.0 99%
EU086570.1 Bacterium TLCL3 16S ribosomal RNA gene, partial sequence
2525 2525 100% 0.0 99%
AB334768.1 Pseudomonas veronii gene for 16S ribosomal RNA, partial sequence
2525 2525 100% 0.0 99%
DQ536516.1 Pseudomonas trivialis strain BIHB 745 16S ribosomal RNA gene, partial sequence
2525 2525 100% 0.0 99%
AY599721.1 Pseudomonas sp. TB3-6-I 16S ribosomal RNA gene, partial sequence
2525 2525 100% 0.0 99%
Phylogenetic Analysis
The phylogenetic tree showed the interrelationship between BS12FAnGS and top 10 Blast hits from NCBI.
AJ583501 Pseudomonas extremaustralis
AY014806 Pseudomonas sp. NZ024
DQ136048 Bacterium PT03
AB056120 Pseudomonas veronii gene
AM421982 Pseudomonas sp. NJ-61
BS12FAnGS
AY599721 Pseudomonas sp. TB3-6-I
FJ179366 Pseudomonas trivialis strain...
DQ536516 Pseudomonas trivialis strain...
EU086570 Bacterium TLCL3
AB334768 Pseudomonas veronii gene
EF422070 Bacillus cereus (outgroup)
53
17
12
51
49
33
37
3593
334
Service Report Microorganism Identification By 16S/18S rRNA Sequencing
Customer Name : Dr. Azmi Aris/ Date: 12th February 2009 Ms Khalida Muda Institute/Company : Dept of Environmental Eng.
Faculty of Civil Eng. UTM
Sample ID Our Label Comment Remark
1. BS1FAnGS G1 Phylogenetic analysis using both 5’ (forward) and 3’ (reverse) sequences showed that this bacteria is closely related to Pseudomonas veronii
Sequencing results, phylogenetic trees, multiple alignment and top 10 sequences match were supplied.
2. BS6FAnGS 02 Phylogenetic analysis using both 5’ (forward) and 3’ (reverse) sequences showed that this bacteria is closely related to Bacillus cereus
Sequencing results, phylogenetic trees, multiple alignment and top 10 sequences match were supplied.
3. BS7FAnGS 03 Phylogenetic analysis using both 5’ (forward) and 3’ (reverse) sequences showed that this bacteria is closely related to Pseudomonas sp.
Sequencing results, phylogenetic trees, multiple alignment and top 10 sequences match were supplied.
4. BS10FAnGS N2 Phylogenetic analysis using both 5’ (forward) and 3’ (reverse) sequences showed that this bacteria is closely related to Pseudomonas sp.
Sequencing results, phylogenetic trees, multiple alignment and top 10 sequences match were supplied.
5. BS11FAnGS N3 Phylogenetic analysis using both 5’ (forward) and 3’ (reverse) sequences showed that this bacteria is closely related to Enterobacter sp.
Sequencing results, phylogenetic trees, multiple alignment and top 10 sequences match were supplied.
6. BS12FAnGS N4 Phylogenetic analysis using both 5’ (forward) and 3’ (reverse) sequences showed that this bacteria is closely related to Pseudomonas sp.
Sequencing results, phylogenetic trees, multiple alignment and top 10 sequences match were supplied.
In order to view the sequencing result, the Chromas software need to be used. The Chromas freeware is available at http://www.technelysium.com.au/chromas_lite.html Prepared by: Chan Toh Theng Lab Manager Vivantis Technologies Sdn. Bhd.
VIVANTIS TECHNOLOGIES SDN. BHD. (587389-D)
Suite 2-B, No. 23, Jalan U1/15, Hicom Glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia. Tel: 603-5569 5785 Fax: 603-5569 5786 URL: http://www.vivantis.com E-mail: [email protected]
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APPENDIX E: FACTORIAL DESIGN AND RESPONSE SURFACE METHODOLOGY DATA ANALYSIS FOR COAGGREGATION AND
SURFACE HDROPHOBICITYASSAY E-1: Factorial Design Analysis for Coaggregation Assay (1 hour) Fractional Factorial Fit: CAgg_1h versus Substrate, pH, Temperature Estimated Effects and Coefficients for CAgg_1h (coded units) Term Effect Coef SE Coef T P Constant 53.025 1.006 52.70 0.000 Substrat 7.775 3.887 1.006 3.86 0.005 pH -9.725 -4.862 1.006 -4.83 0.001 Temperat 15.325 7.663 1.006 7.62 0.000 Substrat*pH -6.600 -3.300 1.006 -3.28 0.011 Substrat*Temperat -2.200 -1.100 1.006 -1.09 0.306 pH*Temperat -0.650 -0.325 1.006 -0.32 0.755 Substrat*pH*Temperat 1.325 0.663 1.006 0.66 0.529 Analysis of Variance for CAgg_1h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 3 1559.53 1559.53 519.843 32.10 0.000 2-Way Interactions 3 195.29 195.29 65.097 4.02 0.051 3-Way Interactions 1 7.02 7.02 7.023 0.43 0.529 Residual Error 8 129.57 129.57 16.196 Pure Error 8 129.57 129.57 16.196 Total 15 1891.41 E-2: Factorial Design Analysis for Coaggregation Assay (2 hour) Fractional Factorial Fit: CAgg_2h versus Substrate, pH, Temperature Estimated Effects and Coefficients for CAgg_2h (coded units) Term Effect Coef SE Coef T P Constant 57.775 1.318 43.83 0.000 Substrat 11.350 5.675 1.318 4.31 0.003 pH -15.375 -7.687 1.318 -5.83 0.000 Temperat 12.925 6.463 1.318 4.90 0.001 Substrat*pH -7.475 -3.737 1.318 -2.84 0.022 Substrat*Temperat -4.775 -2.388 1.318 -1.81 0.108 pH*Temperat -4.100 -2.050 1.318 -1.56 0.159 Substrat*pH*Temperat -4.500 -2.250 1.318 -1.71 0.126 Analysis of Variance for CAgg_2h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 3 2129.07 2129.07 709.69 25.53 0.000 2-Way Interactions 3 381.95 381.95 127.32 4.58 0.038 3-Way Interactions 1 81.00 81.00 81.00 2.91 0.126 Residual Error 8 222.41 222.41 27.80 Pure Error 8 222.41 222.41 27.80 Total 15 2814.43
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E-3: Factorial Design Analysis for Coaggregation Assay (3 hour) Fractional Factorial Fit: CAgg_3h versus Substrate, pH, Temperature Estimated Effects and Coefficients for CAgg_3h (coded units) Term Effect Coef SE Coef T P Constant 59.363 2.417 24.56 0.000 Substrat 17.375 8.687 2.417 3.59 0.007 pH -11.225 -5.612 2.417 -2.32 0.049 Temperat 9.200 4.600 2.417 1.90 0.094 Substrat*pH -1.775 -0.887 2.417 -0.37 0.723 Substrat*Temperat 0.800 0.400 2.417 0.17 0.873 pH*Temperat -6.900 -3.450 2.417 -1.43 0.191 Substrat*pH*Temperat -1.700 -0.850 2.417 -0.35 0.734 Analysis of Variance for CAgg_3h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 3 2050.12 2050.12 683.37 7.31 0.011 2-Way Interactions 3 205.60 205.60 68.53 0.73 0.561 3-Way Interactions 1 11.56 11.56 11.56 0.12 0.734 Residual Error 8 747.75 747.75 93.47 Pure Error 8 747.75 747.75 93.47 Total 15 3015.04 E-4: Factorial Design Analysis for Coaggregation Assay (4 hour) Fractional Factorial Fit: CAgg_4h versus Substrate, pH, Temperature Estimated Effects and Coefficients for CAgg_4h (coded units) Term Effect Coef SE Coef T P Constant 61.944 2.134 29.02 0.000 Substrat 18.637 9.319 2.134 4.37 0.002 pH -9.637 -4.819 2.134 -2.26 0.054 Temperat 5.462 2.731 2.134 1.28 0.236 Substrat*pH -4.637 -2.319 2.134 -1.09 0.309 Substrat*Temperat -2.088 -1.044 2.134 -0.49 0.638 pH*Temperat -6.562 -3.281 2.134 -1.54 0.163 Substrat*pH*Temperat 1.238 0.619 2.134 0.29 0.779 Analysis of Variance for CAgg_4h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 3 1880.31 1880.31 626.769 8.60 0.007 2-Way Interactions 3 275.72 275.72 91.907 1.26 0.351 3-Way Interactions 1 6.13 6.13 6.126 0.08 0.779 Residual Error 8 583.03 583.03 72.878 Pure Error 8 583.03 583.03 72.878 Total 15 2745.18
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E-5: Factorial Design Analysis for Coaggregation Assay (5 hours) Fractional Factorial Fit: CAgg_5h versus Substrate, pH, Temperature Estimated Effects and Coefficients for CAgg_5h (coded units) Term Effect Coef SE Coef T P Constant 58.406 0.4839 120.69 0.000 Substrat 19.362 9.681 0.4839 20.01 0.000 pH -13.837 -6.919 0.4839 -14.30 0.000 Temperat 5.562 2.781 0.4839 5.75 0.000 Substrat*pH -0.587 -0.294 0.4839 -0.61 0.561 Substrat*Temperat 0.713 0.356 0.4839 0.74 0.483 pH*Temperat -12.287 -6.144 0.4839 -12.70 0.000 Substrat*pH*Temperat -0.737 -0.369 0.4839 -0.76 0.468 Analysis of Variance for CAgg_5h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 3 2389.30 2389.30 796.432 212.56 0.000 2-Way Interactions 3 607.34 607.34 202.447 54.03 0.000 3-Way Interactions 1 2.18 2.18 2.176 0.58 0.468 Residual Error 8 29.98 29.98 3.747 Pure Error 8 29.97 29.97 3.747 Total 15 3028.79
E-6: Factorial Design Analysis for Surface Hydrophobicity Assay Fractional Factorial Fit: SHb (%) versus Substrate, pH, Temperature Estimated Effects and Coefficients for SHb (coded units) Term Effect Coef SE Coef T P Constant 30.15 0.4889 61.66 0.000 Substrat 4.95 2.47 0.4889 5.06 0.001 pH -8.05 -4.02 0.4889 -8.23 0.000 Temperat -4.43 -2.21 0.4889 -4.53 0.002 Substrat*pH -11.25 -5.62 0.4889 -11.50 0.000 Substrat*Temperat -0.18 -0.09 0.4889 -0.18 0.862 pH*Temperat -20.82 -10.41 0.4889 -21.30 0.000 Substrat*pH*Temperat -5.13 -2.56 0.4889 -5.24 0.001 Analysis of Variance for SHb (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 3 435.54 435.54 145.181 37.96 0.000 2-Way Interactions 3 2241.09 2241.09 747.032 195.30 0.000 3-Way Interactions 1 105.06 105.06 105.063 27.47 0.001 Residual Error 8 30.60 30.60 3.825 Pure Error 8 30.60 30.60 3.825 Total 15 2812.30
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E-7: Response Surface Modeling Analysis for Coaggregation Assay (Transforms) (Full Quadratic Terms) (5 hours) Response Surface Regression: Coaggregation versus Substrate, pH and Temperature Transform: Power Lambda: 2.05 Constant: 0 Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 6.307E+007 9 7.007E+006 7.20 0.0024 significant A-Substrate 1.128E+007 1 1.128E+007 11.59 0.0067 B-pH 1.623E+007 1 1.623E+007 16.67 0.0022 C-Temperature 5.639E+006 1 5.639E+006 5.79 0.0369 AB 2.362E+005 1 2.362E+005 0.24 0.6329 AC 56360.44 1 56360.44 0.058 0.8147 BC 7.442E+006 1 7.442E+006 7.65 0.0200 A2 1.322E+007 1 1.322E+007 13.58 0.0042 B2 5.244E+006 1 5.244E+006 5.39 0.0427 C2 1.521E+006 1 1.521E+006 1.56 0.2397 Residual 9.734E+006 10 9.734E+005 Lack of Fit 9.242E+006 5 1.848E+006 18.81 0.0029 significant Pure Error 4.912E+005 5 98249.40 Cor Total 7.280E+007 19 Std. Dev. 986.59 R-Squared 0.8663 Mean 4133.89 Adj R-Squared 0.7460 C.V. % 23.87 Pred R-Squared 0.0221
PRESS `7.119E+007 Adeq Precision 11.147 Coefficient Standard 95% CI 95% CI
Factor Estimate df Error Low High VIF Intercept 4113.74 1 402.38 3217.19 5010.30 A-Substrate 908.95 1 266.97 314.11 1503.80 1.00 B-pH -1090.06 1 266.97 -1684.90 -495.21 1.00 C-Temperature 642.60 1 266.97 47.75 1237.45 1.00 AB -171.85 1 348.81 -949.05 605.36 1.00 AC 83.93 1 348.81 -693.27 861.14 1.00 BC -964.48 1 348.81 -1741.68 -187.27 1.00 A2 957.62 1 259.89 378.56 1536.69 1.02 B2 -603.20 1 259.89 -1182.26 -24.13 1.02 C2 -324.93 1 259.89 -903.99 254.14 1.02
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E-8: Response Surface Modeling Analysis for Coaggregation Assay (Transforms) (5 hours) (Linear + Square + pH x Tempareture) Response Surface Regression: Coaggregation versus Substrate, pH and Temperature Transform: Power Lambda: 2. Constant: 0 Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 3.912E+007 6 6.520E+006 11.43 0.0002 significant A-Substrate 7.112E+006 1 7.112E+006 12.47 0.0037 B-Ph 1.047E+007 1 1.047E+007 18.36 0.0009 C-Temperature 3.590E+006 1 3.590E+006 6.29 0.0262 BC 4.676E+006 1 4.676E+006 8.20 0.0133 A2 9.044E+006 1 9.044E+006 15.85 0.0016 B2 3.152E+006 1 3.152E+006 5.53 0.0352 Residual 7.416E+006 13 5.705E+005 Lack of Fit 7.105E+006 8 8.881E+005 14.25 0.0048 significant Pure Error 3.116E+005 5 62320.34 Cor Total 4.654E+007 19 Std. Dev. 755.31 R-Squared 0.8406 Mean 3363.73 Adj R-Squared 0.7671 C.V. % 22.45 Pred R-Squared 0.4608 PRESS 2.509E+007 Adeq Precision 13.947 Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High VIF Intercept 3143.25 1 261.49 2578.33 3708.17 A-Substrate 721.65 1 204.39 280.10 1163.19 1.00 B-pH -875.70 1 204.39 -1317.25 -434.15 1.00 C-Temperature 512.68 1 204.39 71.13 954.23 1.00 BC -764.56 1 267.04 -1341.47 -187.65 1.00 A2 788.26 1 197.98 360.55 1215.98 1.01 B2 -465.39 1 197.98 -893.10 -37.67 1.01
340
E-9: Response Surface Modeling Analysis for Surface Hydrophobicity Assay
ANOVA for Response Surface Quadratic Model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 9065.62 9 1007.29 3.50 0.0321 significant A-Substrate 267.37 1 267.37 0.93 0.3582 B-pH 1137.96 1 1137.96 3.95 0.0750 C-Temperature 3.10 1 3.10 0.011 0.9194 AB 265.65 1 265.65 0.92 0.3596 AC 6.30 1 6.30 0.022 0.8854 BC 822.15 1 822.15 2.85 0.1221 A2 1559.95 1 1559.95 5.41 0.0423 B2 5472.23 1 5472.23 18.99 0.0014 C2 290.17 1 290.17 1.01 0.3393 Residual 2881.90 10 288.19 Lack of Fit 2862.19 5 572.44 145.17 < 0.0001 significant Pure Error 19.72 5 3.94 Cor Total 11947.53 19 Std. Dev. 16.98 R-Squared 0.7588 Mean 51.96 Adj R-Squared 0.5417 C.V. % 32.67 Pred R-Squared -0.8204 PRESS 21749.41 Adeq Precision 5.870 Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High VIF Intercept 75.43 1 6.92 60.01 90.86 A-Substrate 4.42 1 4.59 -5.81 14.66 1.00 B-pH -9.13 1 4.59 -19.36 1.11 1.00 C-Temperature 0.48 1 4.59 -9.76 10.71 1.00 AB -5.76 1 6.00 -19.14 7.61 1.00 AC -0.89 1 6.00 -14.26 12.49 1.00 BC -10.14 1 6.00 -23.51 3.24 1.00 A2 -10.40 1 4.47 -20.37 -0.44 1.02 B2 -19.49 1 4.47 -29.45 -9.52 1.02 C2 -4.49 1 4.47 -14.45 5.48 1.02
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APPENDIX F: FACTORIAL DESIGN AND RESPONSE SURFACE METHODOLOGY DATA ANALYSIS FOR COD REMOVAL
F1: Factorial Design Analysis for COD Removal Fractional Factorial Fit: ANA_COD versus Substrate, RM Estimated Effects and Coefficients for ANA_COD (coded units) Term Effect Coef SE Coef T P Constant 53.225 1.048 50.78 0.000 Substrate 50.450 25.225 1.048 24.07 0.000 RM -2.100 -1.050 1.048 -1.00 0.373 Substrate*RM 0.700 0.350 1.048 0.33 0.755 Analysis of Variance for ANA_COD (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 2 5099.22 5099.22 2549.61 290.14 0.000 2-Way Interactions 1 0.98 0.98 0.98 0.11 0.755 Residual Error 4 35.15 35.15 8.79 Pure Error 4 35.15 35.15 8.79 Total 7 5135.35 Estimated Coefficients for ANA_COD using data in uncoded units Term Coef Constant 28.1989 Substrate 0.0589095 RM -0.00113257 Substrate*RM 6.625473E-07 F2: Factorial Design Analysis for Aerobic COD Removal Fractional Factorial Fit: AER_COD versus Substrate, RM Estimated Effects and Coefficients for AER_COD (coded units) Term Effect Coef SE Coef T P Constant 44.68 0.4131 108.15 0.000 Substrate -32.45 -16.22 0.4131 -39.28 0.000 RM -7.95 -3.97 0.4131 -9.62 0.001 Substrate*RM 6.35 3.17 0.4131 7.69 0.002 Analysis of Variance for AER_COD (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 2 2232.41 2232.41 1116.20 817.73 0.000 2-Way Interactions 1 80.64 80.64 80.64 59.08 0.002 Residual Error 4 5.46 5.46 1.37 Pure Error 4 5.46 5.46 1.36 Total 7 2318.51
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F3: Factorial Design Analysis for Total COD Removal Fractional Factorial Fit: Total COD versus Substrate, RM Estimated Effects and Coefficients for Total (coded units) Term Effect Coef SE Coef T P Constant 78.175 0.2767 282.53 0.000 Substrate 12.800 6.400 0.2767 23.13 0.000 RM -6.350 -3.175 0.2767 -11.47 0.000 Substrate*RM 5.000 2.500 0.2767 9.04 0.001 Analysis of Variance for Total (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 2 408.325 408.325 204.162 333.33 0.000 2-Way Interactions 1 50.000 50.000 50.000 81.63 0.001 Residual Error 4 2.450 2.450 0.613 Pure Error 4 2.450 2.450 0.612 Total 7 460.775
F4: Central Composite Design Analysis for Anaerobic COD Removal Response Surface Regression: Anaerobic COD removal versus Substrate, RM The analysis was done using coded units. Estimated Regression Coefficients for Anaerobic COD removal Term Coef SE Coef T P Constant 23.940 8.723 2.744 0.029 Substrate 13.374 6.896 1.939 0.094 RM -1.186 6.896 -0.172 0.868 Substrate*Substrate 9.299 7.395 1.257 0.249 RM*RM 6.849 7.395 0.926 0.385 Substrate*RM 0.775 9.753 0.079 0.939 S = 19.51 R-Sq = 46.0% R-Sq(adj) = 7.5% Analysis of Variance for Anaerobic COD removal Source DF Seq SS Adj SS Adj MS F P Regression 5 2270.79 2270.79 454.159 1.19 0.400 Linear 2 1442.09 1442.09 721.044 1.90 0.220 Square 2 826.30 826.30 413.151 1.09 0.388 Interaction 1 2.40 2.40 2.402 0.01 0.939 Residual Error 7 2663.31 2663.31 380.473 Lack-of-Fit 3 2657.02 2657.02 885.673 563.05 0.000 Pure Error 4 6.29 6.29 1.573 Total 12 4934.10
343
F5: Central Composite Design Analysis for Aerobic COD Removal Response Surface Regression: Aerobic COD removal versus Substrate, RM The analysis was done using coded units. Estimated Regression Coefficients for Aerobic COD removal Term Coef SE Coef T P Constant 73.56 7.883 9.332 0.000 Substrate -1.65 6.232 -0.264 0.799 RM -2.28 6.232 -0.366 0.725 Substrate*Substrate -16.33 6.683 -2.443 0.045 RM*RM -4.33 6.683 -0.648 0.538 Substrate*RM 3.45 8.813 0.391 0.707 S = 17.63 R-Sq = 47.9% R-Sq(adj) = 10.7% Analysis of Variance for Aerobic COD removal Source DF Seq SS Adj SS Adj MS F P Regression 5 2000.32 2000.32 400.063 1.29 0.366 Linear 2 63.36 63.36 31.679 0.10 0.904 Square 2 1889.35 1889.35 944.674 3.04 0.112 Interaction 1 47.61 47.61 47.610 0.15 0.707 Residual Error 7 2174.92 2174.92 310.702 Lack-of-Fit 3 2169.46 2169.46 723.155 530.56 0.000 Pure Error 4 5.45 5.45 1.363 Total 12 4175.23
F6: Central Composite Design Analysis for Total COD Removal Response Surface Regression: Total COD removal versus Substrate, RM The analysis was done using coded units. Estimated Regression Coefficients for Total COD removal Term Coef SE Coef T P Constant 79.900 2.144 37.267 0.000 Substrate 8.332 1.695 4.916 0.002 RM -1.917 1.695 -1.131 0.295 Substrate*Substrate -6.706 1.818 -3.690 0.008 RM*RM 1.644 1.818 0.904 0.396 Substrate*RM 2.825 2.397 1.179 0.277 S = 4.794 R-Sq = 85.8% R-Sq(adj) = 75.7% Analysis of Variance for Total COD removal Source DF Seq SS Adj SS Adj MS F P Regression 5 974.42 974.416 194.883 8.48 0.007 Linear 2 584.75 584.746 292.373 12.72 0.005 Square 2 357.75 357.747 178.874 7.78 0.017 Interaction 1 31.92 31.923 31.923 1.39 0.277 Residual Error 7 160.88 160.881 22.983 Lack-of-Fit 3 159.38 159.381 53.127 141.67 0.000 Pure Error 4 1.50 1.500 0.375 Total 12 1135.30
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APPENDIX G: FACTORIAL DESIGN AND RESPONSE SURFACE METHODOLOGY DATA ANALYSIS FOR COLOR REMOVAL
G1: Factorial Design Analysis for Color Removal (Sumifix Navy Blue_600 nm_5 hour) Fractional Factorial Fit: 600_5h versus Substrate, RM Estimated Effects and Coefficients for 600_5h (coded units) Term Effect Coef SE Coef T P Constant 78.8250 0.1199 657.45 0.000 Substrate 5.9500 2.9750 0.1199 24.81 0.000 RM 2.9500 1.4750 0.1199 12.30 0.000 Substrate*RM 0.9500 0.4750 0.1199 3.96 0.017 Analysis of Variance for 600_5h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 2 88.2100 88.2100 44.1050 383.52 0.000 2-Way Interactions 1 1.8050 1.8050 1.8050 15.70 0.017 Residual Error 4 0.4600 0.4600 0.1150 Pure Error 4 0.4600 0.4600 0.1150 Total 7 90.4750
G2: Factorial Design Analysis for Color Removal (Sumifix Navy Blue_600 nm_12 hour) Fractional Factorial Fit: 600_12 versus Substrate, RM Estimated Effects and Coefficients for 600_12 (coded units) Term Effect Coef SE Coef T P Constant 79.9750 0.2604 307.11 0.000 Substrate -1.9000 -0.9500 0.2604 -3.65 0.022 RM 5.5000 2.7500 0.2604 10.56 0.000 Substrate*RM -0.5500 -0.2750 0.2604 -1.06 0.351 Analysis of Variance for 600_12 (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 2 67.7200 67.7200 33.8600 62.41 0.001 2-Way Interactions 1 0.6050 0.6050 0.6050 1.12 0.351 Residual Error 4 2.1700 2.1700 0.5425 Pure Error 4 2.1700 2.1700 0.5425 Total 7 70.4950
345
G3: Factorial Design Analysis for Color Removal (Synozol Red K-4B_542 nm _5 hour) Fractional Factorial Fit: 542_5h versus Substrate, RM Estimated Effects and Coefficients for 542_5h (coded units) Term Effect Coef SE Coef T P Constant 71.738 0.2741 261.68 0.000 Substrate 12.025 6.013 0.2741 21.93 0.000 RM -2.225 -1.112 0.2741 -4.06 0.015 Substrate*RM 4.125 2.063 0.2741 7.52 0.002 Analysis of Variance for 542_5h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 2 299.103 299.103 149.551 248.73 0.000 2-Way Interactions 1 34.031 34.031 34.031 56.60 0.002 Residual Error 4 2.405 2.405 0.601 Pure Error 4 2.405 2.405 0.601 Total 7 335.539
G4: Factorial Design Analysis for Color Removal (Synozol Red K-4B_542 nm _12hour) Fractional Factorial Fit: 542_12h versus Substrate, RM Estimated Effects and Coefficients for 542_12h (coded units) Term Effect Coef SE Coef T P Constant 78.6000 0.1639 479.46 0.000 Substrate -0.8000 -0.4000 0.1639 -2.44 0.071 RM 5.7000 2.8500 0.1639 17.38 0.000 Substrate*RM -1.1000 -0.5500 0.1639 -3.35 0.028 Analysis of Variance for 542_12h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 2 66.2600 66.2600 33.1300 154.09 0.000 2-Way Interactions 1 2.4200 2.4200 2.4200 11.26 0.028 Residual Error 4 0.8600 0.8600 0.2150 Pure Error 4 0.8600 0.8600 0.2150 Total 7 69.5400
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G5: Response Surface Modeling Design Analysis for Color Removal (Sumifix
Navy Blue EXF_600 nm_5 hours) (Full Quadratic Term)
ANOVA for Response Surface Quadratic Model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 105.45 5 21.09 4.06 0.0475 significant A-Substrate 28.64 1 28.64 5.51 0.0512 B-Riboflavin 18.67 1 18.67 3.60 0.0998 AB 0.25 1 0.25 0.048 0.8326 A2 33.1 1 33.10 6.37 0.0396 B2 32.3 1 32.34 6.23 0.0413 Residual 36.36 7 5.19 Lack of Fit 18.04 3 6.01 1.31 0.3865 not significant Pure Error 18.32 4 4.58 Cor Total 141.81 12 Std. Dev. 2.28 R-Squared 0.7436 Mean 81.63 Adj R-Squared 0.5605 C.V. % 2.79 Pred R-Squared -0.1063 PRESS 156.89 Adeq Precision 4.849 Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High VIF Intercept 84.30 1 1.02 81.89 86.71 A-Substrate 1.89 1 0.81 -0.013 3.80 1.00 B-Riboflavin 1.53 1 0.81 -0.38 3.43 1.00 AB 0.25 1 1.14 -2.44 2.94 1.00 A2 -2.18 1 0.86 -4.22 -0.14 1.02 B2 -2.16 1 0.86 -4.20 -0.11 1.02
347
G6: Response Surface Modeling Design Analysis for Color Removal (Sumifix
Navy Blue EXF_600 nm_5 hours) (Reduced Quadratic Term-Linear + Square
Terms)
ANOVA for Response Surface Reduced Quadratic Model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 105.20 4 26.30 5.75 0.0176 significant A-Substrate 28.64 1 28.64 6.26 0.0369 B-Riboflavin 18.67 1 18.67 4.08 0.0780 A2 33.10 1 33.10 7.23 0.0275 B2 32.34 1 32.34 7.07 0.0289 Residual 36.61 8 4.58 Lack of Fit 18.29 4 4.57 1.00 0.5007 not significant Pure Error 18.32 4 4.58 Cor Total 141.81 12 Std. Dev. 2.14 R-Squared 0.7419 Mean 81.63 Adj R-Squared 0.6128 C.V. % 2.62 Pred R-Squared 0.1786 PRESS 116.48 Adeq Precision 5.847 Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High VIF Intercept 84.30 1 0.96 82.09 86.51 A-Substrate 1.89 1 0.76 0.15 3.64 1.00 B-Riboflavin 1.53 1 0.76 -0.22 3.27 1.00 A2 -2.18 1 0.81 -4.05 -0.31 1.02 B2 -2.16 1 0.81 -4.03 -0.29 1.02
348
G7: Response Surface Modeling Design Analysis for Color Removal (Sumifix Navy Blue EXF_600 nm_12 hour) ANOVA for Response Surface Quadratic Model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 158.58 5 31.72 7.23 0.0109 significant A-Substrate 13.55 1 13.55 3.09 0.1221 B-Riboflavin 76.73 1 76.73 17.50 0.0041 AB 1.69 1 1.69 0.39 0.5543 A2 10.16 1 10.16 2.32 0.1717 B2 49.45 1 49.45 11.28 0.0121 Residual 30.69 7 4.38 Lack of Fit 4.08 3 1.36 0.20 0.8885 not significant Pure Error 26.61 4 6.65 Cor Total 189.26 12 Std. Dev. 2.09 R-Squared 0.8379 Mean 80.62 Adj R-Squared 0.7220 C.V. % 2.60 Pred R-Squared 0.6270 PRESS 70.59 Adeq Precision 9.821 Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High VIF Intercept 81.52 1 0.94 79.31 83.73 A-Substrate -1.30 1 0.74 -3.05 0.45 1.00 B-Riboflavin 3.10 1 0.74 1.35 4.85 1.00 AB -0.65 1 1.05 -3.13 1.83 1.00 A2 1.21 1 0.79 -0.67 3.09 1.02 B2 -2.67 1 0.79 -4.54 -0.79 1.02
349
G8: Response Surface Modeling Design Analysis for Color Removal (Synozol Red K-4B_542 nm _5 hours ANOVA for Response Surface Quadratic Model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 377.90 5 75.58 4.21 0.0437 significant A-Substrate 154.57 1 154.57 8.61 0.0219 B-Riboflavin 22.42 1 22.42 1.25 0.3008 AB 11.22 1 11.22 0.62 0.4552 A2 170.80 1 170.80 9.51 0.0177 B2 36.16 1 36.16 2.01 0.1988 Residual 125.71 7 17.96 Lack of Fit 105.54 3 35.18 6.98 0.0456 significant Pure Error 20.17 4 5.04 Cor Total 503.61 12 Std. Dev. 4.24 R-Squared 0.7504 Mean 75.01 Adj R-Squared 0.5721 C.V. % 5.65 Pred R-Squared -0.5528 PRESS 782.01 Adeq Precision 5.778 Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High VIF Intercept 79.46 1 1.90 74.98 83.94 A-Substrate 4.40 1 1.50 0.85 7.94 1.00 B-Riboflavin 1.67 1 1.50 -1.87 5.22 1.00 AB 1.68 1 2.12 -3.34 6.69 1.00 A2 -4.95 1 1.61 -8.75 -1.16 1.02 B2 -2.28 1 1.61 -6.08 1.52 1.02
350
G9: Response Surface Modeling Design Analysis for Color Removal (Synozol Red K-4B_542 nm _12 hours ANOVA for Response Surface Quadratic Model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 144.60 5 28.92 8.31 0.0074 significant A-Substrate 7.85 1 7.85 2.26 0.1769 B-Riboflavin 67.98 1 67.98 19.54 0.0031 AB 2.10 1 2.10 0.60 0.4624 A2 10.10 1 10.10 2.90 0.1322 B2 49.59 1 49.59 14.25 0.0069 Residual 24.35 7 3.48 Lack of Fit 5.04 3 1.68 0.35 0.7938 not significant Pure Error 19.31 4 4.83 Cor Total 168.95 12 Std. Dev. 1.87 R-Squared 0.8559 Mean 79.44 Adj R-Squared 0.7529 C.V. % 2.35 Pred R-Squared 0.6092 PRESS 66.03 Adeq Precision 10.475 Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High VIF Intercept 80.34 1 0.83 78.37 82.31 A-Substrate -0.99 1 0.66 -2.55 0.57 1.00 B-Riboflavin 2.92 1 0.66 1.36 4.47 1.00 AB -0.72 1 0.93 -2.93 1.48 1.00 A2 1.21 1 0.71 -0.47 2.88 1.02 B2 -2.67 1 0.71 -4.34 -1.00 1.02
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