COLLEGE OF SCIENCE AND TECHNOLOGY

SCHOOL OF ENGINEERING

DEPARTMENT OF CHEMICAL ENGINEERING,

COVENANT UNIVERSITY, OTA.

ALKALINE PRETREATMENT AND ENZYMATIC HYDROLYSIS OF

RICE HULLS

A FINAL YEAR RESEARCH PROJECT

BY

OGU RICHARD AFENOKO

09CF09371

APRIL 2014

ii

ALKALINE PRETREATMENT AND ENZYMATIC HYDROLYSIS OF

RICE HULLS

A FINAL YEAR RESEARCH PROJECT

Presented to

College of Science and Technology

School of Engineering

The Department of Chemical Engineering

By

OGU RICHARD AFENOKO

MATRICULATION NO.: 09CF09371

In Partial Fulfilment of the requirements for the Degree Bachelor of Engineering in

Chemical Engineering

APRIL, 2014

iii

CERTICIFICATION

I hereby declare that the contained report on “Alkaline pre-treatment and enzymatic

hydrolysis of rice hulls” was researched, the results thoroughly analysed, under the

supervision of my project supervisor and approved having satisfied the partial

requirements for the award of Bachelor of Engineering in Chemical Engineering

(B.Eng.), Covenant University, Ota.

___________________________ _____________________________

OGU RICHARD AFENOKO Date

__________________________ ___________________________

DR. A.O AYENI Date

Supervisor

___________________________ _____________________________

PROF. F.K. HYMORE Date

Head of Department

iv

DEDICATION

I dedicate this to report to God Almighty, the reason why I live and also to my parents Chief

& Mrs Ogu.

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ACKNOWLEDGEMENT

I want to express my unending gratitude to God Almighty for His extravagant grace upon my

life. Without Him I would be nothing.

I also appreciate my parents greatly for giving me the opportunity to come to Covenant

University and build a career in Chemical Engineering.

I am thankful to my supervisor, Dr. A.O Ayeni for his guidance and meticulous supervision of

my project work.

I also express my gratitude to the academic and non-academic staff of the Chemical

Engineering Department for all the assistance given to me during the course of my research. I

am too grateful.

Furthermore, I would like to appreciate my friends and course mates who gave me their support

and encouragement throughout the research period. You all are the best.

Finally, to my siblings Onuche, Arikpi and Daniel, I say a big thank you for your prayers and

love.

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ABSRTACT

Alkaline pretreatment was performed before the conversion of rice hulls to reducing sugars

through enzymatic hydrolysis using Trichoderma ressei cellulase enzyme. The effects of time,

temperature and hydrogen peroxide concentration were studied. A statistical software,

MINITAB was used to determine the optimum pretreatment conditions which were validated

experimentally. The validated optimized conditions of 49.8ºC, 11.36 hours and 3.68% with a

biomass loading of 4% and 25FPU/g enzyme loading gave the highest reducing sugar yield of

192.89mg/g of dry biomass. When compared with the reducing sugar yield of the untreated

sample which gave 32.8mg/g of dry biomass, it was seen that alkaline pretreatment could be

used to pretreat rice hulls to a substantial level for better reducing sugar yields after enzymatic

hydrolysis.

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TABLE OF CONTENTS

CHAPTER ONE

INTRODUCTION

1.1 BACKGROUND STUDY ............................................................................................... 1

1.2 AIMS AND OBJECTIVES .............................................................................................. 2

1.3 SCOPE ............................................................................................................................. 2

1.4 JUSTIFICATION ............................................................................................................. 4

1.5 RELEVANCE OF STUDY .............................................................................................. 4

1.6 RESEARCH LIMITATIONS .......................................................................................... 5

CHAPTER TWO

LITERATURE REVIEW

2.1 BIOFUEL ......................................................................................................................... 6

2.1.1 Classification Of Biofuels ............................................................................................. 6

2.1.2 Types Of Biofuels ......................................................................................................... 7

2.1.3 Biofuel Vs Fossil Fuel ................................................................................................... 7

2.1.4 Greenhouse Gas (GHG) Emissions And Global Warming ........................................... 9

2.1.5 Current Trends............................................................................................................... 9

2.2 LIGNOCELLULOSIC BIOMASS .................................................................................. 9

2.2.1 Structure Of Lignocellulosic Biomass ........................................................................ 10

2.2.2 Products Of Lignocellulosic Biomass ......................................................................... 12

2.2.3 Production Of Ethanol From Lignocellulosic Biomass .............................................. 12

2.2.3.1 Acid Hydrolysis........................................................................................................ 14

2.2.3.2 Enzymatic Hydrolysis .............................................................................................. 15

2.2.3.2.1 Cellulosic Capability Of Organisms: Difference In The Cellulose-Degrading

Strategy................................................................................................................................. 16

2.2.3.2.2 Characteristics Of The Commercial Hydrolytic Enzymes .................................... 21

2.3 RICE HULLS ................................................................................................................. 24

2.4 ABSORBANCE ............................................................................................................. 26

2.4.1 Measuring The Absorbance Of A Sample Using A Spectrophotometer .................... 26

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2.4.2 The Importance Of Concentration............................................................................... 26

2.4.3 The Importance Of The Container Shape ................................................................... 27

CHAPTER 3

METHODOLOGY

3.1 MATERIALS USED...................................................................................................... 28

3.1.1 Biomass ....................................................................................................................... 28

3.1.2 Chemicals Required .................................................................................................... 28

3.2 EQUIPMENT USED ..................................................................................................... 28

3.3 BRIEF SUMMARY OF WORK DONE ....................................................................... 28

3.4 ALKALINE PRETREATMENT ................................................................................... 32

3.5 ENZYMATIC HYDROLYSIS ...................................................................................... 34

3.6 OPTIMIZATION OF THE ALKALINE PRETREATMENT AND ENZYMATIC

HYDROLYSIS CONDITIONS FOR RICE HULLS. ......................................................... 34

3.7 GLUCOSE ANALYSIS ................................................................................................. 35

CHAPTER 4

RESULTS AND DISCUSSIONS

4.1 SIEVE ANALYSIS ........................................................................................................ 36

4.2 LABORAORY ANALYSIS .......................................................................................... 36

4.3 ALKALINE PRETREATMENT ................................................................................... 41

4.4 ENZYMATIC HYDROLYSIS ...................................................................................... 41

4.5 OPTIMIZATION OF PRETREATMENT CONDITIONS ........................................... 48

4.6 GLUCOSE TEST ........................................................................................................... 51

CHAPTER 5

CONCLUSIONS AND RECOMMENDATIONS

5.1 CONCLUSION .............................................................................................................. 55

5.2 RECOMMENDATIONS ............................................................................................... 55

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REFERENCES ................................................................................................................... 56

APPENDIX

APPENDIX A (EXPERIMENTAL PROCEDURES) ......................................................... 62

APPENDIX B (FORMULAE) ............................................................................................. 67

APPENDIX C (CALCULATIONS) .................................................................................... 70

APPENDIX D (RESULT TABLES) ................................................................................... 73

APPENDIX E (PRECAUTIONS) ………………………………………………………...76

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LIST OF FIGURES

Figure 1.1: Lignocellulosic materials: composition of major compounds. ............................... 3

Figure 2.1: Reductions of GHG emission by first generation (American corn and Brazilian

sugarcane) ethanol and second generation ................................................................................. 8

Figure 2.2: Illustration of a cellulose chain. ............................................................................ 11

Figure 2.3: Scheme of a lignocellulosic biorefinery. ............................................................... 13

Figure 2.4: Process for production ethanol from lignocellulosic biomass. .............................. 13

Figure 2.5: Schematic of the role of pretreatment in the conversion of biomass to fuel. ........ 17

Figure 2.5: Schematic representation of a cellulosoma. .......................................................... 22

Figure 2.6: Mechanism of action of cellulose. ......................................................................... 25

Figure 3.1: Equipment and Experimental set up for the study ................................................ 31

Figure 4.1: Frequency distribution chart for screened rice hulls ............................................. 38

Figure 4.2: Plot of weight fraction against average particle sizes ........................................... 39

Figure 4.3: Graph showing the different contents of rice hulls in % (w/w) ............................ 40

Figure 4.4: Surface plot of Yield vs. Time &Temp ................................................................. 46

Figure 4.5: Surface plot of Yield vs. H2O2 & Temp. ............................................................... 46

Figure 4.6: Surface plot of Yield vs. H2O2 & Time ................................................................. 47

Figure 4.7: Optimization Plot for Pretreatment Conditions ..................................................... 47

Figure 4.8: Graph showing the reducing sugar yields against biomass loading and enzyme

loading variations ..................................................................................................................... 50

Figure 4.9: Chart showing the concentration of glucose and other reducing sugars in the yield

.................................................................................................................................................. 54

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LIST OF TABLES

Table 2.1: Biofuel comparison with fossil fuel. ......................................................................... 8

Table 2.1: Cellulose, hemicellulose, and lignin contents in common agricultural residues and

wastes ....................................................................................................................................... 11

Table 2.3: Methods for biomass lignocellulosic pretreatment ................................................. 18

Table 2.4: Commercial cellulases able to work at temperature ranging from 50 to 60ºC ....... 25

Table 2.5: Typical composition of rice hulls. .......................................................................... 25

Table 3.1: Experimental range and uncoded levels of factors for pretreatment ...................... 33

Table 3.2: Experimental order for pretreatment ...................................................................... 33

Table 4.1a: Table showing the particle sizes and sample weights for each batch of sample

during the sieve analysis .......................................................................................................... 37

Table 4.1b: Table showing average particle sizes and weight fractions of different batches of

the sieve analysis...................................................................................................................... 37

Table 4.2: Average weight fractions and average particle sizes .............................................. 38

Table 4.3: Contents of Rice hulls ............................................................................................. 39

Table 4.4: Pretreatment Data ................................................................................................... 43

Table 4.5: Various Pretreatment conditions and total reducing sugars yield of rice hulls after

enzymatic hydrolysis ............................................................................................................... 44

Table 4.6: Variation of hydrolysis biomass loading and enzyme loading at 45°c using samples

that were not soaked before pretreatment ................................................................................ 49

Table 4.7: Variation of hydrolysis biomass loading and enzyme loading at 45°c using soaked

samples. .................................................................................................................................... 49

Table 4.8: Glucose data for optimized samples ....................................................................... 53

1

CHAPTER ONE

INTRODUCTION

1.1 BACKGROUND STUDY

The quick depletion of fossil fuels and the negative impacts such as greenhouse gas

emissions into the atmosphere through combustion of these fuels has driven the world to

utilize renewable-energy sources such as biofuel in order to reduce the total dependency on

non-renewable energy sources. The growing industrialization has derived in an increasing

demand of fuels attempting to satisfy both the industrial and domestic demands. Second

generation bioethanol is based on raw materials rich in complex carbohydrates, resulting

an interesting alternative to reduce competition with food industry. The process to obtain

second generation bioethanol involves four basic steps: feedstock pretreatment, enzymatic

or acid hydrolysis, sugars fermentation, and ethanol recovery (Gómez Sandra, Andrade

Rafael, Santander, Costa, & Maciel, 2010).

Lignocellulosic agricultural residues are promising raw materials for sugar-platform

biorefinery on a large scale. These residues or wastes do not compete with primary food

production. However, few biorefinery processes based on sugar-platform are cost-

competitive in current markets because of the low efficiency and high cost of enzymatic

conversion processes (Himmel M. , et al., 2007). Lignocellulose is a generic term for

describing the main constituents in most plants, namely cellulose, hemicelluloses, and

lignin. Lignocellulose is a complex matrix, comprising many different polysaccharides,

phenolic polymers and proteins. Cellulose, the major component of cell walls of land

plants, is a glucan polysaccharide containing large reservoirs of energy that provide real

potential for conversion into biofuels. Lignocellulosic biomass consists of a variety of

materials with distinctive physical and chemical characteristics. It is the non-starch based

fibrous part of plant material.

The largest potential feedstock for ethanol is lignocellulosic biomass. Lignocellulosic

biomass includes materials such as agricultural residues (corn stover, crop straws, rice hulls

and bagasse), herbaceous crops (alfalfa, switchgrass), short rotation woody crops, forestry

residues, waste paper and other wastes (municipal and industrial). Bioethanol production

from these feedstock could be an attractive alternative for disposal of these residues.

Importantly, lignocellulosic feedstock do not interfere with food security. Moreover,

bioethanol is very important for both rural and urban areas in terms of energy security

reason, environmental concern, employment opportunities, agricultural development,

foreign exchange saving, socioeconomic issues etc.

2

1.2 AIMS AND OBJECTIVES

Rice hulls, which represent 20% dry weight of the harvested rice, can serve as a low cost

abundant feedstock for production of fuel (Saha B., 2007). They are considered waste

materials because of their low value as animal feed due to low digestibility, peculiar size

distribution, low bulk density, high ash/silica contents, and abrasive characteristics. They

can be easily collected from rice-processing sites and contain about 36% cellulose and 12%

hemicellulose, so they can be used after transformation for bioethanol production. For this

purpose, these polymers must be hydrolyzed to simple sugars, which are subsequently

fermented to ethanol. However, rice husks also contain high quantities of ash (20%) and

lignin (16%), which combined with hemicelluloses, results a complex structure around the

cellulose, being more difficult its use as a lignocellulosic feedstock for conversion to

ethanol. For this reason, pretreatments are generally applied in order to make these

polymers more accessible to the enzymes to be converted into fermentable sugars (Mosier,

et al., 2005).

The aim of this research is to study the capacity and functioning of rice hulls as feedstock

for ethanol production. Specific objectives of this research are as follows:

1. To study the effect of alkaline pretreatment of the rice hulls for effective enzymatic

hydrolysis.

2. To study the effects of hydrolysis of the pretreated rice hulls using cellulase enzyme.

3. To perform analysis using a 2 level, 3 factor central composite design, a form of response

surface design for the optimization of the pretreatment conditions. Time, temperature and

hydrogen peroxide concentration are the factors to be considered.

4. To validate the optimized pretreatment conditions

5. To investigate the influence of enzyme loading and biomass loading on enzymatic

hydrolysis yield.

1.3 SCOPE

In this work, milled rice hulls with a screen size of 1.18 mm was analyzed. This substrate

was used for comparison of reducing sugar production by commercially prepared

Trichoderma ressei cellulase. Factors considered in this work included; pretreatment

temperature, pretreatment time and alkaline hydrogen peroxide (H2O2) concentration. The

optimum conditions were evaluated using the Central Composite Design (CCD).

This research work was limited to alkaline pretreatment and enzymatic hydrolysis of rice

hulls.

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Figure 1.1: Lignocellulosic materials: composition of major compounds (Kumar, Barrett,

Delwiche, & Stroeve, 2009).

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1.4 JUSTIFICATION

The environmental impact from the production of fuels is an important factor in

determining its feasibility as an alternative to fossil fuels. Over the long run, small

differences in production cost, environmental ramifications, and energy output may have

large effects. It has been found that cellulosic ethanol can produce a positive net energy

output. The reduction in greenhouse gas (GHG) emissions from corn ethanol and cellulosic

ethanol compared with fossil fuels is drastic. Corn ethanol may reduce overall GHG

emissions by about 13%, while that figure is around 88% or greater for cellulosic ethanol.

As well, cellulosic ethanol can reduce carbon dioxide emissions to nearly zero.

Also, despite its lower energy content than gasoline, ethanol’s high octane rating reduces

engine knock thereby improving engine performance even in dilute ethanol–gasoline

blends (Bromberg L. et al., 2006).

Pretreatment is done because enzyme hydrolysis is greatly hindered by the crystallinity of

cellulose and the protective sheath of lignin and hemicellulose that wrap around cellulose

(Laureano-Perez, Teymouri, Alizadeh, & Dale, 2005). An effective pretreatment method

can weaken all these hindrances and exposes cellulose to cellulase enzymes for effective

hydrolysis. (Alizadeh, Teymouri, Gilbert, & Dale, 2005) Reported that only less than 20 %

glucose is released from lignocellulosic biomass without pretreatment while the yield can

be as high as 90 % with proper pretreatment.

The hydrolysis of cellulolytic materials with diluted acids is well known, but this process

generates toxic products of hydrolysis. Other negatives factors related to the acid hydrolysis

are the corrosion and the high amounts of salts resulting from the acid neutralization.

Enzymatic hydrolysis is preferred because of the higher conversion yields and less

corrosive, less toxic conditions compared to acid hydrolysis.( Ngamveng J. et al. 1990)

1.5 RELEVANCE OF STUDY

Long-term economic and environmental concerns have resulted in a great amount of

research in the past couple of decades on renewable sources of liquid fuels to replace fossil

fuels. Burning fossil fuels such as coal and oil releases CO2, which is a major cause of

global warming. With only 4.5% of the world’s population, the United States is responsible

for about 25% of global energy consumption and 25% of global CO2 emissions. The

average price of gasoline in 2005 was $2.56 per gallon, which was $0.67 higher than the

average price of gasoline in the previous year.

Yet, in June 2008, the average price of gasoline in the United States reached $4.10 per

gallon. Conversion of abundant lignocellulosic biomass to biofuels as transportation fuels

presents a viable option for improving energy security and reducing greenhouse emissions.

Unlike fossil fuels, which come from plants that grew millions of years ago, biofuels are

produced from plants grown today. They are cleaner-burning than fossil fuels, and the short

cycle of growing plants and burning fuel made from them does not add CO2 to the

atmosphere. It has been reported that cellulosic ethanol and ethanol produced from other

biomass resources have the potential to cut greenhouse gas emissions by 86%.

Lignocellulosic materials such as agricultural residues (e.g., wheat straw, sugarcane

bagasse, corn stover, rice hulls), forest products (hardwood and softwood), and dedicated

crops (switchgrass, salix) are renewable sources of energy. These raw materials are

sufficiently abundant and generate very low net greenhouse emissions

5

1.6 RESEARCH LIMITATIONS

Lignocellulosic agricultural residues are promising raw materials for sugar platform

biorefinery on a large scale. However, few biorefinery processes based on sugar-platform

are cost-competitive in current markets because of the low efficiency and high cost of

enzymatic conversion processes (Himmel M. E., et al., 2007).

Rice hulls also contain high quantities of ash (20%) and lignin (16%), which combined

with hemicelluloses, results a complex structure around the cellulose, being more difficult

its use as a lignocellulosic feedstock for conversion to ethanol. For this reason,

pretreatments are generally applied in order to make these polymers more accessible to the

enzymes to be converted into fermentable sugars (Mosier, et al., 2005). Pretreatment

processes can be physical, chemical, biological or a combination of these methods (Ana,

Julie, Ana, Ignacio, & Ildefonso, 2013). Although many different types of pretreatments

were tested in different conditions over the past years, advances are still needed for overall

costs to become competitive.

Pre-treatment is considered to be the most expensive step to convert lignocellulosic

biomass into ethanol. Most pretreatment methods disrupt cell walls of the plant fibers to

expose the sugar polymers, but do not remove much lignin. But, alkaline and alkaline

peroxide pre-treatments which belong to chemical methods are effective processes for

pretreating lignocellulose material (Ana, Julie, Ana, Ignacio, & Ildefonso, 2013).

The use of enzymes in the hydrolysis of cellulose is more advantageous than use of

chemicals, because enzymes are highly specific and can work at mild process conditions.

Despite these advantages, the use of enzymes in industrial applications is still limited by

several factors: the costs of enzymes isolation and purification are high; the specific activity

of enzyme is low compared to the corresponding starch degrading enzymes. As

consequence, the process yields increase at raising the enzymatic proteins dosage and the

hydrolysis time (up to 4 days) while, on the contrary, decrease at raising the solids loadings

(Berlin, Maximenco, Gilkes, & Saddeler, 2007).

Despite the challenges of using lignocellulose, there is a vast supply of this biomass

available across many climates. Crops intended for lignocellulosic ethanol production are

sometimes cheaper to grow and harvest than sugar or starch-rich crops (Adetayo, 2013).

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CHAPTER TWO

LITERATURE REVIEW

2.1 BIOFUEL

Biofuel is a hydrocarbon fuel made by or from a living organism that we humans can use

to power something. This definition of a biofuel is rather formal. In practical consideration,

any hydrocarbon fuel that is produced from organic matter (living or once living material)

in a short period of time (days, weeks, or even months) is considered a biofuel. This

contrasts with fossil fuels, which take millions of years to form and with other types of fuel

which are not based on hydrocarbons (nuclear fission, for instance).

What makes biofuels tricky to understand is that they need not be made by a living

organism, though they can be. Biofuels can also be made through chemical reactions,

carried out in a laboratory or industrial setting, that use organic matter (called biomass) to

make fuel. The only real requirements for a biofuel are that the starting material must be

CO2 that was fixed (turned into another molecule) by a living organism and the final fuel

product must be produced quickly and not over millions of years. Biomass is simply organic

matter. In others words, it is dead material that was once living. Kernels of corn, mats of

algae, and stalks of sugar cane are all biomass. Before global warming related to burning

fossil fuels became a major factor in determining where energy came from, the major

concern was that fossil fuels, which are considered limited in supply, would run out over

the next century. It was thought that if we could produce hydrocarbons another way, and

quickly, then we could meet our energy demands without much problem. This leads to one

of the major separating factors between a biofuel and a fossil fuel - renewability.

Fossil fuel is not considered renewable because it takes millions of years to form and

humans really cannot wait that long. Biofuel, on the other hand, comes from biomass,

which can be produced year after year through sustainable farming practices. This means

biomass and biofuel are renewable (we can replace used biofuel over a very short period of

time).

It is important to note that 'renewable' energy is not the same thing as 'green' energy.

Renewable energy simply won’t run out any time soon, like biofuels, hydroelectric, wind,

and solar. A “green” energy is one that is also good for the planet because it does not harm

ecosystems, contribute to acid rain, or worsen global warming. Solar energy is a 'green'

energy. All 'green' energy is considered renewable, but not all renewable energy is green.

Biofuels are examples of renewable energy sources that aren’t always green because they

produce greenhouse gases (Biofuel Facts, 2010).

2.1.1 Classification of Biofuels

Biofuels are often broken into three generations.

1st generation biofuels are also called conventional biofuels. They are made

from things like sugar, starch, or vegetable oil. Note that these are all food

products. Any biofuel made from a feedstock that can also be consumed as a

human food is considered a first generation biofuel.

2nd generation biofuels are produced from sustainable feedstock. The

sustainability of a feedstock is defined by its availability, its impact on

greenhouse gas emissions, its impact on land use, and by its potential to threaten

the food supply. No second generation biofuel is also a food crop, though certain

food products can become second generation fuels when they are no longer

7

useful for consumption. Second generation biofuels are often called “advanced

biofuels.”

Though not a traditional category of biofuel, some people refer to 3rd generation

biofuels. In general, this term is applied to any biofuel derived from algae. These

biofuels are given their own separate class because of their unique production

mechanism and their potential to mitigate most of the drawbacks of 1st and 2nd

generation biofuels.

2.1.2 TYPES OF BIOFUELS

The chemical structure of biofuels can differ in the same way that the chemical structure

of fossil fuels can differ. For the most part, our interest is in liquid biofuels as they are

easy to transport. The table below compares various biofuels with their fossil fuel

counterparts.

In Table 1 only limited list of the biofuels are available, covering only the most popular

and widely used. It is worth nothing that ethanol is found in almost all gasoline

mixtures. In Brazil, gasoline contains at least 95% ethanol. In other countries, ethanol

usually makes up between 10 and 15% of gasoline.

2.1.3 BIOFUEL VS FOSSIL FUEL

Biofuels are not new. In fact, Henry Ford had originally designed his Model T to run

on ethanol. There are several factors that decide the balance between biofuel and fossil

fuel use around the world. Those factors are cost, availability, and food supply.

All three factors listed above are actually interrelated. To begin, the availability of fossil

fuels has been of concern almost from day one of their discovery. Pumping fuel from

the ground is a difficult and expensive process, which adds greatly to the cost of these

fuels. Additionally, fossil fuels are not renewable, which means they will run out at

some point. As our ability to pump fossil fuels from the ground diminishes, the

available supply will decrease, which will inevitably lead to an increase in price.

It was originally thought that biofuels could be produced in almost limitless quantity

because they are renewable. Unfortunately, our energy needs far out-pace our ability to

grown biomass to make biofuels for one simple reason, land area. There is only so much

land fit for farming in the world and growing biofuels necessarily detracts from the

process of growing food. As the population grows, our demands for both energy and

food grow. At this point, we do not have enough land to grow both enough biofuel and

enough food to meet both needs. The result of this limit has an impact on both the cost

of biofuel and the cost of food. For wealthier countries, the cost of food is less of an

issue. However, for poorer nations, the use of land for biofuels, which drives up the

cost of food, can have a tremendous impact.

The balance between food and biofuel is what keeps the relatively simple process of

growing and making biofuels from being substantially cheaper than fossil fuel. When

this factor is combined with an increased ability (thanks to advances in technology) to

extract oil from the ground, the price of fossil fuel is actually lower than that of biofuel

for the most part.

8

Table 2.1: Biofuel comparison with fossil fuel.

Biofuel Fossil Fuel Differences

Ethanol Gasoline/Ethane Ethanol has about half the energy per mass of

gasoline, which means it takes twice as much

ethanol to get the same energy. Ethanol burns

cleaner than gasoline, however, producing less

carbon monoxide. However, ethanol produces

more ozone than gasoline and contributes

substantially to smog. Engines must be modified

to run on ethanol.

Biodiesel Diesel Has only slightly less energy than regular diesel.

It is more corrosive to engine parts than standard

diesel, which means engines have to be designed

to take biodiesel. It burns cleaner than diesel,

producing less particulate and fewer sulphur

compounds.

Methanol Methane Methanol has about one third to one half as much

energy as methane. Methanol is a liquid and easy

to transport whereas methane is a gas that must

be compressed for transportation.

Biobutanol Gasoline/Butane Biobutanol has slightly less energy than

gasoline, but can run in any car that uses gasoline

without the need for modification to engine

components.

Figure 2.1: Reductions of GHG emission by first generation (American corn and

Brazilian sugarcane) ethanol and second generation (cellulosic) ethanol (adapted from

(Wang, Wu, & Huo, 2007))

9

2.1.4 GREENHOUSE GAS (GHG) EMISSIONS AND GLOBAL WARMING

With the exception of cultivating sugarcane in warm climates (like Brazil‘s),

production of first generation biofuels is far from an ideal closed carbon cycle, since

there is a significant petroleum usage during the whole process (to make fertilizers,

power farm equipment, transport feedstock’s), which make greenhouse gas

reductions in the order of 20% to 50%.

The appearance of second and third generation biofuels came as a possible solution to

avoid direct competition for commodities, while benefiting from increased GHG

reductions. Second generation biofuels are produced from non-food crops or waste

materials, such as food wastes, manure and agricultural residues. Third generation

biofuels use algae to produce carbohydrates and lipids, which can be used for

producing bio-ethanol and biodiesel, respectively. This technology is still not very

mature, but has potentially very high yields per terrain usage, while not displacing

terrain for food production.

2.1.5 CURRENT TRENDS

Most gasoline and diesel fuels in North America and Europe are blended with

biofuel.

Biodiesel accounts for about 3% of the German market and 0.15% of the U.S.

market.

About 1 billion gallons of biodiesel are produced annually.

Bioethanol is more popular in the Americas while biodiesel is more popular in

Europe.

The U.S. and Brazil produce 87% of the world's fuel ethanol.

More than 22 billion gallons of fuel ethanol are produced each year.

Ethanol is added to gasoline to improve octane and reduce emissions.

Biodiesel is added to petroleum-based diesel to reduce emissions and improve

engine life. (Biofuel Facts, 2010)

2.2 LIGNOCELLULOSIC BIOMASS

Lignocellulose refers to plant dry matter (biomass), so called lignocellulosic biomass. It is

the most abundantly available raw material on the Earth for the production of bio-fuels,

mainly bio-ethanol. It is composed of carbohydrate polymers (cellulose, hemicellulose),

and an aromatic polymer (lignin). These carbohydrate polymers contain different sugar

monomers (six and five carbon sugars) and they are tightly bound to lignin. Lignocellulosic

biomass can be broadly classified into virgin biomass, waste biomass and energy crops.

Virgin biomass includes all naturally occurring terrestrial plants such as trees, bushes and

grass. Waste biomass is produced as a low value byproduct of various industrial sectors

such as agricultural (corn stover, sugarcane bagasse, straw etc), forestry (saw mill and paper

mill discards). Energy crops are crops with high yield of lignocellulosic biomass produced

to serve as a raw material for production of second generation biofuel examples include

switch grass (Panicum virgatum) and Elephant grass.

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2.2.1 Structure of Lignocellulosic Biomass

Lignocellulose is the primary building block of plant cell walls. Plant biomass is mainly

composed of cellulose, hemicellulose, and lignin, along with smaller amounts of pectin,

protein, extractives (soluble nonstructural materials such as nonstructural sugars,

nitrogenous material, chlorophyll, and waxes), and ash. The composition of these

constituents can vary from one plant species to another. For example, hardwood has

greater amounts of cellulose, whereas wheat straw and leaves have more hemicellulose

(Table 1). In addition, the ratios between various constituents within a single plant vary

with age, stage of growth, and other conditions.

Cellulose is the main structural constituent in plant cell walls and is found in an

organized fibrous structure. The structure of cellulose is shown in Figure 2. This linear

polymer consists of D-glucose subunits linked to each other by β-(1, 4)-glycosidic

bonds. Cellobiose is the repeat unit established through this linkage, and it constitutes

cellulose chains. The long-chain cellulose polymers are linked together by hydrogen

and van der Waals bonds, which cause the cellulose to be packed into microfibrils.

Hemicelluloses and lignin cover the microfibrils. Fermentable D-glucose can be

produced from cellulose through the action of either acid or enzymes breaking the β-

(1, 4)-glycosidic linkages. Cellulose in biomass is present in both crystalline and

amorphous forms. Crystalline cellulose comprises the major proportion of cellulose,

whereas a small percentage of unorganized cellulose chains form amorphous cellulose.

Cellulose is more susceptible to enzymatic degradation in its amorphous form.

The main feature that differentiates hemicellulose from cellulose is that hemicellulose

has branches with short lateral chains consisting of different sugars. These

monosaccharides include pentoses (xylose, rhamnose, and arabinose), hexoses

(glucose, mannose, and galactose), and uronic acids (e.g., 4-o-methylglucuronic, D-

glucuronic, and D-galactouronic acids). The backbone of hemicellulose is either a

homopolymer or a heteropolymer with short branches linked by β-(1, 4)-glycosidic

bonds and occasionally β-(1, 3)-glycosidic bonds. Also, hemicelluloses can have some

degree of acetylation, for example, in heteroxylan. In contrast to cellulose, the polymers

present in hemicelluloses are easily hydrolyzable. These polymers do not aggregate,

even when they cocrystallize with cellulose chains.

Lignin is a complex, large molecular structure containing cross-linked polymers of

phenolic monomers. It is present in the primary cell wall, imparting structural support,

impermeability, and resistance against microbial attack. Three phenyl propionic

alcohols exist as monomers of lignin: coniferyl alcohol (guaiacyl propanol), coumaryl

alcohol (p-hydroxyphenyl propanol), and sinapyl alcohol (syringyl alcohol). Alkyl-

aryl, alkyl-alkyl, and aryl-aryl ether bonds link these phenolic monomers together. In

general, herbaceous plants such as grasses have the lowest contents of lignin, whereas

softwoods have the highest lignin contents (Table 2.1).

11

Table 2.1: Cellulose, hemicellulose, and lignin contents in common agricultural

residues and wastes (Adapted from (Jorgensen, Kristensen, & Felby, 2007)

.

Figure 2.2: Illustration of a cellulose chain.

12

2.2.2 Products of Lignocellulosic Biomass

Lignocellulosic biomass is a potential source of several bio-based products according

to the biorefinery approach. Currently, the products made from bioresources represent

only a minor fraction of the chemical industry production. However, the interest in the

bio-based products has increased because of the rapidly rising barrel costs and an

increasing concern about the depletion of the fossil resources in the near future (Hatti-

Kaul et al., 2007). The goal of the biorefinery approach is the generation of energy and

chemicals from different biomass feedstock, through the combination of different

technologies (FitzPatrick et al. 2010).

The biorefinery scheme involves a multi-step biomass processing. The first step

concerns the feedstock pretreatment through physical, biological, and chemical

methods. The outputs from this step are platform (macro) molecules or streams that can

be used for further processing (Cherubini & Ulgiati, 2010). Recently, a detailed report

has been published by DOE describing the value added chemicals that can be produced

from biomass (Werpy, 2004).

Besides ethanol, several other products can be obtained following the hydrolysis of the

carbohydrates in the lignocellulosic materials. For instance, xylan/xylose contained in

hemicelluloses can be thermally transformed into furans (2-furfuraldeyde,

hydroxymethil furfural), short chain organic acids (formic, acetic, and propionic acids),

and cheto compounds (hydroxy-1-propanone, hydroxy-1-butanone) (Güllü, 2010;

Bozell & Petersen, 2010). Furfural can be further processed to form some building

blocks of innovative polymeric materials (i.e. 2, 5-furandicarboxylic acid). In addition,

levulinic acid could be formed by the degradation of hydroxymethil furfural

(Demirabas, 2008). Another product prepared either by fermentation or by catalytic

hydrogenation of xylose is xylitol (Bozell & Petersen, 2010). Furthermore, through the

chemical reduction of glucose it is possible to obtain several products, such as sorbitol

(Bozell & Petersen, 2010). The residual lignin can be an intermediate product to be

used for the synthesis of phenol, benzene, toluene, xylene, and other aromatics.

Similarly to furfural, lignin could react to form some polymeric materials (i.e.

polyurethanes) (Demirabas, 2008).

2.2.3 PRODUCTION OF ETHANOL FROM LIGNOCELLULOSIC BIOMASS

Ethanol is the most common renewable fuel recognized as a potential alternative to

petroleum-derived transportation fuels. It can be produced from lignocellulosic

materials in various ways characterized by common steps: hydrolysis of cellulose and

hemicellulose to monomeric sugars, fermentation and product recovery. The main

differences lie in the hydrolysis phase, which can be performed by dilute acid,

concentrated acid or enzymatically (Galbe & Zacchi, 2002).

13

Figure 2.3: Scheme of a lignocellulosic biorefinery. The shape of each step describes

the type of process used, chemical, biological, and physical (legend) (FitzPatrick,

Champagne, Cunningham, & Whitney, 2010)

Figure 2.4: Process for production ethanol from lignocellulosic biomass. The circle in

the scheme indicates two alternative process routes: simultaneous hydrolysis and

fermentation (SSF); separate hydrolysis and fermentation (SHF). (Adapted from

(Alessandra, Isabella, Emanuele, & Vincenza, 2012)

14

2.2.3.1 Acid Hydrolysis

The main advantage of the acid hydrolysis is that acids can penetrate lignin without any

preliminary pretreatment of biomass, thus breaking down the cellulose and

hemicellulose polymers to form individual sugar molecules. Several types of acids,

concentrated or diluted, can be used, such as sulphurous, sulphuric, hydrocloric,

hydrofluoric, phosphoric, nitric and formic acid (Galbe & Zacchi, 2002). Sulphuric and

hydrochloric acids are the most commonly used catalysts for hydrolysis of

lignocellulosic biomass (Lenihan, et al., 2010). The acid concentration used in the

concentrated acid hydrolysis process is in the range of 10-30%. The process occurs at

low temperatures, producing high hydrolysis yields of cellulose (i.e. 90% of theoretical

glucose yield) (Iranmahboob, Nadim, & Monemi, 2002). However, this process

requires large amounts of acids causing corrosion problems to the equipment. The main

advantage of the dilute hydrolysis process is the low amount of acid required (2-5%).

However this process is carried out at high temperatures to achieve acceptable rates of

cellulose conversion. The high temperature increases the rates of hemicellulose sugars

decomposition thus causing the formation of toxic compounds such as furfural and 5-

hydroxymethyl-furfural (HMF).These compounds inhibit yeast cells and the

subsequent fermentation stage, causing a lower ethanol production rate (Alessandra,

Isabella, Emanuele, & Vincenza, 2012). In addition, these compounds lead to reduction

of fermentable sugars (Kootstra, Beeftink, Scott, & Sanders, 2009). In addition, high

temperatures increase the equipment corrosion (Jones & Semrau, 1984).

In 1999, the BC International (BCI) of United States has marketed a technology based

on two-step dilute acid hydrolysis: the first hydrolysis stage at mild conditions (170-

190°C) to hydrolyze hemicellulose; the second step at more severe conditions to

hydrolyze cellulose 200-230°C (Wyman, 1999). In 1991, the Swedish Ethanol

Development Foundation developed the CASH process. This is a two-stage dilute acid

process that provides the impregnation of biomass with sulphur dioxide followed by a

second step in which diluted hydrochloric acid is used. In 1995, this foundation has

focused researches on the conversion of softwoods using sulphuric acid (Galbe &

Zacchi, 2002).

15

2.2.3.2 Enzymatic Hydrolysis

A pretreatment step is necessary for the enzymatic hydrolysis process. It is able to

remove the lignin layer and to decrystallize cellulose so that the hydrolytic enzymes

can easily access the biopolymers. The pretreatment is a critical step in the cellulosic

bioethanol technology because it affects the quality and the cost of the carbohydrates

containing streams (Balat., Balat, & Oz, 2008). Pretreatments methods can be classified

into different categories: physical, physiochemical, chemical, biological, electrical, or

a combination of these (Kumar, Barrett, Delwiche, & Stroeve, 2009), (Table 2.3).

On the whole, the final yield of the enzymatic process depends on the combination of

several factors: biomass composition, type of pretreatment, dosage and efficiency of

the hydrolytic enzymes (Alvira, Tomás-Pejó, Ballesteros, & Negro, 2010).

The use of enzymes in the hydrolysis of cellulose is more advantageous than use of

chemicals, because enzymes are highly specific and can work at mild process

conditions. Despite these advantages, the use of enzymes in industrial applications is

still limited by several factors: the costs of enzymes isolation and purification are high;

the specific activity of enzyme is low compared to the corresponding starch degrading

enzymes. As consequence, the process yields increase at raising the enzymatic proteins

dosage and the hydrolysis time (up to 4 days) while, on the contrary, decrease at raising

the solids loadings. One typical index used to evaluate the performances of the cellulase

preparations during the enzymatic hydrolysis is the conversion rate to say the obtained

glucose concentration per time required to achieve it (g glucose/L/h/).Some authors

reported conversion rates of softwoods substrates (5%w/v solids loading) in the range

0.3-1.2 g/L/h (Berlin, Maximenco, Gilkes, & Saddeler, 2007). In general, compromise

conditions are necessary between enzymes dosages and process time to contain the

process costs.

In 2001, the cost to produce cellulase enzymes was 3-5$ per gallon of ethanol (0.8-

1.32$/liter ethanol), (Novozymes and NREL). In order to reduce the cost of cellulases

for bioethanol production, in 2000 the National Renewable Laboratory (NREL) of USA

has started collaborations with Genencor Corporation and Novozymes. In particular, in

2004, Genencor has achieved an estimated cellulase cost in the range $0.10-0.20 per

gallon of ethanol (0.03-0.05$/liter ethanol) in NREL´s cost model (Genencor, 2004).

Similarly, collaboration between Novozymes and NREL has yielded a cost reduction

in the range $0.10-0.18 per gallon of ethanol (0.03-0.047$/liter ethanol), a 30-fold

reduction since 2001 (Mathew, Sukumaran, Singhania, & Pamdey, 2008).

16

Unlike the acid hydrolysis, the enzymatic hydrolysis, still has not reached the industrial

scale. Only few plants are available worldwide to investigate the process (pretreatment

and bioconversion) at demo scale. More recently, the steam explosion pretreatment,

investigated for several years in Italy at the ENEA research Center of Trisaia (De Bari,

et al., 2002) (De Bari, Nanna, & Braccio, SO2-catalyzed steam fractionation of aspen

chips for bioethahnol production: Optimization of the catalyst impregnation, 2007), is

now going to be developed at industrial scale thanks to investments from the Italian

Mossi & Ghisolfi Group.

2.2.3.2.1 Cellulosic Capability of Organisms: Difference in the Cellulose-Degrading

Strategy

Different strategies for the cellulose degradation are used by the cellulase-

producing microorganisms: aerobic bacteria and fungi secrete soluble extracellular

enzymes known as non-complexed cellulase system; anaerobic cellulolytic

microorganisms produce complexed cellulase systems, called cellulosomes (Sun &

Cheng, 2002). A third strategy was proposed to explain the cellulose-degrading

action of two recently discovered bacteria: the aerobic Cytophaga hutchinsonii and

the anaerobic Fibrobacter succinogenes (Ilmén, Saloheimo, Onnel, & Pentillä,

1997).

Non-complexed cellulose system.

One of the most fully investigated non-complexed cellulase system is the

Trichoderma reesei model. T. reesei (teleomorph Hypocrea jecorina) is a

saprobic fungus, known as an efficient producer of extracellular enzymes

(Bayer, Chanzy, Lamed, & Shoham, 1998). Its non-complexed cellulase

system includes two cellobiohydrolases, at least seven endo-glucanases,

and several β-glucosidases. However, in T. reesei cellulases, the amount

of β-glucosidase is lower than that needed for the efficient hydrolysis of

cellulose into glucose. As a result, the major product of hydrolysis is

cellobiose. This is a dimer of glucose with strong inhibition toward endo-

and exo-glucanases so that the accumulation of cellobiose significantly

slows down the hydrolysis process (Gilkes, Henrissat, Kilburn, Miller, &

Warren, 1991). By adding β-glucosidase to cellulases from either external

sources, or by using co-culture systems, the inhibitory effect of cellobiose

can be significantly reduced (Ting & Makarov, 2009).

17

Figure 2.5: Schematic of the role of pretreatment in the conversion of biomass to fuel.

18

Table 2.3: Methods for biomass lignocellulosic pretreatment (Kumar et al., 2009)

19

20

It has been observed that the mechanism of cellulose enzymatic hydrolysis

by T.reesei involves three simultaneous processes (Ting & Makarov,

2009):

1. Chemical and physical changes in the cellulose solid phase. The

chemical stage includes changes in the degree of polymerization, while

the physical changes regard all the modifications in the accessible

surface area. The enzymes specific function involved in this step is the

endo-glucanase.

2. Primary hydrolysis. This process is slow and involves the release of

soluble intermediates from the cellulose surface. The activity involved

in this step is the cellobiohydrolase.

3. Secondary hydrolysis. This process involves the further hydrolysis of

the soluble fractions to lower molecular weight intermediates, and

ultimately to glucose. This step is much faster than the primary

hydrolysis and β-glucosidases play a role for the secondary hydrolysis.

Complexed cellulose system.

Cellulosomes are produced mainly by anaerobic bacteria, but their presence

have also been described in a few anaerobic fungi from species such as

Neocallimastix, Piromyces, and Orpinomyces (Alessandra, Isabella, Emanuele,

& Vincenza, 2012). In the domain Bacteria, organisms possessing cellulosomes

are only found in the phylum Firmicutes, class Clostridia, order Clostridiales

and in the Lachnospiraceae and Clostridiaceae families. In this latter family,

bacteria with cellulosomes are found in various clusters of the genus Clostridium

(McCarter & Whiters, 1994; Wilson, 2008). Cellulosomes are protuberances

produced on the cell wall of the cellulolytic bacteria grown on cellulosic

materials. These protuberances are stable enzyme complexes tightly bound to

the bacteria cell wall but flexible enough to bind strongly to cellulose (Lentig &

Warmoeskerken, 2001). A cellulosome contains two types of subunits: non-

catalytic subunits, called scaffoldins, and enzymatic subunits. The scaffoldin is

a functional unit of cellusome, which contain multiple copies of cohesins that

interact selectively with domains of the enzymatic subunits, CBD (cellulose

binding domains) and CBM (carbohydrates binding modules). These have

complementary cohesins, called dockerins, which are specific for each bacterial

species (Alessandra, Isabella, Emanuele, & Vincenza, 2012).

21

For the bacterial cell, the biosynthesis of a cellulosome enables a specific

adhesion to the substrate of interest without competition with other

microorganisms. The cellulosome allows several advantages: (1) synergism of

the cellulases; (2) absence of unspecific adsorption (Alessandra, Isabella,

Emanuele, & Vincenza, 2012). Thanks to its intrinsic Lego-like architecture,

cellulosomes may provide great potential in the biofuel industry. The concept of

cellulosome was firstly discovered in the thermophilic cellulolytic and anaerobic

bacterium, Clostridium thermocellum (Wyman, Handbook on bioethanol:

production and utilization, 1996). It consists of a large number of proteins,

including several cellulases and hemicellulases. Other enzymes that can be

included in the cellulosome are lichenases.

Third cellulose-degrading strategy

The third strategy was recently proposed to explain the cellulose-degrading

behavior of two recently sequenced bacteria: Cytophaga hutchinsonii and

Fibrobacter succinogenes (Ilmén, Saloheimo, Onnel, & Pentillä, 1997). C.

hutchinsonii is an abundant aerobic cellulolytic soil bacterium (Fägerstam &

Pettesson, 1984), while F. succinogenes is an anaerobic rumen bacterium which

was isolated by the Rockville, (Maryland), and San Diego (California) Institute

of Genomic Research (TIGR) (Mansfieldet al., 1998). In the aerobic C.

hutchinsonii no genes were found to code for CBM and in the anaerobic F.

succinogenes no genes were identified to encode dockerin and scaffoldin. Thus,

a third cellulose degrading mechanism was proposed. It includes the binding of

individual cellulose molecules by outer membrane proteins of the

microorganisms followed by the transport into the periplasmic space where they

are degraded by endoglucanases (Ilmén, Saloheimo, Onnel, & Pentillä, 1997).

2.2.3.2.2 Characteristics of the Commercial Hydrolytic Enzymes

Most cellulase enzymes are relatively unstable at high temperatures. The maximum

activity for most fungal cellulases and β-glucosidase occurs at 50±5°C and a pH

4.5- 5 (Taherzadeh, 2007) (Galbe & Zacchi, 2002). Usually, they lose about 60%

of their activity in the temperature range 50–60 °C and almost completely lose

activity at 80°C (Gautam et. al.2010). However, the enzymes activity depends on

the hydrolysis duration and on the source of the enzymes (Tengborg, Galbe, &

22

Zacchi, 2001). In general, cellulases are quite difficult to use for prolonged

operations. As mentioned before, the enzyme production costs mainly depend on

the productivity of the enzymes-producing microbial strain. Filamentous fungi are

the major source of cellulases and mutant strains of Trichoderma (T. viride, T.

reesei, T. longibrachiatum) have long been considered to be the most productive

(Gusakov, et al., 2005) (Galbe & Zacchi, 2002).

Figure 2.5: Schematic representation of a cellulosoma. Adapted from

(Alessandra, Isabella, Emanuele, & Vincenza, 2012)

23

Preparations of cellulases from a single organism may not be highly efficient for

the hydrolysis of different feedstock. For example, Thrichoderma reesei produces

endo-glucanases and exo-glucanases in large quantities, but its β-glucosidase

activity is low, resulting in an inefficient biomass hydrolysis. For this reason, the

goal of the enzymes producing companies has been to form cellulases cocktails by

enzymes assembly (multienzyme mixtures) or to construct engineered

microrganisms to express the desired mixtures (Mathew, Sukumaran, Singhania, &

Pamdey, 2008). Enzyme mixtures often derive from the co-fermentation of several

micro-organisms (Ahamed, 2008) (Berlin, Maximenco, Gilkes, & Saddeler, 2007)

(Table 2.4). All the commercial cellulases listed in table 4 have an optimal condition

at 50°C and pH of 4.0-5.0. More recently, some enzymes producers have marked

new mixtures able to work in a higher temperature ranging from 50 to 60°C (Table

2.4).

In 2010, new enzymes were produced by two leading companies, Novozymes and

Genencor, supported by the USA Department of Energy (DOE). Genencor has

launched four new blends: Accelerase®1500, Accelerase®XP, Accelerase®XC

and Accelerase®BG. Accelerase®1500 is a cellulases complex (exo-glucanase,

endo-glucanase, hemi-cellulase and β-glucosidase) produced from a genetically

modified strain of T. reesei. All the other Accelerase are accessory enzymes

complexes: Accelerase®XP enhances both xylan and glucan conversion;

Accelerase®XC contains hemicellulose and cellulase activities; Accelerase® BG

is a β-glucosidase enzyme. In February 2010, Genencor has developed an enzyme

complex known as Accellerase®Duet which is produced with a genetically

modified strain of T. reesei and that contains not only exo-glucanase, endo-

glucanase, β-glucosidase, but includes also xylanase. This product is capable of

hydrolyzing lignocellulosic biomass into fermentable monosaccharides such as

glucose and xylose (Genencor, 2010). Similarly, Novozymes has produced and

commercialized two new enzymatic mixtures: cellic Ctec, and cellic Htec. Cellic

CTecis used in combination with Cellic HTec and this mixture is capable to work

with a wide variety of pretreated feedstock, such as sugarcane bagasse, corn cob,

corn fiber, and wood pulp, for the conversion of the carbohydrates in these materials

into simple sugars (Novozyme, 2010).

In order to meet the future challenges, innovative bioprocesses for the production

of new generation of enzymes are needed. As already described, conventional

24

cellulases work within a range of temperature around 50°C and they are typically

inactivated at temperatures above 60-70 °C due to disorganization of their three

dimensional structures followed by an irreversible denaturation (Viikari,

Alapuranen, Puranen, Vehmaanperä, & Siika-aho, 2010). Some opportunities of

process improvement derive from the use of thermostable enzymes.

a) One FPU (filter paper unit) is the amount of enzyme that forms 1 µmol of

reducing sugars/min during the hydrolysis reaction of filter paper Whatman

No.1

b) One CBU (cellobiase unit) corresponds to the amount of enzyme which forms

2 µmol of glucose/min from cellobiose

2.3 RICE HULLS

Rice hulls, a byproduct generated during dehulling of rough rice (Oryza sativa), are

important lignocellulosic materials that could be considered for production of fuels and

chemicals. According to the world production of rice (FAO Food Outlook, 2009) and

based on the 20% yield of hulls of the harvested rice (Kim & Dale, 2004), the global

potential of rice hulls is around 139 million tonnes year.

Although there are several potential applications (Govindarao, 1980), rice hulls are

generally landfilled or burnt (Koopmans & Koppejan, 1997). The availability and

quality of rice hulls depend on the type and size of the rice mills.

Large rice mills generate high amounts of rather uniform hulls, whereas small village-

type (‘‘artisan’’) mills produce lower amount of rather heterogeneous hulls. Small

homemade mills, which are common for example in rural areas in Cuba, often lack a

good control on the milling, thus a high degree of grain breakage occurs during the

process and the hulls contain grain fragments and bran. Rice hulls are promising for

economical ethanol production as their carbohydrate content is high and they are readily

available from large production units without causing high transportation costs

(Moniruzzaman & Ingram, 1998); (Saha, Iten, Cotta, & Wu, 2005) (Martin, Lopez,

Plasencia, & Hernandez, 2006); (Martin, Alriksson, Sjode, Nilvebrant, & Jonsson,

2007a).

However, rice husks also contain high quantities of ash (20%) and lignin (16%), which

combined with hemicelluloses, results a complex structure around the cellulose, being

more difficult its use as a lignocellulosic feedstock for conversion to ethanol. For this

25

Table 2.4: Commercial cellulases able to work at temperature ranging from 50 to 60ºC

Figure 2.6: Mechanism of action of cellulose. (Alessandra, Isabella, Emanuele, & Vincenza,

2012)

Table 2.5: Typical composition of rice hulls. (Ang, et al., 2011)

26

2.4 ABSORBANCE

2.4.1 Measuring the Absorbance of a Sample Using a Spectrophotometer

For each wavelength of light passing through the spectrometer, the intensity of the light

passing through the reference cell is measured. This is usually referred to as I0 - that's I

for Intensity.

The intensity of the light passing through the sample cell is also measured for that

wavelength - given the symbol, I.

If I is less than I0, then obviously the sample has absorbed some of the light. A simple

bit of mathematics is then done in the computer to convert this into something called

the absorbance of the sample - given the symbol, A.

For reasons to do with the form of the Beer-Lambert Law (below), the relationship

between A (the absorbance) and the two intensities is given by:

A= log 10(I0/I)

On most of the diagrams you will come across, the absorbance ranges from 0 to 1, but

it can go higher than that.

An absorbance of 0 at some wavelength means that no light of that particular

wavelength has been absorbed. The intensities of the sample and reference beam are

both the same, so the ratio I0/I is 1. Log10 of 1 is zero.

An absorbance of 1 happens when 90% of the light at that wavelength has been

absorbed - which means that the intensity is 10% of what it would otherwise be.

In that case, I0/I is 100/10 (=10) and log10 of 10 is 1.

2.4.2 The Importance of Concentration

The proportion of the light absorbed will depend on how many molecules it interacts

with. Suppose you have got a strongly coloured organic dye. If it is in a reasonably

concentrated solution, it will have a very high absorbance because there are lots of

molecules to interact with the light.

However, in an incredibly dilute solution, it may be very difficult to see that it is colored

at all. The absorbance is going to be very low.

Suppose then that you wanted to compare this dye with a different compound. Unless

you took care to make allowance for the concentration, you couldn't make any sensible

comparisons about which one absorbed the most light.

27

2.4.3 THE IMPORTANCE OF THE CONTAINER SHAPE

Suppose this time that you had a very dilute solution of the dye in a cube-shaped

container so that the light travelled 1 cm through it. The absorbance isn't likely to be

very high. On the other hand, suppose you passed the light through a tube 100 cm long

containing the same solution. More light would be absorbed because it interacts with

more molecules.

Again, if you want to draw sensible comparisons between solutions, you have to allow

for the length of the solution the light is passing through. (Clark, 2007)

28

CHAPTER 3

METHODOLOGY

3.1 MATERIALS USED

3.1.1 Biomass

Rice hulls were used during this work. It was sourced from Ifo Local Government

Area in Ogun State. The hulls were sun dried and milled and kept in covered drums

at room temperature. The hulls were used in the entire process in this work.

3.1.2 Chemicals Required

a) Sodium hydroxide pallets – 40g

b) Hydrogen Peroxide – 600ml

c) 3-5, Dinitro Salicylic Acid – 25g

d) Crystalline Phenol – 500g

e) Sodium Metabisulphite – 10g

f) Sodium Potassium tartrate – 100g

g) Citric Acid – 40g

h) Sodium Citrate – 40g

i) Cellulase Enzyme

j) Acetone

k) Sulpuric Acid

l) Distilled Water

3.2 EQUIPMENT USED

a) Sieve Shaker

b) Conventional Oven

c) Soxhlet Extractor

d) Water Bath

e) Autoclave

f) Furnace

g) Magnetic Hotplate with Stirrer

h) Micropipette

i) Incubator

j) UV-Spectrophotometer

3.3 BRIEF SUMMARY OF WORK DONE

The milled rice hulls samples were sieved in order to get the right particle sizes for analysis.

The sieved samples were then analyzed in the laboratory to determine the extractives,

29

moisture, lignin, ash, hemicellulose and cellulose contents. After this, the samples were

pretreated at the various conditions generated by the design of experiment, then enzymatic

hydrolysis was done on the pretreated samples.

The reducing sugar yields after enzymatic hydrolysis were analyzed and the pretreatment

conditions were optimized using the MINITAB software and a target yield value was

obtained and further pretreatments and hydrolysis were carried out in order to validate the

pretreatment conditions. Tests were also carried out to find the glucose concentration in the

reducing sugars.

30

(a) (b)

(c) (d)

(e) (f)

31

(g) (h)

(i) (j)

Figure 3.1: Equipment and Experimental set up for the study. (a) Oven, (b) Vacuum

Filtration Setup, (c) UV-Spectrophotometer, (d) pH Meter, (e) Water Bath, (f) Weighing

Balance, (g) Incubator, (h) Magnetic Hotplate , (i) Furnace, (j) Soxhlet Extractor

Figure 3.2: 0.15mm rice hulls (far left); unscreened rice hulls (2nd from left);

1.18mm rice hulls (2nd from right); 0.075mm rice hulls (far right)

32

3.4 ALKALINE PRETREATMENT

The pretreatment was carried out in beakers. 5g of dry biomass was soaked in 100ml

mixture of distilled water and 1-3% of 30% H2O2. The pH of the mixture was raised to 11.5

with sodium hydroxide pellets. The temperature range for the pretreatment was 60-90ºc,

the time range was between 6-10hours.

The design of experiment was done using the statistical software MINITAB 16. The

response surface design method was used for the experimental design. A 2 level, 3 factor

central composite design was selected under the response surface design and 1 block was

selected in order to account for effects on the experiments due to the surroundings. The

levels of parameters for experimental design are shown in Table 3.1 and the total number

of experimental runs with the three variables that was designed according to the Central

composite design (CCD) is shown in Table 3.2.

33

Table 3.1: Experimental range and uncoded levels of factors for pretreatment

Factors Symbols Levels

(-1.68) Low

(-1)

(0) High

(+1)

(+1.68)

Temperature X1 49.7731 60 75 90 100.227

Time X2 4.63641 6 8 10 11.3636

% H2O2 X3 0.31821 1 2 3 3.68179

Table 3.2: Experimental order for pretreatment

STD Order Run Order TEMP (°C) TIME (hours) H2O2 (%)

20 1 75 8 2

3 2 60 10 1

15 3 75 8 2

18 4 75 8 2

13 5 75 8 0.31820

4 6 90 10 1

14 7 75 8 3.681793

1 8 60 6 1

19 9 75 8 2

11 10 75 4.636414 2

16 11 75 8 2

10 12 100.2269 8 2

7 13 60 10 3

2 14 90 6 1

8 15 90 10 3

5 16 60 6 3

17 17 75 8 2

9 18 49.77311 8 2

6 19 90 6 3

12 20 75 11.36359 2

34

After the pretreatment, the dry weight analysis of each sample was done by putting 2g of

each sample in the oven for 3hours, cooling, weighing and putting them back into the oven

for another hour. The samples were dried till constant weight and the dry weight of the

pretreated samples was recorded.

The wet samples were stored in sample bottles and kept in the refrigerator before the

enzymatic hydrolysis.

3.5 ENZYMATIC HYDROLYSIS

The pretreated samples were hydrolyzed by the cellulase enzyme in order to check for the

efficiency of the alkaline pretreatment. The initial dry substance: liquid ratio was

maintained at 20gL-1 i.e. solid dry fraction of 2% (w/v). The solids were loaded into 100ml

sample bottles. 5ml of the 0.1M citrate buffer was added to the loaded biomass in order to

maintain the pH of the reaction at 4.8. The Trichoderma ressei cellulase enzymes were

prepared commercially. The activity of the enzymes was 57.8 FPU/ml and was added at a

loading of 25 FPU/g. The total volume was made to reach 20ml by adding an appropriate

amount of distilled water. The samples were then put into the incubator at 50ºc and

intermittent shaking was done. The experimental period was 96 hours.

3.6 OPTIMIZATION OF THE ALKALINE PRETREATMENT AND ENZYMATIC

HYDROLYSIS CONDITIONS FOR RICE HULLS.

After the yield of reducing sugars was obtain from calculations, optimization was done in

order to get the optimal process parameters for pretreatment. This was done using the

response optimizer of MINITAB 16.

After the yield of reducing sugars were obtained, the response surface design was analyzed.

The yield data was selected as the response of the pretreatment factors and model was set

up in order to examine the effects of the factors on the yield. Surface diagrams were drawn

to determine the individual and interactive effects of the factors on reducing sugar yield

and the optimal value of each factor to optimize the process response was generated using

the response optimizer.

The temperature during the optimized enzymatic hydrolysis was also changed to 45ºc, the

biomass loading was varied 2%, 3%, 4% & 5% biomass loading, the enzyme loading was

also varied between using 15FPU/g, 20FPU/g, 25FPU/g, 30FPU/g & 35FPU/g and some

35

samples were soaked for 3 days while others were not. All these were the variations used

in the optimization process.

The untreated sample was also analyzed at 2% biomass loading and 25FPU/ (g biomass

loading) and the yields were compared.

3.7 GLUCOSE ANALYSIS

This test was performed on the optimized samples that were soaked. The test was done in

order to know the glucose content in the reducing sugars. The randox glucose test kit was

used in determination of the glucose concentration. The test kit had a buffer constituting of

phosphate buffer and phenol. The buffer had a pH value of 7.0. The kit also had a glucose

oxidase reagent. 4-aminophenazone, glucose oxidase and peroxibase where the

constituents of the glucose oxidase reagent. The final constituent of the kit was standard

glucose. The glucose was determined after enzymatic oxidation in the presence of glucose

oxidase. The hydrogen peroxide formed reacted under catalysis of peroxidase, with phenol

and 4-aminophenazone to form a red-violet quinoneimine dye as indicator. The reaction

principle is stated below;

Glucose + O2 + H2O GOD→ gluconic acid + H2O2

2H2O2 + 4-aminophenazone + Phenol POD→ quinoneimine + 4H2O

The procedure for the analysis is stated in appendix A7.

36

CHAPTER 4

RESULTS AND DISCUSSIONS

4.1 SIEVE ANALYSIS

The milled rice hulls were subjected to particle size distribution. The arrangement for

the distribution was in the order (from bottom): Collection pan, 0.075mm, 0.15mm,

1.18mm and 2.36mm. Tables 4.1 (a) and 4.1 (b) show the obtained results.

After the sieve analysis, the 1.18mm particle sizes of rice hulls were kept and used for

the experiments. This was done because 1.18mm was the desired size amongst the other

sizes and there was considerable yield of this size after the sieve analysis.

4.2 LABORAORY ANALYSIS

The 1.18mm particle sizes of the rice hulls went through different tests in the laboratory

in order to obtain the various amounts lignocellulosic contents. Tests for moisture,

lignin, ash, hemicellulose and extractives contents were performed and the cellulose

content was obtained by;

Cellulose content % (w/w) = 100% - (Extractives + Hemicellulose + Lignin +Ash

+ Moisture) content % (w/w)

37

Table 4.1a: Table showing the particle sizes and sample weights for each batch of

sample during the sieve analysis

Table 4.1b: Table showing average particle sizes and weight fractions of different

batches of the sieve analysis

BATCH 1

BATCH 2

BATCH 3

BATCH 4

BATCH 5

Particle Size (mm)

Sample Weight (g)

Particle Size (mm)

Sample Weight (g)

Particle Size (mm)

Sample Weight (g)

Particle Size (mm)

Sample Weight (g)

Particle Size (mm)

Sample Weight (g)

2.36 0 2.36 0 2.36 0 2.36 0 2.36 0

1.18 10 1.18 14 1.18 14 1.18 10 1.18 8

0.15 36 0.15 36 0.15 36 0.15 36 0.15 40

0.075 2 0.075 2 0.075 0 0.075 4 0.075 2

Pan 0 Pan 0 Pan 0 Pan 0 Pan 0

Total=

48

Total=

52

Total=

50

Total=

50

Total=

50

BATCH 1 BATCH 2 BATCH 3 BATCH 4 BATCH 5

Average

Particle

Size

(mm)

Wt.

Fraction

Average

Particle

Size

(mm)

Wt.

Fraction

Average

Particle

Size

(mm)

Wt.

Fraction

Average

Particle

Size

(mm)

Wt.

Fraction

Average

Particle

Size

(mm)

Wt.

Fraction

1.77 0 1.77 0 1.77 0 1.77 0 1.77 0

0.665 0.2083 0.665 0.2692 0.665 0.28 0.665 0.2 0.665 0.16

0.1125 0.75 0.1125 0.6923 0.1125 0.72 0.1125 0.72 0.1125 0.8

0.0375 0.0417 0.0375 0.0385 0.0375 0 0.0375 0.08 0.0375 0.04

0 0 0 0 0 0 0 0 0 0

Total 1.0 1.0 1.0 1.0 1.0

38

Figure 4.1: Frequency distribution chart for screened rice hulls

Table 4.2: Average weight fractions and average particle sizes

Average particle sizes (mm) Average weight fraction

1.77 0

0.665 0.2235

0.1125 0.7365

0.0375 0.0400

0 0

2.36 1.18 0.15 0.075 Pan

BATCH 1 0 0.208333333 0.75 0.041666667 0

BATCH 2 0 0.269230769 0.692307692 0.038461538 0

BATCH 3 0 0.28 0.72 0 0

BATCH 4 0 0.2 0.72 0.08 0

BATCH 5 0 0.16 0.8 0.04 0

00.10.20.30.40.50.60.70.80.9

WEI

GH

T FR

AC

TIO

N

PARTICLE SIZES (mm)

Weight Fraction vs Particle Sizes (mm)

BATCH 1 BATCH 2 BATCH 3 BATCH 4 BATCH 5

39

Figure 4.2: Plot of weight fraction against average particle sizes

Table 4.3: Contents of Rice hulls

CONTENT %(w/w)

Cellulose

Content

Extractives

Content

Hemicellulose

Content

Lignin Ash

Content

Moisture

Content Insoluble

Lignin

Soluble

Lignin

36.71 4.97 12.93 17.7 0.521 13.867 13.3

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Ave

rage

wt.

fra

ctio

n

Average particle sizes (mm)

Average weight fraction vs Average particle sizes (mm)

40

Figure 4.3: Graph showing the different contents of rice hulls in % (w/w)

0

5

10

15

20

25

30

35

40

Cellulose Hemicellulose

Lignin Ash Moisture Extractives

Series1 36.71 12.93 18.221 13.867 13.3 4.97

Co

nte

nt

%(w

/w)

Contents of Rice Hulls %(w/w)

41

From the Figure 4.3, it is seen that the percentage of cellulose present in the rice hulls is

36.71% which is the highest among the other contents. This proves rice hulls to be a very

good lignocellulosic material due to its high concentration of cellulose. However, the

percentage of lignin and ash which combine with hemicellulose to form a complex

structure around the rice hulls is seen to be high also. This makes it difficult to use rice

hulls as an appropriate biomass for ethanol production. For this reason, pretreatment was

employed in order to break the complex chain of the lignin, ash and hemicellulose. Thus

making the cellulose easily accessible for hydrolysis and further fermentation for ethanol

production.

4.3 ALKALINE PRETREATMENT

The alkaline pretreatment was done with the aim of fractionating the rice hull biomass into

a solid fraction containing as much cellulose and less lignin as possible and the liquid

fraction of the pretreatment containing solubilized hemicellulose. The order and variables

of the pretreatment is shown in table 3.1 and 3.2. The dry weight analysis was done in

order to estimate how much lignin, ash and hemicellulose was removed from the rice hull

sample. It was also done in order to calculate the mass of the biomass to use for enzymatic

hydrolysis. The weight of dry biomass in the solid fraction after drying 2g of the pretreated

sample ranged from 0.575g to 0.7556g.

The yield gotten after the enzymatic hydrolysis of each pretreated samples was studied

and used to optimize the alkaline pretreatment conditions using hydrogen peroxide as the

oxidant.

4.4 ENZYMATIC HYDROLYSIS

The results of the experiments after the enzymatic hydrolysis were analyzed by

considering the total reducing sugar yield of the pretreated rice hull samples as the

response variable. This analysis was done using the MINITAB software. The total

reducing sugar yield was expressed as milligram per gram dry biomass. Table 4.5 shows

the experimental design and the response column showing the yield of total reducing

sugars. From Table 4.5, it is seen that standard order 3 produced the maximum yield of

reducing sugars, followed by standard order 7. This shows that the enzymatic

hydrolysis of rice hulls for standard order 3 and 7 was affected by factors such as

42

cellulose swelling, decrease of polymerization degree and crystallinity, increase in

internal surface area, disruption of the lignin structure and separation of structural

linkages between lignin and carbohydrates.

43

Table 4.4: Pretreatment Data

Where;

X1 = Wet weight after pretreatment (g)

X2 = Weight after drying 2g of pretreated solid (g)

X3 = Equivalent dry mass of sample (g)

Y1 = % of equivalent dry mass of biomass

Y2 = % of solids dissolved during pretreatment

StdOrder Temp (°C) Time(hr) H2O2 (%) X1(g) X2(g) X3(g) Y1(%) Y2(%)

20 75 8 2 12.5844 0.6172 3.8711 77.42 22.58

3 60 10 1 11.9086 0.6633 3.949 78.99 21.01

15 75 8 2 7.68 0.711 2.7302 54.6048 45.3952

18 75 8 2 12.315 0.609 3.75 74.9984 25.0016

13 75 8 0.318207 10.589 0.575 3.044 60.89 39.11

4 90 10 1 12.2887 0.732 4.498 89.95 10.05

14 75 8 3.681793 7.2291 0.5829 2.1069 42.138 57.862

1 60 6 1 9.8751 0.6797 3.3561 67.121 32.879

19 75 8 2 11.396 0.6126 3.491 69.81 30.19

11 75 4.636414 2 11.3439 0.6615 3.752 75.04 24.96

16 75 8 2 10.144 0.6603 3.349 66.981 33.019

10 100.2269 8 2 7.41 0.6059 2.245 44.9 55.1

7 60 10 3 13.459 0.5901 3.971 79.42 20.58

2 90 6 1 7.09 0.6633 2.3514 47 53

8 90 10 3 10.14 0.6071 3.078 61.56 38.44

5 60 6 3 7.0872 0.6107 2.164 43.3 56.7

17 75 8 2 10.4773 0.7165 3.753 75.07 24.93

9 49.77311 8 2 13.22 0.6095 4.0295 80.59 19.41

6 90 6 3 7.2291 0.7556 2.7312 54.62 45.38

12 75 11.36359 2 6.8908 0.7299 2.515 50.296 49.704

44

Table 4.5: Various Pretreatment conditions and total reducing sugars yield of rice hulls

after enzymatic hydrolysis

STD Order Run Order TEMP (°C) TIME (hours) H2O2 (%) Yield (mg/g)

20 1 75 8 2 68.97084

3 2 60 10 1 134.2094

15 3 75 8 2 46.13333

18 4 75 8 2 29.22503

13 5 75 8 0.31820 33.98261

4 6 90 10 1 74.79184

14 7 75 8 3.681793 36.06092

1 8 60 6 1 80.01907

19 9 75 8 2 105.5364

11 10 75 4.636414 2 47.75922

16 11 75 8 2 34.66491

10 12 100.2269 8 2 29.60691

7 13 60 10 3 125.9128

2 14 90 6 1 103.7393

8 15 90 10 3 51.16491

5 16 60 6 3 83.08139

17 17 75 8 2 48.76847

9 18 49.77311 8 2 51.17324

6 19 90 6 3 37.03249

12 20 75 11.36359 2 40.17336

45

From Table 4.5, one could also assume that other factors might affect enzymatic

hydrolysis yield. A potential factor could be the conversion of alkali into irrecoverable

salts and the incorporation of salts into the biomass during the pretreatment reactions.

Also, the alkaline reagents can also remove acetyl and various acid substitutions on

hemicellulose, thus reducing the accessibility of hemicellulose and cellulose to

enzymes.

From scientific literature, we see that alkaline pretreatment is more effective on

agricultural residues with low lignin content than on softwood with high lignin content.

(Bjerre, Olesen, & Fernqvist, 1996)

46

Figure 4.4: Surface plot of Yield vs. Time, Temperature

Figure 4.5: Surface plot of Yield vs. H2O2, Temperature

50

100

5075

150

6

4100

10

8

(mg/g)

Time(hr)

Temp(°C)

h2o2 2

Hold Values

Surface plot of Yield vs Time, Temperature

0

40

80

5075

80

120

1

0100

3

2

(mg/g)

H2O 2(%)

Temp(°C)

time 8

Hold Values

Surface plot of Yield vs H2O2, Temperature

47

Figure 4.6: Surface plot of Yield vs. H2O2, Time

Figure 4.7: Optimization plot for pretreatment conditions

40

60

46

810

80

1

010

3

2

(mg/g)

H2O2(%)

Time(hr)

temp 75

Hold Values

Surface plot of Yield vs H2O2, Time

CurHigh

Low0.96074D

Optimal

d = 0.96074

Maximum

Yield

y = 193.4294

0.96074

Desirability

Composite

0.3182

3.6818

4.6364

11.3636

49.7731

100.2269time h2o2temp

[49.7731] [11.3636] [3.6818]

48

Figures 4.4-4.6 show the surface plots of the interactive effect of pretreatment

temperature, time and percentage H2O2 on reducing sugar yield. The response

optimization was done and the graph is shown in figure 4.7. From the response

optimization, pretreatments at 49.8°C, time of 11.36 hours and 3.68% of H2O2 were the

optimum variables in order to attain a maximum reducing sugar yield of 193.43 mg/g

with a composite desirability of 0.96074.

4.5 OPTIMIZATION OF PRETREATMENT CONDITIONS

The response optimizer was used to obtain the optimum pretreatment conditions in

order to get a solid fraction with high cellulose content, low lignin and hemicellulose,

and a liquid fraction with low concentration of reducing sugars. The optimized

conditions were for pretreatments to occur at 49.77ºC, Time of 11.36 hours and

Hydrogen peroxide concentration of 3.68%. Additional experiments were carried out

in order to validate the optimized conditions. The experimental response gave a

maximum yield of 192.89 mg/g dry biomass with a predicted response of 193.43 mg/g

dry biomass, thus confirming the optimization process. This was gotten at 4% biomass

loading and 25 FPU/g enzyme loading.

The results of the different variations used in the optimization process and the

comparisons of their yields is shown below;

49

Table 4.6: Variation of hydrolysis biomass loading and enzyme loading at 45°c using

samples that were not soaked before pre-treatment.

Table 4.7: Variation of hydrolysis biomass loading and enzyme loading at 45°c using

soaked samples.

Time

(hours)

Untreated

Sample

Yield at

2%

loading

(mg/g)

Optimization yields for different variables (mg/g) (Soaked Samples)

2%

Biomass

loading

&

25FPU/g

3%

Biomass

loading

&

25FPU/g

4%

Biomass

loading

&

25FPU/g

5%

Biomass

loading

&

25FPU/g

15FPU/g

&

2%

Biomass

loading

20FPU/g

&

2%

Biomass

loading

25FPU/g

&

2%

Biomass

loading

30FPU/g

&

2%

Biomass

loading

35FPU/g

&

2%

Biomass

loading

2 24.77 37.32 46.23 49.23 61.34 37.5 45.2 42.16 31.9 39.19

24 29.12 64.46 73.16 107.06 135.04 47.23 53.31 48.04 37.67 39.53

72 30.79 67.01 74.43 163.79 139.59 52.93 57.16 55.07 40.21 44.68

96 32.80 68.07 75.07 192.89 147.61 55.18 61.42 62.72 43.27 47.68

Time

(h)

Untreated

Sample

Yield at

2%

loading

(mg/g)

Optimization yields for different variables (mg/g) ( unsoaked samples)

2%

loading

&

25FPU/g

3%

loading

&

25FPU/g

4%

loading

&

25FPU/g

5%

loading

&

25FPU/g

15FPU/g

&

2%

Biomass

loading

20FPU/g

&

2%

Biomass

Loading

25FPU/g

&

2%

Biomass

loading

30FPU/g

&

2%

Biomass

loading

35FPU/g

&

2%

Biomass

Loading

2 24.77 45.29 55.15 104.82 65.24 41.99 53.26 45.75 45.97 46.71

24 29.12 59.97 67.31 168.15 87.89 42.21 55.47 46.86 48.84 50.92

72 30.79 69.41 75.7 179.8 103.34 51.5 59.9 56.61 53.26 53.46

96 32.80 70.04 79.89 184.66 110.36 54.67 61 61.71 57.68 60.24

50

Figure 4.8: Graph showing the reducing sugar yields against biomass loading and enzyme

loading variations

Untreated

Sample

2% B.Loading

3% B.Loading

4% B.Loading

5% B.Loading

15FPU/g 20FPU/g 25FPU/g 30FPU/g 35FPU/g

Unsoaked Pretreaments 32.8 70.04 79.89 184.66 110.36 54.67 61 61.71 57.68 60.24

Soaked Pretreatments 32.8 68.07 75.07 192.89 147.61 55.18 61.42 62.72 43.27 47.68

0

50

100

150

200

250R

edu

cin

g Su

gar

Yiel

d(m

g/g

dry

so

lid)

Variations

Reducing Sugar Yields from different Variations at 45°C

Unsoaked Pretreaments Soaked Pretreatments

51

From the results obtained, Figure 4.8 shows that increase in the biomass loading favored

higher reducing sugar yields. The highest yields of reducing sugars were obtained from

4% biomass loading at 25 FPU/g enzyme loading. These conditions gave the best

productivity at 45°C.

For the enzyme loading, the highest yields at constant biomass loading were obtained

between 20 FPU/g biomass and 25 FPU/g biomass. Thus the optimum enzyme loading

for rice hulls should be between 20–25 FPU/g biomass. This condition is very important

as overloading the biomass with enzymes can cause saturation of the substrate which

does not improve the yield, also insufficient loading could cause low enzyme

concentration and thus reduce the yield of reducing sugars.

Looking at the soaking, the soaked pretreated samples gave a better yield at higher

biomass loading than the pretreated samples that were not soaked. It is assumed that

due to the length of soaking, there was enough time to break the lignin complex and

dissolve more hemicellulose of the rice hulls more efficiently, thus enabling better

hydrolysis. Considering the yield of the untreated sample and comparing with the other

treated samples, it is clear that pretreatment greatly affects enzymatic hydrolysis yield.

The lowest yield on the graph was that of the untreated sample. This justifies the

pretreatment done on the samples.

The optimization of the pretreatment conditions helped in the achievement of a yield of

193.43 mg/g while the validated value was 192.89 mg/g

4.6 GLUCOSE TEST

The results of the glucose test are shown in Table 4.8

From Figure 4.9, it is seen that the glucose concentration in the reducing sugars was

very high in the optimized sample. This means that more cellulose was hydrolyzed in

the enzymatic hydrolysis, which is another indicator of effective pretreatment of the

biomass. The other reducing sugars in the yield were formed as a result of hydrolysis

of the hemicellulose. These other reducing sugars include uronic acids, pentoses,

hexoses and cellubiose. The highest yield of glucose was obtained at 4% biomass

loading and 25 FPU/g enzyme loading. Looking at the untreated sample, it is seen that

the glucose concentration after hydrolysis is far less than that of the reducing sugars.

52

This validates the effect of pretreatment when comparing the glucose concentration of

the untreated sample to the yields of the other pretreated samples. The concentration of

the other reducing sugars is higher because more hemicellulose is hydrolyzed in the

untreated sample and the complex lignin structure around the cellulose prevents

efficient hydrolysis of the cellulose.

Possibly, the concentration of glucose in the reducing sugars could be higher if another

enzyme was used in the enzymatic hydrolysis. From (Gilkes, Henrissat, Kilburn, Miller,

& Warren, 1991), it is seen that in the Trichoderma ressei cellulase enzyme, the amount

of β-glucosidase is lower than the amount needed for efficient hydrolysis of cellulose

to glucose. As a result, a major product of the hydrolysis is cellubiose which is a dimer

of glucose.

53

Table 4.8: Glucose data for optimized samples

Variation

Reducing

Sugar

Conc.

(mg/ml)

Absorbance

(500nm)

Glucose

conc.

(mg/ml)

% of glucose

in reducing

Sugar

% of other

reducing

sugars

Untreated Sample

0.0991 0.003 0.027 27.27 72.73 2% Biomass

Loading 0.86 0.046 0.41 48.05 51.95 3% Biomass

Loading 1.43 0.057 0.51 36 64 4% Biomass

Loading 1.51 0.083 0.75 49.62 50.38 5% Biomass

Loading 1.71 0.069 0.62 36.41 63.59 15FPU/g

0.67 0.034 0.31 45.83 54.17 20FPU/g

0.74 0.035 0.32 42.38 57.62 25FPU/g

0.76 0.039 0.35 46.24 53.76 30FPU/g

0.44 0.019 0.17 39.02 60.98 35FPU/g

0.48 0.021 0.19 39.14 60.86

The absorbance of the standard solution was 0.111.

54

Figure 4.9: Chart showing the concentration of glucose and other reducing sugars in the

yield

0 10 20 30 40 50 60 70 80

2% Loading

3% Loading

4% Loading

5% Loading

15FPU/g

20FPU/g

25FPU/g

30FPU/g

35FPU/g

Untreated

48.05

36

49.62

36.41

45.83

42.38

46.24

39.02

39.14

27.27

51.95

64

50.38

63.59

54.17

57.62

53.76

60.98

60.86

72.73

% Concentration of glucose and other reducing sugars in the yield

% Other reducing sugars % Glucose

55

CHAPTER 5

CONCLUSIONS AND RECOMMENDATIONS

5.1 CONCLUSION

The investigation showed that alkaline pretreatment could be used to pretreat rice

hulls in order to give a substantial yield of reducing sugars after enzymatic

hydrolysis. Validated optimized conditions of 49.8°c, time of 11.36 hours and

3.68% of H2O2 with biomass loading of 4% and 25FPU/g enzyme loading gave a

reducing sugar yield of 192.89 mg/g dry biomass.

From the variations used during the enzymatic hydrolysis, it was seen that soaking

the samples in the pretreatment solution for 3 days before the actual pretreatment

helped in improving the reducing sugar yield. It was also noticed that enzyme

loading between 20FPU/g-25FPU/g gave higher yields than the other enzyme

loadings at 45°C. Also, increasing the biomass loading improved the reducing sugar

yield. The optimum biomass loading for best yield of reducing sugars was 4% at

45ºC.

The glucose concentration in the reducing sugars also helped validate the

pretreatment. The glucose concentration in the reducing sugars was higher in the

pretreated sample than the untreated sample.

5.2 RECOMMENDATIONS

1. More variations should be included in the optimization of the enzymatic

hydrolysis in order to get even better yields. Factors like hydrolysis temperature

can be varied in order to see how the hydrolysis responds to temperature

changes.

2. An incubator which stirs samples automatically during hydrolysis is

recommended, as the human factor in stirring or shaking samples is not

efficient.

3. A further evaluation is needed to determine the reducing sugar content in the

liquid fraction after pretreatment.

4. Other enzymes can be investigated in order the check for their various effects

on glucose yields after hydrolysis.

56

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APPENDIX

APPENDIX A (EXPERIMENTAL PROCEDURES)

1. Sieve Analysis

Procedure

a) The sieve apparatus was setup using the following aperture sizes of sieves

respectively, 2.36 mm, 1.18 mm, 0.15 mm, 0.075 mm, and the final pan.

b) Weight of the different sieve trays was taken and recorded.

c) 50 g of the unscreened rice hulls sample was weighed and kept on the 2.36

mm aperture size tray before agitation started.

d) The sieve sizes were kept on the shaking stand carefully.

e) Then, the samples were agitated at 100 revolutions per minute for 4

minutes.

f) After the agitation was complete, the sieve setup was removed and each of

the trays were reweighed and the new weights were recorded.

g) The difference in weight between the empty tray and the tray after

agitation gave the weight of the sample on each tray.

h) The weight fraction per tray and the average particle sizes were calculated

i) This was repeated for 5 other samples.

j) A plot of average weight fraction versus average particle size was done to

show the graphical representation of the distribution of particle sizes in the

sample.

2. Moisture Content Determination

Procedure

a) 5g of 3 samples were weighed and put into 3 different crucibles of known

weight. This was done with the aid of the weighing balance.

b) The crucibles were put into the oven at 105ºc for 3 hours.

c) After oven drying, the samples were kept in the desiccator for 15mins to

cool.

d) The weight of the samples were taken and recorded.

e) The samples were further dried by putting it into the oven for another hour

at the same 105ºc until constant weight.

f) The samples were removed and placed inside the desiccator for 15mins to

cool.

g) The weight of the samples were taken and recorded.

63

h) After four hours of drying at 105ºc temperature, the samples were now been

dried at 30mins interval. kept in the desiccator to cool and were weighed

until constant weight was achieved.

i) After constant weight achieved and all the weights of samples recorded the

moisture content calculation was done using appropriate formula. (See

appendix B1)

3. Determination Of Extractive Content

Procedure

a) The samples were dried till constant weight.

b) The water bath was switched on and set to the temperature of 70ºc.

c) 2.5g was kept into the cellulose thimble.

d) With the aid of a volumetric flask 150ml of acetone was weighed measured

into the round bottom flask.

e) The Soxhlet extractor was set up with the thimble and was immersed into a

water bath set at 70ºc for 4hours.

f) After extraction, the sample was air dried for 1 hour and put into the oven

set at 105ºc until constant weight was achieved.

g) The samples were weighed and the values were recorded.

h) % Extractive was obtained from calculation. (See appendix B2)

4. Determination Of Hemicellulose Content

Procedure

a) The sodium hydroxide was standardized to 0.5M concentration with

distilled water.

b) The water bath was filled with distilled water.

c) It was switched on and temperature set to 100ºc.

d) 1g of the sample was weighed and kept inside the Erlenmeyer flask.

e) With the aid of volumetric flask 150ml of NaOH was weighed and poured

into the flask containing 1g of the sample.

f) The Erlenmeyer flask was placed inside the water bath and allowed to boil

for 3.5hr.

g) Vacuum Filtration of sample was done with the aid of the buchner filter and

buchner flask.

64

h) The sample was washed with distilled water until it reaches a pH value of 7

i.e. the sample was neutralized.

i) The sample was dried in the oven until constant weight.

j) The sample was weighed with weighing balance and the readings were

recorded.

k) The Hemicellulose content was obtained from calculation. (See appendix

B3)

5. Determination Of Lignin And Ash Content

Procedure

This experiment was done in order to know the amount of lignin and ash present

in the rice hulls. There are 2 types of lignin to be accounted for i.e. soluble and

insoluble lignin.

i. Insoluble Lignin

a) 300mg of extracted sample was weighed and kept inside a test tube.

b) 3ml of 72% H2SO4 was measured and added to the sample. The shake

gently to allow complete mixing.

c) The sample was kept at room temperature for 2hrs with 30mins

interval shaking to allow for complete hydrolysis.

d) The sample was transferred to an Erlenmeyer flask and 84ml of

distilled water was added to it.

e) The sample was autoclaved for 1hr at 121ºC and then cooled to room

temperature.

f) The sample was filtered with the aid of the filtering crucible and filter

paper.

g) Then the residue was dried at 105ºc in an oven till constant weight

which is about 4hr.

h) The weight of the residue was taken and recorded.

i) The insoluble residue was obtained from calculation.

j) The residue was kept in the muffle furnace for 6hrs at 575ºc to obtain

the ash content.

k) %Ash content was subtracted from %insoluble residue to obtain the

insoluble lignin.

65

ii. Soluble Lignin

a) 3ml of the filtrate was taken to the UV-Spectrometer to determine the

absorbance of the sample.

b) The Soluble lignin was also obtained from calculation.

(% total lignin = % Soluble lignin + % insoluble lignin)

(See appendix B4)

6. Sugar Measurement

Procedure

The enzymatic digestibility of pretreated solids was measured by removing

0.5ml aliquot after 2hours, 24hours, 72hours, and 96hours experimental period.

It was done using the DNS reagent. The steps include;

a) 0.5ml of the samples was pipetted into different test-tubes.

b) The test-tubes were placed into the water bath for 15minutes at 100ºc in

order to denature the enzymes.

c) 1.5ml of the DNS reagent was added to each test-tube using the pipette and

the mixture was shaken thoroughly.

d) The test-tubes containing the mixture were placed back in to the water bath

at 100ºc for another 15minutes

e) 1ml of Rochelle salt solution (i.e. Sodium potassium tartrate) was added to

the warm mixture in the test-tubes.

f) The test-tubes were cooled for 3minutes and 6ml of distilled water was

added.

g) The mixture was thoroughly shaken and taken to the spectrophotometer,

where the absorbance of each sample was obtained. Water was used as the

blank reagent.

h) The reducing sugar concentration was calculated using the calibration curve.

(See appendix C3)

7. Glucose Analysis

Procedure

a) The contents of the glucose oxidase reagent were reconstituted with a

portion of the buffer.

66

b) The mixture was well shaken and transferred to the buffer container. The

reagent bottle was rinsed severally with the buffer.

c) 2 test tubes were gotten and labelled appropriately. Test tube A was for the

standard glucose and test tube B was for the sample to be tested.

d) 1ml of the reagent in the buffer was pipetted into each test tube.

e) Then 10μl of the standard glucose and the sample were pipetted into their

appropriate test tubes.

f) The test tubes were shaken properly and incubated for 5 minutes at 37ºC.

g) The absorbance of the 2 different samples was taken at 500nm.

h) The absorbance of the standard glucose and the sample were recorded and

appropriate calculation was done. (See appendix B7)

8. Pretreatment

Procedure

a) 100 ml of 1-3% of 30 %Hydrogen peroxide was kept inside the beaker and

the pH value was taken (4.4).

b) Then 1.8 g of sodium hydroxide pellets was added to the mixture in order to

raise the pH value to 11.5.

c) 5g of dried sample was added to the beaker containing hydrogen peroxide

and distilled water.

d) The soaked sample was kept for 3 days.

e) After 3 days, the mixture was placed on a hotplate with a magnetic stirrer

set at temperatures between 60-90 °C and was left at time ranges between

6-10 hours. The order followed for the times and temperatures of the

pretreatment was designed using the MINITAB software.

f) After pre-treatment, the mixture was cooled and then filtered using the

Buchner flask and funnel.

g) The residue was properly washed with distilled water.

h) The wet weight of the residue was taken and recorded.

i) 2 g of the wet sample was weighed from the treated biomass and dried at

105°C for 4hrs to estimate the total dry solid left in the pretreated sample.

67

APPENDIX B (FORMULAE)

1. Moisture Content

% (w/w) Moisture Content = 𝑊𝑠−𝑊𝑑

𝑊𝑠˟ 100

Where:

Ws = Wr – Wc

Wd = Wf – Wc

Ws = Weight of raw sample

Wr = Weight of raw sample + Weight of crucible.

Wc = Weight of crucible

Wf = Weight of dry sample + Weight of crucible

Wd = Weight of dry sample

2. Extractive Content

% (w/w) Extractive = 𝑊𝑢−𝑊𝑧

𝑊𝑢 ˟ 100

Where:

Wz = Wt - Wy

Wu = Wk - Wy

Wz = Weight of sample after extraction

Wy = Weight of filter paper

Wt = Weight of filter paper + Weight of Sample after extraction

Wk = Weight of dry sample + filter before extraction

Wu = Weight of dry sample.

3. Hemicellulose Content

% (w/w) Hemicellulose = 𝑊𝑣−𝑊𝑥

𝑊𝑣 ˟ 100

Where:

Wv = 1g

Wx = Wa - Wb

Wa = Weight of sample + weight of crucible

Wb = Weight of crucible

68

4. Lignin & Ash Content

a) % (w/w) Soluble Lignin = 𝑈𝑉 𝐴𝑏𝑠𝑜𝑟𝑏𝑎𝑛𝑐𝑒 × 𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑓𝑖𝑙𝑡𝑟𝑎𝑡𝑒

𝐸 ×𝑂𝐷𝑊 𝑆𝑎𝑚𝑝𝑙𝑒× 100

Where:

E = 30

ODW Sample = Oven dry weight sample = 0.3g

Volume of filtrate = 0.087 l

b) % (w/w) Insoluble Residue = 𝑊𝑛

𝑊𝑚˟ 100

Where:

Wm = Oven dry weight sample = 0.3g

Wn = Weight of insoluble residue = Wo - Wp

Wo = Weight of Sample + Weight of crucible

Wp = Weight of empty crucible

c) % (w/w) Ash = 𝑊𝑘

𝑊𝑚× 100

Where:

Wm = Oven dry weight sample

Wk = Weight of Ash = Wf - We

We = Weight of empty crucible

Wf = Weight of crucible + ash (After burning insoluble residue in furnace)

d) % (w/w) Insoluble Lignin = % Insoluble Residue - % Ash

e) % (w/w) Total Lignin = % Insoluble Lignin + % Soluble Lignin

5. Cellulose Content

% (w/w) Cellulose = 100% - (% Moisture + % Extractives + % Hemicellulose + %

Lignin + % Ash)

6. Reducing Sugar Yield

Y= 𝑆 × 𝐷 × 𝑉

𝑊

Where:

Y = Reducing sugar yield from enzymatic hydrolysis (mg/g of dry biomass)

S = Sugar concentration in diluted sample (mg equivalent glucose/ml)

D = Dilution factor

69

V = Working Volume (ml)

W = Weight of dry biomass (g)

7. Glucose Concentration Calculation

Glucose Concentration (mg/dl) = 𝐴𝑏𝑠𝑜𝑟𝑏𝑎𝑛𝑐𝑒 𝑜𝑓 𝑆𝑎𝑚𝑝𝑙𝑒

𝐴𝑏𝑠𝑜𝑟𝑏𝑎𝑛𝑐𝑒 𝑜𝑓 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑× 100

70

APPENDIX C (CALCULATIONS)

1. H2O2 Volume Determination During Pretreatment

To obtain 1% H2O2 in 100 ml of hydrogen peroxide and distilled water mixture

from 30% (v/w) H2O2

CaVa = CbVb

Where:

Ca = 1% H2O2

Va = 100ml

Cb = 30% H2O2

Vb =?

Vb = 𝐶𝑎𝑉𝑎

𝐶𝑏 =

0.01 ×100

0.3 = 3.33 ml

The same process was repeated for 2% and 3% H2O2.

2. Calculations For Sample And Enzyme Preparation Before Enzymatic

Hydrolysis

a) 2% Substrate Loading

For Standard order 3 from pretreatment Table

2g of wet sample = 0.6633g of dry sample (After dry weight analysis)

Therefore,

1g of wet sample = 0.33165g of dry sample

Total volume for enzymatic hydrolysis = 20ml

For 2% Substrate Loading,

2g → 100 ml

X g → 20 ml

X = 20 ×2

100 = 0.4 g dry sample

Therefore,

X g of wet sample = 0.4 g of dry sample

X = 0.4 ×1

0.33165 = 1.2061 g of Wet Sample

b) 25 FPU/g Enzyme Loading

Cellulase Enzyme Activity = 57.8 FPU/ml

25 FPU → 1 g dry biomass

Y FPU → 0.4 g dry biomass

Y = 25 ×0.4

1 = 10 FPU

71

57.8 FPU → 1 ml of cellulase

Therefore 10 FPU → J ml of cellulose

J = 10 ×1

57.8 = 0.1730 ml

The amount of enzyme needed is 0.173 ml for 25 FPU/g enzyme loading.

3. Absorbance versus Reducing sugars Calibration Curve at 550nm

(Adetayo, 2013)

The concentration of reducing sugars is obtained using this curve.

4. Reducing Sugars Yield Calculation

For Run Order 1 (Standard Order 20)

Absorbance of Sample = 0.637

S = Concentration of the reducing sugar from the calibration curve = 0.894

mg/ml

V = Working Volume = 20 ml

D = Dilution Factor = 5ml enzyme to 20 ml distilled water

D = 5+20

5 = 5

W = 1.2962

y = 0.747x - 0.031R² = 0.9853

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 0.2 0.4 0.6 0.8 1 1.2

AB

SOR

BA

NC

E at

55

0n

m

SUGAR CONCENTRATION (mg/ml)

ABSORBANCE VS CONCENTRATION (mg/ml)

b Linear (b) Linear (b)

72

Therefore,

Y= 𝑆 × 𝐷 × 𝑉

𝑊

Where Y = Reducing sugars yield (mg/g of dry biomass)

Y = 0.894 × 5 × 20

1.2962 = 68.97084 mg/g

73

APPENDIX D (RESULT TABLES)

1. Reducing Sugars Yield Data For Pretreated Samples

Standard order

Dilution Factor

Working Volume (ml)

Wt. of biomass (g)

Absorbance at 550nm

Sugar Concentration (mg/ml)

Yield (mg/g)

1 5 20 1.6781 0.972 1.3428 80.0191

2 5 20 1.2061 0.904 1.2512 103.7393

3 8 35 1.2061 0.401 0.5781 134.2094

4 8 35 1.0929 0.187 0.2919 74.7918

5 5 20 1.3098 0.782 1.0882 83.0814

6 5 20 1.0588 0.262 0.3921 37.0325

7 5 20 1.3557 1.244 1.707 125.9128

8 5 20 1.3177 0.473 0.6742 51.1649

9 5 20 1.3126 0.471 0.6717 51.1732

10 5 20 1.3203 0.261 0.3909 29.6069

11 5 20 1.2094 0.400 0.5776 47.7592

12 5 20 1.096 0.298 0.4403 40.1734

13 5 20 1.3913 0.322 0.4728 33.9826

14 5 20 1.3724 0.339 0.4949 36.0609

15 5 20 1.125 0.357 0.519 46.1333

16 5 20 1.2116 0.283 0.420 34.6649

17 5 20 1.1165 0.376 0.5445 48.7685

18 5 20 1.3136 0.256 0.3839 29.2250

19 5 20 1.3059 0.999 1.3782 105.5364

20 5 20 1.2962 0.637 0.894 68.9708

74

2. Reducing Sugars Yield data For Optimized (Unsoaked) Samples

Variations

Dilution

Factor

Working

Volume

(ml)

Wt. Of

Biomass

(g)

Absorbance

at 550nm

Sugar

Conc.

(mg/ml)

Yield

(mg/g)

2%

Biomass

Loading &

25 FPU/g 5 20 1.2531 0.276 0.4110 70.04

3%

Biomass

Loading &

25 FPU/g 5 20 1.8797 1.091 1.5017 79.89

4%

Biomass

Loading &

25 FPU/g 8 35 2.9001 1.398 1.9126 184.66

5%

Biomass

Loading &

25 FPU/g 8 35 3.1959 0.910 1.2596 110.36

15 FPU/g

& 2%

Biomass

Loading 5 20 1.3206 0.508 0.7220 54.67

20 FPU/g

& 2%

Biomass

Loading 5 20 1.3206 0.571 0.8056 61

25 FPU/g

& 2%

Biomass

Loading 5 20 1.3206 0.578 0.8149 61.71

30 FPU/g

& 2%

Biomass

Loading 5 20 1.3206 0.538 0.7617 57.68

35 FPU/g

& 2%

Biomass

Loading 5 20 1.1233 0.474 0.6767 60.24

75

3. Reducing Sugars Yield Data For Optimized (Soaked) Samples

Variations

Dilution

Factor

Working

Volume

(ml)

Wt. Of

Biomass

(g)

Absorbance

at 550nm

Conc.

(mg/ml)

Yield

(mg/g)

2%

Biomass

Loading at

25FPU/g 5 20 1.267 0.279 0.4156 68.07

3%

Biomass

Loading at

25 FPU/g 5 20 1.9005 1.035 1.4267 75.07

4%

Biomass

Loading at

25 FPU/g 8 35 2.187 1.094 1.5066 192.89

5%

Biomass

Loading at

25 FPU/g 8 35 3.2383 1.244 1.7072 147.61

15 FPU/g

at 2%

Biomass

Loading 5 20 1.2114 0.468 0.6685 55.18

20 FPU/g

at 2%

Biomass

Loading 5 20 1.2114 0.525 0.7440 61.42

25 FPU/g

at 2%

Biomass

Loading 5 20 1.2114 0.537 0.7598 62.72

30 FPU/g

at 2%

Biomass

Loading 5 20 1.0139 0.297 0.4387 43.27

35 FPU/g

at 2%

Biomass

Loading 5 20 1.0139 0.330 0.4834 47.68

76

APPENDIX E (PRECAUTIONS)

1. All experiments were done very carefully and without haste.

2. Protective clothing and coverings were used during every experiment.

3. Experiments were repeated at least once in order to ensure accuracy of results.

4. There were proper tutorials and guidance before using any equipment.

5. All laboratory apparatus were handled with care.

6. Samples for weighing were kept in the desiccator in order to prevent atmospheric

interference with weights.

7. Accuracy was ensured in weighing of sample by using the weighing balance that

read results to four decimal places.

8. All prepared chemicals were properly stored at the right conditions.

9. Equipment were properly switched off after use.

10. Laboratory apparatus were kept clean at all times.