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COAGULATION OPTIMIZATION TO MINIMIZE
AND PREDICT THE FORMATION OF
DISINFECTION BY-PRODUCTS
by
Justin Wassink
A thesis submitted in conformity with the requirements
for the degree of Master of Applied Science
Graduate Department of Civil Engineering
University of Toronto
© Copyright by Justin Wassink 2011
ii
Coagulation Optimization to Minimize and Predict the
Formation of Disinfection By-Products
Master’s of Applied Science, 2011
Justin Wassink
Department of Civil Engineering, University of Toronto
ABSTRACT
The formation of disinfection by-products (DBPs) in drinking water has become an issue
of greater concern in recent years. Bench-scale jar tests were conducted on a surface water to
evaluate the impact of enhanced coagulation on the removal of organic DBP precursors and the
formation of trihalomethanes (THMs) and haloacetic acids (HAAs). The results of this testing
indicate that enhanced coagulation practices can improve treated water quality without
increasing coagulant dosage. The data generated were also used to develop artificial neural
networks (ANNs) to predict THM and HAA formation. Testing of these models showed high
correlations between the actual and predicted data. In addition, an experimental plan was
developed to use ANNs for treatment optimization at the Peterborough pilot plant.
iii
ACKNOWLEDGEMENTS
This work was funded by the Natural Sciences and Engineering Research Council of
Canada (NSERC) Chair in Drinking Water Research. Provision of financial and in-kind support by
the Peterborough Utilities Commission (PUC) was invaluable; special thanks go to Wayne Stiver,
John Armour, Kevan Light and René Gagnon for their assistance.
I would like to thank my supervisor, Dr. Robert Andrews, for his expertise, guidance and
support of my research. Fariba Amiri was very helpful when dealing with analytical equipment in
the lab. The assistance of Jennifer Lee, Dana Zheng, Emily Zhou and Sabrina Diemert is also greatly
appreciated. Thanks also to the rest of the Drinking Water Research Group for their help and
support.
I would like to thank my parents for their love and support over the years. Finally, I
would like to thank Erica for her love and patience during this process.
iv
TABLE OF CONTENTS
ABSTRACT.................................................................................................................................... ii
ACKNOWLEDGEMENTS........................................................................................................... iii
TABLE OF CONTENTS............................................................................................................... iv
LIST OF TABLES......................................................................................................................... ix
LIST OF FIGURES ...................................................................................................................... xii
LIST OF FIGURES ...................................................................................................................... xii
NOMENCLATURE .................................................................................................................... xvi
1. Introduction and Research Objectives .................................................................................... 1
1.1 Research Objectives........................................................................................................ 1
1.2 Description of Chapters .................................................................................................. 2
2. Literature Review.................................................................................................................... 3
2.1 Disinfection By-Products (DBPs)................................................................................... 3
2.1.1 Introduction............................................................................................................. 3
2.1.2 Health Risks and Regulations ................................................................................. 4
2.1.3 Precursors................................................................................................................ 4
2.1.4 Formation of DBPs ................................................................................................. 6
2.1.5 Modeling of DBPs .................................................................................................. 7
2.2 Enhanced Coagulation .................................................................................................... 7
2.2.1 Introduction............................................................................................................. 7
2.2.2 Optimization of the Coagulation Process ............................................................... 9
2.2.2.1 Coagulant Type................................................................................................. 10
2.2.2.2 Coagulation pH ................................................................................................. 10
v
2.2.2.3 Coagulant Dose................................................................................................. 11
2.2.2.4 Effects on Water Treatment .............................................................................. 11
2.2.3 Removal of NOM, Humic Matter and DBP Formation Potential ........................ 11
2.3 Artificial Neural Networks ........................................................................................... 12
2.3.1 Introduction........................................................................................................... 12
2.3.2 ANN Components and Architecture..................................................................... 13
2.3.2.1 Structure and Operation .................................................................................... 13
2.3.2.2 Options and Variations ..................................................................................... 13
2.3.3 Model Development and Use................................................................................ 18
2.3.3.1 General Model Development Process............................................................... 18
2.3.3.2 Raw Data Analysis............................................................................................ 18
2.3.3.3 Selection of Input Parameters ........................................................................... 19
2.3.3.4 Network Training.............................................................................................. 19
2.3.3.5 Analysis of Results and Performance Evaluation............................................. 20
2.3.4 ANNs in Water Treatment .................................................................................... 22
2.4 Peterborough Water Treatment Plant............................................................................ 24
3. Materials and Methods.......................................................................................................... 26
3.1 Experimental Protocols................................................................................................. 26
3.1.1 Treatment Sequence for Bench-Scale Testing...................................................... 26
3.1.2 Enhanced Coagulation Conditions........................................................................ 29
3.1.3 Water Samples and Data Collection ..................................................................... 29
3.2 Quality Assurance and Quality Control........................................................................ 30
3.3 Analytical Methods....................................................................................................... 32
vi
3.3.1 Trihalomethanes (THMs)...................................................................................... 32
3.3.2 Haloacetic Acids (HAAs) ..................................................................................... 37
3.3.3 Total Organic Carbon (TOC)................................................................................ 40
3.3.4 Ultraviolet Absorbance (UV254)............................................................................ 43
3.3.5 pH Measurement................................................................................................... 43
3.3.6 Chlorine Residual.................................................................................................. 43
3.3.7 Fluorescence Excitation-Emission........................................................................ 43
3.3.8 Liquid Chromatography - Organic Carbon Detection (LC-OCD)........................ 44
3.4 Artificial Neural Network (ANN) Development .......................................................... 44
3.4.1 Modeling Software................................................................................................ 44
3.4.2 Input Parameter Selection ..................................................................................... 45
3.4.3 ANN Architecture Selection ................................................................................. 45
3.4.4 Training and Validation ........................................................................................ 46
4. Evaluation of Enhanced Coagulation for DBP Minimization .............................................. 47
4.1 Introduction................................................................................................................... 47
4.2 Experimental Design..................................................................................................... 49
4.3 Methods......................................................................................................................... 49
4.3.1 Bench-Scale Testing ............................................................................................. 49
4.3.2 Analyses................................................................................................................ 50
4.4 Bench-Scale Simulation of Full-Scale Treatment......................................................... 53
4.5 Influence of Enhanced Coagulation.............................................................................. 54
4.5.1 Removal of Natural Organic Matter (NOM) ........................................................ 54
4.5.2 DBP Formation ..................................................................................................... 58
vii
4.6 Relationships between Measured Parameters............................................................... 64
4.6.1 Linear Correlations ............................................................................................... 64
4.6.2 Predictive Models ................................................................................................. 69
4.7 Seasonal Changes in Water Quality.............................................................................. 71
4.8 Summary ....................................................................................................................... 78
5. Artificial Neural Network (ANN) Modelling ....................................................................... 80
5.1 Parameter Selection ...................................................................................................... 80
5.2 ANN Development ....................................................................................................... 82
5.3 Results and Discussion ................................................................................................. 84
5.4 Implementation ............................................................................................................. 88
5.4.1 ANN Development ............................................................................................... 89
5.4.2 Pilot Plant ANN Data............................................................................................ 90
5.4.3 Parallel Treatment Train Operation for ANN Evaluation..................................... 92
5.4.4 Full Scale Plant (FSP) and ANNs......................................................................... 96
6. Summary, Conclusions and Recommendations.................................................................... 97
6.1 Summary ....................................................................................................................... 97
6.2 Conclusions................................................................................................................... 98
6.3 Recommendations......................................................................................................... 98
7. References............................................................................................................................. 99
8. Appendices.......................................................................................................................... 106
8.1 Sample Calculations.................................................................................................... 106
8.1.1 Point of Diminishing Returns (PODR) ............................................................... 106
8.1.2 Bromine Incorporation Factor (BIF)................................................................... 108
viii
8.2 Bench Scale Testing Raw Data................................................................................... 109
8.2.1 Post-Filter Water Quality.................................................................................... 109
8.2.2 DBP Formation Potential (DBPFP) .................................................................... 111
8.2.3 Winter Bench Scale Test Results........................................................................ 118
8.3 Artificial Neural Network Performance Parameters................................................... 120
8.4 ANN Development in Neurosolutions®..................................................................... 120
8.4.1 Neural Builder Wizard........................................................................................ 120
8.4.2 Training and Testing ........................................................................................... 127
ix
LIST OF TABLES
Table 2.1: DBP regulations and MCLs........................................................................................... 5
Table 2.2: Models from the literature for the formation of halo-organic DBPs ............................. 8
Table 2.3: TOC removal required by the USEPA D/DBPR for enhanced coagulation ................. 9
Table 2.4: Activation function equations...................................................................................... 16
Table 2.5: Important input parameters for neural network models .............................................. 21
Table 3.1: Bench-scale testing - Reagents .................................................................................... 27
Table 3.2: Bench-scale testing – Coagulant dosing details........................................................... 27
Table 3.3: Bench-scale testing – Method outline.......................................................................... 27
Table 3.4: Bench-scale testing – Method outline (continued) ...................................................... 28
Table 3.5: Locations for collection and analysis of water samples from Peterborough WTP...... 31
Table 3.6: Locations for collection and analysis of water samples for bench-scale tests............. 31
Table 3.7: Vials and preservatives used for sample collection..................................................... 32
Table 3.8: Trihalomethanes – Instrument conditions ................................................................... 33
Table 3.9: Trihalomethanes – Reagents........................................................................................ 33
Table 3.10: Trihalomethanes – Method outline............................................................................ 34
Table 3.11: Trihalomethanes – Method detection limits .............................................................. 35
Table 3.12: Haloacetic acids – Reagents ...................................................................................... 37
Table 3.13: Haloacetic acids – Instrument conditions .................................................................. 38
Table 3.14: Haloacetic acids – Method Outline............................................................................ 38
Table 3.15: Haloacetic acids – Standard solutions ....................................................................... 39
Table 3.16: Haloacetic acids – Method detection limits............................................................... 40
Table 3.17: Total organic carbon – Reagents ............................................................................... 41
x
Table 3.18: Total organic carbon – Instrument conditions ........................................................... 41
Table 3.19: Total organic carbon – Method outline ..................................................................... 41
Table 4.1: TOC removal required by the USEPA D/DBPR for enhanced coagulation ............... 48
Table 4.2: Post-filter water quality comparison for full-scale plant (FSP) and bench scale test.. 54
Table 4.3: 24-Hour DBP formation comparison for full-scale plant (FSP) and bench scale test. 54
Table 4.4: Method detection limits for DBP species of THMs, HAAs, HANs, HKs, and CP... 622
Table 4.5: Comparison of DBP formation at coagulant dosages required to achieve 35% TOC
reduction ............................................................................................................................... 62
Table 4.6: Average ratio of DBP formation by class for four coagulants .................................... 62
Table 4.7: Correlations of NOM fractions detected by FEEM with TOC, UV254, and SUVA for
post-filter waters in bench-scale tests ................................................................................... 66
Table 4.8: Breakdown of NOM in Peterborough raw water via LC-OCD analysis ..................... 67
Table 4.9: Models to predict removal of TOC and UV254 using coagulant dosage...................... 70
Table 4.10: Models to predict formation of TTHM and HAA9 using TOC, UV254, and pH........ 71
Table 4.11: Peterborough raw water quality................................................................................ 72
Table 4.12: R-squared values for linear correlations between measures of filtered water NOM
content and 24-hour DBP formation..................................................................................... 78
Table 4.13: Summary of water quality resulting from recommended treatment conditions with
alum, acid + alum, HI 705 PACl, and HI 1000 PACl........................................................... 79
Table 4.14: R2 values for linear correlations between key performance parameters.................... 79
Table 5.1: Variability in raw and filtered water quality, as well as DBP formation, for the data
generated via bench-scale testing.......................................................................................... 81
Table 5.2: Summary of bench-scale data used to develop ANNs to predict DBP formation....... 82
xi
Table 5.3: Final network architecture selected for TTHM and HAA9 ANNs .............................. 84
Table 5.4: Comparison of performance statistics for TTHM and HAA9 ANNs .......................... 84
Table 8.1: Example data for PODR calculation.......................................................................... 106
Table 8.2: Conversion of mass concentrations to molar concentrations .................................... 108
Table 8.3: pH, TOC, UV254, and fluorescence excitation-emission data for bench scale tests post-
filter water........................................................................................................................... 109
Table 8.4: THM, TCAN, and TCP concentrations for 24-hour DBPFP tests ............................ 112
Table 8.5: HAA concentrations for 24-hour DBPFP tests.......................................................... 115
Table 8.6: pH, TOC, UV254, and fluorescence excitation-emission data for February bench scale
tests post-filter water........................................................................................................... 118
Table 8.7: THM concentrations for February DBPFP tests........................................................ 118
Table 8.8: HAA concentrations for February DBPFP tests........................................................ 119
xii
LIST OF FIGURES
Figure 2.1: An artificial neuron .................................................................................................... 14
Figure 2.2: A multilayer perceptron network ............................................................................... 14
Figure 2.3: A forward process model and corresponding inverse process model ........................ 15
Figure 2.4: Activation functions ................................................................................................... 16
Figure 2.5: Process flow diagram for Peterborough WTP with sampling points for data collection
............................................................................................................................................... 25
Figure 3.1: Treatment Steps for Bench-Scale Testing .................................................................. 26
Figure 3.2: Example trihalomethanes calibration curves.............................................................. 35
Figure 3.3: Example haloacetonitriles calibration curves............................................................. 36
Figure 3.4: Example haloketones and chloropicrin calibration curves......................................... 36
Figure 3.5: Example haloacetic acids calibration curves.............................................................. 40
Figure 3.6: Example total organic carbon calibration curve......................................................... 42
Figure 3.7: Total organic carbon – Quality control chart (3.0 mg/L) ........................................... 42
Figure 4.1: Example 3-D image of a fluorescence excitation-emission spectrum ....................... 51
Figure 4.2: LC-OCD chromatograph for raw water with identified peaks for DOC fractions..... 52
Figure 4.3: Average percent reduction of TOC from Peterborough water ................................... 55
Figure 4.4: Average percent reduction of UV254 from Peterborough water. ................................ 56
Figure 4.5: Example TOC curve for determination of point of diminishing returns (PODR)...... 56
Figure 4.6: Jar test removal of DOC detected by LC-OCD.......................................................... 58
Figure 4.7: 24-hour TTHMFP of for bench-scale tests with four coagulant types....................... 59
Figure 4.8: 24-hour HAA9FP for bench-scale tests with four coagulant types ............................ 60
Figure 4.9: 24-hour TCANFP for bench-scale tests with four coagulant types............................ 60
xiii
Figure 4.10: 24-hour TCPFP for bench-scale tests with four coagulant types ............................. 61
Figure 4.11: TTHM speciation in bench-scale tests ..................................................................... 63
Figure 4.12: HAA9 speciation in bench-scale tests....................................................................... 64
Figure 4.13: Correlation between TOC and UV254 for bench-scale tests using alum, acid + alum,
HI 705 PACl, and HI 1000 PACl.......................................................................................... 65
Figure 4.14: Correlations of humic-like substances with TOC and UV254 for bench-scale tests
using alum, acid + alum, HI 705 PACl, and HI 1000 PACl ................................................. 67
Figure 4.15: Correlations between TOC and DBPFP for bench-scale tests using alum, acid +
alum, HI 705 PACl, and HI 1000 PACl................................................................................ 68
Figure 4.16: Correlations between UV254 and DBPFP for bench-scale tests using alum, acid +
alum, HI 705 PACl, and HI 1000 PACl................................................................................ 68
Figure 4.17: Correlations between HS and DBPFP for bench-scale tests using alum, acid + alum,
HI 705 PACl, and HI 1000 PACl.......................................................................................... 69
Figure 4.18: Percent reduction of TOC from Peterborough water in February jar tests .............. 72
Figure 4.19: Maximum intensity for fluorescence peak at excitation/emission of 340/430 nm for
February jar tests................................................................................................................... 73
Figure 4.20: Seasonal comparison for removal of TOC and UV254 by alum.. ............................ 74
Figure 4.21: Percent reduction of 24-hour TTHM formation in February tests with Peterborough
water...................................................................................................................................... 75
Figure 4.22: Percent reduction of 24-hour HAA9 formation in February tests with Peterborough
water...................................................................................................................................... 75
Figure 4.23: 24-hour TTHM formation for tests conducted in summer (left) and winter (right) 76
Figure 4.24: 24-hour HAA9 formation for tests conducted in summer (left) and winter (right) .. 76
xiv
Figure 4.25: Removal of NOM fractions detected by LC-OCD in February test with alum ...... 77
Figure 5.1: Preliminary architecture for ANN to predict formation of THMs or HAAs using data
from bench-scale testing ....................................................................................................... 83
Figure 5.2: Correlation plot for the predicted versus actual TTHM formation ............................ 85
Figure 5.3: Correlation plot for the predicted versus actual HAA9 formation.............................. 86
Figure 5.4: TTHM formation error histogram .............................................................................. 87
Figure 5.5: HAA9 formation error histogram ............................................................................... 87
Figure 5.6: Q-Q plot to test the normality of the error distribution for DBP models ................... 88
Figure 5.7: Preliminary architecture for ANN to predict TTHM formation in the pilot plant ..... 90
Figure 5.8: Preliminary architecture for ANN to predict optimal alum dosage in the pilot plant 91
Figure 5.9: Comparison of TOC in Peterborough full-scale plant (FSP) and two pilot-scale
treatment trains...................................................................................................................... 92
Figure 5.10: Comparison of TTHM formation in Peterborough full-scale plant (FSP) and two
pilot-scale treatment trains.................................................................................................... 93
Figure 5.11: Data flow between PP1 (simplified process flow diagram) and the process model
ANN...................................................................................................................................... 94
Figure 5.12: Data flow between PP2 (simplified process flow diagram) and the inverse process
model ANN........................................................................................................................... 94
Figure 5.13: Flow diagram for a pilot plant ANN software application....................................... 95
Figure 8.1: Example of exponential approximation of jar test TOC data to find the PODR...... 107
Figure 8.2: Selection of ANN architecture using the Neural Builder tool in NeuroSolutions® 121
Figure 8.3: Selection of training/testing data using the Neural Builder tool in NeuroSolutions®
............................................................................................................................................. 122
xv
Figure 8.4: Selection of input and output parameters using the Neural Builder tool in
NeuroSolutions® ................................................................................................................ 122
Figure 8.5: Selecting how much data to use for cross-validation and testing using the Neural
Builder tool in NeuroSolutions®........................................................................................ 123
Figure 8.6: Specifying the number of hidden layers using the Neural Builder tool in
NeuroSolutions® ................................................................................................................ 124
Figure 8.7: Configuring the hidden layer using the Neural Builder tool in NeuroSolutions®... 124
Figure 8.8: Configuring the output layer using the Neural Builder tool in NeuroSolutions® ... 125
Figure 8.9: Supervised learning options in the Neural Builder tool in NeuroSolutions®.......... 126
Figure 8.10: Probe configuration options in the Neural Builder tool in NeuralSolutions® ....... 126
Figure 8.11: Simulation window showing training progress ...................................................... 127
Figure 8.12: Error curve generated by the Data Graph probe..................................................... 127
Figure 8.13: Choosing the source files for input and output testing data ................................... 128
Figure 8.14: Choosing how NeuroSolutions® should output the results of ANN testing.......... 129
xvi
NOMENCLATURE
% Percent
%MAE Percent mean absolute error
°C Degree(s) Celsius
γ Learning rate
η Learning rate
μ Momentum coefficient
a.u. Arbitrary units (of fluorescence intensity)
Al2(SO4)3 Aluminum sulphate (alum)
ANN Artificial neural network
BAC Biological activated carbon
BAT Best available technology
BB Building blocks
BCAA Bromochloroacetic acid
BCAN Bromochloroacetonitrile
BDCAA Bromodichloroacetic acid
BDCM Bromodichloromethane
B-HAA Sum of six brominated haloacetic acids: monobromoacetic acid,
dibromoacetic acid, bromochloroacetic acid, bromodichloroacetic acid,
dibromochloroacetic acid, and tribromoacetic acid
BIF Bromine incorporation factor
Br- Bromide
C TOC and UV254 values following filtration
C0 TOC and UV254 values in raw water (prior to jar test)
CDBM Chlorodibromomethane
cm Centimetre(s)
CP Chloropicrin
CPM Colloidal / particulate matter
DBAA Dibromoacetic acid
DBAN Dibromoacetonitrile
xvii
DBCAA Dibromochloroacetic acid
DBPs Disinfection by-products
DBPFP Disinfection by-product formation potential
DCAA Dichloroacetic acid
DCAN Dichloroacetonitrile
DCP 1,1-Dichloropropanone
D/DBPR Disinfectants and Disinfection Byproducts Rule
DLL Dynamic library link
DOC Dissolved organic carbon, mg/L
DWRG Drinking Water Research Group
DXAA Di-haloacetic acids
Ek(n) Difference between actual and desired network output
FeCl3 Ferric chloride
FEEM Fluorescence excitation-emission matrix
FPM Forward process model
FSP Full-scale plant
FW Filtered water
g Gram(s)
GAC Granular activated carbon
GC-ECD Gas chromatography with electron capture detection
g/L Gram(s) per litre
H2SO4 Sulfuric acid
HAA Haloacetic acid
HAAFP Haloacetic acid formation potential
HAA9 Total haloacetic acids (sum of monochloroacetic acid, monobromoacetic
acid, dichloroacetic acid, trichloroacetic acid, dibromoacetic acid,
tribromoacetic acid, bromochloroacetic acid, bromodichloroacetic acid, and
dibromochloroacetic acid)
HAN Haloacetonitrile
HI 705 Hyper+Ion 705 PACl
HI 1000 Hyper+Ion 1000 PACl
xviii
HK Haloketone
hr Hour(s)
HRT Hydraulic retention time
HS Humic substances
I-THM Iodinated trihalomethanes
IPM Inverse process model
KHP Potassium hydrogen phthalate
L Litre(s)
LC-OCD Liquid chromatography with organic carbon detection
L/min Litre(s) per minute
LMW Low-molecular-weight
m3 Cubic metre(s)
MAE Mean absolute error
MBAA Monobromoacetic acid
MCAA Monochloroacetic acid
MCL Maximum contaminant level
MDL Method detection limit
mg/L Milligrams per litre
min Minutes
mL Millilitre(s)
ML/d Million litres per day
MLP Multi-layer perceptron
MSE Mean squared error
MTBE Methyl-tert-butyl ether
μg/L Microgram(s) per litre
n Number of measurements
NaOCl Sodium hypochlorite
NOM Natural organic matter
NSERC National Science and Engineering Research Council
PACl Polyaluminum chloride
PC Principle component
xix
PCA Principle component analysis
PE Processing element
PFD Process flow diagram
pjk+ conditional correlation between the states of neurons j and k
pjk- unconditional correlation between the states of neurons j and k
PLC Programmable logic controller
PM Protein-like matter
PODR Point of diminishing returns
PP1 First pilot-scale treatment train
PP2 Second pilot-scale treatment train
PUC Peterborough Utilities Commission
Q-Q Quantile-quantile
r2 Correlation coefficient
rpm Revolutions per minute
RW Raw water
SUVA Specific ultraviolet absorbance
SW Settled Water
TBM Tribromomethane (bromoform)
TBAA Tribromoacetic acid
TCAA Trichloroacetic acid
TCAN Trichloroacetonitrile
TCM Trichloromethane (chloroform)
TCP 1,1,1-Trichloropropanone
THAA Total haloacetic acids
THM Trihalomethane
THMFP Trihalomethane formation potential
THM4 Trihalomethanes (sum of trichloromethane, bromodichloromethane,
dibromochloromethane, and tribromomethane)
TTHM Total trihalomethanes
TOC Total organic carbon, mg/L
TOX Total organic halide
xx
TW Treated water
TXAA Tri-haloacetic acids
UofT University of Toronto
USEPA United States Environmental Protection Agency
UV Ultraviolet light or radiation
UV254 Absorbance of 254 nm-wavelength ultraviolet light, cm-1
ΔWjk Change made to weight connecting neuron j to neuron k
Wi Weight value for connection of neuron i
Wjk Weight connecting neuron j to neuron k
WTP Water treatment plant
Xi Real model output value for exemplar i
Xj(n) Input from neuron j at time n
Xpi Predicted model output value for exemplar i
Yi(n) Output from neuron i at time n
Z Weighted sum of neuron inputs
1
1. Introduction and Research Objectives
Many countries have adopted regulations limiting the formation of disinfection by-
products (DBPs) in treated drinking water. These regulations are established to protect public
health, as some DBPs are suspected carcinogens and/or mutagens. Maximum contaminant levels
(MCLs) for DBP regulations are typically focused on the trihalomethane (THM) group of
compounds. Halo-organic DBPs, such as THMs and haloacetic acids (HAAs), are formed when
natural organic matter (NOM) reacts with free chlorine, which is the most commonly used
method of disinfection for drinking water in North America (Routt et al., 2008). DBP formation
is directly related to the concentration and type of NOM present in the water, as well as the
chlorine dosage, among other factors. Enhanced coagulation has been identified as a best
available technology for removal of DBP precursor material (NOM) prior to disinfection to limit
the formation of DBPs (USEPA, 1998). This can involve changing the type and/or dosage of
coagulant applied, as well as pH depression or polymer addition for use as a flocculant aid.
Many studies have been conducted to use mathematical models to predict the formation
of DBPs (Chowdhury et al., 2009). While these efforts have met with some success, Rodriguez
& Sérodes (1999, 2004) have shown that artificial neural networks (ANNs) are better able to
predict DBP formation than conventional equation-based models. ANNs are robust artificial
intelligence models based on the structure of the human brain. They have been shown to be
excellent tools for optimizing water treatment processes (Guan et al., 2005; Wu & Zhao, 2007;
Mälzer & Strugholtz, 2008).
1.1 Research Objectives
The specific research objectives of this study were as follows:
1. To evaluate the potential of enhanced coagulation practices to reduce DBP formation by increasing removal of precursor material, while maintaining finished water quality and limiting coagulant dosage to avoid increasing sludge formation.
2. To investigate fluorescence excitation-emission and liquid chromatography – organic carbon detection as alternative methods of quantifying the removal of NOM during enhanced coagulation.
3. To create artificial neural networks which can successfully predict the formation of both trihalomethanes and haloacetic acids.
2
1.2 Description of Chapters
• Chapter 2 provides background information on disinfection by-products, enhanced
coagulation and artificial neural networks.
• Chapter 3 describes the approach used to evaluate enhanced coagulation for DBP
minimization and to create ANNs to predict DBP concentrations. Details are given for:
collection of water samples and data, laboratory analyses, data analysis, experimental
methods, and ANN development.
• Chapter 4 presents the results of enhanced coagulation bench-scale tests. The performance
for alternative coagulation treatments was evaluated in terms of NOM removal and DBP
formation. Alternative measures for NOM detection are assessed, and correlations between
different parameters are presented.
• Chapter 5 presents the test results for ANN models trained with bench-scale data to
predict the formation of THMs and HAAs. Performance was evaluated using correlation
plots, error histograms, and several performance parameters. A description of potential
pilot-scale implementation is also provided.
3
2. Literature Review
2.1 Disinfection By-Products (DBPs)
2.1.1 Introduction
Disinfection is a key part of the drinking water treatment process, as it is used for the
reduction of pathogens, for taste and odour control, to oxidize iron and manganese, to improve
the efficiency of coagulation and filtration, and to inhibit bacteria regrowth (USEPA, 1999b).
Unfortunately, chemical disinfectants react with natural organic matter (NOM) to produce
unwanted disinfection by-products (DBPs). Free bromine and iodine can also be incorporated
into various DBPs when present in the source water (McQuarrie & Carlson, 2003). According to
a 2007 survey of 312 drinking water treatment plants in the United States, use of chlorine
dioxide, ozone and UV for disinfection have increased in the last 20 years, but free chlorine is
still the most popular disinfectant: 63% of respondents reported using chlorine gas, 31% use
liquid hypochlorite, 8% use chlorine/hypochlorite generated onsite, and 8% use dry hypochlorite
(Routt et al., 2008). The use of other disinfecting agents such as ozone and chlorine dioxide also
result in DBP formation, but chlorine forms twice as many different classes of DBPs in higher
concentrations (McBean et al., 2008). While it may be possible to remove DBPs from treated
water, it is always more efficient and therefore preferable to prevent their formation when
possible (Singer, 1994). Regulations have been implemented to limit the allowable
concentrations of DBPs in drinking water due to the health risks associated with them.
The most commonly occurring groups of DBPs produced by chlorination are the four
trihalomethanes (chloroform, bromodichloromethane, dibromochloromethane, and bromoform)
and the nine haloacetic acids (monochloroacetic acid, monobromoacetic acid, dichloroacetic
acid, trichloroacetic acid, bromochloroacetic acid, dibromoacetic acid, bromodichloroacetic acid,
dibromochloroacetic, and tribromoacetic acid). Other identified DBPs include haloacetonitriles
(HANs), haloketones (HKs), iodinated trihalomethanes (I-THMs), chloropicrin (CP), cyanogen
halides, chloral hydrate, haloaldehydes, halophenols, and halogenated furanon (Singer, 1994;
Archer & Singer, 2006a), which occur at much lower concentrations than trihalomethanes
(THMs) or haloacetic acids (HAAs) (Hua & Reckhow, 2008). More than 250 different DBPs
have been identified (McBean et al., 2008), but together these account for only half of the total
4
organic halides (TOX) produced by chlorination (Singer, 1994; Hua & Reckhow, 2008), which
encompasses all known and unknown halogenated organic DBPs. Detection of DBPs in treated
water requires laboratory analyses, with the associated cost and time delay for results; DBP
concentrations cannot be directly measured via any online detectors, making it difficult to
optimize a treatment train process to minimize DBP formation (Lewin et al., 2004). It is
generally more practical to use surrogate measures such as total organic carbon (TOC)
concentration and absorbance of ultraviolet light.
2.1.2 Health Risks and Regulations
Many studies have been done in recent years exploring the health risks associated with
certain DBPs present in drinking water (Uyak et al., 2008; Hua & Reckhow, 2008). THMs have
been linked to cancer, as well as diseases affecting the nervous system, liver and kidneys, and
can have reproductive effects; similarly, HAAs are associated with cancer and diseases affecting
the kidneys and spleen, and can also have reproductive and developmental effects (McBean et
al., 2008). As a result, regulations have been implemented based on the perceived potential risk.
These are generally met by average annual measurements of DBP concentrations. Since the US
Environmental Protection Agency (USEPA) first established a limit for TTHM (total
trihalomethanes) in 1979, similar regulations have been promulgated around the world (see
Table 2.1). Since the health risks of DBPs are still generally not well known (especially because
so many are not identified), it is very important to minimize the levels of these compounds in
distributed drinking water.
2.1.3 Precursors
The natural organic matter in raw water that reacts with free chlorine to form DBPs
consists mainly of humic substances. The NOM found in natural water is generally a result of
decomposed plant matter (Archer & Singer, 2006a). The hydrophobic fraction of NOM in the
water is more reactive and therefore forms most of the DBPs (Liang & Singer, 2003).
Fortunately, this fraction is also more easily removed by coagulation, flocculation and
sedimentation (Uyak et al., 2008).
The greater the concentration of NOM, the more by-products will be formed.
Unfortunately, NOM concentration cannot be measured directly. Various surrogate measures are
therefore used to predict and control the formation of DBPs. These include concentration of total
5
organic carbon (TOC) or dissolved organic carbon (DOC), absorbance of ultraviolet light using a
wavelength of 254 nm (UV254), specific UV absorbance (SUVA, UV254 normalized to the DOC
concentration), and DBP formation potential (DBPFP) (Rizzo et al., 2005; Uyak et al., 2008).
TOC has traditionally been used to indicate concentrations of DBP precursors (Singer, 1994;
Liang & Singer, 2003; Najm et al., 1994). More recent studies have shown DOC to be more
accurate (Edzwald & Tobiason, 1999; Uyak & Toroz, 2007; McBean et al., 2008). UV254 has
also been shown to be a good indicator of NOM (Liang & Singer 2003, Edzwald et al., 1985,
Najm et al., 1994).
Besides measuring the quantity of DBP precursors, it is also important to characterize the
quality and reactivity of NOM in the water (Uyak et al., 2008). To this end, SUVA can be a very
useful surrogate parameter (Liang & Singer, 2003; McQuarrie & Carlson, 2003), and is closely
correlated to DBP formation (Vrijenhoek et al., 1998). UV254 is especially important for
monitoring water raw water quality online, since it is less expensive, less time-consuming and
less difficult to measure than TOC or DOC. Liang & Singer (2003) and Najm et al. (1994) have
found UV254 to be more suitable than TOC for predicting DBP formation.
Table 2.1: DBP regulations and MCLs
DBP Class Compounds Health Canada
(2006) USEPA (1998) United Kingdom (2000)
Australia - New Zealand (2004)
World Health Organization (2004)
TTHM 100 80 100 250TCM 300
BDCM 60DBCM 100TBM 100HAA5 60MCAA 150DCAA 100 50TCAA 100 100
HAAs
THMs
Health Canada (2006) Guidelines for Canadian Drinking Water Quality: Guideline Technical Document – Trihalomethanes. USEPA. (1998) Stage 1 Disinfectants and Disinfection Byproducts: Final Rule. Federal Register, 60(241), 69389. UK Water supply (water quality) (2000) Regulations for England and Wales. Australian drinking water guidelines (2004) Australian National Health and Medical Research Council. World Health Organization (2004) Guidelines for drinking-water quality. Recommendations, 3rd eds. Geneva.
6
2.1.4 Formation of DBPs
The formation of disinfection by-products depends on many factors, including raw water
quality, operational treatment conditions, and the point of addition of disinfectant (Archer &
Singer, 2006a). Disinfecting drinking water after coagulation and settling has been shown to
reduce DBPs significantly (Archer & Singer, 2006a, Liang & Singer, 2003). Temperature and
pH are both important factors, as are concentrations of NOM and bromide (Najm et al., 1994;
Singer, 1994). The initial dose, contact time and residual concentration of chlorine are also key
factors (McQuarrie & Carlson, 2003; Najm, et al., 1994). Because of the temperature effect on
reaction kinetics, more DBPs are generally formed during warmer months (Sohn et al., 2001).
Most DBPs are formed in greater quantities at lower pH, the exception being THMs (McBean et
al., 2008; Singer, 1994; Liang & Singer, 2003). The incorporation of bromine depends directly
on the concentration of free bromide ions in the water and to a lesser extent on the chlorine dose
applied (McQuarrie & Carlson, 2003; Singer, 1994).
The end-of-pipe concentration can be significantly greater than the DBP levels measured
immediately following disinfection. Sohn et al. (2001) observed TTHM to be 150 to 300%
greater in post-distribution than in water treatment plant effluent. Many DBPs, such as THMs,
are chemically stable; their concentrations increase with time as excess chlorine reacts with
organic precursors (McBean et al., 2008). Others, like HANs, HKs, and HAAs, form quickly
and decay during distribution (Singer, 1994; Sadiq & Rodriguez, 2004).
While it is possible to remove DBPs after disinfection, it is more efficient to prevent them
from forming by focusing on removing NOM precursors before disinfection (Singer, 1994).
Methods for the removal of NOM from source water include using GAC and membrane
technologies. While these have been shown to be effective, it is generally more cost effective to
implement enhanced coagulation practices by optimizing existing water treatment (Crozes et al.,
1995; Uyak & Toroz, 2007). Removing more NOM from the raw water not only decreases the
amount of DBPs formed, but can also reduce the chlorine demand and inhibit bacteria regrowth
during distribution (Crozes et al., 1995). Aquifer storage and recovery of treated water has been
shown to have significant potential for reducing DBPs (McQuarrie & Carlson, 2003). The use of
chloramines instead of free chlorine for secondary disinfection also results in less DBPs being
formed during distribution (Hua & Reckhow, 2008).
7
2.1.5 Modeling of DBPs
Since DBPs were first discovered and identified as health risks, there have been many
attempts to model the formation and reduction of DBPs (McBean et al., 2008; Hua & Reckhow,
2008). The majority of these models have focused exclusively on THMs, with some work being
done on HAAs as well (Hua & Reckhow, 2008; Sadiq & Rodriguez, 2004). Conventional
models use some combination of pH, temperature, TOC or DOC, UV254, SUVA, bromide ion
concentration, and chlorine dose and contact time to predict DBP formation (Sadiq & Rodriguez,
2004). Table 2.2 summarizes the models produced over the past 26 years, which simulate DBP
formation to varying degrees of accuracy (R2 correlation values between 0.34 and 0.98 for
predicted vs. actual DBP concentrations).
Since these models do not incorporate upstream treatment parameters such as coagulant
dosage, they cannot be used to directly select optimal operating conditions for limiting DBP
formation. But by establishing empirical relationships between DBP formation and water quality
parameters, they can be useful tools for identifying ways to improve treatment; as such, they can
make water treatment more cost effective and help to protect public health (Fisher et al., 2004).
Unfortunately, these models often use the same data sets for calibration as for performance
testing, which provides no indication of the model’s ability to generalize when applied to new
data not used during calibration.
There have been several attempts to use artificial neural networks to model DBPs. Lewin
et al. (2004), Rodriguez et al. (2003) and Rodriguez & Sérodes (2004) have demonstrated the
capacity of ANNs to predict THM formation better than mathematical models. ANNs have the
advantage of being unspecific, nonlinear mappings: using an ANN does not assume a specific
mathematical relationship. This allows ANNs to generalize better than conventional models. In
fact, part of the standard modeling procedure is to test the model with data not used for
calibration.
2.2 Enhanced Coagulation
2.2.1 Introduction
Enhanced coagulation is optimized to achieve maximum removal of NOM and DBP
precursors while maintaining good turbidity reduction (Mesdaghinia et al., 2006; Childress et al.,
1999; Uyak & Toroz, 2007). This includes the selection of coagulant type, optimization of the
8
Table 2.2: Models from the literature for the formation of halo-organic DBPs Author &
Year N R2 Output Units Model Description
0.97 TCM 0.442(pH)2(D)0.229(DOC)0.912(Br-)-0.116
0.86 BDCM 17.5(pH)1.01(D)0.0367(DOC)0.228(Br-)0.513
0.94 DBCM 26.6(pH)1.80(D)-0.0928(DOC)-0.758(Br-)1.2
0.78 TBM 0.29(pH)3.51(D)-0.347(DOC)-0.330(Br-)1.84
0.94 TTHM 12.72(TOC)0.291(t)0.271(D)-0.072
0.97 TTHM 108.8(TOC)0.2466(t)0.2956(UV254)0.9919(D)0.126
0.95 TTHM 131.75(t)0.2931(UV254)1.075(D)0.1064
174 0.34 TTHM 1.392(DOC)1.092(pH)0.531(T)0.255
1800 0.90 TTHM 0.044(DOC)1.030(t)0.262(pH)1.149(D)0.277(T)0.968
0.89 log(HAAs) 2.72 + 0.653(TOC) + 0.458(D) + 0.295(t)0.80 log(HAAs) 1.33 + 2.612(TOC) + 0.102(D) + 0.255(T) + 0.102(t)0.92 HAAs -8.202 + 4.869(TOC) + 1.053(D) + 0.364(t)0.78 TTHM 16.9 + 16.0(TOC) + 3.319(D) - 1.135(T) + 1.139(t)0.89 log(TTHM) -0.101 + 0.335(THM0) + 3.914(TOC) + 0.117(t)0.56 TTHM 21.2 + 2.447(D) + 0.449(t)0.90 TTHM 10-1.385(DOC)1.098(D)0.152(Br-)0.068(pH)1.601(t)0.263
0.70 TTHM 0.24(UV254)0.482(D)0.339(Br-)0.023(T)0.617(pH)1.601(t)0.261
0.81 TTHM 0.283(DOC·UV254)0.421(D)0.145(Br-)0.041(T)0.614(pH)1.606(t)0.261
0.87 TTHM 3.296(DOC)0.801(D)0.261(Br-)0.223(t)0.264
0.90 TTHM 75.7(UV254)0.593(D)0.332(Br-)0.0603(t)0.264
0.92 TTHM 23.9(DOC·UV254)0.403(D)0.225(Br-)0.141(t)0.264
0.92 TTHM (THMpH=7.5,T=20°C)·1.156(pH-7.6)1.0263(T-20)
0.87 HAA6 9.89(DOC)0.935(D)0.443(Br-)-0.031(T)0.387(pH)-0.655(t)0.178
0.80 HAA6 171.4(UV254)0.584(D)0.398(Br-)-0.091(T)0.396(pH)-0.645(t)0.178
0.85 HAA6 101(DOC·UV254)0.452(D)0.194(Br-)-0.0698(T)0.346(pH)-0.06235(t)0.18
0.92 HAA6 5.228(DOC)0.585(D)0.565(Br-)-0.031(t)0.153
0.92 HAA6 63.7(UV254)0.419(D)0.640(Br-)-0.066(t)0.161
0.94 HAA6 30.7(DOC·UV254)0.302(D)0.541(Br-)-0.012(t)0.161
0.85 HAA6 (HAA6pH=7.5,T=20°C)·0.932(pH-7.6)1.021(T-20)
Uyak & Toroz, 2005 30 0.83 TTHM μg/L 11.967(TOC)0.398(T)0.158(D)0.702
0.87 TCM 0.0422(t)0.258(D/DOC)0.194(pH)1.695(T)0.507(Br-)0.218
0.87 BDCM 0.0050(t)0.297(pH)2.878(T)0.414(Br-)-0.371
0.86 TCM 0.179(t)0.210(D/DOC)0.221(pH)1.374(T)0.532(Br-)-0.184
0.73 TTHM 5.188(DOCraw)0.322(DOCtreated)0.761(D0)
0.206(D1)0.184(T)0.204
0.72 HAAs 2152.8(DOCraw)0.380(DOCtreated)0.774(D0)
0.102(pH)-0.2599McBean et al. , 2008 μg/L180
NR
Hong et al. , 2007 NR μg/L
Sérodes et al. , 2003 NR μg/L
Sohn et al. , 2004 NR μg/L
Rodriguez et al. , 2000 μg/L
μg/LRathburn, 1996
Chang et al. , 1996 120 μg/L
NR = not reported; TCM = trichloromethane; BDCM = bromodichloromethane; DBCM = dibromochloromethane; TBM = tribromomethane; TTHM = total trihalomethane; HAAs = total haloacetic acids; HAA6 = sum of six haloactic acids; TOC = total organic carbon (mg/L); DOC = dissolved organic carbon (mg/L); UV254 = ultraviolet absorbance at 254 nm wavelength (cm-1); D = chlorine dose (mg/L); T = temperature (°C); t = reaction time (hours); Br- = bromide ion concentration (mg/L).
9
coagulant dose, and adjustment of pH. The USEPA has identified enhanced coagulation as the
best available technology (BAT) for the removal of NOM and DBP precursors (Pontius, 1996).
While GAC or membranes can also be used, these are generally more costly and complex to
implement. The USEPA Disinfectants and Disinfection Byproducts Rule (D/DBPR) dictates that
all surface and GUDI waters must be treated using enhanced coagulation or softening to ensure
adequate removal of DBP precursors (1998). Minimum percent TOC removals are specified
based on raw water alkalinity and TOC concentration (see Table 2.3). Higher alkalinity makes it
more difficult to achieve a low pH, making coagulation less effective (Rizzo et al., 2004).
Iriarte-Velasco et al. (2007) have found that TOC reduction is not necessarily a good indication
of removal of precursors. Archer & Singer (2006b) have proposed that TOC removal be based
on raw water SUVA instead of TOC and alkalinity. The USEPA also recommends that ferric
chloride (FeCl3) or alum (AlSO4) be used for enhanced coagulation.
Table 2.3: TOC removal required by the USEPA D/DBPR for enhanced coagulation
0-60 60-120 >1202.0-4.0 35.0% 25.0% 15.0%4.0-8.0 45.0% 35.0% 25.0%
>8.0 50.0% 40.0% 30.0%
Source Water TOC (mg/L)
Source Water Alkalinity (mg/L as CaCO3)
Crozes et al. (1995) have identified several potential disadvantages to using enhanced
coagulation. First, increasing the amount of coagulant applied produces more sludge, which may
in turn require larger systems for sludge removal and dewatering than are already available.
Second, enhanced coagulation may require upgrading existing chemical storage and feed
systems. Third, it is possible that the conditions that achieve the greatest removal of DBP
precursors are not optimal for turbidity removal. Finally, enhanced coagulation does result in
greater overall use of chemicals for coagulation and pH adjustment, which in turn increases
operating costs.
2.2.2 Optimization of the Coagulation Process
It is very important that enhanced coagulation include the consideration of coagulant
type, pH and dose, since each of these can have a significant impact on process performance
(Bell-Ajy et al., 2000; Fisher et al., 2004; Crozes et al., 1995). When these three factors are
10
selected properly, enhanced coagulation can produce comparable or even less sludge than before
(Bell-Ajy et al., 2000).
2.2.2.1 Coagulant Type
Literature reports vary as to which coagulant is best suited to removing NOM (Bell-Ajy
et al., 2000). This needs to be evaluated on a case-by-case basis, as each source water and
treatment plant is different. Bell-Ajy et al. (2000) and Uyak & Toroz (2007) report that ferric
chloride (FeCl3) removes more organic carbon, UV254 and THMFP than alum. Crozes et al.
(1995) also found that ferric coagulants perform better than alum for removing NOM. Rizzo et
al. (2005) showed that using polyaluminum chloride (PACl) can shift THM speciation to
produce less brominated substances, reducing the potential health risks, while Iriarte-Velasco et
al. (2007) found that alum is better able to remove bromine-reactive NOM when the raw water
has high alkalinity. Iriarte-Velasco et al. (2007) found that PACl removes more DOC, UV254
and THMFP than alum, while Rizzo et al. (2005) ranked three coagulants by capacity for NOM
removal as FeCl3 > alum > PACl, with the order being reversed for removing turbidity. Rizzo et
al. (2004) showed that less PACl was needed than alum and FeCl3 to meet the TOC requirements
of the D/DBPR. Rizzo et al. (2005) found that using PACl allows for the highest THMFP.
2.2.2.2 Coagulation pH
A drop in pH reduces the charge density of the humic and fulvic acids that make up a
large part of most NOM. This charge neutralization increases the hydrophobicity of NOM,
making it more susceptible to adsorption by metal-organic complexes (Uyak, 2007). Crozes et
al. (1995) and Bell-Ajy et al. (2000) have identified pH as the most important factor for removal
of NOM by coagulation. In general, turbidity removal is maximized near ambient pH (about 7);
removal of DBP precursors and surrogates such as TOC, DOC, UV254 and DBPFP is maximized
at lower pH, often between 5 and 5.5 for aluminum coagulants (Childress et al., 1999;
Mesdaghinia et al., 2006; Vrijenhoek et al., 1998; Uyak, 2007; Bell-Ajy et al., 2000). A slightly
higher pH (about 6) is better for ferric coagulants (Crozes et al., 1995). It has been shown that
using a lower pH can also minimize particle counts (Childress et al., 1999). Another advantage
of adjusting pH is that it reduces coagulant demand, which in turn decreases sludge production
(Mesdaghinia et al., 2006), both of which reduce operating costs.
11
2.2.2.3 Coagulant Dose
Enhanced coagulation generally results in higher coagulant dose being used. This has
been shown to achieve greater reduction of turbidity, particle counts, TOC, DOC, UV254, and
ultimately TTHM and TTHMFP (Childress et al., 1999; Mesdaghinia et al., 2006). Effective pH
adjustment can eliminate the need for excessively high dosing, which generally results in
diminishing returns above a certain point. In fact, Bell-Ajy et al. (2000) have found that optimal
coagulant dose can be comparable to conventional practice for turbidity removal when pH
optimization is practiced. It should be noted that the actual value for optimal coagulant dose and
maximum removal of DBP precursors vary with raw water quality and coagulant type.
2.2.2.4 Effects on Water Treatment
Implementing enhanced coagulation can have several effects on the water treatment
process. The main objective is to remove organic content from the water to prevent reactions
forming disinfection by-products. Enhanced coagulation can therefore have a positive impact by
protecting public health. Removing additional NOM may also result in improved performance
by more conventional criteria such as turbidity measurements. This may require increasing the
amount of chemicals added during treatment, which can result in more sludge being produced
and higher residual concentrations of coagulant metals. These changes may increase the overall
cost of treating drinking water.
2.2.3 Removal of NOM, Humic Matter and DBP Formation Potential
The humic fraction of NOM (indicated by a value of SUVA > 4) has a high DBPFP
(Childress et al., 1999). SUVA is a measure of the aromatic content and chlorine reactivity of
NOM (Archer & Singer, 2006b). Fortunately, most humic and aromatic substances are removed
during treatment because they are hydrophobic. Raw waters with SUVA below 3 (less humic
content) therefore do not gain much from enhanced coagulation, but also have low DBPFPs to
begin with. The primary methods of NOM removal by coagulation are charge neutralization,
metal-humic complex precipitation, and adsorption of humic substances onto metal-hydroxide
floc (Mesdaghinia et al., 2006). The pH and coagulant dose determine which of these is
dominant (Childress et al., 1999).
Parameters such as TOC, DOC, UV254 and SUVA are useful as indicators of the
concentration, nature and reactivity of NOM, and can be used as surrogate measures for NOM
12
removal (Archer & Singer, 2006b; Mesdaghinia et al., 2006). Bell-Ajy et al. (2000) found that
using TOC under-predicts DBPFP removal by 18% on average; Uyak & Toroz (2007) found that
coagulation on average removes DOC and SUVA less than DBPFP by 12% and 26%,
respectively, while UV254 is more closely correlated to DBPFP (r2 = 0.80). TOC monitoring can
lead to overestimating of required coagulant dose, which is costly and produces unnecessary
excess sludge (Edzwald & Tobiason, 1999). Iriarte-Velasco et al. (2007) showed that optimizing
coagulation to remove DOC and UV254 does not necessarily remove the most THM precursors.
Bell-Ajy et al. (2000) showed that enhanced coagulation can increase average TOC removal by
11% while also reducing effluent turbidity, particle counts, UV254, residual coagulant metal
concentrations, colour, and organic matter. Crozes et al. (1995) and Fisher et al. (2004) suggest
that removing more NOM may have advantages besides preventing DBPs, such as reducing the
chlorine demand and inhibiting bacteria regrowth during distribution.
2.3 Artificial Neural Networks
2.3.1 Introduction
Artificial neural networks (ANNs), often referred to simply as neural networks, are a
form of artificial intelligence roughly based on the structure of the human brain. As highly-
interconnected networks, they mimic the way in which the brain stores information by adjusting
the relative weights of synapses that connect layers of nodes, or neurons. A neural network is
able to learn patterns from data, which allows it to map complex input-output relationships
(Rodriguez & Sérodes, 1999). An ANN requires no micro- or macroscopic description of the
process, since the network makes no assumptions about the relationships to be modeled (Zhang
& Stanley, 1999; Lewin et al., 2004). In fact, a priori knowledge of the system generally plays a
relatively small role in the actual modeling process, which has led to ANNs being labelled as
“black box” models.
There are several advantages to using neural networks. They can make predictions from
simultaneous and independent variations of multiple inputs (Baxter et al., 2004). They are also
fault-tolerant, since performance does not deteriorate significantly even if some input data is
missing or parts of the network malfunction (Baxter et al., 2002b). No complicated
programming or algorithms are required to create an ANN. In fact, there are multiple user-
friendly software platforms available for building and using neural networks. Finally, ANNs are
13
able to handle a wide variety of nonlinear relationships and data trends (Haykin, 1994). This has
led to ANNs being applied to many different types of problems, including prediction and
forecasting, and process control (Baxter et al., 1999).
2.3.2 ANN Components and Architecture
2.3.2.1 Structure and Operation
A neural network has several key components: processing units (often called perceptrons
or neurons), connections with associated weights, an activation function, a learning rule, and
functions for input and output scaling (Haykin, 1994). Figure 2.1 shows how information is
processed by a single neuron. Input signals are multiplied by their assigned weights and
summed; the resulting value is the input for a nonlinear activation function, which can take
several different forms:
( )zfy
xwzi
ii
=
= ∑ 2.1
where xi is the ith input value to the neuron, wi is the weight multiplier for the ith input, z is the
sum of the weighted inputs, and y is the output value for the neuron. In a multilayer perceptron
(MLP), the type of network shown in Figure 2.2, the neurons are organized in layers. The
neurons in the input layer are not true processing units, as they only perform a linear scaling
function, which typically maps the input values onto a range of [0,1] or [-1,1]. The connecting
synapses are assigned random initial weights, which are modified during the training process
according to a learning rule. The network shown in Figure 2.2 is referred to as “fully
connected,” since each neuron is linked to all of the neurons in the adjacent layers (Haykin,
1994). The middle layer is referred to as the hidden layer, since it only receives and sends
signals not seen by the user.
2.3.2.2 Options and Variations
There are two basic types of MLPs: forward process model (FPM) and inverse process model
(IPM) networks (Nørgaard et al., 2000). A normal process model uses input parameters that
describe a situation to predict their outcome or result. While this is useful when applied to some
problem types such as categorisation and image identification, it only allows for indirect process
14
control. If the outcome is to be optimized by manipulation of one of the inputs, a trial and error
method must be used to find the best setting. On the other hand, inverse process models are
designed for direct control. Switching the output with a control parameter input, as shown in
Figure 2.3, allows the user or operator to specify a target outcome. The trained network will
automatically produce the optimal value of the control input to achieve a desired condition
(Baxter et al., 2002b).
Figure 2.1: An artificial neuron
Figure 2.2: A multilayer perceptron network
15
Figure 2.3: A forward process model and corresponding inverse process model
The arrangements of neuron connections also fall into two categories: feed-forward and
feed-backward (Graupe, 2007). In a feed-forward network, the outputs of each neuron serve as
inputs only for neurons in subsequent layers. Each set of input signals is processed by the
network independently. A feed-back network, also called a recurrent network, includes neurons
whose output signals are used as inputs to neurons in previous layers, with an associated time
delay (Dreyfus, 2005). These connections can improve performance if future process conditions
are influenced by past events.
Several types of activation functions can be used in neural network design (Nørgaard et
al., 2000), the most common of which are shown in Table 2.4 and Figure 2.4. The functions as
shown take the input z, which can be any real number, and map it within the range [0,1]. They
can also be modified to produce values in the range [-1,1], or be shifted left or right, creating a
threshold for each neuron. In practice this is done using bias neurons. A bias neuron is not
actually a processing unit. Instead, it acts as a fixed input to shift the activation function left or
right depending on the sign of the value assigned to it. A bias neuron with a value of -1 and
connected by different weights to each neuron in the hidden and ouput layers creates different
threshold values for the activation functions in each neuron (Hassoun, 1995).
Another key component of a neural network is the learning rule, which defines the way in
which weights are adjusted during training so that the model best fits the data. There are four
basic learning rule types: error correction, Boltzmann, Hebbian and competitive learning rules.
In error correction learning, weight adjustments are based on the magnitude of the error
16
produced in the network output, gradually reducing the overall network error (Basheer &
Hajmeer, 1994):
)()()( nXnEnW jkjk η=Δ 2.2
Table 2.4: Activation function equations
Sigmoid
Binary Piecewise
tanh
⎩⎨⎧
<≥
=0.00.1
i
ii zfor
zfory
( )ii z
y−+
=exp1
1
⎪⎩
⎪⎨
⎧
>
<<<
=
1.110.
0.0
i
ii
i
i
zforzforz
zfory
( )2
tanh1 ii
zy
+=
0
0.5
1
-5 0 5
Activation Function Input (z)
Neu
ron
Out
put (
y)
BinaryPiecewiseSigmoidtanh
Figure 2.4: Activation functions
17
where ΔWjk(n) is the change made to weight connecting neuron j to neuron k at time n, η is a
positive constant that determines the learning rate, Ek(n) is the difference between the actual
network output and the desired output at time n, and Xj(n) is the input from neuron j to neuron k
at time n that produced the output. Boltzmann learning is a stochastic learning method based on
information theory and thermodynamic principles in which each neuron creates an output signal
based on the Boltzmann distribution function (Hinton & Sejnowski, 1986):
( )−+ −=Δ jkjkjk nW ρρη)( 2.3
where ρjk
+ and ρjk- are the conditional and unconditional correlations between the states of
neurons j and k, respectively. The oldest learning rule is the Hebbian rule, which is based on
neurobiological experiments, and states that a synaptic connection between two neurons is
strengthened when the two neurons are repeatedly activated at the same time (Hebb, 1949).
Weight adjustments can be expressed as:
)()()( nYnYnW kjjk η=Δ 2.4
where Yj(n) and Yk(n) are the output signals at time n for neurons j and k, respectively. In
competitive learning, output neurons compete such that only one of them is activated (Hassoun,
1995). The algorithm for weight modification is:
( )
⎭⎬⎫
⎩⎨⎧ −
=Δlosesjneuronif
winsjneuronifnWnXnW jkj
jk 0
)()()(
η 2.5
where Xj(n) is the input signal connected to output neuron k by weight Wjk.
Neural networks can have multiple outputs, but often only a single output is used. It has
been suggested in the literature that neural network performance is best when only one output is
used (Baxter et al., 1999). Maier et al. (2004) have shown that a single network with two
outputs can provide comparable results. If it is necessary to model more than one output
parameter, a single network with multiple outputs can be developed in addition to several single-
output networks for comparison.
18
2.3.3 Model Development and Use
2.3.3.1 General Model Development Process
A general procedure for creating neural networks is as follows: data collection and
statistical analysis, selection of input and output parameters, selection of architecture, training,
fine-tuning of network parameters, and evaluation of network stability and performance (Baxter
et al., 2002b). The order in which these are done is not exactly a simple step-by-step procedure.
For example, it is necessary to collect data before finalizing which inputs and outputs are to be
used, since subsequent steps in the process are required to determine which parameters should be
used. But there must also be some selection of what to measure and monitor before the data can
be collected. Depending on which methods are used, it may not become clear until the end
which inputs are actually important and which are redundant or unnecessary, in which case the
process must be repeated. Building, training and evaluating the model itself is also a very
iterative process. In order to achieve the best possible model, a trial-and-error approach must
often be employed to find the optimum arrangement of neurons, scaling and activation function,
learning rule, weight initialization, learning rate and momentum term (Maier et al., 2004).
2.3.3.2 Raw Data Analysis
Once the data is collected, it is important to conduct certain statistical analyses before
developing and training a network (Baxter et al., 2002b). Measures of central tendency and
variation, as well as maximum and minimum boundaries, are used to characterize each
parameter. Measurement noise is evaluated and any outliers removed from the data set. Input
data sets with non-normal distributions should be normalized; as this will improve network
performance. The data must also be separated into sets for training, validation and testing;
division ratios of 5:3:2 or 3:1:1 have been suggested in the literature (Baxter et al., 2001b;
Rodriguez & Sérodes, 2004). Each set should be representative of the entire range of data
collected (Baxter et al., 2001b).
If two inputs are very closely correlated, only one need be included in the model as an
input (Murray et al., 1995). A linear correlation between a proposed input and the network
output indicates that the input is an important factor and should be used to train the network. But
lack of such a correlation does not rule out a parameter as an important input, since water
19
treatment can be a very non-linear process (Bowden et al., 2005). Further evaluation and
selection of inputs can be done once the network has been trained and evaluated.
2.3.3.3 Selection of Input Parameters
Proper selection of neural network input parameters is important, but unfortunately this
step in network development is often skipped or done improperly (Bowden et al., 2005). Since
some parameters may be correlated to each other (redundant), have too much measurement
noise, or not be related to the output at all, it is important to evaluate which inputs are
appropriate. Input parameters are initially selected based on probability of relationship to the
output(s) and availability of data or feasibility of data collection (Baxter et al., 1999). One
approach is to start with many parameters and later improve the model by removing those that
are not needed via sensitivity analyses (Lewin et al., 2004). Bowden et al. (2005) have identified
several common methods used to choose inputs, which include: use of a priori knowledge of the
system to be modeled; linear cross-correlation with output data; extraction of information
directly from a trained and tested network; and other heuristics. But there are several
disadvantages to relying on the network itself to identify key inputs (Bowden et al., 2005);
model training becomes more complex and requires more computer memory, making the
learning process more difficult; more data is often required; performance deteriorates; and the
model is more difficult to understand due to the irrelevant inputs initially included. Rodriguez &
Sérodes (1999) successfully used correlation analysis prior to model development to identify the
parameters closely linked to the output. Many inputs have been found to be important when
using ANNs to model removal of NOM via enhanced coagulation or to predict DBP formation,
as shown in Table 2.5.
2.3.3.4 Network Training
Training of a neural network is done by a computer program such as NeuroSolutions.
The network is presented with historical input data and outputs a value to be compared to the
known value corresponding to the inputs provided (Garson, 1998). The weights connecting the
neurons are automatically adjusted so that the error between the output produced by the network
and the known output is minimized (Nørgaard et al., 2000). It is important that the network be
trained enough to learn the trends in the relationships to be modeled without memorizing the
noise in the training data set, which is known as overtraining or overfitting (Dreyfus, 2005). To
20
this end, the network is presented with cross-validation data patterns at regular intervals
throughout the training process (Fine, 1999). If the network becomes overtrained, the errors
from the validation data will increase, and the connecting weights will revert to the values that
produced the least error. Training is complete when the error reaches a minimum (Fine, 1999) or
a specified maximum number of training epochs (presenting the entire available training data set)
have been done.
2.3.3.5 Analysis of Results and Performance Evaluation
Once training has been stopped, the network is evaluated using the testing data set, which
was not used in training and therefore has not yet been “seen” by the network. Network stability
can be evaluated by randomly dividing the data into new sets for training, validation and testing
(Baxter et al., 2001a). If the network is stable, performance will not change when the network is
retrained and tested using the new data divisions.
Neural network performance evaluation is typically based on an r-squared value and the
mean absolute error (MAE) (Baxter et al., 1999; Maier et al., 2004); mean squared error (MSE)
is also used (Rodriguez & Sérodes, 2004). The r2 is obtained by plotting model-predicted values
versus known output data, and is a direct indication of the level of correlation between the two
data sets. A high r2 indicates a close correlation and a good model; a low value indicates a very
little correlation and poor model performance. MAE is calculated as:
n
XXMAE
n
iPii∑
=
−= 1 2.6
where Xi and XPi are the real and predicted model output values, and n is the number of data
points used to test the model. The equation for MSE is similar:
( )
n
XXMSE
n
iPii∑
=
−= 1
2
2.7
21
Table 2.5: Important input parameters for neural network models Authors and Year Parameters Used Output
• pH• temperature• turbidity• colour• chemical dose concentrations: alum, PAC and polymer aid• flow rate• pH• temperature• turbidity• colour• hardness• alkalinity• chemical dose concentrations: alum, PAC and polymer aid• pH• temperature• colour• chemical dose concentrations: alum, PAC, chlorine• chlorine contact time• pH• turbidity• colour• DOC concentration• UV254• alkalinity• pH• temperature• TOC or DOC concentration• UV254• bromine concentration• chlorine dose• chlorine contact time
Rodriguez & Sérodes, 2004 THM formation
Lewin et al ., 2004 THM formation
Maier, Morgan & Chow, 2004
Optimal alum dose for NOM removal
Baxter, Stanley & Zhang, 1999 NOM removal
Baxter et al ., 2001 NOM and colour removal
A low value of MAE or MSE (typically 0 to 15% of the mean parameter value) indicates good
model performance. In addition to these calculations, analysis of residual model errors is also
important: error values should be normally distributed and independent, and have constant
variance and a mean of zero (Baxter et al., 1999; Baxter, Smith & Stanley, 2004). Sensitivity
analyses can also be done to evaluate the relative importance of input parameters and observe
parameter interactions (Lewin et al., 2004; Baxter et al., 2004). If an input is found to have little
to no effect on the output, performance may be improved by removing it from the data and
22
retraining the model. It may also be beneficial or necessary to re-evaluate the network
architecture at this point. Finally, a key component to network evaluation is the ability to predict
peak data (Lewin et al., 2004).
2.3.4 ANNs in Water Treatment
Because physical and chemical water treatment processes are inherently complex and
often not entirely understood, they can be difficult to model (Zhang & Stanley, 1999). For this
reason, advanced control schemes are rare, and process control often relies on general heuristics
and operator experience, which can be inefficient and slow. But recent regulations will require
some treatment plants to implement improved control to reduce formation of DBPs. While
strictly statistical or mechanistic approaches have enjoyed some success in modeling DBP
formation, neural networks are ideally suited to this task for several reasons. First, using ANNs
requires the modeller to make no assumptions about the fundamental kinetics or mechanics of
the process being modeled (Lewin et al., 2004). Since the fundamental reaction mechanisms for
DBPs are not well known (Hua & Reckhow, 2008), the general structure of ANNs makes them
ideal for simulating DBP formation. The network extracts knowledge directly from the data,
allowing it to accurately mimic a wide variety of nonlinear functions (Zhang & Stanley, 1999).
Neural networks have also been shown to handle data noise very well (Lewin et al., 2004) and
generalize well when presented with new data within the calibration range (Baxter et al., 2004).
Another advantage is that neural networks are able to isolate single-variable effects while
simultaneously handling both fixed and randomly varying inputs (Baxter et al., 2004).
Unfortunately, neural network models are site-specific, meaning that a model created
using data from one treatment plant cannot be used at other plants (Lewin et al., 2004). Because
of variations in water quality, treatment steps and operating conditions, each plant must have a
model developed using measurements taken on-site. Therefore, it is impossible to use ANNs to
develop a single process control model to be used at various locations, which would be more
cost-effective. On the other hand, this eliminates scale-up issues that can prevent bench-scale
models from being applied to full-scale processes, since the data used to train and test the
network must be obtained from the full-scale process itself (Baxter et al., 2001b).
Neural networks used in water treatment serve as “inferential” sensors, meaning they are
predictive models that establish relationships between easy-to-measure surrogate parameters and
23
output parameters which are more difficult, costly and time-consuming to measure (Lewin et al.,
2004). Baxter et al. (2001) describe two types of neural network interface: process optimization
and virtual laboratory. The first can be used to optimize online unit processes by adjusting
chemical doses and other operating conditions. The second allows users to conduct virtual
experiments and can also be used as a tool for operator training.
Neural networks perform very well when used to model the removal of NOM in
enhanced coagulation processes (Baxter et al., 1999). Baxter et al. (2001) found that when
colour is used as a surrogate for NOM concentration, the output MAE was even less than the
error associated with colour measurements. ANNs can also be used to reduce precursor
concentrations by predicting DBP formation (Lewin et al., 2004). According to Rodriguez
(2004), neural networks are comparable to conventional models when used to model chlorine
decay. But ANNs perform much better than other model types when predicting NOM removal
(Maier et al., 2004) and TTHM formation (Rodriguez & Sérodes, 2004), especially for peak high
and low values (Rodriguez & Sérodes, 1999).
Researchers have discovered several advantages to using neural networks in water
treatment process control. Rodriguez & Sérodes (1999) have found that using these predictive
models eliminates the time-delay inherent in traditional monitor feedback control. According to
Zhang et al. (2007), using ANNs results in chemical savings, less frequent filter backwash,
improved remote monitoring and greater overall efficiency. Certain practices and network types
have also been shown to perform better than others. Inverse process models not only allow for
more direct control, but they also provide more accuracy and noise reduction than forward
process models (Zhang & Stanley, 1999). Dividing the data to create seasonal neural networks
instead of using a single network year-round can increase model accuracy by about 40%
(Rodriguez & Sérodes, 1999). It is also necessary to create a new ANN when a change is made
in the treatment process, such as using a new coagulant (Zhang et al., 2007).
In the last 15-20 years, a lot of research has been done to improve drinking water
treatment (Delgrange-Vincent et al., 2000; Mälzer & Strugholtz, 2008; Rodriguez et al., 1997),
water distribution (Rodriguez & Sérodes, 1999; Wu & Zhao, 2007; Skipworth et al., 1999) and
wastewater treatment (Dogan et al., 2008; Guan et al., 2005) systems using neural networks.
These not only demonstrate the capability of neural networks to optimize many different
24
processes in water treatment, but also underscore the need for further research, development and
implementation of neural network control systems in the industry.
2.4 Peterborough Water Treatment Plant
The Peterborough Water Treatment Plant is located on the Otonabee River in the City of
Peterborough, Ontario, about 20 km north of Rice Lake. It was built in 1922 and expanded in
1952, 1965 and 1995. The current rated capacity of the plant is 104 ML/d. Raw water TOC
concentrations are consistently between 6 and 7 mg/L.
A process flow diagram is shown in Figure 2.5. When raw water temperature is below
12°C, chlorine is applied at the intake for zebra mussel control. Water from the low lift pumping
station passes through two inline mixers and a flash mixer; alum coagulant is added before each
of these units. This is followed by the flocculation tanks, which are divided into four hydraulic
trains, each with multiple tanks. These feed into six sedimentation basins, two of which have
parallel plate settlers. After the settled water conduit, flow is divided across eleven granular
media filters. Filter effluents are combined in the filtered water conduit and enter the chlorine
contact tank, where chlorine is added for chemical disinfection. Additional dosage of chlorine,
as well as hydrofluosilicic acid and sodium silicate, occurs in the clearwell. From there the
finished water is moved by the high lift pumps to the distribution system.
25
ChlorineContact
Tank
Al
1
116a
4
10
Clearwell 3(CW3)
Screens
Clearwell 2(CW2)
Operating Conditions DataSCADA:� flow rate� alum dosage� primary chlorine dosage� secondary chlorine dosage� sodium silicate dose
Low LiftPumps
Raw WaterSCADA:� temperature� pH
Water Samples :� TOC / UV254
Settled WaterSCADA:� pH
Water Samples :� TOC / UV254
Filtered WaterSCADA:� pH
Water Samples:� TOC / UV254
Chlorine Addition(zebra mussel control)
Alum Addition (3 possible locations)
MultimediaFilters (x 11, flow through each filter can be controlled individually)Raw
Water
6b 6c
5a 5b 5c
3
2
Sedimentation Basins (x 6, only basins 5&6
have parallel plate settlers)
3a
5
6
9
8
7
6
5
4
3
2
1
Chlorine Addition(Disinfection)
Hydrofluosilicic Acid (Fluoride)
Post Chlorine Addition
Inline Mixers (x2)
Sodium Silicate
Addition(pH control)
Treated WaterSCADA:� pH� chlorine
residual
Water Samples:� TOC / UV254� THMs� HAAs
High LiftPumps
Flocculation Tanks( 4x hydraulic trains)
Flash Mixer
Al
4
3b
1a2
1b
Drain by gravity from CW3 to CW2
Distribution System Zone 1
Distribution System Zone 2
High LiftPumps
Weir
Al
Figure 2.5: Process flow diagram for Peterborough WTP with sampling points for data collection
26
3. Materials and Methods
3.1 Experimental Protocols
3.1.1 Treatment Sequence for Bench-Scale Testing
A bench-scale testing treatment sequence, as shown in Figure 3.1, was developed in order
to meet the project’s objectives. Raw water from the Peterborough WTP (Peterborough, ON)
was transported weekly to the University of Toronto (UofT) for use in testing. Treatment steps
were chosen to mimic full-scale treatment in the Peterborough WTP (Figure 2.6). Sodium
hypochlorite dosing solution (12% Cl2, BioShop Canada, Inc., Burlington, ON) was used for pre-
chlorination and post-filter disinfection by-product (DBP) formation tests. Pre-chlorine was
allowed to react with raw water for 2.5 minutes before coagulant addition. This was done to
match the WTP pre-chlorine contact time prior to alum addition. Jar tests were conducted using
a PB-700 Standard Jar Tester paddle stirrer with six square, acrylic 2-L jars (Phipps & Bird,
Richmond, VA). Coagulant addition was followed by 90 seconds of rapid mixing (100 rpm), 15
minutes of slow mixing for flocculation (30 rpm), and 30 minutes of settling. The Enhanced
Coagulation Guidance Manual (USEPA, 1999) recommends 1 minute of rapid mix, 30 minutes
flocculation, and 60 minutes settling; other researchers have shortened this protocol to 15
minutes flocculation and 30 minutes settling (Mesdaghinia et al., 2005; Gao & Yue, 2005; Yan
et al., 2006). In this case, shorter times were used to more closely mimic the residence times for
unit processes at Peterborough WTP. This also allowed for a greater number of tests to be
conducted each week. Settled water was filtered using 42.5 mm diameter glass microfibre filters
with a 1.5-μm pore size (Whatman Inc., Florham Park, NJ). DBP formation (DBPF) tests were
conducted for 24 hours. The reagents used for bench-scale testing are listed in Table 3.1, and the
details of coagulant dosing are presented in Table 3.2. The method steps are outlined in Table 3.3
and Table 3.4.
Figure 3.1: Treatment Steps for Bench-Scale Testing
27
Table 3.1: Bench-scale testing - Reagents Reagent Source
Aluminum Sulphate (Al3(SO4)3·18H20 General Chemical (Parsipanny, NJ), 48.5% Sulphuric Acid (H2SO4) VWR International (Mississauga, ON), 98% Polyaluminum Chloride (Hyper+ Ion 705 PACl) General Chemical (Parsipanny, NJ), 100% Polyaluminum Chloride (Hyper+ Ion 1000 PACl) General Chemical (Parsipanny, NJ), 100% Sodium Hypochlorite (NaOCl) BioShop Canada, Inc. (Burlington, ON), 12%
Table 3.2: Bench-scale testing – Coagulant dosing details
Alum HI 705 HI 100020 61.8 30.3 31.330 92.7 45.5 46.940 123.6 60.6 62.550 154.4 75.8 78.160 185.3 90.9 93.870 216.2 106.1 109.4
Dosage (mg/L)Coagulant Volume (μL)
Table 3.3: Bench-scale testing – Method outline
Chlorine Dosing Solution Preparation (600 mg/L Cl2)
Partially fill a 100 mL volumetric flask with Milli-Q water.
Add 1000 μL of 12% NaOCl stock solution and fill to the mark with MilliQ.
Store raw water in the dark at 4°C.
Allow raw water to reach room temperature (23±2°C) before testing.
Invert the flask 5 times to mix the solution. Transfer to a 125 mL amber bottle. Cap with a Teflon-lined septa screw cap and store at 4°C.
Acid Solution Preparation (0.02 M)
Partially fill a 100 mL volumetric flask with Milli-Q water.
In the fume hood, add 109 μL of concentrated sulfuric acid and fill to the mark with Milli-Q.
Invert the flask 5 times to mix the solution.
Transfer to a 125-mL amber bottle. Label the bottle and store in the acids cabinet below the fume hood.
28
Table 3.4: Bench-scale testing – Method outline (continued)
Simulated Distribution System (SDS) Test
After 24 hours, measure the free chlorine residual concentration in each bottle and collect samples for THM and HAA analyses.
Measure the pH of the filtrate, and collect 40 mL samples for UV254 and TOC measurement.
Determine Volume of Acid Required
For each coagulant dosage, transfer 250 mL of filtered water to a 250 mL amber bottle. Each bottle should have 3 mL of headspace remaining.
Collect 500 mL supernatant from each jar. Measure the pH of the supernatant, and filter using 1.5 μm
Add ¼ of the volume of alum required for a 2 L jar. For example, add 15.5 μL of alum to achieve a
Turn off stirrer, raise paddles and allow settling for 30 minutes.
Collect 40 mL supernatant from each jar for UV254 and TOC measurement.
Jar Testing
Using a buret, titrate with 0.02 M sulfuric acid until the target pH is achieved. The target value is the pre-
If pre-chlorination is being used, add 833 μL of chlorine dosing solution. Mix at 30 rpm for 2.5 mintues before proceeding to coagulant addition.
Take a 500 mL raw water sample and measure its pH. From this take a 40 mL sample for UV254 and
Fill six jars with raw water using the 1-L graduated cylinder so each jar contains 2 L.
Pour 500 mL of raw water into a 1 L beaker.
Repeat for each dosage to be used in the jar test with alum + acid.
Add the required volumes of coagulant (and acid, if required) to each jar. If acid is being used, add the acid and mix well before adding the coagulant.
Stir at 100 rpm for 90 seconds for rapid mixing.
Stir at 30 rpm for 15 minutes for flocculation.
Add chlorine dosing solution to each bottle to achieve the full-scale dosage concentration used for primary disinfection: 3.64 ± 0.17 mg/L, or 1517 ± 71 μL per 250 mL bottle.
Fill each bottle to the top of the neck with Milli-Q water to ensure there will be no headspace. Cap with Teflon-lined screw caps and place in a a temperature-controlled incubator for 24 hours. The incubator
should be set to the water temperature measured at Peterborough WTP (23 ± 2°C).
29
3.1.2 Enhanced Coagulation Conditions
Aluminum sulphate (alum) is the chemical currently in use for coagulation at the
Peterborough WTP. Therefore, bench-scale testing with alum (General Chemical, Parsippany,
NJ) was conducted as a control for comparison with both full-scale treated water quality and
bench-scale performance of alternative coagulants.
According to the Enhanced Coagulation and Enhanced Softening Guidance Manual
(USEPA, 1999), coagulant dosage, pH, and coagulant type are important operational factors to
be considered when optimizing the coagulation process. It is widely reported that the removal
of TOC, UV254, NOM, and THMFP via coagulation with alum is maximized at lower pH,
typically about 5-6 (Hubel & Edzwald, 1987; Childress et al., 1999; Mesdaghinia et al., 2006).
Therefore, testing with alum was also conducted with the addition of concentrated sulphuric acid
(Sigma-Aldrich, St. Louis, MO) for pH depression.
Two polyaluminum chloride coagulants were also investigated: Hyper+Ion (HI) 705 and
HI 1000 (General Chemical, Parsippany, NJ). These were selected based on preliminary jar tests
conducted by General Chemical, in which they demonstrated good performance in terms of
removal of colour and turbidity compared with alum and other coagulants.
Bench-scale testing for each coagulant type was conducted with and without pre-
chlorination. Therefore, each week eight tests were performed: alum, alum with acid, HI 705,
and HI 1000 (all without pre-chlorine), and alum, alum with acid, HI 705, and HI 1000 (all with
pre-chlorine).
Since the paddle stirrer used for jar tests has six jars, each test was done with six different
coagulant dosages: 20, 30, 40, 50, 60, and 70 mg/L. For tests with alum, the dosage for one of
the jars was set to be the same as the dosage concentration at the full-scale plant (45 ± 3 mg/L).
3.1.3 Water Samples and Data Collection
Bench-scale testing on Peterborough water was conducted between July 19 and August
17, 2010. Raw water was collected each week from Peterborough WTP using 25-L carboys and
shipped to the UofT for use in bench-scale testing. Water was stored in the dark at 4°C to inhibit
any biological or chemical action until used for testing. Before each test, raw water was brought
to room temperature (20 to 25°C) to match the water temperature at Peterborough WTP
(23_±_2°C during testing period).
30
For each weekly collection of raw water for bench-scale tests, data was collected to
characterize full-scale treatment. This included water quality and operational data provided by
the SCADA system at Peterborough WTP, as well as water samples for analysis at UofT. This
was done to determine how well the water quality from bench-scale tests with alum matched the
finished water quality at Peterborough WTP. Table 3.5 shows the locations of sample and data
collection and analysis. Important water quality parameters monitored include temperature, pH,
TOC, UV254, NOM content, chlorine residual, and DBP formation. The operational parameters
recorded were flow rate and chemical dosages of pre-chlorine, alum, and primary and secondary
chlorine. Table 3.6 shows the locations of sample and data collection for all bench-scale tests
conducted. Descriptions of sample vials and preservatives used are shown in Table 3.7 (TOC
and UV254 were analysed from a single sample).
3.2 Quality Assurance and Quality Control
Quality assurance was established by analysing running standards during analysis of total
organic carbon (TOC), ultraviolet absorption (UV254), trihalomethanes (THMs), haloacetic acids
(HAAs), haloacetonitriles (HANs), haloketones (HKs), and chloropicrin (CP). Standards within
the expected sample concentration range, as well as method blanks and instrument blanks, were
analysed after every tenth sample. The running standards were used to create quality control
charts, which were used to evaluate the method performance as per Standard Method 1020
(APHA, 2005). The method was recalibrated if any of the following trends were observed
(where M is the historical mean and SD is the historical standard deviation):
- 2 consecutive measurements outside the control limits of M ± 3 x SD;
- 3 out of 4 consecutive measurements were outside of M ± 2 x SD;
- 5 out of 6 consecutive measurements were outside of M ± SD;
- 5 out of 6 consecutive measurements were following a trend of increasing or
decreasing;
- 7 consecutive measurements were greater than M, or 7 consecutive measurements
were less than M.
The warning limits were set at M ± 2 x SD and the control limits were set at M ± 3 x SD.
The historical mean and standard deviation were calculated from the results of 8 standards
prepared individually and analysed consecutively.
31
Table 3.5: Locations for collection and analysis of water samples from Peterborough WTP
Raw a Pre-filter
Post-filter
Post-clearwell
24-Hour SDS
University of Toronto
Peterborough WTP
University of Waterloo
Temperature x xpH x x x x x
TOC x x x x xUV254 x x x x xNOM x x x x x
Chlorine residual x x xTrihalomethanesb x x x x x xHaloacetic Acids x x x x x x
Sampling Location Analysis LocationParameter
aRaw water samples were collected prior to the point of addition for pre-chlorination. bAnalysis of THM samples enables simultaneous detection of haloacetonitriles, haloketones, and chloropicrin.
Table 3.6: Locations for collection and analysis of water samples for bench-scale tests
Raw a Pre-filter b
Post-filter b
24-Hour SDS
University of Toronto
University of Waterloo
Temperature x xpH x x x x
TOC x x x xUV254 x x x xNOMc x x x x
Chlorine residual x xTrihalomethanesd x xHaloacetic Acids x x
ParameterSampling Location Analysis Location
aRaw water samples were analyzed at the same time as settled and filtered water to determine the effect of raw water storage before jar tests were performed. bSeparate pre-filter (settled) and post-filter samples were collected for all six jars in each test. cNOM samples were collected and analyzed only for jar tests without pre-chlorine. dAnalysis of THM samples enables simultaneous detection of haloacetonitriles, haloketones, and chloropicrin.
The method detection limits (MDLs) for TOC, UV254, THMs, HAAs, HANs, HKs, and
CP were determined by multiplying the standard deviation of eight replicates by the Student-t
value for a 99% confidence level (3.0). The eight replicates were made to be ten times the
expected MDL.
32
Table 3.7: Vials and preservatives used for sample collection
Sample Volume (mL)a Preservatives DescriptionTOCb 40 Sulphuric acid (H2SO4), 2 drops VWR International, 98+%NOM 40 none NA
Ammonium chloride, 0.025 g Caledon Laboratories Ltd., 99.5%
Phosphate buffer, 0.4 g99% Potassium phosphate (KH2PO4)
1% Sodium phosphate (Na2HPO4) ACS grade
HAAs 25 Ammonium chloride, 0.025 g Caledon Laboratories Ltd., 99.5%
THMsc 25
aVolumes are for amber vials used to collect water quality samples. bTOC samples from bench-scale tests were also used to analyze for UV254. cAnalysis of THM samples enables simultaneous detection of haloacetonitriles, haloketones, and chloropicrin.
For every tenth sample, additional QA/QC measures were taken when analysing TOC,
UV254 and all DBPs. Method duplicates were done to evaluate the entire procedure. Replicate
analysis was also done to evaluate the instrument precision. Because the analysis for UV
absorbance is relatively quick and easy, replicate analyses were done for all samples and
standards. Finally, matrix spike and recoveries were performed to determine the effect of the
sample matrix on the methods of analyses.
3.3 Analytical Methods
3.3.1 Trihalomethanes (THMs)
Trihalomethanes (THMs) (chloroform (trichloromethane, TCM), bromodichloromethane
(BDCM), dibromochloromethane (DBCM), and bromoform (tribromomethane, TBM)) analyses
were conducted using a liquid-liquid extraction gas chromatographic method as described in
Standard Method 6232 B (APHA, 2005). This method also allowed for the simultaneous
extraction and detection of haloacetonitriles (HANs) (dichloroacetonitrile (DCAN),
trichloroacetonitrile (TCAN), dibromoacetonitrile (DBAN), and bromochloroacetonitrile
(BCAN)), haloketones (HKs) (1,1-dichloropropanone (DCP) and 1,1,1-trichloropropanone
(TCP)), and chloropicrin (CP). All analyses were conducted at the University of Toronto
laboratory facility (Toronto, ON) using a Hewlett Packard 5890 Series II Plus Gas
Chromatograph (Mississauga, ON) equipped with an electron capture detector (GC-ECD) and a
33
DB 5.625 capillary column (Agilent Technologies Canada Inc., Mississauga, ON). The
instrument conditions are described in Table 3.8, and the required reagents are listed in Table
3.9. The method steps are outlined in Table 3.10. The concentration of THM stock solution was
2000 μg/L. The concentration of stock solution for HANs, HKs, and CP was 5000 μg/L. The
concentrations of all 11 DBP species in the intermediate solution and in running standards were
10 μg/mL and 10 μg/L, respectively. Calibration standards were prepared at concentrations of 0,
2, 4, 10, 20, 30, 40, and 80 μg/L. Method detection limits (MDLs) are provided in Table 3.11.
MDLs were determined by multiplying the standard deviation of 8 replicates, prepared in the same
order of magnitude as the expected MDL, by the Student-t value (3.0). Examples of typical
calibration curves for THMs, HANs, and HKs and CP are shown in Figure 3.2, Figure 3.3, and
Figure 3.4, respectively.
Table 3.8: Trihalomethanes – Instrument conditions Parameter DescriptionInjector Temperature 200°CDetector Temperature 300°C
40°C for 4.0 min4°C/min temperature ramp to 95°C60°C/min temperature ramp to 200°C
Carrier Gas HeliumFlow Rate 1.2 mL/min at 35°C
Temperature Program
Table 3.9: Trihalomethanes – Reagents Reagent SourceAmmonium chloride [NH4Cl] Caledon Laboratories Ltd., 99.5%Hydrochloric acid [HCl] E.M. Science, ACS GradeTrihalomethane concentrated stock for calibration Supleco, 2000 μg/mL in methanol (48140-U)Sodium sulphate [Na2SO4] E.M. Science, ACS GradeMethyl-tert -butyl-ether (MTBE) Aldrich, >99.8%
34
Table 3.10: Trihalomethanes – Method outline
Analyze using a GC-ECD.
Extraction
Extract 2 mL from the organic layer using a Pasteur pipette and place in a 1.8 mL GC vial (there should not be any water in the vial). Use a clean pipette for each sample. Fill the vial to the top and cap
immediately, ensuring that there is no headspace. To ensure only the MTBE layer was taken, examine the vials after several minutes to see if there is only one phase visible.
If not analyzing immediately, store the samples in the freezer (-11°C) for up to 21 days.
Place all the vials upright in a rack and shake for 2 minutes.
Let samples stand for 10 minutes for phase separation.
Shake sample vial vigorously for approx. 30 seconds and place on counter on its side.Repeat and complete for all samples, blanks and standards before proceeding.
Add 1 tsp of sodium sulphate (Na2SO4) using scoop in order to increase extraction efficiency.
Add 4 mL of MTBE extraction solvent and cap with Teflon®-lined silicon septa and screw cap.
Include blanks and running standards every 10 samples.
Transfer the contents of each sample vial into a clean 40 mL vial.
Top flask to 5 mL and cap with glass stopper and mix by inverting at least 10 times.
Running Standards: (10 μg/L):Add 25 μL of working solution to 25 mL of Milli-Q® water in a 40 mL vial and process alongside
samples. (Salt and MTBE should be added right after adding working solution)
Working Solution: (10 μg/mL):Fill a 5 mL volumetric flask partially with methanol.
Using a 50 μL syringe and the "sandwich technique", add 25 μL of THM stock (2000 μg/mL each - Supelco 48140-U) to volumetric flask below the surface of the solution.
** Wipe the syringe tip with a Kimwipe before measuring out the THM stock and before adding stock to solution. When injecting stock, submerge tip below surface of methanol in the volumetric flask.
Blanks: Transfer 25 mL of Milli-Q® water into 40 mL vials and process alongside samples.
Store samples in the dark at 4°C for up to 14 days.
To begin preparing samples, remove from refrigerator and bring to room temperature.
Collect samples in 25 mL amber vials quenched with 0.025 g of ammonium chloride (100 mg/mL).
If samples will not be analysed immediately, lower pH to < 2 with 1 or 2 drops of 1:1 HCl. Ensure that samples are headspace-free.
35
Table 3.11: Trihalomethanes – Method detection limits Analyte MDL (μg/L) Std. Dev. (μg/L)
TCM 0.78 0.26BDCM 0.56 0.19DBCM 0.00 0.00TBM 0.20 0.07
DCAN 0.83 0.28TCAN 0.05 0.02BCAN 0.13 0.04DBAN 0.13 0.04DCP 0.12 0.04TCP 0.19 0.06CP 0.60 0.20
0
5
10
15
20
25
0 10 20 30 40 50 60 70 80 90Concentration (ug/L)
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TCMBDCMDBCMTBM
TCM (μg/L) = (area response ratio - 0.2684)/0.1032, R2 = 0.9883BDCM (μg/L) = (area response ratio - 0.3932)/0.2485, R2 = 0.9939DBCM (μg/L) = (area response ratio - 0.2851)/0.2409, R2 = 0.9961TBM (μg/L) = (area response ratio + 0.0247)/0.1525, R2 = 0.9934
Figure 3.2: Example trihalomethanes calibration curves
36
0
5
10
15
20
25
0 10 20 30 40 50 60 70 80 90Concentration (ug/L)
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DCANTCANBCANDBAN
DCAN (μg/L) = (area response ratio - 0.1958)/0.2037, R2 = 0.9949TCAN (μg/L) = (area response ratio + 0.3271)/0.2745, R2 = 0.9927BCAN (μg/L) = (area response ratio + 0.0712)/0.2652, R2 = 0.9956DBAN (μg/L) = (area response ratio + 0.2957)/0.2514, R2 = 0.9936
Figure 3.3: Example haloacetonitriles calibration curves
0
5
10
15
20
25
30
35
0 10 20 30 40 50 60 70 80 90Concentration (ug/L)
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DCPTCPCP
DCAN (μg/L) = (area response ratio - 0.3675)/0.3642, R2 = 0.9938TCAN (μg/L) = (area response ratio + 0.4311)/0.3775, R2 = 0.9929CP (μg/L) = (area response ratio - 0.3917)/0.1060, R2 = 0.9670
Figure 3.4: Example haloketones and chloropicrin calibration curves
37
3.3.2 Haloacetic Acids (HAAs)
Haloacetic acids (HAAs) (monochloroacetic acid (MCAA), monobromoacetic acid
(MBAA), dichloroacetic acid (DCAA), trichloroacetic acid (TCAA), bromochloroacetic acid
(BCAA), dibromoacetic acid (DBAA), bromodichloroacetic acid (BDCAA),
dibromochloroacetic acid (DBCAA), and tribromoacetic acid (TBAA)) analyses were conducted
using a liquid-liquid extraction gas chromatographic method as described in Standard Method
6251 B (APHA, 2005). All analyses were conducted at the University of Toronto laboratory
facility (Toronto, ON) using a Hewlett Packard 5890 Series II Plus Gas Chromatograph
(Mississauga, ON) equipped with an electron capture detector (GC-ECD) and a DB 5.625
capillary column (Agilent Technologies Canada Inc., Mississauga, ON). The required reagents
are listed in Table 3.12, and the instrument conditions are described in Table 3.13. The method
steps are outlined in Table 3.14. Details of the standard solution are presented in Table 3.15.
Method detection limits (MDLs) are provided in Table 3.16. MDLs were determined by
multiplying the standard deviation of 8 replicates, prepared in the same order of magnitude as the
expected MDL, by the Student-t value (3.0).
Table 3.12: Haloacetic acids – Reagents Reagent SourceAmmonium chloride [NH4Cl] Caledon Laboratories Ltd., 99.5%Diethyl ether [C2H5OCH2CH2OCH2CH2OH] Aldrich, 99+%N-methyl-N-nitroso-p-toluene sulfonamide (Diazald) [CH3C6H4SO2N(CH3)NO] Aldrich, 99+%
Ether [C4H10O] Aldrich, 99.9%Potassium hydroxide [KOH] BDH, 85.0+%, ACD GradeSulphuric acid [H2SO4] E.M. Science, 98+%Haloacetic acids concentrated stock for calibration EPA 552.2 Acids Calibration Mix in MTBESodium sulphate [Na2SO4] E.M. Science, ACS GradeMethyl-tert -butyl-ether (MTBE) Aldrich, >99.8%
38
Table 3.13: Haloacetic acids – Instrument conditions Parameter DescriptionInjector Temperature 200°CDetector Temperature 300°C
35°C for 10.0 min2.5°C/min temperature ramp to 65°C10°C/min temperature ramp to 85°C20°C/min temperature ramp to 205°C, hold for 7 min
Carrier Gas HeliumFlow Rate 1.2 mL/min at 35°C
Temperature Program
Table 3.14: Haloacetic acids – Method Outline Collect samples in 25 mL amber vials. Ensure that samples are headspace-free.
Store samples in the dark at 4°C for up to 9 days.
Diazomethane GenerationSet up the generation apparatus as shown in Figure 6521:3 in Standard Methods (APHA, 2005).
Sulfonamide Solution:Before preparing samples, add 15 mL of diethylene glycol, 15 mL of ether and 3 g of N-methyl-N-nitroso-p-
toluene sulfonamide (Diazald) to a 40 mL amber vial.
To begin preparing samples, remove from refrigerator and bring to room temperature.
Blanks: Transfer 23 mL of Milli-Q® water into 23 mL vials and process alongside samples.
Working Solution (10 μg/mL): Fill a 5 mL volumetric flask partially with MTBE.
Using a 50 μL syringe and the "sandwich technique", add 50 μL of HAA stock (2000 μg/mL each) to
Running Standards (varying concentrations):
Fill the first tube with ether to a depth of 3 cm.
Add potaqssium hydroxide solution (370 g/L KOH solution in Milli-Q® water) to the second tube so that it is just touching the base of the impinger.
Add 50 μL of working solution to 25 mL of Milli-Q® water, process alongside samples.
Include blanks and running standards every 10 samples.
Add sulfonamide solution above the KOH using a long Pasteur pipette; ensure no mixing occurs.
Shake vial until completely dissolved. This solution is used to make diazomethane and can be stored at 4°C for up to 30 days.
Top flask to 5 mL and cap with glass stopper and mix by inverting at least 10 times.
** Wipe the syringe tip with a Kimwipe before measuring out the HAA stock and before adding stock to
39
Table 3.14: Haloacetic acids – Method Outline (continued)
Add 1 tsp of sodium sulphate (Na2SO4) using scoop in order to increase extraction efficiency.
Transfer the contents of each sample vial into a clean 40 mL vial.
Add 1 mL of sulphuric acid (H2SO4) to reduce the pH of the sample.
Add two drops of sodium sulphite (Na2SO3) solution.Add 1 tsp of sodium sulphate (Na2SO4) using scoop in order to increase extraction efficiency.
Extraction
Analyze using a GC-ECD.
Add 100 μL of diazomethane to the GC vial (sibmerge tip before injection) and cap immediately.
If not analyzing immediately, store the samples in the freezer (-11°C) for up to 21 days.
Extract 2 mL from the organic layer using a Pasteur pipette and place in a 1.8 mL GC vial. Use a clean pipette for each sample. Fill vial to the midpoint of the neck to allow for addition of 100 μL diazomethane.
Complete this procedure for all samples, blanks and standards before proceeding.
Place all the vials upright in a rack and shake for 2 minutes. Let samples stand for 10 minutes for phase separation.
Add 4 mL of MTBE extraction solvent and cap with Teflon®-lined silicon septa and screw cap.
Shake sample vial vigorously for approx. 30 seconds and place on counter on its side.
Add 4 mL of MTBE to the last tube and put in a beaker of ice such that the MTBE is submerged.
When ready to prepare samples, remove from refrigerator and warm up to room temperature.
Connect the nitrogen gas feed to the in port of the apparatus.
Slowly turn on the gas flow and bubble the nitrogen gas through the apparatus slowly until the MTBE solution becomes yellow.
This solution may be stored at 4°C in a 23 mL amber vial for up to 24 hours.
Table 3.15: Haloacetic acids – Standard solutions
MCAA 600 6.0 0 1.2 2.4 6 12 18 24 48MBAA 400 4.0 0 0.8 1.6 4 8 12 16 32DCAA 600 6.0 0 1.2 2.4 6 12 18 24 48TCAA 200 2.0 0 0.4 0.8 2 4 6 8 16BCAA 400 4.0 0 0.8 1.6 4 8 12 16 32DBAA 200 2.0 0 0.4 0.8 2 4 6 8 16
BDCAA 400 4.0 0 0.8 1.6 4 8 12 16 32DBCAA 1000 10.0 0 2 4 10 20 30 40 80TBAA 2000 20.0 0 4 8 20 40 60 80 160
Level 7 Level 8
Calibration Standard Concentration (μg/L)Compound Level 3 Level 4 Level 5 Level 6Stock
SolutionIntermediate
Solution
Concentration (mg/L)
Level 1 Level 2
40
Table 3.16: Haloacetic acids – Method detection limits Analyte Std. Dev. (μg/L) MDL (μg/L)MCAA 0.09 0.30MBAA 0.05 0.14DCAA 0.14 0.45TCAA 0.03 0.10BCAA 0.10 0.32DBAA 0.17 0.54BDCAA 0.20 0.63DBCAA 0.45 1.42TBAA 0.75 2.37
0.0
0.5
1.0
1.5
2.0
2.5
0 10 20 30 40 50 60 70 80 90Concentration (ug/L)
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MCAA MBAADCAA TCAABCAA DBAABDCAA DBCAATBAA
MCAA (μg/L) = (area response ratio - 0.0010)/0.0027, R2 = 0.9919MBAA (μg/L) = (area response ratio - 0.0053)/0.0257, R2 = 0.9909DCAA (μg/L) = (area response ratio - 0.0062)/0.0226, R2 = 0.9940TCAA (μg/L) = (area response ratio - 0.0057)/0.0501, R2 = 0.9959BCAA (μg/L) = (area response ratio - 0.0081)/0.0414, R2 = 0.9973DBAA (μg/L) = (area response ratio - 0.0132)/0.0495, R2 = 0.9904BDCAA (μg/L) = (area response ratio + 0.0031)/0.0278, R2 = 0.9973DBCAA (μg/L) = (area response ratio + 0.0163)/0.0217, R2 = 0.9897TBAA (μg/L) = (area response ratio + 0.0344)/0.0176, R2 = 0.9883
Figure 3.5: Example haloacetic acids calibration curves
3.3.3 Total Organic Carbon (TOC)
Total organic carbon (TOC) was analysed using an O-I Corporation Model 1030
Analytical TOC Analyzer and Model 1051 Vial Multi-Sampler (College Station, Texas).
Analysis was based on the wet oxidation method described in Standard Method 5310 D (APHA,
2005). The required reagents are listed in Table 3.17, and the instrument conditions are described
41
in Table 3.18. The method steps are outlined in Table 3.19. Samples were collected in 40-mL
vials with Teflon® lined silicon septa and screw cap (VWR International, Mississauga, ON),
acidified to pH <2 with 3 drops of concentrated (98+%) sulphuric acid (H2SO4) and stored in the
dark at 4°C until analyzed. The concentration of the samples was determined through correlation
with standards made from dry potassium hydrogen phthalate (KHP) (Sigma-Aldrich
Corporation, Oakville, ON) in Milli-Q® water that were run with every sample set. Blanks
(Milli-Q® water), and running standards were run every 10 samples. An example of a typical
TOC calibration curve is presented in Figure 3.6. The method detection limit for TOC was
0.2_mg/L, determined by multiplying the standard deviation of 8 replicates by the Student-t
value (3.0).
Table 3.17: Total organic carbon – Reagents Reagent Supplier and PuritySodium persulphate [Na2S2O8] (100 g/L) Aldrich, 98+%Potassium hydrogen phthalate [C8H5KO4] Aldrich, 98+%Sulphuric acid, concentrated [H2SO4] VWR International, 98+% Table 3.18: Total organic carbon – Instrument conditions Parameter DescriptionAcid, volume 200 μL of 5% phosphoric acidOxidant volume 1000 μL of 100 g/L sodium persulphateSample volume 15 mLRinses per sample 1Volume per rinse 15 mLReaction time (min:sec) 02:30Detection time (min:sec) 02:00Purge gas NitrogenLoop size 5 mL
Table 3.19: Total organic carbon – Method outline Blanks: Use 40 mL of Milli-Q® water.
Stock solution: Mix 2.13 g potassium hydrogen phthalate in 1 L Milli-Q® water. Store at pH < 2by acidifying with H2SO4.
Standard (3.0 mg/L): Add 300 μL of stock solution to 100 mL Milli-Q® water.
Samples: Follow SOP for TOC analyzer.
42
0.0E+00
1.0E+04
2.0E+04
3.0E+04
4.0E+04
5.0E+04
6.0E+04
7.0E+04
8.0E+04
9.0E+04
0.0 2.0 4.0 6.0 8.0 10.0 12.0
Concentration (mg/L)
Are
a C
ount
TOC (mg/L) = (area response - 1733.1)/7766.6, R2 = 0.9995
Figure 3.6: Example total organic carbon calibration curve
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
Con
cent
ratio
n (m
g/L)
Upper CLUpper WL
Mean
Lower WLLower CL
Dec. 10, 2009 Feb. 24, 2010 Aug. 23, 2010Jun. 16, 2010
Figure 3.7: Total organic carbon – Quality control chart (3.0 mg/L)
43
3.3.4 Ultraviolet Absorbance (UV254)
Ultraviolet absorbance at a wavelength of 254 nm (UV254) was analysed with a CE 3055
model spectrophotometer (Cecil Instruments, Cambridge, England) using a 1 cm quartz cell
(Hewlett Packard, Mississauga) and following the method described in Standard Method 5910 B
(APHA, 2005). The spectrophotometer was blanked with Milli-Q® water.
3.3.5 pH Measurement
The pH of solutions was determined using a pH meter (Model 8015, VWR Scientific
Inc., Mississauga, ON). Standard pH buffers at pH 4, 7 and 10 were used to calibrate the pH
meter prior to the start of each experiment. Samples were mixed using a magnetic stirrer and stir
bar during pH measurement.
3.3.6 Chlorine Residual
Free chlorine residual was determined following the DPD colorimetric method as
described in Standard Method 4500-Cl G (APHA, 2005). The instrument used was a HP 8452A
Diode Array Spectrophotometer (Hewlett Packard, Palo Alto, CA). The spectrophotometer was
blanked using Milli-Q® water. To analyze for free chlorine, the contents of a DPD free chlorine
powder pack were added to a 25_mL vial, which was capped with a Teflon® top and mixed by
inverting. The sample was then transferred to a 3_mL glass vial and analyzed for absorbance at
530 nm.
3.3.7 Fluorescence Excitation-Emission
Fluorescence excitation-emission matrices (FEEMs) of water samples were obtained
using a Cary Eclipse Fluorescence Spectrofluorometer (Varian Inc., Palo Alto, CA) at 25°C, as
described by Peiris, et al. (2010). All analyses were conducted at the University of Waterloo
(Waterloo, ON). Signal acquisition was accomplished using a Peltier multicell colder and a
Fluorescence Remote Read Fibre Optic Probe coupled to an Eclipse Fibre Optic Coupler with a
20 mm fluorescence probe tip. Samples were analysed in UV-grade polymethylmethacrylate
cuvettes with four optical windows. The excitation and emission ranges used were 250-380 nm
and 300-600 nm, respectively. Multiple photomultiplier tube voltages, scanning rates, and
emission and excitation slit widths were used to obtain fluorescence emission spectra. Raman
scattering and other background noise was reduced by subtracting the spectra obtained for Milli-
44
Q® water from all sample spectra. Results were processed by a principle component analysis
approach using Matlab 7.3.0 software (The Mathworks Inc., Natick, MA).
3.3.8 Liquid Chromatography - Organic Carbon Detection (LC-OCD)
Liquid chromatography/organic carbon detection (LC-OCD) was conducted using the
method described by Huber et al. (2010). Samples were passed through a 0.45-μm filter before
analysis to remove particulates. Chromatographic separation was achieved using a weak cation
exchange column (250 mm × 20 mm, Toso, Japan). The mobile phase used was a phosphate
buffer exposed to UV irradiation in an annular UV reactor, delivered at a flow rate of
1.1_mL/min to an autosampler (MLE, Dresden, Germany, 1 mL injection volume).
Chromatographic separation was followed by UV254 detection (UVD), and then OCD. At the
OCD inlet, the solution was acidified to convert carbonates to carbonic acid. A column bypass
was also used to obtain a total DOC value for each chromatographic run. OCD and UVD
calibration was based on potassium hydrogen phthalate. Data acquisition and processing was
achieved using a customized software program (ChromCALC, DOC-LABOR, Karlsruhe,
Germany).
3.4 Artificial Neural Network (ANN) Development
3.4.1 Modeling Software
The software used to develop neural networks was NeuroSolutions® version 5.07,
created by NeuroDimension Inc. (Gainesville, FL). The Microsoft Excel (Microsoft Corp.,
Redmond, WA) random number generator was used to randomize each data set so that
NeuroSolutions could select data sets for training, validation, and testing. For each neural
network, 60 percent of the data were used for training, with 20 percent dedicated to validation
and the final 20 percent for testing. Validation data were used to test the network during
training, to ensure that models were learning the trends in the training data, rather than
memorizing the training data set itself. Testing data were used to evaluate model performance
by predicting data not seen by the network during the training process.
The NeuralBuilder tool was used to develop ANNs. This allows the developer to
customize each aspect of the neural network architecture, including the network type (i.e. multi-
layer perceptron), the number of hidden layers and neurons, the transfer function to be used, and
45
the learning algorithm. The NeuralBuilder tool also allowed for data saved in Microsoft Excel
(Microsoft Corp., Redmond, WA) as comma delimited files to be imported directly into
NeuroSolutions. All data were automatically scaled by NeuroSolutions to the proper range for
the transfer function being used.
3.4.2 Input Parameter Selection
To model the formation of DBPs in bench-scale tests, several water quality parameters
and operating conditions were selected as model inputs. These decisions were made primarily
using a priori knowledge of the system, combined with a review of previous efforts by
researchers to model DBP formation, with a focus on those studies which employed ANNs to do
so. Lewin et al. (2004) and Rodriguez & Sérodes (2004) identified several parameters which
were found to be important when using ANNs to predict the formation of THMs. These include
TOC, UV254, pH, temperature, bromide ion concentration, chlorine dosage, and chlorine contact
time. Various other studies have identified these as important factors in the formation of DBPs
(Amy et al., 1999; Edzwald et al., 1985; Singer, 1994; Liang & Singer, 2003). Of these seven
parameters, bromide concentration and contact time did not vary during bench-scale testing. For
a more robust model that can account for the impact of bromide concentration and/or chlorine
contact time, ANNs must be trained and tested using a data set which includes variation in these
parameters. Therefore, TOC, UV254, pH, temperature, and chlorine dosage were selected as
ANN inputs to predict the formation of TTHM and HAA9.
3.4.3 ANN Architecture Selection
Selecting network architecture is an important step in the process of creating an ANN.
The basic ANN structure, transfer function, and learning rule were chosen using the
recommendations found in the NeuroSolutions software manual (NeuroDimension Inc., 2008).
The basic network structure used was the multilayer perceptron, as it is well-suited for regression
models. For the hidden layer transfer function, the TanhAxon function was chosen, as it is also
recommended for regression problems. The momentum learning rule was chosen because it is
the most stable.
The number of hidden neurons, momentum coefficient (μ), and learning rate (γ) were
chosen by trial and error. As a starting point, the number of hidden neurons used was 75% of the
number of inputs (Baily & Thompson, 1990); trials were conducted using up to two more or two
46
less hidden neurons. Each ANN had a single layer of hidden nodes, since using two or more
layers did not improve network performance. Values for μ and γ ranged from 0.3 to 1.0 for all
ANNs developed.
3.4.4 Training and Validation
After each training epoch (i.e. each time the full training data set is sent through the
neutral network), the cross-validation data set was sent through the network and the resulting
errors were calculated. The maximum training epochs used was 1000, with training proceeding
until the cross-validation error was observed to increase. Therefore, the cross-validation set was
used to determine the optimum number of training epochs for each network. For each
configuration of ANN architecture (number of hidden neurons, learning rate, and momentum
coefficient), the ANN was trained and tested in triplicate (Griffiths, 2010); connection weights
were randomized before each network was trained. This was done to ensure that the minimum
cross-validation error was found for each ANN. For each configuration, the global minimum
was taken to be the smallest cross-validation error of the three replicates.
47
4. Evaluation of Enhanced Coagulation for DBP Minimization
4.1 Introduction
Currently, the formation of disinfection by-products (DBPs) in treated drinking water is
regulated in many developed countries. These regulations have been established to protect
public health, as some DBPs are known to be carcinogenic and/or mutagenic (Singer, 1994).
Most regulations use a maximum contaminant level (MCL) for total trihalomethanes (TTHM) as
a surrogate measure for all halo-organic DBPs. MCLs range from 25 μg/L in the Netherlands to
250 μg/L in Australia (Chowdhury et al., 2009). The MCL for THMs in Canada is currently 100
μg/L (Health Canada, 2006). It is expected that this will soon be lowered to match the MCL of
80 μg/L established by the USEPA, and that a new MCL for haloacetic acids (HAAs) will be
introduced to match the USEPA level of 60 μg/L (Health Canada, 2009).
Halo-organic DBPs, such as THMs and HAAs, are formed when natural organic matter
(NOM) reacts with free chlorine (Best et al., 2001), which is the most commonly used method of
disinfection for drinking water in North America (Routt et al., 2008). Formation of DBPs is
therefore directly related to the NOM type and concentration in the source water being treated, as
well as the chlorine dose applied, among other factors. While it may be possible to remove
DBPs after they have formed, it is more efficient to remove their precursors before the point of
addition of chlorine. The Disinfectants/Disinfection By-Product Rule (USEPA, 1998), or
D/DBPR, identifies enhanced coagulation as a best available technology for removing precursors
of DBPs, and establishes requirements in the US for precursor removal (Table 4.1). These
requirements are given in terms of percent reduction of total organic carbon (TOC), which is a
commonly used surrogate measure of NOM content in water. The percent removal necessary
depends on the source water TOC and alkalinity, since TOC removal becomes more difficult as
TOC decreases and alkalinity increases. In addition to TOC, ultraviolet absorbance at a
wavelength of 254 nm (UV254) is often used as a measure of DBP precursors in water.
Enhanced coagulation involves the modification of the coagulation process to improve
DBP precursor reduction without adversely affecting other important aspects of water treatment,
such as turbidity removal, filter run times, and sludge formation and disposal. This can include
changing the coagulant dose, using pH depression, changing the coagulant type, and/or
48
introducing polymer addition as a flocculant aid (USEPA, 1998). The first step in identifying
which of these will be most effective for a given source water is to conduct a series of bench-
scale jar tests. This should be followed by pilot-scale testing to evaluate the potential impact of
enhanced coagulation changes on the full-scale treatment process.
Table 4.1: TOC removal required by the USEPA D/DBPR for enhanced coagulation
0-60 60-120 >1202.0-4.0 35.0% 25.0% 15.0%4.0-8.0 45.0% 35.0% 25.0%
>8.0 50.0% 40.0% 30.0%
Source Water TOC (mg/L)
Source Water Alkalinity (mg/L as CaCO3)
Enhanced coagulation may result in a higher coagulant dosage being used, which has
been shown to achieve greater reductions of turbidity, particle counts, TOC, UV254, and THM
formation potential (THMFP) (Childress et al., 1999; Mesdaghinia et al., 2006). Bell-Ajy et al.
(2000) reported that implementation of pH adjustment with acidified alum can result in an
optimal dosage for turbidity removal comparable to the optimal dosage of alum. Switching to a
polyaluminum chloride (PACl) coagulant has been reported to remove more bromine reactive
NOM (Rizzo et al., 2005; Iriarte-Velasco et al., 2007), resulting in less brominated DBPs being
formed. Rizzo et al. (2004) reported that using PACl instead of a ferric coagulant can reduce the
dosage needed to meet TOC removal requirements of the D/DBPR.
TOC and UV254 have historically been the most commonly used metrics for evaluating
the presence and removal of NOM (and DBP precursors), due to their ease of analysis and the
fact that they are gross measurement parameters. Recently, however, fluorescence excitation-
emission matrices (FEEM) have been explored as a tool for detecting specific fractions of NOM
in a variety of water-related applications (Henderson et al., 2009). Likewise, liquid
chromatography – organic carbon detection (LC-OCD) is a new technique which has been
shown to quantitatively separate organic carbon into specific components (Huber et al., 2011).
The first objective of this study was to investigate the potential of enhanced coagulation
practices to maintain or improve finished water quality at Peterborough WTP while limiting
sludge formation. Principle measures of water quality used were DBP formation and precursor
49
removal; changes in coagulant dosage were used to evaluate the potential impact on sludge
formation. The second objective was to evaluate surrogate measures of NOM, including FEEM
and LC-OCD, as methods of quantifying the removal of both NOM and DBP precursors during
enhanced coagulation.
4.2 Experimental Design
In order to meet the objectives of this study, bench scale treatability tests were conducted
at the UofT drinking water laboratory using raw water shipped from the Peterborough WTP.
These tests were conducted weekly for five weeks in July and August of 2010. Bench scale tests
consisted of coagulation, flocculation, sedimentation, and vacuum filtration, followed by the
addition of free chlorine for 24-hour DBP formation tests. Each week three coagulants were
tested: aluminum sulphate (alum), Hyper+Ion (HI) 705 polyaluminum chloride (PACl), and HI
1000 PACl; in addition, alum was used with pH depression (acid + alum). For each test with
alum, acid + alum, HI 705 PACl, or HI 1000 PACl, coagulant dosages between 20 and 70 mg/L
were used. Performance evaluation was based on coagulant dosage; post-filter TOC, UV254, and
pH; and 24-hour formation of THMs, HAAs, haloacetonitriles, haloketones, and chloropicrin.
Comparison was also made between bench scale tests and water quality at the WTP.
4.3 Methods
For detailed experimental and analytical methods, see Sections 3.1 and 3.3, respectively.
4.3.1 Bench-Scale Testing
Bench-scale tests consisted of jar tests followed by 24-hour DBP formation potential
(DBPFP) tests. Aluminum sulphate (alum, Al2(SO4)3·H2O) and two polyaluminum chloride
(PACl) coagulants (Hyper+Ion (HI) 705 and HI 1000) were used. The PACl coagulants were
selected based on preliminary jar tests conducted by General Chemical (Parsippany, NJ), in
which they demonstrated good performance in terms of removal of colour and turbidity
compared with alum and other coagulants. Alum was also used in conjunction with sulfuric acid
for pH depression. Prior to testing, the necessary volume of acid to add with each alum dosage
was determined by titration to pH 6.8, the pH in settled water at the WTP. In jar tests, acid was
added before alum to decrease the coagulant demand exerted by the raw water alkalinity
(84_mg/L CaCO3).
50
Coagulant dosages of 20 to 70 mg/L were added to six 2-L raw water samples for each
test. Jar tests were conducted using a PB-700 Standard Jar Tester paddle stirrer (Phipps & Bird,
Richmond, VA), with 90 seconds of rapid mixing (100 rpm), 15 minutes of slow mixing for
flocculation (30 rpm), and 30 minutes of settling. The USEPA Enhanced Coagulation Guidance
Manual (1999) recommends 30 minutes flocculation and 60 minutes settling be used for jar tests;
shorter times were used to more closely mimic the hydraulic residence times for unit processes at
Peterborough WTP, and to allow a greater number of tests to be conducted each week. From
each jar, 500 mL of supernatant was filtered using 0.45-μm pore size glass microfibre filters
(Whatman Inc., Florham Park, NJ), of which 250 mL was spiked with 73 μL of 12% sodium
hypochlorite (NaOCl) dosing solution to achieve the concentration applied at the WTP (3.5 mg/L
Cl2). Reaction time (24 hours) was based on Standard Method 5710 (APHA, 2005) for DBP
formation potential, after which the reaction was stopped by quenching the free chlorine with
ammonium chloride. Raw water for testing was collected weekly prior to the point of addition
for pre-chlorine at the Peterborough WTP. Tests using each coagulant type (alum, acid + alum,
HI 705 PACl, and HI 1000 PACl) were conducted weekly for five weeks in July and August of
2010.
4.3.2 Analyses
Samples for TOC and UV254 analyses were collected from settled water supernatant and
post-filter water using 40 mL amber vials. TOC was analyzed using a Model 1030 Analytical
TOC Analyzer and a Model 1051 Vial Multi-Sampler (O-I Corporation, College Station, Texas).
The method used is based on the wet oxidation method described in Standard Method 5310 D
(APHA, 2005). Samples were acidified to pH 3 with two drops of concentrated sulfuric acid in
each 40 mL vial and stored in the dark at 4°C until analysis. Calibration standards were prepared
in Milli-Q® water with dry potassium hydrogen phthalate (Sigma-Aldrich Corporation, Oakville,
ON).
UV254 was analysed using a CE 3055 model spectrophotometer (Cecil Instruments,
Cambridge, England) using a 1 cm quartz cell (Hewlett Packard, Mississauga) and following the
method described in Standard Method 5910 B (APHA, 2005). The spectrophotometer was
blanked with Milli-Q® water.
51
Samples for fluorescence excitation-emission analysis were also collected from settled
and filtered waters. Fluorescence excitation-emission matrices (FEEMs) of water samples were
obtained using a Cary Eclipse Fluorescence Spectrofluorometer (Varian Inc., Palo Alto, CA) at
25°C, as described by Peiris, et al. (2010). Signal acquisition was accomplished using a Peltier
multicell holder and a Fluorescence Remote Read Fibre Optic Probe coupled to an Eclipse Fibre
Optic Coupler with a 20 mm fluorescence probe tip. Samples were analysed in UV-grade
polymethylmethacrylate cuvettes with four optical windows. The excitation and emission ranges
used were 250-380 nm and 300-600 nm, respectively. Multiple photomultiplier tube voltages,
scanning rates, and emission and excitation slit widths were used to obtain fluorescence emission
spectra (see Figure 4.1 for example). Raman scattering and other background noise was reduced
by subtracting the spectra obtained for Milli-Q® water from all sample spectra. Results were
processed by a principle component analysis (PCA) approach using Matlab 7.3.0 software (The
Mathworks Inc., Natick, MA). PCA takes a large set of data, such as an excitation-emission
matrix, and extracts a smaller set of values which account for the variation in the original data
matrix, which allows them to describe underlying trends in the original data (Peiris et al., 2010).
FEEM spectra showed peaks corresponding to humic substances (HS, at excitation / emission
wavelengths of 325 / 450 nm), protein-like matter (PM, 280 / 320 nm), and colloidal/particulate-
like matter (CPM, 290-380 / 300-400 and 250-300 / 500-600 nm).
Figure 4.1: Example 3-D image of a fluorescence excitation-emission spectrum
52
Liquid chromatography - organic carbon detection (LC-OCD) was conducted using the
method described by Huber et al. (2010). Samples were passed through a 0.45-μm filter
(Whatman Inc., Florham Park, NJ) before analysis to remove particulates. Chromatographic
separation was achieved using a weak cation exchange column (250 mm × 20 mm, Toso, Japan).
The mobile phase used was a phosphate buffer exposed to UV irradiation in an annular UV
reactor, delivered at a flow rate of 1.1_mL/min to an autosampler (MLE, Dresden, Germany, 1
mL injection volume). Chromatographic separation was followed by UV254 detection (UVD),
and then OCD. At the OCD inlet, the solution was acidified to convert carbonates to carbonic
acid. A column bypass was also used to obtain a total DOC value for each chromatographic run.
OCD and UVD calibration was based on potassium hydrogen phthalate. Data acquisition and
processing was achieved using a customized software program (ChromCALC, DOC-LABOR,
Karlsruhe, Germany). An example of an LC-OCD chromatograph for Peterborough raw water is
shown in Figure 4.2.
Figure 4.2: LC-OCD chromatograph for raw water with identified peaks for DOC fractions (LMW = low-molecular-weight)
53
Disinfection by-product samples were collected in 25 mL amber vials at the end of each
24-hour DBP formation test. Analyses of THMs and HAAs were conducted using a 5890 Series
II Plus Gas Chromatograph (Hewlett Packard, Mississauga, ON) equipped with an electron
capture detector (GC-ECD) and a DB 5.625 capillary column (Agilent Technologies Canada,
Inc., Mississauga, ON). The carrier gas used was helium at a flow rate of 1.2 mL/min.
Standards were prepared in Milli-Q® water, and calibration curves were linear for the range of
sample concentrations.
THM (chloroform (trichloromethane, TCM), bromodichloromethane (BDCM),
dibromochloromethane (DBCM), and bromoform (tribromomethane, TBM)) analyses were
conducted using a liquid-liquid extraction gas chromatographic method as described in Standard
Method 6232 B (APHA, 2005). This method also allowed for the simultaneous extraction and
detection of four haloacetonitriles (HANs) (dichloroacetonitrile (DCAN), trichloroacetonitrile
(TCAN), bromochloroacetonitrile (BCAN), and dibromoacetonitrile (DBAN)), two haloketones
(HKs) (1,1-dichloropropanone (DCP) and 1,1,1-trichloropropanone (TCP)), and chloropicrin
(CP). Of the four HANs, two HKs, and CP, only TCAN and TCP were observed in water
samples at concentrations greater than their respective MDLs.
HAA (monochloroacetic acid (MCAA), monobromoacetic acid (MBAA), dichloroacetic
acid (DCAA), trichloroacetic acid (TCAA), bromochloroacetic acid (BCAA), dibromoacetic acid
(DBAA), bromodichloroacetic acid (BDCAA), dibromochloroacetic acid (DBCAA), and
tribromoacetic acid (TBAA)) analyses were conducted using a liquid-liquid extraction gas
chromatographic method as described in Standard Method 6251 B (APHA, 2005). HAA
samples were derivatized after extraction by adding 100 μL of dibromopropanone.
4.4 Bench-Scale Simulation of Full-Scale Treatment
Data for post-filter water quality (pH, TOC, and UV254), 24-hour DBP formation (THMs,
HAAs, HANs, HKs, and CP) and operational conditions (alum and chlorine dosages) for
Peterborough WTP were collected each week for comparison with bench scale testing. DBP
formation samples were held for 24 hours following collection from the clearwell. Post-filter
SUVA values (the ratio of UV254 to TOC) were also calculated. For each weekly set of tests, one
jar was treated with the same alum dosage as the full scale plant (45 ± 3 mg/L) to evaluate how
well the bench scale tests mimicked the full scale treatment process.
54
Results for post-filter water quality and 24-hour DBP formation are shown in Table 4.2
and Table 4.3, respectively. Post-filter TOC for full scale and bench scale agree to within
0.2_mg/L (6% of the average post-filter TOC value), while UV254 values were consistently
higher for bench-scale tests (average difference of 0.024 cm-1). For DBP formation, full-scale
and bench-scale results agree to within 14.9, 13.6, 2.4, and 1.1 μg/L for TTHM, HAA9, TCAN,
and TCP, respectively.
Table 4.2: Post-filter water quality comparison for full-scale plant (FSP) and bench scale test
FSP Bench Scale FSP Bench
Scale FSP Bench Scale FSP Bench
ScaleJuly 19 (42 mg/L) 6.84 7.32 3.5 3.4 0.053 0.089 1.52 2.60July 27 (43 mg/L) 6.80 7.20 3.4 3.6 0.059 0.072 1.75 1.98
August 4 (47 mg/L) 6.81 7.38 3.7 3.6 0.056 0.082 1.53 2.29August 9 (48 mg/L) 6.76 7.23 3.4 3.7 0.057 0.085 1.67 2.30August 17 (45 mg/L) 6.85 7.21 3.4 3.6 0.051 0.068 1.50 1.91
SUVA (L/mg*m)pH TOC (mg/L) UV254 (cm-1)Date (alum dosage)
Table 4.3: 24-Hour DBP formation comparison for full-scale plant (FSP) and bench scale test (values are μg/L)
FSP Bench Scale FSP Bench
Scale FSP Bench Scale FSP Bench
ScaleJuly 19 (42 mg/L) 85.7 96.2 52.4 56.8 13.6 14.4 4.4 5.4July 27 (43 mg/L) 89.9 76.7 46.8 46.3 10.0 10.2 2.4 3.5
August 4 (47 mg/L) 82.1 99.6 63.3 49.7 11.3 12.5 3.8 NAAugust 9 (48 mg/L) 83.4 89.0 49.9 58.9 10.7 13.1 3.1 NAAugust 17 (45 mg/L) 74.2 82.9 55.6 61.1 12.8 11.9 4.2 NA
TCPDate (alum dosage)
TTHM HAA9 TCAN
NA = TCP not measured for bench scale tests in August due to interference from acetone.
4.5 Influence of Enhanced Coagulation
4.5.1 Removal of Natural Organic Matter (NOM)
In order to evaluate removal of precursors for DBPs (including THMs and HAAs), TOC
and UV254 values (C) following filtration were compared with the initial values measured in raw
water (C0) before each jar test. All measured TOC and UV254 values are presented in Section
8.2.1 of Appendix 8.2 (Table 8.3). Percent removal was calculated as (C0 – C)/C0 for each
coagulant dosage in tests with alum, acid + alum, HI 705 PACl, and HI 1000 PACl.
Average percent removal of TOC and UV254 in bench-scale tests are shown in Figure 4.3
and Figure 4.4, respectively. Based on the average raw water TOC (5.9 mg/L) and alkalinity
55
(84_mg/L CaCO3) the USEPA requirement for TOC removal via enhanced coagulation is 35%
(see Table 4.1). The alum dosage required to meet this removal in bench-scale tests was 43
mg/L. The required dosages for acid + alum, HI 705, and HI 1000 to match this reduction in
TOC are 29, 31, and 56 mg/L, respectively. Charge neutralization via pH depression increases
the hydrophobicity of NOM, which results in more effective coagulation and lower coagulant
demand (Uyak, 2007; Mesdaghinia et al., 2006). Despite similar performance when evaluated
for removal of colour and turbidity in tests conducted by General Chemical (data not shown), the
two PACl coagulants performed very differently in terms of TOC and UV254 removal. Based on
these findings, the Peterborough WTP may be able to maintain removal of DBP precursors while
producing less sludge by switching the type of coagulant applied and using a lower dosage.
0%
10%
20%
30%
40%
50%
60%
70%
20 30 40 50 60 70Coagulant Dosage (mg/L)
Post
-Filt
er %
Red
uctio
n of
TO
C
AlumAcid + AlumHI 705HI 1000
Figure 4.3: Average percent reduction of TOC from Peterborough water (error bars represent standard deviation). Raw water TOC = 5.9 ± 0.3 mg/L.
Increasing coagulant dosage results in a greater reduction of TOC. However, as
coagulant dosage increases, further incremental increases in dosage result in smaller and smaller
gains in TOC reduction, illustrated in Figure 4.5. The USEPA Enhanced Coagulation Guidance
Manual (1999) recommends that the point of diminishing returns (PODR) is the dosage above
which an increase of 10 mg/L in coagulant achieves an additional TOC reduction of ≤0.3 mg/L.
56
This is defined mathematically as the point on the TOC curve where the slope is -0.03, as shown
in Figure 4.5.
0%
10%
20%
30%
40%
50%
60%
70%
80%
20 30 40 50 60 70Coagulant Dosage (mg/L)
Post
-Filt
er %
Red
uctio
n of
UV 2
54
AlumAcid + AlumHI 705HI 1000
Figure 4.4: Average percent reduction of UV254 from Peterborough water (error bars represent standard deviation). Raw water UV254 = 0.140 ± 0.007 cm-1.
2
3
4
5
6
7
0 10 20 30 40 50 60 70
Alum Dosage (mg/L)
TOC
(mg/
L)
Slope = 0.3 mg/L per 10 mg/L alum = -0.03
PODR = 35 mg/L
Figure 4.5: Example TOC curve for determination of point of diminishing returns (PODR)
57
A PODR value was determined for each individual jar test as shown in Figure 4.5. The
average PODR was calculated for alum, for acid + alum, for HI 705 PACl, and for HI 1000
PACl. Sample calculations for the determination of PODR are provided in Section 8.1.1 of
Appendix 8.1. The average PODRs for alum, acid + alum, HI 705, and HI 1000 are 54, 40, 41,
and 48 mg/L, respectively. Therefore, the cut-off point at which increasing coagulant dosage
does not achieve sufficient gains in TOC reduction (USEPA, 1999) is highest for the alum
coagulant. For acid + alum and HI 705, this reinforces the conclusion above that switching from
alum to either of these alternatives would allow for a lower coagulant dosage to be applied. But
since the PODR is related to the rate of change only and not the magnitude of TOC removal, the
same cannot be said about HI 1000. In the case of HI 1000, using a lower dosage would result in
a decrease in TOC (and UV254) removal, making it unsuitable as a replacement for alum at the
Peterborough WTP.
In addition to the weekly testing, a single jar test was conducted in December of 2010 to
examine the raw water presence and removal of NOM fractions detected via LC-OCD. The
concentrations of alum, acid + alum, HI 705 PACl, and HI 1000 PACl used (40, 30, 25, and 50
mg/L, respectively) were chosen as estimates of the dosages required to achieve a post-filter
TOC of 4.0 mg/L, based on the results of the weekly tests conducted in July and August.
Figure 4.6 shows the total DOC present in raw water (6.6 mg/L) and in filtered samples
from the jar test (4.4 ± 0.4 mg/L). Hydrophilic and hydrophobic DOC fractions are also shown;
hydrophilic DOC accounts for 88 ± 4% of total DOC. Percent removal from raw to filtered
water of hydrophilic DOC (34 ± 3%) is very similar to that for total DOC (33 ± 6%). The order
of total DOC remaining in filtered waters is: alum < HI 1000 < acid + alum < HI 705. The order
of hydrophilic DOC remaining is: alum < acid + alum ≈ HI 705 ≈ HI 1000. Hydrophilic DOC is
further separated into five components: humic substances (HS), building blocks (BB), low-
molecular-weight (LMW) neutrals, bio-polymers, and LMW acids. HS form the bulk of DOC
(63% in raw water), and are removed to a greater extent (54 ± 5%) than total or hydrophilic
DOC. This suggests that coagulation is effective at removing the more reactive fraction of NOM
present in raw water, thereby greatly reducing the DBP formation potential prior to the primary
point of chlorine addition. The order of HS remaining in filtered waters is: alum < acid + alum
< HI 1000 ≈ HI 705. BB, LMW neutrals, bio-polymers, and LMW acids represent 17, 12%, 8%,
and 0% of raw water DOC, respectively. Reductions in bio-polymers and LMW neutrals are
58
limited to less than 0.3 mg/L and 0.1 mg/L, respectively. Concentrations of BB and LMW acids
actually increase by up to 0.1 mg/L and 0.3 mg/L, respectively. This may be the result of larger
humic substances breaking into smaller molecules during jar testing.
0
1
2
3
4
5
6
7
Raw Water Alum (40) Acid + Alum (30) HI 705 (25) HI 1000 (50)
Coagulant (dosage in mg/L)
Post
-Filt
er C
once
ntra
tion
(mg/
L)
Total DOC Hydrophilic DOCHydrophobic DOC Humic SubstancesBuilding Blocks LMW NeutralsBio-polymers LMW Acids
Figure 4.6: Jar test removal of DOC detected by LC-OCD
Similar results have been reported in the literature. Baghoth et al. (2009) reported a 72%
reduction in HS detected via LC-OCD for coagulation combined with BAC filtration at an
Amsterdam WTP; however, the percent contribution of HS to total DOC (70%) did not change
between raw and treated water. This study also found a slight increase (< 0.1 mg/L) in BB
following coagulation. Cornelissen et al. (2008) reported that HS (55% of raw water TOC)
showed a percent removal in ion exchange batch experiments that was typically 5-10% greater
than the removal of TOC.
4.5.2 DBP Formation
Based on the results of 24-hour DBPFP tests, the concentrations of four trihalomethane
species (TCM, BDCM, DBCM, and TBM) were added together to calculate total THM
concentration (TTHM). Total HAA concentration (HAA9) was calculated by summing the
59
concentrations of nine haloacetic acids (MCAA, MBAA, DCAA, TCAA, BCAA, DBAA,
BDCAA, DBCAA, TBAA). Of the four HANs, two HKs, and CP included in the analysis of
DBP samples, only TCAN and TCP were observed at levels above their respective MDLs (see
Table 4.4). All measured DBP formation values are presented in Section 8.2.2 of Appendix 8.2
(Table 8.4 and Table 8.5).
Results from the second week of tests (water collected July 27, 2010) serves as a typical
example of the effects of enhanced coagulation on the formation of TTHM, HAA9, TCAN, and
TCP, as shown in Figure 4.7 to Figure 4.10, respectively. In Section 4.5.1, it was shown that
lower dosages of acid + alum and HI 705 PACl (29 and 31 mg/L, respectively) were able to
achieve the same reduction in TOC as 43 mg/L of alum, while a higher dosage (56 mg/L) was
required for HI 1000 PACl. The formation of TTHM, HAA9, TCAN, and TCP for these four
conditions was calculated via linear interpolation of data from July 27 tests. The results, shown
in Table 4.5, indicate that using acid + alum (29 mg/L), HI 705 PACl (31 mg/L), or HI 1000
PACl (56 mg/L) yield lower TTHM and HAA9 than 43 mg/L of alum.
0
20
40
60
80
100
120
20 30 40 50 60 70Coagulant Dosage (mg/L)
TTH
M C
once
ntra
tion
(μg/
L)
Alum Acid + Alum HI 705 HI 1000
Figure 4.7: 24-hour TTHMFP of for bench-scale tests with four coagulant types (results from July 27, 2010)
60
0
10
20
30
40
50
60
70
80
90
20 30 40 50 60 70Coagulant Dosage (mg/L)
HA
A9
Con
cent
ratio
n (μ
g/L)
Alum Acid + Alum HI 705 HI 1000
Figure 4.8: 24-hour HAA9FP for bench-scale tests with four coagulant types (results from July 27, 2010)
0
2
4
6
8
10
12
14
20 30 40 50 60 70Coagulant Dosage (mg/L)
TCA
N C
once
ntra
tion
(μg/
L)
Alum Acid + Alum HI 705 HI 1000
Figure 4.9: 24-hour TCANFP for bench-scale tests with four coagulant types (results from July 27, 2010)
61
0
1
2
3
4
5
6
7
8
9
20 30 40 50 60 70Coagulant Dosage (mg/L)
TCP
Con
cent
ratio
n (μ
g/L)
Alum Acid + Alum HI 705 HI 1000
Figure 4.10: 24-hour TCPFP for bench-scale tests with four coagulant types (results from July 27, 2010)
These results are consistent with those reported in Section 4.5.1: acid + alum or HI 705
PACl could be used for coagulation at the Peterborough WTP instead of alum. Either of these
would reduce DBP formation while being applied at a lower dosage. While these four
coagulation treatments have been shown to achieve the same reduction of TOC and UV254, the
relative formation of DBPs reveals that acid + alum and HI 705 PACl are actually more efficient
than alum at targeting DBP precursor material. These findings are inconsistent with those
reported in the literature. Edzwald & Tobiason (1999) found that using pH depression to achieve
the same reduction in DOC at a lower alum dosage resulted in the no change in TTHMFP and
HAA9FP. Rizzo et al. (2004) reported that while alum and PACl achieved that same removal of
UV254, PACl was generally able to remove less TTHMFP.
62
Table 4.4: Method detection limits for DBP species of THMs, HAAs, HANs, HKs, and CP Species MDL Species MDL Species MDL Species MDL Species MDL
TCM 0.78 MCAA 0.30 BCAA 0.32 TBAA 2.37 DBAN 0.13BDCM 0.56 MBAA 0.14 DBAA 0.54 DCAN 0.83 DCP 0.12DBCM 0.00 DCAA 0.45 BDCAA 0.63 TCAN 0.05 TCP 0.19TBM 0.20 TCAA 0.10 DBCAA 1.42 BCAN 0.13 CP 0.60
Table 4.5: Comparison of DBP formation at coagulant dosages required to achieve 35% TOC reduction
Coagulant Dosage (mg/L) TTHM (μg/L) HAA9 (μg/L) TCAN (μg/L) TCP (μg/L)Alum 43 74 62 10 2
Acid + Alum 29 64 46 10 7HI 705 PACl 31 66 45 8 1
HI 1000 PACl 56 58 56 10 4
For the DBP data shown above, concentrations of HAA9, TCAN, and TCP were divided
by corresponding TTHM concentrations for each dosage, and the average was calculated for
each coagulant for dosages from 20 to 70 mg/L. The average values for HAA9, TCAN and TCP
expressed as a fraction of TTHM formation are shown in Table 4.6. Formation of HAA9 relative
to TTHM for alum, acid + alum, and HI 705 PACl (0.78, 0.78, and 0.71, respectively) was
similar to the ratio of MCLs for HAA5 and TTHM (60 and 80 μg/L, respectively) established by
the USEPA (1998). HAA9 formation was almost equal to TTHM formation when HI 1000 PACl
was used (0.91). Other researchers have reported HAA to THM ratios as low as 0.4 (Goslan et
al., 2009) and as high as 1.0 (Reckhow & Singer, 1990) in chlorinated finished waters.
Table 4.6: Average ratio of DBP formation by class for four coagulants Coagulant HAA9:TTHM TCAN:TTHM TCP:TTHM
Alum 0.78 0.14 0.03Acid + Alum 0.78 0.19 0.12HI 705 PACl 0.71 0.14 0.02HI 1000 PACl 0.91 0.16 0.06
Bromine incorporation factor (BIF) was calculated for THMs, dihaloacetic acids
(DXAAs), and trihaloacetic acids (TXAAs) using the method reported by Goslan et al. (2009).
Sample calculations for the determination of BIF are provided in Section 8.1.2 of Appendix 8.1.
For THMs, the BIF ranges from 0 (no bromine) to 3 (only TBM present). Maximum BIF values
for DXAA and TXAA are 2 and 3, respectively. Average BIF for THMs, DXAA, and TXAA
are 0.10, 0.15, and 0.01, respectively. No variation in BIF was observed between tests with
63
different coagulant types. The low BIF values are expected, since bromide levels in
Peterborough water are very low (2 μg/L, measured in raw water and post-filter samples).
Goslan et al. (2009) reported maximum BIF values of 1.4 for waters with bromide levels up to
200 μg/L.
Figure 4.11 shows the speciation of TTHM for a single dosage each of alum, alum + acid,
HI 705 PACl, and HI 1000 PACl. Dosages were selected to demonstrate TTHM composition
and removal of individual compounds. TCM, BDCM, DBCM, and TBM account for 91%, 7%,
2%, and 0% by weight of TTHM, respectively. Since TCM comprises the majority of TTHM,
reduction of TCM accounts for 97% of TTHM reduction. HAA9 speciation is shown in Figure
4.12. Brominated HAAs (B-HAA, the sum of MBAA, BCAA, DBAA, BDCAA, DBCAA, and
TBM) are shown as a group, since they account for only 6% of HAA9 by weight, while TCAA,
DCAA, and MBAA account for 63%, 21%, and 10% of HAA9 by weight, respectively. While
TCAA comprises less than two-thirds of HAA9 formation, reduction in TCAA accounts for 94%
of HAA9 reduction. Therefore, the formation and reduction of THMs and HAAs is primarily
associated with non-brominated compounds.
0
10
20
30
40
50
60
70
80
90
Alum (40 mg/L) Acid + Alum (30 mg/L) HI 705 (30 mg/L) HI 1000 (50 mg/L)
Coagulant (dosage)
Con
cent
ratio
n (u
g/L)
TBMDBCMBDCMTCM
Figure 4.11: TTHM speciation in bench-scale tests (results from July 27, 2010)
64
0
10
20
30
40
50
60
70
80
Alum (40 mg/L) Acid + Alum (30 mg/L) HI 705 (30 mg/L) HI 1000 (50 mg/L)
Coagulant (dosage)
Con
cent
ratio
n (u
g/L)
B-HAAMCAADCAATCAA
Figure 4.12: HAA9 speciation in bench-scale tests (results from July 27, 2010). B-HAA = sum of six brominated HAAs.
The four coagulation treatment conditions shown in the above figures were also used in
the jar test conducted in December, described in Section 4.5.1, for which LC-OCD analysis was
conducted on raw and filtered waters. For the same dosages of alum, acid + alum, HI 705, and
HI 1000 (40, 30, 30, and 50 mg/L, respectively), alum achieved the lowest concentrations of
DOC and humic substances. In contrast, for jar tests followed by DBP formation tests on July 27
(Figure 4.11 and Figure 4.12), using alum allowed for the highest concentrations of TTHM and
HAA9 (specifically, TCM and TCAA).
4.6 Relationships between Measured Parameters
4.6.1 Linear Correlations
TOC and UV254 were measured for bench-scale tests following filtration, when using
dosages between 20 and 70 mg/L of alum, acid + alum, HI 705 PACl, and HI 1000 PACl. TOC
and UV254 have been shown to be closely related (Edzwald et al., 1985; Baghoth et al., 2011;
65
Singer & Chang, 1992). A linear relationship was observed between post-filter TOC and UV254
(Figure 4.13, R2 = 0.88), which can be expressed as:
( ) ( )[ ] 0281.0/0273.01254 −⋅=− LmgTOCcmUV 4.1
0.02
0.04
0.06
0.08
0.10
0.12
0.14
2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0
Post-Filter TOC (mg/L)
Post
-Filt
er U
V 254
(cm
-1)
R2 = 0.878
Figure 4.13: Correlation between TOC and UV254 for bench-scale tests using alum, acid + alum, HI 705 PACl, and HI 1000 PACl (coagulant dosages between 20 and 70 mg/L)
The effects of enhanced coagulation practices on TOC and UV254 are therefore very
similar. Values for linear correlation coefficients between DOC and UV254 have been reported in
the literature as low as 0.77 (Uyak & Toroz, 2007) and as low as 0.97 (Braul et al., 2001); an R2
value of 0.88 was reported by Baghoth et al. (2011). Singer and Chang (1992) reported a
relationship between TOC and UV254 similar to Equation 4.1 for waters treated via enhanced
coagulation:
( ) ( )[ ] 0257.0/0225.01254 −⋅=− LmgTOCcmUV 4.2
TOC data used to generate Equation 4.1 were used as inputs into Equation 4.2. The set of UV254
values generated by Equation 4.2 showed a close correlation with the UV254 values used to
66
generate Equation 4.1 (R2 = 0.88). The relationship observed between TOC and UV254 is
therefore the same as reported in the literature.
Post-filtration samples from bench-scale tests were also analyzed to generate
fluorescence excitation-emission matrices (FEEM) (see Section 3.3.7), which were analyzed
using principle component analysis (PCA). These results enabled detection of three fractions of
natural organic matter (NOM): humic-like substances (HS), protein-like matter (PM), and
colloidal / particulate-like matter (CPM) (Peiris et al., 2010). Table 4.7 shows R2 values for
linear correlations of these three components to TOC, UV254, and SUVA. Only HS shows good
correlation with TOC and UV254 (R2 of 0.78 and 0.84, respectively), as shown in Figure 4.14.
Findings by other researchers vary greatly: reported correlations between FEEM peak intensities
and surrogate measures of NOM (TOC, DOC, or UV254) range from none (Baker, 2001; Bieroza
et al., 2009) to R2 values of 0.99 for HS–TOC and HS–UV254 (Macraith et al., 1994) and 0.80 for
PM–TOC (Reynolds, 2002).
Table 4.7: Correlations of NOM fractions detected by FEEM with TOC, UV254, and SUVA for post-filter waters in bench-scale tests with alum, acid + alum, HI 705 PACl, and HI 1000 PACl (coagulant dosages between 20 and 70 mg/L)
Excitation EmissionTOC 0.78UV254 0.84SUVA 0.62TOC 0.05UV254 0.02SUVA 0.00TOC 0.20UV254 0.27SUVA 0.33
NOM Fraction Wavelengths (nm) Surrogate Measure R2
Colloidal / Particulate-like Matter 290-380 250-300
300-400 500-600
Humic-like Substances 325 450
Protein-like Matter 280 320
Table 4.8 shows the results of LC-OCD analysis of Peterborough raw water. The lack
of a strong correlation between PM and TOC or UV254 can be attributed to the fact that bio-
polymers (which include proteins) comprise only 7% of the total DOC present in Peterborough
raw water. TOC and UV254 are reliable indicators of total NOM content, and are readily
removed via coagulation. On the other hand, since protein-like matter makes up such a small
fraction of NOM in Peterborough water, its removal may not increase with coagulant dosage.
67
0
1
2
3
4
5
6
-60 -40 -20 0 20 40 60
Post-Filter Humic Substances FEEM PCA Score
Post
-Filt
er T
OC
(mg/
L)
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
Post
-Filt
er U
V 254
(cm
-1)
TOC
UV254
R2 = 0.778
R2 = 0.843
Figure 4.14: Correlations of humic-like substances with TOC and UV254 for bench-scale tests using alum, acid + alum, HI 705 PACl, and HI 1000 PACl (dosages between 20 and 70 mg/L)
Table 4.8: Breakdown of NOM in Peterborough raw water via LC-OCD analysis
Total Hydrophobic HydrophilicConcentration (mg/L) 6.64 0.77 5.87 0.47 3.71 0.98 0.70 0.00Percent of Total DOC 100% 12% 88% 7% 56% 15% 11% 0%
NOM Fraction DOC Bio-polymers
Humic Substances
Building Blocks
LMW Neutrals
LMW Acids
LMW = low molecular weight
TOC and UV254 have been shown to correlate well with DBP formation potentials
(DBPFP) (van Leeuwen et al., 2005; Najm et al., 1994; Singer, 1994; Liang & Singer, 2003;
Edzwald et al., 1985), but few studies have been reported that relate humic substances detected
via fluorescence excitation-emission to DBPFP. Hua et al. (2010) have reported a strong
correlation between 7-day THMFP and HS. Strong correlations (r > 0.8) exist between 24-hour
HAA9FP and TOC, UV254, and HS, while weaker correlations (r < 0.6) were observed between
24-hour TTHMFP and TOC, UV254, and HS, as shown in Figure 4.15, Figure 4.16, and Figure
4.17, respectively. It should be noted that the order of correlation strength for both TTHMFP
and HAA9FP was HS > UV254 > TOC.
68
0
20
40
60
80
100
120
2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0Post-Filter TOC (mg/L)
24-H
our S
DS
DB
P C
once
ntra
tion
(μg/
L)
TTHM HAA9 Linear (TTHM) Linear (HAA9)
R2 = 0.249
R2 = 0.672
Figure 4.15: Correlations between TOC and DBPFP for bench-scale tests using alum, acid + alum, HI 705 PACl, and HI 1000 PACl (coagulant dosages between 20 and 70 mg/L)
0
20
40
60
80
100
120
0.02 0.04 0.06 0.08 0.10 0.12 0.14
Post-Filter UV254 (cm-1)
24-H
our S
DS
DB
P C
once
ntra
tion
(μg/
L)
TTHM HAA9 Linear (TTHM) Linear (HAA9)
R2 = 0.297
R2 = 0.705
Figure 4.16: Correlations between UV254 and DBPFP for bench-scale tests using alum, acid + alum, HI 705 PACl, and HI 1000 PACl (coagulant dosages between 20 and 70 mg/L)
69
0
20
40
60
80
100
120
-50 -30 -10 10 30 50 70Post-Filter Humic Substances FEEM PCA Score
24-H
our S
DS
DB
P C
once
ntra
tion
(μg/
L)
TTHM HAA9 Linear (TTHM) Linear (HAA9) Linear (TTHM)
R2 = 0.315
R2 = 0.741
Figure 4.17: Correlations between HS and DBPFP for bench-scale tests using alum, acid + alum, HI 705 PACl, and HI 1000 PACl (coagulant dosages between 20 and 70 mg/L)
4.6.2 Predictive Models
Bench-scale test results were used to model the relationship between coagulant dosage
and DBP precursor content using an equation of the form:
( ) cxbay +⋅−⋅= exp 4.3
where a, b, and c are constants, x is the coagulant dosage in mg/L, and y is the post-filter TOC
(mg/L) or UV254 (cm-1). In Section 4.5.1, this form of equation was used to find the PODR, and
it was shown that the relationship between dosage and TOC or UV254 varies by coagulant type.
Therefore, data were divided by coagulant type to calibrate separate equations for alum, acid +
alum, HI 705 PACl, and HI 1000 PACl. Values for the empirical constants were determined
using Microsoft Excel Solver (Microsoft Corp., Redmond, WA) to minimize the sum of squared
errors between model predictions and known values of TOC and UV254. Performance was
evaluated using R2 and mean absolute error (MAE) between model predictions and known data
(post-filter TOC or UV254).
Table 4.9 shows the equations, along with values for R2 and MAE. The exponential
models resulted in strong correlations between model predictions and actual data: R2 > 0.88 for
70
TOC and R2 > 0.86 for UV254. Equations predicting post-filter removal of TOC and UV254 with
HI 705 PACl coagulant showed the best performance (R2 = 0.96 and 0.95, and MAE = 0.09
mg/L and 0.003 cm-1, respectively). Based on these findings, coagulant dosage (and type) may
be used to predict DBP precursor removal via coagulation and filtration at the Peterborough
WTP. This assumes a constant influent water quality, since there was very little variation in raw
water TOC and UV254 (5.86±0.34 mg/L and 0.140±0.009 cm-1, respectively) for the period of
data collection (July and August, 2010).
Table 4.9: Models to predict removal of TOC and UV254 using coagulant dosage
Output R2 MAE Model EquationTOC (mg/L)1 0.90 0.18 = 5.23·exp[-0.019(Alum Dosage)]+1.38TOC (mg/L)2 0.88 0.18 = 4.14·exp[-0.017(Alum Dosage)]+1.50TOC (mg/L) 0.96 0.09 = 3.54·exp[-0.031(HI 705 Dosage)]+2.36TOC (mg/L) 0.90 0.19 = 5.65·exp[-0.008(HI 1000 Dosage)]+0.14UV254 (cm-1)1 0.87 0.006 = 0.140·exp[-0.027(Alum Dosage)]+0.031UV254 (cm-1)2 0.86 0.005 = 0.091·exp[-0.029(Alum Dosage)]+0.037UV254 (cm-1) 0.95 0.003 = 0.101·exp[-0.031(HI 705 Dosage)]+0.033UV254 (cm-1) 0.86 0.006 = 0.120·exp[-0.020(HI 1000 Dosage)]+0.037
MAE = mean absolute error, n = 30 for each equation 1For TOC and UV254 removal by alum (no pH depression) 2For TOC and UV254 removal by acid + alum
It is important to note that these relationships are not necessarily applicable during all
seasons. The data used were generated via tests conducted with a relatively constant raw water
quality (TOC = 5.86±0.34 mg/L, UV254 = 0.140±0.009 cm-1, and pH = 7.9±0.1) and operating
temperature (23±2°C).
Bench-scale data were also used to model the formation of TTHM and HAA9. Results
from five weeks of tests with alum, acid + alum, HI 705 PACl, and HI 1000 PACl were used as a
single data set. Post-filter TOC, UV254, and pH were used as input variables; other important
factors for DBP formation (disinfectant dosage, bromide concentration, reaction time, and
temperature) did not vary during bench-scale testing. Values for the empirical constants were
determined using Microsoft Excel Solver (Microsoft Corp., Redmond, WA) to minimize the sum
71
of squared errors between model predictions and known values of TTHM and HAA9.
Performance was evaluated using R2 and MAE between model predictions and known data.
Table 4.10 shows the equations along with values for R2 and MAE. Performance did not
improve with different combinations of TOC and UV254 used as inputs to the models (TOC only,
UV254 only, TOC and UV254, or TOC·UV254). Linear correlations are similar to those observed
between DPB precursors and DBP formation (R2 ≈ 0.3 for TTHM, R2 ≈ 0.7 for HAA9).
Table 4.10: Models to predict formation of TTHM and HAA9 using TOC, UV254, and pH
Output R2 MAE Model EquationTTHM (μg/L) 0.32 13.9 = 2.07(TOC)0.66(pH)1.35
TTHM (μg/L) 0.37 13.6 = 18.74(UV254)0.49(pH)1.34
TTHM (μg/L) 0.38 13.5 = 41.41(TOC)-0.29(UV254)0.68(pH)1.39
TTHM (μg/L) 0.36 13.7 = 7.45(TOC·UV254)0.29(pH)1.34
HAA9 (μg/L) 0.65 6.6 = 2.30(TOC)1.24(pH)0.68
HAA9 (μg/L) 0.65 6.3 = 88.55(UV254)0.83(pH)0.76
HAA9 (μg/L) 0.67 6.1 = 24.22(TOC)0.55(UV254)0.50(pH)0.61
HAA9 (μg/L) 0.67 6.1 = 21.30(TOC·UV254)0.51(pH)0.71
MAE = mean absolute error, n = 120 for each equation
4.7 Seasonal Changes in Water Quality
A set of bench scale tests with alum, acid + alum, HI 705 PACl, and HI 1000 PACl was
also conducted with water collected February 8th, 2011. This was done to evaluate the impact of
enhanced coagulation with seasonal change in raw water matrix and temperature (comparison of
winter and summer conditions). (It should be noted that for the test using acid + alum, pH was
depressed to 5.8 with acid prior to the addition of alum, whereas for summer tests pH was only
depressed down to 7.) During jar tests, water temperature was kept below 7°C using an ice bath;
DBP formation tests were conducted in an environmental chamber at 4°C. Table 4.11 shows the
change in raw water quality between July and August 2010 and February 2011. In addition to
raw water quality, the pH, TOC, UV254, and fluorescence excitation-emission of filtered water
were measured, as well as TTHM and HAA9 concentrations after 24 hours. (A 24-hour DBP
formation test was also conducted using the untreated raw water.) All measured values are
presented in Section 8.2.3 of Appendix 8.2 (Table 8.6 to Table 8.8).
72
Table 4.11: Peterborough raw water quality (summer values are averages of five measurements)
Season pH TOC (mg/L) UV254 (cm-1)Summer 7.9 6.1 0.169Winter 7.7 5.9 0.140
Percent reduction of TOC in February tests is shown in Figure 4.18. Similar trends were
observed for UV254, with percent removal being on average 20% greater for UV254 than for TOC.
(R2 between post-filter TOC and UV254 was 0.98). These results are consistent with those
discussed in Section 4.5.1. The dosages of alum, acid + alum, HI 705 PACl, and HI 1000 PACl
needed to achieve the 35% reduction in TOC required by the USEPA are 40, 20, 30, and
58_mg/L, respectively. For summer tests, it was found that coagulant dosages of 43, 29, 31, and
56 mg/L, respectively, were needed to achieve 35% reduction in TOC. These results indicate
that using acid + alum or HI 705 PACl at lower dosages than alum may be able to maintain
adequate reduction in natural organic matter, even at lower temperatures and with seasonal
changes in water quality. The increased TOC removal for acid + alum compared with summer
tests is attributable to the discrepancy in pH depression (down to 5.8 instead of 7).
0%
10%
20%
30%
40%
50%
60%
70%
20 30 40 50 60 70
Dosage (mg/L)
Post
-Filt
er %
Red
uctio
n of
TO
C
AlumAcid + AlumHI 705HI 1000
Figure 4.18: Percent reduction of TOC from Peterborough water in February jar tests. Raw water TOC = 6.1 mg/L.
73
Filtered (and raw) waters were also analyzed using FEEM, with a peak corresponding to
humic-like substances being observed at excitation / emission wavelengths of 340 / 430. The
maximum fluorescence intensities, shown in Figure 4.19, exhibit the same trends as TOC and
UV254 data; R2 values for correlation with maximum FEEM intensity were 0.96 and 0.99,
respectively. As with TOC and UV254, the order of removal for the coagulants tested is alum +
acid > HI 705 PACl > alum > HI 1000 PACl. While this analysis does not quantify the
concentration of organic matter present or removed, it does provide a reliable method for
detecting relative levels of NOM and their removal, with a focus on the humic substances
fraction.
60
80
100
120
140
160
180
200
0 10 20 30 40 50 60 70 80
Dosage (mg/L)
Filte
red
Wat
er M
ax F
EEM
Inte
nsity
(a.u
)
AlumAcid + AlumHI 705HI 1000
Figure 4.19: Maximum intensity for fluorescence peak at excitation/emission of 340/430 nm for February jar tests.
TOC and UV254 results from February jar tests were directly compared with the results of
tests conducted in July and August using the equations generated in Section 4.6.2 and shown in
Table 4.9. An example is shown in Figure 4.20 for the alum jar test. The equations calibrated
74
with data from summer jar tests were generally able to predict filtered water TOC and UV254 in
February tests to within 0.2 mg/L and 0.01 cm-1, respectively. The relationships established by
repeated jar tests in summer are accurate to within 10% despite changes in raw water quality and
temperature.
0
1
2
3
4
5
6
7
0 10 20 30 40 50 60 70 80
Alum Dosage (mg/L)
Filte
red
Wat
er T
OC
(mg/
L)
0.00
0.03
0.06
0.09
0.12
0.15
0.18
0.21
Filte
red
Wat
er U
V 254
(cm
-1)
Summer TOCWinter TOCSummer UV254Winter UV254
TOC = 5.23•exp(-0.019•[dosage]) + 1.38
UV254 = 0.140•exp(-0.027•[dosage]) + 0.037
Figure 4.20: Seasonal comparison for removal of TOC and UV254 by alum. Equations were generated using results from repeated tests conducted during the summer.
Percent reduction in formation of TTHM and HAA9 from February bench scale tests
shown in Figure 4.21 and Figure 4.22, respectively; DBP formation tests with raw water resulted
in TTHM and HAA9 concentrations of 93.3 and 114.2 μg/L, respectively. These results are
consistent with those discussed in Section 4.5.2, with acid + alum and HI 705 PACl achieving
greater reduction in formation of TTHM and HAA9, while HI 1000 PACl achieved less reduction
when compared with alum. Percent reduction increases with coagulant dosage, as expected. The
exception to this is acid + alum, for which reduction of DBP formation levels off at 30 mg/L. As
with removal of TOC, the better performance of acid + alum compared with other coagulants is
attributed to the fact that pH was depressed down to 5.8, whereas for summer tests the pH was
only depressed down to 7.
75
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
20 30 40 50 60 70
Dosage (mg/L)
Red
uctio
n of
24-
Hou
r TTH
M F
orm
atio
nAlumAcid + AlumHI 705HI 1000
Figure 4.21: Percent reduction of 24-hour TTHM formation in February tests with Peterborough water. Raw water = 93.3 μg/L.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
20 30 40 50 60 70
Dosage (mg/L)
Red
uctio
n of
24-
Hou
r HA
A9
Form
atio
n
AlumAcid + AlumHI 705HI 1000
Figure 4.22: Percent reduction of 24-hour HAA9 formation in February tests with Peterborough water. Raw water = 114.2 μg/L.
76
It has been widely reported in the literature that higher DBP levels are observed in
warmer waters (Singer, 1994; Hua & Reckhow, 2008; Ristoiu et al., 2009), since the rate of
reactions between organic precursors and halogens present (chlorine and bromine) increases with
temperature. Figure 4.23 and Figure 4.24 show the formation of TTHM and HAA9, respectively,
in both summer and winter tests. The average 24-hour formation of TTHM was 30% lower than
in summer tests, while the average HAA9 formation did not change. Since summer and winter
tests generally achieved the same levels of TOC and UV254 in filtered water, the decrease in
THMs can be attributed to the fact that winter DBP formation tests were conducted at a lower
temperature (4°C compared with 23±2°C for summer tests).
20 30 40 50 60 70
0
20
40
60
80
100
120
TTH
M F
orm
atio
n (u
g/L)
Coagulant Dosage (mg/L)
AlumAcid + AlumHI 705 PAClHI 1000 PACl
20 30 40 50 60 70
0
20
40
60
80
100
120
Coagulant Dosage (mg/L)
AlumAcid + AlumHI 705 PAClHI 1000 PACl
Figure 4.23: 24-hour TTHM formation for tests conducted in summer (left) and winter (right)
20 30 40 50 60 70
0
20
40
60
80
100
HA
A9 F
orm
atio
n (u
g/L)
Coagulant Dosage (mg/L)
AlumAcid + AlumHI 705 PAClHI 1000 PACl
20 30 40 50 60 70
0
20
40
60
80
100
Coagulant Dosage (mg/L)
Alum Acid + AlumHI 705 HI 1000
Figure 4.24: 24-hour HAA9 formation for tests conducted in summer (left) and winter (right)
77
Filtered waters from the jar test with alum were also tested for NOM content using LC-
OCD. The results, which are similar to the LC-OCD results discussed in Section 4.5.1, are
shown in Figure 4.25. Total DOC detected via LC-OCD was generally within 0.2 mg/L of the
value measured using the TOC analyzer (data not shown). Hydrophilic DOC accounted for 80%
of total DOC, while humic substances comprised 60% of raw water DOC, with all others less
than 10%. Like UV254, percent removal for humic substances is 10-20% greater than for total
DOC. This is consistent with the results of FEEM analysis, which indicated that the presence
and removal of humic substances is more closely correlated with UV254 than with TOC.
0
1
2
3
4
5
6
7
0 20 30 40 50 60 70
Alum Dosage (mg/L)
Filte
red
Wat
er D
OC
(mg/
L)
Total DOC Hydrophilic DOCHydrophobic DOC Humic SubstancesBuilding Blocks LMW NeutralsBio-Polymers
(no data)
Figure 4.25: Removal of NOM fractions detected by LC-OCD in February test with alum (LMW acids were not detected in any samples)
LC-OCD data shown in Figure 4.25 were also compared directly with measurements of
TOC, UV254, maximum fluorescence intensity of humic substances, and DBP formation (TTHM
and HAA9 concentrations) by calculating R2 values for linear correlations between all parameters
(Table 5). TOC, UV254, FEEM HS, total and hydrophilic DOC, and the humic substances and
bio-polymer fractions of LC-OCD were all strongly correlated to each other (R2 > 0.95). These
parameters were also found to be closely correlated with the formation of TTHM (R2 > 0.78) and
78
HAA9 (R2 > 0.92). (Similar trends were observed for HAA5 and HAA6, since HAA9 is 98%
non-brominated species by weight.) Linear correlations of DBPs with FEEM and LC-OCD
results were not better than those of DBPs with TOC and UV254. While FEEM and LC-OCD are
good surrogate measures of NOM and DBP precursors, they were not able to improve upon the
simpler and more conventional measures of TOC and UV254.
Table 4.12: R-squared values for linear correlations between measures of filtered water NOM content and 24-hour DBP formation
Total DOC
Hydro-philic
Hydro-phobic
Humic Substances
Building Blocks
LMW Neutrals
Bio-Polymers
LMW Acids TTHM HAA9
TOC 1.00 0.98 0.96 0.98 1.00 0.79 0.98 0.30 0.00 0.99 0.50 0.91 0.94
UV254 1.00 0.99 0.99 0.99 0.85 1.00 0.38 0.03 0.99 0.61 0.84 0.95
FEEM HS 1.00 0.97 0.96 0.83 0.99 0.45 0.07 0.95 0.69 0.78 0.92
Total DOC 1.00 0.99 0.88 0.98 0.32 0.02 1.00 0.56 0.86 0.97
Hydrophilic DOC 1.00 0.80 0.98 0.30 0.01 0.99 0.51 0.91 0.95
Hydrophobic DOC 1.00 0.82 0.34 0.12 0.86 0.64 0.57 0.87
Humic Substances 1.00 0.42 0.03 0.98 0.59 0.84 0.92
Building Blocks 1.00 0.51 0.31 0.53 0.13 0.22
LMW Neutrals 1.00 0.01 0.48 0.05 0.02
Bio-Polymers 1.00 0.51 0.88 0.96
LMW Acids 1.00 0.23 0.57
TTHM 1.00 0.81
HAA9 1.00
ParametersLC-OCD Fractions DBP Formation
TOC UV254FEEM
HS
4.8 Summary
Bench scale testing was conducted on water obtained from the Peterborough WTP using
alum, acid + alum, HI 705 PACl, and HI 1000 PACl coagulants. The chemical currently in use
for coagulation at Peterborough WTP is alum, typically applied at a dosage of 45 mg/L. The
objective was to maintain or improve water quality in terms of removing DBP precursors (TOC
and UV254) and limiting DBP formation (focusing on THMs and HAAs), while applying an
equal or lower coagulant dosage to potentially reduce sludge handling costs. Results indicate
that both acid + alum and HI 705 PACl can achieve the same reduction in DBP precursors at
79
lower dosages (29 and 30 mg/L, respectively) than are currently applied at the WTP (Table
4.13). In addition, applying these concentrations of acid + alum and HI 705 PACl was shown to
decrease the formation of TTHM and HAA9. The required dosage of HI 1000 PACl to maintain
DBP precursor removal and decrease formation of TTHM and HAA9 was 56 mg/L. In addition,
HI 705 PACl dose not have the same impact on pH as the other options; using this coagulant
would decrease or eliminate the need for subsequent addition of sodium silicate to raise the pH,
which is the current practice at the Peterborough WTP. Cold-water tests conducted during the
winter achieved the same levels of TOC and UV254 reduction as summer tests; THM formation
was 30% lower, while HAA formation did not change. Table 4.14 shows correlation coefficients
between measures of NOM and DBPFP.
Table 4.13: Summary of water quality resulting from recommended treatment conditions with alum, acid + alum, HI 705 PACl, and HI 1000 PACl
pH TOC (mg/L) UV254 (cm-1) TTHM HAA9 TCAN TCPAlum 43 7.3 3.8 0.076 77 68 10 2
Acid + Alum 29 7.4 3.8 0.076 61 46 10 7HI 705 PACl 31 7.9 3.8 0.073 67 45 8 3HI 1000 PACl 56 7.4 3.8 0.077 67 58 10 4
Post-filter Water Quality 24-Hour DBP Formation (μg/L)Dosage (mg/L)Coagulant
Table 4.14: R2 values for linear correlations between key performance parameters Parameter TOC UV254 HS TTHMFP HAA9FP
TOC 1UV254 0.88 1
HS 0.78 0.84 1TTHMFP 0.25 0.30 0.32 1HAA9FP 0.67 0.71 0.74 0.30 1
The three parameters used as indicators of DBP precursor content (TOC, UV254, and
FEEM humic substances) were shown to be closely correlated to each other (R2 > 0.78), with
weaker correlations to HAA9FP (0.67 < R2 < 0.74) and very weak correlations to TTHMFP (0.25
< R2 < 0.32). Fitting TOC and UV254 data to exponential equations to relate them to coagulant
dosage resulted in very good predictions of post-filter TOC and UV254 (R2 > 0.86). Equations
created to relate TOC, UV254, and pH to TTHMFP and HAA9FP were not able to improve upon
the linear correlations summarized in the table above.
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5. Artificial Neural Network (ANN) Modelling
Artificial neural networks (ANNs) have been used to improve drinking water treatment
(Delgrange-Vincent et al., 2000; Mälzer & Strugholtz, 2008; Rodriguez et al., 1997), water
distribution (Rodriguez & Sérodes, 1999; Wu & Zhao, 2007; Skipworth et al., 1999) and
wastewater treatment (Dogan et al., 2008; Guan et al., 2005) systems. Several researchers have
used ANNs to predict the removal of NOM via coagulation (Baxter et al., 1999; Baxter et al.,
2001; Maier et al., 2004) or to predict the formation of THMs in treated drinking water (Lewin et
al., 2004; Rodriguez & Sérodes, 2004). The objective of this study was to create neural
networks which can successfully predict the formation of both THMs and HAAs in bench scale
tests. The successful demonstration of the capability of ANNs to predict DBP formation may
lead to the implementation of ANNs for direct process control at the Peterborough pilot plant.
5.1 Parameter Selection
Researchers employing ANNs to predict the formation of DBPs or the removal of NOM
have used a combination of water quality and chemical dosage parameters as inputs to these
models (Baxter et al., 2001; Maier et al., 2004; Rodriguez & Sérodes, 2004). Inputs used in
previous studies to predict THM formation include: pH, temperature, chlorine dosage, chlorine
contact time, bromide concentration, and various measures of NOM (Rodriguez & Sérodes,
2004; Lewin et al., 2004). Several researchers have used colour and/or turbidity inputs as NOM
surrogates (Baxter et al., 1999; Baxter et al., 2001; Lewin et al., 2004; Maier et al., 2004).
While these parameters can be measured quickly and easily, TOC and UV254 are more
quantitative surrogate measures for NOM, and have been shown to be closely correlated with
DBP formation (Section 4.7). In addition, the Peterborough water treatment facility has the
capability to provide automated online analysis of both parameters at several locations in the
full-scale and pilot plants.
During bench-scale testing, two additional analytical methods were used to detect organic
matter in raw and filtered waters. Fluorescence excitation-emission matrices (FEEMs) were able
to indicate the removal of humic substances, protein-like substances, and colloidal / particulate-
like matter. Likewise, liquid chromatography – organic carbon detection (LC-OCD) was
employed to quantify the hydrophobic and hydrophilic fractions of DOC, the latter of which was
divided into humic substances, bio-polymers, building blocks, low-molecular-weight (LMW)
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acids, and LMW neutrals. While the results of FEEM and LC-OCD analyses also provided good
correlations with the formation of TTHM and HAA9, they did not improve upon the linear
correlations between DBPs and TOC or UV254 (Section 4.7). In addition, these are complex,
time-consuming analytical procedures which cannot be conducted at the Peterborough WTP.
Therefore, neither FEEM nor LC-OCD was used as ANN inputs.
In order for ANNs to model the relationships between input and output parameters, the
data set used for training and testing must contain sufficient variability, as indicated by percent
standard deviation. If the inputs are relatively constant, the network will not be able to learn any
cause-and-effect relationships between inputs and output. For water quality input parameters
(pH, TOC, and UV254), filtered water data were used instead of raw water due to the difference in
variability (Table 5.1). The variability of filtered water TOC and UV254 (19% and 28%
respectively) are similar to that of THM and HAA formation (28% and 31% respectively).
Conversely, the variability of raw water quality during the period of data generation was almost a
whole order of magnitude less (4% for TOC and UV254). Likewise, the chlorine contact time and
bromide concentration were not used as inputs since they did not change during bench-scale
tests. Therefore, the following parameters were selected as inputs for ANNs created to predict
DBP formation: pH, TOC, and UV254 of filtered water, temperature, and chlorine dosage.
Table 5.1: Variability in raw and filtered water quality, as well as DBP formation, for the data generated via bench-scale testing. Percent Std. Dev. is calculated by dividing the standard deviation by the average value.
Parameter Location Average Standard Deviation % Std. Dev.RW 7.89 0.11 1%FW 7.39 0.37 5%RW 5.9 0.2 4%FW 3.7 0.7 19%RW 0.140 0.005 4%FW 0.074 0.020 28%
TTHM (μg/L) 24-hour formation 71.4 19.8 28%HAA9 (μg/L) 24-hour formation 46.5 14.3 31%
pH
TOC (mg/L)
UV254 (cm-1)
an = 6 for raw water (RW), n = 144 for filtered water (FW) and 24-hour DBP formation
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5.2 ANN Development
Detailed methods for ANN development are provided in Section 3.4. ANNs were trained
and tested using the data generated from the bench-scale tests described in Chapter 4 (all
measured values are presented in Appendix 9.2). These data consist of 145 exemplars for THM
and HAA formation and the corresponding inputs (pH, TOC, UV254, temperature, and chlorine
dosage), which were randomized using the random number generator in Microsoft Excel
(Microsoft Corp., Redmond, WA). The minimum, maximum, average, and interquartile values
for the entire data set are shown in Table 5.2. The randomized data were divided so that 60%
was used for training, 20% for cross-validation, and 20% for testing. Testing data were used to
evaluate the ANN performance on data not seen by the network during training.
Table 5.2: Summary of bench-scale data used to develop ANNs to predict DBP formation
Paramter FW TOC (mg/L)
FW UV254
(cm-1)FW pH Temperature
(°C)Cl2 Dosage
(mg/L)
TTHM Formation
(μg/L)
HAA9
Formation (μg/L)
Minimum 2.5 0.037 4.92 4.0 3.49 18.1 16.425th Percentile 3.1 0.056 6.96 23.1 3.50 51.7 35.4
Average 3.8 0.074 7.33 21.1 3.58 67.6 47.775th Percentile 4.3 0.089 7.76 23.1 3.55 84.9 57.8
Maximum 6.1 0.169 8.06 25.6 3.82 113.5 114.2
The general architecture for ANNs that predict the formation of DBPs is shown in Figure
5.1. The output for such a network is either TTHM or HAA9 concentration (networks with two
output parameters were not developed). Multiple networks were trained and tested to find the
optimal ANN architecture settings (number of hidden neurons, learning rate, and momentum
term). For each unique network architecture, the ANN was trained and tested three times to
ensure that the optimal arrangement was achieved and that the results were reproducible. The
performance of each trial was evaluated by comparing known values and ANN predictions for
the output parameter (THM or HAA formation). Quantitative comparisons were made using the
following performance parameters: r2 values for linear correlation, mean absolute error (MAE),
normalized (or percent) mean absolute error (%MAE), and mean squared error (MSE). In
addition, correlation plots and error histograms were also generated. MAE and MSE are
83
indicators of the difference between ANN output predictions and the actual value of the output
parameter being estimated. %MAE is found by dividing the MAE by the average actual output
value. The formula for MAE is:
n
XXMAE
n
iPii∑
=
−= 1
5.1
where Xi and XPi are the real and predicted model output values respectively, and n is the
number of data points used to test the model. The equation for MSE is similar:
( )
n
XXMSE
n
iPii∑
=
−= 1
2
5.2
Figure 5.1: Preliminary architecture for ANN to predict formation of THMs or HAAs using data from bench-scale testing
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5.3 Results and Discussion
The ANN architecture selected is shown in Table 5.3. These settings were used for the
TTHM model and for the HAA9 model. The performance of both models was evaluated by
comparing performance statistics and by preparing correlation plots and error histograms.
Performance statistics for TTHM and HAA9 ANNs are shown in Table 5.4.
Table 5.3: Final network architecture selected for TTHM and HAA9 ANNs Architecture Parameter Selection
Input neurons 5Input scale -1 to 1
Hidden layers 1Hidden neurons 4
Hidden transfer function TanhAxonLearning rate 0.5
Momentum coefficient 0.7Output neurons 1
Output scale -1 to 1Output transfer function BiasAxon
Epochs 1000Stopping Criteria 1000 epochs or increase in cross-validation error
Table 5.4: Comparison of performance statistics for TTHM and HAA9 ANNs
r2 MAE MSE %MAETTHM 63.5 0.85 7.0 89.3 11%HAA9 51.4 0.77 5.8 64.0 11%
Output Average Value (μg/L) Performance Measures
r2 = correlation coefficient, MAE = mean absolute error, %MAE = percent mean absolute error MSE = mean squared error
Comparison of the correlation coefficients shows that the TTHM model (r2 = 0.85)
performed somewhat better than the HAA9 model (r2 = 0.77). The MAE and MSE are both
higher for the TTHM model (MAE = 7.0, MSE = 89.3 respectively) than for the HAA9 model
(MAE = 5.8, MSE = 64.0). This disparity can be attributed to the fact that the average TTHM
formation (63.5 μg/L) was higher than the average HAA9 formation (51.4 μg/L). The %MSE
shows the relative error to be very similar for the two neural networks (11% for both TTHM and
HAA9 models). In contrast, the exponential regression equations described in Section 4.6.2 were
not able to predict TTHM and HAA9 formation nearly as well: these models produced r2 values
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of 0.38 and 0.67, with MAE of 13.5 and 6.1 μg/L, respectively. Therefore, neural networks are
much better suited to modeling the complex processes involved in DBP formation.
Plots created using the predicted and actual DBP formation data sets were also used to
investigate model performance. The correlation plot for TTHM formation is shown in Figure 5.2,
with a 45-degree line to illustrate the ideal performance (slope = 1, y-intercept of zero, r2 = 1).
The regression line between predicted and actual TTHM formation has slope of 0.78 and a y-
intercept of 13.38 μg/L. Therefore, the model tends to under-predict TTHM formation for values
over 60 μg/L and over-predict TTHM formation for values under 60 μg/L. The correlation plot
for HAA9 formation is shown in Figure 5.3. The regression line has a slope of 0.81 and a y-
intercept of 6.27 μg/L. This model generally under-predicts HAA9 formation for values above
33 μg/L and over-predicts HAA9 formation for values below 33 μg/L.
y = 0.78x + 13.38R2 = 0.85
0
20
40
60
80
100
120
0 20 40 60 80 100 120
Actual TTHM Formation (μg/L)
Pred
icte
d TT
HM
For
mat
ion
(μg/
L)
Figure 5.2: Correlation plot for the predicted versus actual TTHM formation
In a previous study that used ANNs to predict THM formation in a conventional WTP,
Lewin et al. (2004) reported an r2 value of 0.90, a MSE of 9.4, and a MAE of 2.1 μg/L. The low
error values are likely due to the mean THM concentration being much lower (13 μg/L) than in
86
this study (64 μg/L). The %MAE for the results reported by Lewin et al. (16%) is actually
higher than the 11% found in this study. Hashem & Karkory (2007) also used ANNs to model
the formation of THMs in bench-scale chlorination experiments, and reported an r2 of 0.98. The
testing data set used by Hashem & Karkory (200 exemplars) was much larger than that used in
this study (35 exemplars). They also reported a MSE of 2143, due to the high THM
concentration data used in ANN development (mean = 280 μg/L, maximum = 2843 μg/L).
Studies by Rodriguez & Sérodes (2004) and by Kulkarni & Chellam (2010) to predict THM
formation using ANNs yielded performance similar to this study (r2 = 0.89 and 0.90,
respectively).
y = 0.81x + 6.27R2 = 0.77
0
20
40
60
80
100
0 20 40 60 80 100Actual HAA9 Formation (μg/L)
Pred
icte
d H
AA
9 For
mat
ion
(μg/
L)
Figure 5.3: Correlation plot for the predicted versus actual HAA9 formation
The accuracy of model predictions was also assessed by creating histograms to show the
distribution of errors between predicted and actual values of DBP formation. The error
histograms for the TTHM and HAA9 formation ANNs are shown in Figure 5.4 and Figure 5.5,
respectively. Ideally the mean error value would be zero, and if the measurement noise is
normally distributed then the errors should also have a normal distribution (Swingler, 1996). For
the TTHM model the error histogram is centered very close to zero (-0.7 μg/L), with a standard
87
deviation of 9.6 μg/L. The error histogram for the HAA9 model is centered on -3.6 μg/L and has
a standard deviation of 7.3 μg/L.
0%
5%
10%
15%
20%
25%
30%
35%
-25 -20 -15 -10 -5 0 5 10 15 20 25
Error (μg/L)
Freq
uenc
y (p
erce
nt o
ccur
ence
)
Figure 5.4: TTHM formation error histogram
0%
5%
10%
15%
20%
25%
30%
35%
-25 -20 -15 -10 -5 0 5 10 15 20 25
Error (μg/L)
Freq
uenc
y (p
erce
nt o
ccur
ence
)
Figure 5.5: HAA9 formation error histogram
The normality of the error distribution was tested by constructing a quantile-quantile plot
(Q-Q plot). Quantiles are points on the cumulative distribution function of a random variable; in
this case, the variable is the model error. For comparison with the normal distribution (mean of
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zero and standard deviation of 1), the TTHM error data were normalized by subtracting the mean
value and dividing by the standard deviation (-0.7 and 9.6 μg/L, respectively); the HAA9 error
data were also normalized. The values of the 10th, 20th, 30th, 40th, 50th, 60th, 70th, 80th, and 90th
percentiles were then calculated for the normal distribution and for the normalized TTHM and
HAA9 error data sets. The resulting Q-Q plot is shown in Figure 5.6, with a 45-degree (y = x)
line for a theoretical data set which is perfectly normally distributed. Since all of the points on
the Q-Q plot lie approximately on the y = x line, the error values are normally distributed for
both models.
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Normal Distibution Quantiles
Erro
r Dis
trib
utio
n Q
uant
iles
TTHM Error QuantilesHAA9 Error Quantiles
Figure 5.6: Q-Q plot to test the normality of the error distribution for DBP models (values shown are the 10th, 20th, 30th, 40th, 50th, 60th, 70th, 80th, and 90th percentiles); 45-degree line shows theoretical perfectly normal distribution
5.4 Implementation
Since ANNs have been shown to successfully predict the formation of DBPs in bench-
scale tests, they may also be used to provide predictions for the Peterborough pilot plant (and
ultimately the full scale plant). This would require that new models be developed using data
collected from the pilot plant itself, following the procedures described in Sections 5.2 and 3.4.
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By making predictions for DBP formation as well as an optimal alum dosage, ANNs can be a
useful tool for process control to minimize DBP formation.
5.4.1 ANN Development
Using data generated from the pilot plant itself, two types of ANN models could be
developed. The first is a process model with TTHM formation as the output (similar to the
model created using bench-scale data), shown in Figure 5.7. This would demonstrate the
capability of ANNs to predict DBP formation in the pilot plant using water quality parameters
(TOC, UV254, pH, and temperature) and operating conditions (flow rate and dosages of alum and
chlorine). It can be used to conduct theoretical experiments by manually modifying the inputs
and observing the changes in the output, which can serve as a tool for training operators. In
addition to a process model with TTHM formation as the output, a similar neural network can be
created to predict HAA9 formation. The second type of ANN is an inverse process model with
optimal alum dosage as the output and the formation of THMs and HAAs used as inputs (Figure
5.8). This network can be used for real-time direct process control by specifying the alum
dosage required to achieve desired DBP concentrations.
The development of neural networks should follow the methods described in Sections 5.2
and 3.4, using the NeuralBuilder tool in the NeuroSolutions software. The available data should
be randomized and divided such that 60% is used for network training, 20% for validation, and
20% for testing. Validation data are used to test the network during training, to ensure that
models learn the general trends in the data, rather than memorizing the training data set itself.
Testing data are used to evaluate model performance by predicting data not seen by the network
during the training process. ANNs should be developed using the multilayer perceptron
structure, the momentum learning rule, and the TanhAxon transfer function for the hidden layer.
The number of hidden neurons, momentum coefficient, and learning rate are chosen by trial and
error.
Unlike the batch experiments conducted at bench-scale, pilot plant treatment involves a
continuous flow. Therefore, the hydraulic retention times (HRT) of the different unit processes
should be used to establish time lags for the input parameters. For example, a change in influent
water quality will not have an immediate impact on DBP formation; instead, the change will be
delayed by approximately one HRT, representing the entire treatment train. Griffiths (2010)
90
used various time lags for inputs to ANNs for predicting turbidity, particle counts, and optimal
alum dosage in a full scale plant; these lags were based on the minimum, maximum, and average
flow rate for the plant. Since the flow rate in the Peterborough pilot is constant (3.0_L/min), it
would not be difficult to identify the appropriate time lag for each input parameter.
Figure 5.7: Preliminary architecture for ANN to predict TTHM formation in the pilot plant (RW = raw water)
5.4.2 Pilot Plant ANN Data
In order for a neural network to be used for pilot plant control, it must be calibrated using
data collected from the actual pilot-scale treatment train. Ideally, data should be collected for at
least one year such that the model can learn how annual variations in water quality (pH,
temperature, UV254, and TOC) interact with changes in chemical dosages to affect DBP
formation. Alternatively, models may be developed using data collected during a specific season
91
(Griffiths, 2010). In addition, the data generated to train the ANNs should cover the expected
range for all parameters, such that the model does not have to extrapolate beyond the range of the
training data when it is used to control the treatment train (Baxter et al., 2001).
Figure 5.8: Preliminary architecture for ANN to predict optimal alum dosage in the pilot plant (RW = raw water)
Pilot plant testing of alum and the HI 705 PACl coagulants has been conducted
continuously since January 2011 at the Peterborough WTP. DBP formation tests were conducted
at the pilot plant with filtered water using the same method as in the bench-scale testing (see
Section 4.3). DBP analyses were conducted bi-weekly at the University of Toronto laboratory;
all other parameters are monitored regularly at Peterborough. The data collected to-date can be
used to develop ANNs to evaluate the applicability of neural networks at pilot scale. Neural
networks developed with data generated using alum as the coagulant should not make use of data
generated while dosing PACl, and vice versa, since changing the coagulant type represents a
significant change in the treatment process (Lewin et al., 2004). To date, pilot tests have
92
involved the first treatment train (PP1) using the same alum dosage as the full scale plant (FSP)
(typically 40-45 mg/L), with the second treatment train (PP2) either mimicking PP1 or using
PACl. In order for the ANN to be able to predict optimal alum dosages outside the range
typically used in the FSP, pilot tests should be conducted using a wider range of dosages (Baxter
et al., 2002a).
5.4.3 Parallel Treatment Train Operation for ANN Evaluation
Once the ANN models described above have been successfully trained and tested, they
can be used to develop a process optimization system for the dual-train Peterborough pilot plant.
The pilot plant uses the same source water as the FSP, which it has been designed to mimic.
Initial testing has shown that the two pilot-scale treatment trains are both able to produce very
similar water quality to that of the FSP. For example, post-filter TOC and TTHM formation are
shown in Figure 5.9 and Figure 5.10, respectively. For each day, the TOC data agree to within
0.2 mg/L and the maximum difference for TTHM formation is 12 μg/L.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
1/27/11 2/03/11 3/10/11 3/17/11 3/24/11
Filte
red
Wat
er T
OC
(mg/
L)
FSPPP1PP2
Figure 5.9: Comparison of TOC in Peterborough full-scale plant (FSP) and two pilot-scale treatment trains (PP1 and PP2) all using alum coagulant (47 mg/L)
93
To determine the applicability of the ANN models, PP1 would be operated using the
same alum dosage as the FSP, with the water quality and operating parameters used as inputs to
the process model ANNs. The predictions made by the process models (TTHM and HAA9
formation) would be compared to the actual DBP concentrations measured in PP1.
Simultaneously, the inverse process model ANN would provide an optimal alum dosage based
on the water quality, operating conditions, and target DBP formation inputs. (It is suggested that
the target DBP concentrations be set to 80% of the US maximum contaminant levels (USEPA,
1998): 64 μg/L for TTHM and 48 μg/L for HAA9). This optimal alum dosage would be used to
control the dosage in PP2, with the target DBP levels (TTHM and HAA9) compared with the
actual formation measured in PP2. Figure 5.11 and Figure 5.12 show the flow of data between
the ANN models and the PP1 and PP2 treatment trains, respectively. Since the analyses for DBP
concentrations are conducted bi-weekly at UofT, there will be significant delay in evaluating the
performance of these models. Fortunately, this will not affect the actual operation of the pilot
plant and ANN control system.
0
10
20
30
40
50
60
70
80
90
100
3/07/11 3/08/11 3/09/11 3/10/11 3/14/11 3/15/11 3/16/11 3/17/11
24-H
our T
THM
For
mat
ion
(μg/
L)
FSP PP1 PP2
Figure 5.10: Comparison of TTHM formation in Peterborough full-scale plant (FSP) and two pilot-scale treatment trains (PP1 and PP2) all using alum coagulant (47 mg/L)
94
Coag. / Floc. / Sed. Filtration Disinfection
Process Model ANN
Alum(FSP Dosage)
RawWater
Chlorine(FSP Dosage)
Actual DBPs Formation
Predicted DBPs Formation
Figure 5.11: Data flow between PP1 (simplified process flow diagram) and the process model ANN. Dashed lines indicate data input/output.
Coag. / Floc. / Sed. Filtration Disinfection
Inverse Process Model ANN
Optimal Alum Dosage
Chlorine(FSP Dosage)
Actual DBPs Formation
Target DBPs Formation
RawWater
Alum(Optimal Dosage)
Figure 5.12: Data flow between PP2 (simplified process flow diagram) and the inverse process model ANN. Dashed lines indicate data input/output.
95
Each of the ANNs developed will be used to generate a Dynamic Library Link (DLL) file
using the Custom Solution Wizard tool in NeuroSolutions (for details see Griffiths, 2010). These
DLLs can be embedded into a software application using Visual Basic, Access, Excel, Visual
C++, or Active Server Pages (NeuroDimension Inc., 2008). This program will collect the
SCADA data required for ANN inputs from the programmable logic controller (PLC) and send
them to the DLLs. Before the ANN models are run, a validity check will be conducted to
determine if the inputs are within the range of the training data. If any of the inputs are outside
of the training data range, a flag will be triggered to show that the current model outputs may be
invalid. An invalid flag indicates that the model outputs may be less accurate than would be
expected based on previous testing. The output values from the DLLs will be returned to the
PLC to be stored, with the optimal alum dosage being used to control the dosage of PP2. A flow
diagram for the application is shown in Figure 5.13.
SCADA (Start)
Inputs withintraining range?
Run DBPFormation ANN
Run Optimal Alum Dosage ANN
+
- -Turn on flag for invalid outputs
Turn on flag for invalid outputs
Record predicted value for DBP formation
Record output value for optimal alum dosage
Send optimal alum dosage to PP2
End
Figure 5.13: Flow diagram for a pilot plant ANN software application
96
5.4.4 Full Scale Plant (FSP) and ANNs
If neural networks are demonstrated to perform well at pilot scale, they may also be used
to predict DBP formation in the FSP. This can be done with the ANNs developed for the pilot
plant by re-testing them using full-scale data. This would require establishing new data lags,
since full scale unit processes have greater hydraulic retention times than at pilot scale. Ideally,
testing pilot models with full-scale data would provide similar performance (in terms of r2, slope,
MAE, MSE, and %MAE) as when testing with pilot data. Otherwise, new models can be
developed using the data being collected from the FSP in conjunction with the pilot testing.
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6. Summary, Conclusions and Recommendations
6.1 Summary
Bench-scale tests were conducted to evaluate the potential of enhanced coagulation to
remove natural organic matter (NOM) and decrease disinfectant by-product (DBP) formation.
Water from the Otonabee River was collected at the Peterborough plant and transported to the
University of Toronto for testing with four coagulation alternatives: alum, acid + alum (pH
depression), and two polyaluminum chloride (PACl) coagulants (HI 705 and HI 1000). Key
parameters were total organic carbon (TOC) and ultraviolet absorbance (UV254) as surrogates for
NOM content, the formation of trihalomethanes (THMs) and haloacetic acids (HAAs), as well as
pH and coagulant dosage. In addition, fluorescence excitation-emission matrices (FEEM) and
liquid chromatography – organic carbon detection (LC-OCD) were used as advanced methods of
quantifying NOM removal. Finally, the data from these tests was used to create artificial neural
network (ANN) models to predict the formation of THMs and HAAs.
The first objective of this research was to determine the effect of enhanced coagulation
practices. The results of bench-scale tests indicated that acid + alum and HI 705 PACl were both
able to achieve the same reduction of TOC and UV254 with lower coagulant dosages when
compared with alum alone. These coagulation alternatives also produced lower formation of
THMs and HAAs. The HI 705 PACl was observed to have very little impact on pH.
The second objective of this study was to investigate FEEM and LC-OCD as alternative
methods of quantifying the removal of NOM during enhanced coagulation. These two methods
were able to identify specific fractions of NOM. The largest of these was humic substances,
which was found to be very strongly correlated with TOC, UV254, and THM and HAA
formation. FEEM and LC-OCD both provide good surrogate measures of DBP precursor
material.
The third objective was to create ANNs which can successfully predict the formation of
both trihalomethanes and haloacetic acids. Performance was assessed using correlation plots,
error histograms, and calculated values for mean absolute error, mean squared error, and r2
correlation coefficient. Testing of these models showed good correlations between the actual
and predicted data for THMs (r2 = 0.85) and for HAAs (r2 = 0.77).
98
6.2 Conclusions
The conclusions of this research are:
1. Enhanced coagulation can be used to decrease DBP formation in treated drinking water by
improving the removal of NOM prior to disinfection. Alternatively, finished water quality
can be maintained by changing the type of coagulant being used and applying a lower
dosage.
2. FEEM and LC-OCD are both useful analytical tools for characterizing the nature of NOM
present in a source water and identifying which components are effectively removed via
treatment.
3. ANNs can successfully predict the formation of THM and HAAs for a range of conditions
using conventional bench-scale treatment.
6.3 Recommendations
The recommendations from this research are: 1. Conduct further testing of HI 705 PACl coagulant at the Peterborough pilot plant, with one
treatment train being used to match full-scale treatment conditions and the other to observe
how well the PACl performs in comparison.
2. Develop new ANN models to be used for pilot plant optimization and control using data
collected during pilot testing.
99
7. References
Archer, A.D. & Singer, P.C. (2006a) Effect of SUVA and enhanced coagulation on removal of TOX precursors. J.Am.Water Works Assoc., 98(8), 97-107.
Archer, A.D. & Singer, P.C. (2006b) An evaluation of the relationship between SUVA and NOM
coagulation using the ICR database. J.Am.Water Works Assoc., 98(7), 110-123. Australian drinking water guidelines (2004) Australian National Health and Medical
Research Council. Baghoth, S.A., Dignum, M., Grefte, A., Kroesbergen, J., & Amy, G.L. (2009) Characterization
of NOM in a drinking water treatment process train with no disinfectant residual. Water Science & Technology: Water Supply, 9(4), 379-386.
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106
8. Appendices
8.1 Sample Calculations
8.1.1 Point of Diminishing Returns (PODR)
For each jar test, TOC data was used to find the point of diminishing returns (PODR).
The PODR is defined by the Enhanced Coagulation and Enhanced Precipitative Softening Guide
(USEPA, 1999a) as the coagulant dosage at which the rate of removal of TOC is equal to
0.3_mg/L per 10 mg/L of coagulant dosage. The PODR is found using the following procedure.
The example data shown below are from a jar test conducted with water collected on July 19,
2010, using alum coagulant with acid for pH depression.
1. Fit the TOC and coagulant dosage data to an equation of the form:
( ) cxbaxy +⋅−⋅= exp)(
where y is TOC in mg/L, x is dosage in mg/L, and a, b, and c are constants. For example, for the TOC and dosage values shown in Table 8.1, the exponential approximation of the data is shown graphically in Figure 8.1. For each set of TOC and dosage data, values for a, b, and c are found in Excel using the Solver tool to minimize the squared errors between the known TOC data and the equation-predicted values. For the example data, values of a, b, and c were found to be 3.53, 0.0535, and 2.72, respectively.
Table 8.1: Example data for PODR calculation (July 19 jar test, alum with acid). Dosage (mg/L) 0 10 20 30 42.3 50 60
TOC (mg/L) 5.56 4.80 3.92 3.44 3.09 3.00 2.84 2. Find the point at which the slope of the exponential line is equal to -0.03 (or 0.3 mg/L per
10_mg/L dosage). This is done using the derivative of the equation shown in Step 1:
( )
⎟⎠⎞
⎜⎝⎛
⋅−
−=
⋅−⋅⋅−=
baxf
bx
xbbaxf)('ln1
exp)('
107
For the example data, this yields:
Lmgx
x
/4.340535.053.3
)03.0(ln0535.01
=
⎟⎠⎞
⎜⎝⎛
⋅−
−=
This result is shown graphically in Figure 8.1, where the intersection between the exponential curve and the tangent line of slope -0.03 occurs approximately halfway between 30 and 40_mg/L alum dosage (indicated by the vertical line).
2
3
4
5
6
7
0 10 20 30 40 50 60 70
Alum Dosage (mg/L)
TOC
(mg/
L)
Figure 8.1: Example of exponential approximation of jar test TOC data to find the PODR.
108
8.1.2 Bromine Incorporation Factor (BIF)
Bromine incorporation in DBPs was evaluated using the bromine incorporation factor
(BIF), which was calculated via the method described by Goslan et al. (2009). The BIF indicates
the contribution of brominated species on a molar basis. BIF can range from 0 to 3 for THMs,
from 0 to 2 for dihalogenated HAAs (DXAA), and from 0 to 3 for trihalogenated HAAs
(TXAA). The BIF values for THMs, DXAAs, and TXAAs are calculated using these equations:
( ) [ ] [ ] [ ][ ] [ ] [ ] [ ]TBMDBCMBDCMTCM
TBMDBCMBDCMTHMsBIF+++
++=
32
( ) [ ] [ ][ ] [ ] [ ]DBAABCAADCAA
DBAABCAADXAAsBIF++
+=
2
( ) [ ] [ ] [ ][ ] [ ] [ ] [ ]TBAADBCAABDCAATCAA
TBAADBCAABDCAATXAAsBIF+++
++=
32
where all DBP species concentrations are on a molar basis.
For example, Table 8.2 shows mass concentrations for the four THM species. These
values are divided by their respective molecular weights to calculate molar concentrations, which
are used to compute the BIF in the equation below.
Table 8.2: Conversion of mass concentrations to molar concentrations (data are 24-hour THM formation for July 19 test with 60 mg/L HI 705 coagulant).
Species TCM BDCM DBCM TBMConcentration (μg/L) 55.5 5.1 2.1 0.0
MW (g/mol) 119.4 163.8 208.3 252.7Concentration (μmol/L) 0.465 0.031 0.010 0.000
( ) ( ) ( )( ) ( ) ( ) ( ) 101.0
506.0051.0
0010.0031.0465.003010.02031.0
==+++
++=THMsBIF
109
8.2 Bench Scale Testing Raw Data
8.2.1 Post-Filter Water Quality
The measured values for filtered water quality parameters in bench scale tests are
presented in Table 8.3.
Table 8.3: pH, TOC, UV254, and fluorescence excitation-emission data for bench scale tests post-filter water
HS PM CPM19/07/2010 Alum 20 7.46 4.80 0.112 28.8 -16.1 -15.919/07/2010 Alum 30 7.38 4.38 0.099 18.0 -4.1 -6.119/07/2010 Alum 42 7.32 4.12 0.089 13.7 -4.0 -7.919/07/2010 Alum 50 7.22 3.68 0.075 -1.7 -3.0 -21.419/07/2010 Alum 60 7.06 3.03 0.058 -27.3 -3.8 -28.219/07/2010 Alum 70 6.98 2.78 0.051 -30.5 -6.2 -41.219/07/2010 Acid + Alum 10 6.74 4.87 0.104 19.5 -19.2 -11.819/07/2010 Acid + Alum 20 6.67 4.05 0.080 7.4 -3.0 -14.219/07/2010 Acid + Alum 30 6.81 3.35 0.060 -14.4 -5.4 -26.519/07/2010 Acid + Alum 42 6.70 3.24 0.055 -24.4 -6.7 -28.119/07/2010 Acid + Alum 50 6.78 2.95 0.048 -24.8 -4.1 -28.319/07/2010 Acid + Alum 60 6.71 2.82 0.046 -32.9 2.2 -29.019/07/2010 HI 705 PACl 20 7.95 4.13 0.086 5.7 -16.8 -10.719/07/2010 HI 705 PACl 30 7.88 3.62 0.071 -2.2 1.7 -8.119/07/2010 HI 705 PACl 40 7.84 3.25 0.061 -23.4 2.7 -16.619/07/2010 HI 705 PACl 50 7.85 3.15 0.056 -16.1 -9.0 -35.819/07/2010 HI 705 PACl 60 7.80 2.80 0.046 -37.3 -6.0 -35.619/07/2010 HI 705 PACl 70 7.75 2.61 0.043 -39.7 0.0 -35.119/07/2010 HI 1000 PACl 20 7.59 5.31 0.114 37.8 -13.8 -4.919/07/2010 HI 1000 PACl 30 7.55 4.87 0.102 31.9 -5.5 -0.419/07/2010 HI 1000 PACl 40 7.42 4.53 0.090 17.1 -8.9 -13.619/07/2010 HI 1000 PACl 50 7.35 4.26 0.081 9.1 -12.5 -32.919/07/2010 HI 1000 PACl 60 7.28 4.02 0.074 -3.9 -2.2 -13.119/07/2010 HI 1000 PACl 70 7.25 3.62 0.066 -7.9 -6.5 -31.427/07/2010 Alum 20 7.48 4.92 0.113 -5.5 3.1 -13.427/07/2010 Alum 30 7.42 4.50 0.099 21.3 -4.4 -3.427/07/2010 Alum 43 7.20 3.63 0.072 -3.1 3.1 -18.127/07/2010 Alum 50 7.15 3.52 0.068 36.6 -10.9 -23.127/07/2010 Alum 60 7.04 2.98 0.054 -11.6 -5.9 -31.127/07/2010 Alum 70 6.95 2.92 0.051 -30.7 -0.5 -11.527/07/2010 Acid + Alum 20 6.81 4.52 0.097 39.2 1.1 8.427/07/2010 Acid + Alum 30 6.87 4.00 0.088 30.3 2.6 18.927/07/2010 Acid + Alum 43 6.85 3.48 0.061 -0.6 15.2 7.427/07/2010 Acid + Alum 50 6.84 2.93 0.055 -5.8 12.7 2.227/07/2010 Acid + Alum 60 6.93 2.96 0.050 -20.6 0.7 -22.127/07/2010 Acid + Alum 70 6.89 2.76 0.047 -23.2 13.4 2.4
TOC (mg/L)
UV254
(cm-1)FEEM PCA ScoreDate
(dd/mm/yyyy) Coagulant Dosage (mg/L) pH
110
Table 8.3: pH, TOC, UV254, and fluorescence excitation-emission data for bench scale tests post-filter water (continued)
HS PM CPM27/07/2010 HI 705 PACl 20 7.91 4.33 0.093 30.2 -10.1 10.527/07/2010 HI 705 PACl 30 7.85 3.91 0.074 8.0 -2.1 -8.327/07/2010 HI 705 PACl 40 7.80 3.65 0.067 -3.2 -8.2 -21.527/07/2010 HI 705 PACl 50 7.77 3.10 0.058 -13.7 6.1 -13.727/07/2010 HI 705 PACl 60 7.72 2.87 0.050 -22.1 -6.3 -29.327/07/2010 HI 705 PACl 70 7.68 2.81 0.046 -26.7 28.7 -31.127/07/2010 HI 1000 PACl 20 7.66 5.34 0.124 49.2 -6.4 0.627/07/2010 HI 1000 PACl 30 7.56 4.87 0.108 35.7 3.6 10.927/07/2010 HI 1000 PACl 40 7.45 4.38 0.094 26.3 1.6 8.527/07/2010 HI 1000 PACl 50 7.39 3.95 0.086 14.9 -1.7 -11.527/07/2010 HI 1000 PACl 60 7.32 3.70 0.078 11.0 -7.9 -16.527/07/2010 HI 1000 PACl 70 7.23 3.36 0.067 -4.5 4.1 -18.204/08/2010 Alum 20 7.57 4.90 0.122 45.4 8.6 21.704/08/2010 Alum 30 7.48 4.27 0.103 30.3 24.3 36.104/08/2010 Alum 40 7.43 3.87 0.088 15.5 17.4 18.304/08/2010 Alum 47 7.38 3.58 0.082 9.6 7.5 -0.304/08/2010 Alum 60 7.28 3.15 0.067 -6.4 4.5 -8.104/08/2010 Acid + Alum 20 6.76 4.5 0.095 24.0 -1.8 36.104/08/2010 Acid + Alum 30 6.76 3.8 0.070 1.8 26.1 32.604/08/2010 Acid + Alum 40 6.77 3.6 0.063 -17.3 7.7 22.604/08/2010 Acid + Alum 47 6.76 3.4 0.059 -3.5 22.6 11.404/08/2010 Acid + Alum 60 6.78 3.0 0.049 -24.0 13.0 -1.404/08/2010 Acid + Alum 70 6.86 2.7 0.049 -16.8 16.2 -2.604/08/2010 HI 705 PACl 20 8.03 4.3 0.088 5.2 -4.5 20.304/08/2010 HI 705 PACl 30 7.96 3.9 0.076 -11.3 11.0 -5.704/08/2010 HI 705 PACl 40 7.93 3.5 0.067 -25.9 3.6 -10.504/08/2010 HI 705 PACl 50 7.89 3.1 0.053 -16.0 2.7 -23.904/08/2010 HI 705 PACl 60 7.83 2.9 0.047 -32.3 -4.3 -29.004/08/2010 HI 705 PACl 70 7.79 2.5 0.042 -36.9 15.5 -36.004/08/2010 HI 1000 PACl 20 7.78 5.5 0.113 41.5 19.1 29.704/08/2010 HI 1000 PACl 30 7.73 4.6 0.097 33.0 27.3 41.604/08/2010 HI 1000 PACl 40 7.58 4.4 0.086 27.6 -1.6 13.004/08/2010 HI 1000 PACl 50 7.47 4.0 0.076 6.1 16.6 9.404/08/2010 HI 1000 PACl 60 7.35 3.8 0.070 0.5 4.9 0.904/08/2010 HI 1000 PACl 70 7.28 3.8 0.062 -13.0 12.7 0.709/08/2010 Alum 20 7.5 4.8 0.12 55.2 -3.9 26.409/08/2010 Alum 30 7.4 4.4 0.10 34.5 9.7 24.809/08/2010 Alum 40 7.3 4.0 0.09 21.6 17.1 22.109/08/2010 Alum 48 7.2 3.7 0.09 11.4 0.7 11.909/08/2010 Alum 60 7.2 3.2 0.07 3.0 13.3 10.409/08/2010 Alum 70 7.0 2.6 0.06 -18.5 21.9 5.309/08/2010 Acid + Alum 20 7.0 4.4 0.10 29.9 18.0 22.409/08/2010 Acid + Alum 30 7.0 4.0 0.09 20.0 27.9 38.809/08/2010 Acid + Alum 40 6.9 3.7 0.08 12.3 24.5 24.009/08/2010 Acid + Alum 48 6.9 3.1 0.07 5.5 20.2 13.909/08/2010 Acid + Alum 60 6.9 3.0 0.06 -9.4 15.1 7.409/08/2010 Acid + Alum 70 6.9 2.8 0.06 -23.1 8.1 -5.7
Date (dd/mm/yyyy) Coagulant Dosage
(mg/L) pH TOC (mg/L)
UV254
(cm-1)FEEM PCA Score
111
Table 8.3: pH, TOC, UV254, and fluorescence excitation-emission data for bench scale tests post-filter water (continued)
HS PM CPM09/08/2010 HI 705 PACl 20 7.9 4.2 0.09 27.2 7.5 27.809/08/2010 HI 705 PACl 30 7.9 3.7 0.08 12.3 25.9 23.509/08/2010 HI 705 PACl 40 7.9 3.5 0.07 7.5 24.5 16.609/08/2010 HI 705 PACl 50 7.8 3.1 0.06 -13.3 1.7 11.409/08/2010 HI 705 PACl 60 7.8 2.9 0.06 -30.7 0.0 8.809/08/2010 HI 705 PACl 70 7.8 2.7 0.05 -34.4 17.5 2.509/08/2010 HI 1000 PACl 20 7.7 5.2 0.12 43.2 4.3 28.509/08/2010 HI 1000 PACl 30 7.6 5.0 0.11 44.6 1.2 18.109/08/2010 HI 1000 PACl 40 7.6 4.4 0.10 30.4 6.6 19.909/08/2010 HI 1000 PACl 50 7.4 4.2 0.09 18.3 -1.2 11.709/08/2010 HI 1000 PACl 60 7.4 3.8 0.08 10.3 3.5 0.509/08/2010 HI 1000 PACl 70 7.3 3.5 0.08 -0.7 10.9 6.017/08/2010 Alum 20 7.44 4.7 0.106 43.7 19.8 28.317/08/2010 Alum 30 7.36 4.4 0.088 13.7 27.8 25.117/08/2010 Alum 40 7.27 3.7 0.076 -2.8 28.7 20.717/08/2010 Alum 45 7.21 3.6 0.068 -10.5 18.2 10.417/08/2010 Alum 50 7.16 3.1 0.062 -8.3 5.7 -7.517/08/2010 Alum 60 7.05 3.1 0.059 -18.7 24.1 -7.917/08/2010 Acid + Alum 20 7.02 4.2 0.091 23.3 21.3 22.917/08/2010 Acid + Alum 30 7.17 4.0 0.080 15.4 31.1 34.517/08/2010 Acid + Alum 40 7.15 3.4 0.068 2.9 29.8 21.617/08/2010 Acid + Alum 45 7.12 3.2 0.065 3.1 31.7 16.217/08/2010 Acid + Alum 50 7.07 3.2 0.060 -1.6 3.5 10.217/08/2010 Acid + Alum 60 6.99 2.9 0.058 -16.3 21.7 8.517/08/2010 HI 705 PACl 20 7.72 4.2 0.093 24.2 7.3 18.617/08/2010 HI 705 PACl 30 7.04 3.8 0.081 14.5 30.3 12.417/08/2010 HI 705 PACl 40 7.57 3.3 0.067 -6.8 18.5 9.917/08/2010 HI 705 PACl 50 7.41 3.2 0.059 -6.3 24.7 4.017/08/2010 HI 705 PACl 60 7.36 2.9 0.052 -18.2 22.3 2.117/08/2010 HI 705 PACl 70 7.29 2.6 0.048 -23.6 29.9 1.917/08/2010 HI 1000 PACl 20 7.91 5.4 0.118 35.4 -0.7 8.317/08/2010 HI 1000 PACl 30 7.89 4.9 0.107 23.1 4.4 5.417/08/2010 HI 1000 PACl 40 7.87 4.4 0.096 19.4 5.3 4.417/08/2010 HI 1000 PACl 50 7.85 4.1 0.084 2.7 0.9 -11.317/08/2010 HI 1000 PACl 60 7.77 3.8 0.077 2.3 1.5 -14.917/08/2010 HI 1000 PACl 70 7.73 3.5 0.070 -6.8 8.3 -14.4
Date (dd/mm/yyyy) Coagulant Dosage
(mg/L) pH TOC (mg/L)
UV254
(cm-1)FEEM PCA Score
8.2.2 DBP Formation Potential (DBPFP)
The 24-hour DBP concentrations measured in bench scale tests are presented in Table 8.4
and Table 8.5 below.
112
Table 8.4: THM, TCAN, and TCP concentrations for 24-hour DBPFP tests
TCM BDCM DBCM TBM7/19/2010 Alum 20 84.1 8.3 14.3 < 0.20 17.1 3.27/19/2010 Alum 30 85.2 8.9 14.8 < 0.20 18.9 4.57/19/2010 Alum 42 73.7 8.0 14.4 < 0.20 17.9 4.57/19/2010 Alum 50 67.0 8.0 14.5 < 0.20 18.1 5.57/19/2010 Alum 60 45.9 4.2 3.9 < 0.20 11.1 3.67/19/2010 Alum 70 47.3 4.9 4.2 < 0.20 13.7 6.07/19/2010 Acid + Alum 10 69.7 4.7 2.2 < 0.20 15.7 7.37/19/2010 Acid + Alum 20 50.6 3.6 1.9 < 0.20 11.7 5.37/19/2010 Acid + Alum 30 53.4 4.3 1.9 < 0.20 11.6 4.67/19/2010 Acid + Alum 42 46.0 4.0 1.9 < 0.20 10.8 4.47/19/2010 Acid + Alum 50 44.4 4.0 2.1 < 0.20 10.5 4.47/19/2010 Acid + Alum 60 39.7 3.5 1.9 < 0.20 9.8 4.07/19/2010 HI 705 PACl 20 100.7 6.9 2.3 < 0.20 12.0 0.37/19/2010 HI 705 PACl 30 80.2 5.9 2.2 < 0.20 10.4 0.47/19/2010 HI 705 PACl 40 62.6 5.0 2.2 < 0.20 9.6 0.47/19/2010 HI 705 PACl 50 55.7 4.7 2.1 < 0.20 8.1 0.57/19/2010 HI 705 PACl 60 55.5 5.1 2.1 < 0.20 7.2 0.57/19/2010 HI 705 PACl 70 41.8 3.8 2.2 < 0.20 6.5 0.57/19/2010 HI 1000 PACl 20 98.2 5.8 2.1 < 0.20 12.9 1.07/19/2010 HI 1000 PACl 30 72.5 4.5 2.0 < 0.20 11.5 0.77/19/2010 HI 1000 PACl 40 64.0 4.4 1.9 < 0.20 11.7 1.57/19/2010 HI 1000 PACl 50 61.4 4.5 2.2 < 0.20 11.0 1.47/19/2010 HI 1000 PACl 60 56.8 4.4 2.3 < 0.20 11.7 2.27/19/2010 HI 1000 PACl 70 61.3 5.2 2.3 < 0.20 11.3 1.87/27/2010 Alum 20 100.6 6.3 0.2 < 0.20 12.2 1.77/27/2010 Alum 30 93.6 6.4 0.2 < 0.20 11.5 2.07/27/2010 Alum 43 70.8 5.6 0.2 < 0.20 10.0 2.57/27/2010 Alum 50 61.9 5.0 0.2 < 0.20 9.9 2.57/27/2010 Alum 60 50.8 4.6 0.2 < 0.20 7.9 1.67/27/2010 Alum 70 38.9 3.7 0.2 < 0.20 7.6 2.47/27/2010 Acid + Alum 20 53.1 3.3 2.6 < 0.20 11.2 6.27/27/2010 Acid + Alum 30 44.7 3.0 2.6 < 0.20 9.6 5.17/27/2010 Acid + Alum 43 51.9 4.2 2.8 < 0.20 9.6 5.87/27/2010 Acid + Alum 50 50.4 4.3 2.7 < 0.20 8.3 4.37/27/2010 Acid + Alum 60 46.7 4.1 2.8 < 0.20 8.5 4.97/27/2010 Acid + Alum 70 42.7 3.8 2.7 < 0.20 7.9 4.57/27/2010 HI 705 PACl 20 60.3 4.6 2.8 < 0.20 9.1 0.77/27/2010 HI 705 PACl 30 68.8 5.9 2.9 < 0.20 9.2 0.87/27/2010 HI 705 PACl 40 56.5 5.2 2.9 < 0.20 8.3 0.97/27/2010 HI 705 PACl 50 41.2 3.9 3.0 < 0.20 7.7 0.97/27/2010 HI 705 PACl 60 44.7 4.5 2.9 < 0.20 7.1 0.97/27/2010 HI 705 PACl 70 36.0 3.2 2.7 < 0.20 7.9 1.1
TCAN TCPDate (dd/mm/yyyy) Coagulant Dosage
(mg/L)Trihalomethanes
“< #” indicates less than method detection limit
113
Table 8.4: THM, TCAN, and TCP concentrations for 24-hour DBPFP tests (continued)
TCM BDCM DBCM TBM7/27/2010 HI 1000 PACl 20 73.2 5.2 2.7 < 0.20 11.7 2.07/27/2010 HI 1000 PACl 30 80.1 5.9 2.8 < 0.20 11.2 2.27/27/2010 HI 1000 PACl 40 66.4 5.3 2.8 < 0.20 10.9 3.17/27/2010 HI 1000 PACl 50 62.7 5.2 2.9 < 0.20 10.4 2.87/27/2010 HI 1000 PACl 60 64.2 5.8 3.0 < 0.20 10.5 3.87/27/2010 HI 1000 PACl 70 48.2 4.5 2.9 < 0.20 9.2 3.28/4/2010 Alum 20 151.8 6.8 0.2 < 0.20 10.2 NA8/4/2010 Alum 30 123.0 5.5 0.1 < 0.20 9.7 NA8/4/2010 Alum 40 114.0 5.5 0.2 < 0.20 9.5 NA8/4/2010 Alum 47 143.3 7.3 0.3 < 0.20 9.5 NA8/4/2010 Alum 60 123.0 7.0 0.3 < 0.20 9.1 NA8/4/2010 Acid + Alum 20 57.7 4.1 0.1 < 0.20 10.8 NA8/4/2010 Acid + Alum 30 55.6 4.9 0.2 < 0.20 12.2 NA8/4/2010 Acid + Alum 40 49.5 4.5 0.1 < 0.20 11.8 NA8/4/2010 Acid + Alum 47 54.0 5.3 0.3 < 0.20 9.8 NA8/4/2010 Acid + Alum 60 33.5 3.4 0.1 < 0.20 9.3 NA8/4/2010 Acid + Alum 70 34.1 3.5 0.1 < 0.20 8.4 NA8/4/2010 HI 705 PACl 20 116.6 5.4 0.2 < 0.20 8.2 NA8/4/2010 HI 705 PACl 30 117.4 5.8 0.3 < 0.20 7.8 NA8/4/2010 HI 705 PACl 40 120.2 5.7 0.3 < 0.20 6.9 NA8/4/2010 HI 705 PACl 50 109.1 5.3 0.2 < 0.20 8.2 NA8/4/2010 HI 705 PACl 60 103.3 5.3 0.3 < 0.20 6.1 NA8/4/2010 HI 705 PACl 70 118.5 6.3 0.3 < 0.20 5.9 NA8/4/2010 HI 1000 PACl 20 159.1 6.6 0.2 < 0.20 10.3 NA8/4/2010 HI 1000 PACl 30 161.2 6.5 0.2 < 0.20 9.1 NA8/4/2010 HI 1000 PACl 40 97.4 4.6 0.1 < 0.20 8.9 NA8/4/2010 HI 1000 PACl 50 110.0 5.7 0.2 < 0.20 9.4 NA8/4/2010 HI 1000 PACl 60 86.2 4.8 0.2 < 0.20 9.1 NA8/4/2010 HI 1000 PACl 70 58.4 3.7 0.2 < 0.20 8.7 NA8/9/2010 Alum 20 95.1 5.8 0.1 < 0.20 12.6 NA8/9/2010 Alum 30 61.5 3.5 0.0 < 0.20 11.0 NA8/9/2010 Alum 40 46.2 3.3 0.1 < 0.20 10.2 NA8/9/2010 Alum 48 62.5 5.1 0.2 < 0.20 10.0 NA8/9/2010 Alum 60 36.0 2.9 0.1 < 0.20 9.0 NA8/9/2010 Alum 70 39.9 3.6 0.2 < 0.20 8.7 NA8/9/2010 Acid + Alum 20 83.0 6.1 3.7 1.3 15.7 NA8/9/2010 Acid + Alum 30 77.9 6.0 3.7 1.2 14.4 NA8/9/2010 Acid + Alum 40 77.7 6.2 3.7 1.3 13.8 NA8/9/2010 Acid + Alum 48 71.5 5.8 3.6 1.1 13.1 NA8/9/2010 Acid + Alum 60 81.7 7.1 3.8 1.3 13.4 NA8/9/2010 Acid + Alum 70 77.5 7.1 3.9 1.5 14.0 NA
TCAN TCPDate (dd/mm/yyyy) Coagulant Dosage
(mg/L)Trihalomethanes
“< #” indicates less than method detection limit NA = Not Available
114
Table 8.4: THM, TCAN, and TCP concentrations for 24-hour DBPFP tests (continued)
TCM BDCM DBCM TBM8/9/2010 HI 705 PACl 20 160.0 8.8 5.8 2.0 13.5 NA8/9/2010 HI 705 PACl 30 123.7 6.2 5.7 2.0 11.2 NA8/9/2010 HI 705 PACl 40 129.1 6.7 5.9 2.1 10.8 NA8/9/2010 HI 705 PACl 50 117.6 6.3 5.6 2.0 10.0 NA8/9/2010 HI 705 PACl 60 121.9 6.7 6.0 2.2 10.4 NA8/9/2010 HI 705 PACl 70 148.0 9.1 6.0 2.2 10.6 NA8/9/2010 HI 1000 PACl 20 163.7 9.0 5.9 2.0 15.1 NA8/9/2010 HI 1000 PACl 30 153.6 8.4 5.9 2.1 14.6 NA8/9/2010 HI 1000 PACl 40 166.5 9.6 6.0 2.2 14.5 NA8/9/2010 HI 1000 PACl 50 157.8 9.2 6.0 2.0 13.8 NA8/9/2010 HI 1000 PACl 60 137.5 7.6 5.7 2.1 11.2 NA8/9/2010 HI 1000 PACl 70 103.8 7.0 4.0 1.5 11.6 NA
8/17/2010 Alum 20 88.4 7.9 10.2 < 0.20 15.6 NA8/17/2010 Alum 30 73.0 7.3 10.5 < 0.20 15.1 NA8/17/2010 Alum 40 60.6 6.8 10.3 < 0.20 14.4 NA8/17/2010 Alum 45 70.2 8.2 10.6 < 0.20 16.7 NA8/17/2010 Alum 50 53.1 6.8 10.6 < 0.20 13.4 NA8/17/2010 Alum 60 53.4 7.4 11.5 < 0.20 14.5 NA8/17/2010 Acid + Alum 20 60.7 4.0 2.0 < 0.20 14.6 NA8/17/2010 Acid + Alum 30 62.0 4.6 1.9 < 0.20 13.2 NA8/17/2010 Acid + Alum 40 44.2 3.5 2.0 < 0.20 11.2 NA8/17/2010 Acid + Alum 45 41.5 3.6 2.4 < 0.20 11.0 NA8/17/2010 Acid + Alum 50 38.5 3.2 1.8 < 0.20 10.1 NA8/17/2010 Acid + Alum 60 32.2 2.7 1.8 < 0.20 9.3 NA8/17/2010 HI 705 PACl 20 72.2 7.6 11.0 < 0.20 12.7 NA8/17/2010 HI 705 PACl 30 75.4 8.5 11.2 < 0.20 12.8 NA8/17/2010 HI 705 PACl 40 65.4 7.8 11.6 < 0.20 12.9 NA8/17/2010 HI 705 PACl 50 56.2 7.2 11.1 < 0.20 11.2 NA8/17/2010 HI 705 PACl 60 55.6 7.5 11.4 < 0.20 12.9 NA8/17/2010 HI 705 PACl 70 50.7 7.0 11.1 < 0.20 10.9 NA8/17/2010 HI 1000 PACl 20 90.6 8.7 11.5 < 0.20 16.1 NA8/17/2010 HI 1000 PACl 30 80.1 8.2 11.5 < 0.20 15.3 NA8/17/2010 HI 1000 PACl 40 71.2 8.0 11.2 < 0.20 15.0 NA8/17/2010 HI 1000 PACl 50 70.2 8.0 11.7 < 0.20 15.9 NA8/17/2010 HI 1000 PACl 60 78.1 9.4 12.3 < 0.20 18.7 NA8/17/2010 HI 1000 PACl 70 61.6 7.6 11.4 < 0.20 15.4 NA
TCAN TCPDate (dd/mm/yyyy) Coagulant Dosage
(mg/L)Trihalomethanes
“< #” indicates less than method detection limit NA = Not Available
115
Table 8.5: HAA concentrations for 24-hour DBPFP tests
MCAA MBAA DCAA TCAA BCAA DBAA BDCAA DBCAA TBAA7/19/2010 Alum 20 < 0.09 < 0.05 31.3 37.8 < 0.10 0.6 1.1 < 0.45 < 0.757/19/2010 Alum 30 < 0.09 < 0.05 26.8 32.9 < 0.10 0.6 1.1 < 0.45 < 0.757/19/2010 Alum 42 < 0.09 < 0.05 23.8 31.2 < 0.10 0.6 1.3 < 0.45 < 0.757/19/2010 Alum 50 < 0.09 < 0.05 20.7 27.9 < 0.10 0.4 1.3 < 0.45 < 0.757/19/2010 Alum 60 < 0.09 < 0.05 14.3 20.3 < 0.10 0.3 1.1 < 0.45 < 0.757/19/2010 Alum 70 < 0.09 < 0.05 12.8 19.7 < 0.10 0.7 1.6 < 0.45 < 0.757/19/2010 Acid + Alum 10 < 0.09 < 0.05 7.4 17.3 < 0.10 < 0.17 0.1 < 0.45 < 0.757/19/2010 Acid + Alum 20 < 0.09 < 0.05 7.6 18.0 < 0.10 < 0.17 0.5 < 0.45 < 0.757/19/2010 Acid + Alum 30 < 0.09 < 0.05 8.6 17.1 < 0.10 < 0.17 0.7 < 0.45 < 0.757/19/2010 Acid + Alum 42 < 0.09 < 0.05 3.1 8.6 < 0.10 < 0.17 0.1 < 0.45 < 0.757/19/2010 Acid + Alum 50 < 0.09 < 0.05 3.5 9.4 < 0.10 < 0.17 0.2 < 0.45 < 0.757/19/2010 Acid + Alum 60 < 0.09 < 0.05 1.0 6.1 < 0.10 < 0.17 0.0 < 0.45 < 0.757/19/2010 HI 705 PACl 20 < 0.09 < 0.05 23.9 25.8 < 0.10 0.9 0.9 < 0.45 < 0.757/19/2010 HI 705 PACl 30 < 0.09 < 0.05 18.8 22.5 < 0.10 0.8 1.0 < 0.45 < 0.757/19/2010 HI 705 PACl 40 < 0.09 < 0.05 15.4 19.5 < 0.10 0.9 1.0 < 0.45 < 0.757/19/2010 HI 705 PACl 50 < 0.09 < 0.05 13.7 15.7 < 0.10 0.8 0.9 < 0.45 < 0.757/19/2010 HI 705 PACl 60 < 0.09 < 0.05 11.1 13.6 < 0.10 0.8 0.8 < 0.45 < 0.757/19/2010 HI 705 PACl 70 < 0.09 < 0.05 0.0 0.0 < 0.10 < 0.17 < 0.20 < 0.45 < 0.757/19/2010 HI 1000 PACl 20 1.1 < 0.05 33.7 37.3 0.1 1.5 1.3 < 0.45 < 0.757/19/2010 HI 1000 PACl 30 0.4 < 0.05 33.7 37.4 0.1 1.5 1.5 < 0.45 < 0.757/19/2010 HI 1000 PACl 40 0.5 < 0.05 25.8 32.8 < 0.10 1.2 1.5 < 0.45 < 0.757/19/2010 HI 1000 PACl 50 0.2 < 0.05 23.4 28.4 < 0.10 1.2 1.4 < 0.45 < 0.757/19/2010 HI 1000 PACl 60 < 0.09 < 0.05 19.0 26.6 < 0.10 0.9 1.4 < 0.45 < 0.757/19/2010 HI 1000 PACl 70 < 0.09 < 0.05 17.5 23.8 < 0.10 0.9 1.4 < 0.45 < 0.757/27/2010 Alum 20 < 0.09 < 0.05 25.5 30.8 < 0.10 0.8 0.6 < 0.45 < 0.757/27/2010 Alum 30 < 0.09 < 0.05 22.8 30.4 < 0.10 0.8 1.0 < 0.45 < 0.757/27/2010 Alum 43 < 0.09 < 0.05 17.8 26.5 < 0.10 0.7 1.4 < 0.45 < 0.757/27/2010 Alum 50 < 0.09 < 0.05 15.2 23.2 < 0.10 0.6 1.2 < 0.45 < 0.757/27/2010 Alum 60 < 0.09 < 0.05 12.0 19.4 < 0.10 0.7 1.3 < 0.45 < 0.757/27/2010 Alum 70 < 0.09 < 0.05 11.1 19.1 < 0.10 0.2 1.5 < 0.45 < 0.757/27/2010 Acid + Alum 20 < 0.09 < 0.05 16.9 29.6 < 0.10 < 0.17 0.6 < 0.45 < 0.757/27/2010 Acid + Alum 30 < 0.09 < 0.05 10.5 20.2 < 0.10 < 0.17 0.8 < 0.45 < 0.757/27/2010 Acid + Alum 43 < 0.09 < 0.05 2.5 9.2 < 0.10 < 0.17 0.1 < 0.45 < 0.757/27/2010 Acid + Alum 50 < 0.09 < 0.05 3.6 10.0 < 0.10 < 0.17 0.3 < 0.45 < 0.757/27/2010 Acid + Alum 60 < 0.09 < 0.05 6.7 13.4 < 0.10 < 0.17 0.7 < 0.45 < 0.757/27/2010 Acid + Alum 70 < 0.09 < 0.05 7.7 13.7 < 0.10 < 0.17 0.6 < 0.45 < 0.757/27/2010 HI 705 PACl 20 < 0.09 < 0.05 21.2 26.7 < 0.10 0.9 0.8 < 0.45 < 0.757/27/2010 HI 705 PACl 30 < 0.09 < 0.05 18.3 23.6 < 0.10 0.9 1.0 < 0.45 < 0.757/27/2010 HI 705 PACl 40 < 0.09 < 0.05 14.8 20.3 < 0.10 0.8 1.0 < 0.45 < 0.757/27/2010 HI 705 PACl 50 < 0.09 < 0.05 11.6 17.2 < 0.10 0.6 1.0 < 0.45 < 0.757/27/2010 HI 705 PACl 60 < 0.09 < 0.05 11.1 16.2 < 0.10 0.7 1.2 < 0.45 < 0.757/27/2010 HI 705 PACl 70 < 0.09 < 0.05 8.2 15.8 < 0.10 0.4 0.7 < 0.45 < 0.75
Date (dd/mm/yyyy)
Haloacetic AcidsCoagulant Dose (mg/L)
“< #” indicates less than method detection limit
116
Table 8.5: HAA concentrations for 24-hour DBPFP tests (continued)
MCAA MBAA DCAA TCAA BCAA DBAA BDCAA DBCAA TBAA7/27/2010 HI 1000 PACl 20 < 0.09 0.3 30.4 38.2 < 0.10 1.1 1.1 < 0.45 < 0.757/27/2010 HI 1000 PACl 30 < 0.09 < 0.05 26.2 34.9 < 0.10 1.0 1.4 < 0.45 < 0.757/27/2010 HI 1000 PACl 40 < 0.09 0.5 22.5 32.3 < 0.10 0.8 1.5 < 0.45 < 0.757/27/2010 HI 1000 PACl 50 < 0.09 < 0.05 20.1 28.8 < 0.10 0.7 1.4 < 0.45 < 0.757/27/2010 HI 1000 PACl 60 < 0.09 < 0.05 19.5 30.9 < 0.10 0.8 2.0 < 0.45 < 0.757/27/2010 HI 1000 PACl 70 < 0.09 < 0.05 17.1 26.1 < 0.10 0.6 1.8 < 0.45 < 0.758/4/2010 Alum 20 7.0 < 0.05 1.7 32.7 < 0.10 0.9 < 0.20 < 0.45 < 0.758/4/2010 Alum 30 8.6 < 0.05 1.7 35.4 < 0.10 1.1 < 0.20 < 0.45 < 0.758/4/2010 Alum 40 7.3 < 0.05 1.7 28.8 < 0.10 0.9 < 0.20 < 0.45 < 0.758/4/2010 Alum 47 6.7 < 0.05 1.7 25.5 < 0.10 0.8 < 0.20 < 0.45 < 0.758/4/2010 Alum 60 6.7 < 0.05 1.7 24.8 < 0.10 0.8 < 0.20 < 0.45 < 0.758/4/2010 Acid + Alum 20 2.4 0.8 12.9 35.0 1.9 0.9 < 0.20 < 0.45 < 0.758/4/2010 Acid + Alum 30 2.2 1.1 11.2 33.2 1.9 0.9 < 0.20 < 0.45 < 0.758/4/2010 Acid + Alum 40 1.9 1.0 6.8 24.3 1.5 1.1 < 0.20 < 0.45 < 0.758/4/2010 Acid + Alum 47 1.8 1.0 6.9 22.6 1.5 1.0 < 0.20 < 0.45 < 0.758/4/2010 Acid + Alum 60 1.8 1.0 6.5 20.5 1.4 0.7 < 0.20 < 0.45 < 0.758/4/2010 Acid + Alum 70 1.9 1.3 4.5 21.1 1.6 1.2 < 0.20 < 0.45 < 0.758/4/2010 HI 705 PACl 20 4.5 0.7 12.7 20.7 1.4 1.4 < 0.20 < 0.45 < 0.758/4/2010 HI 705 PACl 30 5.4 0.8 12.8 22.7 1.6 1.7 < 0.20 < 0.45 < 0.758/4/2010 HI 705 PACl 40 5.1 0.8 8.6 18.8 1.3 1.2 < 0.20 < 0.45 < 0.758/4/2010 HI 705 PACl 50 6.4 0.8 8.0 18.3 1.3 1.5 < 0.20 < 0.45 < 0.758/4/2010 HI 705 PACl 60 6.1 0.9 5.5 15.5 1.2 1.3 < 0.20 < 0.45 < 0.758/4/2010 HI 705 PACl 70 6.0 1.0 4.8 13.8 1.1 1.2 < 0.20 < 0.45 < 0.758/4/2010 HI 1000 PACl 20 8.2 0.9 18.7 40.9 2.0 2.5 < 0.20 < 0.45 < 0.758/4/2010 HI 1000 PACl 30 8.9 0.9 17.7 41.6 2.1 2.7 < 0.20 < 0.45 < 0.758/4/2010 HI 1000 PACl 40 9.1 1.1 11.2 43.7 2.1 2.5 < 0.20 < 0.45 < 0.758/4/2010 HI 1000 PACl 50 7.6 1.1 8.2 36.3 1.8 2.2 < 0.20 < 0.45 < 0.758/4/2010 HI 1000 PACl 60 8.6 1.2 6.8 35.5 1.9 2.4 < 0.20 < 0.45 < 0.758/4/2010 HI 1000 PACl 70 8.1 1.7 6.2 41.1 2.3 2.4 < 0.20 < 0.45 < 0.758/9/2010 Alum 20 5.1 0.2 15.9 43.5 0.3 1.6 < 0.20 < 0.45 < 0.758/9/2010 Alum 30 4.9 0.8 12.0 45.7 0.7 1.5 < 0.20 < 0.45 < 0.758/9/2010 Alum 40 6.4 1.0 11.6 46.7 0.1 1.8 < 0.20 < 0.45 < 0.758/9/2010 Alum 48 4.9 0.9 9.2 42.5 < 0.10 1.4 < 0.20 < 0.45 < 0.758/9/2010 Alum 60 4.5 0.9 3.1 32.0 < 0.10 1.2 < 0.20 < 0.45 < 0.758/9/2010 Alum 70 6.5 0.7 2.9 23.4 < 0.10 1.1 < 0.20 < 0.45 < 0.758/9/2010 Acid + Alum 20 0.9 0.8 12.2 50.7 1.1 0.4 < 0.20 < 0.45 < 0.758/9/2010 Acid + Alum 30 1.7 0.9 11.1 44.5 1.0 0.4 < 0.20 < 0.45 < 0.758/9/2010 Acid + Alum 40 2.1 0.8 7.5 38.1 1.0 0.4 < 0.20 < 0.45 < 0.758/9/2010 Acid + Alum 48 1.4 1.0 4.1 32.1 < 0.10 0.3 < 0.20 < 0.45 < 0.758/9/2010 Acid + Alum 60 3.1 0.9 3.1 28.7 < 0.10 0.5 < 0.20 < 0.45 < 0.758/9/2010 Acid + Alum 70 3.1 0.8 1.7 25.0 < 0.10 0.2 < 0.20 < 0.45 < 0.75
Date (dd/mm/yyyy) Coagulant Dose
(mg/L)Haloacetic Acids
“< #” indicates less than method detection limit
117
Table 8.5: HAA concentrations for 24-hour DBPFP tests (continued)
MCAA MBAA DCAA TCAA BCAA DBAA BDCAA DBCAA TBAA8/9/2010 HI 705 PACl 20 6.2 0.1 18.0 40.8 0.7 1.2 < 0.20 < 0.45 < 0.758/9/2010 HI 705 PACl 30 7.2 0.3 12.0 37.2 0.7 1.2 < 0.20 < 0.45 < 0.758/9/2010 HI 705 PACl 40 7.9 0.9 12.9 41.0 1.3 1.7 < 0.20 < 0.45 < 0.758/9/2010 HI 705 PACl 50 7.5 0.9 7.6 31.3 0.4 1.2 < 0.20 < 0.45 < 0.758/9/2010 HI 705 PACl 60 7.6 0.8 4.6 26.3 < 0.10 0.9 < 0.20 < 0.45 < 0.758/9/2010 HI 705 PACl 70 7.7 0.9 4.6 24.1 < 0.10 1.0 < 0.20 < 0.45 < 0.758/9/2010 HI 1000 PACl 20 6.0 0.6 21.7 48.5 0.7 1.5 < 0.20 < 0.45 < 0.758/9/2010 HI 1000 PACl 30 6.2 0.7 16.7 45.0 < 0.10 1.5 < 0.20 < 0.45 < 0.758/9/2010 HI 1000 PACl 40 7.3 0.7 16.8 43.4 1.0 1.7 < 0.20 < 0.45 < 0.758/9/2010 HI 1000 PACl 50 6.6 0.7 11.3 37.5 0.5 1.5 < 0.20 < 0.45 < 0.758/9/2010 HI 1000 PACl 60 6.8 0.8 8.2 36.2 0.6 1.4 < 0.20 < 0.45 < 0.758/9/2010 HI 1000 PACl 70 6.1 0.8 5.4 34.7 0.4 0.9 < 0.20 < 0.45 < 0.758/17/2010 Alum 20 3.0 < 0.05 7.1 39.6 < 0.10 0.6 < 0.20 < 0.45 < 0.758/17/2010 Alum 30 2.7 < 0.05 7.6 42.7 0.1 0.2 < 0.20 < 0.45 < 0.758/17/2010 Alum 40 2.0 < 0.05 4.7 34.0 < 0.10 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 Alum 45 0.7 < 0.05 1.5 32.8 < 0.10 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 Alum 50 1.0 < 0.05 1.3 30.2 < 0.10 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 Alum 60 0.4 < 0.05 < 0.14 26.4 < 0.10 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 Acid + Alum 20 1.1 < 0.05 8.3 38.9 < 0.10 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 Acid + Alum 30 1.2 < 0.05 6.3 34.8 < 0.10 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 Acid + Alum 40 1.1 < 0.05 6.2 28.5 < 0.10 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 Acid + Alum 45 1.8 < 0.05 3.6 27.5 < 0.10 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 Acid + Alum 50 2.0 < 0.05 1.4 27.0 < 0.10 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 Acid + Alum 60 1.6 < 0.05 < 0.14 25.2 < 0.10 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 HI 705 PACl 20 7.3 < 0.05 16.0 34.3 0.3 0.3 < 0.20 < 0.45 < 0.758/17/2010 HI 705 PACl 30 6.3 < 0.05 11.0 29.3 < 0.10 0.2 < 0.20 < 0.45 < 0.758/17/2010 HI 705 PACl 40 7.3 < 0.05 5.9 26.0 < 0.10 0.1 < 0.20 < 0.45 < 0.758/17/2010 HI 705 PACl 50 6.8 < 0.05 5.0 23.2 < 0.10 0.1 < 0.20 < 0.45 < 0.758/17/2010 HI 705 PACl 60 6.1 < 0.05 2.8 20.0 < 0.10 0.1 < 0.20 < 0.45 < 0.758/17/2010 HI 705 PACl 70 5.4 < 0.05 1.6 17.2 < 0.10 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 HI 1000 PACl 20 4.4 < 0.05 17.8 40.5 < 0.10 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 HI 1000 PACl 30 5.1 < 0.05 16.9 41.1 0.4 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 HI 1000 PACl 40 5.6 < 0.05 15.5 37.5 0.2 0.1 < 0.20 < 0.45 < 0.758/17/2010 HI 1000 PACl 50 4.4 < 0.05 12.8 36.5 0.2 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 HI 1000 PACl 60 2.7 < 0.05 6.1 32.9 < 0.10 < 0.17 < 0.20 < 0.45 < 0.758/17/2010 HI 1000 PACl 70 2.8 < 0.05 3.6 30.3 < 0.10 < 0.17 < 0.20 < 0.45 < 0.75
Date (dd/mm/yyyy) Coagulant Dose
(mg/L)Haloacetic Acids
“< #” indicates less than method detection limit
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8.2.3 Winter Bench Scale Test Results
Table 8.6: pH, TOC, UV254, and fluorescence excitation-emission data for February bench scale tests post-filter water
08/02/2011 Alum 20 7.78 5.46 0.131 138.108/02/2011 Alum 30 7.77 4.43 0.091 113.808/02/2011 Alum 42 7.39 3.96 0.073 99.608/02/2011 Alum 50 7.26 3.59 0.063 88.308/02/2011 Alum 60 7.18 3.20 0.055 86.208/02/2011 Alum 70 7.14 3.11 0.052 82.608/02/2011 Acid + Alum 10 5.95 3.85 0.071 104.408/02/2011 Acid + Alum 20 5.64 3.25 0.058 91.108/02/2011 Acid + Alum 30 5.48 3.07 0.053 84.908/02/2011 Acid + Alum 42 5.19 3.07 0.054 85.508/02/2011 Acid + Alum 50 5.10 3.06 0.053 88.608/02/2011 Acid + Alum 60 4.92 3.31 0.062 94.608/02/2011 HI 705 PACl 20 8.04 4.33 0.089 118.308/02/2011 HI 705 PACl 30 8.03 3.97 0.075 107.608/02/2011 HI 705 PACl 40 7.95 3.57 0.063 96.908/02/2011 HI 705 PACl 50 7.93 3.19 0.053 85.208/02/2011 HI 705 PACl 60 7.86 2.90 0.045 76.308/02/2011 HI 705 PACl 70 7.77 2.75 0.043 71.008/02/2011 HI 1000 PACl 20 7.94 5.32 0.124 139.108/02/2011 HI 1000 PACl 30 7.89 4.97 0.107 126.908/02/2011 HI 1000 PACl 40 7.82 4.59 0.096 119.808/02/2011 HI 1000 PACl 50 7.66 4.39 0.091 115.308/02/2011 HI 1000 PACl 60 7.60 3.87 0.072 100.908/02/2011 HI 1000 PACl 70 7.41 3.52 0.065 92.7
Date (dd/mm/yyyy) Coagulant Dosage
(mg/L) pH TOC (mg/L)
UV254
(cm-1)Fluorescence Intensity (au)
Table 8.7: THM concentrations for February DBPFP tests
TCM BDCM DBCM TBM2/8/2011 Alum 20 87.9 12.6 < 0.01 < 0.202/8/2011 Alum 30 65.3 10.6 < 0.01 < 0.202/8/2011 Alum 42 43.6 8.1 < 0.01 < 0.202/8/2011 Alum 50 41.1 9.0 < 0.01 < 0.202/8/2011 Alum 60 28.3 6.3 < 0.01 < 0.202/8/2011 Alum 70 27.1 6.3 < 0.01 < 0.202/8/2011 Acid + Alum 10 30.2 7.7 < 0.01 < 0.202/8/2011 Acid + Alum 20 18.5 6.2 < 0.01 < 0.202/8/2011 Acid + Alum 30 15.7 5.2 < 0.01 < 0.202/8/2011 Acid + Alum 42 19.1 5.4 < 0.01 < 0.202/8/2011 Acid + Alum 50 13.0 5.1 < 0.01 < 0.202/8/2011 Acid + Alum 60 17.2 6.1 < 0.01 < 0.20
Coagulant Dosage (mg/L)
TrihalomethanesDate (dd/mm/yyyy)
“< #” indicates less than method detection limit
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Table 8.7: THM concentrations for February DBPFP tests (continued)
TCM BDCM DBCM TBM2/8/2011 HI 705 PACl 20 62.4 10.2 < 0.01 < 0.202/8/2011 HI 705 PACl 30 45.5 8.0 < 0.01 < 0.202/8/2011 HI 705 PACl 40 41.2 7.5 < 0.01 < 0.202/8/2011 HI 705 PACl 50 30.6 6.0 < 0.01 < 0.202/8/2011 HI 705 PACl 60 22.8 5.6 < 0.01 < 0.202/8/2011 HI 705 PACl 70 22.6 5.3 < 0.01 < 0.202/8/2011 HI 1000 PACl 20 61.2 10.6 < 0.01 < 0.202/8/2011 HI 1000 PACl 30 51.1 9.3 < 0.01 < 0.202/8/2011 HI 1000 PACl 40 48.5 8.7 < 0.01 < 0.202/8/2011 HI 1000 PACl 50 45.2 9.3 < 0.01 < 0.202/8/2011 HI 1000 PACl 60 33.5 6.9 < 0.01 < 0.202/8/2011 HI 1000 PACl 70 33.9 7.7 < 0.01 < 0.20
Date (dd/mm/yyyy) Coagulant Dosage
(mg/L)Trihalomethanes
“< #” indicates less than method detection limit
Table 8.8: HAA concentrations for February DBPFP tests
MCAA MBAA DCAA TCAA BCAA DBAA BDCAA DBCAA TBAA2/8/2011 Alum 20 2.0 < 0.05 37.9 50.1 0.4 0.3 0.9 < 0.45 < 0.752/8/2011 Alum 30 2.2 < 0.05 24.3 30.0 0.2 0.2 0.8 < 0.45 < 0.752/8/2011 Alum 42 NA NA NA NA NA NA NA NA NA2/8/2011 Alum 50 10.1 < 0.05 18.9 28.6 0.1 0.3 1.0 < 0.45 < 0.752/8/2011 Alum 60 2.6 < 0.05 13.4 21.0 0.1 0.1 0.8 < 0.45 < 0.752/8/2011 Alum 70 3.0 < 0.05 11.6 17.8 < 0.10 0.1 0.7 < 0.45 < 0.752/8/2011 Acid + Alum 10 2.7 < 0.05 18.5 30.7 0.1 < 0.17 1.0 < 0.45 < 0.752/8/2011 Acid + Alum 20 1.2 < 0.05 14.4 22.0 < 0.10 < 0.17 0.9 < 0.45 < 0.752/8/2011 Acid + Alum 30 5.2 < 0.05 12.4 19.3 0.1 < 0.17 0.9 < 0.45 < 0.752/8/2011 Acid + Alum 42 3.6 < 0.05 13.0 19.2 < 0.10 < 0.17 0.9 < 0.45 < 0.752/8/2011 Acid + Alum 50 4.1 < 0.05 11.7 17.9 < 0.10 < 0.17 0.8 < 0.45 < 0.752/8/2011 Acid + Alum 60 3.9 < 0.05 14.3 19.7 < 0.10 < 0.17 0.8 < 0.45 < 0.752/8/2011 HI 705 PACl 20 1.9 < 0.05 28.7 37.5 0.3 0.4 1.0 < 0.45 < 0.752/8/2011 HI 705 PACl 30 3.1 < 0.05 22.6 30.6 0.3 0.3 1.0 < 0.45 < 0.752/8/2011 HI 705 PACl 40 2.3 < 0.05 20.2 26.1 0.2 0.3 1.0 < 0.45 < 0.752/8/2011 HI 705 PACl 50 0.9 < 0.05 15.5 17.8 0.2 0.1 0.7 < 0.45 < 0.752/8/2011 HI 705 PACl 60 6.7 < 0.05 13.5 17.2 0.1 0.2 0.8 < 0.45 < 0.752/8/2011 HI 705 PACl 70 4.2 < 0.05 11.3 14.6 0.1 0.1 0.7 < 0.45 < 0.752/8/2011 HI 1000 PACl 20 1.7 < 0.05 39.5 56.1 0.5 0.3 1.2 < 0.45 < 0.752/8/2011 HI 1000 PACl 30 2.0 < 0.05 31.1 44.1 0.4 0.3 1.1 < 0.45 < 0.752/8/2011 HI 1000 PACl 40 3.1 < 0.05 25.8 39.2 0.4 0.3 1.1 < 0.45 < 0.752/8/2011 HI 1000 PACl 50 3.6 < 0.05 26.9 38.4 0.3 0.4 1.1 < 0.45 < 0.752/8/2011 HI 1000 PACl 60 2.6 < 0.05 19.5 31.2 0.3 0.2 1.2 < 0.45 < 0.752/8/2011 HI 1000 PACl 70 2.7 < 0.05 17.3 23.1 0.1 0.2 0.8 < 0.45 < 0.75
Coagulant Dosage (mg/L)
Haloacetic AcidsDate (dd/mm/yyyy)
“< #” indicates less than method detection limit
120
8.3 Artificial Neural Network Performance Parameters
Mean absolute error (MAE) and mean squared error are indicators of the difference
between ANN output predictions and the actual value of the output parameter being estimated.
The formula for MSE is:
n
XXMAE
n
iPii∑
=
−= 1
where Xi and XPi are the real and predicted model output values respectively, and n is the
number of data points used to test the model. The equation for MSE is similar:
( )
n
XXMSE
n
iPii∑
=
−= 1
2
8.4 ANN Development in Neurosolutions®
Neural networks were developed using NeuroSolutions® software version 5.07
(NeuroDimension Inc., Gainesville, FL). Using the Neural Builder wizard, users can specify the
architecture and learning parameters, which the software will use to build the ANN in a
breadboard window. The user can then manually modify the ANN components with the
properties inspector tool. The software automatically divides the data into sets for training,
cross-validation, and testing. This appendix serves as a step-by-step guide for the development
of the neural networks discussed in Chapter 5.
8.4.1 Neural Builder Wizard
Using the Neural Builder wizard, users can specify the type of ANN architecture, the
number of hidden layers and neurons, the type of transfer function, and the learning algorithm to
be used. The Neural Builder can be started by clicking on the “NBuilder” button in the main
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menu, which will open the wizard in a separate window (Figure 8.2), which will walk the user
through the necessary steps to create an ANN. The first step is to choose the architecture type.
Multilayer perceptron (MLP) is a feed-forward ANN arrangement that has been successfully
used to model water treatment processes (Lewin et al., 2004; Griffiths, 2010). Therefore, MLP
was chosen for all ANNs developed in this study.
Figure 8.2: Selection of ANN architecture using the Neural Builder tool in NeuroSolutions®
The second step is to select the data file to be used for training and testing (Figure 8.3).
Excel spreadsheets should be saved as .csv (comma delimited) files to be used by
NeuroSolutions®. The column headings for input and output parameters should all be listed in
the training data window (Figure 8.4) after the data file has been selected. (In order for the
parameter labels to correspond to the right data columns, headings should not include any
spaces.) All parameters are designated as inputs by default. This can be changed by selecting
the parameter and clicking on the appropriate button below. Output parameters are tagged as
“Desired”, and parameters which are not used as inputs or outputs (e.g. date) are tagged as
“Skip”. GA boxes next to each parameter should remain blank (unchecked). The data may be
randomly re-ordered by clicking on the “Randomize” button. For this study, randomization was
accomplished by adding an extra column to each data file with the Rand() function, which
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randomly generates a number between 0 and 1. The data rows were then sorted in order of these
random values.
Figure 8.3: Selection of training/testing data using the Neural Builder tool in NeuroSolutions®
Figure 8.4: Selection of input and output parameters using the Neural Builder tool in NeuroSolutions®
The third step is to divide the data separate sets for training, cross-validation, and testing.
Cross-validation data is used to test the model during training to make sure the ANN learns the
trends in the data without memorizing the training data itself. Testing data is used to evaluate
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ANN performance with data not seen by the neural network during the training process. In the
“Cross Val. & Test Data” window (Figure 8.5), select “Read from Existing File”. For the
percent of data for both CV and test, enter 20; the remaining data (60%) will be used for training.
Figure 8.5: Selecting how much data to use for cross-validation and testing using the Neural Builder tool in NeuroSolutions®
The fourth step is to configure the hidden layer(s). In this study, a single hidden layer
was used for all ANNs (Figure 8.6). Once this has been done done, the number of hidden
processing elements (PEs, or neurons), transfer function, learning rule, step size, and momentum
coefficient are selected (Figure 8.7). As a starting point, the number of PEs used was the number
of inputs multiplied by 0.75 (Bailey & Thompson, 1990). The “Tanh Axon” transfer function is
recommended when using ANNs to solve regression problems (NeuroDimension Inc., 2008).
The momentum learning rule was chosen with a step size of 0.5 and a momentum coefficient of
0.7; these parameters can be varied to improve model performance.
The fifth step is to configure the output layer by choosing the transfer function, learning
rule, step size, and momentum coefficient (Figure 8.8). The “Bias Axon” was used instead of the
“Tanh Axon” since it is a linear function. The other configuration options were selected to be
the same as the hidden layer.
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Figure 8.6: Specifying the number of hidden layers using the Neural Builder tool in NeuroSolutions®
Figure 8.7: Configuring the hidden layer using the Neural Builder tool in NeuroSolutions®
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Figure 8.8: Configuring the output layer using the Neural Builder tool in NeuroSolutions®
The sixth step is to check the options for the Supervised Learning Control window
(Figure 8.9). For this study, none of these settings were modified from their defaults.
“Maximum Epochs” specifies the maximum number of times the training data may be processed
through the ANN before terminating the training process. The criterion for termination of
training is when there is an increase in the mean squared error (MSE) of the cross-validation
data. Having the “Load Best on Test” box checked ensures that program saves the optimal
combination of connection weight values (to be used for testing), which occurs when the cross-
validation error is minimized. The weight update option is set to “Batch” so that the weights will
be modified only after the entire training data set is processed by the ANN, and not after each
exemplar.
The final step in the Neural Builder wizard is Probe Configuration (Figure 8.10), which
determines how the training and testing processes will be viewed by the user. For this study, the
default settings were used. Checking the “General” box will automatically provide performance
parameters during training and testing. Clicking on “Build” will generate a new ANN based on
all of the specifications entered into the Neural Builder wizard.
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Figure 8.9: Supervised learning options in the Neural Builder tool in NeuroSolutions®
Figure 8.10: Probe configuration options in the Neural Builder tool in NeuralSolutions®
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8.4.2 Training and Testing
Once an ANN has been created using the Neural Builder, the user can begin training by
clicking the “Start” button on the main menu. The simulation window (Figure 8.11) indicates
the training progress by showing the number of epochs sent through the ANN, how many data
exemplars have been sent through the ANN, the elapsed training time, and the estimated time to
complete training. During training, the Data Graph probe shows an error curve (Figure 8.12) that
is automatically generated as the network weights are repeatedly modified during training to
reduce error. It is important to save the ANN before testing it.
Figure 8.11: Simulation window showing training progress
Figure 8.12: Error curve generated by the Data Graph probe
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After an ANN has been trained, it is tested using data not seen by the network during
training. The testing wizard is started by clicking the “Testing” button in the main menu. The
first step is to choose the source files to be used for input and output (“desired”) data during
testing (Figure 8.13). Since testing data was designated during the ANN development process,
the program has automatically created files containing the appropriate data; these data files are
chosen by default.
Figure 8.13: Choosing the source files for input and output testing data
The second step in the training process is to choose how the program should output the
results of ANN training (Figure 8.14). Either “Display in a Window” or “Export to a File” can
be selected; for this study, the first option was used, and the data were copied from the resulting
window into an Excel spreadsheet. In order for quantitative comparison to be made between the
known data and predicted ANN output data, the “Include the Desired data” box must be checked.
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Figure 8.14: Choosing how NeuroSolutions® should output the results of ANN testing
