COAGULATION OPTIMIZATION TO MINIMIZE AND PREDICT THE FORMATION OF DISINFECTION BY-PRODUCTS. Justin Wassink

Transcription

1 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

2 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.

3 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.

4 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 Research Objectives Description of Chapters Literature Review Disinfection By-Products (DBPs) Introduction Health Risks and Regulations Precursors Formation of DBPs Modeling of DBPs Enhanced Coagulation Introduction Optimization of the Coagulation Process Coagulant Type Coagulation ph... 10

5 v Coagulant Dose Effects on Water Treatment Removal of NOM, Humic Matter and DBP Formation Potential Artificial Neural Networks Introduction ANN Components and Architecture Structure and Operation Options and Variations Model Development and Use General Model Development Process Raw Data Analysis Selection of Input Parameters Network Training Analysis of Results and Performance Evaluation ANNs in Water Treatment Peterborough Water Treatment Plant Materials and Methods Experimental Protocols Treatment Sequence for Bench-Scale Testing Enhanced Coagulation Conditions Water Samples and Data Collection Quality Assurance and Quality Control Analytical Methods... 32

6 vi Trihalomethanes (THMs) Haloacetic Acids (HAAs) Total Organic Carbon (TOC) Ultraviolet Absorbance (UV 254 ) ph Measurement Chlorine Residual Fluorescence Excitation-Emission Liquid Chromatography - Organic Carbon Detection (LC-OCD) Artificial Neural Network (ANN) Development Modeling Software Input Parameter Selection ANN Architecture Selection Training and Validation Evaluation of Enhanced Coagulation for DBP Minimization Introduction Experimental Design Methods Bench-Scale Testing Analyses Bench-Scale Simulation of Full-Scale Treatment Influence of Enhanced Coagulation Removal of Natural Organic Matter (NOM) DBP Formation... 58

7 vii 4.6 Relationships between Measured Parameters Linear Correlations Predictive Models Seasonal Changes in Water Quality Summary Artificial Neural Network (ANN) Modelling Parameter Selection ANN Development Results and Discussion Implementation ANN Development Pilot Plant ANN Data Parallel Treatment Train Operation for ANN Evaluation Full Scale Plant (FSP) and ANNs Summary, Conclusions and Recommendations Summary Conclusions Recommendations References Appendices Sample Calculations Point of Diminishing Returns (PODR) Bromine Incorporation Factor (BIF)

8 viii 8.2 Bench Scale Testing Raw Data Post-Filter Water Quality DBP Formation Potential (DBPFP) Winter Bench Scale Test Results Artificial Neural Network Performance Parameters ANN Development in Neurosolutions Neural Builder Wizard Training and Testing

9 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 Table 2.5: Important input parameters for neural network models Table 3.1: Bench-scale testing - Reagents Table 3.2: Bench-scale testing Coagulant dosing details Table 3.3: Bench-scale testing Method outline Table 3.4: Bench-scale testing Method outline (continued) Table 3.5: Locations for collection and analysis of water samples from Peterborough WTP Table 3.6: Locations for collection and analysis of water samples for bench-scale tests Table 3.7: Vials and preservatives used for sample collection Table 3.8: Trihalomethanes Instrument conditions Table 3.9: Trihalomethanes Reagents Table 3.10: Trihalomethanes Method outline Table 3.11: Trihalomethanes Method detection limits Table 3.12: Haloacetic acids Reagents Table 3.13: Haloacetic acids Instrument conditions Table 3.14: Haloacetic acids Method Outline Table 3.15: Haloacetic acids Standard solutions Table 3.16: Haloacetic acids Method detection limits Table 3.17: Total organic carbon Reagents... 41

10 x Table 3.18: Total organic carbon Instrument conditions Table 3.19: Total organic carbon Method outline Table 4.1: TOC removal required by the USEPA D/DBPR for enhanced coagulation 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 Table 4.5: Comparison of DBP formation at coagulant dosages required to achieve 35% TOC reduction Table 4.6: Average ratio of DBP formation by class for four coagulants Table 4.7: Correlations of NOM fractions detected by FEEM with TOC, UV 254, and SUVA for post-filter waters in bench-scale tests Table 4.8: Breakdown of NOM in Peterborough raw water via LC-OCD analysis Table 4.9: Models to predict removal of TOC and UV 254 using coagulant dosage Table 4.10: Models to predict formation of TTHM and HAA 9 using TOC, UV 254, and ph Table 4.11: Peterborough raw water quality Table 4.12: R-squared values for linear correlations between measures of filtered water NOM content and 24-hour DBP formation Table 4.13: Summary of water quality resulting from recommended treatment conditions with alum, acid + alum, HI 705 PACl, and HI 1000 PACl Table 4.14: R 2 values for linear correlations between key performance parameters Table 5.1: Variability in raw and filtered water quality, as well as DBP formation, for the data generated via bench-scale testing Table 5.2: Summary of bench-scale data used to develop ANNs to predict DBP formation... 82

11 xi Table 5.3: Final network architecture selected for TTHM and HAA 9 ANNs Table 5.4: Comparison of performance statistics for TTHM and HAA 9 ANNs Table 8.1: Example data for PODR calculation Table 8.2: Conversion of mass concentrations to molar concentrations Table 8.3: ph, TOC, UV 254, and fluorescence excitation-emission data for bench scale tests postfilter water Table 8.4: THM, TCAN, and TCP concentrations for 24-hour DBPFP tests Table 8.5: HAA concentrations for 24-hour DBPFP tests Table 8.6: ph, TOC, UV254, and fluorescence excitation-emission data for February bench scale tests post-filter water Table 8.7: THM concentrations for February DBPFP tests Table 8.8: HAA concentrations for February DBPFP tests

12 xii LIST OF FIGURES Figure 2.1: An artificial neuron Figure 2.2: A multilayer perceptron network Figure 2.3: A forward process model and corresponding inverse process model Figure 2.4: Activation functions Figure 2.5: Process flow diagram for Peterborough WTP with sampling points for data collection Figure 3.1: Treatment Steps for Bench-Scale Testing Figure 3.2: Example trihalomethanes calibration curves Figure 3.3: Example haloacetonitriles calibration curves Figure 3.4: Example haloketones and chloropicrin calibration curves Figure 3.5: Example haloacetic acids calibration curves Figure 3.6: Example total organic carbon calibration curve Figure 3.7: Total organic carbon Quality control chart (3.0 mg/l) Figure 4.1: Example 3-D image of a fluorescence excitation-emission spectrum Figure 4.2: LC-OCD chromatograph for raw water with identified peaks for DOC fractions Figure 4.3: Average percent reduction of TOC from Peterborough water Figure 4.4: Average percent reduction of UV 254 from Peterborough water Figure 4.5: Example TOC curve for determination of point of diminishing returns (PODR) Figure 4.6: Jar test removal of DOC detected by LC-OCD Figure 4.7: 24-hour TTHMFP of for bench-scale tests with four coagulant types Figure 4.8: 24-hour HAA 9 FP for bench-scale tests with four coagulant types Figure 4.9: 24-hour TCANFP for bench-scale tests with four coagulant types... 60

13 xiii Figure 4.10: 24-hour TCPFP for bench-scale tests with four coagulant types Figure 4.11: TTHM speciation in bench-scale tests Figure 4.12: HAA 9 speciation in bench-scale tests Figure 4.13: Correlation between TOC and UV 254 for bench-scale tests using alum, acid + alum, HI 705 PACl, and HI 1000 PACl Figure 4.14: Correlations of humic-like substances with TOC and UV 254 for bench-scale tests using alum, acid + alum, HI 705 PACl, and HI 1000 PACl Figure 4.15: Correlations between TOC and DBPFP for bench-scale tests using alum, acid + alum, HI 705 PACl, and HI 1000 PACl Figure 4.16: Correlations between UV 254 and DBPFP for bench-scale tests using alum, acid + alum, HI 705 PACl, and HI 1000 PACl Figure 4.17: Correlations between HS and DBPFP for bench-scale tests using alum, acid + alum, HI 705 PACl, and HI 1000 PACl Figure 4.18: Percent reduction of TOC from Peterborough water in February jar tests Figure 4.19: Maximum intensity for fluorescence peak at excitation/emission of 340/430 nm for February jar tests Figure 4.20: Seasonal comparison for removal of TOC and UV 254 by alum Figure 4.21: Percent reduction of 24-hour TTHM formation in February tests with Peterborough water Figure 4.22: Percent reduction of 24-hour HAA 9 formation in February tests with Peterborough water Figure 4.23: 24-hour TTHM formation for tests conducted in summer (left) and winter (right) 76 Figure 4.24: 24-hour HAA 9 formation for tests conducted in summer (left) and winter (right).. 76

14 xiv Figure 4.25: Removal of NOM fractions detected by LC-OCD in February test with alum Figure 5.1: Preliminary architecture for ANN to predict formation of THMs or HAAs using data from bench-scale testing Figure 5.2: Correlation plot for the predicted versus actual TTHM formation Figure 5.3: Correlation plot for the predicted versus actual HAA 9 formation Figure 5.4: TTHM formation error histogram Figure 5.5: HAA 9 formation error histogram Figure 5.6: Q-Q plot to test the normality of the error distribution for DBP models Figure 5.7: Preliminary architecture for ANN to predict TTHM formation in the pilot plant 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 Figure 5.10: Comparison of TTHM formation in Peterborough full-scale plant (FSP) and two pilot-scale treatment trains Figure 5.11: Data flow between PP1 (simplified process flow diagram) and the process model ANN Figure 5.12: Data flow between PP2 (simplified process flow diagram) and the inverse process model ANN Figure 5.13: Flow diagram for a pilot plant ANN software application Figure 8.1: Example of exponential approximation of jar test TOC data to find the PODR 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

15 xv Figure 8.4: Selection of input and output parameters using the Neural Builder tool in NeuroSolutions Figure 8.5: Selecting how much data to use for cross-validation and testing using the Neural Builder tool in NeuroSolutions 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 Figure 8.8: Configuring the output layer using the Neural Builder tool in NeuroSolutions 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 Figure 8.11: Simulation window showing training progress Figure 8.12: Error curve generated by the Data Graph probe Figure 8.13: Choosing the source files for input and output testing data Figure 8.14: Choosing how NeuroSolutions should output the results of ANN testing

16 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) Al 2 (SO 4 ) 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 UV 254 values following filtration C 0 CDBM cm CP CPM DBAA DBAN TOC and UV 254 values in raw water (prior to jar test) Chlorodibromomethane Centimetre(s) Chloropicrin Colloidal / particulate matter Dibromoacetic acid Dibromoacetonitrile

17 xvii DBCAA DBPs DBPFP DCAA DCAN DCP D/DBPR DLL DOC DWRG DXAA E k (n) FeCl 3 FEEM FPM FSP FW g GAC GC-ECD g/l H 2 SO 4 HAA HAAFP HAA 9 HAN HI 705 HI 1000 Dibromochloroacetic acid Disinfection by-products Disinfection by-product formation potential Dichloroacetic acid Dichloroacetonitrile 1,1-Dichloropropanone Disinfectants and Disinfection Byproducts Rule Dynamic library link Dissolved organic carbon, mg/l Drinking Water Research Group Di-haloacetic acids Difference between actual and desired network output Ferric chloride Fluorescence excitation-emission matrix Forward process model Full-scale plant Filtered water Gram(s) Granular activated carbon Gas chromatography with electron capture detection Gram(s) per litre Sulfuric acid Haloacetic acid Haloacetic acid formation potential Total haloacetic acids (sum of monochloroacetic acid, monobromoacetic acid, dichloroacetic acid, trichloroacetic acid, dibromoacetic acid, tribromoacetic acid, bromochloroacetic acid, bromodichloroacetic acid, and dibromochloroacetic acid) Haloacetonitrile Hyper + Ion 705 PACl Hyper + Ion 1000 PACl

18 xviii HK hr HRT HS I-THM IPM KHP L LC-OCD L/min LMW m 3 MAE MBAA MCAA MCL MDL mg/l min ml ML/d MLP MSE MTBE μg/l n NaOCl NOM NSERC PACl PC Haloketone Hour(s) Hydraulic retention time Humic substances Iodinated trihalomethanes Inverse process model Potassium hydrogen phthalate Litre(s) Liquid chromatography with organic carbon detection Litre(s) per minute Low-molecular-weight Cubic metre(s) Mean absolute error Monobromoacetic acid Monochloroacetic acid Maximum contaminant level Method detection limit Milligrams per litre Minutes Millilitre(s) Million litres per day Multi-layer perceptron Mean squared error Methyl-tert-butyl ether Microgram(s) per litre Number of measurements Sodium hypochlorite Natural organic matter National Science and Engineering Research Council Polyaluminum chloride Principle component

19 xix PCA PE PFD + p jk - p jk PLC PM PODR PP1 PP2 PUC Q-Q r 2 rpm RW SUVA SW TBM TBAA TCAA TCAN TCM TCP THAA THM THMFP THM 4 TTHM TOC TOX Principle component analysis Processing element Process flow diagram conditional correlation between the states of neurons j and k unconditional correlation between the states of neurons j and k Programmable logic controller Protein-like matter Point of diminishing returns First pilot-scale treatment train Second pilot-scale treatment train Peterborough Utilities Commission Quantile-quantile Correlation coefficient Revolutions per minute Raw water Specific ultraviolet absorbance Settled Water Tribromomethane (bromoform) Tribromoacetic acid Trichloroacetic acid Trichloroacetonitrile Trichloromethane (chloroform) 1,1,1-Trichloropropanone Total haloacetic acids Trihalomethane Trihalomethane formation potential Trihalomethanes (sum of trichloromethane, bromodichloromethane, dibromochloromethane, and tribromomethane) Total trihalomethanes Total organic carbon, mg/l Total organic halide

20 xx TW Treated water TXAA Tri-haloacetic acids UofT University of Toronto USEPA United States Environmental Protection Agency UV Ultraviolet light or radiation UV 254 Absorbance of 254 nm-wavelength ultraviolet light, cm -1 ΔW jk W i W jk WTP X i X j (n) X pi Y i (n) Z Change made to weight connecting neuron j to neuron k Weight value for connection of neuron i Weight connecting neuron j to neuron k Water treatment plant Real model output value for exemplar i Input from neuron j at time n Predicted model output value for exemplar i Output from neuron i at time n Weighted sum of neuron inputs

21 1 1. Introduction and Research Objectives Many countries have adopted regulations limiting the formation of disinfection byproducts (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.

22 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.

23 3 2. Literature Review 2.1 Disinfection By-Products (DBPs) 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

24 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 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 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

25 5 organic carbon (TOC) or dissolved organic carbon (DOC), absorbance of ultraviolet light using a wavelength of 254 nm (UV 254 ), specific UV absorbance (SUVA, UV 254 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). UV 254 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). UV 254 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 UV 254 to be more suitable than TOC for predicting DBP formation. Table 2.1: DBP regulations and MCLs DBP Class THMs HAAs Health Canada United Kingdom Australia - New Compounds USEPA (1998) (2006) (2000) Zealand (2004) TTHM TCM 300 BDCM 60 DBCM 100 TBM 100 HAA5 60 MCAA 150 DCAA TCAA World Health Organization (2004) 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), 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.

26 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).

27 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, UV 254, 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 (R 2 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 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

28 8 Table 2.2: Models from the literature for the formation of halo-organic DBPs Author & Year Rathburn, 1996 Chang et al., 1996 Rodriguez et al., 2000 Sérodes et al., 2003 Sohn et al., 2004 N R 2 Output Units Model Description 0.97 TCM 0.442(pH) 2 (D) (DOC) (Br - ) NR 0.86 BDCM 17.5(pH) 1.01 (D) (DOC) (Br - ) μg/l 0.94 DBCM 26.6(pH) 1.80 (D) (DOC) (Br - ) TBM 0.29(pH) 3.51 (D) (DOC) (Br - ) TTHM 12.72(TOC) (t) (D) TTHM μg/l 108.8(TOC) (t) (UV 254 ) (D) TTHM (t) (UV 254 ) (D) TTHM 1.392(DOC) (ph) (T) μg/l TTHM 0.044(DOC) (t) (ph) (D) (T) log(haas) (TOC) (D) (t) 0.80 log(haas) (TOC) (D) (T) (t) NR 0.92 HAAs (TOC) (D) (t) μg/l 0.78 TTHM (TOC) (D) (T) (t) 0.89 log(tthm) (THM 0 ) (TOC) (t) 0.56 TTHM (D) (t) 0.90 TTHM (DOC) (D) (Br - ) (ph) (t) TTHM 0.24(UV 254 ) (D) (Br - ) (T) (ph) (t) TTHM 0.283(DOC UV 254 ) (D) (Br - ) (T) (ph) (t) TTHM 3.296(DOC) (D) (Br - ) (t) TTHM 75.7(UV 254 ) (D) (Br - ) (t) TTHM 23.9(DOC UV 254 ) (D) (Br - ) (t) NR 0.92 TTHM (THM ph=7.5,t=20 C ) (ph-7.6) (T-20) μg/l 0.87 HAA (DOC) (D) (Br - ) (T) (ph) (t) HAA (UV 254 ) (D) (Br - ) (T) (ph) (t) HAA 6 101(DOC UV 254 ) (D) (Br - ) (T) (ph) (t) HAA (DOC) (D) (Br - ) (t) HAA (UV 254 ) (D) (Br - ) (t) HAA (DOC UV 254 ) (D) (Br - ) (t) HAA 6 (HAA6 ph=7.5,t=20 C ) (ph-7.6) (T-20) Uyak & Toroz, TTHM μg/l (TOC) (T) (D) Hong et al., 2007 McBean et al., TCM (t) (D/DOC) (ph) (T) (Br - ) NR 0.87 BDCM μg/l (t) (ph) (T) (Br - ) TCM 0.179(t) (D/DOC) (ph) (T) (Br - ) TTHM 5.188(DOC raw ) (DOC treated ) (D 0 ) (D 1 ) (T) μg/l 0.72 HAAs (DOC raw ) (DOC treated ) (D 0 ) (ph) NR = not reported; TCM = trichloromethane; BDCM = bromodichloromethane; DBCM = dibromochloromethane; TBM = tribromomethane; TTHM = total trihalomethane; HAAs = total haloacetic acids; HAA 6 = sum of six haloactic acids; TOC = total organic carbon (mg/l); DOC = dissolved organic carbon (mg/l); UV 254 = 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).

29 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 (FeCl 3 ) or alum (AlSO 4 ) be used for enhanced coagulation. Table 2.3: TOC removal required by the USEPA D/DBPR for enhanced coagulation Source Water TOC (mg/l) 0-60 Source Water Alkalinity (mg/l as CaCO 3 ) > % 25.0% 15.0% % 35.0% 25.0% > % 40.0% 30.0% 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 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

30 10 selected properly, enhanced coagulation can produce comparable or even less sludge than before (Bell-Ajy et al., 2000) 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 (FeCl 3 ) removes more organic carbon, UV 254 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, UV 254 and THMFP than alum, while Rizzo et al. (2005) ranked three coagulants by capacity for NOM removal as FeCl 3 > alum > PACl, with the order being reversed for removing turbidity. Rizzo et al. (2004) showed that less PACl was needed than alum and FeCl 3 to meet the TOC requirements of the D/DBPR. Rizzo et al. (2005) found that using PACl allows for the highest THMFP 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, UV 254 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.

31 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, UV 254, 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 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 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, UV 254 and SUVA are useful as indicators of the concentration, nature and reactivity of NOM, and can be used as surrogate measures for NOM

32 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 UV 254 is more closely correlated to DBPFP (r 2 = 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 UV 254 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, UV 254, 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 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 highlyinterconnected 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 userfriendly software platforms available for building and using neural networks. Finally, ANNs are

33 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) ANN Components and Architecture 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: z = wi x y = i f ( z) where x i is the i th input value to the neuron, w i is the weight multiplier for the i th 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 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 i 2.1

34 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

35 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

36 16 produced in the network output, gradually reducing the overall network error (Basheer & Hajmeer, 1994): Δ W ( n) = ηe ( n) X ( n) 2.2 jk k j Table 2.4: Activation function equations y i = Binary 1 for. z 0 for. z i i < 0 0 y i = Piecewise 0 for. z i < z i for. 0 < 1 for. z i > 0 z 1 i < 1 y i = Sigmoid exp ( z ) i y i = tanh ( z ) 1 + tanh i 2 1 Neuron Output (y) 0.5 Binary Piecewise Sigmoid tanh Activation Function Input (z) Figure 2.4: Activation functions

37 17 where ΔW jk (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, E k (n) is the difference between the actual network output and the desired output at time n, and X j (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): ΔW jk ( ρ + ρ ) ( n) = η 2.3 jk jk where ρ + - jk and ρ jk 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: Δ W ( n) = ηy ( n) Y ( n) 2.4 jk j k where Y j (n) and Y k (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: ΔW jk η ( n) = ( X ( n) W ( n) ) j 0 jk if neuron j wins if neuron j loses 2.5 where X j (n) is the input signal connected to output neuron k by weight W jk. 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 singleoutput networks for comparison.

38 Model Development and Use 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) 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

39 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 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 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

40 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 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 r 2 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 r 2 indicates a close correlation and a good model; a low value indicates a very little correlation and poor model performance. MAE is calculated as: MAE n X X i Pi i= = n where X i and X Pi 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: MSE n i= = 1 ( X X ) i n Pi 2 2.7

41 21 Table 2.5: Important input parameters for neural network models Authors and Year Parameters Used Output ph temperature Baxter, Stanley & Zhang, 1999 Baxter et al., 2001 Lewin et al., 2004 Maier, Morgan & Chow, 2004 Rodriguez & Sérodes, 2004 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 NOM removal NOM and colour removal THM formation Optimal alum dose for NOM removal THM formation 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

42 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) 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

43 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 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

44 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 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.

45 25 Raw Water Raw Water SCADA: temperature ph Chlorine Addition (zebra mussel control) Low Lift Pumps Screens Operating Conditions Data SCADA: flow rate alum dosage primary chlorine dosage secondary chlorine dosage sodium silicate dose Water Samples: TOC / UV 254 Alum Addition (3 possible locations) Al Al Al Inline Mixers (x2) Distribution System Zone 2 Clearwell 2 (CW2) Flash Mixer High Lift Pumps Distribution System Zone 1 6a High Lift Pumps Flocculation Tanks ( 4x hydraulic trains) 6b 6c 5a 5b 5c 3a 1a Sodium Silicate Addition (ph control) 4 3b 2 1b Hydrofluosilicic Acid (Fluoride) Sedimentation Basins (x 6, only basins 5&6 have parallel plate settlers) Chlorine Addition (Disinfection) Post Chlorine Addition Settled Water SCADA: ph Water Samples: TOC / UV 254 Multimedia Filters (x 11, flow through each filter can be controlled individually) Treated Water SCADA: Water Samples: ph TOC / UV 254 chlorine THMs residual HAAs Drain by gravity from CW3 to CW2 Clearwell 3 (CW3) Weir Chlorine Contact Tank Filtered Water SCADA: Water Samples: ph TOC / UV 254 Figure 2.5: Process flow diagram for Peterborough WTP with sampling points for data collection

46 26 3. Materials and Methods 3.1 Experimental Protocols 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% Cl 2, BioShop Canada, Inc., Burlington, ON) was used for prechlorination 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

47 27 Table 3.1: Bench-scale testing - Reagents Reagent Source Aluminum Sulphate (Al 3 (SO 4 ) 3 18H 2 0 General Chemical (Parsipanny, NJ), 48.5% Sulphuric Acid (H 2 SO 4 ) 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 Dosage (mg/l) Coagulant Volume (μl) Alum HI 705 HI Table 3.3: Bench-scale testing Method outline Store raw water in the dark at 4 C. Allow raw water to reach room temperature (23±2 C) before testing. Chlorine Dosing Solution Preparation (600 mg/l Cl 2 ) 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. 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.

48 28 Table 3.4: Bench-scale testing Method outline (continued) Determine Volume of Acid Required Pour 500 ml of raw water into a 1 L beaker. Add ¼ of the volume of alum required for a 2 L jar. For example, add 15.5 μl of alum to achieve a Using a buret, titrate with 0.02 M sulfuric acid until the target ph is achieved. The target value is the pre- Repeat for each dosage to be used in the jar test with alum + acid. Jar Testing 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. 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. 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. Turn off stirrer, raise paddles and allow settling for 30 minutes. Collect 40 ml supernatant from each jar for UV 254 and TOC measurement. Collect 500 ml supernatant from each jar. Measure the ph of the supernatant, and filter using 1.5 μm Measure the ph of the filtrate, and collect 40 ml samples for UV 254 and TOC measurement. Simulated Distribution System (SDS) Test 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. 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). After 24 hours, measure the free chlorine residual concentration in each bottle and collect samples for THM and HAA analyses.

49 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, UV 254, 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 prechlorination. 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) Water Samples and Data Collection Bench-scale testing on Peterborough water was conducted between July 19 and August 17, 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).

50 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, UV 254, 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 UV 254 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 (UV 254 ), 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.

51 31 Table 3.5: Locations for collection and analysis of water samples from Peterborough WTP Sampling Location Analysis Location Parameter Raw a Prefiltefilteclearwell SDS of Toronto WTP Waterloo Post- Post- 24-Hour University Peterborough University of Temperature x x ph x x x x x TOC x x x x x UV 254 x x x x x NOM x x x x x Chlorine residual x x x Trihalomethanes b x x x x x x Haloacetic Acids x x x x x x a Raw water samples were collected prior to the point of addition for pre-chlorination. b Analysis 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 Sampling Location Analysis Location Parameter Raw a Prefilter filter b SDS Toronto Waterloo Post- 24-Hour University of University of Temperature x x ph x x x x TOC x x x x UV 254 x x x x NOM c x x x x Chlorine residual x x Trihalomethanes d x x Haloacetic Acids x x a Raw 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. b Separate pre-filter (settled) and post-filter samples were collected for all six jars in each test. c NOM samples were collected and analyzed only for jar tests without pre-chlorine. d Analysis of THM samples enables simultaneous detection of haloacetonitriles, haloketones, and chloropicrin. The method detection limits (MDLs) for TOC, UV 254, 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.

52 32 Table 3.7: Vials and preservatives used for sample collection Sample Volume (ml) a Preservatives Description TOC b 40 Sulphuric acid (H 2 SO 4 ), 2 drops VWR International, 98+% NOM 40 none NA Ammonium chloride, g Caledon Laboratories Ltd., 99.5% THMs c 25 Phosphate buffer, 0.4 g 99% Potassium phosphate (KH 2 PO 4 ) 1% Sodium phosphate (Na 2 HPO 4 ) ACS grade HAAs 25 Ammonium chloride, g Caledon Laboratories Ltd., 99.5% a Volumes are for amber vials used to collect water quality samples. b TOC samples from bench-scale tests were also used to analyze for UV 254. c Analysis of THM samples enables simultaneous detection of haloacetonitriles, haloketones, and chloropicrin. For every tenth sample, additional QA/QC measures were taken when analysing TOC, UV 254 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 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

53 33 DB 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 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 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 Description Injector Temperature 200 C Detector Temperature 300 C 40 C for 4.0 min Temperature Program 4 C/min temperature ramp to 95 C 60 C/min temperature ramp to 200 C Carrier Gas Helium Flow Rate 1.2 ml/min at 35 C Table 3.9: Trihalomethanes Reagents Reagent Source Ammonium chloride [NH 4 Cl] Caledon Laboratories Ltd., 99.5% Hydrochloric acid [HCl] E.M. Science, ACS Grade Trihalomethane concentrated stock for calibration Supleco, 2000 μg/ml in methanol (48140-U) Sodium sulphate [Na 2 SO 4 ] E.M. Science, ACS Grade Methyl-tert -butyl-ether (MTBE) Aldrich, >99.8%

54 34 Table 3.10: Trihalomethanes Method outline Collect samples in 25 ml amber vials quenched with 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. 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. Blanks: Transfer 25 ml of Milli-Q water into 40 ml vials and process alongside samples. 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 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. 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) Include blanks and running standards every 10 samples. Extraction Transfer the contents of each sample vial into a clean 40 ml vial. Add 1 tsp of sodium sulphate (Na 2 SO 4 ) 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. 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. Place all the vials upright in a rack and shake for 2 minutes. Let samples stand for 10 minutes for phase separation. 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. Analyze using a GC-ECD.

55 35 Table 3.11: Trihalomethanes Method detection limits Analyte MDL (μg/l) Std. Dev. (μg/l) TCM BDCM DBCM TBM DCAN TCAN BCAN DBAN DCP TCP CP TCM (μg/l) = (area response ratio )/0.1032, R 2 = BDCM (μg/l) = (area response ratio )/0.2485, R 2 = DBCM (μg/l) = (area response ratio )/0.2409, R 2 = TBM (μg/l) = (area response ratio )/0.1525, R 2 = Area Response Ratio Concentration (ug/l) TCM BDCM DBCM TBM Figure 3.2: Example trihalomethanes calibration curves

56 DCAN (μg/l) = (area response ratio )/0.2037, R 2 = TCAN (μg/l) = (area response ratio )/0.2745, R 2 = BCAN (μg/l) = (area response ratio )/0.2652, R 2 = DBAN (μg/l) = (area response ratio )/0.2514, R 2 = Area Response Ratio Concentration (ug/l) DCAN TCAN BCAN DBAN Figure 3.3: Example haloacetonitriles calibration curves DCAN (μg/l) = (area response ratio )/0.3642, R 2 = TCAN (μg/l) = (area response ratio )/0.3775, R 2 = CP (μg/l) = (area response ratio )/0.1060, R2 = Area Response Ratio DCP TCP CP Concentration (ug/l) Figure 3.4: Example haloketones and chloropicrin calibration curves

57 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 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 The method steps are outlined in Table Details of the standard solution are presented in Table Method detection limits (MDLs) are provided in Table 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 Source Ammonium chloride [NH 4 Cl] Caledon Laboratories Ltd., 99.5% Diethyl ether [C 2 H 5 OCH 2 CH 2 OCH 2 CH 2 OH] Aldrich, 99+% N-methyl-N-nitroso-p-toluene sulfonamide (Diazald) [CH 3 C 6 H 4 SO 2 N(CH 3 )NO] Aldrich, 99+% Ether [C 4 H 10 O] Aldrich, 99.9% Potassium hydroxide [KOH] BDH, 85.0+%, ACD Grade Sulphuric acid [H 2 SO 4 ] E.M. Science, 98+% Haloacetic acids concentrated stock for calibration EPA Acids Calibration Mix in MTBE Sodium sulphate [Na 2 SO 4 ] E.M. Science, ACS Grade Methyl-tert -butyl-ether (MTBE) Aldrich, >99.8%

58 38 Table 3.13: Haloacetic acids Instrument conditions Parameter Description Injector Temperature 200 C Detector Temperature 300 C 35 C for 10.0 min Temperature Program 2.5 C/min temperature ramp to 65 C 10 C/min temperature ramp to 85 C 20 C/min temperature ramp to 205 C, hold for 7 min Carrier Gas Helium Flow Rate 1.2 ml/min at 35 C 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. 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 ** Wipe the syringe tip with a Kimwipe before measuring out the HAA stock and before adding stock to Top flask to 5 ml and cap with glass stopper and mix by inverting at least 10 times. Running Standards (varying concentrations): Add 50 μl of working solution to 25 ml of Milli-Q water, process alongside samples. Include blanks and running standards every 10 samples. Sulfonamide Solution: Before preparing samples, add 15 ml of diethylene glycol, 15 ml of ether and 3 g of N-methyl-N-nitroso-ptoluene sulfonamide (Diazald) to a 40 ml amber vial. Shake vial until completely dissolved. This solution is used to make diazomethane and can be stored at 4 C for up to 30 days. Diazomethane Generation Set up the generation apparatus as shown in Figure 6521:3 in Standard Methods (APHA, 2005). 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 sulfonamide solution above the KOH using a long Pasteur pipette; ensure no mixing occurs.

59 39 Table 3.14: Haloacetic acids Method Outline (continued) Add 4 ml of MTBE to the last tube and put in a beaker of ice such that the MTBE is submerged. 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. When ready to prepare samples, remove from refrigerator and warm up to room temperature. Extraction Transfer the contents of each sample vial into a clean 40 ml vial. Add two drops of sodium sulphite (Na 2 SO 3 ) solution. Add 1 tsp of sodium sulphate (Na 2 SO 4 ) using scoop in order to increase extraction efficiency. Add 1 ml of sulphuric acid (H 2 SO 4 ) to reduce the ph of the sample. Add 1 tsp of sodium sulphate (Na 2 SO 4 ) 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. Shake sample vial vigorously for approx. 30 seconds and place on counter on its side. 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. 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. 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. Analyze using a GC-ECD. Table 3.15: Haloacetic acids Standard solutions Concentration (mg/l) Calibration Standard Concentration (μg/l) Compound Stock Intermediate Solution Solution Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Level 8 MCAA MBAA DCAA TCAA BCAA DBAA BDCAA DBCAA TBAA

60 40 Table 3.16: Haloacetic acids Method detection limits Analyte Std. Dev. (μg/l) MDL (μg/l) MCAA MBAA DCAA TCAA BCAA DBAA BDCAA DBCAA TBAA Area Response Ratio MCAA (μg/l) = (area response ratio )/0.0027, R2 = MBAA (μg/l) = (area response ratio )/0.0257, R2 = DCAA (μg/l) = (area response ratio )/0.0226, R2 = TCAA (μg/l) = (area response ratio )/0.0501, R2 = BCAA (μg/l) = (area response ratio )/0.0414, R2 = DBAA (μg/l) = (area response ratio )/0.0495, R2 = BDCAA (μg/l) = (area response ratio )/0.0278, R2 = DBCAA (μg/l) = (area response ratio )/0.0217, R2 = TBAA (μg/l) = (area response ratio )/0.0176, R2 = Concentration (ug/l) MCAA DCAA BCAA BDCAA TBAA MBAA TCAA DBAA DBCAA Figure 3.5: Example haloacetic acids calibration curves 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

61 41 in Table The method steps are outlined in Table 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 (H 2 SO 4 ) 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 Purity Sodium persulphate [Na 2 S 2 O 8 ] (100 g/l) Aldrich, 98+% Potassium hydrogen phthalate [C 8 H 5 KO 4 ] Aldrich, 98+% Sulphuric acid, concentrated [H 2 SO 4 ] VWR International, 98+% Table 3.18: Total organic carbon Instrument conditions Parameter Description Acid, volume 200 μl of 5% phosphoric acid Oxidant volume 1000 μl of 100 g/l sodium persulphate Sample volume 15 ml Rinses per sample 1 Volume per rinse 15 ml Reaction time (min:sec) 02:30 Detection time (min:sec) 02:00 Purge gas Nitrogen Loop 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 < 2 by acidifying with H 2 SO 4. Standard (3.0 mg/l): Add 300 μl of stock solution to 100 ml Milli-Q water. Samples: Follow SOP for TOC analyzer.

62 42 9.0E E+04 TOC (mg/l) = (area response )/7766.6, R 2 = E E+04 Area Count 5.0E E E E E E Concentration (mg/l) Figure 3.6: Example total organic carbon calibration curve Upper CL Upper WL 3.0 Mean Concentration (mg/l) Lower WL Lower CL Dec. 10, 2009 Feb. 24, 2010 Jun. 16, 2010 Aug. 23, 2010 Figure 3.7: Total organic carbon Quality control chart (3.0 mg/l)

63 Ultraviolet Absorbance (UV 254 ) Ultraviolet absorbance at a wavelength of 254 nm (UV 254 ) 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 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 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 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 nm and 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-

64 44 Q water from all sample spectra. Results were processed by a principle component analysis approach using Matlab software (The Mathworks Inc., Natick, MA) 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 UV 254 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 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. multilayer perceptron), the number of hidden layers and neurons, the transfer function to be used, and

65 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 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, UV 254, 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, UV 254, ph, temperature, and chlorine dosage were selected as ANN inputs to predict the formation of TTHM and HAA 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

66 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 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.

67 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 (UV 254 ) 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

68 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 benchscale 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 Source Water TOC (mg/l) 0-60 Source Water Alkalinity (mg/l as CaCO 3 ) > % 25.0% 15.0% % 35.0% 25.0% > % 40.0% 30.0% Enhanced coagulation may result in a higher coagulant dosage being used, which has been shown to achieve greater reductions of turbidity, particle counts, TOC, UV 254, 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 excitationemission 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

69 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 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, UV 254, 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 Bench-Scale Testing Bench-scale tests consisted of jar tests followed by 24-hour DBP formation potential (DBPFP) tests. Aluminum sulphate (alum, Al 2 (SO 4 ) 3 H 2 O) 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 CaCO 3 ).

70 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 Cl 2 ). 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 Analyses Samples for TOC and UV 254 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). UV 254 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.

71 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 nm and 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 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/particulatelike matter (CPM, / and / nm). Figure 4.1: Example 3-D image of a fluorescence excitation-emission spectrum

72 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 UV 254 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)

73 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 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 UV 254 ), 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 UV 254 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.

74 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 UV 254 values were consistently higher for bench-scale tests (average difference of 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, HAA 9, TCAN, and TCP, respectively. Table 4.2: Post-filter water quality comparison for full-scale plant (FSP) and bench scale test ph TOC (mg/l) UV 254 (cm -1 ) SUVA (L/mg*m) Date (alum dosage) Bench Bench Bench Bench FSP FSP FSP FSP Scale Scale Scale Scale July 19 (42 mg/l) July 27 (43 mg/l) August 4 (47 mg/l) August 9 (48 mg/l) August 17 (45 mg/l) Table 4.3: 24-Hour DBP formation comparison for full-scale plant (FSP) and bench scale test (values are μg/l) TTHM HAA TCP Date 9 TCAN (alum dosage) Bench Bench Bench Bench FSP FSP FSP FSP Scale Scale Scale Scale July 19 (42 mg/l) July 27 (43 mg/l) August 4 (47 mg/l) NA August 9 (48 mg/l) NA August 17 (45 mg/l) NA NA = TCP not measured for bench scale tests in August due to interference from acetone. 4.5 Influence of Enhanced Coagulation Removal of Natural Organic Matter (NOM) In order to evaluate removal of precursors for DBPs (including THMs and HAAs), TOC and UV 254 values (C) following filtration were compared with the initial values measured in raw water (C 0 ) before each jar test. All measured TOC and UV 254 values are presented in Section of Appendix 8.2 (Table 8.3). Percent removal was calculated as (C 0 C)/C 0 for each coagulant dosage in tests with alum, acid + alum, HI 705 PACl, and HI 1000 PACl. Average percent removal of TOC and UV 254 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

75 55 (84_mg/L CaCO 3 ) 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 UV 254 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. Post-Filter % Reduction of TOC 70% 60% 50% 40% 30% 20% 10% Alum Acid + Alum HI 705 HI % Coagulant Dosage (mg/l) 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.

76 56 This is defined mathematically as the point on the TOC curve where the slope is -0.03, as shown in Figure 4.5. Post-Filter % Reduction of UV % 70% 60% 50% 40% 30% 20% Alum Acid + Alum HI 705 HI % 0% Coagulant Dosage (mg/l) Figure 4.4: Average percent reduction of UV 254 from Peterborough water (error bars represent standard deviation). Raw water UV 254 = ± cm Slope = 0.3 mg/l per 10 mg/l alum = TOC (mg/l) PODR = 35 mg/l Alum Dosage (mg/l) Figure 4.5: Example TOC curve for determination of point of diminishing returns (PODR)

77 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 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 In the case of HI 1000, using a lower dosage would result in a decrease in TOC (and UV 254 ) 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 Hydrophilic DOC is further separated into five components: humic substances (HS), building blocks (BB), lowmolecular-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

78 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. 7 Total DOC Hydrophilic DOC Post-Filter Concentration (mg/l) Hydrophobic DOC Building Blocks Bio-polymers Humic Substances LMW Neutrals LMW Acids 0 Raw Water Alum (40) Acid + Alum (30) HI 705 (25) HI 1000 (50) Coagulant (dosage in mg/l) 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 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 (HAA 9 ) was calculated by summing the

79 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 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, HAA 9, 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, HAA 9, 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 HAA 9 than 43 mg/l of alum. 120 Alum Acid + Alum HI 705 HI TTHM Concentration (μg/l) Coagulant Dosage (mg/l) Figure 4.7: 24-hour TTHMFP of for bench-scale tests with four coagulant types (results from July 27, 2010)

80 Alum Acid + Alum HI 705 HI 1000 HAA9 Concentration (μg/l) Coagulant Dosage (mg/l) Figure 4.8: 24-hour HAA 9 FP for bench-scale tests with four coagulant types (results from July 27, 2010) 14 Alum Acid + Alum HI 705 HI TCAN Concentration (μg/l) Coagulant Dosage (mg/l) Figure 4.9: 24-hour TCANFP for bench-scale tests with four coagulant types (results from July 27, 2010)

81 Alum Acid + Alum HI 705 HI TCP Concentration (μg/l) Coagulant Dosage (mg/l) 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 UV 254, 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 HAA 9 FP. Rizzo et al. (2004) reported that while alum and PACl achieved that same removal of UV 254, PACl was generally able to remove less TTHMFP.

82 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 0.13 BDCM 0.56 MBAA 0.14 DBAA 0.54 DCAN 0.83 DCP 0.12 DBCM 0.00 DCAA 0.45 BDCAA 0.63 TCAN 0.05 TCP 0.19 TBM 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) HAA 9 (μg/l) TCAN (μg/l) TCP (μg/l) Alum Acid + Alum HI 705 PACl HI 1000 PACl For the DBP data shown above, concentrations of HAA 9, 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 HAA 9, TCAN and TCP expressed as a fraction of TTHM formation are shown in Table 4.6. Formation of HAA 9 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 HAA 5 and TTHM (60 and 80 μg/l, respectively) established by the USEPA (1998). HAA 9 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 HAA 9 :TTHM TCAN:TTHM TCP:TTHM Alum Acid + Alum HI 705 PACl HI 1000 PACl 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 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

83 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. HAA 9 speciation is shown in Figure 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 HAA 9 by weight, while TCAA, DCAA, and MBAA account for 63%, 21%, and 10% of HAA 9 by weight, respectively. While TCAA comprises less than two-thirds of HAA 9 formation, reduction in TCAA accounts for 94% of HAA 9 reduction. Therefore, the formation and reduction of THMs and HAAs is primarily associated with non-brominated compounds TBM DBCM BDCM TCM Concentration (ug/l) Alum (40 mg/l) Acid + Alum (30 mg/l) HI 705 (30 mg/l) HI 1000 (50 mg/l) Coagulant (dosage) Figure 4.11: TTHM speciation in bench-scale tests (results from July 27, 2010)

84 B-HAA MCAA DCAA TCAA Concentration (ug/l) Alum (40 mg/l) Acid + Alum (30 mg/l) HI 705 (30 mg/l) HI 1000 (50 mg/l) Coagulant (dosage) Figure 4.12: HAA 9 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 HAA 9 (specifically, TCM and TCAA). 4.6 Relationships between Measured Parameters Linear Correlations TOC and UV 254 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 UV 254 have been shown to be closely related (Edzwald et al., 1985; Baghoth et al., 2011;

85 65 Singer & Chang, 1992). A linear relationship was observed between post-filter TOC and UV 254 (Figure 4.13, R 2 = 0.88), which can be expressed as: 1 ( cm ) = TOC ( mg / L) [ ] UV Post-Filter UV 254 (cm -1 ) R 2 = Post-Filter TOC (mg/l) Figure 4.13: Correlation between TOC and UV 254 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 UV 254 are therefore very similar. Values for linear correlation coefficients between DOC and UV 254 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 R 2 value of 0.88 was reported by Baghoth et al. (2011). Singer and Chang (1992) reported a relationship between TOC and UV 254 similar to Equation 4.1 for waters treated via enhanced coagulation: 1 ( cm ) = TOC ( mg / L) [ ] UV TOC data used to generate Equation 4.1 were used as inputs into Equation 4.2. The set of UV 254 values generated by Equation 4.2 showed a close correlation with the UV 254 values used to

86 66 generate Equation 4.1 (R 2 = 0.88). The relationship observed between TOC and UV 254 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 R 2 values for linear correlations of these three components to TOC, UV 254, and SUVA. Only HS shows good correlation with TOC and UV 254 (R 2 of 0.78 and 0.84, respectively), as shown in Figure Findings by other researchers vary greatly: reported correlations between FEEM peak intensities and surrogate measures of NOM (TOC, DOC, or UV 254 ) range from none (Baker, 2001; Bieroza et al., 2009) to R 2 values of 0.99 for HS TOC and HS UV 254 (Macraith et al., 1994) and 0.80 for PM TOC (Reynolds, 2002). Table 4.7: Correlations of NOM fractions detected by FEEM with TOC, UV 254, 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) NOM Fraction Colloidal / Particulate-like Matter Wavelengths (nm) Excitation Emission Humic-like Substances Protein-like Matter Surrogate Measure R 2 TOC 0.78 UV SUVA 0.62 TOC 0.05 UV SUVA 0.00 TOC 0.20 UV SUVA 0.33 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 UV 254 can be attributed to the fact that biopolymers (which include proteins) comprise only 7% of the total DOC present in Peterborough raw water. TOC and UV 254 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.

87 TOC UV Post-Filter TOC (mg/l) R 2 = Post-Filter UV 254 (cm -1 ) 1 R 2 = Post-Filter Humic Substances FEEM PCA Score Figure 4.14: Correlations of humic-like substances with TOC and UV 254 for bench-scale tests using alum, acid + alum, HI 705 PACl, and HI 1000 PACl (dosages between 20 and 70 mg/l) 0.02 Table 4.8: Breakdown of NOM in Peterborough raw water via LC-OCD analysis DOC Biopolymers Substances Blocks Neutrals Acids Humic Building LMW LMW NOM Fraction Total Hydrophobic Hydrophilic Concentration (mg/l) Percent of Total DOC 100% 12% 88% 7% 56% 15% 11% 0% LMW = low molecular weight TOC and UV 254 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 HAA 9 FP and TOC, UV 254, and HS, while weaker correlations (r < 0.6) were observed between 24-hour TTHMFP and TOC, UV 254, 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 HAA 9 FP was HS > UV 254 > TOC.

88 Hour SDS DBP Concentration (μg/l) R 2 = R 2 = TTHM HAA9 Linear (TTHM) Linear (HAA9) Post-Filter TOC (mg/l) 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) Hour SDS DBP Concentration (μg/l) 100 R 2 = R 2 = TTHM HAA9 Linear (TTHM) Linear (HAA9) Post-Filter UV 254 (cm -1 ) Figure 4.16: Correlations between UV 254 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)

89 Hour SDS DBP Concentration (μg/l) R 2 = R 2 = TTHM HAA9 Linear (TTHM) Linear (HAA9) Linear (TTHM) Post-Filter Humic Substances FEEM PCA Score 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) 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: ( b x) c y = a exp 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 UV 254 (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 UV 254 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 UV 254. Performance was evaluated using R 2 and mean absolute error (MAE) between model predictions and known data (post-filter TOC or UV 254 ). Table 4.9 shows the equations, along with values for R 2 and MAE. The exponential models resulted in strong correlations between model predictions and actual data: R 2 > 0.88 for

90 70 TOC and R 2 > 0.86 for UV 254. Equations predicting post-filter removal of TOC and UV 254 with HI 705 PACl coagulant showed the best performance (R 2 = 0.96 and 0.95, and MAE = 0.09 mg/l and 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 UV 254 (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 UV 254 using coagulant dosage Output R 2 MAE Model Equation TOC (mg/l) = 5.23 exp[-0.019(alum Dosage)]+1.38 TOC (mg/l) = 4.14 exp[-0.017(alum Dosage)]+1.50 TOC (mg/l) = 3.54 exp[-0.031(hi 705 Dosage)]+2.36 TOC (mg/l) = 5.65 exp[-0.008(hi 1000 Dosage)]+0.14 UV 254 (cm -1 ) = exp[-0.027(alum Dosage)] UV 254 (cm -1 ) = exp[-0.029(alum Dosage)] UV 254 (cm -1 ) = exp[-0.031(hi 705 Dosage)] UV 254 (cm -1 ) = exp[-0.020(hi 1000 Dosage)] MAE = mean absolute error, n = 30 for each equation 1 For TOC and UV 254 removal by alum (no ph depression) 2 For TOC and UV 254 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, UV 254 = 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 HAA 9. 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, UV 254, 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

91 71 of squared errors between model predictions and known values of TTHM and HAA 9. Performance was evaluated using R 2 and MAE between model predictions and known data. Table 4.10 shows the equations along with values for R 2 and MAE. Performance did not improve with different combinations of TOC and UV 254 used as inputs to the models (TOC only, UV 254 only, TOC and UV 254, or TOC UV 254 ). Linear correlations are similar to those observed between DPB precursors and DBP formation (R for TTHM, R for HAA 9 ). Table 4.10: Models to predict formation of TTHM and HAA 9 using TOC, UV 254, and ph Output R 2 MAE Model Equation TTHM (μg/l) = 2.07(TOC) 0.66 (ph) 1.35 TTHM (μg/l) = 18.74(UV 254 ) 0.49 (ph) 1.34 TTHM (μg/l) = 41.41(TOC) (UV 254 ) 0.68 (ph) 1.39 TTHM (μg/l) = 7.45(TOC UV 254 ) 0.29 (ph) 1.34 HAA 9 (μg/l) = 2.30(TOC) 1.24 (ph) 0.68 HAA 9 (μg/l) = 88.55(UV 254 ) 0.83 (ph) 0.76 HAA 9 (μg/l) = 24.22(TOC) 0.55 (UV 254 ) 0.50 (ph) 0.61 HAA 9 (μg/l) = 21.30(TOC UV 254 ) 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 8 th, 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 In addition to raw water quality, the ph, TOC, UV 254, and fluorescence excitation-emission of filtered water were measured, as well as TTHM and HAA 9 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 of Appendix 8.2 (Table 8.6 to Table 8.8).

92 72 Table 4.11: Peterborough raw water quality (summer values are averages of five measurements) Season ph TOC (mg/l) UV 254 (cm -1 ) Summer Winter Percent reduction of TOC in February tests is shown in Figure Similar trends were observed for UV 254, with percent removal being on average 20% greater for UV 254 than for TOC. (R 2 between post-filter TOC and UV 254 was 0.98). These results are consistent with those discussed in Section 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). Post-Filter % Reduction of TOC 70% 60% 50% 40% 30% 20% Alum Acid + Alum HI 705 HI % 0% Dosage (mg/l) Figure 4.18: Percent reduction of TOC from Peterborough water in February jar tests. Raw water TOC = 6.1 mg/l.

93 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 UV 254 data; R 2 values for correlation with maximum FEEM intensity were 0.96 and 0.99, respectively. As with TOC and UV 254, 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. 200 Filtered Water Max FEEM Intensity (a.u) Alum Acid + Alum HI 705 HI Dosage (mg/l) Figure 4.19: Maximum intensity for fluorescence peak at excitation/emission of 340/430 nm for February jar tests. TOC and UV 254 results from February jar tests were directly compared with the results of tests conducted in July and August using the equations generated in Section and shown in Table 4.9. An example is shown in Figure 4.20 for the alum jar test. The equations calibrated

94 74 with data from summer jar tests were generally able to predict filtered water TOC and UV 254 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. 7 6 TOC = 5.23 exp( [dosage]) Filtered Water TOC (mg/l) Summer TOC Winter TOC Summer UV254 Winter UV254 UV 254 = exp( [dosage]) Alum Dosage (mg/l) Figure 4.20: Seasonal comparison for removal of TOC and UV 254 by alum. Equations were generated using results from repeated tests conducted during the summer Filtered Water UV 254 (cm -1 ) Percent reduction in formation of TTHM and HAA 9 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 HAA 9 concentrations of 93.3 and 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 HAA 9, 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.

95 75 Reduction of 24-Hour TTHM Formation 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Alum Acid + Alum HI 705 HI Dosage (mg/l) Figure 4.21: Percent reduction of 24-hour TTHM formation in February tests with Peterborough water. Raw water = 93.3 μg/l. Reduction of 24-Hour HAA9 Formation 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Alum Acid + Alum HI 705 HI Dosage (mg/l) Figure 4.22: Percent reduction of 24-hour HAA 9 formation in February tests with Peterborough water. Raw water = μg/l.

96 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 HAA 9, respectively, in both summer and winter tests. The average 24-hour formation of TTHM was 30% lower than in summer tests, while the average HAA 9 formation did not change. Since summer and winter tests generally achieved the same levels of TOC and UV 254 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) Alum Acid + Alum HI 705 PACl HI 1000 PACl Alum Acid + Alum HI 705 PACl HI 1000 PACl TTHM Formation (ug/l) Coagulant Dosage (mg/l) Coagulant Dosage (mg/l) 70 Figure 4.23: 24-hour TTHM formation for tests conducted in summer (left) and winter (right) Alum Acid + Alum HI 705 PACl HI 1000 PACl Alum Acid + Alum HI 705 HI 1000 HAA 9 Formation (ug/l) Coagulant Dosage (mg/l) Coagulant Dosage (mg/l) 70 Figure 4.24: 24-hour HAA 9 formation for tests conducted in summer (left) and winter (right)

97 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 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 UV 254, 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 UV 254 than with TOC. Filtered Water DOC (mg/l) Total DOC Hydrophobic DOC Building Blocks Bio-Polymers Hydrophilic DOC Humic Substances LMW Neutrals 1 0 (no data) Alum Dosage (mg/l) 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, UV 254, maximum fluorescence intensity of humic substances, and DBP formation (TTHM and HAA 9 concentrations) by calculating R 2 values for linear correlations between all parameters (Table 5). TOC, UV 254, FEEM HS, total and hydrophilic DOC, and the humic substances and bio-polymer fractions of LC-OCD were all strongly correlated to each other (R 2 > 0.95). These parameters were also found to be closely correlated with the formation of TTHM (R 2 > 0.78) and

98 78 HAA9 (R 2 > 0.92). (Similar trends were observed for HAA 5 and HAA 6, since HAA 9 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 UV 254. 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 UV 254. Table 4.12: R-squared values for linear correlations between measures of filtered water NOM content and 24-hour DBP formation Parameters Total DOC LC-OCD Fractions Building Blocks Humic Substances LMW Neutrals Hydrophilic Hydrophobic Bio- Polymers LMW Acids TTHM HAA 9 TOC UV FEEM HS Total DOC Hydrophilic DOC Hydrophobic DOC Humic Substances Building Blocks LMW Neutrals TOC UV 254 FEEM HS DBP Formation Bio-Polymers LMW Acids TTHM HAA 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 UV 254 ) 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

99 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 HAA 9. The required dosage of HI 1000 PACl to maintain DBP precursor removal and decrease formation of TTHM and HAA 9 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 UV 254 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 Coagulant Dosage Post-filter Water Quality 24-Hour DBP Formation (μg/l) (mg/l) ph TOC (mg/l) UV 254 (cm -1 ) TTHM HAA 9 TCAN TCP Alum Acid + Alum HI 705 PACl HI 1000 PACl Table 4.14: R 2 values for linear correlations between key performance parameters Parameter TOC UV254 HS TTHMFP HAA9FP TOC 1 UV HS TTHMFP HAA 9 FP The three parameters used as indicators of DBP precursor content (TOC, UV 254, and FEEM humic substances) were shown to be closely correlated to each other (R 2 > 0.78), with weaker correlations to HAA 9 FP (0.67 < R 2 < 0.74) and very weak correlations to TTHMFP (0.25 < R 2 < 0.32). Fitting TOC and UV 254 data to exponential equations to relate them to coagulant dosage resulted in very good predictions of post-filter TOC and UV 254 (R 2 > 0.86). Equations created to relate TOC, UV 254, and ph to TTHMFP and HAA 9 FP were not able to improve upon the linear correlations summarized in the table above.

100 80 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 UV 254 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 / particulatelike 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)

101 81 acids, and LMW neutrals. While the results of FEEM and LC-OCD analyses also provided good correlations with the formation of TTHM and HAA 9, they did not improve upon the linear correlations between DBPs and TOC or UV 254 (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 UV 254 ), 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 UV 254 (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 UV 254 ). 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 UV 254 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. ph TOC (mg/l) UV 254 (cm -1 ) RW RW RW % 4% 4% FW FW FW % 19% 28% TTHM (μg/l) 24-hour formation % HAA 9 (μg/l) 24-hour formation % a n = 6 for raw water (RW), n = 144 for filtered water (FW) and 24-hour DBP formation

102 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, UV 254, 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 UV 254 (cm -1 ) FW ph Temperature ( C) Cl 2 Dosage (mg/l) TTHM Formation (μg/l) HAA 9 Formation (μg/l) Minimum th Percentile Average th Percentile Maximum 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 HAA 9 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: r 2 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

103 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 X i X Pi i= MAE = 1 n 5.1 where X i and X Pi 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: MSE n i= = 1 ( X X ) i n Pi Figure 5.1: Preliminary architecture for ANN to predict formation of THMs or HAAs using data from bench-scale testing

104 Results and Discussion The ANN architecture selected is shown in Table 5.3. These settings were used for the TTHM model and for the HAA 9 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 HAA 9 ANNs are shown in Table 5.4. Table 5.3: Final network architecture selected for TTHM and HAA 9 ANNs Architecture Parameter Selection Input neurons 5 Input scale -1 to 1 Hidden layers 1 Hidden neurons 4 Hidden transfer function TanhAxon Learning rate 0.5 Momentum coefficient 0.7 Output neurons 1 Output scale -1 to 1 Output transfer function BiasAxon Epochs 1000 Stopping Criteria 1000 epochs or increase in cross-validation error Table 5.4: Comparison of performance statistics for TTHM and HAA 9 ANNs Output Average Value (μg/l) Performance Measures r 2 MAE MSE %MAE TTHM % HAA % r 2 = 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 (r 2 = 0.85) performed somewhat better than the HAA 9 model (r 2 = 0.77). The MAE and MSE are both higher for the TTHM model (MAE = 7.0, MSE = 89.3 respectively) than for the HAA 9 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 HAA 9 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 HAA 9 models). In contrast, the exponential regression equations described in Section were not able to predict TTHM and HAA 9 formation nearly as well: these models produced r 2 values

105 85 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, r 2 = 1). The regression line between predicted and actual TTHM formation has slope of 0.78 and a y- intercept of 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 HAA 9 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 HAA 9 formation for values above 33 μg/l and over-predicts HAA 9 formation for values below 33 μg/l. 120 Predicted TTHM Formation (μg/l) y = 0.78x R 2 = Actual TTHM Formation (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 r 2 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

106 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 r 2 of 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 (r 2 = 0.89 and 0.90, respectively). 100 Predicted HAA 9 Formation (μg/l) y = 0.81x R 2 = Actual HAA 9 Formation (μg/l) Figure 5.3: Correlation plot for the predicted versus actual HAA 9 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 HAA 9 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

107 87 deviation of 9.6 μg/l. The error histogram for the HAA 9 model is centered on -3.6 μg/l and has a standard deviation of 7.3 μg/l. 35% Frequency (percent occurence) 30% 25% 20% 15% 10% 5% 0% Error (μg/l) Figure 5.4: TTHM formation error histogram 35% Frequency (percent occurence) 30% 25% 20% 15% 10% 5% 0% Error (μg/l) Figure 5.5: HAA 9 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

108 88 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 HAA 9 error data were also normalized. The values of the 10 th, 20 th, 30 th, 40 th, 50 th, 60 th, 70 th, 80 th, and 90 th percentiles were then calculated for the normal distribution and for the normalized TTHM and HAA 9 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 TTHM Error Quantiles HAA9 Error Quantiles Error Distribution Quantiles Normal Distibution Quantiles Figure 5.6: Q-Q plot to test the normality of the error distribution for DBP models (values shown are the 10 th, 20 th, 30 th, 40 th, 50 th, 60 th, 70 th, 80 th, and 90 th 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 benchscale 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.

109 89 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 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, UV 254, 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 HAA 9 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)

110 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) 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, UV 254, and TOC) interact with changes in chemical dosages to affect DBP formation. Alternatively, models may be developed using data collected during a specific season

111 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

112 92 involved the first treatment train (PP1) using the same alum dosage as the full scale plant (FSP) (typically 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) 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. Filtered Water TOC (mg/l) FSP PP1 PP /27/11 2/03/11 3/10/11 3/17/11 3/24/11 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)

113 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 HAA 9 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 HAA 9 ). This optimal alum dosage would be used to control the dosage in PP2, with the target DBP levels (TTHM and HAA 9 ) 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 FSP PP1 PP2 24-Hour TTHM Formation (μg/l) /07/11 3/08/11 3/09/11 3/10/11 3/14/11 3/15/11 3/16/11 3/17/11 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)

114 94 Process Model ANN Predicted DBPs Formation Alum (FSP Dosage) Chlorine (FSP Dosage) Raw Water Coag. / Floc. / Sed. Filtration Disinfection Actual DBPs Formation Figure 5.11: Data flow between PP1 (simplified process flow diagram) and the process model ANN. Dashed lines indicate data input/output. Target DBPs Formation Inverse Process Model ANN Optimal Alum Dosage Alum (Optimal Dosage) Chlorine (FSP Dosage) Raw Water Coag. / Floc. / Sed. Filtration Disinfection Actual DBPs Formation Figure 5.12: Data flow between PP2 (simplified process flow diagram) and the inverse process model ANN. Dashed lines indicate data input/output.

115 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 SCADA (Start) Turn on flag for invalid outputs Inputs within - training range? - + Turn on flag for invalid outputs Run DBP Formation ANN Run Optimal Alum Dosage ANN 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

116 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 r 2, 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.

117 97 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 (UV 254 ) 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 UV 254 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, UV 254, 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 r 2 correlation coefficient. Testing of these models showed good correlations between the actual and predicted data for THMs (r 2 = 0.85) and for HAAs (r 2 = 0.77).

118 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.

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126 Appendices 8.1 Sample Calculations 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: ( b x) c y ( x) = a 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, , and 2.72, respectively. Table 8.1: Example data for PODR calculation (July 19 jar test, alum with acid). Dosage (mg/l) TOC (mg/l) Find the point at which the slope of the exponential line is equal to (or 0.3 mg/l per 10_mg/L dosage). This is done using the derivative of the equation shown in Step 1: f '( x) = a b exp 1 f '( x) x = ln b a b ( b x)

127 107 For the example data, this yields: 1 (0.03) x = ln x = 34.4 mg / L This result is shown graphically in Figure 8.1, where the intersection between the exponential curve and the tangent line of slope occurs approximately halfway between 30 and 40_mg/L alum dosage (indicated by the vertical line). 7 6 TOC (mg/l) Alum Dosage (mg/l) Figure 8.1: Example of exponential approximation of jar test TOC data to find the PODR.

128 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: BIF BIF BIF ( THMs) = ( DXAAs) ( TXAAs) [ BDCM ] + 2[ DBCM ] + 3[ TBM ] [ TCM ] + [ BDCM ] + [ DBCM ] + [ TBM ] = = [ BCAA] + 2[ DBAA] [ DCAA] + [ BCAA] + [ DBAA] [ BDCAA] + 2[ DBCAA] + 3[ TBAA] [ TCAA] + [ BDCAA] + [ DBCAA] + [ TBAA] 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 TBM Concentration (μg/l) MW (g/mol) Concentration (μmol/l) BIF ( THMs) = ( 0.010) + 3( 0) ( 0.465) + ( 0.031) + ( 0.010) + ( 0) = =

129 Bench Scale Testing Raw Data 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, UV 254, and fluorescence excitation-emission data for bench scale tests post-filter water Date (dd/mm/yyyy) Coagulant Dosage (mg/l) ph TOC (mg/l) UV 254 FEEM PCA Score (cm -1 ) HS PM CPM 19/07/2010 Alum /07/2010 Alum /07/2010 Alum /07/2010 Alum /07/2010 Alum /07/2010 Alum /07/2010 Acid + Alum /07/2010 Acid + Alum /07/2010 Acid + Alum /07/2010 Acid + Alum /07/2010 Acid + Alum /07/2010 Acid + Alum /07/2010 HI 705 PACl /07/2010 HI 705 PACl /07/2010 HI 705 PACl /07/2010 HI 705 PACl /07/2010 HI 705 PACl /07/2010 HI 705 PACl /07/2010 HI 1000 PACl /07/2010 HI 1000 PACl /07/2010 HI 1000 PACl /07/2010 HI 1000 PACl /07/2010 HI 1000 PACl /07/2010 HI 1000 PACl /07/2010 Alum /07/2010 Alum /07/2010 Alum /07/2010 Alum /07/2010 Alum /07/2010 Alum /07/2010 Acid + Alum /07/2010 Acid + Alum /07/2010 Acid + Alum /07/2010 Acid + Alum /07/2010 Acid + Alum /07/2010 Acid + Alum

130 110 Table 8.3: ph, TOC, UV254, and fluorescence excitation-emission data for bench scale tests post-filter water (continued) UV 254 Date Dosage TOC FEEM PCA Score Coagulant ph (dd/mm/yyyy) (mg/l) (mg/l) (cm -1 ) HS PM CPM 27/07/2010 HI 705 PACl /07/2010 HI 705 PACl /07/2010 HI 705 PACl /07/2010 HI 705 PACl /07/2010 HI 705 PACl /07/2010 HI 705 PACl /07/2010 HI 1000 PACl /07/2010 HI 1000 PACl /07/2010 HI 1000 PACl /07/2010 HI 1000 PACl /07/2010 HI 1000 PACl /07/2010 HI 1000 PACl /08/2010 Alum /08/2010 Alum /08/2010 Alum /08/2010 Alum /08/2010 Alum /08/2010 Acid + Alum /08/2010 Acid + Alum /08/2010 Acid + Alum /08/2010 Acid + Alum /08/2010 Acid + Alum /08/2010 Acid + Alum /08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl /08/2010 Alum /08/2010 Alum /08/2010 Alum /08/2010 Alum /08/2010 Alum /08/2010 Alum /08/2010 Acid + Alum /08/2010 Acid + Alum /08/2010 Acid + Alum /08/2010 Acid + Alum /08/2010 Acid + Alum /08/2010 Acid + Alum

131 111 Table 8.3: ph, TOC, UV254, and fluorescence excitation-emission data for bench scale tests post-filter water (continued) UV 254 Date Dosage TOC FEEM PCA Score Coagulant ph (dd/mm/yyyy) (mg/l) (mg/l) (cm -1 ) HS PM CPM 09/08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl /08/2010 Alum /08/2010 Alum /08/2010 Alum /08/2010 Alum /08/2010 Alum /08/2010 Alum /08/2010 Acid + Alum /08/2010 Acid + Alum /08/2010 Acid + Alum /08/2010 Acid + Alum /08/2010 Acid + Alum /08/2010 Acid + Alum /08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 705 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl /08/2010 HI 1000 PACl 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.

132 112 Table 8.4: THM, TCAN, and TCP concentrations for 24-hour DBPFP tests Date (dd/mm/yyyy) Dosage (mg/l) Trihalomethanes Coagulant TCM BDCM DBCM TBM TCAN TCP 7/19/2010 Alum < /19/2010 Alum < /19/2010 Alum < /19/2010 Alum < /19/2010 Alum < /19/2010 Alum < /19/2010 Acid + Alum < /19/2010 Acid + Alum < /19/2010 Acid + Alum < /19/2010 Acid + Alum < /19/2010 Acid + Alum < /19/2010 Acid + Alum < /19/2010 HI 705 PACl < /19/2010 HI 705 PACl < /19/2010 HI 705 PACl < /19/2010 HI 705 PACl < /19/2010 HI 705 PACl < /19/2010 HI 705 PACl < /19/2010 HI 1000 PACl < /19/2010 HI 1000 PACl < /19/2010 HI 1000 PACl < /19/2010 HI 1000 PACl < /19/2010 HI 1000 PACl < /19/2010 HI 1000 PACl < /27/2010 Alum < /27/2010 Alum < /27/2010 Alum < /27/2010 Alum < /27/2010 Alum < /27/2010 Alum < /27/2010 Acid + Alum < /27/2010 Acid + Alum < /27/2010 Acid + Alum < /27/2010 Acid + Alum < /27/2010 Acid + Alum < /27/2010 Acid + Alum < /27/2010 HI 705 PACl < /27/2010 HI 705 PACl < /27/2010 HI 705 PACl < /27/2010 HI 705 PACl < /27/2010 HI 705 PACl < /27/2010 HI 705 PACl < < # indicates less than method detection limit

133 113 Table 8.4: THM, TCAN, and TCP concentrations for 24-hour DBPFP tests (continued) Date Dosage Trihalomethanes Coagulant (dd/mm/yyyy) (mg/l) TCM BDCM DBCM TBM TCAN TCP 7/27/2010 HI 1000 PACl < /27/2010 HI 1000 PACl < /27/2010 HI 1000 PACl < /27/2010 HI 1000 PACl < /27/2010 HI 1000 PACl < /27/2010 HI 1000 PACl < /4/2010 Alum < NA 8/4/2010 Alum < NA 8/4/2010 Alum < NA 8/4/2010 Alum < NA 8/4/2010 Alum < NA 8/4/2010 Acid + Alum < NA 8/4/2010 Acid + Alum < NA 8/4/2010 Acid + Alum < NA 8/4/2010 Acid + Alum < NA 8/4/2010 Acid + Alum < NA 8/4/2010 Acid + Alum < NA 8/4/2010 HI 705 PACl < NA 8/4/2010 HI 705 PACl < NA 8/4/2010 HI 705 PACl < NA 8/4/2010 HI 705 PACl < NA 8/4/2010 HI 705 PACl < NA 8/4/2010 HI 705 PACl < NA 8/4/2010 HI 1000 PACl < NA 8/4/2010 HI 1000 PACl < NA 8/4/2010 HI 1000 PACl < NA 8/4/2010 HI 1000 PACl < NA 8/4/2010 HI 1000 PACl < NA 8/4/2010 HI 1000 PACl < NA 8/9/2010 Alum < NA 8/9/2010 Alum < NA 8/9/2010 Alum < NA 8/9/2010 Alum < NA 8/9/2010 Alum < NA 8/9/2010 Alum < NA 8/9/2010 Acid + Alum NA 8/9/2010 Acid + Alum NA 8/9/2010 Acid + Alum NA 8/9/2010 Acid + Alum NA 8/9/2010 Acid + Alum NA 8/9/2010 Acid + Alum NA < # indicates less than method detection limit NA = Not Available

134 114 Table 8.4: THM, TCAN, and TCP concentrations for 24-hour DBPFP tests (continued) Date Dosage Trihalomethanes Coagulant (dd/mm/yyyy) (mg/l) TCM BDCM DBCM TBM TCAN TCP 8/9/2010 HI 705 PACl NA 8/9/2010 HI 705 PACl NA 8/9/2010 HI 705 PACl NA 8/9/2010 HI 705 PACl NA 8/9/2010 HI 705 PACl NA 8/9/2010 HI 705 PACl NA 8/9/2010 HI 1000 PACl NA 8/9/2010 HI 1000 PACl NA 8/9/2010 HI 1000 PACl NA 8/9/2010 HI 1000 PACl NA 8/9/2010 HI 1000 PACl NA 8/9/2010 HI 1000 PACl NA 8/17/2010 Alum < NA 8/17/2010 Alum < NA 8/17/2010 Alum < NA 8/17/2010 Alum < NA 8/17/2010 Alum < NA 8/17/2010 Alum < NA 8/17/2010 Acid + Alum < NA 8/17/2010 Acid + Alum < NA 8/17/2010 Acid + Alum < NA 8/17/2010 Acid + Alum < NA 8/17/2010 Acid + Alum < NA 8/17/2010 Acid + Alum < NA 8/17/2010 HI 705 PACl < NA 8/17/2010 HI 705 PACl < NA 8/17/2010 HI 705 PACl < NA 8/17/2010 HI 705 PACl < NA 8/17/2010 HI 705 PACl < NA 8/17/2010 HI 705 PACl < NA 8/17/2010 HI 1000 PACl < NA 8/17/2010 HI 1000 PACl < NA 8/17/2010 HI 1000 PACl < NA 8/17/2010 HI 1000 PACl < NA 8/17/2010 HI 1000 PACl < NA 8/17/2010 HI 1000 PACl < NA < # indicates less than method detection limit NA = Not Available

135 115 Table 8.5: HAA concentrations for 24-hour DBPFP tests Date (dd/mm/yyyy) Dose (mg/l) Haloacetic Acids Coagulant MCAA MBAA DCAA TCAA BCAA DBAA BDCAA DBCAA TBAA 7/19/2010 Alum 20 < 0.09 < < < 0.45 < /19/2010 Alum 30 < 0.09 < < < 0.45 < /19/2010 Alum 42 < 0.09 < < < 0.45 < /19/2010 Alum 50 < 0.09 < < < 0.45 < /19/2010 Alum 60 < 0.09 < < < 0.45 < /19/2010 Alum 70 < 0.09 < < < 0.45 < /19/2010 Acid + Alum 10 < 0.09 < < 0.10 < < 0.45 < /19/2010 Acid + Alum 20 < 0.09 < < 0.10 < < 0.45 < /19/2010 Acid + Alum 30 < 0.09 < < 0.10 < < 0.45 < /19/2010 Acid + Alum 42 < 0.09 < < 0.10 < < 0.45 < /19/2010 Acid + Alum 50 < 0.09 < < 0.10 < < 0.45 < /19/2010 Acid + Alum 60 < 0.09 < < 0.10 < < 0.45 < /19/2010 HI 705 PACl 20 < 0.09 < < < 0.45 < /19/2010 HI 705 PACl 30 < 0.09 < < < 0.45 < /19/2010 HI 705 PACl 40 < 0.09 < < < 0.45 < /19/2010 HI 705 PACl 50 < 0.09 < < < 0.45 < /19/2010 HI 705 PACl 60 < 0.09 < < < 0.45 < /19/2010 HI 705 PACl 70 < 0.09 < < 0.10 < 0.17 < 0.20 < 0.45 < /19/2010 HI 1000 PACl < < 0.45 < /19/2010 HI 1000 PACl < < 0.45 < /19/2010 HI 1000 PACl < < < 0.45 < /19/2010 HI 1000 PACl < < < 0.45 < /19/2010 HI 1000 PACl 60 < 0.09 < < < 0.45 < /19/2010 HI 1000 PACl 70 < 0.09 < < < 0.45 < /27/2010 Alum 20 < 0.09 < < < 0.45 < /27/2010 Alum 30 < 0.09 < < < 0.45 < /27/2010 Alum 43 < 0.09 < < < 0.45 < /27/2010 Alum 50 < 0.09 < < < 0.45 < /27/2010 Alum 60 < 0.09 < < < 0.45 < /27/2010 Alum 70 < 0.09 < < < 0.45 < /27/2010 Acid + Alum 20 < 0.09 < < 0.10 < < 0.45 < /27/2010 Acid + Alum 30 < 0.09 < < 0.10 < < 0.45 < /27/2010 Acid + Alum 43 < 0.09 < < 0.10 < < 0.45 < /27/2010 Acid + Alum 50 < 0.09 < < 0.10 < < 0.45 < /27/2010 Acid + Alum 60 < 0.09 < < 0.10 < < 0.45 < /27/2010 Acid + Alum 70 < 0.09 < < 0.10 < < 0.45 < /27/2010 HI 705 PACl 20 < 0.09 < < < 0.45 < /27/2010 HI 705 PACl 30 < 0.09 < < < 0.45 < /27/2010 HI 705 PACl 40 < 0.09 < < < 0.45 < /27/2010 HI 705 PACl 50 < 0.09 < < < 0.45 < /27/2010 HI 705 PACl 60 < 0.09 < < < 0.45 < /27/2010 HI 705 PACl 70 < 0.09 < < < 0.45 < 0.75 < # indicates less than method detection limit

136 116 Table 8.5: HAA concentrations for 24-hour DBPFP tests (continued) Date Dose Haloacetic Acids Coagulant (dd/mm/yyyy) (mg/l) MCAA MBAA DCAA TCAA BCAA DBAA BDCAA DBCAA TBAA 7/27/2010 HI 1000 PACl 20 < < < 0.45 < /27/2010 HI 1000 PACl 30 < 0.09 < < < 0.45 < /27/2010 HI 1000 PACl 40 < < < 0.45 < /27/2010 HI 1000 PACl 50 < 0.09 < < < 0.45 < /27/2010 HI 1000 PACl 60 < 0.09 < < < 0.45 < /27/2010 HI 1000 PACl 70 < 0.09 < < < 0.45 < /4/2010 Alum < < < 0.20 < 0.45 < /4/2010 Alum < < < 0.20 < 0.45 < /4/2010 Alum < < < 0.20 < 0.45 < /4/2010 Alum < < < 0.20 < 0.45 < /4/2010 Alum < < < 0.20 < 0.45 < /4/2010 Acid + Alum < 0.20 < 0.45 < /4/2010 Acid + Alum < 0.20 < 0.45 < /4/2010 Acid + Alum < 0.20 < 0.45 < /4/2010 Acid + Alum < 0.20 < 0.45 < /4/2010 Acid + Alum < 0.20 < 0.45 < /4/2010 Acid + Alum < 0.20 < 0.45 < /4/2010 HI 705 PACl < 0.20 < 0.45 < /4/2010 HI 705 PACl < 0.20 < 0.45 < /4/2010 HI 705 PACl < 0.20 < 0.45 < /4/2010 HI 705 PACl < 0.20 < 0.45 < /4/2010 HI 705 PACl < 0.20 < 0.45 < /4/2010 HI 705 PACl < 0.20 < 0.45 < /4/2010 HI 1000 PACl < 0.20 < 0.45 < /4/2010 HI 1000 PACl < 0.20 < 0.45 < /4/2010 HI 1000 PACl < 0.20 < 0.45 < /4/2010 HI 1000 PACl < 0.20 < 0.45 < /4/2010 HI 1000 PACl < 0.20 < 0.45 < /4/2010 HI 1000 PACl < 0.20 < 0.45 < /9/2010 Alum < 0.20 < 0.45 < /9/2010 Alum < 0.20 < 0.45 < /9/2010 Alum < 0.20 < 0.45 < /9/2010 Alum < < 0.20 < 0.45 < /9/2010 Alum < < 0.20 < 0.45 < /9/2010 Alum < < 0.20 < 0.45 < /9/2010 Acid + Alum < 0.20 < 0.45 < /9/2010 Acid + Alum < 0.20 < 0.45 < /9/2010 Acid + Alum < 0.20 < 0.45 < /9/2010 Acid + Alum < < 0.20 < 0.45 < /9/2010 Acid + Alum < < 0.20 < 0.45 < /9/2010 Acid + Alum < < 0.20 < 0.45 < 0.75 < # indicates less than method detection limit

137 117 Table 8.5: HAA concentrations for 24-hour DBPFP tests (continued) Date Dose Haloacetic Acids Coagulant (dd/mm/yyyy) (mg/l) MCAA MBAA DCAA TCAA BCAA DBAA BDCAA DBCAA TBAA 8/9/2010 HI 705 PACl < 0.20 < 0.45 < /9/2010 HI 705 PACl < 0.20 < 0.45 < /9/2010 HI 705 PACl < 0.20 < 0.45 < /9/2010 HI 705 PACl < 0.20 < 0.45 < /9/2010 HI 705 PACl < < 0.20 < 0.45 < /9/2010 HI 705 PACl < < 0.20 < 0.45 < /9/2010 HI 1000 PACl < 0.20 < 0.45 < /9/2010 HI 1000 PACl < < 0.20 < 0.45 < /9/2010 HI 1000 PACl < 0.20 < 0.45 < /9/2010 HI 1000 PACl < 0.20 < 0.45 < /9/2010 HI 1000 PACl < 0.20 < 0.45 < /9/2010 HI 1000 PACl < 0.20 < 0.45 < /17/2010 Alum < < < 0.20 < 0.45 < /17/2010 Alum < < 0.20 < 0.45 < /17/2010 Alum < < 0.10 < 0.17 < 0.20 < 0.45 < /17/2010 Alum < < 0.10 < 0.17 < 0.20 < 0.45 < /17/2010 Alum < < 0.10 < 0.17 < 0.20 < 0.45 < /17/2010 Alum < 0.05 < < 0.10 < 0.17 < 0.20 < 0.45 < /17/2010 Acid + Alum < < 0.10 < 0.17 < 0.20 < 0.45 < /17/2010 Acid + Alum < < 0.10 < 0.17 < 0.20 < 0.45 < /17/2010 Acid + Alum < < 0.10 < 0.17 < 0.20 < 0.45 < /17/2010 Acid + Alum < < 0.10 < 0.17 < 0.20 < 0.45 < /17/2010 Acid + Alum < < 0.10 < 0.17 < 0.20 < 0.45 < /17/2010 Acid + Alum < 0.05 < < 0.10 < 0.17 < 0.20 < 0.45 < /17/2010 HI 705 PACl < < 0.20 < 0.45 < /17/2010 HI 705 PACl < < < 0.20 < 0.45 < /17/2010 HI 705 PACl < < < 0.20 < 0.45 < /17/2010 HI 705 PACl < < < 0.20 < 0.45 < /17/2010 HI 705 PACl < < < 0.20 < 0.45 < /17/2010 HI 705 PACl < < 0.10 < 0.17 < 0.20 < 0.45 < /17/2010 HI 1000 PACl < < 0.10 < 0.17 < 0.20 < 0.45 < /17/2010 HI 1000 PACl < < 0.17 < 0.20 < 0.45 < /17/2010 HI 1000 PACl < < 0.20 < 0.45 < /17/2010 HI 1000 PACl < < 0.17 < 0.20 < 0.45 < /17/2010 HI 1000 PACl < < 0.10 < 0.17 < 0.20 < 0.45 < /17/2010 HI 1000 PACl < < 0.10 < 0.17 < 0.20 < 0.45 < 0.75 < # indicates less than method detection limit

138 Winter Bench Scale Test Results Table 8.6: ph, TOC, UV254, and fluorescence excitation-emission data for February bench scale tests post-filter water UV 254 Date Dosage TOC Fluorescence Coagulant ph (dd/mm/yyyy) (mg/l) (mg/l) (cm -1 ) Intensity (au) 08/02/2011 Alum /02/2011 Alum /02/2011 Alum /02/2011 Alum /02/2011 Alum /02/2011 Alum /02/2011 Acid + Alum /02/2011 Acid + Alum /02/2011 Acid + Alum /02/2011 Acid + Alum /02/2011 Acid + Alum /02/2011 Acid + Alum /02/2011 HI 705 PACl /02/2011 HI 705 PACl /02/2011 HI 705 PACl /02/2011 HI 705 PACl /02/2011 HI 705 PACl /02/2011 HI 705 PACl /02/2011 HI 1000 PACl /02/2011 HI 1000 PACl /02/2011 HI 1000 PACl /02/2011 HI 1000 PACl /02/2011 HI 1000 PACl /02/2011 HI 1000 PACl Table 8.7: THM concentrations for February DBPFP tests Date Dosage Trihalomethanes Coagulant (dd/mm/yyyy) (mg/l) TCM BDCM DBCM TBM 2/8/2011 Alum < 0.01 < /8/2011 Alum < 0.01 < /8/2011 Alum < 0.01 < /8/2011 Alum < 0.01 < /8/2011 Alum < 0.01 < /8/2011 Alum < 0.01 < /8/2011 Acid + Alum < 0.01 < /8/2011 Acid + Alum < 0.01 < /8/2011 Acid + Alum < 0.01 < /8/2011 Acid + Alum < 0.01 < /8/2011 Acid + Alum < 0.01 < /8/2011 Acid + Alum < 0.01 < 0.20 < # indicates less than method detection limit

139 119 Table 8.7: THM concentrations for February DBPFP tests (continued) Date Dosage Trihalomethanes Coagulant (dd/mm/yyyy) (mg/l) TCM BDCM DBCM TBM 2/8/2011 HI 705 PACl < 0.01 < /8/2011 HI 705 PACl < 0.01 < /8/2011 HI 705 PACl < 0.01 < /8/2011 HI 705 PACl < 0.01 < /8/2011 HI 705 PACl < 0.01 < /8/2011 HI 705 PACl < 0.01 < /8/2011 HI 1000 PACl < 0.01 < /8/2011 HI 1000 PACl < 0.01 < /8/2011 HI 1000 PACl < 0.01 < /8/2011 HI 1000 PACl < 0.01 < /8/2011 HI 1000 PACl < 0.01 < /8/2011 HI 1000 PACl < 0.01 < 0.20 < # indicates less than method detection limit Table 8.8: HAA concentrations for February DBPFP tests Date Dosage Haloacetic Acids Coagulant (dd/mm/yyyy) (mg/l) MCAA MBAA DCAA TCAA BCAA DBAA BDCAA DBCAA TBAA 2/8/2011 Alum < < 0.45 < /8/2011 Alum < < 0.45 < /8/2011 Alum 42 NA NA NA NA NA NA NA NA NA 2/8/2011 Alum < < 0.45 < /8/2011 Alum < < 0.45 < /8/2011 Alum < < < 0.45 < /8/2011 Acid + Alum < < < 0.45 < /8/2011 Acid + Alum < < 0.10 < < 0.45 < /8/2011 Acid + Alum < < < 0.45 < /8/2011 Acid + Alum < < 0.10 < < 0.45 < /8/2011 Acid + Alum < < 0.10 < < 0.45 < /8/2011 Acid + Alum < < 0.10 < < 0.45 < /8/2011 HI 705 PACl < < 0.45 < /8/2011 HI 705 PACl < < 0.45 < /8/2011 HI 705 PACl < < 0.45 < /8/2011 HI 705 PACl < < 0.45 < /8/2011 HI 705 PACl < < 0.45 < /8/2011 HI 705 PACl < < 0.45 < /8/2011 HI 1000 PACl < < 0.45 < /8/2011 HI 1000 PACl < < 0.45 < /8/2011 HI 1000 PACl < < 0.45 < /8/2011 HI 1000 PACl < < 0.45 < /8/2011 HI 1000 PACl < < 0.45 < /8/2011 HI 1000 PACl < < 0.45 < 0.75 < # indicates less than method detection limit

140 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: MAE n i= = 1 X i n X Pi where X i and X Pi 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: MSE n i= = 1 ( X X ) i n Pi 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 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

141 121 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

142 122 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

143 123 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.

144 124 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

145 125 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 crossvalidation 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.

146 126 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

147 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

148 128 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.

149 Figure 8.14: Choosing how NeuroSolutions should output the results of ANN testing 129