optimisation of coagulation process in water...

OPTIMISATION OF COAGULATION PROCESS IN WATER TREATMENT

PLANT USING STATISTICAL APPROACH

MUZAFFAR BIN ZAINAL ABIDEEN

A thesis submitted in fulfilment of the

requirements for the award of the degree of

Doctor of Philosophy (Civil Engineering)

Faculty of Civil Engineering

Universiti Teknologi Malaysia

JUNE 2016

ii

I declare that this thesis entitled “Optimisation Of Coagulation Process In Water

Treatment Plant Using Statistical Approach” is the result of my own research except

as cited in the references. This thesis has not been accepted for any degree and is not

concurrently submitted in candidature of any other degree.

Signature : ………………………………

Name : MUZAFFAR BIN ZAINAL ABIDEEN

Date : 5th JUNE, 2016

iii

Love, Thoughts and Du’a to

my late mak (Raja Arbaiah) and abah (Hj Zainal Abideen);

my beloved wife, Dzurina;

my children, Iffah, Harraz, Asyraf and Ammar;

my lovely makcik (Hjh Jamaliah) and mama (Hjh Rokiah).

iv

ACKNOWLEDGMENTS

In the name of Allah, the Most Gracious, the Most Merciful. All the praises and thanks are to Allah, the Lord of the Alamin. May His peace and blessings be upon His messenger, Prophet Muhammad Sallahu Alaihi Wassalam, his family and his companions.

First and foremost, I would like to express my gratefulness to Allah Azza wa

Jalla for giving me the ability and strength to complete my doctorate study. I sincerely extend gratitude and appreciation to my main supervisor, Professor Dr. Azmi bin Aris, for very many reasons, especially for his guidance, advice, patience and words of wisdom. His confidence in me and his undivided support given over countless days and nights, have been very important for me in completing this long and tumultuous task. My thanks also go to Associate Professor Dr. Zaiton binti Abdul Majid and Associate Professor Dr. Fadhilah binti Yusof for serving as co-supervisors. Their help and commitment in the discussion and interpretation of results presented in this thesis are immensely invaluable.

I would also like to take this opportunity to thank my parents and my parents-

in-law for their love and support throughout my life. My wholehearted love and thanks are also dedicated to my beloved wife, Dzurina; the apples of my eye, Iffah, Harraz, Asyraf and Ammar; for their continuous du’a, sacrifice and undivided support throughout this study. My beloved friends all over the world and especially in Johor, whom I have considered to be my brothers and sisters, have helped me a lot, physically and emotionally. They, as well as all family members deserve my utmost thanks too.

My appreciation also goes to Syarikat Air Johor Holdings for providing fund

and permitting me to carry out my study at the Bandar Tenggara, Sungai Gembut and Sri Gading Water Treatment Plants. I would also like to thank the Ministry of Higher Education (MOHE) for generously awarding me the scholarship. Last but not least, I would like to thank Universiti Teknologi Malaysia, and especially the Faculty of Civil Engineering and Faculty of Science, for allowing me to use their laboratories, as well as to all who contributed directly or indirectly in accomplishing this thesis.

v

ABSTRACT

The typical jar test practice at water treatment plants to determine the optimum coagulation conditions is statistically inappropriate as it is based on one-factor-at-a-time (OFAT) approach. This approach, which does not cover the whole experimental space, could miss the actual optimum values results in higher cost and less effective coagulation performance. Additionally, the jar test exercise is time consuming and is therefore conducted once a day or when it is really needed. This study carried out a long-term comparison between the use of Response Surface Method (RSM) and the traditional (a modified OFAT) jar test in optimising the coagulation process. The study was carried out at Bandar Tenggara WTP (BTWTP), Sungai Gembut WTP (SGWTP) and Sri Gading WTP (SRGWTP) as these WTPs have different raw water characteristics. Characterisation of natural organic matters (NOMs) in terms of dissolved organic carbon (DOC) and UV254 and their removal via coagulation process were explored. The relationship between the raw water characteristics and coagulation optimum conditions were also developed. The results showed that the RSM and traditional approaches acquired almost identical optimum coagulation conditions at BTWTP and SGWTP. At SRGWTP, RSM technique was found to be better than the traditional method as the coagulant (alum) and flocculant (polymer) optimum concentrations produced by the RSM technique were respectively 50% and 20% of the optimum values obtained by the traditional method. The raw waters of BTWTP and SGWTP were identified as hydrophilic, non-humic, with low molecular mass of NOMs. At optimum coagulation conditions (based on turbidity removal), the average DOC and UV254 removals were about 11% and 70%, respectively. The models predicting optimum pH and coagulant dosing from raw water quality data were formulated from the RSM and traditional approach and was found to be reliable. From the models, it was found that NH3–N in the raw water at BTWTP and turbidity, Mn and NH3–N in the raw water at SGWTP did not affect the optimum coagulation conditions. The detail optimisation model derivation procedures were also developed.

vi

ABSTRAK

Ujian balang yang biasa digunakan untuk menentukan keadaan optimum koagulasi adalah tidak tepat secara statistik kerana ia menggunakan pendekatan satu-faktor-dalam-satu-masa (OFAT). Pendekatan yang tidak merangkumi keseluruhan ruang eksperimen ini, mungkin terlepas di dalam mendapatkan nilai optimum sebenar yang menyebabkan peningkatan kos serta menghasilkan prestasi koagulasi yang kurang efektif. Tambahan lagi, pelaksanaan ujian balang agak mengambil masa yang panjang dan oleh itu, ia hanya dilakukan sehari sekali atau apabila berkeperluan. Kajian ini membuat perbandingan jangka panjang di antara penggunaan ujian balang berasaskan Kaedah Permukaan Maklumbalas (RSM) dengan ujian balang berasaskan kaedah tradisional (OFAT Terubahsuai) di dalam mengoptimumkan proses koagulasi. Kajian dilaksanakan di Loji Olahan Air Bandar Tenggara (BTWTP), Sungai Gembut (SGWTP) dan Sri Gading (SRGWTP) kerana loji-loji ini mempunyai ciri-ciri air mentah yang berbeza. Pencirian bahan organik semulajadi (NOM) dalam bentuk karbon organik terlarut (DOC) dan UV254 serta penyingkirannya melalui proses koagulasi juga diterokai. Hubungan di antara ciri-ciri air mentah dan keadaan optimum koagulasi juga dihasilkan. Berdasarkan pemerhatian, kaedah RSM dan tradisional yang dijalankan di BTWTP dan SGWTP memberikan keadaan optimum yang hampir serupa. Di SRGWTP, kaedah RSM yang dilaksanakan didapati lebih baik daripada kaedah tradisional kerana dos optimum koagulan (alum) dan flokulan (polimer) yang diperoleh daripada kaedah RSM adalah masing-masing 50% dan 20% daripada dos optimum yang diperoleh melalui kaedah tradisional. Air mentah dari BTWTP dan SGWTP dikenalpasti mengandungi NOM yang hidrofilik, bukan humik dan rendah jisim molekulnya. Pada keadaan koagulasi yang optimum (berasaskan penyingkiran kekeruhan), purata penyingkiran DOC dan UV254 adalah masing-masing sebanyak kira-kira 11% dan 70%. Model-model yang dijanakan untuk meramalkan hubungan di antara dos koagulan dan pH optimum dengan data kualiti air mentah melalui pendekatan RSM dan tradisional, didapati boleh dipercayai. Menerusi model-model ini, kandungan NH3–N di dalam air mentah di BTWTP dan kekeruhan, Mn dan NH3–N di dalam air mentah di SGWTP ini tidak memberi kesan terhadap keadaan optimum koagulasi loji terbabit. Perincian tentang prosedur penerbitan model pengoptimuman juga dihasilkan di dalam kajian ini.

vii

TABLE OF CONTENTS

CHAPTER TITLE PAGE DECLARATION ii DEDICATION iii ACKNOWLEDGMENT iv ABSTRACT v ABSTRAK vi TABLE OF CONTENTS vii LIST OF TABLES xii

LIST OF FIGURES xv LIST OF ABBREVIATIONS xxiv LIST OF SYMBOLS xxvi LIST OF APPENDICES xxvii

1 INTRODUCTION 1 1.1 Preamble 1 1.2 Problem Statement 3 1.3 Objectives of Study 6 1.4 Scope of Study 7 1.5 Significance of Study 7 1.6 Thesis Outline 9 2 LITERATURE REVIEW 10 2.1 Impurities in Water 10 2.1.1 Colloids 10

viii

2.1.2 Natural Organic Matters 11

2.1.3 Removal of Natural Organic Matters by Coagulation

16

2.2 Coagulation Process 18 2.2.1 Theory of Coagulation Process 18 2.2.2 Mechanisms of Coagulation 22 2.2.2.1 Double Layer Compression 23 2.2.2.2 Adsorption and Charge Neutralisation 24 2.2.2.3 Sweep Coagulation 27 2.2.2.4 Adsorption and Interparticle Bridging 27 2.2.2.5 Complexation and Co-precipitation 28 2.2.3 Factors Affecting Coagulation Process 29 2.2.3.1 Characteristics of Raw Water 31 2.2.3.2 Temperature 32 2.2.3.3 Mixing 32 2.2.3.4 pH 34 2.2.3.5 Coagulants and Coagulant Aids

(Flocculants)

38 2.2.4 Determination of Optimum Condition for

Coagulation Process

44 2.2.4.1 Measurement of Zeta Potential 44 2.2.4.2 Jar Test 44 2.3 Design of Experiments: Traditional versus Statistical 45 2.3.1 Response Surface Method 47 2.4 Conclusions 52

3 METHODOLOGY 53 3.1 Introduction 53 3.2 Study Sites 53 3.3 Materials and Equipment 57 3.4 Analytical Procedures 57 3.4.1 Temperature and pH 60 3.4.2 Turbidity 60 3.4.3 Colour 60 3.4.4 Aluminium 61

ix

3.4.5 Iron (III), Manganese And Ammonium Ions 61 3.4.6 Total Dissolved Solids 62 3.4.7 Characterisation of Natural Organic Matters 62 3.4.7.1 Dissolved Organic Carbon 63 3.4.7.2 UV254 Absorbance 64 3.5 Experimental Procedures 64 3.5.1 Historical Data Collection 64 3.5.2 Determination of Raw Water Quality Parameters 65 3.5.3 Jar Test Procedure 65

3.5.3.1 Preparation of Stock Solution 66 3.5.3.2 Traditional Jar Test 67

3.5.3.3 Response Surface Methodological Jar Test 69 3.5.3.4 Optimum Conditions for Jar Tests 70 3.5.4 Development of Statistical Models 71 3.5.5 Natural Organic Matters Characteristics and

Removal

74 3.5.6 Development of Standard Procedure 74

4 OPTIMISATION OF JAR TESTS AND NATURAL ORGANIC MATTERS REMOVAL

75

4.1 Introduction 75

4.2 Characteristics of the Raw Water 76 4.2.1 Bandar Tenggara WTP 76

4.2.2 Sungai Gembut WTP 86 4.2.3 Sri Gading WTP 95

4.2.4 Conclusion on the Raw Water Quality Parameters at the three WTPs

101

4.3 Derivation of Jar Test Experimental Conditions 104 4.3.1 Chemicals Dosage and operating pH for Traditional

Jar Test

105 4.3.1.1 Bandar Tenggara WTP 105 4.3.1.2 Sungai Gembut WTP 107 4.3.1.3 Sri Gading WTP 109 4.3.1.4 Summary of Chemicals Dosage and

Operating pH for Traditional Jar Tests

111

x

4.3.2 Chemicals Dosing and operating pH for Response Surface Methodology Jar Test

112

4.3.2.1 Bandar Tenggara WTP 113 4.3.2.2 Sungai Gembut WTP 115 4.3.2.3 Sri Gading WTP 116 4.4 Comparison between Traditional and RSM Jar Test 116 4.4.1 Bandar Tenggara WTP 118 4.4.1.1 Determination of Optimum Coagulation

Conditions from Traditional and RSM Jar Test at Bandar Tenggara WTP

118 4.4.1.2 Verification of RSM Jar Test Optimisation

Experimental Results at Bandar Tenggara WTP

130 4.4.1.3 Traditional versus RSM Optimisation at

Bandar Tenggara WTP

132 4.4.2 Sungai Gembut WTP 136 4.4.2.1 Determination of Optimum Coagulation

Conditions from Traditional and RSM Jar Test at Sungai Gembut WTP

136 4.4.2.2 Verification of RSM Jar Test Optimisation

Experimental Results at Sungai Gembut WTP

148 4.4.2.3 Traditional versus RSM Optimisation at

Sungai Gembut WTP

149 4.4.3 Sri Gading WTP 155 4.4.3.1 Determination of Optimum Coagulation

Conditions from Traditional and RSM Jar Test at Sri Gading WTP

155 4.4.3.2 Traditional versus RSM Optimisation at Sri

Gading WTP

166 4.4.4 Conclusions on Comparison between Traditional

and RSM Jar Test

172 4.5 Natural Organic Matters Removal By Coagulation

Process

173

4.5.1 Natural Organic Matter Removal Through Traditional Jar Test

173

4.5.1.1 Bandar Tenggara WTP 173

xi

4.5.1.2 Sungai Gembut WTP 177

4.5.2 Natural Organic Matter Removal at Optimum Turbidity Removal Conditions by Traditional Jar Test

180

4.5.2.1 Bandar Tenggara WTP 180

4.5.2.2 Sungai Gembut WTP 183 4.5.3 Conclusion on Natural Organic Matters Removal

Rates by Traditional Jar Test at Bandar Tenggara and Sungai Gembut WTPs

186

5 REGRESSION MODEL – FINDINGS AND

DERIVATION PROCEDURES

187 5.1 Introduction 187 5.2 Establishment of Statistical Models 187

5.2.1 Bandar Tenggara WTP 188 5.2.2 Sungai Gembut WTP 192

5.2.3 Sri Gading WTP 197 5.2.4 Validation of the Statistical Models 203

5.2.4.1 Validation at Bandar Tenggara WTP 204 5.2.4.2 Validation at Sungai Gembut WTP 208

5.2.5 Conclusions on the Statistical Models Findings 211 5.3 Statistical Models Derivation Procedure 214 5.3.1 Historical Data Gathering 216 5.3.2 Regression Models Generation 216 5.3.3 Model Validation and Selection 219 5.3.4 Conclusion on Statistical Models Derivation

Procedure

220 6 CONCLUSIONS AND RECOMMENDATIONS 221 6.1 Conclusions 221 6.2 Recommendations 223 REFERENCES 225 Appendices A – C 243 – 279

xii

LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Selected review of techniques applied to characterise

NOM

14

2.2 NOM and turbidity recorded for some natural water 16

2.3 SUVA, composition of NOM, coagulation process and

DOC removal (Matilainen et al., 2010)

17

2.4 Selected studies on effect of temperature on coagulation

efficiency

33

2.5 Selected studies on effect of mixing on coagulation

efficiency

35

2.6 Selected studies on effect of pH on coagulation efficiency 39

2.7 Summary on coagulants and flocculants 40

3.1 List of equipment used in the study 58

3.2 List of chemicals and reagent used in the study 59

4.1 UV254 versus raw water sources 84

4.2 Summary of the raw water quality parameters at Bandar

Tenggara, Sungai Gembut and Sri Gading WTP

103

4.3 Ranges of PAC and pH dosage used in the traditional jar

test at Bandar Tenggara WTP

106

4.4 Ranges of alum and pH dosage used in the traditional jar

test at Sungai Gembut WTP

108

4.5 Ranges of initial pH, alum, and polymer concentrations

used in the traditional jar test at Sri Gading WTP

110

xiii

4.6 Summary of Chemicals Dosage and operating pH for

Traditional Jar Tests at Bandar Tenggara, Sungai Gembut

and Sri Gading WTPs

112

4.7 RSM Jar Test conditions for Bandar Tenggara WTP 114

4.8 RSM Jar Test conditions for Sungai Gembut WTP 116

4.9 RSM Jar Test conditions for Sri Gading WTP 117

4.10 Raw water quality of Bandar Tenggara WTP 119

4.11 Traditional jar test result for Bandar Tenggara WTP 120

4.12 Experimental range and levels of factors for Bandar

Tenggara WTP

120

4.13 CCD and response results for the study of two

experimental factors (variables) in coded units for Bandar

Tenggara WTP

121

4.14 ANOVA results for response parameters in RSM jar test 123

4.15 Experimental and predicted values of the responses at the

optimum levels predicted by RSM

130

4.16 Raw water quality of Sungai Gembut WTP 137

4.17 Traditional jar test result of Sungai Gembut WTP 138

4.18 Experimental range and levels of factors of Sungai

Gembut WTP

139

4.19 CCD and response results for the study of two

experimental factors (variables) in coded units for Sungai

Gembut WTP

140



optimum levels predicted by RSM at Sungai Gembut

WTP

148

4.22 Water quality parameters for raw water sample from Sri

Gading WTP

155

4.23 Traditional jar test result for Sri Gading WTP 156

4.24 Experimental range and levels of factors for Sri Gading

WTP

156

xiv

4.25 CCD and response results for the study of three

experimental factors (variables) in coded units for Sri

Gading WTP

157



optimum levels predicted by RSM at Sri GadingWTP

161

4.28 Comparison between traditional, direct reading from

RSM table and RSM optimisation techniques on the

amount and cost of chemicals at Sri Gading WTP

168

4.29 Raw and treated turbidity, DOC, UV254 and SUVA at

optimum traditional jar test conditions based on the

turbidity, settling pH and residual aluminium at Bandar

Tenggara WTP

182

4.30 Raw and treated turbidity, DOC, UV254 and SUVA at

optimum traditional jar test conditions based on the

turbidity, settling pH and residual aluminium at Sungai

Gembut WTP

185

5.1 Final “refined” regression models for Sungai Gembut

WTP

209

xv

LIST OF FIGURES

FIGURE NO. TITLE PAGE

1.1 Design of Experiment; (a) OFAT Approach and (b) All

Factors Varied Systematically Approach

4

2.1 Hypothetical molecular structure of humic acid

(Matilainen et al., 2010)

12

2.2 Schematic diagram showing the electrical double layer

and potentials (Sawyer et al., 1994)

20

2.3 Forces acting between colloids (Peavy et al., 1995) 22

2.4 (a) Forces acting on colloids without the presence of

chemical coagulants; (b) Compression of the double-layer

charge on colloids (destabilisation) by addition of

chemical coagulants (Hammer and Hammer, 2005).

24

2.5 Interparticle bridging with polymers (Hammer and

Hammer, 2005)

28

2.6 The model interaction of coagulant hydrolysed species and

organic matters (Yan et al., 2008a)

30

2.7 OFAT Jar Test Experimental Approach 46

2.8 Graphical representation between factors and response in

RSM (Mouafi et al., 2016)

48

2.9 Central Composite Design (CCD) of a RSM (Multifactor

RSM Tutorial, 2007)

49

3.1 Research Framework 54

xvi

3.2 Locations of (a) the WTPs on the Peninsular Malaysia

map; (b) Bandar Tenggara WTP and Sungai Pengeli; (c)

Sungai Gembut WTP and Sungai Gambul; (d) Sri Gading

WTP and Sungai Bekok

55

3.3 The WTPs involved in the study: (a) Bandar Tenggara;

(b) Sungai Gembut and (c) Sri Gading

56

4.1 Raw water pH profile of Bandar Tenggara WTP from

November 2008 to December 2009

77

4.2 Raw water turbidity profile of Bandar Tenggara WTP

from November 2008 to December 2009

78

4.3 Raw water colour profile of Bandar Tenggara WTP from


78

4.4 Variation of NH3–N and Fe present in raw water of

Bandar Tenggara WTP (November 2008 – December

2009)

79

4.5 Variations of Al levels in raw water of Bandar Tenggara

WTP from November 2008 to December 2009

80

4.6 Mn profile in raw water of Bandar Tenggara WTP from


81

4.7 DOC profile in raw water of Bandar Tenggara WTP

(April – July 2011)

82

4.8 UV254 profile in raw water of Bandar Tenggara WTP


83

4.9 SUVA profile in raw water of Bandar Tenggara WTP


85

4.10 Raw water pH profile of Sungai Gembut WTP in 2007,

and June 2008 to January 2010

87

4.11 Raw water turbidity profile of Sungai Gembut WTP in

2007, and June 2008 until January 2010

88

4.12 Raw water colour profile of Sungai Gembut WTP in

2007, and June 2008 to January 2010

89

4.13 Raw water Fe profile of Sungai Gembut WTP in 2007,


90

xvii

4.14 Raw water NH3-N profile of Sungai Gembut WTP in

2007, and June 2008 to January 2010

90

4.15 Raw water Al profile of Sungai Gembut WTP in 2007,


91

4.16 Raw water Mn profile of Sungai Gembut WTP in 2007,


92

4.17 Raw Water DOC profile of Sungai Gembut WTP (March

– July 2011)

93

4.18 Raw Water UV254 profile of Sungai Gembut WTP (March

– July 2011)

94

4.19 Raw Water SUVA profile of Sungai Gembut WTP

(March – July 2011)

95

4.20 pH profile of raw water of Sri Gading WTP in 2007, and

from May to December 2008

96

4.21 Raw water turbidity profile of Sri Gading WTP in 2007,

and from May to December 2008

97

4.22 Colour profile of raw water of Sri Gading WTP in 2007,


98

4.23 NH3–N profile of raw water of Sri Gading WTP in 2007,


98

4.24 Variation of Fe in raw water at Sri Gading WTP in 2007,

and from May 2008 to December 2008

99

4.25 Al profile of raw water of Sri Gading WTP in 2007, and

from May 2008 to December 2008

100

4.26 Mn profile of raw water of Sri Gading in 2007, and from

May 2008 to December 2008

101

4.27 Total Dissolved Solids (TDS) profile of raw water of Sri

Gading in 2007, and from May 2008 to December 2008

102

4.28 Minimum and maximum PAC dosing commonly applied

at Bandar Tenggara WTP (Bandar Tenggara WTP, 2009)

106

4.29 Operating pH used in the traditional jar test obtained from

historical data at Bandar Tenggara WTP (Bandar

Tenggara WTP, 2009)

107

xviii

4.30 Maximum and minimum alum dosing commonly applied

at Sungai Gembut WTP (Sungai Gembut WTP, 2008)

108


historical data at Sungai Gembut WTP (Sungai Gembut

WTP, 2008)

109

4.32 Maximum and minimum alum dosing commonly applied

at Sri Gading WTP (Sri Gading WTP, 2008)

110


historical data at Sri Gading WTP (Sri Gading WTP,

2008)

111

4.34 Optimum PAC dosage obtained from Bandar Tenggara

WTP historical data (Bandar Tenggara WTP, 2009)

114

4.35 Optimum alum dosing obtained from Sungai Gembut

WTP historical data (Sungai Gembut WTP, 2008)

115

4.36 Optimum alum dosing obtained from Sri Gading WTP

historical data (Sri Gading WTP, 2008)

117

4.37 One-to-one plot; predicted vs. observed values plot for (a)

turbidity, (b) settling pH, (c) residual aluminium of

treated water sample by RSM jar test

127

4.38 Overlay plot for optimum region for sample taken from

Bandar Tenggara WTP

128

4.39 Overall desirability to obtain predicted responses of final

turbidity, coagulated pH and residual aluminium at

suggested optimum conditions for samples taken from

Bandar Tenggara WTP in (a) response surface and (b)

contour plots

129

4.40 Predicted obtained values from RSM optimisation of (a)

final turbidity (b) coagulated pH (c) residual aluminium

and their average observed values

131

4.41 Optimum PAC dosage obtained at Bandar Tenggara WTP 133

4.42 Optimum pH setting for coagulation process at Bandar

Tenggara WTP

134

xix

4.43 Turbidity after flocs settling when applying optimum

coagulation conditions at Bandar Tenggara WTP

135

4.44 Coagulated pH after optimum conditions at Bandar

Tenggara WTP

136

4.45 Residual aluminium after applying optimum coagulation

conditions at Bandar Tenggara WTP

137


square root of turbidity, (b) settling pH, (c) residual

aluminium of treated water sample through RSM jar test

144

4.47 Overlay plot for optimum region for sample taken from

Sungai Gembut WTP

146



suggested optimum conditions for samples taken from

Sungai Gembut WTP in (a) response surface and (b)

contour plots

147

4.49 Predicted – observed values from RSM optimisation of

(a) final turbidity (b) coagulated pH (c) residual

aluminium at Sungai Gembut WTP

150

4.50 Optimum alum dosage obtained at Sungai Gembut WTP 151

4.51 Optimum pH setting for coagulation process at Sungai

Gembut WTP

152

4.52 Turbidity after flocs settling when applying optimum

coagulation conditions at Sungai Gembut WTP

152

4.53 Coagulated pH after optimum coagulation conditions at

Sungai Gembut WTP

153


conditions at Sungai Gembut WTP

154


turbidity, (b) settling pH, (c) of

coagulated water sample through RSM jar test

162

Aluminium Residual1

xx

4.56 Overlay plot for optimum region when polymer dosing

0.004 mg/L for a sample taken from Sri Gading WTP

163



suggested optimum conditions for samples taken from Sri

Gading WTP in (a) response surface and (b) contour plots

164

4.58 Optimum alum dosing obtained at Sri Gading WTP 166

4.59 Optimum polymer dosing obtained at Sri Gading WTP 167

4.60 Optimum initial pH setting for coagulation at Sri Gading

WTP

169

4.61 Turbidity after flocs settling at optimum coagulation

conditions at Sri Gading WTP

170

4.62 Coagulated pH after optimum coagulation conditions at

Sri Gading WTP

170


conditions at Sri Gading WTP

171

4.64 Percentage removal of dissolved organic carbon (DOC)

through traditional jar test carried out at Bandar Tenggara

WTP

174

4.65 DOC concentration in 1% solution of CK442B and soda

used in the jar test

175

4.66 Percentage removal of UV254 by traditional jar test carried

out at Bandar Tenggara WTP

176

4.67 Percentage removal of dissolved organic carbon (DOC)

by traditional jar test carried out at Sungai Gembut WTP

177

4.68 Amount of DOC in 1% stock solution of alum and soda

ash dense used in the jar test

178

4.69 Percentage removal of UV254 by traditional jar test carried

out at Sungai Gembut WTP

179

4.70 Percentage removal of turbidity, DOC and UV254 by

optimum coagulation conditions obtained by traditional

jar test carried out at Bandar Tenggara WTP

180

xxi

4.71 Percentage removal of DOC and UV254 by optimum

coagulation conditions obtained by traditional jar test

carried out at Sungai Gembut WTP

183

5.1 RMSE values for optimum dosing for PAC regression

models acquired from traditional jar test method

189

5.2 Actual versus predicted optimum pH setting obtained

through traditional jar test at Bandar Tenggara WTP

190

5.3 RMSE values for optimum dosing for PAC regression

models acquired from RSM optimisation method

191


through RSM Optimisation at Bandar Tenggara WTP

192

5.5 RMSE values for optimum dosing for alum regression

models acquired from traditional jar test method at Sungai

Gembut WTP

193

5.6 RMSE values for optimum dosing for initial pH

regression models acquired through traditional jar test

method at Sungai Gembut WTP

194

5.7 RMSE values for optimum dosing for initial pH

regression models acquired through direct reading from

RSM table method at Sungai Gembut WTP

195


models acquired from RSM Optimisation at Sungai

Gembut WTP

196


models acquired from traditional jar test method at Sri

Gading WTP

198

5.10 RMSE values for optimum pH setting regression models

acquired from traditional jar test method at Sri Gading

WTP

198

5.11 RMSE values for optimum alum dosing regression

models acquired through direct reading from RSM table

data at Sri Gading WTP

200

xxii


through direct reading from RSM table data at Sri Gading

WTP

201

5.13 Actual versus predicted optimum dosing for polymer

obtained through direct reading from RSM table data at

Sri Gading WTP

201


models acquired from RSM optimisation method at Sri

Gading WTP

202

5.15 RMSE values for optimum dosing for polymer regression

models acquired from RSM optimisation method

203

5.16 Turbidity observed after applying optimum Bandar

Tenggara WTP coagulation conditions calculated from

traditional and RSM regression models

205

5.17 The pH of water observed after applying optimum Bandar

Tenggara WTP coagulation conditions calculated from

traditional and RSM regression models

206

5.18 Residual aluminium concentration observed after

applying optimum Bandar Tenggara WTP coagulation

conditions calculated from traditional and RSM

regression models

206

5.19 Comparison between optimum PAC dosing generated by

regression models (Equations 5.1 and 5.3) and the actual

dosing used at Bandar Tenggara WTP

207

5.20 Comparison between optimum pH dosing generated by

regression models (Equations 5.2 and 5.4) and the actual

dosing used at Bandar Tenggara WTP

207

5.21 Turbidity observed after applying optimum Sungai

Gembut WTP coagulation conditions calculated from

traditional, direct reading of RSM table and RSM

regression models

208

xxiii

5.22 pH of water observed after applying optimum Sungai

Gembut WTP coagulation conditions calculated from

traditional, direct reading of RSM table and RSM

regression models

209

5.23 Residual aluminium concentration observed after

applying optimum Sungai Gembut WTP coagulation

conditions calculated from traditional and RSM

regression models

210

5.24 Comparison between optimum alum dosing generated by

regression models (Equations 5.19, 5.21 and 5.23) and the

actual dosing used at Sungai Gembut WTP

211

5.25 Comparison between optimum pH dosing generated by

regression models (Equations 5.20, 5.22 and 5.24) and the

actual dosing used at Sungai Gembut WTP

212

5.26 Development of empirical relationship regression model 215

5.27 Example of a Data View for raw water quality parameters

and optimum jar test conditions in SPSS

217

5.28 Producing empirical relationship between independent

and dependent variables through clicking linear regression

in SPSS

218

5.29 Choosing either stepwise or forward method, filling the

dependent and independent to analyse

218

xxiv

LIST OF ABBREVIATIONS

BBD – Box Behnken Design

BTWTP – Bandar Tenggara WTP

CCD – Central Composite Design

COD – Chemical Oxygen Demand

CRD – Completely Randomised

DOC – Dissolved Organic Carbon

DoE – Design of Experiment

FCD – Face-centred Central Composite Design

FCS – Ferric Chloride Sludge

G – Velocity Gradient

IC – Inorganic Carbon

nC60 – nanoscale colloidal product of carbon fullerene

NOM – Natural Organic Matters

OFAT – One-Factor-at-A-Time

PAC – Polyaluminium chloride

PACS – Polyaluminium chloride Sludge

RCBD – Randomised Complete Block

RMSE – Root Mean Square Error

RSM – Respond Surface Method

SGWTP – Sungai Gembut Water Treatment Plant

SRGWTP – Sri Gading Water Treatment Plant

SUVA – Specific Ultra-Violet Absorbance

SVI – Sludge Volume Index

TC – Total Carbon

TOC – Total Organic Carbon

xxv

TSS – Total Suspended Solids

USEPA – United States of America Environmental Protection Agency

UV254 – Ultra-violet 254 nm

WTP – Water Treatment Plant

xxvi

LIST OF SYMBOLS

α − constants associated with raw water quality

β − constants associated with raw water quality

γ − constants associated with raw water quality

X1 − pH

X2 − turbidity

X3 − colour (apparent colour)

X4 − Iron (Fe)

X5 − Aluminium (Al)

X6 − temperature

X7 − Manganese (Mn)

X8 − Ammonium (NH3–N)

X9 − Total Dissolved Solids (TDS)

xxvii

LIST OF APPENDICES

APPENDIX TITLE PAGE

A1 Borang LRA/B_Tenggara 243

A2 Borang LRA/Sg_Gembut 245

A3 Borang LRA/S_Gading 247

B1 ANOVAs for Turbidity, Settling pH & Residual Aluminium for

Bandar Tenggara WTP

251


Sungai Gembut WTP

254


Sri Gading WTP

257

C1 Statistical Models Generated From Traditional Jar Test at

Bandar Tenggara WTP

264

C2 Statistical Models Generated From RSM Optimisation at

Bandar Tenggara WTP

266

C3 Statistical Models Generated From Traditional Jar Test at

Sungai Gembut WTP

268

C4 Statistical Models Generated From Direct Reading from RSM

Table at Sungai Gembut WTP

270

C5 Statistical Models Generated From RSM Optimisation at

Sungai Gembut WTP

272

C6 Statistical Models Generated From Traditional Jar Test at Sri

Gading WTP

274

C7 Statistical Models Generated From Direct Reading from RSM

Table at Sri Gading WTP

276

xxviii

C8 Statistical Models Generated From RSM Optimisation at Sri

Gading WTP

278

CHAPTER 1

INTRODUCTION

1.1 Preamble

The presence of colloids and natural organic matters (NOM) in raw water

sources excessively can cause a lot of problems. During intensive raining period,

excessive soil erosion occurs and this increases the concentration of colloids and

subsequently the raw water surface turbidity will multiply considerably. Having

high turbidity for a raw water source may disturb the operation at the water treatment

plant (WTP) and can be very costly (Chang and Liao, 2012). A huge amount of

chemicals and coagulants must be used to ensure the turbidity is removed until it

reaches the allowable turbidity for drinking water of less than 5 NTU outlined by the

National Standard for Drinking Water Quality (NSDWQ) (Ministry of Health, 2004).

Large pre-sedimentation basins may need to be constructed prior to the upstream of a

WTP to remove the colloids. High concentration of sediments trapped in these

basins needs to be treated too (Lin et al., 2008).

The NOM is known to cause colour (Jiang and Graham, 1998; Fabris et al.,

2008; Matilainen et al., 2010), unpleasant taste and odour problems in surface water

(Matilainen et al., 2010). It also acts as substrate for microbial and causes

2

bacterial regrowth problem (Yan et al., 2008b; Liang and Ma, 2009) and able to

block membranes and activated carbon pores for adsorption site hence reducing

adsorption efficiency (Fabris et al., 2008; Matilainen et al., 2010) in water supplies.

More importantly, they have been reported to be one of the precursors for

disinfection by-products (DBPs) (Amirtharajah and O'Melia, 1990; McGhee, 1991;

Edzwald, 1993; Iriarte-Velasco et al., 2007b) that may cause cancer to human (Bob

and Walker, 2001; Abdullah et al., 2003; Matilainen et al., 2010; Chow et al., 2011;

Fooladvand et al., 2011; Gan et al., 2013).

Looking at the adverse impacts of colloids and NOM, it is therefore essential

to remove these impurities. Coagulation is known to be an effective method to

remove the colloidal particles of high and intermediate molecular weight that result

in high turbidity (Lin et al., 2008; AlMubaddal et al., 2009) and NOM (Fabris et al.,

2008). It is an established agglomeration process to transform small particles into

larger aggregates (flocs), which involves addition of chemicals. These chemicals will

destabilise kinetically stable suspensions such as dissolved and colloid impurities

hence producing larger flocs that can be removed at the clarification or filtration

stage (Jiang and Graham, 1998; Gao et al., 2002).

The most widely used chemical in this process is aluminium (Al) based

coagulant. Though Al based coagulant is known to be effective in coagulation

process, it raises serious concerns due to the increase of residual Al in treated water.

Among the problems associated with the presence of Al in potable water are it may

increase the turbidity owing to the formation of Al precipitates, raise the pressure of

water distribution network, inhibit to the effect of disinfection and damage human’s

nervous system (Jiao et al., 2015). Medical reports state that aluminium may induce

Alzheimer’s disease too (Zouboulis et al., 2004; Kimura et al., 2013).

Therefore, coagulation process must be carried out in an optimum way as it

has direct impact on the reliability of the treatment plant operations and the final

water quality. It also has significant contribution to the operational cost of the

3

treatment plant. Any Al concentrations in treated water detected to be greater than

0.3 mg/L shows a lack of optimisation in coagulation process of the conventional

water treatment method (Qaiyum et al., 2011). The effectiveness of the process is

highly dependent on several factors including pH of the operation and dosage of

coagulant and coagulant aids.

1.2 Problem Statement

Over the years, jar test is the most common method to determine the best pH

and dosage of the chemicals used in the coagulation process. It is normally carried

out at the WTP once or twice a day or whenever any significant changes occurred in

the raw water due to many factors including the change in the weather conditions and

contaminant intrusion from the urbanization activities. Since the raw water quality

parameters are continuously changing over time resulting the optimum doses of pH

and chemical should be changing as well, it is insufficient to carry out the jar test at

the said rate above.

In the current jar test approach, the WTP operator determines the best pH and

chemical dosages by systematically changing the level of the factors, that are pH and

dosage, one at a time while holding the level of other factors constant. The level of

that factor which results in the best response (for example, lowest turbidity value) is

then selected and used in subsequent tests which continue in the same manner on

other factors. While the approach, termed as One-Factor-at-A-Time (OFAT) (Figure

1.1 (a)) is rather straightforward, it suffers from shortcomings which may lead to a

wrong conclusion. It is time-consuming and does not fully explore the space of

possible solutions and hence, it is incapable of identifying the interaction effects

resulted from the factors being considered (Montgomery, 2005). Due to these

reasons, the current jar test approach could have missed the actual best pH and

4

dosage that are possibly hidden in the experimental space not covered by the OFAT

approach.

To overcome this problem, a better method to acquire the best pH and

chemical dosage is to adopt an experimental approach as shown in Figure 1.1 (b). In

this approach, all levels of factors are altered systematically (one-by-one) to ensure

that the experimental space is fully covered. The best response could not possibly be

missed through this approach. Nonetheless, it is very time-consuming and costly.

(a) (b)

Figure 1.1: Design of Experiment; (a) OFAT Approach and (b) All Factors

Varied Systematically Approach

Response surface methodology (RSM) jar test has been proposed to

overcome these problems. The RSM is a statistical technique for designing

experiment and involves relatively small number of experiments. It is able to build

mathematical models from the outcome of the experiments and search for the

optimum conditions for desirable factors and responses. With RSM, the interaction

between factors and responses can also be determined (Montgomery, 2005).

pH

Coagulant

Coagulant

pH Flocculant Flocculant

5

RSM has been used in many optimisation experiments (Wang et al., 2007,

Pinzi et al., 2010; Moradi and Ghanbari, 2014). Ahmad et al. (2005) managed to

explain the effect and interaction between coagulant dose, flocculant dose and pH

when treating palm oil mill effluent (POME) through coagulation-flocculation

process incorporated with membrane separation technology. Wang et al. (2007)

optimised the coagulation-flocculation process to achieve minimum turbidity and

sludge volume index (SVI) for paper-recycling wastewater treatment. Pinzi et al.

(2010) used RSM to optimise the transesterification reaction for several types of

vegetable oils. Moradi and Ghanbari (2014) applied RSM technique to treat landfill

leachate and successfully obtained the optimum conditions for an integrated

coagulation-fenton process in removing the chemical oxygen demand, colour and

total suspended solids of the leachate. Despite the application of RSM in many

experimental studies, its usage in optimising coagulation conditions through jar test

in water treatment is apparently uncommon.

The raw water quality at any WTP keeps changing very frequently but the jar

test is only carried out once or twice a day. Therefore, the optimum chemical doses

obtained through the jar test are only valid for the period of sampling. It is

impractical to carry out the jar test whenever the quality of the water changes, as it is

very time-consuming. Therefore, there is a need to develop statistical models

relating the historical data of a WTP’s raw water quality parameters and the best

coagulation conditions acquired through jar test. Once the statistical models are

established, the optimum chemical doses may be known almost instantaneously by

plugging the raw water parameters data into the models. The generated statistical

models may also be used to monitor or check whether the chemical doses used in the

plant are at optimum condition. The steps taken to produce the statistical models has

got to be properly recorded and verified so that a standard procedure to develop the

statistical models relating raw water quality parameters and optimum doses of

chemical used for any WTP may be established.

Even though the negative effects of NOM present in water and how

coagulation process is able to remove NOM are widely known, no maximum

6

concentration limit for NOM is stated in the National Standard for Drinking Water

Quality (NSDWQ) (Ministry of Health, 2004). It is believed that due to this fact,

NOM are hardly analysed at any WTPs in Malaysia even though simple technique

such as the analysis of dissolved organic carbon (DOC) and the ultra-violet 254 nm

absorbance (UV254) may be applied to characterise the NOM present in the WTP’s

raw water. Hence, the performance of coagulation process carried out daily at any

WTP in removing NOM has never been explored by most, if not all WTPs in

Malaysia, unlike turbidity that is clearly spelt out in the NSDWQ.

1.3 Objectives of Study

The objectives are as follows:

i) To carry out a long-term comparison between the optimum coagulation

conditions obtained from the traditional (an adjusted OFAT) jar test

with the one obtained from the RSM jar test for different sources of raw

water

ii) To develop statistical models that correlate the relationship between

raw water quality parameters normally analysed at WTP and the best

coagulation conditions in terms of pH, chemical dosing and residual

aluminium concentration.

iii) To characterise the NOM present in the raw water source in terms of its

affinity towards water as well as to quantify the removal of NOM based

on typical traditional jar test carried out at the WTP.

iv) To establish a standard procedure to obtain statistical model that

correlates optimum coagulation conditions and raw water quality

parameters for any WTP.

7

1.4 Scope of Study

The coagulation process in this study focussed solely on the water treatment

process. The research was carried out for three Syarikat Air Johor Holdings Sdn.

Bhd. (SAJ) WTPs that have different characteristics of raw water. These WTPs were

Bandar Tenggara WTP (high turbidity water), Sungai Gembut WTP (yellowish-

orange coloured acidic water) and Sri Gading WTP (low turbidity acidic water). The

traditional and RSM jar tests were carried out at the WTPs by the operators on a

daily basis for a minimum of six months and at Universiti Teknologi Malaysia

(UTM) on weekly basis. Raw water quality parameters analysed daily at these

WTPs were pH, turbidity, colour (apparent colour), ammonia nitrogen (NH3-N), iron

(Fe), aluminium (Al), manganese (Mn) and other parameters that suited the

requirement of the WTPs. Parameters analysed for the jar tests were coagulant dose

(and coagulant aid dose for Sri Gading WTP), pH (during initial setting for the jar

test and after flocs settlement), water turbidity and residual aluminium after

conducting the jar tests. The NOM present at the WTP was characterised by their

specific ultra-violet absorbance (SUVA) values through the analysis of the dissolved

organic carbon (DOC) and the ultra-violet 254 nm absorbance (UV254). Design

Expert Version 7.1 and Statistical Package for the Social Sciences Version 10.0

(SPSS) softwares were used to assist the results analyses.

1.5 Significance of Study

The study is important as it develops a more systematic approach to

overcome the shortcomings of the traditional jar test typically carried out at most

WTPs in Malaysia. In Malaysia, most WTPs have adopted an adjusted OFAT jar

test whereby only the coagulant dosing is systematically altered and not the

coagulant aid nor the pH of the coagulation. This is because these WTPs are more

concerned on the amount of coagulant used as compared to any other chemicals

8

since its usage is considerably higher than the others. The adjusted OFAT jar test

has definitely neglected the determination of optimum values of pH and coagulant

aid and hence it will not lead to the actual best values for these entities. As for the

optimum coagulant, the jar test does not fully explore the experimental space of

possible solutions and hence, it may miss the actual optimum value too. The wastage

of chemicals used in the coagulation process at these WTPs is unavoidable.

The RSM jar test experiment is implemented in this study to overcome the

stated problems earlier as RSM has been proven to be very successful in optimisation

experiments (Wang et al., 2007, Pinzi et al., 2010; Moradi and Ghanbari, 2014).

RSM involves small number of experiments, builds mathematical models from the

outcome of the experiments and searches the optimum conditions for desirable

factors and responses. With RSM, the interaction between factors and responses can

also be determined (Montgomery, 2005). This study also eases the determination of

optimum pH and chemicals dosage of coagulation process in producing acceptable

water quality through the establishment of statistical models relating the raw water

quality parameters and the jar test optimum conditions.

NOM characteristics present in the raw waters for two WTPs in Johor in

terms of their affinity towards water and their interaction with coagulation process

will be determined conducted by the traditional jar test at these WTPs. This study

also determines the characteristics of the NOM present in the raw water for several

WTPs in terms of their affinity towards water and how it interacts with the

coagulation process carried out by the traditional jar test at these WTPs. Finally, this

study establishes a standard procedure to obtain statistical model that correlates

optimum coagulation conditions and their raw water quality parameters for any

WTP.

9

1.6 Thesis Outline

This thesis is divided into six chapters. Chapter 1 is an introduction chapter.

It contains the problem statement, objectives, scope and the significance of the study

as well as the thesis outline. Chapter 2 consists of the literature review on

coagulation process, jar test, NOM, design of experiment (DoE) and RSM topics.

Chapter 3 describes the research methodology in detail followed by Chapter 4. In

Chapter 4, the analysis of water characteristics, long-term comparison on the

traditional and RSM jar tests, the generation of regression models relating the

optimum jar test conditions with the raw water quality parameters and NOM removal

through traditional jar test are related. Chapter 5 relates the standard procedure

obtained from this study to acquire statistical model that correlates optimum

coagulation conditions and their raw water quality parameters for any WTP. Chapter

6, the final chapter of the thesis, presents the conclusions and recommendations for

future study.

225

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