Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Sharif, Muhammad Nadeem
Doctor of Philosophy - PhD
University of British Columbia
A water distribution system (WDS) is the most expensive and energy-intensive component of a water supply system. Maintaining acceptable water quality (WQ) and adequate pressure with minimum energy use is important in drinking water management. At the very least, water utilities must maintain a certain residual chlorine level to inactivate the pathogens and minimize human health risks. In terms of energy use, inefﬁcient pump scheduling is a common problem identified in water distribution, leading to high energy costs. Due to increasing urbanization, population growth, high energy costs, and related greenhouse gas emissions, there is an increasing need to improve WQ and minimize energy usage simultaneously. Thus, leading to the development of sustainable water management strategies for large WDS. The optimization in the context of WQ and energy usage for large WDS alone is a challenging task. Until now, no established solution is available, as most of the past studies to find an optimal solution were either having a compromised WQ or unable to deal with the increasing energy demands of a large WDS. This research developed an integrated WQ and energy use optimization framework for an existing large WDS. At first, the water quality index and risk-based fuzzy failure modes effect analysis methods were applied to assess and optimize WQ. Later, optimal pump scheduling was identified to minimize energy use by employing a mixed integer goal programming model. In the last phase, both hydraulics and WQ performances were evaluated by developing a unique optimization approach. The proposed framework was demonstrated using a WDS in Al-Khobar (a large-sized city in Saudi Arabia). The case study results reflected the merits of the developed framework as ~20% energy cost reduction was obtained. Similarly, the use of the proposed optimization framework predicted > 40% WQ improvement. The proposed framework is flexible and can be applied to WDS of different sizes. It can assist water utilities in achieving higher performance with minimal energy use.
OPTIMIZATION OF WATER QUALITY AND ENERGY USE IN LARGE WATER DISTRIBUTION SYSTEMS by Muhammad Nadeem Sharif M.Eng. University of Waterloo, 2006 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) Feburary 2022 © Muhammad Nadeem Sharif, 2022 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the dissertation entitled: OPTIMIZATION OF WATER QUALITY AND ENERGY USE IN LARGE WATER DISTRIBUTION SYSTEMS Submitted by Muhammad Nadeem Sharif in partial fulfilment of the requirements for the degree of Doctor of Philosophy Examining Committee: Dr. Rehan Sadiq (School of Engineering) Supervisor Dr. Kasun Hewage (School of Engineering) Supervisory Committee Member Dr. Shahria Alam (School of Engineering) Supervisory Committee Member Dr. Kevin Hanna (Faculty of Science) University Examiner Dr. Salim Ahmed (Memorial University) External Examiner iii Abstract A water distribution system (WDS) is the most expensive and energy-intensive component of a water supply system. Maintaining acceptable water quality (WQ) and adequate pressure with minimum energy use is important in drinking water management. At the very least, water utilities must maintain a certain residual chlorine level to inactivate the pathogens and minimize human health risks. In terms of energy use, inefficient pump scheduling is a common problem identified in water distribution, leading to high energy costs. Due to increasing urbanization, population growth, high energy costs, and related greenhouse gas emissions, there is an increasing need to improve WQ and minimize energy usage simultaneously. Thus, leading to the development of sustainable water management strategies for large WDS. The optimization in the context of WQ and energy usage for large WDS alone is a challenging task. Until now, no established solution is available, as most of the past studies to find an optimal solution were either having a compromised WQ or unable to deal with the increasing energy demands of a large WDS. This research developed an integrated WQ and energy use optimization framework for an existing large WDS. At first, the water quality index and risk-based fuzzy failure modes effect analysis methods were applied to assess and optimize WQ. Later, optimal pump scheduling was identified to minimize energy use by employing a mixed integer goal programming model. In the last phase, both hydraulics and WQ performances were evaluated by developing a unique optimization approach. The proposed framework was demonstrated using a WDS in Al-Khobar (a large-sized city in Saudi Arabia). The case study results reflected the merits of the developed framework as ~20% energy cost reduction was obtained. Similarly, the use of the proposed optimization framework predicted > 40% WQ improvement. The proposed framework is flexible and can be applied to WDS of different sizes. It can assist water utilities in achieving higher performance with minimal energy use. iv Lay Summary Increasing urbanization and ecological changes led to increased drinking water and energy demand, requiring water utilities to adopt an effective and efficient approach in supplying water with acceptable water quality (WQ) and minimum energy usage. This study developed an integrated framework, which employs WQ & energy-use modeling techniques to identify, evaluate, and simultaneously measure the causes of depleted WQ and high energy use. Hydraulic and goal programming mathematical models were developed to optimize the WQ and energy usage of the system. The proposed framework also evaluated the trade-off between WQ and energy use and was validated using a large water distribution system (WDS) to show its practicality. Water utilities and municipalities worldwide can use this research's developed models and assessment tools to assess and optimize their respective WDS. Thus, improving WQ while ensuring energy efficiency over the WDS lifetimes. v Preface I, Muhammad Nadeem Sharif, developed all the contents of this thesis under the supervision of Dr. Rehan Sadiq. Most of the contents have been published in scientific journals as outlined below: Sharif, M. N., Haider, H., Farahat, A., Hewage, K., & Sadiq, R. (2019). Water–energy nexus for water distribution systems: a literature review. Environmental Reviews, 27(4), 519-544. DOI: 10.1139/er-2018-0106. (Chapter 3) Sharif, M. N., Farahat, A., Al-Zahrani, M. A., Islam, N., Rodriguez, M. J., & Sadiq, R. (2016). Optimization of chlorination boosters in drinking water distribution network for Al-Khobar City in Saudi Arabia. Arabian Journal of Geosciences, 9(9), 1-11. DOI: 10.1007/s12517-016-2552-1. (Chapter 4) Sharif, M. N., Farahat, A., Haider, H., Al-Zahrani, M. A., Rodriguez, M. J., & Sadiq, R. (2017). Risk-based framework for optimizing residual chlorine in large water distribution systems. Environmental monitoring and assessment, 189(7), 1-19., DOI: 0.1007/s10661-017-5989-0. (Chapter 5) Sharif, M. N., E. Bakhtavar., H. Haider., Guangji Hu., K. Hewage., Sadiq, R. (2022). Staged energy and water quality optimization for large water distribution systems, accepted January 19, 2022, in Environmental monitoring and assessment (Chapter 6) vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ..................................................................................................................................x List of Figures .............................................................................................................................. xii List of Abbreviations ...................................................................................................................xv List of Symbols ......................................................................................................................... xviii Acknowledgements .................................................................................................................... xix Chapter 1: Introduction ................................................................................................................1 Background ..................................................................................................................... 1 Research gaps.................................................................................................................. 5 Research motivation........................................................................................................ 6 Research objectives ......................................................................................................... 7 Thesis organization ......................................................................................................... 8 Chapter 2: Research Methodology .............................................................................................10 Phases of the research ................................................................................................... 10 Chapter 3: Literature Review .....................................................................................................13 Background ................................................................................................................... 13 Water Distribution System Modeling and Optimization .............................................. 14 Water Quality in Water Distribution System ................................................................ 15 3.3.1 Hydraulic Modeling .................................................................................................. 16 3.3.2 Water Quality Modeling ........................................................................................... 18 Energy use in Water Distribution System ..................................................................... 20 vii 3.4.1 Energy Indicators ...................................................................................................... 23 3.4.2 Energy Saving Solutions ........................................................................................... 26 3.4.2.1 Watergy ............................................................................................................. 26 3.4.2.2 Maintenance of pumping system ...................................................................... 26 3.4.2.3 Leakage Management ....................................................................................... 27 Optimization of Water Distribution System ................................................................. 30 3.5.1 Optimal Design ......................................................................................................... 30 3.5.2 Optimal Water Quality .............................................................................................. 33 3.5.2.1 Index-Based Approach...................................................................................... 34 3.5.2.2 Risk-Based Approach ....................................................................................... 35 3.5.2.3 Fuzzy - Failure Mode and Effect Analysis (Fuzzy-FMEA) ............................. 36 3.5.2.4 Fuzzification and Defuzzification Methods ...................................................... 36 3.5.3 Optimal Energy Use .................................................................................................. 38 3.5.3.1 Water Pumping System..................................................................................... 38 3.5.3.2 Multi-objective Optimization............................................................................ 43 3.5.4 Renewable Energy use in Water Pumping System ................................................... 45 3.5.5 Energy-Efficient Control System .............................................................................. 47 3.5.5.1 An Example of Small Water Utility (Lisbon, Portugal) ................................... 48 3.5.6 Sustainability of Water Distribution System ............................................................ 51 3.5.6.1 Life Cycle Assessment ...................................................................................... 51 3.5.6.2 Life Cycle Energy Analysis .............................................................................. 52 3.5.6.3 An Example of Midwestern USA ..................................................................... 57 Summary ....................................................................................................................... 62 Chapter 4: Water Quality Index-Based Optimization .............................................................63 Background ................................................................................................................... 63 Methodology ................................................................................................................. 63 viii 4.2.1 EPANET Modeling ................................................................................................... 64 4.2.2 Water Quality Index .................................................................................................. 67 Results and Discussion ................................................................................................. 70 Summary ....................................................................................................................... 76 Chapter 5: Water Quality Risk-Based Optimization ...............................................................77 Background ................................................................................................................... 77 Methodology ................................................................................................................. 78 5.2.1 Risk-based Optimization ........................................................................................... 78 5.2.2 Residual Chlorine Modeling ..................................................................................... 79 5.2.2.1 CCME- CWQI .................................................................................................. 79 5.2.2.2 Risk Assessment ............................................................................................... 79 5.2.2.3 Fuzzy - Failure Mode and Effect Analysis (Fuzzy-FMEA) ............................. 80 5.2.2.4 Fuzzification and Defuzzification Methods ...................................................... 81 5.2.2.5 Risk Management ............................................................................................. 82 Results and Discussion ................................................................................................. 83 5.3.1 Case Study ................................................................................................................ 83 5.3.2 Risk Identification - Estimation of CWQI ................................................................ 83 5.3.3 Scenario Analysis...................................................................................................... 83 5.3.4 Scenario Evaluation .................................................................................................. 86 Summary ....................................................................................................................... 89 Chapter 6: Integrated Framework of WQ and Energy Use Optimization ............................90 Background ................................................................................................................... 90 Methodology ................................................................................................................. 91 6.2.1 Staged Energy and Water Quality Optimization Model ........................................... 91 6.2.2 Case Study ................................................................................................................ 93 6.2.2.1 Stage 1 - Optimization of Chlorine Boosters .................................................... 95 ix 6.2.2.2 Stage 2 – Energy-use Optimization .................................................................. 95 6.2.2.3 Stage 3 - Combined Energy-use and WQ Optimization ................................. 100 Results and Discussion ............................................................................................... 100 6.3.1 Stage 1 - Chlorine Boosters Optimization .............................................................. 100 6.3.2 Stage 2 - Energy Optimization ................................................................................ 102 6.3.3 Stage 3 - Combined Energy and Water Quality Performance Evaluation .............. 107 Summary ..................................................................................................................... 108 Chapter 7: Conclusions and Recommendations .....................................................................109 Background ................................................................................................................. 109 Summary and Conclusions ......................................................................................... 109 Originality and Contributions ..................................................................................... 110 Limitations and Recommendations............................................................................. 111 References ...................................................................................................................................113 Appendices ..................................................................................................................................139 Appendix A1 ........................................................................................................................... 139 Appendix A2 ........................................................................................................................... 140 Appendix A3 ........................................................................................................................... 141 Appendix A4 ........................................................................................................................... 142 Appendix A5 ........................................................................................................................... 143 x List of Tables Table 3.1 Water quality standards [37] ......................................................................................... 17 Table 3.2 Disinfectant dosage and modeling methods used in various past studies [10] ............. 20 Table 3.3 An estimated energy use in urban drinking WDSs of various regions of the world [35]....................................................................................................................................................... 22 Table 3.4 Summary of past studies on water-energy efficiency indicators for WDSs [35] ........ 24 Table 3.5 Summary of past studies on water-energy saving solutions through a water management system [35] .................................................................................................................................... 29 Table 3.6 Summary of the past studies used multi-objective optimization methods in the design of WDSs [35] .................................................................................................................................... 32 Table 3.7 Summary of previous studies for optimizing booster chlorination in WDS [150] ....... 37 Table 3.8 Summary of past studies on pump optimization algorithms used in small-medium size WDSs [35] .................................................................................................................................... 42 Table 3.9 Past Multi-objective approaches used for pump energy cost optimization and GHG in WDSs [35] .................................................................................................................................... 46 Table 3.10 Characteristics of energy-efficient control system used for the water pumping system [35] ................................................................................................................................................ 48 Table 3.11 Summary of past studies considered various life cycle indicators w.r.t energy use in WDS [35] ...................................................................................................................................... 56 Table 4.1 Lengths, material and sizes of pipes used in Al-Khobar WDS [43] ............................. 71 Table 4.2 Temporal and spatial analysis of results ....................................................................... 73 Table 4.3 Average MWQIn for each combination ....................................................................... 74 Table 5.1 Linguistically defined vulnerability, consequences, and detectability [150] ................ 80 Table 5.2 Description of different risk management scenarios [150] ........................................... 85 Table 5.3 RPN ranges & corresponding risk levels for scenario evaluation [6] ........................... 87 Table 5.4 Rank and average RPN values of all scenarios ............................................................. 87 xi Table 6.1 Maximum flow rate and power of each pump used in the study of city of Al-Khobar WDS .............................................................................................................................................. 94 Table 6.2 Input and output parameters of the pump scheduling optimization model................... 98 Table 6.3 Network input parameters and their values used for the energy optimization model 103 Table 6.4 An average pump operational optimized energy consumption of all three scenarios for 24-hours ...................................................................................................................................... 104 Table 6.5 Optimized flow rate and energy cost of all 15 pumps active during 24 hours of Scenario 3................................................................................................................................................... 106 xii List of Figures Figure 1.1 An integrated framework of WQ and energy use optimization in WDSs ..................... 7 Figure 2.1 Synthesis of chapters and research phases .................................................................. 12 Figure 3.1 Review study in the context of water-energy-nexus for WDSs [35] ........................... 15 Figure 3.2 Energy use in each step of the drinking water system from source to end-user [65] .. 22 Figure 3.3 Water lost due to leakage in the large WDSs [30] ...................................................... 28 Figure 3.4 Nonlinear optimization algorithms used in the design of WDSs [35]......................... 31 Figure 3.5 Comparison between original and optimized WDS proposed by Dandy et al. (2008) [86] ................................................................................................................................................ 33 Figure 3.6 Various evolutionary algorithms used for pump operational optimization of WDS [22]....................................................................................................................................................... 41 Figure 3.7 Energy consumption and corresponding GHG emissions in; (a) FSP (b) VSP [93]. .. 44 Figure 3.8 Combination of water-turbine and wind-power system used for water pumping in Portugal [77] ................................................................................................................................. 49 Figure 3.9 (a) Electricity generated by the pump as turbine (b) with wind turbine [77] .............. 50 Figure 3.10 Typical life cycle stages of WDS [205][206] ............................................................ 51 Figure 3.11 Life cycle stages, energy consumed, and GHG emissions [226] .............................. 53 Figure 3.12 A few past LCA studies considered the environmental impact and operation of WDS [205][210] ..................................................................................................................................... 55 Figure 3.13 (a) lost volume due to leakage (b) energy lost due to leakage in all proposed plans [80]....................................................................................................................................................... 59 Figure 3.14 (a) annual operational energy in each plan (b) operational energy saving in each plan [80] ................................................................................................................................................ 60 Figure 4.1 Water quality index-based optimization [143] ............................................................ 65 Figure 4.2 EPANET model for the city of Al-Khobar WDS ........................................................ 66 Figure 4.3 Residual chlorine concentration at node 22 of Al-Khobar WDS ................................ 66 Figure 4.4 WQI & amended functions [10] .................................................................................. 68 xiii Figure 4.5 Fuzzy excursion approximation using modified function [10] ................................... 68 Figure 4.6 Number of water mains in the city of Al-Khobar [43] ................................................ 72 Figure 4.7 Existing chlorine booster points in each zone ............................................................. 72 Figure 4.8 Water quality in each zone .......................................................................................... 73 Figure 4.9 3D-Surface optimization ............................................................................................. 75 Figure 4.10 Optimization with proposed chlorine booster’s location........................................... 75 Figure 5.1 Risk-based WQ assessment approach [150] ................................................................ 79 Figure 5.2 Fuzzification method; M2, N2, O2, and P2 are the elements of the ‘low’ membership function [150]................................................................................................................................ 81 Figure 5.3 Defuzzification of RPN using Chen’s Method [150] .................................................. 82 Figure 5.4 Existing chlorine booster points and average CWQI in each zone of Al-Khobar WDS scenario 1 ...................................................................................................................................... 84 Figure 5.5 Proposed chlorine booster points and average CWQI in each zone of scenario 2 ...... 85 Figure 5.6 Proposed chlorine booster points and average CWQI in each zone of scenario 3 ...... 86 Figure 5.7 Proposed pipe replacement in each zone of scenario 4 and average CWQI ............... 87 Figure 5.8 Fuzzification and defuzzification of all scenarios [150] ............................................. 88 Figure 5.9 Cumulative RPN of all scenarios: S1: no action; S2 and S3: three additional booster stations in different locations of the network; S4: rehabilitation and replacement of pipes [150] 89 Figure 6.1 Energy and WQ optimization approach for large WDS .............................................. 92 Figure 6.2 A schematic diagram of the pumping system of the city of Al-Khobar water network....................................................................................................................................................... 95 Figure 6.3 WQ improvement results, a) average pressures and CWQI in each zone for existing base case scenario, b) improved CWQI after applying Stage 2 in each zone (average pressures* shown are same as base case scenario without energy optimization). ........................................ 101 Figure 6.4 24 hours demand variation of Al-Yarmouk water tank (EPANET model); negative demand period corresponds to the filling of the tank ................................................................. 102 xiv Figure 6.5 The normal and optimized energy costs and pump flow rates for each scenario, a) and b) for scenario 1, c) and d) for scenario 2, e) and f) for scenario 3 ............................................ 105 Figure 6.6 Comparison of percentage cost reduction of each scenario over 24 hours ............... 106 Figure 6.7 Stage 3 results showed average pressures* with both the energy and WQ optimization with optimal locations of booster chlorinators............................................................................ 107 xv List of Abbreviations AWWA American Water Works Association ANN Artificial Neural Networks BC British Columbia MoE Ministry of Environment CAN Canadian CCME Canadian Council of Ministers of the Environment CWQI Canadian Water Quality Index CPS Central pumping station DP Dynamic programming DBPs Disinfection by Products U.S. EPA United States Environmental Protection Agency EPANET Environmental Protection Agency Network EWQMS Energy Water Quality Management System FMEA Failure Mode Effect Analysis GAs Genetic Algorithms GP Goal Programming GHG Green House Gases GIS Geographic Information System IWA International Water Association KWH Kilowatt Hour KFUPM King Fahd University of Petroleum & Minerals LP Linear Programming LCA Life Cycle Assessment LCEA Life Cycle Energy Analysis LWDS Large Water Distribution System xvi MH Metaheuristic algorithms MIGP Mixed Integer Goal Programming MGD Million Gallons per Day MRR Maintenance, Rehabilitation and Renewal MO Multi-objective NOM Natural Organic Matter NLP Non-Linear Programming NRC National Research Council NTU Nephelometric Turbidity Units NWC National Water Commission OP Operational OPS Optimal Pump Scheduling PAT Pump as a Turbine PVC Polyvinyl chloride PH Physical PRV Pressure release valve RPN Risk Probability Number SMWU Small to Medium-Sized Water Utilities SAR Saudi Riyal SO Single Objective THMs Trihalomethanes TDS Total Dissolved Solids UOD Universe of Discourse USEPA United National Environmental Protection Agency UBCO University of British Columbia, Okanagan WEN Water-Energy Nexus xvii WHO World Health Organization WQ Water Quality WQI Water Quality Index WSS Water supply system WDS Water Distribution System xviii List of Symbols ,tE Energy consumption cost for pump  in period t jS  The cross-section of a cylindrical tank j ,j tD  The demand of tanks j in period t (the expected value for the goal of tanks' demands) ,0jW  The initial water level (hydraulics head) in the tank j at t = 0 jW  The minimum water level (hydraulics head) in the tank j at any time jW  The maximum water level (hydraulics head) in the tank j at any time F The minimum water flow of the pump  F The maximum water flow of the pump   The fixed potential difference for pump  𝛽ij nodal demand from node i to node j at any time ∅ij head losses in pipes from node i to node j at any time ij Potential differences for pipeline segment between nodes i and j ,ty A binary integer variable: 1 if the pump  is ON over period t, 0 otherwise ,tx The flow rate of pump  over period t P Pumps V Vulnerability C Consequences D Detectability xix Acknowledgements In the name of Allah (ىلاعتو ناحبس), the creator, most gracious, merciful, who gave me the strength to materialize this research in the present form and many blessings upon the last Prophet Muhammad peace be upon him (ملسو هيلع الله ىلص). I want to express my sincere gratitude and thanks to my supervisor (Dr. Rehan Sadiq), who has been a tremendous mentor and guide during this journey. Dr. Sadiq is a humble person, a high-quality researcher, and a dedicated professor. The experience and exposure under his supervision have given me the confidence to conduct, manage, and complete research projects. I am forever grateful to him for the immense support, kindness, inspiration, and motivation he gave me. I also thank Dr. Kasun Hewage for his continuous support and guidance during my doctoral studies. He has reviewed my research work and provided me with critical feedback. I would like to thank my friends Dr. Husnain Haider and Dr. Ezzeddin Bakhtavar for their valuable guidance and feedback throughout my research. Also, I want to thank Dr. Haroon and Dr. Guangji, who helped me shape the thesis structure in its current form and Mr. Syed Athar for facilitating English proofreading. My special thanks to Engr. Sohail Almomin, Engr. Abbas, and Engr. Umar from the WDS of Al-Khobar, Saudi Arabia, provided baseline data and shared their valuable experiences. Dr. Abdulaziz Alassaf also facilitated me in this regard. My friends and colleagues, Dr. Gyan, Mrs. Shannon Hohl from UBCO and Dr. Ashraf, Dr. Ali, Dr. Ibrahim, Dr. Qudrat-Ullah from KFUPM, also helped me during this journey. I also acknowledge the University of British Columbia for providing me with the graduate fellowship awards/grants to cover my tuition fee partially. My heartfelt thanks to all my family members, including sisters, elder brothers (Mr. Anees Chaudhry, Mr. Javed Iqbal. Mr.Imtiaz Sharif, Mr.Asif Sharif), nephews, nieces, and parents-in-law for always encouraging me and whole-heartedly praying for me to achieve this prestigious goal. Finally, I would like to thank my wife and beloved sons (Ahmed and Ibrahim), who shared all the hardships during this study period. At last, May Allah guides and bless all of us to work for humanity, peace, and love. xx Dedicated to My Parents Ch. Muhammad Sharif (late) Mumtaz Begum (late) 1 Chapter 1: Introduction Background Water and energy are vital for the sustenance of life. Due to increasing urbanization and rapid population growth, ensuring a safe and sustainable drinking water supply has become a significant challenge around the globe. A water supply system (WSS), consisting of pipes, pumps, booster chlorination stations, and overhead water reservoirs, delivers treated water to consumers. A significant amount of energy is used in various components of WSS, including source water extraction, transmission, treatment, and distribution. Water distribution system (WDS) is the most expensive component contributing 60-80% of total WSS costs. The WDSs typically consist of treated water reservoirs, distribution pumps, chlorine boosters, and many linear assets (pipes) widely spread over the service areas [1]. Maintaining safe water quality (WQ) and adequate pressure with minimum operation and maintenance costs is one of the primary objectives of a WDS. In addition, water utilities are striving to achieve 21st-century sustainability goals by maximizing energy efficiency and minimizing greenhouse gas (GHG) emissions [2]. Water contamination at the source, lack of effective treatment, and deteriorated infrastructure adversely affect WQ [3]. The presence of pathogens in the contaminated water is one of the main causes of waterborne diseases, including cryptosporidiosis, diarrhea, typhoid, gastroenteritis, and hepatitis-A [4]. It is estimated that diarrhea alone is responsible for about 1.8 million deaths/yr globally [5]. To ensure a safe drinking water supply, purveyors add disinfectants to water in WDS to inactivate pathogens and reduce contamination risks. Standard disinfection methods include the application of ultraviolet radiation, ozonation, and chlorination. However, chlorination is one of the most commonly used, inexpensive, but effective disinfection processes that produce residual chlorine in the WDS [6]. A high chlorine dosage may produce undesirable odour and disinfection by-products (DBPs), while a low dosage may not sufficiently deactivate pathogens [7] [8]. Thus, it is essential to optimize the chlorine dosage used in WDS to achieve adequate disinfection and avoid the formation of DBPs. There are no established universal guidelines for the maximum chlorine levels in a WDS. However, a free residual chlorine concentration between 0.2 and 4 mg/L has been recommended [9]. Besides, the residual chlorine concentration also depends on numerous factors, such as physicochemical and microbiological parameters of the water and chlorine decay rates [10]. The decay of residual chlorine in large WDNs leads to lower disinfectant concentrations, especially at the 2 farthest points, increasing the risk of microbial contamination. These microbial contaminations can severely affect the sensitive zones of an urban area, e.g., residential, public (hospitals and schools), and commercial centres. Thus to avoid microbial risks, chlorine booster stations are needed to maintain optimal residual chlorine levels throughout WDS, which increases the operational costs [11]. Hence, optimally locating booster stations should take the consequence of WQ failure into account depending on land-use variations in the network [12]. Modeling WQ can be a way to monitor the change of water characteristics throughout the WDS and identify failures in WQ [10]. Mechanistic WQ models typically assume an advective-reactive transport, including bulk flow and pipe wall reactions. These models can estimate the required reduction in contaminants to meet the guidelines/standards and facilitate a decision-making process for operational control. Moreover, optimization methods combined with WQ modeling can assist design, operation, locating boosters, sampling, monitoring, and calibration [10]. Therefore, developing simulation algorithms to model WQ in WDSs can provide water utilities and regulators to monitor, simulate contamination incidents, and develop future guidelines [10]. Modeling residual chlorine due to its association with pathogens and DBPs can be an important parameter for utility managers to evaluate the WQ in large WDS [10]. A large WDS usually consists of hundreds of pipes, nodes, several pumps, valves, reservoirs, elevated tanks, and an urban population of 100,000 and above [13]. Several studies have been conducted to model and optimize residual chlorine and the locations of chlorine booster stations in WDS. Many of these models are based on mathematical and statistical optimization techniques [14][15][16][17]. However, these models are based on small or hypothetical networks (e.g., one reservoir, one tank, one pump, and a few nodes). Most optimization problems in large WDSs are non-linear, affecting the implementation of these models due to the high computation time and effort. A detailed discussion on relevant optimization studies from the recent past, their limitations, and their applications are discussed in Chapter 3. Besides, discussions on typical risk-based methods and their limitations and applications are also discussed. The Failure Mode Effect Analysis (FEMA) is the most commonly used risk-based analysis in the water management sector. Water quality index (WQI) can be used as a better surrogate to represent overall WQ in the WDSs. WQI is based on residual chlorine, DBPs, turbidity, water age, etc. It can bring real value to improve the system. Several WQI approaches have been established; however, the WQI proposed by the 3 Canadian Council of Ministers of the Environment (CCME), i.e., Canadian WQI (CWQI), is the most commonly used alternative to represent overall WQ in the WDSs [10]. CWQI is flexible and facilitates integrating complex data and parameters and generating the score based on the WQ status [10]. The CWQI is a straightforward and commonly applied WQ assessment approach due to its flexibility in setting benchmarks, such as WQ standards or site-specific background concentrations. Furthermore, it is easy to change the number, type, and set objectives of selected parameters in CWQI [18]. From the perspective of energy consumption, 8% of the world’s total generated energy is used for the drinking water sector [19]. A significant portion of this energy is used for distribution, i.e., pumping, chlorination, and maintenance [20]. In general, an efficient WDS consumes approximately 0.21-0.51 kWh energy to supply one cubic meter of water. Due to high energy prices and the pressure of reducing associated GHG emissions, most water utilities strive to operate their WDSs at a low energy cost while keeping the WQ at an acceptable level [2]. WQ has been compromised against pump operating energy costs because those objectives are conflicting. It is reported that pumping costs are considerably reduced if WQ is unclear from the optimization model of WDS. On the other hand, the best WQ outcome agrees with the highest pump operating costs [21][35]. Besides, the WQ cannot be compromised in residential areas such as schools and hospitals at the expense of reducing operational energy use (i.e., fewer booster stations). Therefore, a water utility must achieve acceptable WQ and consider minimum energy use. Furthermore, water utilities worldwide have adopted intelligent pumping management systems, i.e., optimal pump scheduling (OPS), to reduce the pumping energy consumption [22]. Several heuristic and mathematical models have been developed to minimize the pumping energy cost [23], as discussed in Chapter 3. Nonetheless, previous studies have identified a suitable schedule for pumps to fulfil variable water demand and simultaneously reduce the energy consumption for a real WDS. The OPS in WDS is constrained by an integer, such as switching pumps ON/OFF and adjusting water tank levels. Hydraulics constraints, such as velocity and flow rate, also lead to complex mixed integer nonlinear programming (MINLP) formulations [24][21]. Developing the MINLP of the dynamic pump scheduling is still under investigation for large WDS. A Mixed-Integer Goal Programming (MIGP) approach can be adopted to resolve OPS problems [25]. Day-ahead pump scheduling based on optimal energy consumption may affect the residual chlorine in simultaneous WQ and energy optimization schemes. Conversely, the best WQ may influence high 4 pumping energy costs due to the competing nature of the trade-off between WQ and energy costs [21]. Hence, the present research is part of continuing efforts to improve hydraulics and WQ performance with minimum possible energy utilization in a WDS. An optimization framework, energy-water quality-management-system (EWQMS), was introduced [26]. The framework was implemented in several stages, i.e., by optimizing the operation in the service area of a distribution system and progressively expanding to the entire network once the predicted saving and return on investments were validated [26]. EWQMS implementation has encountered many challenges related to the system infrastructure and boundaries, embedded energy, and geographical configuration [26]. Water age was used as a surrogate in the EWQMS framework, which was not a helpful indicator to predict WQ at dead-ends of WDS. The variability of chlorine decay rates exists in the different segments of WDS. Hence, the water age may not accurately reflect chlorine residual or DBPs formation potential if multiple water sources of different ages are combined in tanks and pipe junctions. The CWQI provides a simple scale to represent the overall WQ. Applying CWQI by considering residual chlorine, DBPs, turbidity, and water age indicators can bring more comprehensive information to improve the existing EWQMS framework. For energy use optimization (pump scheduling), an advanced meta-heuristic technique combined with EPANET software was used in the previous EWQMS framework, which reported different simulation results in each run [26]. A considerable computational effort was required to implement such an approach to optimize the large WDSs [22][27]. However, in the proposed framework, integrating EPANET with the MIGP-based modeling can provide an optimal solution with less computational effort for pump scheduling in a large real-world WDS. The MIGP-based mathematical model can effectively handle the flexibility with some constraints, such as expected water demands in a WDS, by minimizing undesirable deviations from the expected levels of the goals. Such flexibility helps to achieve an acceptable solution with less computational complexity. This research aims to provide an integrated framework for optimizing energy use and improving WQ in a large real-world WDS to consider the challenges above. The proposed framework used CWQI and risk-based Fuzzy-FMEA to evaluate WQ. Various combinations of factorial analysis were used to optimize residual chlorine dosage and booster locations. Besides, EPANET integrated with a MIGP-based modeling solution was used to optimize the day-ahead pump scheduling. The proposed framework was validated by applying to a WDS in Al-Khobar, Saudi Arabia. The proposed 5 optimization framework can assist the water utilities and regulatory agencies in assessing critical WQ risk factors, optimizing pump scheduling, and developing WQ-energy-based best management practices. Research gaps This research originated based on the challenges water utilities face around the globe. Substantial research has addressed the operational optimization of WDSs in the context of WQ and pump operation [28][29]. Some specific gaps identified are discussed in the following discussion. Fewer applications of WDS optimization in real-world networks: Test network models such as Anytown and EPANET have been used to optimize WDSs. These networks vary in size and complexity, from small systems with one source, one pump, and a few nodes to large WDSs with multiple pumps and reservoirs and hundreds of nodes [21]. The possible reasons for these popular test networks are their data availability and flexibility to be modified to suit a range of optimization models. However, an open question remains: how can these tools or benchmark networks be adapted to the needs of operational optimization of large real-world WDSs? Residual chlorine and infrastructure asset management: Depending on the shape and size of the distribution network, it is sometimes difficult to maintain the desirable range of residual chlorine in a large WDS. Particularly near the booster station, where the chlorine levels are high and at the remote points where most residual could have been consumed entirely. The vulnerability of water main breaks can also increase the possibility of water quality failure irrespective of the location of chlorination points. Effective infrastructure asset management efforts such as prioritized rehabilitation, replacement, and planning of water mains can reduce the possibility of water quality failure [6]. Therefore, there is a need to develop a model to optimize residual chlorine in large water distribution systems operating under environmental conditions which can integrate both the water quality and structural failures for effective decision making. Optimal pump scheduling to minimize operational energy costs: Various water utilities have adopted Optimal pump scheduling to reduce the pumping energy costs [22]. Heuristic and mathematical models were proposed to minimize the pumping energy cost [23]. However, these techniques have some limitations and cannot be applied to a real large-sized WDS due to the long computational time, usually several hours to a month, to generate a solution [21]. Thus, there is a need to find the best schedule for pumps supplying water to satisfy a variable demand and reduce the energy consumption 6 for a real WDS. The optimal pump scheduling algorithm needs to provide an optimal solution in less computational time and be universally applicable. Integrating WQ and energy-use optimization models: WQ has been traded-off to some extent against pumping energy costs because of their conflicting nature. It is identified that pumping costs can be significantly reduced if WQ is absent from the optimization model. Conversely, the best WQ outcome corresponds to the highest pumping energy costs. However, operational energy use in the WDS cannot be reduced at the expense of compromising WQ. Therefore, a water utility must achieve acceptable WQ with minimum energy use. This hypothesis represents a further research challenge to test different case studies of various configurations to ascertain whether the objectives are conflicting or integrated for reduced optimization complexity. Although WQ and energy use optimization methods are well studied, there is no agreement on the best optimization method for a particular WDS operation problem. The research community requires a concerted effort to develop objective comparison and validation strategies. Furthermore, there is no universal agreement on formulating an operational optimization problem and including all relevant objectives and constraints. There is a need to integrate WQ and energy use optimization models to minimize pumping energy costs and maximize WQ and hydraulics performances. Research motivation The motivation for the research originates from the need to optimize operational energy use without compromising WQ in large WDSs. Current water distribution strategies often pose complex coordination issues between energy savings, WQ concerns, and operation and maintenance [30]. Distribution systems control and operations have typically monitored water consumption for water utilities. They have mainly aimed to achieve WQ goals, but less attention was paid towards energy costs and carbon footprint reduction. Therefore, there is stress on water utilities to provide customers with a water supply that continuously meets the required WQ and quantity considering various constraints. There is an urgent need to explore economic, energy-efficient, environmentally friendly, and flexible strategies to meet the water supply-demand under varied conditions. Furthermore, no state-of-the-art information on integrated WQ and energy use is available in the literature. Therefore, developing a comprehensive framework proposed in this study can help water utilities to provide better WQ and energy management plans to reduce operational energy costs, maximize hydraulics reliability, and minimize WQ failure risks. 7 Research objectives This research aims to develop an integrated WQ and energy use optimization framework to improve WQ and minimize energy use in large WDS. The objectives of the research are outlined below. Figure 1.1 presents the conceptual framework adopted in this research. Start• WQI-based approach• WQ failure risk-based approachLegends:WQ: Water Quality WDS: Water Distribution SystemWQI: Water Quality IndexMIGP: Mix Integer Goal Programing Critical review of WQ and energy-use in the WDSs• MIGP-based approach for optimal pump scheduling• Integration of both WQ and energy-use • Scenario analysis • Propose best management Energy-use optimization in WDS• WQ and energy use optimization in WDSWater quality Energy-useENDObjective 2 Objective 3Objective 4WQ improvement in WDSObjective 1 WQ and Energy-use Optimization Framework Figure 1.1 An integrated framework of WQ and energy use optimization in WDSs Objective 1: Identify technical, economic, environmental, and social constraints and requirements to improve WQ and energy use optimization techniques in the large WDSs. Objective 2: Develop empirical models to evaluate and improve water quality in large WDS. Objective 3: Identify a cost-effective pump scheduling system to minimize energy use in large WDS. 8 Objective 4: Develop an integrated approach to simultaneously maintain good water quality and ensure minimum energy use in large WDS. The significant outcomes of this research are the development of: 1. Innovative scheme for maintaining residual chlorine levels, identifying optimal chlorine dosages and booster locations in the water distribution system. 2. Energy use model to reduce the challenges of poor pump scheduling. The developed model minimizes the pumping energy costs and simultaneously stabilizes the water flow rates to meet the demand. 3. Integrated optimization framework to ensure desired water quality and reduced energy use in real large-sized WDS. The developed models/methods in the proposed research are flexible and can be applied to any WDS to improve energy use performance and water quality. The field data related to hydraulics and WQ are sourced from the city of Al-Khobar, Saudi Arabia, and were used to build and validate the developed framework of this research. Thesis organization This thesis consists of the following seven chapters: Chapter 1: This chapter lays the foundation of the thesis by introducing the background, research gaps, motivation, objectives, and basic concepts on water quality and energy use management in WDS. Chapter 2: An overall methodology adopted for this research was discussed in this chapter. The chapter also provides an insight into the key research phases and the methods followed in achieving the objectives of each phase. Chapter 3: This chapter describes the literature on WQ and energy use in the WDS, water-energy nexus, modeling and optimization methods, and their applications and limitations in large real-world WDS. Chapter 4: In this chapter, the WQI-based method was used to evaluate the WQ in WDS. EPANET and CWQI were applied to model the residual chlorine and assess WQ. Eight factorial designs were formulated and used to calculate optimized chlorine dosage. 9 Chapter 5: This chapter presents a risk-based method for WQ modeling and optimization based on the model developed in Chapter 4. Fuzzy-FMEA was used to perform the risk analysis. Chapter 6: An integrated approach of WQ and energy use (pump scheduling) was developed in this chapter. The risk-based method for WQ developed in Chapter 5 was used along with the mixed integer goal programming (MIGP)-based model to optimize the energy use in the WDS. Best management practices were also proposed based on the optimization results. Chapter 7: This chapter provides the conclusions, limitations, recommendations and lists the avenues for future research. 10 Chapter 2: Research Methodology This chapter provides an overview of the methodology used for developing the integrated framework. As shown in Figure 2.1, the objectives are achieved by completing five phases. Details of the proposed framework, such as its components that focus on WQ and energy use optimizations, are described in Chapters 3 to 6. To demonstrate the application of the developed models and proposed framework, the city of Al-Khobar was selected as a case study. Phases of the research Phase I – Literature Review and Content Analysis The first phase involves a comprehensive literature review exploring existing WQ and energy use studies on WDSs. This phase investigated the methods used for modeling and optimizing energy use and WQ in WDSs and analyzed their trade-off relationship. WQI-based and risk-based approaches were used to evaluate WQ, while mathematical-based modeling was used to optimize energy use. The hydraulics and energy use data were collected from existing literature and the WDS of Al-Khobar. Case studies of real-world WDSs were reviewed, and the potential research gaps were identified. Phase II – Water Quality Index-Based Optimization In this phase, a preliminary WQ assessment was performed in the large WDS of Al-Khobar to identify poor WQ locations that failed to meet the regulatory requirements. CWQI was used as a measure for the comparative assessment of WQ in terms of residual chlorine. A two-level factorial analysis was performed using Design Expert 9.0 software to optimize chlorine dosage. The developed model improved the WQ by selecting a better booster chlorine dosage. Besides, it can help the water utility in decision-making with limited hydraulics and WQ data. The detailed method is discussed in Chapter 4. Phase III –Water Quality Risk-Based Optimization Phase III contains a risk-based method to optimize residual chlorine dosage in a large WDS. Data such as pipe dimensions/composition and land use distribution were collected from the water utility. FMEA was used to estimate the WQ failure risk by analyzing three parameters, namely vulnerability (refers to pipe break), consequence (relates to land use, e.g., residential, commercial), and detectability (refers to the detection of a water quality failure). These parameters were identified to evaluate the risk of not meeting the desired residual chlorine in the WDS, and an RPN was calculated. 11 The calculated RPN was based on expert judgment from academic researchers and technical managers working in the water distribution sector. Further details of the method used in this phase are discussed in Chapter 5. Phase IV –Energy Use Optimization In this phase, the day ahead pump scheduling of the WDS was investigated. A MIGP-based model was developed to find the optimal pump scheduling to minimize the operational energy cost. MIGP was integrated with EPANET to optimize the day-ahead pump scheduling and minimize energy costs. The developed model was solved using a CPLEX solver linked with the hydraulics model developed in Phase II. The model's performance was assessed in different scenarios, which is further explained in Chapter 6. Phase V – Integration of WQ Risk-Based and Energy Use Optimization In this phase, an integrated approach of WQ and energy use optimization was developed. The developed framework contains three stages (i) to improve WQ (developed in Phase III), (ii) to optimize pumping energy (developed in Phase IV), and (iii) to evaluate a combined hydraulics and WQ performance. The integrated framework evaluates a large WDS's combined hydraulics and WQ performance. Data from the city of Al-Khobar WDS was used to validate the framework. The performance of the integrated framework was evaluated in different scenarios, which is further explained in Chapter 6. 12 Background Research gapResearch motivationResearch objectives Thesis organizationState of the art practices on water quality and energy use Investigate WQ issues, energy-use in the WDSsModelling and optimization techniques Identify research gaps and challengesSummary and conclusionsOriginality and contributionsLimitations and recommendationFuture research Phases of Research Temporal & spatial analysisEvaluate the WQ , WQI-based Identify poor WQ zones in the networkDevelop hydraulic modelOptimized chlorine dosage Scenario analysisSelect the best optimized WQ solutionEvaluate the WQ , Risk-based Identify the RPN not meeting the desired WQ Develop risk priority number (RPN)Risk managementScenario analysisFactorial analysis, optimized chlorine dosage Select the best optimized WQ solutionObjective: Max. WQInvestigate the day ahead pump schedulingAsses the performance of developed modelIdentify best pump scheduling Develop mathematical model, MIGP-based Scenario analysisEvaluate the performance of integrated frameworkDevelop WQ and energy use integrated framework Validate the framework on real WDSsScenario analysis, select the optimum solutionOptimized energy-useObjective: Min. pumping energy costIdentify the challenges to implement the framework on real WDSs Propose best management practices Chapter 1Chapter 3 (Phase 1)Chapter 4 (Phase 2)Chapter 7Combined hydraulics WQ and Energy-use performanceSelect the best optimized energy-use solutionIntroduction Chapter 2 Research MethodologyLiterature ReviewWQ index-based optimization Chapter 4 (Phase 4)Chapter 5 (Phase 3)WQ Risk-based optimizationChapter 6 (Phase 5)WQ and energy-use optimizationConclusion and RecommendationsEnergy use optimization Figure 2.1 Synthesis of chapters and research phases 13 Chapter 3: Literature Review A part of this chapter has been published in Environmental Reviews, an NRC Research Press Journal, as a review article titled “Water-Energy Nexus for Water Distribution Systems: A Literature Review [20]. A state-of-the-art comprehensive review of past research studies has been conducted to identify the research gap evaluate recent developments in WQ and energy modeling and common optimization techniques in the context of WDS management. This review covers the basic concepts and studies on water-energy nexus, energy-saving solutions, and optimization methods for a large WDS's WQ and water pumping systems. This chapter contains five main sections concerning the objectives defined in Chapter 2. This chapter is organized as follows: Section 3.1 explains the WEN in WDSs; Section 3.2 explains WQ and its failure in WDSs; Section 3.3 introduces energy use in water pumping systems; Section 3.4 presents methods for both WQ and energy use optimization; Section 3.5 overall optimization of WDS. Background Water and energy are essential natural resources; both are interlinked. This relationship is often called the Water-Energy-Nexus (WEN). Freshwater is used for energy production, refinement, processing, transportation, storage, and electric power generation. On the other hand, energy is required by all the components of a water supply system (WSS), including groundwater extraction, surface water withdrawals, transmission, treatment, and distribution [56]. A WDS is the last component of the WSS and delivers water for domestic, commercial, and industrial uses [6][31]. A WDS consists of nodes (junction, tanks, & reservoir) and links (pipes). Through a reliable WDS, every water utility (or supplier) aims to provide water with adequate quantity, quality, and suitable pressure to the end-users [6]. In a water supply system (WSS), energy is consumed for source water extraction, transmission, treatment, and distribution. The WDS is the largest energy consumer from among the components of a WSS [32]. It is estimated that about 7-8% of the world's total generated electricity is used for drinking water production and distribution systems [19]. The energy consumption per unit volume in a WDS depends upon the size of the network, the topography, population, water quality (WQ), and demand variations [33][34]. However, water resources managers are putting serious effort into water 14 conservation through performance benchmarking of their WSS [6]. However, the optimum pressure and good quality water demand continually increase with the growing population [34]. A state-of-the-art review of past research studies is conducted to assess and evaluate the energy consumption and the related GHG emissions in the WDSs. This review covers the basic concepts and studies on WEN, WQ, water pumping systems, optimization methods, the life cycle assessment (LCA) of the large WDSs. Case studies of real-world WDSs are reviewed, and the potential research gaps are identified. The approach adopted for the state-of-the-art review to identify the existing knowledge gap in the different research areas of WEN, primarily of large WDSs, is illustrated in Figure 3.1. It includes energy consumption, GHG emissions, WQ and energy use optimization, booster pumps, and pipe life cycle stages (embodied energy) in the network. The research was geographically distributed primarily between North America, Europe, Australia, and Asia. The main components of the overall review process are shown in Figure 3.1 Water Distribution System Modeling and Optimization In general, public water utilities and private suppliers withdraw surface water (lake, river, and seawater desalination) or groundwater sources to meet consumers' demand in their service areas. Modeling a system's WQ and energy use is vital for water utility managers to see how characteristics change under several scenarios [26]. Therefore, developing simulation algorithms to model the WQ and energy use in WDS is helpful for water utilities and regulatory agencies to monitor and optimize pump scheduling, simulate contamination incidents, and develop future guidelines [35]. These models can estimate the required reduction in contaminants to meet the guidelines/standards and facilitate a decision-making process for operational control. Moreover, optimization methods facilitate WDS modeling at various stages, including design, operation, locating booster locations, sampling and monitoring, and calibration [14. The WDS models are broadly categorized in terms of hydraulics, water quality, and energy use in the following sections. 15 Figure 3.1 Review study in the context of water-energy-nexus for WDSs [35] Water Quality in Water Distribution System Types of water sources and treatments significantly affect the distributed water quality. WQ is the term used to express the suitability of drinking water for consumption. WQ in a WDS can be compromised because of many factors. For example, insufficient water treatment may regrow in 16 WDS, e.g., injured microbes [31]. WQ may also deteriorate because of internal corrosion, nitrification, longer water age (retention in the dead ends), biofilm growth, DBPs formation, leaching of lining, pH instability, and scale formation [3]. Moreover, contaminants can enter a WDS through storage tanks, reservoirs, pump stations, cross-connections, water mains, and permeation [15]. WQ problems associated with water age, such as DBPs formation and microbial regrowth, may directly impact human health [31]. Common bacterial infections include typhoid, salmonellosis, shigellosis, bacillary dysentery, Asiatic cholera, Legionnaires disease, and Pontiac fever [36]. Viral water-associated illnesses include hepatitis, poliomyelitis, and gastroenteritis attributable to Norwalk virus and rotavirus [36]. Protozoans such as Giardia and Cryptosporidium can cause gastroenteritis and are resistant to disinfectant [37]. Various water and environmental agencies have established maximum concentration levels for different drinking WQ indicators, as shown in Table 3.1 [37][38]. WQ in distribution systems can be monitored to evaluate the effectiveness in the context of defined standards/ guidelines. Monitoring WQ is an intensive time process, and it is difficult to monitor water quality regularly. Applying modeling techniques can be a good solution to address these complex monitoring challenges. 3.3.1 Hydraulic Modeling The hydraulics analysis is an essential step towards understanding the behaviour of water flows, pressure, velocity, and head loss in a WDS. Various models have been developed to simulate water flows and analyze pressure under steady-state conditions [39]. These models can assist in planning, evaluating, and managing the system’s equipment (e.g., pipes, pumps, valves, pipes). Moreover, hydraulics simulations are critical for modeling WQ [40]. The detailed theory and application of hydraulics models of WDSs have been discussed in these references[39] [41] [42]. Hydraulics models are built on link-node relationships in which links (pipelines) are interconnected with nodes. Nodes do not physically exist in an actual system. However, in WDS designs, the nodes represent the water supply and demand on the pipelines. Nodes can be set either where pipelines are interconnected or at any point of the water main where the demand needs to be measured. When formulating the network, the conservation of mass equation needs to be satisfied at each node. The energy equation, on the other hand, needs to be satisfied for each link. Based on this formulation, a set of non-linear equations are developed, which cannot be solved analytically [43]. 17 Table 3.1 Water quality standards [37] Water Quality Parameters WHO U.S. EPA Health Canada HB NHB MCL1 MCLG MCL2 MAC AO Primary /Physical Odor acceptable pH 6.5-8.5 6.5-8.5 6.5-8.5 Turbidity, NTU <5 0.5-1 <1 Temperature ≤15 Total dissolved solids (mg/l) 1000 500 ≤500 Total suspended solids, (mg/l) Conductivity, (μS/cm) 4.7-5.83 Nutrients Total phosphorus, mg/l Nitrate- N, (mg/l) 10 10 10 10 Nitrite-N, (mg/l) 1 1 1 1 Ammonia, (mg/l) 1.5 Nitrate, (mg/l) Nitrite, (mg/l) Organic Matter TOC, (mg/l) 0.053 BOD, (mg/l) Heavy Metals Arsenic, (mg/l) 0.01 0.05 0.01 0 Lead, (mg/l) 0.01 0.015 0 Mercury, (mg/l) 0.001 0.002 0 0.001 0 Chromium, (mg/l) 0.05 0.1 Cadmium, (mg/l) 0.003 0.005 0.01 0.005 0 Microorganism Fecal coliform, (MPN/100 (ml) 0* 0* Total coliform, (MPN/100 ml) <1 HB- Health based guideline, NHB- Non- a health-based guideline, MCL- Health-related standards called the Maximum Permitted Contaminant Levels (MCLs) system under the Safe Drinking Water Act, MCLG- Maximum Contaminant Level Goals (MCLGs) at levels where no known or anticipated adverse effects on health occur, MAC- The maximum acceptable concentration, AO- Aesthetic objectives, TCB- Trichlorobenzene (1, 2, 4), MPN/ 100ml- Most probable number per 100 ml, 1- Primary MCL, 2- Secondary MCL, 3- USP 23-Standard for purified water imposed by USEPA regulations for drinking water, *Drinking water-related Numerical techniques such as Newton-Rapson are commonly used to solve the resulting set of non-linear equations iteratively until the solution converges [44]. Computer-based hydraulics simulators 18 were first developed in the 1960s and have been widely available since the 1970s [45]. Modeling software such as EPANET, WATERCAD, Info Water, KYPipe, H2OMap Water, HydraulicsAD, WaterGEMs, and GOODwater have simulated flow in pipe networks [10]. Recently, an extension pack of EPNET has been introduced known as EPANET-MSX to model both microbiological and residual chlorine concentrations [10]. These modeling tools can analyze the behaviour of real-world systems and simulate them as closely as possible in terms of spatial and temporal characteristics. However, successful analysis results and predictions depend on the accurate estimation of input parameters. EPANET is the most common water modeling software developed in 1993 by the United States Environmental Protection Agency (USEPA). It has been widely used for more than twenty years for hydraulics and water quality simulations [46]. EPANET tracks the water flow in each pipe, node, and tank and simulates chemical species level, water age, and source throughout the network during a simulation period comprised of multiple time-steps [47]. The EPANET software also models chlorine decay with predefined kinetics and forecasts the residual concentration change for a WDS time series. 3.3.2 Water Quality Modeling Two types of processes occur in WDS: within the main water body (bulk processes) and processes that appear on the internal surfaces of WDS (wall processes). There are various applications related to WQ modeling. The possible application areas include residual disinfectant and DBPs concentration estimation, metal corrosion mechanisms to minimize lead and copper solubility, control bacterial growth, and identify possible pathogen intrusion. WQ models typically assume an advective-reactive transport and all reactions, including bulk flow and at the pipe wall. Water quality modeling consists of two governing mechanisms: (1) conservation of mass and (2) reaction kinetics. Drinking water treatment is an essential factor in WDS. Before supplying water to the consumer at the treatment plants, disinfectants must be added to the WDS to avoid contagious waterborne diseases and safeguards human health [46][48]. Three standard disinfection methods are used: ultraviolet radiation, ozonation, and chlorination [16]. The first two methods are expensive and inappropriate for public water supply in developing countries [5]. Conversely, chlorine has been used in water distribution systems for more than 100 years. Chlorine produces residuals that persist in the distribution system until the water reaches the end-user. It has little capital and operating cost and is easy to handle [16]. Chlorine helps to control biofilm 19 growth in the water pipe. This phenomenon is known as secondary disinfection, usually performed after the water treatment. This secondary disinfection with booster chlorination provides spatial and sufficient free residual chlorine concentration in the WDS. Furthermore, this mechanism helps to maintain the taste and odour [3] and reduces disinfection by-products (DBPs) [49]. In the WDSs, the residual chlorine depends on physical, chemical, and biological factors and chlorine decay and pipes’ materials [16]. Various municipalities have established a minimum chlorine concentration in the water network at the point of entry. It depends mainly on the pH level and water temperature [8]. Chlorine decay occurs in the water due to its antiphons with organic and inorganic matter. The US Environment Protection Agency approves 0.2 mg/L free residual chlorine concentration at the distribution network [8]. It depends upon its climate and water conditions. WHO (2004) recommends 0.2 to 0.5 mg/L of residual chlorine in WDSs. This recommendation is for countries where cholera and dysentery are common problems. However, an increase in chlorine level may produce disinfection by-products (DBPs) [50][51]. Many DBPs can cause serious health issues like cancer, muscle cramps, and fertility problems [3]. Residual chlorine modeling helps to categorize the challenging areas in WDS. It provides a good understanding of the system's applied chlorine dosage consumption. Modeling results improve the amount of dosage to sustain the minimum required chlorine residual throughout the system. Many studies have been conducted on the modeling/optimization of WQ in the distribution network [10][52][53][49]. These studies are founded on predefined concentration values of free residual chlorine. Many stable states and dynamic models have simulated residual chlorine in WDSs [16]. Most of them are used to simulate chlorine residual by first-order kinetics. However, researchers have developed two-dimensional chloride transport and decay equations [54]. Chlorine is the most commonly used disinfectant, and its residual value is a critical WQ indicator. There are many techniques to predict the residual chlorine levels in drinking water. Modeling residual chlorine can be equally important as other WQ parameters for utility managers to evaluate water quality index (WQI) in DBPs formation under different conditions [52]. Some relevant studies are summarized in Table 3.2 20 Table 3.2 Disinfectant dosage and modeling methods used in various past studies [10] Optimization Scheme Method used Residual chlorine (mg/L) References (min) (max) Disinfection points and Chlorine dosage Indexing system 0.2 1 Cozzolino et al. (2005) [53] Chlorine booster Genetic algorithm 0.2 4 Gibbs et al. (2010) [55] Reduce unnecessary chlorine concentrations Genetic algorithm 0.2 4 Kang and Lansey (2012) [56] Disinfectant booster station distributions to maintain chlorine residuals linear programming (Mixed-integer) 0.04 to 0.05 2.25 to 4 Tryby et al. (2002) [57] Location and injection optimization Genetic algorithms (multi-objective) 0.2 4 Prasad et al. (2004) [58] Disinfectant residuals and high dosage optimization EPANET Analysis & linear programming 0.5 4 Lansey et al. (2007) [59] Reduce booster disinfectant doses Linear least squares 0.1 to 0.2 1 to 4 Propato and Uber (2004) [60] Cost effective booster chlorine Genetic algorithm & EPANET 0.2 4 Ostfeld and Salomon’s (2006) [14] Multi-species disinfection contamination EPANET-MSX 0.2 1 Parks and VanBriesen (2009) [61] Many water utilities strive to operate their WDSs at low energy costs while keeping the WQ at an optimal level [2]. An efficient WDS consumes 0.21-0.51 kWh/m3 (approximately) energy because of the large number of chlorine booster stations needed to avoid the possibility of bacterial contamination [10] [11. Extra chlorine booster stations must maintain an optimal WQ in the network, increasing operational energy costs. However, the WQ cannot be conceded at the expense of operational energy use in the public sector. Therefore, the WQ has been suggested a trade-off against pumping energy costs in the network. Energy use in Water Distribution System The primary objective of a water utility is to operate its WDSs at low maintenance and operational costs, maximize WQ and energy efficiency, and minimize GHG emissions throughout its lifecycle [1] [2]. Energy saving in a large WDS depends largely on process improvement, efficiency targets, 21 and pumping efficiency. Due to high energy prices and the associated GHG emissions, there is an increasing demand to minimize energy requirements and develop and implement sustainable water use strategies [62]. The energy utilization in a WDS depends primarily on water quality, type of pumping system, size of the network, the age and network material, and the topography of the area [63] [64]. Table 3.3 summarizes past studies that have presented data about the energy consumed by various WDSs around the globe. The average GHG emissions are shown in Table 3.3. The variation in energy usage in both places is due to the network's topography. An efficient water distribution system that receives good water quality in Auckland (New Zealand) consumes 0.21 kWh/m3 energy. While the WDSs in Taipei, Taiwan consume energy varying from 0.26 to 0.51 kWh/m3 because of the large number of chlorine booster stations needed to avoid the possibility of bacterial contamination due to the ageing pipe infrastructure and the relatively poor source water quality [64]. Figure 3.2 shows the breakdown of total energy consumed (i.e., 0.40 kWh/m3) by different components in a typical North American drinking WSS with a surface-water source on flat terrain [65]. It can be seen that more than 75% (i.e., 0.3 kWh/m3) of the total energy is consumed for water distribution. WDSs in Canada and some European countries are more energy-intensive. For example, in Toronto, Canada, Nantes, France, Oslo, Norway, and Turin, Italy, water utilities have energy demands varying between 0.16 and 0.41 kWh/m3 and emission intensities ranging from 0.006 to 0.16 kg/CO2-e [66]. In Germany, an urban WDS consumes 1.71 kWh/m3 of energy, which is six times higher than in China (i.e., 0.6 kWh/m3) [64][67]. The primary reasons are i) the type of supply, i.e., direct pumping for continuous supply in Europe contrasted with pumping with storage or intermittent supplies in developing countries; and ii) the required pressures along with the network. Countries in the Arabian Peninsula, including Saudi Arabia, Kuwait, Egypt, and Libya, have used fossil fuels to operate their WDS, and thus they produce a large number of carbon emissions [68][69]. 22 Table 3.3 An estimated energy use in urban drinking WDSs of various regions of the world [35] Region Activity Energy consumed kWh/m3 *% of total State/City Energy consumed **Equivalent CO2 emission per of kWh energy (kg) References Louden, Virginia, USA, small town, a mountain valley Water distribution 2.28 1-2 % of the state 1.6 (Alanis 2009) [70] Alexandria-Virginia, USA, a flat terrain Water distribution 0.55 -- 0.38 (Alanis 2009) [70] Spain Urban Water Distribution in general 0.12-0.22 -- 0.15 (Joan Corominas 2009) [71] China Urban Water Distribution in general 0.2-0.3 -- 0.22 (Liu et al. 2013) [72] Toronto, Canada Water Distribution 0.3-0.4 1-2 % of the greater Toronto area 0.1 (Venkatesh et al. 2014) [66] Turin, Italy Water distribution 0.3-0.32 - 0.2 (Venkatesh et al. 2014) [66] Lahore, Pakistan Water distribution to residential area 0.5-0.62 1-2% of the total municipal energy 0.42 (Nasir et al. 2015) [73] Melbourne, Australia Urban Water Distribution 1 – 2 0.2-0.6 % of the total urban Energy 1.5 (Kenway et al. 2016) [74] Taipei, Taiwan Urban Water Distribution 0.26-0.51 -- 0.36 (Lee et al. 2017) [11] India Urban Water Distribution in general 0.3-0.4 0.5-1% total urban energy 0.23 (Huang et al. 2018) [75] Saudi Arabia Urban Water Distribution in general 0.7-0.8 1-5% of eastern province energy 0.6 (Al-mutrafi et al. 2018) [69] “--” Data not available, *The energy consumption % age varies with different topographies, depth, volume, and the technology used **The GHG emission ~ kWh energy (USEPA 2014) [76] Figure 3.2 Energy use in each step of the drinking water system from source to end-user [65] 23 Water utilities relying on natural resources, such as hydel and wind-driven energy, have far less GHG emissions. For instance, in the Portuguese city of Aveiro, a medium-sized WDS consumes electricity generated by hydel and wind power; as a result, negligible CO2 emissions were reported [77]. Various important factors, including climate change, growth in population, health, and environmental impact, can influence the development of an integrated approach to the WEN [62]. The sources of energy and their consumption patterns using different components of a WSS, along with the associated environmental impacts during the complete life cycle, should be carefully analyzed to achieve a sustainable WEN [32][78][79]. 3.4.1 Energy Indicators Over the last two decades, several “energy indicators” have been developed to i) identify the factors responsible for the wastage of energy in a WDS and ii) prioritize decisions that improve system performance [80][81]. A summary of water-energy efficiency indicators and their applications is presented in Table 3.4. Pelli and Hitz (2000) introduced two primary energy efficiency indicators known as quality and infrastructure [82]. A quality indicator is the ratio of minimum theoretical energy to the actual quantity of delivered water in the network. It is used to calculate the individual pressure zones in a simple WDS (Indicator E1 and E2 in Table 3.4). However, the quality indicator can neither effectively estimate the minimum energy for each pressure zone in a complex network nor consider the structural characteristics, e.g., the energy dissipated due to friction, water loss, and the network's total length. Later, Colombo and Karney (2002) proposed additional energy indicators such as pump efficiency and energy dissipation due to friction [83]. Filion et al. (2004, 2009) have analyzed the environmental impact indicator associated with pipe fabrication and its disposal energy, in addition to the end-of-life stages of a WDS (Indicator E3 and E4 in Table 3.4). Boulos et al. (2010) developed ‘water network energy efficiency’ indicators to reduce the additional hydraulics energy dissipated in the network [40]. These indicators apprise the system rezoning, upsizing the water-mains, surplus hydraulics energy use, and suggested pumping trade-offs. However, the study did not consider the energy lost due to leakage in the entire network and the LCEA of distribution systems. 24 Table 3.4 Summary of past studies on water-energy efficiency indicators for WDSs [35] References Description Location Size of system Energy indicators Lifecycle considerations (Pelli and Hitz 2000) [82] Analyzed water quality, infrastructure, and pump efficiency Sagno- Mendrisio Municipality Switzerland Small a E1: Quantity Indicator =0.71 kWh/m3 b E2: Quality Indicator in relation to energy consumption =2.75 × (Colombo and Karney 2002) [84] studied the link between water losses and energy costs EPANET network model Small E3: Energy ratio as a function of leak detection, location, and magnitude About 30% of the system water lost ~ 0.6 kW/m3 of energy × (Filion et al. 2004) [85] Evaluated life cycle energy analysis (LCEA), pipe replacement and environmental impact of WDS New York City primary water supply Large E4: Environmental impact Indicator associated with Pipe fabrication and its disposal energy A 100mm diameter pipe consume 80200 kWh/km manufacturing and 1400 kWh/km disposal energy √ (Dandy et al. 2008) [86] Evaluated the embodied energy and environmental impact indicator, optimized the WDS design Simple network model small E5: Embodied energy and environmental impact indicator, the trade-off between cost and total energy for a simple WDS √ (Cabrera et al. 2010) [81] Evaluated the performance indicators to assess the system from the energetic point of view EPANET test model (later applied to real network) Small E6: Energy balance indicator (kWh/m3): Energy injected into the system to energy lost due to friction and leakage × (Boulos et al. 2010) [40] Developed Water network energy efficiency (WNEE) tool Eight large cities WDSs of Europe Large E7: minimum level of service pressure indicator, optimize the power (kWh/m3) needed to convey water to each customer × (Bolognesi et al. 2014) [2] Proposed the concept of unavoidable minimum energy (UME) as the reference for defining an energy efficiency indicator for WDS Cabrera et al. (2010) model adopted for this study Small E8: Unavoidable minimum energy indicator, maximize the energy efficiency w.r.t. pipe diameter (m), leakage rate, and pump energy (kWh/m3) × (Smith et al. 2016) [67] Evaluated the environmental emissions impact on urban WDS China Large E9: Low carbon emission indicator; controlling leakage (less GHG emissions /unit volume of water: 0.213 kg-e CO2/m3) √ (Scanlan 2016) [87] Developed energy indicator, evaluated energy lost due to friction, leakage, and excess energy Ontario, Canada Large E10: Energy indicators for WDS × “---” Data is not given a- Quantity Indicator (energy used to lift water/year) / (theoretical minimum energy consumption) b- Quality Indicator (The ratio of actual energy used to the minimum energy required (< 2 Very Good, 2-3 Good, 3-4 Need Improvement) 25 Cabrera et al. (2010) proposed an ‘energy balance indicator,’ also known as an ‘excess of supplied energy.’ They also developed five additional indicators to assess the energy efficiency of a WDS, including an excess of the supplied energy, network energy efficiency, leakage energy, standard compliance, and energy dissipated through pipe friction (Indicator E6 in Table 3.4) [81]. EPANET simulations were performed to develop these indicators assuming a flat surface, frictionless pipes with no leakage, and service to low-pressure heads. The study did not consider the energy cost and embodied energy over the life cycle of a WDS. Bolognesi et al. (2014) proposed an energy efficiency indicator, ‘the ratio of unavoidable minimum energy to actual energy’ for WDS [2]. This indicator can be used as a reference starting point to assess the actual energy use, the impact of leakage, and pipe replacement in the network (Indicator E8 in Table 3.4). Bolognesi et al. (2014) used the model of Cabrera et al. (2010) to study the pumping energy variable of WDS [2]. They included the energy efficiency of pumping systems and a pressure-dependent demand to express the leakage rate, which was not considered in previous studies. Scanlan (2016) extended the analysis proposed by Cabrera et al. (2010), applying energy indicators to assist the WDS operators regarding energy use [87]. This study characterized four small to medium-sized WDS in Ontario, Canada, and assessed the energy lost due to friction, leakage, and excess energy (Indicator E10 in Table 3.4). It was found that energy efficiency can be improved from 75% to 94 % without leakage in the system. However, the system characteristics like topography, hydraulics redundancy, and peak demand were assumed in this study. The term ‘minimum energy value’ was used by water utilities as a reference baseline to assess the actual energy consumed. Any system requires this unavoidable energy value to maintain the minimum pressure to run the network. The assessment of the minimum energy baseline is complicated for a larger and more complex network because it varies with system configuration [88]. The decision-makers are planning, prioritizing, and making affordable strategies to reduce energy incompetence in WDS. In the past, few researchers have developed “energy indicators” to assess the wastage of energy during the operations of WDSs. Applications for these indicators in real-world WDSs are limited to date. However, a few studies have described the energy efficiency indicators of a large system to assess the actual use of energy, pipe breakage, leakage reduction, pump repair or replacement, and the related GHG emissions [89]. 26 3.4.2 Energy Saving Solutions 3.4.2.1 Watergy The concept of WEN has not yet been broadly understood or adequately explored through synchronized, holistic efficiency approaches. The water energy-saving approach consists of three main steps: creating the political will, technical management, and implementing efficiency measures [30]. “Watergy” is a recent term developed to provide energy-saving solutions, through technical and managerial interventions, for conserving water without interrupting the desired uses [30]. The successful implementation of this tool worldwide has revealed immediate improvements in water delivery systems, energy-saving, and water price reductions [41]. A USEPA (2013) report states that a water utility in the U.S. can save about $400 million and 5 billion kWh annually through cost-effective energy conservation techniques [90]. Interested readers are referred to Barry et al. (2007) [30] for details. 3.4.2.2 Maintenance of pumping system Recent studies have revealed that most of the operational cost in a WDS is associated with water pumping [91]. For example, more than 6-7% of the electricity in California is being used for water pumping systems [32]. According to the U.S. Department of Energy (2006), about 10 to 30% energy savings can be achieved by optimizing the pump sizes. The appropriate use of variable speed drives can save an average of 10% of the total electricity in the WDS. It also reported that investing 8 cents/kWh on maintenance of pumping systems can: i) increase pump performance by about 20%, ii) provide savings of 145,100 kWh/year of energy and $11,800 in energy costs, and iii) reduce emissions by around 98 tons CO2 per annum. The three types of pumps (centrifugal, vertical, and submersible) that have been used in WDS depend on network size. The most commonly used type of pump for the delivery of large flows with a stable head is the centrifugal pump [92]. Instead of using a fixed-speed pump (FSP), both the energy use and the GHG emissions can be reduced with the help of a variable speed drive pump (VSP). The optimization of the FSP in WDSs has been noted in many studies [93]. The VSP is useful in networks where the water demand varies (close pressure zones), and the pump size is not a deciding factor [94][95][96]. Marchi et al. (2012) evaluated the performance of both the FSP and VSP in a small WDS (population less than 20,000)[91]. They found that the FSP consumed more energy (i.e., 0.4 kWh/m3) than the VSP (i.e., 0.23 kWh/m3), saving around 40% in pumping costs. A pump’s efficiency decreases due to wear and tear, and with age, it starts consuming more 27 energy. A few water utilities in Ontario, Canada, have adopted refurbished pumps in WDS, resulting in significant reductions in energy, operational cost, and GHG emissions. HydraTek (2013) tested the efficiencies of 152 pumps operating in various water utilities spread all over the province of Ontario. The tested pumps' annual energy consumption and GHG emissions were 161,230 MWh and 27,400 tonnes of CO2, respectively [97]. After refurbishing these pumps, it was found that 11% of the pumps recovered their lost energy efficiency. The results show improvements in pump energy indicator and volumetric efficiency after refurbishing 34-year-old pumps in a large WDS [92]. Micro-pump storage activities, such as pumping at night and in-conduit hydropower generation, are energy savings options [91]. Various researchers have investigated the possibility of recovering water energy by applying mini hydro-power plants. Unfortunately, thus far, high equipment costs restrict these applications in WDS. A pump as a turbine (PAT) is an alternative power source in WDS, which can save substantial amounts of energy [91]. Around the world, China has the highest installed capacity of mini-hydro turbines for power generation [98][99]. Pressure reducing valves (PRVs) are commonly used to control hydraulics energy available in the drinking water pipe network. A small hydro turbine can be exercised to control the stream of water for electricity generation with no environmental impact [77]. However, mini-hydropower generation is a less efficient method when compared to other renewable energy resources [100]. 3.4.2.3 Leakage Management Leakage in water mains is one of the most significant operational problems in a WDS. Water leakage usually occurs in broken and old buried pipes, water mains, valves, and taps. A pressure management solution (PMS) can significantly reduce leakage in WDS. Pressure management is more cost-effective than expensive conventional repairs to numerous leaks in buried pipes. Water utilities have saved $2.6 million in energy costs per year in Ontario, Canada, by implementing PMS [101]. Pressure reduction due to leakage results in a significant loss of embodied energy [30] [32]. Around 30-50 % of urban supplied water is lost in developing countries due to leaking pipes and system inefficiencies. For example, in large Indian metropolitan cities, about 40% of the water is lost due to leakage, and this figure varies from 50%-60% in smaller towns. Similarly, in most big cities in South Africa, about 40% of the water is lost in the distribution system [30]. Figure 3.3 shows the % of water lost due to leakage within the WDS in various world countries. 28 Figure 3.3 Water lost due to leakage in the large WDSs [30] In the U.S.A, a water utility requires a 14-m (head) minimum service level pressure (in fire flow condition). In the U.K, this level is about 10-m (head) (at kitchen tap) to deliver water to every single user. Ideally, a 20-m water pressure head demonstrates good pressure throughout the WDS without extensive leakage. A minimum energy level is required to maintain an optimal pressure level in the WDS. Therefore, a 20-m water pressure head (a form of potential energy) is necessary to maintain a balanced pressure at every single customer tap. The European Commission (2010) has reported that living standards and other social aspects can significantly impact water consumption by the end-user; thus, an energy sustainable water management system is required in homes [64]. The results show that an average medium-sized U.S. WDS loses about 7% of water energy in its pipes due to friction, 8% from overpressure, and 85% at the customer taps (plumbing fixtures) [40]. Most of the energy is lost at the customer tap due to leaking tap washers, extra flushing water in the toilet, and lengthy showering times. In the U.K. and Australia, the average time for a person to take a bath or shower is about 8 minutes, consuming about 0.04kWh/m3 of energy [40]. People kept their taps turned on while brushing their teeth. About 25% of the water lost in London alone is due to the unattended taps remaining open for an extended period [40]. An enormous amount of energy is lost at the customer tap. This energy could be conserved by educating the general public and implementing a pressure management system (PRM) at the users’ 29 end. Table 3.5 summarizes the past studies of water energy management systems employed on large WDS and medium-sized water utilities serving two- to three-story residential and commercial buildings. Table 3.5 Summary of past studies on water-energy saving solutions through a water management system [35] References Study area and location Energy-saving methods Population Amount of Energy saved * GHG reduction Limitations California Energy Commission (CEC-2005) [32] State of California, U.S.A. Replace pipes (increased diameter) overhaul pump’s impellers 39 million (2015) 880 million kWh /year (from water & wastewater facilities) 618000 metric-ton CO2 Pipe replacement cost is high (Maas et al. 2010) [101] Province of Ontario, Canada Pressure management and leak detection (0.68 kWh / m3 energy saved) 13.6 million (2014) 4.4 million kWh/year 3200 metric-ton CO2 Infrastructure up-gradation pump repairing cost (Ramos et al. 2011) [77] Lisbon county water utility, Portugal In conduit water turbine installation for electricity generation 2.8 million (2015) 520 kWh/day energy saved 442 kg CO2 assumes daily water consumption Spellman (2013) [102] Chandler water utility Arizona, U.S.A. optimize water storage tank, upgrade the pumps, maintain pressure zones 0.25 million (2014) 1.4 million kWh/year 996 metric-ton CO2 additional equipment cost Watergy, The Alliance to Save Energy (Barry et al. 2007) [30] Fortaleza Brazil WDS Operational procedures improved and standardized 2.5 million (2015) 88 million kWh over four years 68 metric ton CO2 Initial investment of $1.1 million Vishakhapatnam India WDS retrofitting pumps and motors, optimizing the use of contracted demand 1.7 million (2011) 1.4 million kWh/year 2400 metric ton CO2 Bringing water from the river 200 km away Mogale City South Africa Prevent leaking, controlled water supply, applying metering system 0.3 million (2011) 15.4 million kWh/year 13,700 metric tonnes of CO2 Small network, leakage rate high Plappally et al. (2013) [64] Evansville water utility Indiana State, U.S.A. embedded energy in real water losses (about 475-million-gallon water leakage saved) 0.2 million (2014) 750 MWh/year 400-ton CO2 Equipment installation cost * GHG emission factor varies and depends upon the electricity generation methods (coal, gas, thermal and hydel) Numerous water utilities worldwide have successfully applied water management systems to control leakages in the system. One of the best practical applications of PRM is adopted by the water utility of the city of Emfuleni, South Africa. The city has a population of one million and utilizes about 200,000 m3/day of drinkable water. It adopted the PRM system, a cost-effective way of reducing leakage at the end user. After implementing the PRM system, about 8,000,000 m3/year of water and 30 14,250,000 kWh/year energy was conserved, leading to a reduction of about 12,000 metric tonnes of GHG annually within a 3-year payback period [30]. Optimization of Water Distribution System 3.5.1 Optimal Design The development of large WDS cannot fulfil local communities' growing future water demands without being optimized [103]. Given the complexities of a WDS, such as the varying sizes of the pipes and the corresponding head losses, including variations in pressure and demand loads across the network, optimization problems can be resolved by using traditional trial and error techniques [22]. The use of a nonlinear optimization algorithm is more popular and efficient. The optimal solution always means minimizing the cost function or maximizing the network's benefit (reliability) function subject to equality or inequality constraints, i.e., hydraulics system requirements. Various optimization studies have shown that one objective function may be suitable for one system while inadequate for another [104]. No perfect algorithm has been developed so far that can solve all of the optimization problems of urban WDS. In the last three decades, the nonlinear (classical and heuristic) optimization approach has been used frequently to optimize network design and operation [22]. Figure 3.4 illustrates different types of nonlinear optimization algorithms for urban WDS. Other optimization methods for WDS such as ant colony, particle swarm, simulated annealing, and harmony search have also been reported in the literature [22]. However, the main limitations of using these methods are their high computation time and the extensive effort required. Some researchers have used a multi-objective approach to simultaneously optimize (minimize or maximize) some objective functions. The common multi-objective delinquent of WDS considered five parameters, including hydraulics capacity, water quality, flexibility (the system can be expanded with the increase in population), physical integrity, and cost of the system [105]. The purpose of most common multi-objective problems is to maximize the hydraulics benefit and minimize the cost of the network. The application of the multi-objective optimization of WDS is described in section 3.4.4. Many studies have been conducted on optimal design: minimizing cost, planning, and network operation [106]. Most previous studies have focused on network piping systems [80]. At the same time, a small number of studies have simultaneously measured the real size of the network, the tank size, pump scheduling, multiple operating conditions, and demand variations [22]. Table 3.6 summarizes these 31 previous studies that have applied multi-objective optimization to identify the optimal design of WDSs. Figure 3.4 Nonlinear optimization algorithms used in the design of WDSs [35] There are three possible stages of network optimization that can be employed over a life cycle, including deterministic, non-gradient, and real-world optimization [107]. Empirical algorithms, such as genetic algorithms and frog leaping algorithms, are some of the optimization methodologies which have been successfully used in the past [28][89][108]. Over the last three decades, several methods have been developed for the optimal design of WDSs. These optimization methods were based on single and multi-objective genetic algorithms, which have been used to improve WQ, minimize construction costs and extend the network's life [109]. Other algorithms, such as the non-dominated sorting genetic algorithm II and the strength Pareto evolutionary algorithm II, have also been used in some studies [110][111][112]. Usually, the cost has been considered a single object in WDS construction and operation [44]. Recent studies have shown that network reliability should also be considered, in addition to cost, in the sustainability of WDSs[113][114]. Most of the studies described in Table 3.6 applied optimization to either small or hypothetical WDSs. A small number of the above studies considered an integrated approach for overall optimization, considering all relevant parameters. It includes water quality, optimal pump pressure (head), storage sizing, pipe sizes, embodied energy, pumping energy, GHG emissions, and the cost of a large complex actual network. A few studies have pointed out that climate change and environmental factors should be considered an objective function and other optimization methods. 32 Table 3.6 Summary of the past studies used multi-objective optimization methods in the design of WDSs [35] References Multi-objective Variables Study parameters Limitations (Kapelan et al. 2005) [115] cost of the pipes, head lost, minimum pressure Systems design and Reliability The methodology tested on New York Tunnels reinforcement problem only (data assumed) (Dandy et al. 2008) [86] minimize cost, energy consumption, GHG emissions optimum design sustainability objectives identified Applicable for simple WDS for irrigation purposes, not for populated complex WDS (Wu et al. 2010) [116] minimize cost and GHG emissions The impacts of minimizing GHG emissions on the results of WDS optimization Hypothetical network model (Fu et al. 2011) [117] minimization of total design cost and maximization of system performance (pressure, head) provide the cost‐effective and reliable design of WDS to cope with future uncertainties Proposed methodology tested on the New York tunnels and Hanoi network only (Lancy et al. 2012) [118] minimization of cost, GHG emissions Relation between pump cost, GHG emissions, and system integrity The methodology tested on a small network (residential span 11 km only) (Ostfled et al. 2014) [119] minimize pumping costs, water quality, and tank sizing WDS Development of a new tool to check the water quality and pumping operation EPANET model (not real network) (Zheng et al. 2014) [120] minimizing cost and maximization of the network resilience An efficient hybrid approach for the design of (WDS) with multiple objectives Proposed methodology verified on the New York water channels and Hanoi-network only (Bi et al. 2015) [121] optimal pipe diameter and pressure head Use of new heuristic algorithms approach of varying size WDS Pumping power is not considered in the proposed methodology (Siew et al. 2016) [122] minimum node pressure, conservation of mass & energy, pump scheduling, tank sizing spatial and temporal variations in water quality investigated in addition to water age Not real network (Lence et al. 2017) [123] minimize pipe network cost and maximize the reliability proposed fuzzy multi-objective programming for pipe design The methodology applied to design pipe diameter (water-mains only) Previous studies have used multi-objective optimization techniques for the sustainable design of WDS, considering both the costs and the embodied energy[113][114][124][125][126]. Dandy et al. (2008) employed the multi-objective optimization technique for the sustainability of a WDS in New South Wales, Australia, by considering cost, embodied energy, and GHG emissions [86]. The results showed that 8% of integrated energy was saved, along with the benefit of reduced GHG emissions. Figure 3.5 shows the comparison between the existing and the optimized WDS proposed by Dandy et al. (2008) [86]. The optimization of WDS is a challenging task in which a tradeoff is required 33 among these factors: economic, environmental, hydraulics reliability, water quality, energy use, construction, and operation costs. Figure 3.5 Comparison between original and optimized WDS proposed by Dandy et al. (2008) [86] 3.5.2 Optimal Water Quality Water quality management in distribution networks is directly related to the spatial distribution of chlorine boosters and their dosages. Water chlorination is essential to reduce the effects of bacterial and other microbiological contaminants. A higher dosage of chlorine generates harmful by-products in addition to changes in drinking water’s taste and odour. Similarly, a low dosage of chlorine cannot kill all the germs completely in the drinking water. The chlorine dosage of 0.2 mg/L is usually defined as the minimum desired level to safeguard microbiological control. At the same time, it is difficult to maintain this level, especially near the booster station where the chlorine level is high [127]. Therefore, optimizing chlorine dosage is necessary to decrease the microbial contaminants that affect water quality. Several studies have been conducted to optimize residual chlorine and the locations of chlorine booster stations in WDS. Deineinger (1992) obtained optimal locations of monitoring stations by solving different water consumption patterns [128]. Boccelli et al. (1998) employed linear optimization to minimize the total chlorine consumption in a small-sized WDS with a surface water source [129]. Later, Munavalli et 34 al. (2003) applied a GA to select suitable locations of booster stations for a small-sized network [130]. Due to the trial-and-error approach, the objective function came out to be non-linear, and the model parameters (e.g., pipe wall reaction) were altered. The results revealed higher chlorine consumption compared to the findings of Boccelli et al. (1998). Broad et al. (2005) investigated the expansion of a large WDS having a water tunnel with a surface water source in New York by integrating GA and artificial neural network (ANN) [131]. Ostfeld and Salomons (2006) studied the scheduling problem of pumping units and the operational problems at booster stations in a WDS relying on the surface water source[14]. May et al. (2008) combined a non-linear input variable selection algorithm with ANN to forecast chlorine concentration at the 1-hr interval in a WDS [132]. Tabesh et al. (2010) integrated the EPANET with the GA for optimizing the location and dose of residual chlorine in a WDS based on the Munavalli et al. (2003) model [133]. Some studies also integrated the formation of THMs while optimizing residual chlorine in WDS [134]. Recently, Al-Zahrani et al. (2013) and Abu-Monasar et al. (2014) proposed a GA model for locating Al-Khobar WDS's optimal WQ monitoring system [135]. Due to a limited number of consumption patterns with a trial and error approach, these studies might not be efficiently applied to broad and complex distribution networks [136]. Ohar et al. (2014) proposed a multi-objective model to minimize the variations of chlorine injection for taste and odour control in a water supply scheme with groundwater as a source[137]. Haider et al. (2015) integrated EPANET simulations and fuzzy rule-based modeling to optimize residual chlorine for smaller WDS relying on groundwater sources. They implemented it in a case study of a residential neighbourhood in Lahore, Pakistan [16]. Siew et al. (2016) applied a multi-objective evolutionary optimization approach for the design and up-gradation of WDS. Spatial and temporal variations in water quality were investigated [122]. Price and Ostfeld (2016) proposed operational graph optimization (OGO) algorithm to detect the water quality and pump operating cost in the network [17]. The algorithm provided a quick solution and was applied to detect the water quality in the real world. WQ optimizing methods used in this study are described in the following sections. 3.5.2.1 Index-Based Approach WQI is used to make a computable WQ assessment based on the selected WQ parameters. The WQI can be linked with WQ parameters such as residual chlorine concentrations. It is associated with environmental standards and threshold values. Several water quality indices have been reported in the 35 literature [38]. Some researchers integrated the water quality index-based approach with the residual chlorine concentration in distribution networks. Islam et al. (2013) proposed a model to optimize residual chlorine based on EPANET simulations, factorial analysis, and the water quality index (WQI) developed by the Canadian Council of Ministers of the Environment (CCME), i.e., CWQI [10]. Recently, Ty et al. (2017) proposed a model based on continuous performance improvement to investigate the WQ problems in small water supply systems [138] [139]. They used the water quality data of Labrador, Newfoundland, Canada. The proposed CPI model integrates with the water quality index (WQI) to evaluate drinking water quality and identify the vulnerable locations (based on land use variations) in WDS. Shokoohi et al. (2016) developed a water quality reliability index based on water age and free chlorine. They applied the proposed model to the WDS of Jahrom, south of Iran [140]. The Canadian Council of Ministry of Environment (CCME) WQI or Canadian WQI (CWQI) is the most commonly used and globally accepted WQI. The index value reflects the number, frequency, and magnitude where regulatory standards (for various water quality parameters) are not maintained for a specific period. The use of CWQI is described in Section 4.2. In large water distribution networks with a groundwater source, maintaining a desired range of residual chlorine is undoubtedly a daunting task, mainly when the pipes are old and are susceptible to a high breakage rate leading to contaminant intrusion. The high breakage rate also reflects the possibility of leakage, and the problem becomes more critical when the type of supply is intermittent due to water crises. The pipes remain without water for several hours, like in Saudi Arabia. It can also be observed from Table 3.7 that none of the above studies considered the possibility of contaminant intrusions from deteriorated pipes, subsequent controls using R&R planning based on the condition of pipes, and the impacts of effective asset management on residual chlorine in WDS. There is a need to consider the risk-based approach to optimize WQ. 3.5.2.2 Risk-Based Approach WDS is subjected to continuous ageing and deterioration, which ultimately leads to WQFs, which are defined as the “exceedance of one or more WQ indicators from established threshold values either self-imposed or determined by the water regulators [141]. The deterioration of pipe structural integrity can have different adverse WQ impacts. Frequent pipe leaks and breaks increase the possibility of contaminant intrusion through many pathways. Chlorine residual value is a critical WQ indicator. There are many techniques to predict the residual chlorine levels in drinking water, and optimization 36 residual chlorine can be taken equally important as other WQ parameters for utility managers to evaluate WQI in the context of DBPs formation under different conditions [142]. A summary of some relevant studies carried out in the recent past is presented in Table 3.7 [143]. 3.5.2.3 Fuzzy - Failure Mode and Effect Analysis (Fuzzy-FMEA) FMEA is a method based on identifying the root causes, recognizing the types of potential failure modes, and assessing the consequence (impact) of the failure[144]. Traditionally, FMEA determines the criticality of a failure mode in terms of a risk priority number (RPN) which facilitates the management to prioritize the critical events and plan an immediate response for risk control. FMEA method evaluates all possible failure modes concerning their probability of occurrence, consequence, and detectability of failure events [145]. A detailed fuzzy method approach applied in this study is illustrated in Chapter 5. 3.5.2.4 Fuzzification and Defuzzification Methods Zadeh (1974) first developed fuzzy logic based on human intellectualism [146]. Several parameters cannot be estimated for modeling artificial systems and thus contain measurements and human errors uncertainties. Applying empirical approaches established for different environmental conditions and conventional problems has limited available information. These parameters can be classified as fuzzy with the help of membership functions [147]. The literature has frequently reported the application of fuzzy sets for managing artificial and natural systems [148] [149]. Due to the high breakage rate of pipes and deteriorated infrastructure of the network, not only WQ effects but also the water pumping system consume more energy. Therefore, due to the structural failure, there is a need to develop an approach to optimize residual chlorine and operational energy use in the large WDSs operating under environmental conditions. 37 Table 3.7 Summary of previous studies for optimizing booster chlorination in WDS [150] References Study objective Method employed Network Model &location Size of distribution network Free Residual Chlorine Range mg/L Factors considered Min Max P1 C2 D3 Tryby et al. (2002) [57] Disinfection booster location MILP Network model assumed Large 0.05 4    Prasad et al. (2004) [58] Chlorine injection optimization Multi-objective GA Tryby et al. (2002) model Large 0.2 4    Cozzolino et al. (2005) [53] Chlorine dosage and location Index-based model assumed Large 0.2 1    Ostfeld et al. (2006) [14] Cost-effective pumping and chlorine injection EPANET & GA-method model assumed Large 0.2 4    Lansey et al. (2007) [59] Residual chlorine optimization MILP EPANET model Large 0.5 4    Parks et al. (2009) [61] Contamination control response time EPANET-MSX Ostfeld et al. (2006) model small 0.2 4    Gibbs et al. (2010) [55] Chlorine injection optimization Genetic algorithm (GA) Sydney, Australia Large 0.2 4    Tabesh et al. (2011) [133] Optimizing residual chlorine EPANET – GA method Jepson Model (1976) Large 0.2 1    Ahn J. et al. (2012) [46] Residual chlorine & Trihalomethanes EPANET- multiple regression model Seoul city, South Korea Large 0.1 0.5    Islam et al. (2013) [10] Optimizing Chlorine booster stations EPANET-MSX Canadian water quality index Kelowna, B.C. Canada Large 0.2 0.8    Ostfeld et al. (2014) [119] Optimization of residual chlorine and TTHM concentrations EPANET – MSX & GA method Network model assumed Large 0.1 1    Haider et al. (2015) [16] Optimize chlorine dose EPANET, Fuzzy rule-based model (FRBM) Neighborhood Lahore, Pakistan Medium 0.1 1.1    Sharif et al. (2016) [151] Optimization of chlorine booster location EPANET, WQI Al-Khobar, Saudi Arabia Large 0.2 0.8    Calvin et al. (2016) [122] Optimization of Any town Water Distribution Network Multi-objective evolutionary approach Any town network hypothetical Medium 0.2 0.6    Price et al. (2016) [17] Optimal Pump scheduling under hydraulics and chlorine constraints OGO algorithm, EPANET C-Town example application Medium 0.2 0.4    Ty et al. (2017) [138] Performance improvement in small drinking water systems CPI framework, CCME Labrador, NF, Canada Small 0.2 4    1Probability of occurrence, 2Consequence (Land use), 3Detectibility (condition of pipe infrastructure), 4Fuzzy rule-based modeling, 5operational graph optimization, 6Continuous performance improvement, 7Canadian Council of Ministers of the Environment 38 3.5.3 Optimal Energy Use 3.5.3.1 Water Pumping System According to the U.S. Department of Energy (2004), electric motors used for water pumping systems use 20% of the world's total energy [62]. About 75% of the total electricity cost in a municipal distribution system is related to water processing [152]. Incorrect pump selection and inefficient pumping schedules are the most common problems associated with water pumping systems that lead to high energy costs. Therefore, pumping systems need to be optimized by optimal schedule computation and pump combinations. Most past studies on optimal pump scheduling were conducted on fixed speed pumps (FSP) and variable speed pumps (VSP). Some studies show that replacing inefficient pumps can save up to 30% of energy costs [153]. Pumping in WDS is done either through direct (pump scheduling) or indirect (storage trigger level) operations. Pump scheduling is used to control the scheduled status speed of the pumps, while the trigger level is used to monitor the storage level [156]. With the help of pump scheduling, i.e., storage trigger level information and hydraulics simulation, the pumping operational energy consumption can be calculated. More accurate estimation can be achieved by considering multiple operational scenarios for WSS [156] [157]. In some cases, time has also been considered a variable in the optimization process [155]. Nature-based algorithms have been used in more studies than gradient-based optimization as they do not require any simplification of problems [154]. In the last decade, many studies have combined local and global techniques to develop meta-heuristic optimization algorithms for improving the convergence of several methods [155] [156] [170] [171]. Brion et al. (1991) presented well-cited examples of successful applications of classic algorithms in pump schedule optimization [155]. Previously, Cembrano et al. (1988) used the single objective technique to test the Conjugate Gradient in the Barcelona water network model in Spain [157]. Their study selected the decision variables, like pumping stations' flow combination and valves' optimal flow control. Although they successfully reduced the operational costs related to pump scheduling and optimal valve control, high computational efforts were required for analysis. Brion and Mays (1991) applied KYPIPE computer programming to optimize the Austin (Texas) network [155]. They used a generalized Gradient algorithm and Lagrangian methods to optimize the timing of the pump schedule. They found that this optimization method can save about 17% of the operational cost. Later, a similar study performed by Coelho et al. (2012) found that the algorithm used by Brion and Mays (1991) was 39 sensitive to the Lagrangian coefficients [158]. Curi et al. (2012) conducted a study on a WDS located in Campina Grande, Brazil, using linear integer programming optimization methods [159]. They optimized energy for three pumping stations and obtained an estimated 15% savings in energy costs. Many researchers have used Genetic Algorithms (GA) in their studies on pump scheduling optimization of water networks and have obtained favourable results. To optimize the pump operational energy cost, Atkinson et al. (2000) and Van Zyl et al. (2004) studied the Richmond benchmark network with applied GA and a hydraulics simulator. They found that the operational cost can be reduced by 19% [160]. However, it was found that the GA computational time was high because of the hydraulics simulator. Later, Van Zyl et al. (2004) developed a hybrid optimization model by combining GA with two hill climber methods, i.e., the Fibonacci Coordinate Search and the Hooke Jeeves Search optimization method [161]. The optimization variables were defined w.r.t. tank level controls. The hybrid GA-based methodology was applied to a WDS in Richmond, U.K., and achieved an estimated 25% reduction in the operational cost of pumping [22]. However, they faced a problem deciding whether to change from GA to the hill-climber method during the optimization process. It was observed that the hybrid GA produced more reliable results than the pure GA method. Wang et al. (2009) applied the GA method to minimize cost, the number of switches, and the total work time of a groundwater pumping system [162]. Pump time interval optimization was performed using a real number array as an alternative to a binary bit string. The methodology provided several solutions like low electricity cost at the same time as eco-aware pump schedules. However, it was observed that the algorithm convergence speed could be further improved. Shu et al. (2010) used a hybrid GA on a large WSS in China and found substantial reductions in pumping electricity (about 6%) and the water production cost [163]. Lopez-Ibanez et al. (2008) proposed a parallel Ant Colony Optimization (ACO) method, combined with the EPANET library and applied to the Richmond benchmark network [164]. They found that the parallel ACO method required half the computational time to produce better results than other sequential ACO techniques. They also analyzed the case study conducted by Van Zyl et al. (2004) by using a multi-objective approach and combining a second version of the strength Pareto evolutionary algorithm (SPEA2) with EPANET 2.0 to reduce both cost and the number of pump switches [29]. These results verified that by considering the binary encoding of the decision variables, i.e., tank water level and nodal pressure, better results were obtained than the same problem solved by single objective optimization using an evolutionary algorithm. There are various multi-objective optimization evolutionary algorithms (MOEA) like Niched Pareto (NPGA), 40 Non-Dominated Sorting (NSGA), and Strength Pareto (SPEA) genetic algorithms used for the operational optimization of water networks [165]. These MOEAs’ were used to find the optimal solution to minimize energy cost, pump scheduling, power peak time, and water tank level [22]. Figure 3.6 shows the percentage of energy cost savings achieved in past studies (Modified from Coelho et al. 2014) [22]. Figure 3.6 shows the hybrid GA (Fibonacci, Hooke-Jeeves, Search Pattern Method) resulted in more energy cost savings when compared to other computational methods. However, the study only considered a single objective optimization, i.e., the maximum water “tank level”. Rao et al. (2007) developed a methodology based on an artificial neural network (ANN) to predict the best combination (starting conditions and demand) of different pumps and valve control settings [94]. Later, the methodology was applied to reduce the pumping cost by 15 to 25% for a WDS in Haifa (Israel) and Valencia (Spain). However, the operational optimizing methodologies of these pumps cannot be applied to a large complex network due to the high computational time. A super-efficient supercomputer system is needed to solve the algorithms and to save the analysis duration. Hence, there is an advantage to using a combination of algorithms rather than the single objective (SO) algorithms. Table 3.8 illustrates a summary of the previous studies on the optimization of pumping systems and their limitations. Coelho et al. (2014) studied and applied two genetic algorithms to optimize the water pumping energy, with an objective function to minimize cost, of two different WDSs [22]. The applied evaluation algorithms were named the gradient-based Levenberg-Marquardt (LM) and the Evolutionary Algorithm (based on natural evolution). Subsequently, the optimized mathematical model was integrated with EPANET to simulate two networks, including a basic network with an open loop and closed loop networks. The optimized results were obtained by applying both the Levenberg-Marquardt algorithm (LM) and the Evolutionary algorithm (EA). It was observed that the LM method is more time-efficient than the EA. However, the methodology presented by Coelho (2014) applies to elementary and small networks. They did not consider the energy production methods, the wasted energy, and the GHG emissions; neither did they apply the methodology to an actual WDS. 41 Figure 3.6 Various evolutionary algorithms used for pump operational optimization of WDS [22] *SO=Single Objective, MOEA=Multi Objective Evolutionary Algorithms, (SPEA)=Strength Pareto Evolutionary Algorithm, PSO= Particle Swarm Optimization, F.H.J= (Fibonacci, Hooke Jeeves, Pattern Search, NSGA=Non-Dominated Sorting Genetic Algorithm, LM= Levenberg-Marquardt, GSA= Genetic Simulation Annealing, LP= Linear programming Mathematical optimization approaches and hydraulics simulators have been used to pump scheduling. It includes dynamic programming (DP), mixed-integer nonlinear programming (MINLP), model predictive control (MPC), iterative methods, benders decomposition, and branch-bound methods, reported in the literature [134][154][157]. For the pump scheduling problem, the DP methods have been applied commonly to minimize the operational cost. However, to handle more extensive system data, this method undergoes the “curse of dimensionality” when analyzing and organizing data in high-dimensional spaces [169]. The pump switching decisions can be gathered or the network divided into small zones [170]. In the past, most of the studies on pump scheduling have performed on a single objective (to minimize energy cost) and ignored the system's environmental impact. Therefore, further research on a multi-objective (min. cost, min GHG, and max hydraulics benefits) optimization study for large networks is still required. 42 Table 3.8 Summary of past studies on pump optimization algorithms used in small-medium size WDSs [35] References No of tanks & pumps location Hydraulics model Optimization algorithms Objectives (decision variables) Results Limitations (Lopez et al. 2008) [171] -- Van Zyl model Parallel ACO + EPANET Hybrid GA min energy costs and the number of pump switches -- A custom network (Ostfeld et al. 2006) [14] 3, 8 Ostfeld & Salomon’s (2004) network model EPANET Opti GA min. chlorine dosage and pumping costs 25 % pumping energy cost reduction the tradeoff between the energy cost WQ (Bunn et al. 2007) [172] -- EBMUND/ Washington Aqueduct Hybrid GA / MOEA min. pump energy 13 % cost reduction Real-time operation (Salomon et al. 2007) [94] Haifa- A (Mount Camel), Israel EPANET Model DRAGA-ANN Single-objective (min cost) 25% less cost, faster than GA-EPANET SCADA system (Wang et al. 2009) [162] EPANET model -- EPANET Model Genetic algorithms Min. cost and total work time for each pump Good computer simulation results 1st work considering land subsidence due to groundwater pumping (Gibbs et al. 2010) [55] -- Woronora, WDS Sydney, Australia EPANET Model Genetic algorithms min the disinfectant dosage 30 % pumping cost reduce the computationally expensive process to run water quality models (Shu et al. 2010) [163] 12, 30 T- China Genetic simulation annealing (GSA) Hybrid GA min. Energy & cost 6 % energy cost reduction -- (Mouatasim et al. 2012) [173] 5, 8 Kenitra, Agadir City, (Morocco) EPANET Model Lingo solver + Stochastic perturbation of reduced gradients (SPRG) minimize the pump operational cost Pump energy cost reduce -- (Mala et al. 2015) [174] 1, 8 Victoria, Australia Ostfeld network model EPANET Model GA (NSGA-II + GA-NetXL) min pumping energy, Improve Water Quality 10 % reduction in pump energy cost Optimal values for water quality and pumping energy costs (Coelho et al. 2014) [22] 2, 1 Madeira, Portugal EPANET Model Levenberg-Marquardt Algorithm +EA Minimize the pump operational cost 14 % pumping cost reduction Simulation difficulties due to the design complexity WSS “--” No Data Available 43 3.5.3.2 Multi-objective Optimization In pump operational optimization, it is essential to consider other associated parameters like pump electrical energy saving, the source of energy generation (hydel or thermal), GHG emissions, and the system's total cost. Wu et al. (2012) used MOGA for optimizing the total economic cost, pumping energy, and GHG emissions [93]. Also, they investigated the impact of VSPs on their optimization results. This method was incorporated with VSPs in the conceptual design and WDS planning. They compared the optimization results of both VSPs and FSPs and observed that the cost of the network, the pumping energy, and the GHG emissions was lower in the case of VSPs than with the FSPs, as shown in Figure 3.7. Later, the proposed methodology was applied to a small network serving a population of fewer than 1,000 inhabitants. However, FSPs are preferred over VSPs for the optimization of large WDSs. This indicated that there is a trade-off between pump replacement, capital cost, loss of efficiency due to water leakage, and associated GHG emissions. These parameters should be considered in the future studies of the optimization of WDS. Wu et al. (2010) used trigger levels and optimized the pump operation for 24 hours, while Marchi et al. (2012) used both trigger and pump scheduling to optimize the operational energy [116] [91]. Both studies concluded that pump type selection and operational management options might reduce energy and associated GHG emissions. Ertin et al. (2001) and Ramos et al. (2011) compared energy efficiency for both pump scheduling and trigger levels [77] [175]. Ramos et al. (2011) concluded that the total operational energy does not affect the pump schedule [77]. They found that pumping water at off-peak loads can reduce pump operational energy and GHG emissions. Ertin et al. (2001) found that 1 % of energy can be saved by pump scheduling instead of using the storage tank trigger level [175]. However, both the studies did not consider the time-dependent operational and emission factors, such as the scheduling of pumps at low energy tariffs. A study by Stokes et al. (2014) considered the time-dependent emission factors (EFs) to calculate the pumping operational energy and the associated environmental emissions [176]. Other studies also used time-dependent emission factors [93]. Table 3.9 summarizes some recent studies on optimization used in multi-objective approaches to minimize the cost, operational energy, and GHG emissions in WDSs. 44 Figure 3.7 Energy consumption and corresponding GHG emissions in; (a) FSP (b) VSP [93]. A control system in the pumping operation management system can save energy and further reduce GHG emissions. However, multi-objective optimization considering all the influencing parameters in a large WDS is undoubtedly a challenging task, i.e., minimizing the cost, the operational energy, and the GHG emissions, and maximizing the hydraulics reliability and water quality in a large real water network. At the same time, preventing water losses via leakage through pressure management [40]. However, a minimum amount of energy is needed for a pumping system to maintain service pressure to the customer located at the highest point in the system. 45 3.5.4 Renewable Energy use in Water Pumping System A massive amount of CO2 emissions are linked with WSS's power, including water withdrawals, extraction, distribution, and treatment. The electricity used by a water pumping system is usually generated by coal-fired, natural gas, and diesel power plants. These power generation methods produce massive levels of GHG emissions [177]. Around the world, about ten gigatonnes of CO2 are emitted by electrical power generation, which is about 38% of global GHG emissions [177]. In the last two decades, more attention has been given to renewable energy resources for electricity production to reduce GHG emissions. Countries worldwide are investigating the use of environmentally friendly energy generation systems to reduce CO2 emissions under international obligations as agreed in the Kyoto Protocol at the United Nations climate change conference [77]. In urban WSSs, three common renewable resources, i) solar, ii) wind, iii) water turbine/pump as turbine (PAT), has been used as alternative energy sources for water pumping [77]. However, these systems' capital cost, initial investment, and payback period are still high. PAT is being used to convert excess hydraulics energy, i.e., the potential energy due to large head differences that exist in pipes, into electricity[77][178][179][180][181]. It has been found that PAT can produce 5 to 100 kW of energy with approximately 85% efficiency [77]. However, PAT performance depends on flow rate and needs a large head difference between the higher and lower ends of the water mains [178]. The study led by Lopes et al. (2006) verified that PAT in water mains could produce 4 kW of energy with a payback period of between 4 and 22 months for a small distribution system. Similarly, an experimental study was conducted by Fontana et al. (2012) in the city of Naples, Italy, where the WDS used PAT instead of pressure-reducing valves (PRV) to control losses, in addition to using it for electricity production [196][197]. Later, Coelho et al. (2014) used the same model and applied GA to find the optimal location of PRVs to minimize water losses and consequently energy savings. Ramos et al. (2008) proposed a framework to use PAT for power generation in the city of Aveiro, Portugal [182]. They found an optimal model that is cost effective and energy efficient with minimal GHG emissions. In normal water pumping operations, parameters like flow, head, and speed show positive hydraulics values, while the same capacity pump used as a turbine shows a negative hydraulics value. This drawback was demonstrated by [182]. 46 Table 3.9 Past Multi-objective approaches used for pump energy cost optimization and GHG in WDSs [35] References Study area and location Multi-objective variables/ optimization methods WDS layout Energy, cost-saving Energy saved GHG reduction Limitations (Boulos et al. 2010) [40] hypothetical model (later used in Europe’s 8 WDS) Min. service pressure, energy, and GHG emissions Four pumping stations $ 4 million operating costs & 38 % energy saved 754 kW pump potential energy saved 3454x103 kg CO2/year Model is applicable for a small network, topography not considered, (Wu et al. 2009) [96] hypothetical model, Duan et al. (1990) Min. Cost (GHG), max. hydraulics reliability One pump, one storage tank, 36 pipes and 16 demand nodes $10 million saved - 315 kt trade-offs suggested between economic, environmental, and hydraulics reliability (Roshani et al. 2012) [126] Amherst view Ontario min cost, energy, and GHG Two pumps, three tanks, one reservoir /15000 inhabitant - 2 % of total energy saved 10 kt / over 50 years span Small network (Stokes et al. 2014) [176] hypothetical model (D town) derived from Salomon et al. 2012 min pumping operation, GHG time-varying emissions factor Seven storage tanks and 12 pumps in 5 pumping stations, 400 pipes, over 350 demand nodes - - 0.574 kg CO2-e/ kWh EF (emission factor) depends upon various electricity generations methods (Carla et al. 2015) [26] Eastern Municipal Water District (EMWD), California Min. pumping operational & GHG Large WDS 22 % cost reduction 6–15% 6 % less GHG emissions Offline software simulations and real network optimized values not agreed (Ruben et al. 2017) [183] UK (Van Zyl model, 2004) min pump electricity costs and GHG Two tanks, three pumps - 19 % - the extra cost of renewable energy resources, a small network “-” Data not available Mechanically coupled wind turbines have extracted and delivered water for many decades. Recently, small wind turbines with integrated energy were used for water pumping in small WDSs. The main advantage of wind turbine power generation is the independence of pumping locations, while its efficiency depends on its motors and pumps [184]. Rehman et al. (2012) conducted an experimental study in remotely located villages in Saudi Arabia on electricity generation using wind power. These villages were not connected with the national electricity grid [185]. They found that a pump can deliver about 30,000 m3 of water per year from a depth of 50 m. A 2.5 kW wind turbine was used, ranging from 15m to 40m, with an estimated operational pumping cost of 0.0128 US$/m3. However, the annual pumping cost of a wind turbine is still on the higher side compared to other alternative 47 energy sources. Photovoltaic (PV) cells are another renewable energy technology that has been used for water pumping in WSSs[186][187][188]. Past studies concluded that an optimal PV pumping water system is feasible if the PV generator values match the pump’s power requirement [189]. However, a PV power system requires ample space and long hours of annual sunshine, which limits its application in some areas [103]. Among all environment-friendly renewable energy systems, using micro-water turbines in pipes to produce electricity and control pressure is the most feasible option with minimum associated GHG emissions 3.5.5 Energy-Efficient Control System Water utilities adopt energy-efficient water monitoring and delivery systems to control energy costs and GHG emissions. Automation technologies have been used to monitor the consumption of water resources and control water head (pump pressure), flow control (velocity), and water demand [30]. An efficient electro-mechanical control system in a distribution network can maintain an optimal pressure head, balanced flow rate, water demand variations, and relevant variables to conserve energy. A control system can: i) automatically increase the flow rate (on-demand basis), ii) reduce interruption, and prevent pump congestion resulting in decreased GHG emissions [190]. Around 40% of pump operational energy can be saved using an energy-efficient drive instead of a water flow control valve [103]. Energy-efficient control systems can regulate both the pumping operations and chlorine boosters. These pumping control systems are integrated with programmable logic control (PLC), supervisory and data acquisition systems (SCADA), and wireless sensors [103]. However, utilization of these control systems might be restricted in complex WDSs, where the shape of a network changes both temporally and spatially due to an increase in water demand and a growing population. An energy-efficient centrifugal pump coupled with an induction motor delivers the required flow rate and pressure head to its consumers [40]. A frequency-controlled induction drive is used to rotate the centrifugal pump adjust the pressure (head) and the velocity based on the optimum criteria to meet customer needs. This can save up to 27% of electrical energy without altering the pump’s efficiency [206]. However, induction motor-controlled pumps are not suitable for WDSs in hilly areas where pressure variations are frequent. In hilly areas, inverter-controlled parallel pumps have been used [191]. Siemens, ASEA Brown Boveri (ABB), Grundfos, and Pumpenfabrik Ernst Vogel GmbH have 48 developed numerous energy-efficient intelligent controllers to optimize pumping systems in WDSs [103]. The important characteristics of these control systems are summarized in Table 3.10. Table 3.10 Characteristics of energy-efficient control system used for the water pumping system [35] References Objectives/variables Energy saved Contribution Observation (UNICO Inc. 2010) [192] Pressure, flow and level control 20 to 50 % developed synthesis intelligent pump (IP) controller to synchronize multiple drives Volume measurement error can shut down the system (ABB Motors 2012) [193] Pressure head, flow rate, and energy efficiency 10-50 % of total pumping energy developed intelligent pump control software Application in parallel pumps only (Danfoss 2012) [194] Pump head, flow rate 30 to 50 % % energy saved at a lower pump speed Developed sensor-less controller for pump head and flow Less malfunction but the high price (Siemens 2015) [195] Speed, flow rate controller reduce energy cost up to 20 % developed ultrasonic controller for water supply pump scheduling High maintenance cost (Vogel GmbH 2015) [196] Pressure, speed Up to 50 % energy saved to run variable speed pumps Developed Hydra over the sensor-less controller of a centrifugal pump Up to 8 pumps can control at a time (Armstrong 2017) [197] Power, pressure, flow rate 30-50 % energy saved at peak load developed an intelligent variable speed sensorless pump High price, used for a small network Most modern water utilities are currently using energy-efficient water monitoring systems. In addition to water delivery, head loss reduction, and flow control, other features of this monitoring system also promote their customers' energy and water-saving awareness and continuously monitor the consumption of natural water resources [40]. However, most water monitoring and controlling devices are expensive and need regular preventive maintenance to operate at optimal levels. 3.5.5.1 An Example of Small Water Utility (Lisbon, Portugal) Ramos et al. (2011) proposed a sustainable hybrid solution to improve energy and hydraulics efficiency for a small district metering area (DMA) in the county of Lisbon, Portugal [77]. The county of Lisbon, located in a hilly coastal area of Portugal, has a population of around half a million [198]. The small DMA consists of a water source on a hill at an elevation of 130 m above the mean sea level, which supplies water to two villages. One village is located lower than the source (at an elevation of 17m), while the other is located at 177m, i.e., higher than the source. The water is supplied to the upper and lower villages through pipes connected to tanks with 1780 m3 and 2025 m3 capacities. Consequently, water is supplied by pumping to the upper village, while a gravity system is effective for the lower village. The significant available potential energy (head) in the case of the lower village is controlled with the help of a pressure reduction valve (PRV) to prevent leakage. In Portugal, about 40% of electricity is produced by fossil fuels resulting in higher GHG emissions [199]. The remaining 49 electricity is generated through hydropower, wind, and other renewable resources. Ramos et al. (2011) conducted a study on two small Portuguese villages (population less than 10,000) to optimize a water pumping system [77]. Electricity data was obtained from the Portuguese power companies to optimize water pumping for the two villages. PAT was connected (to the lower village’s tank) with pipes to replace the PRV valves in the first option. In the second option, shown in Figure 3.8, wind turbine integrated power was used to operate the second pump (to fill the upper village’s tank). A water turbine system was used to generate electricity for the lower village. A water turbine was installed in the gravity pipe of the lower tank to utilize the potential energy available in the pipe. About 520 kWh/day of electricity is produced by a hydropower plant which emits a corresponding 5.1 kg of CO2 [200]. On the other hand, an equivalent energy level produced by fossil fuels (petrol, gas, coal) emits around 442 kg CO2. A 30 kW wind turbine FL30 (2017) is integrated with the main grid system, which results in reduced electricity consumption from the main grid and about half of the CO2 emissions [201]. Figure 3.8 Combination of water-turbine and wind-power system used for water pumping in Portugal [77] Figure 3.9 shows the power generation with PAT and wind energy pumping water (normal, summer, and winter), respectively, and the corresponding CO2 emissions in the system. However, the methodology proposed by Ramos et al. (2011) has some limitations [77]. In Lisbon County, the water and electricity supply load values remained the same during peak hourly demand. It was suggested Upper village tank at height of 177 m Water source reservoir at height of 130 m Lower village tank at height of 17 m Water pump at height of 80 m (connected with wind mill power) Water turbine/Pressure reducing V/V 50 that the pumping energy could be reduced during off-peak electricity tariffs (night time), which is not possible. Figure 3.9 (a) Electricity generated by the pump as turbine (b) with wind turbine [77] PAT is the only suitable solution to replace PRV valves if the hydraulics potential energy is available in the pipes. Although wind power generation has no CO2 emissions, the initial cost of windmills is high compared to hydropower when used to obtain the same amount of energy. 51 3.5.6 Sustainability of Water Distribution System 3.5.6.1 Life Cycle Assessment Life cycle assessment (LCA) is a procedure to estimate the performance of an entire system by considering a ‘cradle-to-grave’ approach [33] [202]. LCA serves as a useful information tool to examine future substitution scenarios for strategic planning and decisions [203]. Assessment of the system starts with the extraction of raw material, followed by design and manufacturing, eventually culminating in its return to earth [204]. Figure 3.10 shows the typical life cycle phases of a WDS from raw material to the end of its life. LCA has been applied to water technology assessment for nearly three decades [205][206]. Most past studies on water and wastewater life cycles are related to treatment processes [222][223]. Figure 3.10 Typical life cycle stages of WDS [205][206] Since 2005, several LCA studies have investigated the various life cycle stages of WDS, including pipe manufacturing, pipe repairing and replacement, environmental factors, water recycling, embodied energy related to water leakage, and the operational energy requisite to run a distribution system [207][208][106][205][209][210]. The definitive studies of WDS LCAs have focused on comparing different pipe materials, which evaluated the sustainability of WDSs and assessed the Input Raw Material Energy Raw material procurement Manufacturing Maintenance Waste Management/ Recycle Output Emissions, Waterborne waste, Co-products, Other wastes System’s boundary 52 future implications of present decisions [210]. The sustainability of a WDS can be assessed using a triple-bottom-line approach based on three main dimensions, i.e., economic, social, and environmental [211]. Due to the ageing of infrastructure, an enormous amount of investment for rehabilitation, replacement, and expansion is required in each step of the life cycle stages of WDSs [212][218][228][231]. Recent research focused on LCA has considered the economic aspects and the environmental factors of WDS [204]. Dennison et al. (1999) used LCA for poly-vinyl-chloride (PVC) and steel-iron pipes [216]. They concluded that enormous energy was consumed during the pipe manufacturing processes. An LCA study was conducted by Lundie et al. (2004) [214] on a WDS in Sydney, Australia. They forecasted the future water management planning for the city regarding population growth, expansion of infrastructure, demand, and GHG emissions. Filion et al. (2004) computed the lifecycle energy analysis (LCEA) of WDSs and studied its sustainability. The proposed LCA methodology was based on an economic-input-output (EIO) model developed by Carnegie University [217]. They estimated the embodied energy linked with pipe manufacturing (from raw material to its disposal) and the operational energy required for a water pumping system. Stokes et al. (2006) used a similar framework to the one proposed by Filion et al. (2004) and evaluated alternative solutions for drinking water supplies [218]. An index-based LCA model developed by Herstein et al. (2009) was used to assess the eco-friendly impacts of expanding and operating WDSs [106]. The results revealed that the absence of a lifecycle energy perspective would lead to erroneous estimations for the energy and the environmental impacts. Although all of these studies endorsed the usefulness of LCA, most of them were conducted using hypothetical models. At the same time, some of them focused on the rehabilitation or replacement (R&R) of water mains and the optimization of pumping systems in a real-world WDS [215][85]. Due to rising energy prices and GHG emissions, water utilities strive to optimize energy usage in each stage of the WDS. In the following sub-sections, LCA energy perspective models are reviewed in detail to investigate the environmental impacts, GHG emissions, pipe manufacturing energy, replacement (embodied) energy, and the disposal energy of a WDS. 3.5.6.2 Life Cycle Energy Analysis Many water utilities strive to reduce energy costs and environmental emissions through life cycle energy analysis (LCEA) of WDSs [219]. The LCEA of WDS has received more attention in the last decade. The LCEA study includes the embodied energy of pipe manufacturing, disposal energy, pump 53 operational energy, the energy required to overcome friction losses in pipes, and reductions in the GHG emissions of the system [208]. The LCEA tool computes the energy used in each step of a WDS rather than considering the whole life cycle of the system [33]. LCEA methodology needs fewer data requirements and less calculation effort than a normal LCA computation [80]. Figure 3.11 shows the use of energy and the associated GHG releases during the LCA phases of a WDS [226]. In WDS, the environmental effects, such as atmospheric emissions, water pollution, global warming potential (GWP), and energy consumption, can be analyzed with an LCA approach [80]. Various studies have investigated and developed environmental indices with respect to energy consumption in urban water systems. Several advantages of an LCA are grouped in a stand-alone index that helps to categorize the effects instigated by a separate system on the environment [80]. Figure 3.11 Life cycle stages, energy consumed, and GHG emissions [226] Herstein et al. (2009;2011) used this LCA approach to investigate the eco-friendly impact concerning pipe repair and replacement of a large WDS [106] [220]. Later this index was integrated with other WDS optimization methods to provide cost-effective and environmentally friendly solutions. The study proved that the operational energy used in WDS has an essential role in deciding the eco-friendly sustainability of the system. However, obtaining the environmental data from each step of an LCA is a daunting task [221]. According to Zakkour et al. (2002), energy substantially impacts GHG Material/ Extraction/ Production Energy GHG Pipe/ Pump manufacturing Energy GHG Installation/ Transportation Energy GHG Disposal/ Recycling Energy GHG Operation/ Maintenance/Repair Energy GHG GHGGHG Fabrication Stage Use Stage End of life stage Legends: Energy Consumed Greenhouse Gas Emissions LCA Stages 54 emissions in WDS [223]. Therefore, a complete LCA is not required to investigate eco-friendly-related impacts other than energy, especially in a WDS. Filion et al. (2004) also supported this idea and used the LCEA approach rather than investigate the whole LCA of the system [85]. However, the study did not consider the energy required during pipe installation, maintenance, and pipe cleaning. Later, Dandy et al. (2008) applied GA in their study to evaluate two reservoir levels in a WDS [86]. Their study calculated a Pareto optimal solution to find the minimum costs and embodied the energy of two types of PVC pipes. In their case study, the pumping energy cost of the network was not taken into account; in the network, gravitational force distributed water from reservoirs at a higher elevation to the lower end. However, pumping energy is required in a flat ground area where the source end users are on the same elevation. Mo et al. (2010) developed an EIO-LCA model and estimated the embodied energy linked with the infrastructure of a WDS [125]. Later, the same methodology was applied to the WDS in Kalamazoo, Michigan, in the USA. The city consumed around 9.3 MJ/m3 of energy, and about 25-30% of the total was utilized for treatment purposes. The study presented a comprehensive model with inadequate information on operational energy estimation. Herstein and Filion (2011) performed an LCA study on deteriorated pipes' optimized R&R planning [219]. They observed that significant savings could be achieved in embodied and operational energy capacity by reducing leakage and friction losses. The study found that capital cost is directly linked to pumping operational energy, regardless of the pipe replacement plan. However, the environmental index (EI) calculated by Herstein et al. (2009) [106] did not obtain optimized results when combined with the EIO-LCA of the system. Prosser et al. (2013) extended the work of both Filion et al. (2004) and Herstein et al. (2009) [80]. They further performed an embodied energy analysis of pipe manufacturing and the operational life cycle stages of a WDS. They proposed various schemes for pipe replacement planning. In their analysis, the pumping energy was compared with the embodied energy associated with the pipe replacement of the system. Outcomes of the study revealed that substantial energy could be saved by using an effective water mains replacement plan (i.e., asset management). More than 30 studies on the LCA of WDS were reviewed. These studies were primarily focused on environmental impacts with respect to pipe manufacturing, repair and replacement, leakage, buried pipe disposal, infrastructure construction, pumping operation, pump manufacturing and renovation [87] [102] [239] [103] [141] [213] [220] [221] [226] [229] [235] [226]. Figure 3.12 summarizes the types of past studies on the LCA of WDS. About 53% of the studies evaluated the 55 impact of the construction and operations of pipe infrastructure, while only 3% addressed the issues associated with pump manufacturing and renovations, as shown in Figure 3.12. In general, most of the LCEA studies reported in the literature have ignored the geographical, social, and economic characteristics of large WDSs. General system analysis (GSA) is another methodology used for the environmental impact assessment of the network. GSA can evaluate the functions of the entire network, while LCA analysis ignores the social aspects of the system [229]. Figure 3.12 A few past LCA studies considered the environmental impact and operation of WDS [205][210] Table 3.11 summarizes past LCEA studies of urban WDS [221]. The studies described in Table 3.11 primarily investigated the life cycle stages of pipe construction and the operating components of WDS. At the same time, less attention was paid to energy consumption in civil construction works (e.g., excavation), human health, ecosystem quality, and water resources. Although LCA is a widespread assessment tool and can evaluate individual aspects and whole networks, its application regarding energy analysis has been limited to centralized systems rather than decentralized systems. Integrating LCA with GSA can provide more insights concerning the water-energy nexus (WEN) of a drinking WSS. 56 Table 3.11 Summary of past studies considered various life cycle indicators w.r.t energy use in WDS [35] Reference Location Population No. of scenarios Estimated energy use kWh/m3 Lifecycle stages Life cycle indicators Remarks (Lundie et al. 2004) [214] Sydney Australia 4.5 million 8 0.24 Op. Only. Cons. CML-IA Single criteria assessment (Sahely et al. 2005) [230] Toronto Canada 2.6 million 0 0.60 Op. Only TC and CED Single criteria assessment (Lassaux et al. 2006) [231] Walloon region Belgium 3.5 million 5 -- Op. and Cons. Eco-indicator 99 and CML-IA Health risks, GHG emissions, and materials resources not considered in the study (Friedrich et al. 2009) [224] Durban South Africa 3 million 4 0.10 Op. and Cons. CML-IA Excavation recycling energy are not considered (Muñoz et al. 2010) [232] Malaga Spain 0.5 million 2 0.50 Op. and Cons. CML-IA and CED Excavation, landfill, transport energy are not considered (Herstein et al. 2011) [219] Any-town benchmark Model - - - Op only EIO-LCA Measured 14 environmental pollutions in the air Prosser et al. (2013) [80] Mid-Western U.S. water utility 1 million -- -- Op. and Cons Life cycle energy indicator Evaluated the link between pump operational & pipe embodied energy cost (Amores et al. 2013) [227] Tarragona Spain 0.14 Million 3 0.48 Op. and Cons (pipes only) CML-IA Health risks, GHG emissions, and materials resources not considered in the study (Venkatesh et al. 2014) [66] Oslo Norway 0.5 million 0 0.18 Op. Only CML-IA Excavation recycling energy are not considered (Nault et al. 2015) [210] Southern Ontario 12 million 11 0.68 Pump operational energy+ GHG EIO-LCA Pipe embodied (Fabrication) energy not considered (Wakeel et al. 2016) [34] Lahore, Pakistan 12 million - 0.62 Pump operational energy+ GHG LCA Pipe embodied (Fabrication) energy not considered (Sambito et al. 2017) [233] Palermo metropolitan Sicily, Italy 0.8 million 1 0.28 Replace pump + GHG LCA- ISO/TS 14067 (2013) Pipe embodied (Fabrication) energy not considered * TC = Temperature Change, Op = Operation, Cons = Construction, CED = Collective Energy Demand, “---” = No data available To elaborate further about the LCA (energy perspective) of a WDS, a case study presented by Prosser et al. (2013) is discussed in the following section. 57 3.5.6.3 An Example of Midwestern USA Prosser et al. (2013) developed an LCEA methodology for a WDS to integrate embodied energy with repair and replacement (R&R) planning of deteriorated pipes and proposed various energy savings solutions [80]. The proposed framework investigated the energy lost due to leakage and pipe friction energy and calculated its payback period. The LCEA was applied to a real WDS serving 1 million consumers located in the midwestern region of the United States. The water utility supplied 530,000 m3/day of water through 28 pumping stations, three water treatment plants, and around 6000 km of transmissions and distribution mains. The terrain of the area is a combination of hilly and flat land. The cradle to grave analysis was completed using an EIO-LCA method developed by Carnegie university [233]. It was found that water mains, consisting of 140,000 individual pipe segments, hold the largest share of the total embodied energy [85]. For analysis, a hydraulics model with a pump was created to compute the pumping energy of the system. Similarly, the embodied energy data linked with the pipe replacement was obtained from Toronto (Canada) water authority [234]. The historic (since 1969) pipe data were grouped according to the type of pipe material, diameter, and age. Most of the steel and plastic material pipes were 5 to 60 years old; however, pipes up to 150 years of age were also operational in some locations. The developed LCEA methodology considered the pipe manufacturing and operational embodied energy linked with the life cycle stages of pipe R&R. Three R&R schemes referred to as A, B, and C for the period between 2020 and 2070 were proposed. The proposed scheme “C” was based on the age of the water mains, while “A” and “B” were based on the pipe rupture rate. For an initial time step of 2020, 11% water loss (as reported by a partner utility) was assumed for all the scenarios. Three types of leakage data (reported, unreported, and instance) were analyzed, and with minor modification, the leakage event was modeled. The amount of water energy lost due to natural disasters, such as an earthquake, or burst pipes from large water mains, was excluded from this study. It was estimated that the embodied energy linked with pipe R&R is the same as the embodied energy required for manufacturing new pipes. Only the embodied energy linked with new pipe manufacturing was calculated. For old pipes, if the repair time (linked with embodied energy) was longer than three days, it could be used as a landfill or for other recycling purposes. This study did not include another energy linked with pipe production and transportation. EPANET software simulations were performed to estimate the pumping energy consumption and assumed that the pump worked at about 70% of its 58 efficiency. To evaluate the impact of pipe roughness on pumping energy, different values of Hazen-Williams constant “C” factor was used. A roughness coefficient “C” value of between 120-130 was taken for each replacement plan. It was estimated that about 47.3 kWh/MG embodied energy was lost to treated water due to leakage in the WDS [234]. The results in Figure 3.13 (a, b) show a summary of the anticipated outflow volume and the allied embodied energy lost in all planning phases of the system. The results also show that, at the end of the 2070 forecasting period, all pipe-changing plans can save a huge amount of embodied energy that was previously lost in the system due to leakage. The analysis shows that energy can be saved through pipe replacement, leakage reduction, and improving pumping efficiency. About 33% of operational energy can be saved by controlling water leakage (no pipe replacement plan). The analytical results show that efforts made on water leakage detection have a lower impact on energy saving than improving the pump efficiency of the system. If pump efficiency improves from 60 to 80%, around 33% of operational energy will be saved for all plans A, B, C, w.r.t. reference base-point. Figure 3.14 (a,b,c,d) shows the annual operational energy with and without pipe replacement, the embodied energy, % of energy-saving, and the payback period for each plan. Results demonstrated in Figure 3.14 (c) show that the embodied energy linked with pipe changing has more weight when compared to the percent of operational energy required to drive a complex WDS. However, the utility needs to pay the infrastructure construction cost linked with pipe replacement to achieve this energy saving. By the end of the first decade of 2020, the cost invested in embodied energy for Plan “A” will reduce the operational energy by 6.4 million kWh, as shown in Figure 3.14 (b). This embodied energy is about 80 million kWh for plan “C” and 112 million kWh for plan “A” in 2020, as described in Figure 3.14 (c). The results in Figure 3.14 (b) show that, in the first ten years of 2020, about 4.9-6.4 million kWh (reference point) of operational energy was saved through leakage reduction. Figure 3.14 (d) shows the energy payback period for each plan. The data show that the pumping energy system savings increase as more as old pipes are replaced. The analytical results showed that the pipe replacement embodied energy is significant, regardless of whether an enormous amount of energy is required to run a large WDS. Interested readers can see the details in Prosser et al. (2013) [80]. However, the LCEA study by Prosser et al. (2013) was only carried out for ductile iron pipes of the same diameter and made from the same materials. Various materials like PVC and asbestos cement pipes have different embodied energies linked with pipe manufacturing processes. The study also ignored the 59 pipe rehabilitation procedure, saving time and cost. Further research is required on energy balance for pipe rehabilitation by using other materials for a complex WDS. Figure 3.13 (a) lost volume due to leakage (b) energy lost due to leakage in all proposed plans [80] 60 Figure 3.14 (a) annual operational energy in each plan (b) operational energy saving in each plan [80] 61 Figure 3.14 (c) annual embodied energy in each plan (d) payback period of each plan [80] 62 Summary All the components of a drinking WSS, including extraction from the source, treatment, and distribution to the end-user, consistently consume energy. Globally, most of the energy is generated from fossil fuel sources, which WDS utilizes, resulting in more GHG emissions, climate change, and environmental pollution. The literature review has revealed that the WEN of drinkable WDS has received more attention in recent years due to increased energy usage and its effect on global warming. Most of the studies proposed water-energy saving solutions, like optimizing pumping energy, improving WQ, enhancing the network's design, and repairing or replacing pipes through investment. WDS design (pipe diameter) and pump operation (scheduling) optimization have been investigated in the past to decrease both the operational energy cost and the associated environmental pollution. Many studies have been conducted on optimal design, minimizing cost, planning, and network operation, while other studies also considered network-piping systems. Due to the complex nature of WDS, optimization problems can be solved by using trial and error methods. Some studies reported that using a nonlinear (classical and heuristic) optimization algorithm is more effective and efficient to improve the design and operation of WDS. However, fewer studies simultaneously measured the real size of the network, the tank size, pump scheduling, multiple operating conditions, and demand variations. Some studies reviewed above used multi-objective optimization evolutionary algorithms (MOEA) to find the optimal solution to minimize energy cost and optimize pump scheduling, power peak time, and water tank levels. However, these pumps’ operational optimizing methodology cannot be applied to a large complex network due to their high computational time. A super-efficient supercomputer system is needed to solve the integrated MOEA simultaneously. 63 Chapter 4: Water Quality Index-Based Optimization A part of this chapter was published in the Arabian Journal of Geosciences as an article titled “Optimization of Chlorination Boosters in Drinking Water Distribution Network for Al-Khobar city in Saudi Arabia” (Sharif et al. 2016). In this chapter, residual chlorine modeling and optimization were performed to identify the poor booster locations in the water network. EPANET, modeling and simulating software, was applied to evaluate the water quality index (WQI). The proposed optimization model suggested new chlorine booster locations to improve water quality in the WDSs. A review of some past studies on WQ optimization approaches is discussed in chapter 3. Background It is commonly accepted that water shortage and the deficiency of safe drinking water is the most challenging issue of the 21st century [3]. Fresh drinking water sources are pretty threatened due to environmental pollution. Reliable and safe water distribution must supply sufficient quantity and good drinking water quality. Water is contaminated through various pathways. The contaminant (microbial or chemical) intrusions inside WDSs directly influence people’s health [10]. Drinking water treatment is an essential factor in the WDSs. Three standard disinfection methods improve the WQ: ultraviolet radiation, ozonation, and chlorination [16]. However, chlorine has been used for many decades in WDS. Chlorine produces residuals that carry in the network till the water reaches the end-user. It is cheap and is easy to handle [16]. WHO (2004) recommends 0.2 to 0.5 mg/L of residual chlorine in the WDS [9]. The chlorine dosage of 0.2 mg/L is usually considered the minimum desired level to control germs in the water [8]. However, an increase in the level of chlorine may produce disinfection by-products (DBPs), while low-dosage of chlorine cannot kill all the germs completely in the drinking water. At the same time, it isn't easy to maintain this level, especially near the booster station where the chlorine level is high [127]. As a high chlorine level produces odour in the water, the residual chlorine should be optimized to the threshold level to maintain acceptable water quality. This study aims to maintain an optimal chlorine level with no water odour and bacterial threat in the network. Methodology The most popular Canadian water quality index (CWQI) optimized the chlorine dosage under the given physical and environmental conditions. The modeling strategy to optimize chlorine dosage in 64 WDS was used in this study, as shown in Figure 4.1. The methodology mainly performs preliminary water quality assessment to select the area needing further attention or the area with lower water quality. Then, optimization was performed in that area to improve the water quality by selecting a better booster chlorine dosage. The methodology can simply be applied to other WDSs to improve WQ with better booster dosage selection. Water quality at the WDS is better assessed using a modified WQI function, which is flexible and applicable to a real WDS. This methodology can help decision-making procedures with limited data and basic hydraulics and water quality analysis. Based on this, an important decision can be made on booster dosage in a WDS. 4.2.1 EPANET Modeling EPANET software executes an increased simulation period for hydraulics and water quality performance at a given pressure in the water network. The hydraulics model in the EPANET requires input data of all the parameters like pipe length, diameter, water head, and coefficients’ values [237]. The results of modeled output values consist of velocities, junction pressure, and head losses in the network. In this study, the water head losses were calculated using the Hazen-Williams formula, as shown in equation 4.1. In addition to the hydraulics model, EPANET requires different inputs to simulate residual chlorine in a distribution system: chlorine concentration, pH level, and water temperature. The chlorine decay (kb) and pipe wall (kW) values can be assumed. Common approaches to optimize booster chlorination are multi-objective genetic algorithm, mixed-integer, linear programming, linear least squares, and EPANET analysis [52]. The various chlorine optimization methods are described in Tables 3.2 and 3.7. This study performed WQ optimization with the booster chlorination simulated by EPANET. 87.485.168.10DLCQH L = (4.1) HL is the head loss in meter, Q is the flow in the pipe in m3/s, C is the Hazen-Williams roughness coefficient, L is the pipe length in meter, and D is the diameter of the pipe in meter. C values of 140 and 120 have been used for polyvinylchloride (PVC) and asbestos cement (AC) pipes. 65 StartEPANET modelingNetwork zoningSelect poor WQ zone for optimizationFactorial analysisRequired WQ achieved? Data collectionAdd chlorine booster locationEnd analysisYESNOData analysisCalculate Water Quality Index (CWQI) Figure 4.1 Water quality index-based optimization [143] In this study, the city of Al-Khobar WDS was selected. The network was modeled by EPANET software, as shown in Figure 4.2. The chlorine results values were exported from EPANET to XL sheets for further analysis. These results values were used to calculate the water quality index (WQI). In this study, the middle and two extreme nodes are selected as RNs to represent any particular zone. EPANET analysis is performed for all seasons (winter, spring, summer, and fall) using a one-hour time step. In the city of Al-Khobar, the optimal level of free residual chlorine at any location ranges between 0.3 mg/L and 1.2 mg/L [43]. A WDN node possesses a series of free residual chlorine concentrations for a specific time. This study used a maximum of 0.8 mg/L and a minimum of 0.2 mg/L threshold chlorine values. In addition, these chlorine values with objective functions are used to calculate the modified water quality index (MWQI). Figure 4.3 shows for a particular node, Node 22, a few residual chlorine threshold values are above 0.8 mg/L, and a few are below 0.8 mg/L. 66 Figure 4.2 EPANET model for the city of Al-Khobar WDS Figure 4.3 Residual chlorine concentration at node 22 of Al-Khobar WDS 67 4.2.2 Water Quality Index WQI is used for making a computable assessment of WQ. The WQI is linked with the residual chlorine concentration to assess the WQ. The index value reflects the number, frequency and magnitude where regulatory standards (for various water quality parameters) are not maintained for a specific time. Moreover, the WQI can also identify a critical zone with low water quality in a WDN. The Canadian water quality index (CWQI) formula is given in equation 4.2 (CCME, 2000) [235]. 𝐶𝑊𝑄𝐼 = 100 − (√𝑃12 + 𝑃22 + 𝑃3 21.7321) (4.2) where, P1 = Scope = [𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑎𝑖𝑙𝑒𝑑 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠] ∗ 100 P2 = Frequency = [𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑎𝑖𝑙𝑒𝑑 𝑇𝑒𝑠𝑡𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑇𝑒𝑠𝑡] ∗ 100 P3= Amount by which failed tests do not meet their objectives P1 is the percentage of failed variables to the total number of variables. P2 is the percentage of regulatory noncompliance incidence (“failed values”) for all parameters under consideration. P3 refers to the amount by which the noncompliance values do not meet their regulatory guideline. The value 1.732 is a normalization factor to keep the final CWQI from 0 to 100 [236]. The CWQI value ranges between 0 and 100, where 0 represents “very bad” and 100 shows “best” water quality [236]. The CWQI uses a uniform function, where any residual concentration lower than 0.2 mg/L and higher than 0.8 mg/L will contribute a function value of 1. If the value is between 0.2 and 0.8 mg/L, the transformed value will be zero, as shown in Figure 4.4 68 Figure 4.4 WQI & amended functions [10] Temporal and Spatial Analysis: Similarly, the temporal residual chlorine values are incorporated in the modified WQI formula for further calculations. Figure 4.5 shows the modified WQI function referred to in this study. There are two additional points other than the maximum and minimum inception points denoted as “p” and “q.” Decision-makers may define these points based on their needs to maintain the best water quality. For example, P=0.1 mg/L and Q=1.0 mg/L are used in this study. Excursion points called fuzzy excursions (denoted as FE and not related to fuzzy logic) are calculated as shown in Figure 4.5 Figure 4.5 Fuzzy excursion approximation using modified function [10] 69 It is found that values lower than 0.2 mg/L or higher than 0.8 mg/L are a complete failure (i.e., function values “1”), as shown in Figure 4.4. The normalized sum for fuzzy excursion (nsfe) is calculated as follows: 𝑛𝑠𝑓𝑒 =∑ 𝑒𝑥𝑐𝑢𝑟𝑠𝑖𝑜𝑛𝑖𝑛𝑖=1𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑎𝑖𝑙𝑒𝑑 𝑡𝑒𝑠𝑡 (4.3) The modified water quality is calculated after the factor “Q” as follows: 𝑄 =𝑛𝑠𝑓𝑒(0.01 𝑛𝑠𝑓𝑒 + 0.01) (4.4) 𝑀𝑜𝑑𝑖𝑓𝑖𝑒𝑑 𝑊𝑄𝐼 = 100 – 𝑄 (4.5) For example, the modified WQI for the time series is calculated at Node 22. The sum of FE and failed values at node 22 are 46 and 150. Equations 4.2, 4.3, and 4.4 are used to calculate nsfe, factor Q, and modified WQI. 𝑛𝑠𝑓𝑒 = 46150= 0.3 𝑎𝑛𝑑 𝑄 = 47 𝑀𝑜𝑑𝑖𝑓𝑖𝑒𝑑 𝑊𝑄𝐼 = 100 − 47 = 52 The result value of modified WQI (52) shows the water quality for specific node time series and residual chlorine. Critical WQ Zone Selection: It is observed that the central zone (5) of Al-Khobar WDS has more potential for contamination than other zones (1, 2, 3, and 4), and it needs special treatment attention [43]. Therefore, the average water quality of all five zones is estimated. In each zone, 3 RNs are selected for further calculations. 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑀𝑊𝑄𝐼𝑛 =𝑀𝑊𝑄𝐼1 + 𝑀𝑊𝑄𝐼2 + 𝑀𝑊𝑄𝐼33 (4.6) where n is the number of zones (1, 2, 3, …) Comparing WQ between different zones helps select the precarious location in the network. Factorial Analysis: The objective function of this study is to maximize the water quality and minimize the chlorine booster dosage, as shown in Equation 4.7. A two-level factorial analysis is performed using Design Expert 70 9.0 software in the critical zone with different chlorine dosages. 2n numbers of combinations generate a 3-D surface response optimization. 𝑂𝑏𝑗𝑒𝑐𝑖𝑣𝑒 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑡𝑜 𝑚𝑎𝑥𝑖𝑚𝑖𝑧𝑒 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑀𝑊𝑄𝐼𝑛 … … … … (4.7) 𝑇ℎ𝑒 𝑐ℎ𝑙𝑜𝑟𝑖𝑛𝑒 𝑟𝑎𝑛𝑔𝑒 0.2 𝑚𝑔𝐿< 𝐶𝑙 < 0.8 𝑚𝑔𝐿 Where “n” refers to number of zones and “Cl” refers to chlorine dosages. A central part of Al-Khobar WDS was selected to improve WQ with optimized chlorine dosage. It is observed that the location of a new booster station is chosen at random points in the central zone. A complete factorial analysis step by step (1-4) is performed in the following example as a case study. Results and Discussion Case Study The city of Al-Khobar is located on the eastern coast of Saudi Arabia. It has an area of approximately 64 km2 with a population of about 300,000, which is expected to reach 590,000 by 2016 [43]. The water demand is increasing due to the growing population. The water demand in Al-Khobar has increased from 35.60 MCM (million cubic meters in 1990) to 58.52 MCM (million cubic meters in 2004). It is expected to increase 111.95 MCM (million cubic meters) by the end of the year 2020 [43]. The city is served with a WDS that covers all urban areas. The total length of the WDS is approximately 472.6 km. The network consists of pipes with different materials and diameters ranging from 50 mm to 1 m [43]. The WDS is modeled by EPANET software, as shown in Figure 4.2. The city is served by a WDS that extends throughout the metropolitan zones, as shown in Figure 4.2. The central part of the network contains 141 nodes, 2 storage tanks named "Al-Yarmouk" and "Makkah" (for standby), 1 central ground tank, 12 reservoirs, 191 pipes (water mains), and 17 pumps of various capacities. The network consists of different pipe materials, lengths, and diameters, as described in Table 4.1. Al-Khobar municipality reported that 65% asbestos, 25% PVC, and 10% iron pipes were broken in the last 40 years. Hence, there are more chances of contaminants’ intrusions in the WDS. The number of water mains and history of pipe breakage in the city of Al-Khobar is shown in Figure 4.6. There are 44 groundwater wells distributed over the whole network. The Al-Azizia plant is situated on the eastern part adjacent to the Corniche of Al-Khobar city, one of the largest desalination plants in Saudi Arabia. It is the main source of the water supply system. The city's water utility blends 71 the desalinated water with the raw groundwater extracted from wells to obtain the desired concentration of total dissolved solids (TDS) [43]. Table 4.1 Lengths, material and sizes of pipes used in Al-Khobar WDS [43] Length according to material type Diameter(mm) Plastic Ductile Iron Asbestos Fibre Glass Total (m) 50 627 627 60 0 75 8422 8422 80 15209 47500 62709 100 9275 9275 21310 30585 110 54168 54168 150 30464 11376 48403 90243 160 84035 429 84464 200 15120 3519 9528 28167 225 19354 3700 23054 250 306 543 849 280 7687 7684 300 26746 10082 36828 315 6822 6822 350 42 42 380 0 400 9435 160 11520 123 21238 500 4960 4960 600 130 7520 7650 700 0 800 494 494 1000 3643 3643 Total= 472,625 72 Figure 4.6 Number of water mains in the city of Al-Khobar [43] The network has two main storage facility tanks named Makkah and Al-Yarmouk, with one central pumping station. It has 12 reservoirs and 3 chlorine booster stations that continuously inject 1 mg/L of chlorine into the water. The optimal level of free chlorine at any location in the network varies between 0.3 and 1.2 mg/L, and it should neither exceed the chlorine level of 2 mg/L nor be less than 0.2 mg/L [43]. The average water temperature ranges between 25 0C to 28 0C. The chlorine bulk (kb) and wall reaction values (kw) are assumed constant and incorporated into EPANET for water quality analysis [43]. The current chlorine boosters’ locations in the city are shown in Figure 4.7. Figure 4.7 Existing chlorine booster points in each zone 73 The temporal and spatial analysis is performed for all four seasons, 365 days of the year. The data is exported from EPANET to the XL file and is incorporated into Equation 4.5 and Equation 4.6. The calculated values of WQI and modified WQI for each zone are shown in Table 4.2. Table 4.2 Temporal and spatial analysis of results Zone Dec-Feb Mar-May June-Aug Sep-Nov Average WQIn Average MWQIn Average WQIn Average MWQIn Average WQIn Average MWQIn Average WQIn Average MWQIn Z1 = LEFT 4.2 1.46 4.3 1.42 4.44 1.43 3.18 1.57 Z2 =BOTTOM 24.72 22.14 28.51 23.63 28.49 23.56 28.52 23.56 Z5= CENTRE 19.56 23.81 22.6 24.86 22.6 25 22.6 24.28 Z4 = TOP 44.2 49.31 45.7 52.04 45.2 52.17 41.11 47.71 Z3 = RIGHT 42.2 49.52 41.1 52.04 27 52.26 27 25.28 WQI= Water Quality Index; MWQI=Modified Water Quality index; n= number of zones It is clear from the results that water quality is poor during the period September-November, while the water quality in other months of the year remains almost the same, as shown in Figure 4.8. Figure 4.8 Water quality in each zone Figure 4.8 above shows that zones 2, 3, 4 and 5 have marginal water quality. Only zone 1, the industrial area, shows poor water quality. Central zone 5 is more populated. It is considered critical during September- November because it proves poor water quality than the rest of the year. Thus, central zone 5 needs more attention [43]. Therefore, the optimization strategy is applied only at zone 5. 74 Enhancing Booster Chlorination: The existing chlorine booster stations are shown in Figure 4.6. There are 3 booster stations (1, 2 and 3) installed at different nodes. Critical zone 5 is selected for the chlorine enhancing booster station. Several chlorine combination dosages are computed using factorial analysis. A maximum chlorine level of 1 mg/L and a minimum 0.7 mg/ L is used for the objective function. There are eight combinations (23) of factorial analysis. These are A1–A8 used for the calculation of chlorine optimization. Equation 6 is used for these calculations and the results are shown in Table 4.3. Table 4.3 Average MWQIn for each combination Grouping Number Booster Concentration at different nodes (Central Zones- mg/L) MWQI Average MWQI Node=65 Node=71 Node=118 RN1=59 RN2= 76 RN3=91 A1 0.7 0.7 0.7 68.95 0 10.09 26.35 A2 1 0.7 0.7 65.82 0 10.09 25.30 A3 0.7 1 0.7 65.82 0 10.09 25.30 A4 1 1 0.7 65.82 0 10.09 25.30 A5 0.7 0.7 1 65.82 0 10.09 25.30 A6 1 0.7 1 65.82 0 10.09 25.30 A7 0.7 1 1 40.76 9.99 16.28 22.34 A8 1 1 1 40.06 9.99 16.28 22.11 A 3D surface analysis is performed with design expert software, which helps to determine the factor known as the “desirability factor”. The desirability factor is used in optimization analysis. The typical range of desirability factors varies between 0 and 1. The “0” means the poorest, and “1” means the best value. Therefore, a higher desirability factor is required for successful optimization. The optimization function helps to maximize water quality with a high desirability factor and minimum chlorine booster dosage. The graphical result obtained by the Design Expert is shown in Figure 4.9. 75 Figure 4.9 3D-Surface optimization By increasing chlorine booster level up to 0.8 mg/L, the average MWQI reaches 71.24, which is a better water quality than the 0.7 mg/L chlorine dosage i-e 26. However, increasing chlorine booster level may also increase the cost and water odour [128]. Figure 4.9 shows optimal combinations of chlorine dosage 0.7 mg/L for all booster points with desirability factor 0.95, which is nearest to the best value 1. Figure 4.10 Optimization with proposed chlorine booster’s location 76 This case study aims to increase water quality and decrease chlorine dosage. The optimum chlorine dosages with the new suggested booster location are shown in Figure 4.10. Figure 4.10 shows how the old and the newly added booster stations are uniformly distributed to provide optimal free residual chlorine inside the WDS. The 0.7 mg/L proposed chorine booster points are added in different zones, as shown in Figure 4.10, to improve the water quality. The chlorine dosage 0.7 mg/L is selected for this study, which is much less than the local threshold value of 2 mg/L. Lower chlorine dosages can help to reduce odour and disinfection by-products (DBPs) in the water. Summary Water quality management in distribution networks is directly related to the spatial distribution of chlorine boosters and their dosages. Water chlorination is essential to reduce the effects of bacterial and other microbiological contaminants. A higher dosage of chlorine generates harmful by-products in addition to changes in drinking water’s taste and odour. Optimizing chlorine dosage is necessary to decrease the microbial contaminants that affect water quality. Once the chlorine threshold is determined for a microbial contaminant, it will help decision-makers suggest optimal values. These decisions can rely on the estimated Water Quality Index (WQI). WQI is an index to evaluate water quality and can be linked to adequate residual chlorine with optimal booster dosage, numbers and locations in the water distribution network (WDS). The city of Al-Khobar, Saudi Arabia’s WDS, was selected to validate the application of this study. The Al-Khobar water network was divided into five zones based on geographic location. The initial temporal and spatial analysis pointed out poor water quality zones. EPANET, modeling and simulating software, was applied to evaluate the WQI. Those EPANET results were then integrated with an optimization model. The optimization model suggested new chlorine booster locations to improve water quality in the city of Al-Khobar water distribution network. Al-Khobar water authority reported that various pipes in the network were broken in the last 40 years, as described in Figure 4.5. Hence, there is more risk of contaminants’ intrusions in the WDS. The vulnerability of water pipe breaks may increase the possibility of WQ failure regardless of the location of chlorination points. There is a need to consider infrastructure assets such as prioritized rehabilitation and replacement of pipes to reduce the possibility of WQ failure. In the next section, a risk-based approach is used to optimize the residual chlorine in the network. 77 Chapter 5: Water Quality Risk-Based Optimization A part of this chapter was published in the Journal of Environmental Monitoring and Assessment as an article titled “Risk-based Model for Optimizing Residual Chlorine in Large Water Distribution Systems” (Sharif et al. 2017). In this chapter, risk-based residual chlorine modeling and optimization was performed. The EPANET model developed in Chapter 4 is used in this study. Failure mode effect analysis and fuzzy set theory were used to perform the risk analysis. The proposed optimization methodology can be helpful to optimize residual chlorine effectively in large WDS. A comprehensive review of the proposed risk-based WQ optimization is discussed in chapter 3; detailed selection is carried out in chapter 5. Background After treatment, potential microbial or chemical intrusion through cross-connections and broken sewer lines, old and cracked water mains, contaminated groundwater and soil can pose human health risks. It is the responsibility of the concerned municipality (or water utility) to ensure the safe delivery of drinking water up to the consumers. Depending on the shape and size of the distribution network, it is sometimes difficult to maintain the desirable range of residual chlorine in a large WDS. In particular, near the booster station where the chlorine levels are high and at the remote points where most of the residual could have been fully consumed. A detailed review of water quality failure in terms of residual chlorine has been discussed in Section 3.4. The vulnerability of water main breaks can also increase the possibility of water quality failure irrespective of the location of chlorination points. Effective infrastructure asset management efforts such as prioritized rehabilitation and replacement (R&R) planning of water mains can reduce the possibility of water quality failure (i.e., intrusion of soil and pathogens from broken or leaking mains) by improving the reliability of the WDS. Therefore, an approach needs to be developed to optimize residual chlorine in large water distribution systems operating under environmental conditions (i.e., relying on groundwater sources), which can integrate water quality and structural failures for effective decision-making. This study aims to develop a risk-based model to optimize the residual chlorine in large WDS by i) identifying the most desirable locations of booster stations based on land use characteristics and ii) planning R&R scenarios of water pipes to avoid contaminant intrusion. The details of the development of the methodological approach, results, and discussion are described in the subsequent sections. 78 Methodology 5.2.1 Risk-based Optimization The risk-based model for optimizing residual chlorine in large WDS is shown in Figure 5.1. The EPANET model developed in Section 4.2.1 for Al-Khobar WDS is used in this study. Therefore, all hydraulics, EPANET, and WQI parameters used in this study remain the same, as mentioned in Sections 4.4.1 and 4.4.2. Failure mode and effect analysis (FMEA), based on vulnerability, consequence, and detectability of water quality failure, is employed to evaluate the risk of not meeting the desirable range (established below) of residual chlorine in the WDS. Zoning of the entire study area is done based on the land use characteristic to evaluate the ‘Consequence’ of water quality failure regarding the severity of the possible human health risk in each land use, e.g., residential, industrial, and public. The possibility of contaminant intrusion due to pipe breaks is considered ‘Vulnerability’ for the risk-based optimization of residual chlorine. The risk factor ‘Detectability’ regarding the Canadian Council of Ministers of the Environment (CCME) water quality index (CWQI) is used as the decision criteria to optimize the chlorine dosage under the given physical and environmental conditions. Water quality assessment for the current scenario is conducted to identify the areas with relatively poor water quality in terms of residual chlorine. If the water quality is not acceptable, a need for booster stations or infrastructural improvements is established (See Figure 5.1). Subsequently, different scenarios are evaluated for the problem areas (i.e., low CWQI) to obtain the most desirable scenario for the optimal chlorine booster stations and efficient R&R planning of linear assets. The total risk is calculated, for a given scenario, at each node of the distribution network. The developed model is applied to the WDS of Al-Khobar, relying on a groundwater source in Saudi Arabia to evaluate its practicality. 79 Water Distribution SystemPipe diameterPipe lengthPipe material Land use distributionMain breaksPipe ageZoning based on land useVulnerability V Potential of pipe failure (i.e., main beaks) for each nodeConsequence C Severity of water quality failure based on land use variations in each zoneDetectability D EPANET Simulations to estimate water quality (i.e., residual chlorine level) at each nodeWater Quality Index (CCME)Calculating fuzzy Risk Priority Number (RPN) at each nodeRPN = V x C x DIs water quality acceptable?Identification of failure nodes (Risk Prioritization)Improvement action (Risk AlleviationCreate Scenarios Locate optimum Chlorine booster station (Select Best Scenario) Perform factorial analysis to optimize water quality with the help of required number of Chlorine Booster StationsStep 1Data CollectionStep 2FMEAStep 3Risk ManagementNO NOYESEnd Figure 5.1 Risk-based WQ assessment approach [150] 5.2.2 Residual Chlorine Modeling EPANET modeling regarding the residual Chlorine is mentioned in Section 4.2.1 5.2.2.1 CCME- CWQI A complete calculation of CCME-Canadian WQI is given in Section 4.2.2 5.2.2.2 Risk Assessment A detailed risk assessment method is described in Section 3.5.2. 80 In this research, the fuzzy FMEA risk-based assessment approach is used to optimize the residual chlorine in the WDS. 5.2.2.3 Fuzzy - Failure Mode and Effect Analysis (Fuzzy-FMEA) FMEA method evaluates all possible failure modes concerning their probability of occurrence, consequence, and detectability of failure events. Mathematically, RPN is calculated using the following equation [144] [145]. RPN = Vulnerablity (V) ∗ Consequences(C) ∗ Detectability (D) (5.1) In this study, the probability of structural failure (i.e., pipe break) refers to vulnerability (V). The severity of the failure relates to consequence (C) which depends on the type of land use, e.g., residential, commercial, or industrial. The third-factor detectability (D) defines the ability to detect the risk. In this study, ‘D’ is the risk mitigation factor regarding chlorine booster stations to manage a desirable range of residual chlorine. All three factors, V, C, and D, vary between 1 and 10, leading to an overall RPN value ranging between 1 and 1000. Experts from academia and technical managers working on actual WDS linguistically define all three factors in equation (5.1). Equation (5.2) has been used to calculate the RPN on each node of the WDS under study. For risk analysis, the linguistically defined universe of discourses for risk parameters V, C, and D are illustrated in Table 5.1. Table 5.1 Linguistically defined vulnerability, consequences, and detectability [150] Vulnerability (V) Consequence (C) Detectability (D) Linguistic Score Land Severity of effect CWQI1 Very low 1 1 2 3 <10 Agricultural (Pure) Very limited exposure is expected, i.e., few persons may consume this water >80 Low 2 3 4 5 10-20 Industrial Limited exposure is expected, i.e., not a high-density population area 60-80 Moderate 4 5 6 7 20-40 Commercial Moderate exposure, i.e., industries have their process specific treatment facilities 40-60 High 6 7 8 9 40-50 Residential/ Commercial The area is primarily residential with some commercial settings 'High exposure is possible, i.e., parks, offices, etc. 20 - 40 Very High 8 9 10 10 >50 Residential/ Public Very High exposure is expected, i.e., residential area, school, hospital, etc. <10 - 20 1Canadian Council of Ministers of the Environment Water Quality Index (CWQI) 81 5.2.2.4 Fuzzification and Defuzzification Methods Chen's (1985) ranking method is employed for defuzzification of the fuzzy risk number obtained from FMEA [237]. In this research, the WDS of the study area is divided into different zones based on the predominant land use in each zone. First, RPN values are calculated at individual nodes. Then the overall RPN has been estimated for each zone based on average RPN values calculated for all the nodes in that specific zone. The zones having a high RPN are more critical due to the following reasons: i) high vulnerability having frequent pipe break which can increase the possibility of contaminant intrusion; ii) high consequence as the primary land use is residential with higher exposure to human health risk, and iii) residual chlorine concentrations either close to the lowest value of desirable range (i.e., 0.1mg/L) or the highest (1.1mg/L) which has been assessed in term of erectility. The risk factors (‘V’, ‘C’, and ‘D’) are linguistically defined as very low, low, moderate, high, and very high as shown in Figure 5.2. Figure 5.2 Fuzzification method; M2, N2, O2, and P2 are the elements of the ‘low’ membership function [150] 82 Figure 5.3 Defuzzification of RPN using Chen’s Method [150] The trapezoidal fuzzy numbers and their elements defining the universe of discourse (UOD) from 1 to 10 are presented in Figure 5.2. The corresponding fuzzy-RPN can be calculated by Equation 5.1 and then the resulting fuzzy-RPN value calculated as: (3, 5, 6, 7) * (2, 3, 4, 5) * (4, 5, 6, 7) = Fuzzy RPN 3*2*4, 5*3*5, 6*4*6, 7*5*7 = (24, 75, 144, 245) The next step is defuzzification, which converts the fuzzy RPN into a crisp value. Chen’s (1985) ranking method shown in Figure 5.3 is used in this study for the trapezoidal fuzzy number. The defuzzified RPN is calculated by the following equation developed by Chen (1985): 𝑼𝑻(𝒙) = ½ [(𝑷𝒊 − 𝑿 𝒎𝒊𝒏)/ (𝑿 𝐦𝐚𝐱 – 𝑿 𝒎𝒊𝒏) − (𝑵𝒊 – 𝑷 𝒊) + 𝟏 − (𝑿 𝐦𝐚𝐱 − 𝑶 𝒊)/ (𝑿 𝐦𝐚𝐱 – 𝑿 𝒎𝒊𝒏) + (𝑴𝒊 – 𝑶 𝒊) (𝟓. 𝟐) X min and X max are the maximum and minimum RPN values. A detailed calculation of the fuzzification and defuzzification method is given in Section 5.3. 5.2.2.5 Risk Management The crisp RPN values calculated by Equation 5.2 at all nodes of the WDS under study are compared with the acceptable risk level (i.e., the threshold value of RPN). The decision-makers establish the threshold RPN based on their experience and the nature of the project. In this research, an RPN of 100 is selected for acceptable risk. The RPN can be reduced by improving detectability by enhancing 83 the CWQI at all the nodes of a WDS. Scenarios analysis with different combinations of booster stations can be evaluated to obtain optimal values of residual chlorine (i.e., high CWQI). Two-level factorial analysis is performed for ‘2n’ numbers of combinations of chlorine dosages. A scenario of ‘effective asset management’ has also been evaluated with R&R of most vulnerable water mains to reduce the RPN accordingly for the location with high vulnerability. Results and Discussion 5.3.1 Case Study The case study of Al-Khobar WDS is used as described in Section 4.3. 5.3.2 Risk Identification - Estimation of CWQI Abu-Monasar et al. (2014) reported that maximum chlorine level approaches 1.07 mg/L in the central zone of Al-Khobar, located near the chlorine booster station, while residual chlorine level in the south of the city was not found in the desired range [136]. The Al-Khobar WDS is divided into five zones based on the type of land use (i.e., residential, commercial, public, and industrial), which defines the level of the exposed population. EPANET simulates hydraulics characteristics of the network for an extended period of 240hrs. Different scenarios have been developed in the following section; however, there is a need to assess the extent of the problem for the present case scenario. In Scenario 1 (i.e., existing situation), three booster stations are located in zone 3, 4, and 5. Estimated CWQI in each zone for Scenario 1 are shown in Figure 5.4. A complete CWQI calculation for Scenario 1 is given in Table A1 Appendix A1. The results of residual chlorine concentrations obtained from EPANET simulations reveal that the CWQI value is less than 60 (i.e., poor) throughout the WDS. These results suggest mitigation actions improve the overall water quality in the distribution system under study. For risk analysis, the linguistically defined universe of discourses for risk parameters V, C, and D are defined in Table 5.1. The detailed fuzzy-FMEA analysis for assessing V, C, and D are attached as Table A2 in Appendix A2. 5.3.3 Scenario Analysis The RPN values for Scenario 1 with three existing booster stations presented in Figure 5.4 are higher than the acceptable range of risk. These results of the analysis show the need for risk reduction. Therefore, three additional scenarios and the existing scenario have been developed in Table 5.2. In scenarios 2 and 3, factorial analyses were performed to obtain the best chlorine booster dosage 84 combination, which improved the CWQI (i.e., detectability factor) in all the zones of WDS. While in Scenario 4 vulnerability factor has been reduced through R&R planning. In scenario 2, three new booster stations are proposed in zones 2, 3 and 4. Zone 1 and 5 lie in the regions that previously encountered a relatively high number of pipe breaks. Based on the factorial analysis, new booster stations increased the CWQI in these zones resulting in an overall reduction in RPN. The results for scenario 2 shown in Figure 5.5 depict an overall improvement in CWQI in all the zones. Scenario 3 was analyzed to improve water quality further in the study area. The objective function maximized the CWQI and minimized the chlorine booster dosage. Figure 5.6 shows that the CWQI values in zone 1 and 2 are improved from “Fair” to “Excellent,” validating improved water quality in scenario 3. The results of scenario 3 have an optimized combination of proposed chlorine booster dosage combinations, i.e., 0.8, 0.8, &1 mg/L in the entire WDS. Figure 5.4 Existing chlorine booster points and average CWQI in each zone of Al-Khobar WDS scenario 1 85 Table 5.2 Description of different risk management scenarios [150] Scenario No. Description Scenario 1 No Action: an existing situation with three chlorine booster stations Scenario 2 Proposed action: add 3 chlorine booster station in residential / commercial zone 1 & 2 Scenario 3 Proposed action: add 3 chlorine booster station in residential / public zone 3& 4 Scenario 4 Proposed action: replacement of structurally deteriorated pipes in zone 1,2,3,4, and 5 Figure 5.5 Proposed chlorine booster points and average CWQI in each zone of scenario 2 86 Figure 5.6 Proposed chlorine booster points and average CWQI in each zone of scenario 3 Scenario 4 is designed to investigate the possibility of water quality improvement through asset management of linear infrastructure. It can be achieved by prioritizing the R&R of water pipes with high vulnerability. A detail of pipe material and breakage is mentioned in Table 4.1 and Figure 4.5. The vulnerability (pipe breakage) in zone 1 and 5 was found to be 40% and 50%, respectively (i.e., ‘High’ to ‘Very High’ as per Table 5.1); thus, these zones need necessary water quality improvements. In scenario 4, it is proposed to replace the pipes older than 30 years, especially in vulnerable zones. After replacing the old pipes in existing scenario 1 without any additional booster station, the improved CWQI results are shown in Figure 5.7. It can be observed from Figure 5.7 that in zone 1, 2, and 5, CWQI improved by replacing old pipes. 5.3.4 Scenario Evaluation After developing and evaluating different scenarios, an overall evaluation of scenarios based on average risk (i.e., an average of RPN values calculated at all the nodes) was also carried out. Average ranges of the crisp (defuzzified) RPNs correspond to a resultant risk level are listed in Table 5.3. Experts from academia and technical managers working on actual WDS define the RPN range values. All the scenarios with their average RPN values, respective risk levels, and overall ranks are presented 87 in Table 5.4. The graphical representation of ranking of all the scenarios according to Chen’s (1985) defuzzification method is shown in Figure 5.8 [237]. Figure 5.7 Proposed pipe replacement in each zone of scenario 4 and average CWQI Table 5.3 RPN ranges & corresponding risk levels for scenario evaluation [6] RPN Risk level > 300 Extremely high 250-300 Very high 150-250 High 100-150 Moderate < 100 Low (acceptable range) Table 5.4 Rank and average RPN values of all scenarios Scenario 1 Scenario 2 Scenario 3 Scenario 4 Average RPN 208 132 90 78 Risk Level High Moderate Low Low Rank of Scenarios 4 3 1 2 88 Figure 5.8 Fuzzification and defuzzification of all scenarios [150] In Scenarios 2 and 3, three additional chlorine boosters are proposed to confirm that these two scenarios (2&3) are significantly different from each other. In Table 5.4, the RPN value of Scenario 1 is higher than 200, and thus the overall risk is ‘High.’ Scenario 4 also showed an average RPN value of less than the acceptable risk. Table 5.4 is based on the average RPNs, and further analysis is required for detailed evaluations. Therefore, 75th percentile, average and 25th percentile RPN values after applying the risk mitigation actions for all scenarios are shown in Figure 5.9. As per Table 5.3, the risk is acceptable (i.e., low) for Scenario 3. It is evident in Figure 5.9 that 75% of the nodes in Scenario 3 have RPN values of less than 100. Although the remaining risk at more than 75% of the nodes after implementing scenario 4 is acceptable, extensive pipe replacement might be an economically unviable option compared to installing additional booster stations. However, prioritized R&R of pipes is a conventional asset management practice in all water utilities. 89 Figure 5.9 Cumulative RPN of all scenarios: S1: no action; S2 and S3: three additional booster stations in different locations of the network; S4: rehabilitation and replacement of pipes [150] Summary Managing residual chlorine in large water distribution systems (WDS) is a daunting task to minimize human health risks. In this research, a novel risk-based model is developed and implemented in a distribution network spanning over 64 km2 for supplying water to the city of Al-Khobar (Saudi Arabia) through 473 km long water mains. The approach integrates the planning of linear assets (i.e., pipes) and the placement of booster stations to optimize residual chlorine in the WDS. Failure mode and effect analysis are integrated with the fuzzy set theory to perform risk analysis. A vulnerability regarding the probability of failure of pipes estimated from historical water main breaks: residual chlorine availability has been associated with the exposed population depending on the land use characteristics (i.e., defined through zoning). EPANET simulations have been conducted to predict residual chlorine at each node of the network. A water quality index is used to assess the effectiveness of chlorine practice. Scenario analysis is also performed to evaluate the impact of changing locations and the number of booster stations, rehabilitation and replacement of vulnerable water mains. The results revealed that the proposed methodology could facilitate the utility managers to optimize residual chlorine effectively in a large WDS. 90 Chapter 6: Integrated Framework of WQ and Energy Use Optimization A part of this Chapter 6 was submitted in the Journal of Environmental Monitoring and Assessment as an original research article titled “Staged Energy and Water Quality Optimization for Large Water Distribution Systems” (accepted for publication, January 2022). This chapter uses risk-based residual chlorine modeling and optimization developed in Chapter 5 in this study. Mixed Integer Goal Programming (MIGP)-based modeling is used for the day-ahead pump scheduling to minimize the pumping energy cost. The proposed framework used three stages to optimize both WQ and energy use. Stage 1 uses CWQI and factorial analysis to locate the chlorine boosters (developed in Chapter 5). Stage 2 uses MIGP, integrating EPANET and CPLEX software, optimizing the day-ahead pump scheduling. Stage 3 evaluates the combined hydraulics and WQ performances by scenario analysis. Background Keeping safe water quality and adequate pressure with minimum operation and maintenance cost is one of the primary objectives of a WDS. In addition, the water utilities are trying to maximize energy efficiency, improve WQ, and minimize environmental impact concerning sustainable development goals [2]. The pumping systems consume approximately 75% of the total energy supplied to an urban WDS [152]. The remaining energy is utilized for pressure, chlorine boosters, and other maintenance activities [35]. Due to inflation in energy prices and related CO2 emissions, there is a need to optimize water-pumping systems with acceptable WQ for sustainable development in the water supply sector. Hydraulics and WQ models have been used for planning, monitoring, and overall management of WDS for the past 30 years. Considering the energy aspect along with WQ increases the complexity of WDS. Besides, energy optimization can be challenging using many pipes, scheduling several pumps, and adjusting water levels in multiple tanks. In addition, demand uncertainties, associated energy costs, and variable electricity tariffs, day-ahead pump scheduling is difficult in large distribution systems. Water utilities worldwide use intelligent pumping management systems, such as optimal pump scheduling (OPS), to minimize the pump operational energy costs [22]. In practice, finding an OPS that satisfies varying demands with minimal energy consumption for a large WDS is highly complex due to some variables' discrete nature and the problem's nonlinear nature [170]. Various OPS optimization approaches have been reported in the literature (mentioned in chapter 3). 91 Besides OPS challenges, there exists a WQ optimization problem, as chlorination is a regular practice that can generate residual chlorine in WDSs to ensure pathogen-free water up to the consumer end. However, a high chlorine dosage produces odour issues and disinfection by-products (DBPs), while a low dosage may not sufficiently deactivate pathogens [7]. Booster stations are often used to maintain optimal residual chlorine concentration. Large cities consist of different land uses, e.g., residential, public, commercial, industrial, and open spaces. Microbiological water quality failure (i.e., absence of chlorine) can severely affect the sensitive zones. Hence, optimally locating the booster stations should take the consequence of a failure event into account depending on the land-use variations in the network. In addition, it is essential to ensure that pump energy consumption and costs are low. Mala-Jetmarova et al. (2017) have comprehensively reviewed studies relevant to optimizing pumping energy and WQ in the WDSs [21] and concluded that no universal optimization method is available for medium-to-large distribution systems that include all relevant objectives and constraints. Some studies on WQ and energy use optimization techniques are already mentioned in chapter 3 and suggest a trade-off because of the conflicting objectives in optimizing WQ and pumping energy cost [21][26]. It was observed that Mixed Integer Goal Programming (MIGP) based solution for pump scheduling does not affect the residual chlorine in simultaneous WQ and energy optimization schemes. Hence, our research is a part of continuing efforts to improve the system's hydraulics and WQ performance with minimum possible energy utilization. Thus, the WQI risk-based model developed in chapter 5 is combined with the energy optimization MIGP-based model developed in the present study to generate integrated energy and WQ optimization solution. Methodology 6.2.1 Staged Energy and Water Quality Optimization Model Figure 6.1 shows the staged model for optimizing pump scheduling (to minimize the energy cost) and water quality (residual chlorine) in a large real-world WDS. Stage 1 of the model identified the optimal chlorine boosters' locations and the impact of pipe breakage on WQ [150]. Stage 2, the MIGP-based mathematical solution, optimizes day-ahead pump scheduling to energy costs. This stage considers the multiple-goal constraints based on the GP concept, such as pump flow for each period, maximum-minimum capacity of the tank, water demand, etc. 92 Figure 6.1 Energy and WQ optimization approach for large WDS Selection of study area and collection of water distribution system’ baseline data - pipe diameter - pipe length - pipe material - pump characteristic curves - energy cost - energy usage per cubic meter - tank water levels - suction and delivery heads of pumps - nodal demands - storage tank capacity - population - peak hour demand - pipe diameter - pipe length - pipe material - pipe age - land use distribution - water main breaks - chlorine demand - locations of chlorine booster stations - nodal demands - storage tank capacity - population - peak hour demand Module 2: Optimization of chlorine boosters START Classify network into different zones based on land use Estimate risk factors • Vulnerability (V) - Potential of pipe failure for each node • Consequence (C) – Severity of water quality failure based on land use variations in each zone • Detectability (D) – Estimation of residual chlorine levels at each node using EPANET simulations (CCEM-WQI) Calculate Risk Priority Number (RPN) = V x C x D Is water quality acceptable? Factorial analysis to optimize water quality using required number of chlorine boosters Identification of failure nodes and create scenarios with different locations of chlorine boosters END No No Yes Module 3: Integrated optimized hydraulics and water quality performance evaluation Overall hydraulic performance in each zone using optimized pump scheduling Overall WQ performance in each zone using optimized pump scheduling and optimized chlorine booster stations Module 1: Energy optimization START Define decision variables Formulate Mixed Integer Programming (MIGP) Model Solve MIGP Model based on EPANET output Perform hydraulic simulations using EPANET Perform scenario analysis based on MIGP outputs Select “most optimal scenarios” END 93 Stage 3 combined both the stages by employing the optimal chlorine doses and locations of booster chlorinators identified in Stage 1 to the optimal pump scheduling determined in Stage 2. Finally, a comparison has been made between different states of the WDS, including the baseline state (normal) without WQ and energy optimization, the state with only WQ optimization, and the best case with both the energy use and WQ optimization. Figure 6.1 shows the WQ and energy use optimization model using a three-stage process. 6.2.2 Case Study A large WDS serving the city of Al-Khobar in Saudi Arabia was selected to evaluate the practical application of the proposed model. The model starts with collecting baseline data of hydraulics parameters from the Ministry of Water and Environment, the city of Al-Khobar WDS (local office). The data was obtained from the city water authority, including pipe diameter and length, characteristic pump curves, energy usage (kWh), energy cost/kWh, tank water levels, discharge at major network nodes, storage tank capacity, population, and peak hour's demand. Additional data for the residual chlorine model was also obtained from the same sources that included pipe age, water main breaks, land use distribution, and locations of existing chlorine boosters. In the initial stage, the hydraulics network simulation is performed by EPANET software. The EPANET model developed in Section 4.2.1 used in this study, integrated with the CPLEX solver. The network hydraulics varies throughout the day, with the hourly demand fluctuating from 2800 to 5900 m3/hr. The utility recorded an average maximum daily flow rate of approximately 4800 m3/h on a hot summer day. The daily demand variations revealed the lowest consumption hours from midnight until 4:00 a.m.; a gradual increase occurs after 6:00 a.m., followed by peak hours from noon to 4:00 p.m. The Al-Khobar WDS was divided into five operational zones based on land-use variations. The overall WDS operates with 17 pumps, including one transmission pump (P625) to supply desalinated water to the Al-Yarmouk tank, ten groundwater well pumps (P6, P7, P9, P10, P11, P13, P16, P17, P19, P23), and main distribution pumps at central pumping station (P82, P94, P420, P421, P220, P221) to supply water to the network. All of them have been included for day-ahead pump scheduling in the present research. The flow rates of the pumps vary from 660 to 1322 m3/hr, while their power consumptions range from 136 to 256 kWh. Table 6.1 presents the maximum flow rate, maximum power, and average working hours per day of each pump in the network. The average energy consumption per unit volume of the flow rate ranges from 0.1 to 0.2 kWh/m3 in the network. Figure 94 6.2 illustrates that the distribution pumps erected after the central water tank ensures a necessary pressure head throughout the network. All distribution pumps are centrifugal with variable speed drives operated at 50 hertz. Most centrifugal pumps deliver large flows with stable heads in the distribution network [92]. The pressure head in the network fluctuates between 2.5 to 3.0 bars (i.e., around 25 to 30 m), and the energy consumption depends upon the flow rate and varies with water demand in the network. The total pumping capacity of the CPS is 2300 m3/hr throughout the day and 1500 m3/hr during the night. The submersible type well pumps, i.e., P11, P13, P16, P19, P17, and P23, inject water into the main network. Each submersible type well pump has a maximum flow rate between 200 and 280 m3/hr with rated power from 66 to 75 kWh. Pump P625 is a dedicated pump that works 24 hours and fills the elevated Al-Yarmouk tank. The maximum pumping capacity is 1417 m3/hr with a rated power of 427 kWh. Water from the AL-Yarmouk tank subsequently fills the Makkah and Central ground tanks by gravity, as shown in Figure 6.2. Table 6.1 Maximum flow rate and power of each pump used in the study of city of Al-Khobar WDS 95 A monitoring study in the past reported 1.07 mg/L as the maximum chlorine level in the central zone of the city located near the central pumping station. Conversely, the desired residual chlorine range was not observed in the southern part of the city [136]. 6.2.2.1 Stage 1 - Optimization of Chlorine Boosters The left side of Figure 6.1 demonstrates the risk-based residual chlorine optimization approach developed by the authors for the same case study area given in Section 5.2. The readers can see Sharif et al. (2017) [150] for details. Figure 6.2 A schematic diagram of the pumping system of the city of Al-Khobar water network 6.2.2.2 Stage 2 – Energy-use Optimization The right side of Figure 6.1 describes the energy optimization stage. The hydraulics performance of a WDS depends on the capacity and efficiency of pumps, valves, junctions, tanks, and reservoirs. In the initial stage, hydraulics simulations of the distribution network were performed using EPANET [238]. Hazen-Williams equation (Section 4.2) was used to calculate the head losses in the network 96 [37]. The average energy cost estimation calculations for all the 17 pumps operating in the WDS under study are as follows. The average energy consumption of pumps (kWh) per unit flow rate (m3/hr) was multiplied by energy cost (SAR/kWh). The Saudi electric distributor obtained the unit electricity cost of 0.32 SAR/kWh (flat rate for 24 hours) [239]. For example, if the maximum power of P82 was 75 kWh and its maximum flow rate was 280 m3/hr, the maximum energy cost of this pump came out to be 0.086 SAR/m3-hr. This cost corresponds to the baseline state (unoptimized) of the WDS before optimization, while the optimized energy cost was found to be 0.061 SAR/m3-hr (see Table 6.4 in the Results section). The average optimized energy cost of all 17 pumps for the first operational hour is 103.9 SAR/m3-hr. Table A3 in Appendix A3 contains the detailed and average energy cost for all the 17 pumps (optimized scenario) for 24 hours. The developed model was updated and solved by CPLEX Studio IDE 12.9.0 on a laptop with Intel® Core™ i7-4710HQ CPU @ 2.50GHz and 8.00 GB RAM using Windows 10, 64-bit operation system. The EPANET was integrated with Mix Integer Goal Programming (MIGP) solvers using CPLEX software to optimize the pump scheduling and minimize the energy cost. The goal-programming concept investigates the versatility of constraints and suitable deviations from the desired goals in the problem. The model's objective functions were considered both the energy cost minimization and unsuitable deviations from goal constraints such as expected water demand level. The developed mathematical model solves the network process using a scheduling theory. The model essentially identifies the active pumps over each hour for daily scheduling, and it estimates the flow for each active pump during the scheduling period. Table 6.2 defines the essential components of the optimization model to formulate the day-ahead pump-scheduling problem. In Table 6.2, small and capital letters describe decision variables and parameters, respectively. In the initial formulation of the pump scheduling problem, some rigid constraints (e.g., the volume of the water, the capacity of the tank, etc.) were converted into flexible constraints (e.g., water demand for tank) based on the concept of GP. Both the tanks and nodal demands and the potential differences along pumps and pipeline segments were formulated as the goal-based constraints within the flexible formulation. The unwanted deviations from the expected values of the goals must be minimized in the objective function of goal-based programming. 97 The pump-scheduling problem was formulated through the following equations by considering the parameters presented in Table 6.2: ( ) ( ) ( ) ( ), , ,, , , , , ,min t t tt T Pt t j t j t t t ij t ij tt T t T j N t T P t T ij Lz E x yd d d d d d d d    − + − + − + − +        =   + + + + + + + +       (6.1) s.t. , , , ,t tx F y t T P         (6.2) , , , ,t tx F y t T P         (6.3) , , ,j t jw W t T j N        (6.4) , , ,j t jw W t T j N        (6.5) ,0 ,0 ,j jw W j N   =   (6.6) ( ), , , 1 , , , , ,ij t j j t j t j t j t j tij Lf S w w d d D t T j N− +     − −  − + − =     (6.7) , , 0,ij t ji t t tij L ji Lf f d d t T− + − + − =    (6.8) ( )( ), , , , , , 0, ,s t r t t t t tw w x y d d t T P     − +− −   + − =     (6.9) , , , , , 0, ,i t j t ij ij t ij t ij tw w f d d t T ij L− +− −  + − =     (6.10) ,, , , , , , ,0 , ,, 0; {0,1};, , , , , , , , 0,, , , , , , , ,tt ij t ji t i t j t j t j r t s td d yx f f w w w w w wP t T ij L ji L i M j N j N r N s N+ −  =                    (6.11) Figure 6.2 illustrates the water distribution network adopted for the mathematical programming model. The first part of equation (6.1) minimizes pumps' total energy consumption cost, and the second minimizes the sum of unwanted deviations from the expected values of the goals. Constraints are given in equation (6.2) to guarantee that the pump flow (  ) at each period must be greater than or equal to the minimum flow of the pump. Constraints are given in equation (6.3) to ensure that the pump flow at each period must be less than or equal to the maximum capacity of that pump. Constraints are given in equations (6.4) and (6.5) to confirm the minimum and maximum capacities 98 of the tanks ( j ) at each period, respectively. The last constraint defined in equation (6.6) fixes the volume of water (hydraulics heads) for the tank ( j ) at the initial time (t = 0). Equations (6.7) to (6.10) present the flexible constraints of the goal programming-based model. Constraint in equation (6.7) shows the water demand for the tank ( j ) in terms of flow conservation at the station node (s) and tank nodes ( j ). These constraints (,j td− and,j td+) provide any acceptable deviations from the expected values of the goals, i.e., the water demand of the tanks at each period. Both the deviations (,j td− and,j td+) are undesirable and must be minimized by considering the objective function given in equation (1). Constraints presented in equation (6.8) guarantee the flow conservations at station nodes and junction nodes at each period to be flexible by considering deviational variables of td− and td+ in this group of constraints and the objective function. Equations (6.9) and (6.10) present the constraints that formulate potential differences along with pumps and pipeline segments, respectively. They are flexibly formulated based on GP using two separate sets of deviational variables (,td− and,td+ ) and (,ij td− and,ij td+ ). These goal variables are undesirable and must be minimized in the objective function given in equation (6.1). Constraints in equation (6.11) provide binary variables and the non-negativity of all goals and the systemic variables. Table 6.2 Input and output parameters of the pump scheduling optimization model Notation Description Sets: M Set of start nodes in pipeline segments (based on the index i ) N Set of end (junction) nodes in pipeline segments (based on the index j ) N  Set of tank nodes (based on the index j ) N Set of reservoir nodes (based on the index j ) N Set of station nodes (based on the index j ) L Set of pipeline segments between nodes i and j (based on the index ij ) L Set of pipeline segments between nodes j and i (based on the index ji ) P Set of pumps (based on the index  ) G Set of goals (based on the index g ) T Set of scheduling periods (based on the index t ) Parameters: ,tE Energy consumption cost for pump  in period t 99 jS  The cross-section of a cylindrical tank j ,j tD  The demand of tanks j in period t (the expected value for the goal of tanks' demands) ,0jW  The initial water level (hydraulics head) in the tank j at t = 0 jW  The minimum water level (hydraulics head) in the tank j at any time jW  The maximum water level (hydraulics head) in the tank j at any time F The minimum water flow of the pump  F The maximum water flow of the pump   The fixed potential difference for pump  𝛽ij Nodal demand from node i to node j at any time ∅ij Head losses in pipes from node i to node j at any time ij Potential differences for pipeline segment between nodes i and j Decision variables: ,ty A binary integer variable: 1 if the pump  is ON over period t, 0 otherwise ,tx The flow rate of pump  over period t ,ij tf Flow-through pipeline segment between nodes i and j over period t ,ji tf Flow-through pipeline segment between nodes j and i over period t ,i tw Water level (hydraulics head) at the node i of pipeline segments over period t ,j tw Water level (hydraulics head) at the node j of pipeline segments over period t ,j tw  Water level (hydraulics head) at tank node j over period t ,0jw  Water level (hydraulics head) in tank node j at t = 0 ,r tw Water level (hydraulics head) at inlet (reservoir) node r based on pumps over period t ,s tw Water level (hydraulics head) at outlet (station) node s based on pumps over period t d + Positive deviation from the expected value of each goal (over achievement variable) d − Negative deviation from the expected value of each goal (below achievement variable) 100 6.2.2.3 Stage 3 - Combined Energy-use and WQ Optimization Stage 1 identified an optimal WQ scenario with the maximum number of nodes with residual chlorine in the desired range. Stage 2 generates the optimized day-ahead pump scheduling with a minimum energy cost scenario. In the absence of additional chlorine boosters identified in Stage 1, the chlorine dosage only at the pumps optimized in Stage 2 does not result in desired WQ (i.e., maximum CWQI and minimum possible risk). Finally, Stage 3 combines the outcomes from Stages 1 and 2 by applying additional chlorine boosters identified by Stage 1 and re-simulating the optimized hydraulics model to validate minimum energy cost and maximum WQ compliance. As the energy consumption of chlorine booster stations is negligible compared to water distribution pumping energy, the energy required for additional chlorine boosters was directly added to Stage 2 energy calculations. Results and Discussion 6.3.1 Stage 1 - Chlorine Boosters Optimization Hydraulics simulations using EPANET software were extended for 240 hrs to estimate residual chlorine levels at the network nodes in each zone. Complete CWQI calculations can be seen in Chapter 4, Section 4.2.2. Figure 6.3a shows the CWQI and pressure levels in different study area zones for the base case condition. Based on the estimated residual chlorine, the base case (existing condition) showed a low CWQI (60, i.e., poor) throughout the WDS (See Figure 4.8 and 5.4). Hence, Stage 1 identified additional booster stations. For details, see Section 4.2.2 and Section 5.3.3. In Figure 6.3, Zone 1 and Zone 2 of the distribution network have a wastewater treatment facility and a few automobile workshops in their vicinity. A severe consequence was considered for these two zones. Moreover, the past studies reported compromised structural integrity towards this side of the city [43]. In the case of a water quality failure, a more severe consequence was considered for high population density zones. For instance, the population density is high in the city centre, decreasing along with the northern and southern segments. The factors, including living style, population density, commercial and industrial activities, number of beds in the hospitals, and the number of students in schools, can affect the consequence severity. Residential zones face more severe health threats than commercial and industrial zones, as more water is directly consumed for drinking, cooking, washing, and bathing. In Zone 3 and Zone 5, patients and children are more susceptible to waterborne illnesses than common healthy adults [240] 101 Figure 6.3 WQ improvement results, a) average pressures and CWQI in each zone for existing base case scenario, b) improved CWQI after applying Stage 2 in each zone (average pressures* shown are same as base case scenario without energy optimization). *Note that pressure colour coding applies to nodes, and chlorine colour-coding corresponds to pipes. 102 Figure 6.4 24 hours demand variation of Al-Yarmouk water tank (EPANET model); negative demand period corresponds to the filling of the tank For the CWQI given in Figure 6.3a, the risk value (i.e., risk priority number (RPN) ranging between 1 and 1000 using FMEA) was found higher than the acceptable threshold of '100'. The primary land uses in Zone 1, and 2 are industrial and commercial, while residential and public land uses are in Zone 3 and 4. In Figure 6.3b, three chlorine boosters were added to Zone 1, 4, and 5 and three existing boosters located in Zone 4 and 5. The factorial analysis (See Table 4.3, Section 4.3) obtained the best combinations of chlorine dosage (main pumping stations and chlorine boosters) for the WQ optimization scenarios. Figure 6.3b shows improvements in both the CWQI and risk values. Comparison between Figure 6.3a and 6.3b indicates around 40% overall improvement in CWQI. 6.3.2 Stage 2 - Energy Optimization EPANET first used the input parameters (baseline data in Figure 6.1) for hydraulics simulations to calculate the nodal demand from node i to node j, 𝛽ij, and head losses in pipes from node i to node j, ∅ij. Figure 6.4 presents 24-hour water demand variations in the Al-Yarmouk tank. Next, the CPLEX model used the input parameters of Table 6.3 along with the output results, 𝛽ij, and ∅ij, of EPANET, where our proposed algorithm optimized the pump scheduling for 24 hours. In this study, three different scenarios were simulated to analyze the performance of the proposed algorithm. All 17 103 pumps were assumed operational in the first scenario, while 15 were selected for scheduling at various network locations in the other two scenarios. In Scenario 2, two distribution booster pumps (P421 and P220) were switched off, while one distribution booster pump (P420) and one well pump (P23) were switched off in Scenario 3. Table 6.4 shows the average pump operational energy consumption per unit volume (kWh/m3) and cost per unit volume for all three scenarios. An electricity tariff of 0.32 Saudi Riyal (SAR)/kWh was used [239]. It can be seen in Table 6.4 for Scenario 1 that the pumps switched off in Scenarios 2 and 3 were associated with higher energy costs than the remaining operational pumps. The present research did not consider the energy lost due to water leakage in the network. Figure 6.5 shows the energy costs against the flow rates for all three scenarios. These results are based on the average input data per day (24 hours) for May-June 2020. Figure 6.5 presents both normal (un-optimized) and optimized results. The results show that the system runs at the maximum flow rate from 12:00 p.m. to 4:00 p.m. to meet the required demand, and the energy costs remain high from 6:00 a.m. to 9:00 p.m. Both Figure 6.5 and Figure 6.6 show the advantages of the proposed optimization algorithm in terms of energy cost reductions, i.e., 13%, 19%, and 22% for Scenario 1, 2, and 3. The energy cost reductions are more significant during off-peak hours (i.e., 1:00 a.m. to 6:00 a.m.). Table A5 (Appendix A5) compares energy cost reductions for all three scenarios normal and optimized for 24 hours. Figure 6.6 compares the percentage cost reduction of each scenario over 24 hours. It can be observed that Scenario 2 has more energy cost reduction than Scenario 1 and 3 during peak hours (10:00 a.m. to 4:00 p.m.). However, Scenario 3 has maximum cost reduction among all scenarios. Table 6.3 Network input parameters and their values used for the energy optimization model 104 Table 6.4 An average pump operational optimized energy consumption of all three scenarios for 24-hours In the present research, the electricity cost was fixed at a constant rate (0.32 SAR/kWh) and considered independent of the time-varying rates during peak and off-peak hours of operation. Figure 6.6 illustrates that Scenario 3 provides the optimal solution with a 22% cost reduction. Scenario 3 shows that the proposed optimization algorithm tries to balance the energy costs and flow rates to meet the required demand in the network over a 24-hour duration. In Scenario 3, P82 (well) and P420 (distribution) were switched off. The proposed algorithm suggests keeping those pumps operational for the maximum period due to their lower energy cost. Table 6.5 shows the optimized energy costs and flow rates for all 15 operational pumps (P82 and P420 are OFF) in Scenario 3. Due to space limitations, the detailed optimization results for Scenario 1 and Scenario 2 are given in Appendix A3 and A4. Table 6.4 shows that pump P625 always needs to be turned ON to fill the Al-Yarmouk tank. This pump is the main input to the WDS and is considered a constraint in the proposed algorithm. In the actual case scenario without optimization, the maximum flow rate of P625 is 1417 m3/hr and remains constant throughout the day. The proposed algorithm reduces the load on P625 by sharing it with low energy cost pumps such as P6, P7, P9, P10, P11, P13, P16, and P17. 105 Figure 6.5 The normal and optimized energy costs and pump flow rates for each scenario, a) and b) for scenario 1, c) and d) for scenario 2, e) and f) for scenario 3 0501001502002503001 3 5 7 9 11 13 15 17 19 21 23Energy cost (SAR/m3-hr) Time (hrs)Normal Energy Cost (SAR/m3 -hr)Optimized Energy Cost (SAR/m3 -hr)(a)010002000300040005000600070001 3 5 7 9 11 13 15 17 19 21 23Flow rate (m3/hr)Time (hrs)Normal Pump Flow Rate (m3/hr)Optimized Pump Flow Rate (m3/hr)(b)0501001502002503003501 3 5 7 9 11 13 15 17 19 21 23Energy cost (SAR/m3-hr) Time (hrs)Normal Energy Cost (SAR/m3-hr)Optimized Energy Cost (SAR/m3)(c)010002000300040005000600070001 3 5 7 9 11 13 15 17 19 21 23Flow rate (m3/hr)Time (hrs)Normal Pump Flow Rate (m3/hr)Optimized Pump Flow Rate (m3/hr)(d) 010002000300040005000600070001 3 5 7 9 11 13 15 17 19 21 23Flow rate (m3/hr)Time (hrs)Normal Pump Flow Rate (m3/hr)Optimized Pump Flow Rate (m3/hr)(f)0501001502002503003501 3 5 7 9 11 13 15 17 19 21 23Energy cost (SAR/m3-hr)Time (hrs)Normal Energy Cost (SAR/m3 -hr)Optimized Energy Cost (SAR/m3 -hr)(e)106 In Scenario 2, the distribution pumps P421 and P220 were switched off. Since P421 and P220 were switched off, the proposed algorithm suggested keeping P19, P23, P82, P94, and P420 ON for the maximum amount of time. The algorithm also suggested that P221 be kept OFF for the whole day due to its high-energy cost (See Table A4 Appendix A4). Figure 6.6 Comparison of percentage cost reduction of each scenario over 24 hours Table 6.5 Optimized flow rate and energy cost of all 15 pumps active during 24 hours of Scenario 3 0%5%10%15%20%25%30%35%40%1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Cost reduction (%)Time (hrs) Scenario 1 Scenario 2 Scenario 3107 6.3.3 Stage 3 - Combined Energy and Water Quality Performance Evaluation In the base case or existing situation, energy is not optimized, and there are three chlorine booster stations, each located in Zone 3, 4, and 5. Figure 6.3a shows the study area's high-energy consumption (23,608 kW/day) and 'poor' WQ (overall average CWQI = 45). Stage 1 identified the optimal locations of booster stations and improved the CWQI to 'excellent' (overall average CWQI = 78). However, Stage 1 results were obtained using the current (unoptimized) pumping rates and the additional energy cost of chlorine boosters, resulting in maximum energy consumption of 23,833 kW/day (Figure 6.3b). Figure 6.3b shows the total energy cost of all 17 pumps, including chlorine boosters, is 5958 SAR/day. Figure 6.7 presents the final integrated results for optimized energy cost and WQ for the study area. Figure 6.7 Stage 3 results showed average pressures* with both the energy and WQ optimization with optimal locations of booster chlorinators * Pressure colour-coding applies to nodes, and chlorine colour-coding corresponds to pipes. Stage 2 found Scenario 3 (15 pumps active) of energy use optimization as the least energy cost scenario. Finally, Stage 3 combines the additional proposed chlorine boosters identified in Stage 1 (as shown in Figure 6.3b) and the day-ahead pump scheduling obtained in Stage 2 (Scenario 3). 108 Hydraulics simulations were performed again with the OPS and chlorine doses suggested in Stage 1. The new CWQI for each zone was recalculated. It can be seen in Figure 6.7 that the total optimized energy cost of 15 pumps is 4603 SAR/day. It means that around 22% of energy cost savings can be achieved through the OPS algorithm developed in the present study with almost the same hydraulics performance, i.e., average pressure ranges between 18m and 32m. At the same time, around 40% improvement in WQ performance was achieved, i.e., the average CWQI increased from 45 to 78. Summary This chapter proposes a three-staged approach to improve WQ and minimize energy use. Stage 1 uses a risk-based approach to optimally locate the chlorine boosters in a large distribution system based on residual chlorine failures and the associated consequences in different land uses of the service area. Integrating EPANET and CPLEX software, Stage 2 uses mixed integer goal programming for optimizing the day-ahead pump scheduling. The objective function minimizes the pumping energy cost and maximizes WQ. Stage 3 evaluates the combined hydraulics and WQ performances at the network level. Integrating EPANET with the MIGP-based mathematical modeling adopted in this research can provide an optimal solution with less computational effort for pump scheduling in a large real-world WDS. The proposed approach is implemented on a real-time large-sized network of Al-Khobar city in Saudi Arabia. Simulating the network with an optimal pumping schedule and chlorine boosters’ locations shows a 40% improvement in WQ performance, desired hydraulics performance with optimal pump scheduling, and an average 20% energy cost reduction compared to the normal (unoptimized) base case scenario. 109 Chapter 7: Conclusions and Recommendations Background Water and energy are two basic and essential components of life. Urbanization and fast population growth have increased demands for water and energy, which has become a significant challenge for utilities worldwide. The water distribution system (WDS) is the most costly and energy-consuming component of the water supply system. Water gets contaminated through many mechanisms and pathways, which has human health impacts. Water pumping has the highest energy cost in the WDSs. Thus, inefficient pump scheduling can affect the performance of a WDS. Ensure good WQ and adequate pressure with minimum operational energy cost is one of the main drivers of a WDS. However, this venture becomes problematic when the distribution pipes get old and prone to breakage. Besides, the contaminant intrusion and high pumping energy costs also affect WDS performance. There is a need to improve WQ and optimize energy use in WDS throughout its lifetime. This research proposed an integrated framework to simultaneously improve WQ and optimize energy use in a real WDS. The framework was implemented in step-wise phases. In the first phase, the proposed framework explored the WQ and energy use modeling techniques investigated identified the poor WQ and inefficient energy use in the WDS. In the second phase, a hydraulics model was developed, evaluated the WQ based on CWQI, identified poor WQ location in WDS. The third phase developed a risk-based Fuzzy-FMEA to evaluate WQ and estimate the impacts of deteriorating drinking WQ due to pipe breakage. Furthermore, the MIGP-based mathematical model was developed to optimize energy use and identify inefficient energy use (pump scheduling) in the fourth phase. In the last stage, both models of WQ (developed in phase three) and energy use (developed in phase four) were integrated to evaluate the combined hydraulics and WQ performance of a WDS. The integrated framework was developed and validated with a WDS in Al-Khobar, Saudi Arabia. Summary and Conclusions Summary of this research and main conclusions from Chapters 3 to 6 are given below: Chapter 3: This chapter provides comprehensive literature and content analysis on WQ and energy use methods in the WDS. Explored modeling and optimization methods, investigated trade-off relationship between WQ and energy use, identified research gaps, application, and limitations in 110 large real-world WDS. Reviewed studies reported that using a nonlinear optimization method is more effective and efficient in improving the design and operation of WDS. However, fewer studies simultaneously measured the actual size of the network, the tank size, pump scheduling, multiple operating conditions, and demand variations. Chapter 4: In this chapter, the hydraulics model was developed. The CWQI-based method evaluated the WQ in terms of residual chlorine. Factorial analysis was performed to estimate an optimal combination of chlorine dosages. The optimal combination of chlorine dosage was determined to be 0.8 mg/L. The average CWQI of 71 was achieved, indicating a better WQ than that (i.e., 26) obtained at a chlorine dosage of 0.7 mg/L. Chapter 5: A risk-based model was developed to optimize the residual chlorine in the network by considering infrastructure assets. FMEA was integrated with the fuzzy set theory to perform risk analysis. Scenario-based analysis was performed to evaluate the impacts of changing locations, the number of booster stations, and the replacement of vulnerable water mains. The study revealed that adding chlorine booster stations randomly without optimizing their locations and chlorine doses might not reduce the WQ risk to an acceptable level. The risk mitigation actions have reduced the RPN value from 300 (high risk) to an acceptable value of 100 (low risk). Chapter 6: An integrated framework of WQ and energy use was developed. A MIGP-based mathematical model was developed to optimize energy use (pump scheduling) in the first step. In the second step, a risk-based method developed in Chapter 5 was integrated with a MIGP-based model to evaluate the combined hydraulics and WQ performances. The network was simulated with optimal day-ahead pump scheduling and booster locations, and the performance was evaluated in different scenarios. The integrated model resulted in an energy cost reduction of around 20% and a 40% improvement in WQ, i.e., the average CWQI increased from 45 to 78. Originality and Contributions The three leading contributions are listed below: Risk-based water quality optimization model: An innovative risk-based model was developed to improve the WQ by considering infrastructure assets in large WDS. Critical locations with a higher potential of WQ failure were identified based on recontamination through deteriorated pipes, exposure to sensitive users in different land uses, and the impact on human health. The new proposed model 111 helps in decision-making with limited data and uses basic hydraulics and WQ analysis for large-sized water distribution networks. Energy-use optimization model: The energy use optimization model was developed due to the high-energy cost of water pumping in large-sized WDS. The developed model identifies inefficient energy use, optimizes the day-ahead pump scheduling to minimize the pumping energy costs, stabilizes the water flow rates to meet the demand, and provides an optimal solution with less computational effort. This new model can be applied to any WDS to optimize pump scheduling and associated energy requirements and costs. Staged WQ and energy-use optimization model: A staged WQ and energy-use optimization model was developed, which can assist water utility in addressing WQ challenges and evaluating the energy use in continuing pumping operations. The model facilitates utility engineers to optimize both the residual chlorine and operational energy costs in large-sized WDS without infrastructural alterations. The mathematical modeling -based pump scheduling solution developed in this approach simultaneously optimizes residual chlorine dosage without conflicting with the optimal pumps scheduling with minimum energy use. Limitations and Recommendations Limitations of the study: Despite developing useful water quality and energy optimization approaches for large-sized WDS, the present study holds the following limitations: The models developed in the present study were based on limited data (single operational season), which did not sufficiently reflect the seasonal variations of WQ and energy consumption. The model simulations took a long time on a personal computer with a large dataset. Nevertheless, using a super-computer can resolve this limitation. Thus, training developed models with additional data will improve generalization and practicality. The present study assumed the Hazen-Williams roughness coefficients (C-values) of 140 for PVC and 120 for AC pipes for hydraulics modeling. As the C-value is a function of pipe age and large-sized WDS have pipes with carrying ages, assuming constant C-values in the risk-based water quality optimization model was not realistic. In a real-time WDS, these values may change with the aging of 112 pipe infrastructure and increase the pump operational energy costs accordingly. The practical implementation of the developed model can yield a difference in actual and estimated energy costs. The present research attempted to simultaneously optimize water quality and energy use in large-sized WDS with many pipes and network nodes. Adding the impact of water quality failure on the consumers in different land uses further complicated the water quality optimization problem, which was resolved by an integrated fractional analysis and risk-based approach. The complexity of the energy use mixed-integer goal programming optimization model is also enhanced by including many pipes, pumps, nodes, and reservoirs. Therefore, a staged optimization approach was adopted that identifies optimal locations of booster stations and doses at the first stage and superimposes these results on the energy optimization model at the second stage. Hence, optimizing the water quality and energy use in large-sized WDS through a single optimization problem is a limitation of the present research. Recommendations: This study used the WQI-based method, which only considers one WQ parameter, i.e., residual chlorine. The use of other parameters, such as dissolved oxygen, pH, biofilm growth, arsenic, lead, and DBPs formation, can be lead to an improvement of overall WQ. An optimization algorithm is developed to minimize pumping energy costs. However, the influence of pipe embodied energy and energy losses due to water leakage can be considered in the overall operational energy cost. By viewing the associated environmental emissions and impact of land use costs, future research can investigate the widespread influence of this additional cost of large WDSs. This study contributed with an integrated optimization model by considering i) WQ ii) energy use in the large WDSs. However, the sustainable operations of such a large WDS depend upon the integrity of the network's physical properties, hydraulics, WQ, and reliability. Future research should investigate the impact of these sustainability factors on the overall operational cost of large WDS. 113 References [1] H. Haider, R. Sadiq, and S. Tesfamariam, “Selecting performance indicators for small and medium-sized water utilities: Multi-criteria analysis using ELECTRE method,” Urban Water J., vol. 12, no. 4, pp. 305–327, May 2015, doi: 10.1080/1573062X.2014.900089. [2] A. Bolognesi, C. Bragalli, C. Lenzi, and S. 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Calculation of RPN value, Fuzzy and Defuzzification at node 72 (All values exported from EPANET- with and without proposed chlorine booster station) Zone Node Vulnerability “V” Consequences “C” Detectability “D” (CCME-CWQI) Universe of Discourse (UOD) Assigned Score V C D 1 (Scenario 1) 72 Medium Residential/ Commercial 55 3 5 6 7 6 7 8 9 4 5 6 7 1 (Scenario 2) 72 Medium Residential/ Commercial 75 3 5 6 7 6 7 8 9 2 3 4 5 1 (Scenario 3) 72 Medium Residential/ Commercial 83 3 5 6 7 6 7 8 9 1 1 2 3 1 (Scenario 4) 72 Medium Residential/ Commercial 75 1 2 2 4 6 7 8 9 2 3 4 5 RPN calculation for all scenarios Defuzz -RPN RPN 3*6* 4=72 5*7*5=175 6*8*6=288 7*9*7=441 0.269 1000* 0.146=269 (Scenario 1) 3*6* 2=36 5*7*3=105 6*8*4=109 7*9*5=315 0.188 1000* 0.146=188 (Scenario 2) 3*6* 1=18 5*7*1=35 6*8*2=96 7*9*3=189 0.102 1000* 0.146=102 (Scenario 3) 1*6* 2=12 2*7*3=42 2*8*4=64 4*9*5=180 0.100 1000* 0.146=100 (Scenario 4) 141 Appendix A3 Energy use Optimization (Pump Scheduling) Table A3. Scenario 1- Optimized flow rate and energy cost of all 17 pumps active during 24 hours of a day 142 Appendix A4 Table A4. Scenario 2 - Optimized flow rate and energy cost of 15 pumps active during 24 hours of a day 143 Appendix A5 Table A5: Percentage of energy cost reduction of all three scenarios normal (baseline) and optimized over 24 hours
Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Sadiq, Rehan
Optimization of water quality and energy use in large water distribution systems
Text