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This paper presents a novel methodology for the coupled optimization of water distribution network (WDN) pipe sizing and isolation valve placement. The methodology is based on the effective application of biobjective genetic algorithm optimization to simultaneously minimize installation cost and average demand shortfall due to segment isolations, followed by a postprocessing financial analysis including assessment of valve maintenance costs. For speeding up convergence, a constraint is implemented in the optimization to enforce the telescopic property of pipe diameters, which are expected to shrink from source(s) to external parts of the WDN. The methodology is applied to two case studies with different levels of complexity, enabling the results to be compared with the traditional approach based on decoupled least-cost optimization of pipe sizing and placement of isolation valves at all (N_valve rule) or all but one (N-1_valve rule) pipes connected to the generic demand node. The least-cost pipe design equipped with the N_valve rule valve placement is always dominated. The adoption of the N-1_valve rule yields very close results to the optimization, as long as the best among the very numerous possible N-1_valve layouts is suitably identified. However, more beneficial solutions featuring lower valve maintenance costs can be selected from the optimal solutions obtained from the novel methodology. The use of the weighted average demand shortfall as objective function, instead of the simple average demand shortfall, tends to produce a larger variety of solutions. [ABSTRACT FROM AUTHOR]

Severe droughts, water-use conflicts, and minimum outflow requirements have caused infeasibilities in modeling large-scale complex hydrothermal systems. This paper presents and discusses a new formulation of objective function and constraints to better represent multiple water uses and deal with infeasibilities in the HIDROTERM model, a nonlinear programming (NLP) model developed to optimize the operation of the Brazilian interconnected hydrothermal system. The system is one of the largest in the world. In the last 10 years, approximately 72% of electricity consumed in Brazil has been supplied by hydropower plants, but with increasing shares from thermal, wind, and, more recently, solar power generation. Because of hydrological changes in recent years and the additional operational constraints imposed by environmental agencies, the use of models for decision making, such as HIDROTERM, frequently encounters the problem of infeasibility. To avoid this problem, a new objective function is proposed in HIDROTERM that incorporates economic penalties when the preset requirements are not met. Based on economic considerations, some hard constraints are removed from the constraint set and added to the objective function as penalty terms. These constraints are tightly linked with multiple water uses, including environmental protection, flood control, consumptive uses, and navigation purposes. The proposed change in the objective function and constraints avoids the problem of infeasibilities and makes it possible to apply the model to extreme conditions. Additionally, this formulation helps identify and minimize the risk, duration, and intensity of unavoidable non-compliance occurrences related to multiple water uses. The presented new version of the HIDROTERM model can be used to study and support decision making in the management and operation of large-scale hydrothermal systems. These systems can be formed by a set of hydraulically connected individual hydropower plants and reservoirs, located within a set of electrically connected subsystems. Each subsystem contains demands and other power plants, including thermal, wind, nuclear, and solar plants, and exchanges. The model has been developed with continued improvements since 2008 and applied to large systems such as the Brazilian Interconnected Power System (BIPS) with over 150 reservoirs. A graphical user interface is provided. The objective function is to minimize the total costs of additional thermal dispatch and exchanges, as well as avoid or minimize deficits in energy supply in subsystems, minimum releases, consumptive uses, and minimum storages in reservoirs. The decision variables are the power and nonpower releases for each hydropower plant and the thermal dispatches and exchanges for each subsystem. Some typical applications include (1) monthly planning operation of the system, considering a planning horizon from 3 to 5 years; (2) diagnostic studies about reliability and resilience of existing systems, processing the same system configuration to a set of different hydrological or energy demand scenarios; (3) impact analysis of changes in operational rules and constraints related to multiple uses of water; and (4) planning and evaluation of expansion alternatives. The model can be applied to a complete hydrothermal system or subsets of it, including subsystems, basins, cascades, or a single hydropower plant. [ABSTRACT FROM AUTHOR]

This paper presents a multistep approach for the rehabilitation of the deteriorated and intermittently operated water distribution network (WDN) proposed by the organizers of the joint WDSA CCWI 2022 Conference as the case study for the battle. In the rehabilitation, various interventions are considered with the final objective to improve the operation of the WDN in terms of hours of service to users and total volume supplied to meet their demands, including pipe and device (pump/valve) replacements, leak repairs, and extended period optimization of device settings. The multistep approach is proposed to tame the extremely large space of decision variables and includes the following steps: (1) subdivision of the WDN into main chain and district metered areas (DMAs), rehabilitation of (2) the main chain and (3) the DMAs while looking at the final year of the rehabilitation, (4) sequencing of the interventions, and (5) optimization of device settings. Compared to other methodologies in the literature, the main novelty and merit of the present approach consist of the way the rehabilitation interventions are selected, i.e., by using engineering judgment and by maximizing the flow capacity of the main chain and the global resilience–failure index in the DMAs. [ABSTRACT FROM AUTHOR]

In recent years, many researchers have recognized pressure reducing valves (PRVs) as potential microhydropower (MHP) sites, aiming to improve the efficiency of water networks. Pump-as-turbines (PATs) have been pointed out as the most suitable technology because of their favorable cost. Most of the methodologies available in the literature for selection of a PAT to replace a PRV follow a traditional approach that is based on scaling aprototype data using affinity curves, thus restricting the solution space only to these curves. The optimization-based methodology presented in this paper uses the classical hydraulic regulation scheme with the Nedler–Mead simplex direct search algorithm to search for the optimal solution within space that is constrained only by the boundaries of available centrifugal PATs on the market. The methodology also defines the PAT's operation limits based on the PAT's relative mechanical power. Improvements gained by using the novel methodology have been demonstrated on real-world case studies from Ireland and Italy that were previously used in the literature. The results of the considered sites also suggest that the maximal global plant's efficiency is around 80% of the maximal efficiency of the theoretically optimal PAT. The paper also examines effects of different objective functions and different PATs' operation limits on the selection of the optimal PAT. [ABSTRACT FROM AUTHOR]

An integrated design method that complements the increasing uptake of water-saving schemes (WSSs) is presented. WSSs may consist of water-saving fittings, appliances, rainwater-harvesting systems (RWHSs), and water-reuse schemes that provide the link between water distribution systems (WDSs) and sanitary sewers (SSs). The method developed here considers the interaction between WDSs, WSSs, and SSs to provide unified solutions. In this paper, a multiobjective optimization problem is formulated and solved considering three objectives, which are (1) minimization of the total cost incurred by implementing water system interventions, i.e., excluding the associated cost savings; (2) maximization of cost savings, which are benefits conferred by interventions; and (3) minimization of system water demand. The decision variables include conventional water network design interventions (i.e., addition of pipes) and the water-saving equipment at a household level. The main constraints include both WDS and SS hydraulic performances. The optimal trade-off solutions are obtained using nondominated sorting genetic algorithm optimization processes. This method was demonstrated in the realistic subsystem of the Tsholofelo Extension water and SS networks in Gaborone, Botswana. The results show that integrating and optimizing WDS, WSS, and SS designs would improve water security and lead to more sustainable water systems. [ABSTRACT FROM AUTHOR]

This paper proposes an agent-based modeling approach to assess water users' behavior for water demand management (WDM) in a river basin. In this procedure, each agent controls its own strategy regarding whether to conserve or consume more water in order to achieve a better economic return based on an initial allocation scheme. The effects of agents' behaviors on their own economic returns and the aggregated impacts of individual behavior on the system are investigated. A positive incentive given to water conservers encourages agents to implement WDM strategies, which in turn improve water use efficiency. A case study using this new agent-based approach reveals that agricultural users are the main contributors to water conservation. Compensation given to water conservers more than covers the benefit loss from lower water consumption, whereas other users gain benefits from the use of the conserved water. The results also indicate that the implementation of WDM strategies is beneficial for the overall system from both economic and ecological perspectives. [ABSTRACT FROM AUTHOR]

This paper presents a new valve positioning algorithm (VPA) for water network pressure reduction. The proposed algorithm is a graph theory-based algorithm combined with EPANET2.2 to position pressure-reducing valves (PRVs) at the most effective locations, having the most effect on downstream pressure reduction, and then calculates the setpoint for locally controlled valves. Each algorithm solution positions one valve on the edge with the highest-pressure reduction indicator calculated using the depth-first search algorithm. The setpoint is then calculated according to downstream pressure differences to the minimum service pressure to be supplied. The PRV location and setpoint found by the algorithm may be used to install and set locally controlled PRVs or serve as a guide for positioning remote-controlled PRVs. The algorithm is demonstrated on a simple example network using demand-driven analysis (DDA), pressure-driven analysis (PDA), and genetic algorithms (GA) and on a medium and a large example application (DDA). Any number of PRVs may be installed using the algorithm, which returns stable results and very short solution times while maintaining minimum service pressure requirements to the critical consumers. [ABSTRACT FROM AUTHOR]

A noncrossover dither creeping mutation-based genetic algorithm (CMBGA) for pipe network optimization has been developed and is analyzed in this paper. This CMBGA differs from the classic genetic algorithm (GA) optimization in that it does not utilize the crossover operator; instead, it only uses selection and a proposed dither creeping mutation operator. The creeping mutation rate in the proposed dither creeping mutation operator is randomly generated in a range throughout a GA run, rather than being set to a fixed value. In addition, the dither mutation rate is applied at an individual chromosome level rather than at the generation level. The dither creeping mutation probability is set to take values from a small range that is centered about (where = number of decision variables of the optimization problem being considered). This is motivated by the fact that a mutation probability of approximately previously has been demonstrated to be an effective value and is commonly used for the GA. Two case studies are used to investigate the effectiveness of the proposed CMBGA. An objective of this paper is to compare the performance of the proposed CMBGA with four other GA variants and other published results. The results show that the proposed CMBGA exhibits considerable improvement over the considered GA variants, and comparable performance with respect to other previously published results. Two big advantages of the CMBGA are its simplicity and the fact that it requires the tuning of fewer parameters compared with other GA variants. [ABSTRACT FROM AUTHOR]

This paper reports the use of many-objective optimization for water distribution system (WDS) design or rehabilitation problems. The term many-objective optimization refers to optimization with four or more objectives. The increase in the number of objectives brings new challenges for both optimization and visualization. This study uses a multiobjective evolutionary algorithm termed the epsilon Nondominated Sorted Genetic Algorithm II (-NSGAII) and interactive visual analytics to reveal and explore the tradeoffs for the Anytown network problem. The many-objective formulation focuses on a suite of six objectives, as follows: (1) capital cost, (2) operating cost, (3) hydraulic failure, (4) leakage, (5) water age, and (6) fire-fighting capacity. These six objectives are optimized based on decisions related to pipe sizing, tank siting, tank sizing, and pump scheduling under five different loading conditions. Solving the many-objective formulation reveals complex tradeoffs that would not be revealed in a lower-dimensional optimization problem. Visual analytics are used to explore these complex tradeoffs and identify solutions that simultaneously improve the overall WDS performance but with reduced capital and operating costs. This paper demonstrates that a many-objective visual analytics approach has clear advantages and benefits in supporting more informed, transparent decision-making in the WDS design process. [ABSTRACT FROM AUTHOR]

Increases in greenhouse gases emissions have encouraged the replacement of fossil fuels with renewable energy sources. This paper investigates the potential of wind energy as a renewable resource for producing agricultural water in Eghlid city, Iran. The purpose of the optimization model herein considered is to maximize the net benefit from crop production by selecting an optimal cropping pattern. This paper’s results demonstrate that wind energy can be efficiently applied to provide irrigation water and optimize cropping patterns. Specifically, the application of wind energy to withdraw irrigation water increases agricultural production benefits in the amount of 1,254 million Rials (US$45,000). [ABSTRACT FROM AUTHOR]

Considerable research has been carried out on the optimization of water distribution systems (WDSs) over the last three decades. In previous research, attention has mainly focused on the minimization of cost, due to the high expenditure associated with the construction and maintenance of such systems. However, the impacts of WDSs on the environment usually have not been considered adequately. The recent increasing awareness of sustainability and climate change, especially global warming, has led to research where greenhouse gas (GHG) emissions are considered. In the study described in this paper a multiobjective genetic algorithm for WDS optimization has been used as an explorative tool to investigate the trade-offs between the traditional economic objective of minimizing costs and an additional environmental objective of minimizing GHG emissions. The impacts of minimizing GHG emissions on the results of WDS optimization have been explored for a case study in this paper. The results indicate that the inclusion of GHG emission minimization as one of the objectives results in significant trade-offs between the economic and environmental objectives. Furthermore, a sensitivity analysis has been conducted by using different discount rates in a present value analysis for computing both ongoing costs and GHG emissions. The results obtained show that the Pareto-optimal front is very sensitive to the discount rates used. As a result, the selection of discount rates has a significant impact on final decision making. [ABSTRACT FROM AUTHOR]

Drinking water systems commonly use manual or grab sampling to monitor water quality, identify or confirm issues, and verify that corrective or emergency response actions have been effective. In this paper, the effectiveness of regulatory sampling locations for emergency response is explored. An optimization formulation based on the literature was used to identify manual sampling locations to maximize overall nodal coverage of the system. Results showed that sampling locations could be effective in confirming incidents for which they were not designed. When evaluating sampling locations optimized for emergency response against regulatory scenarios, the average performance was reduced by 3%–4%, while using optimized regulatory sampling locations for emergency response reduced performance by 7%–10%. Secondary constraints were also included in the formulation to ensure geographical and water age diversity with minimal impact on the performance. This work highlighted that regulatory sampling locations provide value in responding to an emergency for these networks. [ABSTRACT FROM AUTHOR]

This paper evaluates the impacts of multiple water uses on the operation of a system of existing reservoirs that originally were designed for hydropower generation. The research reported in this paper utilizes a nonlinear optimization model, developed to optimize hydropower production for a system of interconnected reservoirs. This model was modified to include water withdrawals for consumptive uses. The formulated multiobjective optimization problem is solved by employing the weighting method. The formulation considers water allocation policy for consumptive uses and individual hydropower plants. By parametrically varying the weighting coefficients, the tradeoff relationship (Pareto front) between hydropower generation and water demand for consumptive uses was traced out. The modified model is applied to the São Francisco River Basin in Brazil, where there are several hydropower plants in operation with a total installed capacity of 10.7 GW. The river basin is an important agricultural land that requires water supply for crop irrigation. The power generation loss as a result of increased water demand due to consumptive uses was evaluated. Water demand scenarios range from current estimated consumptive uses to the amount expected to be granted by the federal government in the future. It is concluded that these demands cannot fully be met in critical drought periods, and the proposed methodology can be helpful in leading to more equitable solutions among competing water users. [ABSTRACT FROM AUTHOR]

The cost of pumping is one of the most significant operational expenditures in a water distribution network. When a network has multiple water sources, which are associated with pumping stations, it may be possible to optimize those costs. One way to minimize costs is to determine the optimal flow rate for each pumping station and for every point of the temporal demand curve, while keeping the energy cost minimized. This also requires that the required minimum pressure at the critical point in the network be satisfied. This paper introduces a principle known as the setpoint curve as the key component of the network optimization methodology. A direct search algorithm based on the Hooke-Jeeves approach is tested in two case studies. Two key cost factors, the electric tariff and the cost of water production, are considered for each water supply source. The model also considers the pressure-dependent consumption, which directly influences the setpoint curve. To test the methodology, a software application is implemented using the EPANET toolkit. The two case studies demonstrate the benefits of the approach in developing optimal pump operating policies which otherwise would be difficult to infer. [ABSTRACT FROM AUTHOR]

This paper presents a case study on the application of a dynamic framework for the intelligent control of flooding in the Boise River system in Idaho. This framework couples a robust and numerically efficient hydraulic routing approach with the popular multi-objective nondominated sorting genetic algorithm II (NSGA-II). The novelty of this framework is that it allows for controlled flooding when the conveyance capacity of the river system is exceeded or is about to exceed. Controlled flooding is based on weight factors assigned to each reach of the system, depending on the amount of damage that would occur, should a flood occur. For example, an urban setting would receive a higher weight factor than a rural or agricultural area. The weight factor for a reach does not need to be constant as it can be made a function of the flooding volume (or water stage) in the reach. The optimization algorithm minimizes flood damage by favoring low-weighted floodplain areas (e.g., rural areas) rather than high-weighted areas (e.g., urban areas) for the overbank flows. The proposed framework has the potential to improve water management and use of flood-prone areas in river systems, especially of those systems subjected to frequent flooding. This work is part of a long-term project that aims to develop a reservoir operation model that combines short-term objectives (e.g., flooding) and long-term objectives (e.g., hydropower, irrigation, water supply). The scope of this first paper is limited to the application of the proposed framework to flood control. Results for the Boise River system show a promising outcome in the application of this framework for flood control. [ABSTRACT FROM AUTHOR]

This paper reviews policy and research initiatives that have begun to address three major challenges to incorporate regional environmental impacts in the design and optimization of drinking water distribution systems. These challenges are to (1) develop a regulatory framework to limit the environmental impacts linked to upgrades and operation of water networks; (2) select indicators and develop models to quantify energy use, environmental impacts, and emissions for the design and operation of water distribution systems; and (3) develop decision support systems to integrate environmental sustainability objectives into the decision-making process for utilities. This paper concludes by discussing realistic steps that water utilities can take to reduce the environmental impact of their business operations. [ABSTRACT FROM AUTHOR]

This paper develops a new domain knowledge–based initial design method for the optimization of water distribution network design. The new initial water distribution network design method, termed as head loss-based design preconditioner (HDP), is based on head loss analysis in the supplying path from source to user. The new HDP-preconditioned search is compared with two algorithms: one preconditioned on a velocity-based initial design method and a simple genetic algorithm without preconditioning. Results show the HDP head loss–based method outperforms the prescreened heuristic sampling method (PHSM) in terms of the quality of the initial solutions and computational efficiency on all three cases. HDP also outperforms stochastic initialization on two of the three cases. The results obtained imply that the proposed domain knowledge–based design method HDP also would be able to provide effective starting conditions for other optimization algorithms, besides genetic algorithm for large water distribution systems, since most optimization methods are greatly assisted by a good starting condition. [ABSTRACT FROM AUTHOR]

The basic objective of long-term optimization of large-scale hydropower system operations (LOLHSO) is to increase water resource use efficiency and construct a clean and low-carbon-intensity energy system. However, limited by rather stochastic inflows, large-scale hydropower systems, and complex objectives and constraints, LOLHSO is characterized by uncertainty, high dimensionality, nonlinearity, and nonconvexity, which pose great challenges in modeling. This paper presents an improved hybrid decomposition-coordination and discrete differential dynamic programming model (IDC-DDDP) for solving the LOLHSO problem. In IDC-DDDP, a decomposition strategy considering the adjustment potential of hydropower plants is designed to reduce the system size. Meanwhile, owing to the sensitivity of DDDP to initial trajectories, a data mining–based strategy is developed as a means of generating superior initial trajectories. A corridor generation strategy is presented to determine the discrete steps and enhance global search abilities. Case studies in a hydropower system in southwestwern China demonstrate the practicability and robustness of the proposed model. [ABSTRACT FROM AUTHOR]

Streams and their associated biological communities are among our most valuable natural resources. Humans rely on the environmental services provided by streams in a myriad of ways. However, in some areas, excessive groundwater pumping exacerbates the already critical pressure on streamflow and must be managed through effective planning. Based on economic and hydrogeological concepts, this study estimates the quantity of streamflow depletion that is attributable to groundwater pumping and the negative impact on the socioeconomic system if groundwater pumping must be constrained to restore streamflow. The primary objective of this paper is to develop a multiobjective nonlinear optimization model to simulate the tradeoffs between streamflow restoration and economic welfare loss in a Chicago suburban county, McHenry County. The multiobjective optimization was conducted at both county and municipality levels. An evolutionary algorithm, the nondominated sorting genetic algorithm, was used to solve the optimization model and to identify the tradeoff curve (Pareto frontier). Comparing municipal Pareto frontiers shows spatially heterogeneous costs of preserving streamflow through various shadow prices and also the different capacities of restoring streamflow. The results include discussion of the shapes of the Pareto frontier, the sensitivity of the pumping boundary constraints, and return flow coefficients. It is concluded that the multiobjective optimization model provides a useful framework to consider conflicting objectives in a typical environmental management and planning process, and that the findings can help decision-makers and planners to formulate effective groundwater pumping strategies. [ABSTRACT FROM AUTHOR]

The cost of electricity used for pumping in water-distribution systems typically represents the largest part of the total operational costs. Therefore, optimization of pump operations is a major concern for water utilities around the world, especially in recent years with significantly increasing energy prices. Recently, to maximize cost and energy savings, pump scheduling is frequently done in real time by integrating the relevant optimization software into the water company's supervisory control and data acquisition (SCADA) system. This, however, requires solving a complex, large, nonlinear optimization problem in a computationally efficient manner, typically in less than 1 h. To achieve this, the pump-scheduling problem is solved in this paper by a novel hybrid optimization method that uses linear programming (LP) and a greedy algorithm: LPG. The new methodology is applied to two case studies: the artificial, benchmark case study of Anytown network and the real-life pump-scheduling problem of the Richmond water-distribution network (WDN) in the UK. The results obtained clearly demonstrate that the LPG hybrid method is capable of solving real-life pump-scheduling problems in an extremely computationally efficient manner while preserving the accuracy (i.e., the near optimality) of the obtained solution. This makes the method particularly appealing for use in real-time pump-scheduling applications. [ABSTRACT FROM AUTHOR]

In contrast to most common methods used in optimal control of reservoir systems requiring a large number of decision variables, parametric rule can make a radical reduction in the number of decision variables without yielding inferior solutions. However, parametric rule employs the standard operating policy to determine releases of reservoirs as much as demand only if there is enough water in the system, which may result in single periods of severe short supply during droughts. The purpose of this paper is to devise an operating rule for multireservoir system by combining parametric rule with the hedging rule to avoid catastrophic water shortage during droughts. In this way, decision variables to be optimized not only make a significant reduction compared with traditional operating rules, but also severe short supply during droughts can be controlled effectively. This paper employs a water supply multireservoir system in northern China to explore the changes of shortage characteristics produced by the proposed rule over a long horizon. In the case study, particle swarm optimization algorithms with a simulation model are used to optimize the decision variables. The results indicate that the extended parametric rule has a significant advantage over the classic parametric rule in dealing with the multireservoir operation problem during droughts. [ABSTRACT FROM AUTHOR]

This paper deals with methods aimed at the effective and efficient detection of accidental and/or intentional contaminant intrusion(s) in water distribution systems. The objective of this paper is to present a methodology entitled sensors local optimal transformation system (SLOTS) to address both single-objective and multiobjective sensor layout problems. SLOTS is tested on two benchmark water distribution networks used for the Battle of the Water Sensors Networks challenge (BWSN), held as part of the Water Distribution Systems Analysis Symposium, in Cincinnati in 2006. The objectives considered are detection likelihood and the expected population affected prior to detection. The results obtained demonstrate that SLOTS sensor placements are often near optimal. For both single-objective and multiobjective cases, SLOTS is shown to be capable of identifying placements which are consistently better performing than one of the best BWSN methodologies, the greedy algorithm. [ABSTRACT FROM AUTHOR]

Contamination emergency in water distribution systems is a complex situation where optimal operation becomes important for public health. In case of emergency corrective operational actions for flushing the pollutant out of the network are needed, which have to be fast and accurate. Under such a stressful situation, trial-and-error simulation experiments with the hydrodynamic and water quality models cannot be applied since significant number of model evaluations may be required to identify the optimal solution. This paper presents a methodology for finding sets of operational interventions in a supply network for flushing a contaminant by minimizing the impact on the population. The situation is treated as both single- and multiobjective optimization problem, which is solved by using evolutionary optimization approaches, in combination with the EPANET solver engine. The methodology is tested on a simple imaginary network configuration, as well as on a real case study for the city of Villavicencio in Colombia. The results prove the usefulness of the approach for advising the operators and decision makers. [ABSTRACT FROM AUTHOR]

The area of systematic identification of contamination sources in water distribution systems is in its infancy and is rapidly growing. The real water distribution network problem poses many challenges that current methods usually assume away to facilitate manageable method development and testing. Current methods may not readily and efficiently address issues, such as multiple sources, unknown contamination types with different reaction kinetics, use of different types of sensors with varying degree of resolution, dynamically varying demand and sensor information, and uncertainty and errors in the data and measurements. With the aim of addressing these imminent challenges, this paper reports the findings of an ongoing research investigation that develops and tests an evolutionary algorithm-based flexible and generic procedure, which is structured within a simulation-optimization paradigm. This paper describes the specific implementation of the method using evolution strategies (ESs), a population-based heuristic global search algorithm. A key component of designing this source characterization method is to define a compact, but comprehensive, solution encoding structure. The new method is constructed using a tree-based encoding design to enable the representation of variable-length decision vectors and a set of associated genetic operators that enable an efficient search. This algorithm is successfully tested and demonstrated to have consistently good performance for several instances of an illustrative water distribution contamination case study. As the ES-based algorithm conducts a probabilistic search, its robustness is tested using multiple random trials, and the method is shown to exhibit a robust behavior. [ABSTRACT FROM AUTHOR]

Reducing energy consumption of water distribution networks has never had more significance than today. The greatest energy savings can be obtained by careful scheduling of operation of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels, or explicitly by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper a new explicit representation is presented. It is based on time controlled triggers, where the maximum number of pump switches is specified beforehand. In this representation a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules (search space) compared to the binary representation. Ant colony optimization (ACO) is a stochastic meta-heuristic for combinatorial optimization problems that is inspired by the foraging behavior of some species of ants. In this paper, an application of the ACO framework was developed for the optimal scheduling of pumps. The proposed representation was adapted to an ant colony Optimization framework and solved for the optimal pump schedules. Minimization of electrical cost was considered as the objective, while satisfying system constraints. Instead of using a penalty function approach for constraint violations, constraint violations were ordered according to their importance and solutions were ranked based on this order. The proposed approach was tested on a small test network and on a large real-world network. Results are compared with those obtained using a simple genetic algorithm based on binary representation and a hybrid genetic algorithm that uses level-based triggers. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of contamination source determination in municipal drinking water networks. In previous work, the authors introduced a large-scale nonlinear programming approach for identifying both the time and location of contamination sources given concentration information from a limited number of sensors. Due to the sparseness of the sensor grid, this problem inherently has nonunique solutions. The problem was therefore regularized, and the regularized solution provided an approximate linear combination of possible injection scenarios. In this paper, a mixed-integer quadratic program is presented to refine the solution provided by the nonlinear programming formulation. We introduce a two-phase approach. In the n-phase, the number of likely injection locations is estimated. Using this information, the e-phase is performed to extract the likely injection scenarios from the family of nonunique solutions. This two-phase approach is tested on a realistic municipal water network model with approximately 400 nodes, simulating five different injection scenarios. In all five examples, the approach was able to determine the correct number of injection locations and identify a set of possible injection scenarios containing the actual simulated injections. [ABSTRACT FROM AUTHOR]

In the past decade, international donors and governments have generally required water supply systems to be financially self-sufficient, with stakeholder participation in their planning and operation to ensure sustainability. Efforts are being made to shift planning from a process that tends to be dominated by governments to one with more involvement of water users. This paper integrates two tools for strengthening demand-based planning. On one hand, contingent valuation (CV) surveys can be used to determine the preferences of households for improved water facilities and the amounts they are willing to pay for them, and on the other, mathematical models built around CV survey results can be used to identify and screen alternatives to meet the objectives of improved water supply. This paper reports a CV study conducted in Rwanda in 2000 and shows how the findings can be embedded in optimization models that can be used by stakeholders for addressing the key planning questions. The models are applied to a test case in Rwanda; the approach, however, is general and is expected to have wide application. [ABSTRACT FROM AUTHOR]

Reservoir systems are essential for water resources management. The application and development of optimization techniques for optimal reservoir operation is therefore a valuable undertaking. This paper presents a modified firefly algorithm (MFA) and applies it to optimally solve reservoir operation problems. Three well-known benchmark multireservoir operation problems are optimized for energy production. The results of the MFA are compared with results obtained with other mathematical programming approaches, such as linear programming (LP), differential dynamic programming (DDP), and discrete DDP (DDDP), the genetic algorithm (GA), the multicolony ant algorithm (MCAA), the honey-bee mating optimization (HBMO) algorithm, the water cycle algorithm (WCA), the bat algorithm (BA), and the biogeography-based optimization (BBO) algorithm. The MFA was found to be more effective than alternative optimization methods in solving the test problems demonstrating its strong potential to tackle multireservoir operation problems. This paper's results indicate that the MFA differed by 0.01 and 0.79% with the LP global optimal solutions of a continuous four-reservoir problem (CFP) and a continuous 10-reservoir problem (CTP), respectively. The objective function of a discrete four-reservoir problem (DFP) obtained with the MFA is equal to the LP's objective function. This paper demonstrates that the MFA is a competitive optimization method with which to solve a variety of reservoir operation problems. [ABSTRACT FROM AUTHOR]

The optimization of pump operations has the potential to reduce operational costs, while still maintaining the high level of reliability required of water distribution systems. The hydraulic software EPANET2 toolkit has been frequently linked to evolutionary algorithms for this purpose; however, only time-based controls and simple controls based on one single condition, e.g., the tank level, could be automatically changed during the optimization. This paper introduces a modification to the original EPANET2 toolkit library, so that the operation of pumps can be optimized taking into account simultaneously several conditions (e.g., the time of the day and the tank level). A problem in the original toolkit associated with computing pump energy and costs using rule-based controls has also been solved. The new ETTAR toolkit has been tested on a case study, in which a genetic algorithm has been used to optimize different types of controls. Results show that it is possible to find more cost-effective solutions compared to simple controls and, although with longer computational times, let the algorithm create the entire pump control rule set. The robustness of the optimized controls found has also been tested. [ABSTRACT FROM AUTHOR]

This paper summarizes an approach for the assessment and control of background leakage on water distribution networks. The methodology was developed for the battle of background leakage assessment for water networks (BBLAWN) held at the Water Distribution System Analysis Conference 2014 in Bari, Italy. The problem instance posed for the conference considers an aging water network with high levels of background leakage. A range of operational and design changes including new valves, pipes, pumps, tanks, and controls are available to reduce the expenditure needed to operate the system. Constraints are imposed on nodal pressures and tank levels to meet service level requirements. The solution methodology proposed in this paper decomposes the problem according to the type of intervention, considering each type separately. An initial diagnosis of the network informs the manner and order of evaluating the various interventions. Custom implementations of network simulation, heuristic algorithms, and optimization models are used to identify improvements. The recommended program of network modifications reduced the annual cost of running the system from €4 million to €1.5 million and had a return on investment in network infrastructure of 430%. [ABSTRACT FROM AUTHOR]

Past studies of commercial, industrial, and institutional (CII) water use efficiency have been limited to simple normalization benchmarking measures. Simple normalization ratios (e.g., water use per employee) account for only one driver of water use and are often misleading because of the predominantly skewed nature of water use intensities. In order to improve on benchmarking systems to assess the water use efficiency of CII customers, this paper explores two popular methods used in the energy efficiency field: ordinary least squares (OLS) and data envelopment analysis (DEA). Parcel-level data from Austin, Texas, is used to demonstrate the methods, where a data-driven approach is employed to include average water use, building area, building value, year built, parcel area, and number of employees as measures of input, and annual sales as the economic measure of output. [ABSTRACT FROM AUTHOR]

The optimal operation of reservoir systems to meet water demand is a complex and nonlinear problem. This paper applies the biogeography-based optimization (BBO) algorithm to solve reservoir operation problems. The BBO algorithm is first verified with the minimization of three mathematical benchmark functions (Sphere, Rosenbrock, and Bukin6). In addition, the BBO algorithm was applied to a single reservoir system and a four-reservoir system. The performance of the BBO algorithm was compared with that of the genetic algorithm (GA) in solving the three optimization problems. The results show that the BBO algorithm minimized the benchmark functions accurately, and outperformed the GA in this respect. In the case of the single-reservoir hydropower optimization problem the BBO reached a near-optimal solution. The values of the objective function averaged 1.228 and 1.746 with the BBO and GA, respectively. The global solution of this problem with the nonlinear programming method equals 1.213. In the four-reservoir system application the BBO converged to 99.94% of the optimal solution in its best-performing history, whereas the GA converged to 97.46% of the optimal solution. The results from the three test problems demonstrated the superior capacity of the BBO to optimize general mathematical problems and the operation of reservoir systems. [ABSTRACT FROM AUTHOR]

This paper presents a coupled self-adaptive multiobjective differential evolution and network flow algorithm for the optimal operation of complex multipurpose reservoir systems. The developed algorithm (i.e., self-adaptive multiobjective differential evolution) is compared to nondominated sorting genetic algorithm II using a set of common test problems and a real-world case study. An out-of- kilter method for minimal-cost flow problems is used to optimize the water resource system from self-adaptive multiobjective differential evolution inputs driven by the evolutionary process. Self-adaptive multiobjective differential evolution is then used to evaluate objective functions based on the outputs from out-of-kilter algorithm and the process continues until the stop criterion is met. The advantages of the proposed approach include (1) flexible evolutionary algorithms for solving highly complex objective function, and (2) efficient network flow method for dealing with large and highly constrained problems. The case study includes one part of a complex water supply system located in southwestern Brazil that provides water for almost 20 million people in Sao Paulo metropolitan area. The objectives of the case study include minimization of demand shortage (the difference between demand for water and available water supply), maximization of water quality (or minimization of the deviation from the water quality standards), and minimization of pumping cost. The coupled model is applied to the case study using one inflow scenario representing a drought period with inflows below historical average. Multiobjective analyses are performed by comparing two pairs of objective functions, as follows: (1) minimization of demand shortage versus minimization of pumping cost, and (2) minimization of demand shortage versus minimization of the deviation from the water quality standards. The problem constraints include reservoir capacity, capacity of tunnels, channel flow limitations, and minimum downstream release for all reservoirs within the system. The proposed coupled model (self-adaptive multiobjective differential evolution and out-of-kilter) is outperforming both pure self-adaptive multiobjective differential evolution and nondominated sorting genetic algorithm II, as it requires significantly smaller number of generations to derive the Pareto front. In addition, the proposed approach is capable of handling larger problems without major computational burden. The coupled model and self-adaptive multiobjective differential evolution also converge closer to, and provide better coverage of the true Pareto front than, nondominated sorting genetic algorithm II. [ABSTRACT FROM AUTHOR]

Most of the energy consumed by a water company is used to operate pumping systems. Identifying the optimal schedule for such systems in near real time will drastically reduce energy costs. The pump scheduling problem comprises three main elements: the pumping system, the tank, and the water demand to be satisfied. In this paper, a mathematical programming model and techniques used to solve this problem are presented. This study analyzed a parallel programming paradigm to solve this problem by introducing stochastic programming techniques (scenario tree evaluation) and multisite problems. Numerical experiments were designed and completed on parallel computers combining classical mathematical programming techniques and parallel tools. As a result, the parallel programming strategy was experimentally proven to be a useful technique for near-real-time pump scheduling applications. [ABSTRACT FROM AUTHOR]

The Battle of the Water Networks II (BWN-II) is the latest of a series of competitions related to the design and operation of water distribution systems (WDSs) undertaken within the Water Distribution Systems Analysis (WDSA) Symposium series. The BWN-II problem specification involved a broadly defined design and operation problem for an existing network that has to be upgraded for increased future demands, and the addition of a new development area. The design decisions involved addition of new and parallel pipes, storage, operational controls for pumps and valves, and sizing of backup power supply. Design criteria involved hydraulic, water quality, reliability, and environmental performance measures. Fourteen teams participated in the Battle and presented their results at the 14th Water Distribution Systems Analysis conference in Adelaide, Australia, September 2012. This paper summarizes the approaches used by the participants and the results they obtained. Given the complexity of the BWN-II problem and the innovative methods required to deal with the multiobjective, high dimensional and computationally demanding nature of the problem, this paper represents a snap-shot of state of the art methods for the design and operation of water distribution systems. A general finding of this paper is that there is benefit in using a combination of heuristic engineering experience and sophisticated optimization algorithms when tackling complex real-world water distribution system design problems. [ABSTRACT FROM AUTHOR]

Municipalities with limited budgets will have to rehabilitate their stock of deteriorated water mains in the coming decades. Optimization and asset management offer municipalities the opportunity to plan the rehabilitation of their water mains in a more rational manner. The aim of this paper is to develop a new event-based approach to optimize the timing of water main rehabilitation. The approach incorporates a new gene-coding scheme and covers the full range of decisions about pipe replacement, duplication, lining, new pipe installation, and asset management strategies (infrastructure adjacency and quantity discounts applied to the installed pipe). The new approach is applied to the Fairfield water network, and results suggest that applying a budget constraint prohibits early investment in pipe rehabilitation with a resulting increase in leakage, pipe breaks, and energy costs. Applying pipe discounts decreases capital and operation costs and favors pipe lining over pipe replacement and duplication in the Fairfield network. A sensitivity analysis suggests that uncertainties in demand, leakage, and break growth rate have a moderate to significant impact on capital and operation costs. [ABSTRACT FROM AUTHOR]

The traditional optimization approach for water distribution mains is that of considering a single design scenario with prefixed nodal demands representing the peak values at the end of the life cycle of the construction. Instead, this paper presents a different approach for the design of water distribution mains aimed at considering the phasing of construction. It makes it possible to identify, on prefixed time steps or intervals (for instance 25 years), the upgrade of the construction rendering the network able to satisfy, during the expected life of the system, growing nodal demands related to the increment in the population served. To show the benefits of this approach in comparison to using a single design flow, an optimization methodology, aimed at introducing new pipes in the network as needed at each time step, was set up and applied to a simple case study, where two different scenarios were considered concerning the growth of the network. Results showed that this approach is able to yield better results when compared with the single flow design, because it enables short-term construction upgrades to be performed while keeping a vision of the expected long term network growth. [ABSTRACT FROM AUTHOR]

This paper presents a method for the reduction of network models described by a system of nonlinear algebraic equations. Such models are, for example, present when modeling water networks, electrical networks, and gas networks. The approach calculates a network model equivalent to the original one, but containing fewer components. This procedure has an advantage compared with straightforward linearization because the reduced nonlinear model preserves the nonlinearity of the original model and approximates the original model in a wide range of operating conditions. The method is applicable to hydraulic analysis and has been validated by simplifying many practical water network models for optimization studies. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for obtaining and adjusting of efficiency curves for hydroelectric generating units. It is based on measured data of power, gross head, and water discharge recorded by the company that manages the plant operation. The objective is to determine the actual performance characteristics of the set: turbine, generator, and penstock. In order to obtain the efficiency functions, an iterative calculation is used. Its input data are the functions currently in use of turbine efficiency, generator efficiency, and penstock head losses. For the adjustment of the efficiency functions, the Generalized Reduced Gradient optimization method is employed. A case study was applied to the data from a large Brazilian hydroelectric plant whose operation is under the coordination of the Electric System National Operator. The benefits of the proposed methodology are analyzed using a simulation tool for the hydroelectric operation. The simulator is used to reproduce the past operation of the plant, first with current data and second with adjusted data. The results show that the optimal unit efficiency functions significantly contribute to bring the real and simulated operation closer. [ABSTRACT FROM AUTHOR]

Long-range infrastructure planning is fraught with uncertainties. Population growth may occur faster or slower than expected, regulations may change, and public sentiment can shift. In the face of these uncertainties, water system managers must plan for large infrastructure investments and the questions about when it is desirable to invest in infrastructure and what is the appropriate infrastructure component size to meet growing demands. One of the most powerful and intuitive ways to incorporate uncertainties is to use scenarios that represent plausible futures. Scenario-based planning is gaining acceptance in the water resources community. Preparing for a range of possible futures provides flexibility and adds robustness to the system so it can respond to uncertain events at reasonable costs while maintaining community confidence in their utilities. In this paper, novel scenario-based planning and optimization approaches are presented for the optimal design of regional-scale water supply infrastructure in a multiperiod planning framework. For demonstration, water demand projections are considered as uncertain and multiperiod construction projects are selected to minimize the economic costs. The solution approaches are applied to a decentralized water reclamation planning project in a green-field development area in southeast Tucson, Arizona, where water reclamation is viewed as a potential component for a sustainable water supply. [ABSTRACT FROM AUTHOR]

Enhanced socioeconomic criteria and temporal changes in the topology of the system often require waste load reallocation (WLRA) in a river-reservoir system to sustain long-term water quality standards. In addition to climate and hydrological changes, hydrologic fragmentation and dam construction may significantly affect the waste-accepting capacity of the water body through changes in its physical, chemical, and even biological characteristics. Deterministic waste load allocation optimization designs are often bounded with a set of rigid constraints. These constraints do not allow any flexibility to account for uncertainties and the possibility of system failure. This paper presents a reliability-based waste load reallocation model in a complex river-reservoir system. We have linked a physical and surrogate simulation model with the Particle Swarm Optimization algorithm to present an efficient methodology for reallocating waste loads in a river-reservoir system with reliability constraints. Reliability requirements are addressed by different sets of constraints in three different formulations for the entire planning horizon. The problem, as defined, contains real and integer variables, and is formulated as a mixed-integer nonlinear programming problem. It finds the maximum values of monthly waste loads that may be discharged into the river-reservoir system under predefined reliability constraints. The surrogate model itself is refined using an online dynamic routine which makes it suitable for planning waste load allocation in multiperiod and high-dimensional system optimization under reliability-based water quality constraints. The proposed model is applied to the Karkheh river-reservoir system to illustrate its performance under various reliabilities. [ABSTRACT FROM AUTHOR]

In this paper, a particle swarm optimization (PSO) algorithm augmented by fly-back and harmony memory features--referred to as the heuristic particle swarm optimization (HPSO) algorithm--is used for solving the sewer network optimization problem. The fly-back and harmony memory mechanisms are meant to avoid ineffective particle flights and to increase the efficiency and computational stability of the PSO algorithm. Problem constraints are checked and observed at two levels through a mechanism that enhances the convergence of the PSO algorithm as compared with those of conventional penalizing methods used in other evolutionary methods. The HPSO algorithm is then combined with dynamic programming (DP) to yield a hybrid algorithm called dynamic programming with heuristic particle swarm optimization (DPHPSO). Eliminating the inadequacies associated with either component method, this hybrid algorithm does not rely on the discretization of elevations, thereby reducing the complexity of the problem and the time required for solving it when compared with the rival DP method. Moreover, compared with the situation in which an evolutionary algorithm is used alone, the DP partitioning employed in HPSO leads to a reduced number of decision variables in the metaheuristic algorithm and also decreases the changes in ultimate objective function. The proposed methods are validated by applying them to three benchmark sewer network problems. Comparison of the results with those obtained from other optimization methods indicates the superiority of these algorithms over those reported in the literature. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for developing efficient and effective operational, short-time response to an unplanned failure event (e.g., a pipe burst or equipment failure) in a water distribution system (WDS). The proposed automated response methodology consists of isolating the failure event followed by the recovery of the affected system part by restoring the flows and pressures to normal conditions. The isolation is typically achieved by manipulating the nearby on/off valves. The recovery, which is the focus of this work, involves selecting an optimal combination of suitable operational interventions from a number of possible choices with the aim to reduce the negative impact of the failure (e.g., volume of water undelivered to consumers) over a prespecified time horizon. The intervention options considered here include valve manipulations, changing the pressure reducing valve's (PRV) outlet pressure, and installation and use of temporary overland bypasses from nearby hydrants in an adjacent, unaffected part of the network. The optimal mix of interventions is identified by using a multiobjective optimization approach driven by the minimization of the negative impact on the consumers and the minimization of the corresponding number of operational interventions (which acts as a surrogate for operational costs). This methodology has been applied to a real-world water distribution network of C-Town. The results obtained demonstrate the effectiveness of the proposed methodology in identifying the Pareto optimal intervention strategies that could be ultimately presented to the control room operator for making a suitable decision in near real time. [ABSTRACT FROM AUTHOR]

This work presents a postoptimization methodology for refining the solutions found by adaptive search algorithms used in the design of large water distribution networks. The approach uses two heuristics to search for an optimal combination of pipes that, after a reduction of their diameters, will maximize cost savings while continuing to meet design constraints. Adaptive search methods are often used to design urban water distribution networks when the number of pipes in the network is insignificant. For complex, real-world networks, however, such methods are computationally demanding, and they have difficulty finding near-global optima. To identify a solution as close to the global optimum (and in which no pipe can be reduced without violating pressure constraint), requires a high-speed computer potentially running for a long time and also probably some good fortune. The postoptimization approach presented in this paper is shown to be an efficient complement to heuristic search algorithms used in the design of real-world networks. In a network created with the aid of a genetic algorithm, the proposed heuristics found that 4.37% of the pipes with a diameter greater than the minimum could be further reduced without causing hydraulic failure. [ABSTRACT FROM AUTHOR]

Optimal design of tunnels and surge tanks is a major concern in the implementation and operation of large hydropower plants, particularly those in which tunnels are long and water hammer is likely. In this paper, flow simulation and an optimization process have been linked in an iterative manner to develop a general model for the optimal design of hydropower tunnels and surge tanks for the emergency condition of operation (maximum surge pressure). The governing equations for the analysis of transient flow in conduits are solved using a finite difference scheme. Furthermore, a genetic algorithm (GA) optimization technique is utilized to select the optimal diameter for the headrace tunnel, penstocks, and surge tanks. The benefit-cost ratio is considered as the objective function in the optimization analysis. The proposed method was used to design diameters for Marun hydropower tunnels (an in-operation large hydropower dam located in southwest Iran). The results were compared with results from a nonlinear optimization technique. They revealed considerable savings in construction cost, attributable to the application of GA. The calculated pressures across the tunnels with and without surge tank agree with valve closure theory and previous studies. The model is able to optimize diameters for a branching system of tunnels and penstocks with surge tanks. [ABSTRACT FROM AUTHOR]

Accumulation of sediment from upstream agricultural land may shorten the lifetime of a reservoir and reduce its long-term benefits. This paper presents a framework for assessment of management strategies that simultaneously considers upstream soil conservation and downstream reservoir-level sediment control. A dynamic model that utilizes optimal control theory is developed for this purpose. Simulation results from the model are expected to provide practical information for policy makers, such as determination of the best sediment removal technology, the optimal timing to install this technology, and the optimal pattern of soil conservation efforts. An illustrative application to Lake Aswan, which is located between the Sudan and Egypt, indicates that the highest permissible level of soil conservation and the unconstrained dredging installed at 22 years are the best management practices, giving a sustainable life of the reservoir and net benefits of $117.8 billion. The cooperative strategy compared to the noncooperative and baseline strategies increases social net benefits by $6.5 billion and $7.8 billion, respectively. To test the robustness of this study's results, sensitive analysis for key parameters, such as interest rate, water price, agricultural sediment contribution, and soil conservation effectiveness was performed. The results are very sensitive to selected parameters. As suspected, interest rate and water price shift the net present value of watershed on both sides. However, the rank of alternatives does not change. [ABSTRACT FROM AUTHOR]

This paper presents an effective approach for calibrating an extended period simulation (EPS) model of water distribution system. By using the benchmark model calibration problem, set up for Battle of Water Calibration Networks (BWCN), the EPS model calibration is solved in a progressive manner, optimizing model parameters and engineering judgments. The model calibration approach consists of multiple steps, including: (1) constructing the initial EPS model with the given system and supervisory control and data acquisition (SCADA) data, (2) calibrating pipe properties and pump curves for static and fire flow test data, and (3) calibrating EPS model demands and controls for given SCADA data over 167 h. The calibration was iteratively conducted per district meter area (DMA) and then fine-tuned for the whole system. Although the field data contained some noise, good model calibration was achieved for BWCN. By using the practically measurable hydraulic attributes of pump flows, pressures, and tank levels provided for BWCN, the performance of the calibrated model is compared with those calibrated by 14 teams around the world participating in the BWCN. [ABSTRACT FROM AUTHOR]

Climate change has made environmental impact a factor of growing importance in decision making for municipalities. Increasingly, the environmental impacts of expanding and operating a water distribution system (WDS) are considered alongside the cost and hydraulic design. This paper presents a nondominated sorting genetic algorithm (NSGA-II) that minimizes capital costs, annual pumping energy use, and environmental impacts in WDS design that adheres to hydraulic constraints. A previously developed environmental impact (EI) index is included in the environmental objective function of the optimization program. The EI index normalizes and aggregates multiple environmental measures evaluated with an economic input-output life-cycle assessment (EIO-LCA) model. The EIO-LCA-based NSGA-II was applied to the Anytown network. Annual pumping energy use was found to dominate the EI index while capital cost and the EI index were inversely related, and the annual pumping energy use and the EI index followed a near linear relationship. The location and shape of the Pareto fronts were sensitive to demand and roughness coefficient (C-factor) adjustments with greater sensitivity observed for changes in demand than changes in the C-factor. [ABSTRACT FROM AUTHOR]

This paper presents an optimization methodology to calibrate the nodal demand in the model of water supply distribution systems. The matrix analysis has been carried out to get the sensitive coefficients matrix for the model of water distribution systems. Singular value decomposition is applied to calculate search director of optimization by solving the sensitive coefficients matrix, which is more efficient than the finite-difference method. Two cases are used to show the performance of this algorithm. The first case is a hypothetical water distribution network showing the calculation of the optimization vector. The second case is the application of real-life water distribution system in Hangzhou, China, which shows the determination of the cutoff singular value and the search step size in one-dimensional search. The calibrated model has been also validated using the measured data. The results have shown that the proposed algorithm is reliable and effective in real-time system. [ABSTRACT FROM AUTHOR]

The implicit goal of water network design is to provide acceptable service during normal and peak demands and to limit damages to property and people during fires. Despite this, fire damages are seldom explicitly included in network design. This paper presents a single-objective particle swarm optimization program that incorporates a new measure of expected conditional fire damages to size local water distribution mains for fire flow protection in residential service areas. The optimization approach generates trade-off information to help utilities determine the cost effectiveness of adding new pipe capacity to reduce the risk of fire damages in water networks. The optimization program was applied to an 8-pipe network and a 34-pipe network to generate trade-off curves for pipe cost and fire damages. A sensitivity analysis indicated that the level of uncertainty in fire flow had a little impact on pipe sizing and cost in the eight-pipe network. Optimization results in the 34-pipe network supported the industry practice of using a minimum 150-mm distribution main sizing to provide fire flow protection. [ABSTRACT FROM AUTHOR]