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Maximizing reliability is imperative for water distribution network (WDN) design. Recent claims about providing adequate and operable isolation valves as a focal aspect of a reliable WDN design have attracted substantial interest. In this direction, this paper attempts to answer the question of which isolation valves in a WDN must be prioritized based on their criticality toward enhancing WDN reliability. Two novel algorithms are proposed, one for ranking every isolation valve in a WDN and the second to identify the location of a new isolation valve to reduce the criticality of a specific valve in a WDN. Unlike the prevailing methodologies, the proposed algorithms are entirely based on the topological attributes of a WDN and do not require a calibrated hydraulic model, which is often a limiting factor in WDN management. Our proof of concept has unveiled the capability of the methodology to deliver preliminary information to water utilities regarding the criticality of valves without any hydraulic simulation. [ABSTRACT FROM AUTHOR]

A least-cost optimization of a water distribution system (WDS) results in a branched network if some system reliability measures are not considered. Using an explicit level of system redundancy is one way to enhance reliability in the WDS network. This approach consists of first dividing the network into subsystems. Then, during the optimization stage, the subsystems are optimized simultaneously by demanding each one to maintain some level of service. Which pair of subsystems is ultimately selected determines the outcome of the optimization problem. However, there is little if no literature on the optimization of the selection of a pair of subsystems. This study addresses both subsystem and component sizing optimization in designing a level-1 redundant network. Candidate subsystems are enumerated using graph theory where st-numbering for the network is assigned first, and then the backups are generated following the decrement and increment orders of the node's st-numbering. [ABSTRACT FROM AUTHOR]

A key challenge in designing algorithms for leakage detection and isolation in drinking water distribution systems is the performance evaluation and comparison between methodologies using benchmarks. For this purpose, the Battle of the Leakage Detection and Isolation Methods (BattLeDIM) competition was organized in 2020 with the aim to objectively compare the performance of methods for the detection and localization of leakage events, relying on supervisory control and data acquisition (SCADA) measurements of flow and pressure sensors installed within a virtual water distribution system. Several teams from academia and the industry submitted their solutions using various techniques including time series analysis, statistical methods, machine learning, mathematical programming, met-heuristics, and engineering judgment, and were evaluated using realistic economic criteria. This paper summarizes the results of the competition and conducts an analysis of the different leakage detection and isolation methods used by the teams. The competition results highlight the need for further development of methods for leakage detection and isolation, and also the need to develop additional open benchmark problems for this purpose. [ABSTRACT FROM AUTHOR]

Contamination events in water distribution systems (WDS) are emergencies that cause public health crises and require fast response by the responsible utility manager. Various models have been developed to explore the reactions of relevant stakeholders during a contamination event, including agent-based modeling. As the COVID-19 pandemic has changed the daily habits of communities around the globe, consumer water demands have changed dramatically. In this study, an agent-based modeling framework is developed to explore social dynamics and reactions of water consumers and a utility manager to a contamination event, while considering regular and pandemic demand scenarios. Utility manager agents use graph theory algorithms to place mobile sensor equipment and divide the network in sections that are endangered of being contaminated or cleared again for water consumption. The status of respective network nodes is communicated to consumer agents in real time, and consumer agents adjust their water demands accordingly. This sociotechnological framework is presented using the overview, design, and details protocol. The results comprise comparisons of reactions and demand adjustments of consumers to a water event during normal and pandemic times, while exploring new methods to predict the fate of a contaminant plume in the WDS. [ABSTRACT FROM AUTHOR]

At the 2013 World Environmental and Water Resources Congress in Cincinnati, Ohio, the ASCE Task Committee on Research Databases for Water Distribution Systems was formed to develop an online open-access repository of water distribution system hydraulic network files for use in applied scientific research. This paper discusses the development of the resulting database, specific model contributions, available model building toolkits, general methods for system classification, and general information related to the database platform and content. It is hoped that the assembled database will promote the advancement of water distribution research into the next decade. [ABSTRACT FROM AUTHOR]

Accurate interpretation of longitudinal dispersion of solutes within distribution pipes increases the reliability of the predictions of water distribution systems (WDS) water quality analysis models. However, the estimation of longitudinal dispersion from fundamental principles is complicated. Thus, a longitudinal diffusion coefficient, a nonphysical constant depending on the flow and pipe properties, is generally applied to characterize the dispersion mechanism during the advective-dispersive-reactive (ADR) modeling in WDS. Many empirical/semiempirical formulas exist for calculating the coefficient values. Several of them have been applied in WDS water quality modeling research also. However, these formulas have shortcomings concerning overestimating and/or underestimating the longitudinal dispersion coefficient value under transitional/turbulent flow regimes and depicting the transient nature of longitudinal dispersion under laminar flow settings. As yet, no effort has been made to comparatively analyze the implications of the performance of these formulas in ADR modeling in WDS. This paper attempted to critically examine the competence of the prevailing state of the art to accurately represent longitudinal dispersion in pipes from a WDS water quality modeling perspective. The results established that the conceptual dissimilarities in incorporating dispersion memory and transient characteristics between the formulas for laminar regimes have significant impacts on regulating the scale of longitudinal dispersion in the ADR model predictions. The relative variances between the formulas for transitional/turbulent flow settings were found comparatively less significant concerning the ADR model outputs. This study's findings can advance the state of the art to minimize the failure risks involved in simulating the concentration profiles when dispersive transport is dominant over advection in WDS. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic affected the operation of water utilities across the world. In the context of utilities, new protocols were needed to ensure that employees can work safely, and that water service is not interrupted. This study reports on how the operations of 27 water utilities worldwide were affected by the COVID-19 pandemic. Interviews were conducted between June and October 2020; respondents represent utilities that varied in population size, location, and customer composition (e.g., residential, industrial, commercial, institutional, and university customers). Survey questions focused on the effects of the pandemic on water system operation, demand, revenues, system vulnerabilities, and the use and development of emergency response plans (ERPs). Responses indicate that significant changes in water system operations were implemented to ensure that water utility employees could continue working while maintaining safe social distancing or alternatively working from home. A total of 23 of 27 utilities reported small changes in demand volumes and patterns, which can lead to some changes in water infrastructure operations and water quality. Utilities experienced a range of impacts on finances, where most utilities discussed small decreases in revenues, with a few reporting more drastic impacts. The pandemic revealed new system vulnerabilities, including supply chain management, capacity of staff to perform certain functions remotely, and finances. Some utilities applied existing guidance developed through ERPs with slight modifications, other utilities developed new ERPs to specifically address unique conditions induced by the pandemic, and a few utilities did not use or reference their existing ERPs to change operations. Many utilities suggested that lessons learned would be used in future ERPs, such as personnel training on pandemic risk management or annual mock exercises for preparing employees to better respond to emergencies. [ABSTRACT FROM AUTHOR]

A real-life water distribution system (WDS) contains uncertainty in numerous stages. This makes the optimal management and design of a WDS a complex problem. Water quality has also become a significant factor in the design and management of a WDS. Our objective was to incorporate water quality uncertainty in the WDS design problem. The mixing level was assumed to be uncertain and used to design the WDS such that the design was immune to the level of mixing. This method aimed to yield designs that satisfied the nodal concentration constraints irrespective of the mixing level in the junctions. Two optimization methodologies, robust optimization and info-gap decision theory combined with a cuckoo search optimization algorithm, were proposed to solve this problem. An illustrative example 4×4 grid network was used to understand nonuniform mixing and explain the design methodology using both methodologies. Then these methodologies were applied to solve a similar treatment plant problem on a modified Fossolo network. The results also exhibited a significant variation in cost between complete mixing and nonuniform mixing. The WDS designs obtained from both methods were evaluated through Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

This study presents an approximated model for pressure transient simulations as well as a local wall shear stress analysis to control and dislodge biofilm growth in water distribution systems. It demonstrates the potential of taming hydraulic transients to manage and disrupt the growth of biofilm colonies attached to the inner walls of pipeline systems. The systems are subjected to consecutive controlled transient pressure waves by manipulating valves positioned strategically along the water distribution system. Because the controlled transient waves are generated at different locations in the system, the interference properties of the waves can be capitalized on at points where different pressure waves can come together and merge in the system, thus creating high pressures and powerful shear stresses. Nevertheless, it is vital to keep the head pressure confined within the allowed pressure range to avert extreme devastating pressures and ensure the integrity of the system. Three case study applications of increasing complexity are presented to demonstrate the potential of this approach. These transient wave simulations implemented the Lagrangian-based wave characteristic transient model, and the optimization of valve operation was governed by an evolutionary genetic algorithm. The results indicate that it is possible to trigger transient events for biofilm management purposes without exceeding the allowed critical pressure in various network layouts. [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]

This paper investigates the problem of optimal placement and operation of valves and chlorine boosters in water networks. The objective is to minimize average zone pressure while penalizing deviations from target chlorine concentrations. The problem formulation includes nonconvex quadratic terms within constraints representing the energy conservation law for each pipe, and discretized differential equations modeling advective transport of chlorine concentrations. Moreover, binary variables model the placement of valves and chlorine boosters. The resulting optimization problem is a nonconvex mixed integer nonlinear program, which is difficult to solve, especially when large water networks are considered. We develop a new convex heuristic to optimally place and operate valves and chlorine boosters in water networks, while estimating the optimality gaps for the computed solutions. We evaluate the proposed heuristic using case studies with varying sizes and levels of connectivity and complexity, including two large operational water networks. The convex heuristic is shown to generate good-quality feasible solutions in all problem instances with bounds on the optimality gap comparable to the level of uncertainty inherent in hydraulic and water quality models. [ABSTRACT FROM AUTHOR]

Cyberattacks on critical infrastructure systems are becoming a significant concern. Sabotage and destruction of critical infrastructures may cause devastating impacts on physical systems, economic security, public health, and safety. The water sector is one of the most critical infrastructures, and as such, identifying and managing cyber threats on the water sector's facilities is crucial to providing a continuous and safe supply of water. This study reports on a stakeholder engagement process conducted in the form of an active workshop that aims to show different stakeholder's perspective on cyber threats, knowledge gaps, and barriers to implementations of cybersecurity procedures and technologies in the water sector. The workshop, demonstrated on the Israeli water and cyber sectors, brought together 45 professionals from the water and cyber sectors (government, academia, utilities, consultants, and commercial suppliers). In the cybersecurity domain, many studies focused on developing algorithms, but only a few considered the organizational and policy aspects of the problem. The present study addresses this gap and presents an example demonstrating the importance of integrating stakeholder engagement into decision making in the water domain. The workshop's findings are summarized and analyzed to highlight top priority activity areas and suggest required actions according to the stakeholders' perspective, which can help shape the landscape of the water sector's cybersecurity. [ABSTRACT FROM AUTHOR]

This study presents a heuristic multiobjective approach for segmenting and operating water distribution systems (WDS). The methodology employs a two-pronged strategy: the first is a heuristic method for dividing the network into clusters (i.e., district metering areas) based on connectivity analysis. The second is the application of the evolutionary multiobjective optimization method non-dominated sorting genetic algorithm (NSGA)-II for trading off the operational cost, excess pressure (serving as a proxy to leakage reduction), and water age (acting as a surrogate to water quality) in the WDS. Three example applications of increasing complexities with various cluster partitioning are explored, showing a clear trade-off among the objectives. This study introduces an unprecedented heuristic approach for jointly solving the multiobjective problem under a given system partitioning. However, by enforcing a priori clustering formation (rather than including it in the optimization), optimality, completeness, and precision are compromised in favor of computational speed and effort. Thus, additional sensitivities need to be conducted outside of the optimization for the clusters' impact. Challenges of extending this study are in embedding the clusters' formations in the optimization considering other objectives such as residual capacity, developments of other optimization frameworks outside of the generic link of simulation-optimization, and uncertainty inclusion (e.g., in demands). All data and codes are included for allowing full replications and comparisons. [ABSTRACT FROM AUTHOR]

This paper presents an analytical algorithm for simultaneous least-cost design and operation of looped water distribution systems (WDSs). This method could be used to replace evolutionary methods (which are typically used to solve the design-operation problem), or it can be used in conjunction with evolutionary algorithms to enhance their performance (i.e., a hybrid approach). Unlike previous studies that propose analytical methods for split-pipe or continuous diameter design, the developed method addresses a more realistic case in which the pipe design is restricted to commercially available discrete diameters. The analytical approach consists of three stages. In the first stage, a reformulated linear programming (LP) method is used to find the least-cost design of a WDS for a given set of flow distribution while allowing a pipe-split in the solution. In the second stage, the equivalent pipe diameters of the split-pipe design are calculated and modified to discrete pipe diameters by applying a rounding-up strategy to the next commercially available pipe diameter. In the third stage, a nonlinear programming (NLP) method is used to find a new flow distribution that reduces the cost of the WDS operation given the design of the second stage. It is shown in this study that the results produced by the analytical method outperform the results of evolutionary methods when compared to previously published studies. Moreover, when a hybrid approach is adapted, the analytical method can be used to initialize the evolutionary algorithm to gain enhanced performance. The results of the hybrid approach fine-tune those obtained from the analytical method and demonstrate a substantial improvement when compared to a standard evolutionary algorithm initialized with a randomly generated initial population. [ABSTRACT FROM AUTHOR]

To incentivize authors to make their results more reproducible, the I Journal i will publish technical papers and case studies with verified reproducible results open access I free i to the authors for the next year. While under review, all data, models, code, directions, and other materials that authors provide will be subject to standard journal confidentiality restrictions (even if the authors permit sharing). Therefore, authors must share code, models, and data, and sufficiently describe and document their use so the AER, a reproducibility reviewer, and I Journal i readers can identify and follow the steps taken to arrive at the desired results ([1]). [Extracted from the article]

Using a system to promptly detect anomalous water quality levels in a water distribution system (WDS) is a critical task to ensure security of a public water supply. Using continuous monitoring stations placed at strategic locations throughout a WDS has shown to be an effective method to detect potential contamination or low water quality; however, the performance of these monitoring stations is highly sensitive to the specific locations at which they are placed throughout the network. As a result, a large amount of research has explored how to determine the locations at which to place monitoring stations in a WDS, which may be composed of tens of thousands of junctions and pipes. These studies have typically used explicit simulations of network hydraulics, and contamination events imposed on a water distribution system, to compare how effectively a network of monitoring stations detects simulated contamination events. Building off these previous studies, the work herein proposes a framework to place fixed monitoring stations and input inline mobile sensors to best detect contamination events under uncertain water quality conditions. An adaptive-noisy-multiobjective-messy genetic algorithm is used to efficiently determine the locations at which to place monitoring stations in two sample water distribution systems for minimum cost. Results show that monitoring stations and sensor networks designed within a demand uncertain framework outperform the solutions designed in a deterministic demand framework when evaluated under more realistic demand uncertain conditions. [ABSTRACT FROM AUTHOR]

The BATtle of the Attack Detection ALgorithms (BATADAL) is the most recent competition on planning and management of water networks undertaken within the Water Distribution Systems Analysis Symposium. The goal of the battle was to compare the performance of algorithms for the detection of cyber-physical attacks, whose frequency has increased in the last few years along with the adoption of smart water technologies. The design challenge was set for the C-Town network, a real-world, medium-sized water distribution system operated through programmable logic controllers and a supervisory control and data acquisition (SCADA) system. Participants were provided with data sets containing (simulated) SCADA observations, and challenged to design an attack detection algorithm. The effectiveness of all submitted algorithms was evaluated in terms of time-to-detection and classification accuracy. Seven teams participated in the battle and proposed a variety of successful approaches leveraging data analysis, model-based detection mechanisms, and rule checking. Results were presented at the Water Distribution Systems Analysis Symposium (World Environmental and Water Resources Congress) in Sacramento, California on May 21-25, 2017. This paper summarizes the BATADAL problem, proposed algorithms, results, and future research directions. [ABSTRACT FROM AUTHOR]

This work contributes a modeling framework to characterize the effect of cyber-physical attacks (CPAs) on the hydraulic behavior of water distribution systems. The framework consists of an attack model and a MATLAB toolbox named epanetCPA. The former identifies the components of the cyber infrastructure (e.g., sensors or programmable logic controllers) that are potentially vulnerable to attacks, whereas the latter allows determining the exact specifications of an attack (e.g., timing or duration) and simulating it with EPANET. The framework is applied to C-Town network for a broad range of illustrative attack scenarios. Results show that the hydraulic response of the network to a cyber-physical attack depends not only on the attack specifications, but also on the system initial conditions and demand at the junctions. It is also found that the same hydraulic response can be obtained by implementing completely different attacks. This has some important implications on the design of attack detection mechanisms, which should identify anomalous behaviors in a water network as well as the cyber components being hacked. Finally, the manuscript presents some ideas regarding the next steps needed to thoroughly assess the risk of cyber attacks on water distribution systems. [ABSTRACT FROM AUTHOR]

Prompt detection of intentional or accidental contamination of the public water supply is vital to maintain public health in any centralized water distribution system. Being able to quickly detect a system contamination event may be the single most influential factor to reduce possible contamination fallout. Consequently, major research has explored how to best protect a water distribution system (WDS) through strategic placement of fixed water quality monitoring stations. Although fixed monitoring stations within a wireless sensor network (WSN) are robust with respect to hydraulic conditions, the stations are expensive to place, and may not provide the highest spatial and temporal resolution of contamination detection. This work sets to build the understanding of a mobile wireless sensor network (MWSN) where inline mobile sensors function within water in pipes to monitor water quality and to wirelessly transmit data to fixed transceivers in real time. Mobile sensor behavior was modeled alongside contamination simulations and the deployment of fixed and mobile sensors was together optimized to minimize the affected population prior contamination event detection constrained by a total system cost. Results show a MWSN to be highly sensitive to sensor battery life, transceiver network coverage, and total system cost. Future obstacles for implementation of a MWSN are highlighted and discussed to be address in future work. [ABSTRACT FROM AUTHOR]

Finding the optimal pump operation in water distribution systems, taking into account hydraulic and water quality constraints, is a complex problem due to the nonlinear relationship between dynamic head loss and flow rate and between chlorine decay and water age, and due to the size of the problem. The proposed algorithm, for minimum cost pump scheduling, utilizes the operational graph algorithm applied to hydraulic and quality constraints. The proposed algorithm utilizes a graph algorithm that considers hydraulic and water quality constraints to find the pump scheduling that minimizes pump operational costs. The algorithm has short solution times and therefore is suitable for real-time water system control or, if used offline, for giving a recommendation on the pump operation taking into account hydraulic and water quality constraints. The algorithm results were compared with the best results found by enumeration to show that the operational graph algorithm returns a global minimal solution and, when combined with quality constraints, returns near-optimal results. The proposed algorithm was successfully demonstrated on a 24-h example application with a single pumping unit and, on the C-Town example application, with 11 pumping units and 168 time steps. [ABSTRACT FROM AUTHOR]

Least-cost operation of water distribution systems (WDS) is a well-known problem in water distribution systems optimization. The formulation of the problem started with deterministic modeling, and the problem was subsequently handled with more sophisticated stochastic models that incorporate uncertainties related to the problem’s parameters. This work applied a recently developed algorithm entitled limited multistage stochastic programming (LMSP) to deal with the stochastic formulation of the least-cost operation of WDS and serves merely as a proof of concept on an illustrative network. The demand is considered as the uncertain parameter in the problem formulation. This algorithm reduces the complexity of the classical multistage stochastic programming (MSP) by adding constraints which result in a linear growth of the problem, as opposed to an exponential growth in the MSP problem. This is accomplished by clustering decision variables based on a postanalysis of the implicit stochastic program of the problem. The clusters allow reduction of the number of decision variables, thus reducing the complexity of the optimization problem. The LMSP is expected to increase the cost because of the additional constraints imposed on the problem; however, a trade-off exists between the computational complexity and the optimality of the objective value to the number of clusters considered. An illustrative example application is provided for demonstrating the suggested methodology abilities. [ABSTRACT FROM AUTHOR]

Least-cost design of water distribution system is a well-known problem in the literature. The formulation of the least-cost design problem started by deterministic modeling and later by more sophisticated stochastic models that incorporate uncertainties related to the problem's parameters. Recently, a new nonprobabilistic modeling, titled the robust counterpart (RC) approach, has been developed for the least-cost design problem to incorporate the uncertainty without the need for full stochastic information. These nonprobabilistic methods, developed in the field of robust optimization, were shown to be advantageous over classical stochastic methods in the following aspects: tractability and computation time, nonnecessity of full probabilistic information, and the ability to integrate correlation of uncertain parameters aspects without adding complexity. Former studies have considered the RC approach for a special case of the least-cost problem with a single load demand uncertainty, and single gravitational source to simplify the problem formulation and facilitate the use of the method. This special case does not handle the joint temporal and spatial correlations between the problem uncertainties and does not include components such as pumping stations and storage facilities. These new components require trading off capital and operation (i.e., energy) costs in the objective function, as the design cost is explicitly influenced by the demand uncertainty, unlike the situation where only capital cost is considered. In this study, the RC approach is expanded to cover the general least-cost design problem, including (1) multiload patterns, (2) pumping stations, and (3) storage facilities. The unknowns are the pipe diameters, pump and tank capacities, and the heads added by the pumping stations. The problem is solved using the cross-entropy method for several possible protection levels, which are defined by the size of the uncertainty set. The results are demonstrated on two examples to show the trade-off between cost and reliability and test the network's ability to cope with unexpected scenarios. [ABSTRACT FROM AUTHOR]

The battle of background leakage assessment for water networks (BBLAWN) challenge was approached using successive linear programming. A linear representation was solved successively for the nonlinear constraints of headloss, leakage, pump energy consumption, and pipe sizing. The optimization model returned minimal cost pump scheduling and pipe sizing while minimizing leakage and maintaining minimum service pressures to the consumers. Pressure reducing valves, pump, and water tank sizing were performed manually and their effect was examined using the optimization model. Parallel pipes were added along the main supply pipes from the pumping stations to the water tanks, to allow for minimum service pressures. Pressure reducing valves were added to pipes branching from the main supply pipes to lower excess pressures to the secondary supply pipes. [ABSTRACT FROM AUTHOR]

This study describes a binary integer programming model for mutually-operating fixed and mobile sensors in water distribution systems. The proposed method applies a deterministic optimization scheme for maximizing the monitored volume within network clusters. For a given budget, the model determines the ratio of mobile sensors to fixed sensors along with their placement and release strategies. Through assessing the benefit of placing each fixed sensor and the time and location of mobile sensors release, the combination of fixed and mobile sensors is determined. Utilizing mobile sensors for water quality monitoring is still in its infancy. Such sensors are equipped with self-powered sensing, sampling, data acquisition, and wireless transmission units. The model initiates with the combined operation of mobile and fixed sensors. It then explores the benefits of mobile sensors compared to fixed. The two battle of the water sensor networks (BWSN) are utilized for demonstrating the model's capabilities. Mobile sensors are found to be beneficial to water distribution system monitoring when operated in conjunction with static sensors. [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]

This work describes a multiobjective model for trading-off pumping cost and water quality for water distribution systems operation. Constraints are imposed on flows and pressures, on periodical tanks operation, and on tanks storage. The methodology links the multiobjective SPEA2 algorithm with EPANET, and is applied on two example applications of increasing complexity, under extended period simulation conditions and variable energy tariffs. The proposed approach enables decision makers to take full advantage of the obtained information on a multiobjective scale for trading-off, cost, water quality, and storage-reliability requirements. Verification of the model outcomes through engineering judgment on all runs for both example applications confirmed the model suitability as a decision tool. Limitations of the proposed model reside in using variable speed pumps with assumed constant efficiency as representing an entire pumping station operation, the storage reliability constraint as an u-priori set parameter, and in the computational intensity required to obtain solutions for real sized systems. [ABSTRACT FROM AUTHOR]

This study addresses the management of a water supply system under uncertainty. Water is taken from sources that include aquifers and desalination plants and conveyed through a distribution system to consumers under constraints of quantity and quality. The replenishment into the aquifers is stochastic, whereas the desalination plants can produce a large and reliable amount, but at a higher cost. The cost is stochastic because it depends on the realization of the replenishment into the aquifer. A new implicit mean-variance approach is developed and applied. It utilizes the advantages of implicit stochastic programming to formulate a small size and easy to solve convex external optimization problem (quadratic objective and linear constraints) that generates the mean-variance tradeoff without the need to solve a large-scale problem. The results are presented as a tradeoff between the expected value versus the standard deviation. At one end of the tradeoff curve, dependence on the aquifer results in low expected cost and higher cost variability. At the other end, when all of the water is taken from desalination, the cost is high with no variability (deterministic). [ABSTRACT FROM AUTHOR]

Bayesian belief networks are graphical probabilistic analysis tools for representing and analyzing problems involving uncertainty. The problem of monitoring the propagation of a contaminant in a water distribution system can be represented by using Bayesian networks (BNs). The presented methodology proposes the use of BN statistics to estimate the likelihood of the injection location of a contaminant and its propagation in the system. A clustering method, previously developed by the authors, is first applied to formulate a simplified representation of the distribution system based on nodal connectivity properties. Given evidence from clusters, information is combined through probabilistic inference using BNs to find the most likely source of contamination and its propagation in the network. The conditional independence assumptions with the BNs allow efficient calculation of the joint probabilities and diagnostic and predictive queries (e.g., the most likely event given evidence or the probability of an outcome given starting conditions). In addition, a theoretic information measure is suggested to evaluate the significance of the clusters relying on the BN model of the system and possible optimal sensor locations. The proposed methodology is developed and tested on two water supply systems. [ABSTRACT FROM AUTHOR]

Convex equations exist in different fields of research. As an example are the Hazen-Williams or Darcy-Weisbach head-loss formulas and chlorine decay in water supply systems. Pure linear programming (LP) cannot be directly applied to these equations and heuristic techniques must be used. This study presents a methodology for linearization of increasing or decreasing convex nonlinear equations and their incorporation into LP optimization models. The algorithm is demonstrated on the Hazen-Williams head-loss equation combined with a LP optimal operation water supply model. The Hazen-Williams equation is linearized between two points along the nonlinear flow curve. The first point is a fixed point optimally located in the expected flow domain according to maximum flow rate expected in the pipe (estimated through maximum flow velocities and pipe diameter). The second point is the calculated flow rate in the pipe resulting from the previous iteration step solution. In each iteration step, the linear coefficients are altered according to the previous step's flow rate result and the fixed point. The solution gradually converges closer to the nonlinear head-loss equation results. The iterative process stops once both an optimal solution is attained and a satisfactory approximation is received. The methodology is demonstrated using simple and complex example applications. [ABSTRACT FROM AUTHOR]

This study introduces a new search method for box-constrained optimization problems called the search method for box optimization (SMBO). SMBO is a population heuristic-based search methodology that solves global optimization problems. SMBO represents the population as a probability density function (PDF) inside the problem bounds. The PDF shape is dynamically adapted during the process to guide to a 'good' search domain. The applicability and the efficiency of the method are demonstrated using two benchmark sets, which include unimodal, multimodal, expanded, and hybrid composition functions. The performance of SMBO is compared with several genetic algorithms (GAs); the first benchmark compares it with nine codes of traditional/classic GAs, and the second compares SMBO with two recent variants of genetic algorithms. The results show that SMBO performs as well as or better than the GAs in both comparisons. The method is demonstrated on a nonlinear model for management of a water supply system (WSS), and the results are compared with the commercial GA toolbox of matrix laboratory (MATLAB). [ABSTRACT FROM AUTHOR]

Calibration is a process of comparing model results with field data and making the appropriate adjustments so that both results agree. Calibration methods can involve formal optimization methods or manual methods in which the modeler informally examines alternative model parameters. The development of a calibration framework typically involves the following: (1) definition of the model variables, coefficients, and equations; (2) selection of an objective function to measure the quality of the calibration; (3) selection of the set of data to be used for the calibration process; and (4) selection of an optimization/manual scheme for altering the coefficient values in the direction of reducing the objective function. Hydraulic calibration usually involves the modification of system demands, fine-tuning the roughness values of pipes, altering pump operation characteristics, and adjusting other model attributes that affect simulation results, in particular those that have significant uncertainty associated with their values. From the previous steps, it is clear that model calibration is neither unique nor a straightforward technical task. The success of a calibration process depends on the modeler's experience and intuition, as well as on the mathematical model and procedures adopted for the calibration process. This paper provides a summary of the Battle of the Water Calibration Networks (BWCN), the goal of which was to objectively compare the solutions of different approaches to the calibration of water distribution systems through application to a real water distribution system. Fourteen teams from academia, water utilities, and private consultants participated. The BWCN outcomes were presented and assessed at the 12th Water Distribution Systems Analysis conference in Tucson, Arizona, in September 2010. This manuscript summarizes the BWCN exercise and suggests future research directions for the calibration of water distribution systems. [ABSTRACT FROM AUTHOR]

Contamination warning systems are being designed to protect water distribution systems against deliberate contamination intrusions. To design a contamination warning system, contamination intrusion events need to be selected. Because contamination intrusions are random, even for a medium-size network the theoretical number of possible injection events is huge, and thus the number of contamination events which can be considered in the design process is limited. To effectively cope with the threat of contamination events there is a need to identify those critical instances. A straightforward approach of enumerating all possible contamination intrusions from which critical events can be selected is limited to small systems. As critical events are rare the probability of revealing them using common Monte Carlo randomized simulations is very small or requires an extensive impractical computational amount of trials. In this study a methodology utilizing importance sampling and cross entropy based on a recent published work of the authors is further tested on real-sized water distribution systems of increasing complexity. The results demonstrate the robustness of the methodology in terms of improved run times, suggesting computational feasibility for problems in which size prevents full enumeration or application of direct Monte Carlo simulation techniques. [ABSTRACT FROM AUTHOR]

For large water-distribution systems fully detailed models result in a substantial amount of data, making it difficult to manage, monitor, and understand how the main structure of the system works. A possible way to cope with this difficulty is to gain insight to the system behavior by simplifying its operation through topological/connectivity analysis. The objective of this study is to develop and demonstrate a generic topological-based scheme to aid in the analysis of water-distribution systems. The methodology relies on clustering, which divides the distribution system into strongly and weakly connected sub-graphs using the depth first search (DFS) and breadth first search (BFS) graph algorithms. The partitioning results in a connectivity matrix that represents the interconnections between clusters, which can support, for example, a response modeling plan in case of a contamination intrusion incident. A detailed illustrative example and a real complex water-distribution system are explored for demonstrating the developed model capabilities. Possible applications of the proposed algorithm are suggested. [ABSTRACT FROM AUTHOR]

This work presents a model for the inclusion of chemical water stability in optimizing the operation of water distribution systems. When desalinated water is mixed with surface water and/or groundwater, the blend can become chemically unstable. Such a state can cause the phenomena of 'red water,' an increase in corrosion rates, and a reduction in disinfection efficiency. In this study, a methodology is developed that links a genetic algorithm, a hydraulic and water quality extended period simulator, a numerical scheme for computing the calcium carbonate precipitation potential (CCPP) [the quantitative measure of the precise thermodynamic potential of a solution to precipitate (or dissolve) CaCO3(s)], and the pH of the water. The model minimizes the cost of pumping and treatment subject to quantities, pressures, CCPP, and pH constraints. Two example applications are utilized for demonstrating the methodology capabilities. Although the model provides a new tool for the explicit inclusion of chemical water stability in optimal operation of water distribution systems, it overlooks variations in pump efficiency at operational points, does not constrain the number of pump switches, the minimum pump operation and off times, the durations between pump start and shutoff, and the plant or source capacity. Those limitations should be considered in possible extensions of this study. [ABSTRACT FROM AUTHOR]

This paper describes and demonstrates an efficient method for online hydraulic state estimation in urban water networks. The proposed method employs an online predictor-corrector (PC) procedure for forecasting future water demands. A statistical data-driven algorithm (M5 Model-Trees algorithm) is applied to estimate future water demands, and an evolutionary optimization technique (genetic algorithms) is used to correct these predictions with online monitoring data. The calibration problem is solved using a modified least-squares (LS) fit method (Huber function) in which the objective function is the minimization of the residuals between predicted and measured pressure at several system locations, with the decision variables being the hourly variations in water demands. To meet the computational efficiency requirements of real-time hydraulic state estimation for prototype urban networks that typically comprise tens of thousands of links and nodes, a reduced model is introduced using a water system-aggregation technique. The reduced model achieves a high-fidelity representation for the hydraulic performance of the complete network, but greatly simplifies the computation of the PC loop and facilitates the implementation of the online model. The proposed methodology is demonstrated on a prototypical municipal water-distribution system. [ABSTRACT FROM AUTHOR]

During the last two decades, the water resources planning and management profession has seen a dramatic increase in the development and application of various types of evolutionary algorithms (EAs). This observation is especially true for application of genetic algorithms, arguably the most popular of the several types of EAs. Generally speaking, EAs repeatedly prove to be flexible and powerful tools in solving an array of complex water resources problems. This paper provides a comprehensive review of state-of-the-art methods and their applications in the field of water resources planning and management. A primary goal in this ASCE Task Committee effort is to identify in an organized fashion some of the seminal contributions of EAs in the areas of water distribution systems, urban drainage and sewer systems, water supply and wastewater treatment, hydrologic and fluvial modeling, groundwater systems, and parameter identification. The paper also identifies major challenges and opportunities for the future, including a call to address larger-scale problems that are wrought with uncertainty and an expanded need for cross fertilization and collaboration among our field’s subdisciplines. Evolutionary computation will continue to evolve in the future as we encounter increased problem complexities and uncertainty and as the societal pressure for more innovative and efficient solutions rises. [ABSTRACT FROM AUTHOR]

In recent years, drinking water distribution systems security has become a major concern. To protect public health and minimize the effected community by a contaminant intrusion, water quality needs to be continuously monitored and analyzed. Contamination warning systems are being designed to detect and characterize contaminant intrusions into water distribution systems. Since contamination injections can occur at any node at any time the theoretical number of possible injection events, even for a medium-size network, is huge and grows substantially with system size. As a result of that contamination warning systems are designed based on a subset of contamination events, which is not necessarily the most critical. To cope with this difficulty a method derived from cross entropy, which originates from rare event simulations, is proposed. The suggested algorithm is able to sample efficiently a rare subset (i.e., a subset of events with a small probability to occur, but with an extreme impact) of the entire set of possible contamination events. The suggested methodology is demonstrated using an illustrative example and two water distribution systems example applications. [ABSTRACT FROM AUTHOR]

Water-distribution systems least-cost pipe sizing/design is probably the most explored problem in water-distribution systems optimization. Attracted numerous studies over the last 4 decades, two main approaches were employed: decomposition in which an “inner” linear programming problem is solved for a fixed set of flows/heads, while the flows/heads are altered at an “outer” problem using a gradient or a subgradient type technique; and the employment of a general evolutionary optimization algorithm. In 1995 Loganathan and his colleagues proposed to couple these two approaches into one framework, thus overcoming the limitations of each. This study employs this framework with two modifications: (1) application of a genetic algorithm for the “outer” optimization search instead of simulated annealing; and (2) constraining the sought solution to the lowest cost spanning tree layout with the spanning tree chords kept at their minimum permissible pipe diameters. A comparison of the methodology to a genetic algorithm application without the refinement of using a spanning tree with minimal chord diameters was explored, showing the proposed methodology dominance. The suggested method is limited to one loading gravitational systems, and is demonstrated using a simple example application. [ABSTRACT FROM AUTHOR]

Following the events of September 11, 2001, in the United States, world public awareness for possible terrorist attacks on water supply systems has increased dramatically. Among the different threats for a water distribution system, the most difficult to address is a deliberate chemical or biological contaminant injection, due to both the uncertainty of the type of injected contaminant and its consequences, and the uncertainty of the time and location of the injection. An online contaminant monitoring system is considered as a major opportunity to protect against the impacts of a deliberate contaminant intrusion. However, although optimization models and solution algorithms have been developed for locating sensors, little is known about how these design algorithms compare to the efforts of human designers, and thus, the advantages they propose for practical design of sensor networks. To explore these issues, the Battle of the Water Sensor Networks (BWSN) was undertaken as part of the 8th Annual Water Distribution Systems Analysis Symposium, Cincinnati, Ohio, August 27–29, 2006. This paper summarizes the outcome of the BWSN effort and suggests future directions for water sensor networks research and implementation. [ABSTRACT FROM AUTHOR]

A contaminant intentional intrusion into a water distribution system is one of the most difficult threats to address. This is because of the uncertainty of the type of the injected contaminant and its consequences, and the uncertainty of the location and intrusion time. An online contaminant sensor network is the main constituent to enhance the security of a water distribution system against such a threat. In this study a multiobjective model for water distribution system optimal sensor placement using the nondominated sorted genetic algorithm II is developed and demonstrated using two water distribution systems of increasing complexity. Tradeoffs between three objectives are explored: (1) sensor detection likelihood; (2) sensor detection redundancy; and (3) sensor expected detection time. Pareto fronts are plotted for pairs of conflicting objectives, and simultaneously for all three. A contamination event heuristic sampling methodology is developed for overcoming the problem of contamination event sampling. [ABSTRACT FROM AUTHOR]

Control and design problems of water distribution systems rely on simulation models for predicting the system behavior under dynamic boundary conditions. A detailed network model may result in thousands to tens of thousands of pipelines and nodes, making the system hydraulics and water quality analysis a complicated task. In this manuscript a methodology and application of a conjunctive hydraulic and water quality model for water distribution systems aggregation is presented. The model outcome provides a reduced network with fewer nodes and links which resembles the original system performance in both the hydraulics (i.e., quantities and pressures) and water quality (i.e., concentrations) at high accuracy. The proposed methodology is demonstrated using the three example applications of EPANET. [ABSTRACT FROM AUTHOR]

Developed and demonstrated in this paper is an ant colony methodology extending previous work on ant colony optimization for least-cost design of gravitational water distribution systems with a single loading case, to the conjunctive least-cost design and operation of multiple loading pumping water distribution systems. Ant colony optimization is a relatively new meta-heuristic stochastic combinatorial computational discipline inspired by the behavior of ant colonies: ants deposit a certain amount of pheromone while moving, with each ant probabilistically following a direction rich in pheromone. This behavior has been used to explain how ants can find the shortest path between their nest and a food source, and inspired the development of ant colony optimization. The optimization problem solved herein is through linking an ant colony scheme with EPANET for the minimization of the systems design and operation costs, while delivering the consumers required water quantities at acceptable pressures. The decision variables for the design are the pipe diameters, the pumping stations maximum power, and the tanks storage, while for the operation—the pumping stations pressure heads and the water levels at the tanks for each of the loadings. The constraints are domain pressures at the consumer nodes, maximum allowable amounts of water withdrawals from the sources, and tanks storage closure. The proposed scheme is explored through base runs and sensitivity analysis using two pumping water distribution systems examples. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for contamination source identification in water distribution systems through a coupled model trees–linear programming algorithm. Model trees are an extension of regression trees (regression trees: tree-based models used to solve prediction problems in which the response variable is a numerical value) in the sense that they associate leaves with multivariate linear models. The model trees replace EPANET through learning (i.e., training and cross validation) after which a linear programming formulation uses the model trees linear rule classification structure to solve the inverse problem of contamination source identification. The use of model trees represents forward modeling (i.e., from root to leaves). The implementation of linear programming on the linear tree structure allows backward (inverse) modeling (i.e., from leaves to root) where the contamination injections characteristics are the problem unknowns. The proposed methodology provides an estimation of the time, location, and concentration of the contamination injection sources. The model is demonstrated using two example applications. [ABSTRACT FROM AUTHOR]

Red team–blue team exercises are methods of evaluating security by creating a “game” where one team (the red team) attempts to “attack” a target and the other team (the blue team) tries to defend it. This paper describes a computer exercise where the red team simulates the contamination of a water distribution system and the blue team defends the system by installing monitors to detect the presence of the contaminant. This exercise was developed and has been used as part of several demonstrations on the effectiveness of contamination monitoring systems for distribution systems. For comparison, a mathematical model is applied to the same network to select monitor locations that provide the optimal solution in terms of a set of objectives. [ABSTRACT FROM AUTHOR]

The events of September 11, 2001 in the United States have brought to the fore the problem of drinking water distribution systems security. As a water distribution system is spatially diverse, limiting physical access to all components is practically impossible. Deliberate intrusions of contaminants directly into tanks, treatment plants, or through connecting devices is considered one of the most serious terrorist threats. An effective means of reducing this threat is online contamination monitoring. This paper extends previous work of the writers for optimal allocation of monitoring stations to secure drinking water distribution systems against deliberate contamination intrusions. The current methodology takes explicitly into account the randomness of the flow rate of the injected pollutants, the randomness in consumer’s demands, and the detection sensitivity and response time of the monitoring stations. The objective is to determine the optimal location of a set of monitoring stations aimed at detecting deliberate external terrorist hazard intrusions through water distribution system nodes: sources, tanks, treatment plant intakes, consumers—subject to extended period hydraulic demands and water quality conditions, and a maximum volume of polluted water exposure to the public at a concentration higher than a minimum hazard level. The methodology is implemented in a noncommercial program entitled optiMQ-S and demonstrated on EPANET Example 3. [ABSTRACT FROM AUTHOR]

A method incorporating a genetic algorithm tailored to EPANET for the conjunctive optimal design and operation of multiquality water distribution systems under unsteady hydraulics is presented and demonstrated. The objective is to minimize the total cost of designing and operating the system for a selected operational time horizon while delivering to consumers the required quantities at acceptable qualities and pressures. The decision variables for the design are the pipe diameters, tank maximum storage, maximum pumping unit power, and maximum removal ratios at the treatment facilities. For the operation phase, the decision variables are set for each time step of the total operational time horizon. These decisions include the scheduling of the pumping units and the treatment removal ratios at the treatment facilities. The constraints are domain heads and concentrations at consumer nodes, maximum removal ratios at the treatment facilities, maximum allowable amounts of water withdrawals at the sources, and return at the end of the operational time horizon to a prescribed total storage in the water distribution system tanks. The model is explored through two example applications. [ABSTRACT FROM AUTHOR]

A water distribution system is a collection of hydraulic control elements jointly connected to convey quantities of water from sources to consumers. Such a system can be described as a graph with the nodes representing the sources and consumers, and the arcs—the connecting elements (e.g., pipes, pumps, and valves). Theoretically, the flow in each arc can reach either direction, resulting in 2n possible digraphs, where n equals the number of arcs. However, this number is substantially reduced as Kirchoff’s Laws 1 and 2 (continuity of mass and energy, respectively) hold, and as in certain arcs the flow is constrained to only one direction (e.g., the pipe leading out of a well). This study describes a methodology for establishing the most flexible pair: Operational and backup digraphs of a water distribution system that maintains Kirchoff’s Laws 1 and 2, and yields (if possible) a one-level system redundancy (i.e., if one arc fails, at least one path from at least one source to all consumers is retained by the operational or backup digraphs). The proposed methodology is cast in a genetic algorithm framework and demonstrated through two example applications. [ABSTRACT FROM AUTHOR]

Deliberate contamination is generally viewed as the most serious potential terrorist threat to water systems. Chemical or biological agents could spread throughout a distribution system and result in sickness or death among the people drinking the water. Since September 11, 2001 the U.S. Environmental Protection Agency’s water protection task force and regional offices have initiated massive actions to improve the security of the drinking water infrastructure. A methodology is presented for finding the optimal layout of an early warning detection system (EWDS). The detection system is comprised of a set of monitoring stations aimed at capturing deliberate external terrorist hazard intrusions through water distribution system nodes—sources, tanks, and consumers. The optimization considers extended period unsteady hydraulics and water quality conditions for a given defensive level of service to the public, defined as a maximum volume of polluted water exposure at a concentration higher than a minimum hazard level. Such a scheme provides an EWDS for a deliberate terrorist external hazard intrusion, as well as for accidental contamination entries under unsteady conditions—a problem that currently has not been solved. The methodology is cast in a genetic algorithm framework for integration with EPANET and is demonstrated through two example applications. [ABSTRACT FROM AUTHOR]

Demonstrates a methodology for finding the optimal layout of a detection system in a municipal water network. Two main reasons in the deterioration of water quality in a distribution system; Concepts wherein the development of the methodology for an optimal layout of a detection system is based on; Methodology for finding the best set of monitoring stations.

Demonstrates the optimal and reliability of a multiquality water supply system. Use of decomposition in formulating and solving model problems; Identification of backup subsystems; Improvement in system costs; Flows and consumption for base run.