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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]

This work presents an algorithm for water distribution systems water age clustering. The objective is to cluster a distribution system into water age sub-zones whose water age variability is minimized within each cluster. The algorithm stages are: (1) water age computation for each system node, (2) kick-off at a number of clusters equal to the number of nodes (i.e., each node initially acts as a cluster), (3) search for the two connected (by link) clusters which have the smallest absolute water age difference, and combine them into a single cluster; characterize their water age value as the weighted arithmetic mean of the two clusters, and (4) repeat step 3 until all nodes are lumped into a single cluster (i.e., the entire water distribution system). The algorithm thus spans all possible clusters starting from the total number of system nodes and up to a one cluster which holds the entire system layout. The model, through a clustering numbering trade-off, is demonstrated on a mid-size water distribution system. [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]

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]

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]

This study presents the potential of integrating Hydrams in modern water distribution systems (WDSs) for managing excess pressure and reducing energy costs. Hydrams, which are also termed Hydraulic ram pumps in the literature, is a cyclic water pump powered by hydropower, generally used to pump drinking and irrigation water in mountainous and rural areas having short of power. The Hydrams is introduced as a sustainable low-cost alternative solution to the more conventional pressure reducing valves (PRVs) approach for managing pressure zones in WDSs. Unlike PRVs, where the pressure is lost and not put into good use, Hydrams mitigate excess pressure at high-pressure zones and direct it to much-needed low-pressure zones. In addition, Hydrams are cheap, simple, environmentally friendly, and require little maintenance. The proposed approach integrates a Hydram in parallel to the original centrifugal pump, where they can be operated interchangeably according to the system's hydraulic needs. Nevertheless, it is vital to correctly size the Hydram at the feed line and accompany it with a proper storage tank at the low-pressure zone. The storage tank serves as a buffer between the intermittent water supply and consumer demand pattern. Moreover, the tank introduces flexibility into the system that allows more sustainable operating schedules. Two case study applications of increasing complexity are presented to demonstrate the potential of this Hybrid system, later referred to as Hybrid Pumping Unit (HPU). The Hydram and tank sizing is done by a simple heuristic approach, while the operation of the system is dictated by a genetic algorithm. The results demonstrate the potential of integrated Hydrams in reducing excess pressures and energy costs. [ABSTRACT FROM AUTHOR]

Water storage tanks are one of the primary and most critical components of water distribution systems (WDSs), which aim to manage water supply by maintaining pressure. In addition, storage provides a surplus source of water in case of an emergency. To gain the mentioned advantages, storage tanks are incorporated in most WDSs. Despite these advantages, storage can also pose negative impacts on water quality, thereby affecting water utilities. Water quality problems are a result of longer residency times and inadequate water mixing. This study aimed to construct a model of a tank's water quantity and quality by formulating and solving governing equations based on inlet/outlet configurations and processes that influence the movement of water and chemical substances inside it. We used a compartment model to characterize the mixing behavior inside a tank. A water quality simulation model with different compartment arrangements was explored for extended filling and draining of storage, which was further validated using a previously published case study. [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]

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]

A simple, straightforward, modified genetic algorithm scheme for contaminant source characterization using imperfect sensors is presented and demonstrated in this study. Previous work on this subject concentrated on developing source-inversion models using sensors that provide accurate, unbiased, contamination concentration measurements. The developed contamination source-detection model is implemented using three sensor types: (1) perfect sensors providing accurate, unbiased, contamination concentration measurements; (2) sensors transmitting fuzzy measured information (i.e., high, medium, and low contamination); and (3) '0-1' (Boolean) sensors indicating only a contamination presence. A comparison between the three sensor types is explored taking into consideration thesystem's response time (i.e., the time elapsed between a contaminant detection and a decision-maker's response action). The methodology capabilities are demonstrated using two example applications of increasing complexity, showing the trade-offs between the sensor types and the model abilities to receive a unique solution to the source-detection problem. [ABSTRACT FROM AUTHOR]

The 2013 Boston Marathon attack demonstrated the complexity of real-time response to such occurrences. The procedures used by the Federal Bureau of Investigation (FBI) at the Boston event to cluster and mitigate the event consequences inspired the development of a method for real-time response to contamination intrusion events in water distribution systems. Similar to the Boston attack, in the event of water contamination events, the shortage of real-time data, coupled with uncertainties in network topology, water consumption, and the event characteristics, set the ground for the need for a real-time response strategy. A methodology that divides the network into separate monitored zones or clusters, often referred to as district metered areas (DMAs), is widely used to cope with water-related problems such as leakage reduction or pressure control. In this study, a water-quality-related criterion called infection delay time (IDT) was introduced to dynamically cluster the network in case of a contamination event. The IDT parameter was combined with the available system resources to meet water quality goals. A coupled DMA-IDT method was developed for real-time response to contamination events. The setup of the DMA-IDT is described and demonstrated on water distribution systems of various complexities. [ABSTRACT FROM AUTHOR]

Looped water distribution system (WDS) repartitioning to district metering areas (DMAs) gained popularity as an effective technique to manage the system and detect and reduce system leakages. However, to apply this method to real WDS, various system properties should be taken into account to ensure efficient water supply. The battle of water networks district meter areas (BWNDMA) is a challenging problem that requires the redesign of the E-Town city network in Colombia. The water utility is looking to repartition the network into manageable DMAs while supplying future demands, keeping minimum and maximum pressures, improving water quality, operating the network at uniform low pressures, balancing water sources, and meeting their seasonal production capabilities. The problem is stated as a multiobjective optimization problem with DMA partitioning being one of eight equal-weighted objectives. They may be reached by (1) closing, opening, or replacing existing pipes, (2) adding parallel pipes, (3) managing storage tanks, pressure valves, and flow-control valves, and (4) utilizing pumps in the dry season. With no known analytical methodology to optimize such a large mixed-integer nonlinear problem, a major difficulty is to find a feasible solution; therefore, a multistage classic engineering approach was taken. First, source allocation and general design were carried out for the operational zones. Then, tank volumes were adjusted to meet their constraints. At this stage, DMAs were introduced to meet pressure regulations. Finally, detailed design and fine-tuning of the operations were carried out. This paper describes the taken procedures and obtained results for the redesign of the E-Town network. [ABSTRACT FROM AUTHOR]

Abstract: In this study, a dynamic thresholds scheme is developed and demonstrated for contamination event detection in water distribution systems. The developed methodology is based on a recently published article of the authors (Perelman et al., 2012). Event detection in water supply systems is aimed at disclosing abnormal hydraulic or water quality events by exploring the time series behavior of routine hydraulic (e.g., flow, pressure) and water quality measurements (e.g., residual chlorine, pH, turbidity). While event detection raises alerts to the possibility of an event occurrence, it does not relate to origins, thus an event may be hydraulically-driven, as a consequence of problems like sudden leakages or pump/pipe malfunctions. Most events, however, are related to deliberate, accidental, or natural contamination intrusions. The developed methodology herein is based on off-line and on-line stages. During the off-line stage, a genetic algorithm (GA) is utilized for tuning five decision variables: positive and negative filters, positive and negative dynamic thresholds, and window size. During the on-line stage, a recursively Bayes'' rule is invoked, employing the five decision variables, for real time on-line event detection. Using the same database, the proposed methodology is compared to Perelman et al. (2012), showing considerably improved detection ability. Metadata and the computer code are provided as Supplementary material. [Copyright &y& Elsevier]