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This study examined the utilization of transient shear stress for cleaning sediment and biofilm materials adhered to the inner walls of pipes in water distribution systems. This approach is presented as a potential method for material resuspension during pipe flushing. This is done to prevent discoloration and low-water-quality events, which can be a major concern for both water utilities and consumers. This work builds and expands on previous work on biofilm detachment under unsteady flows. This study explored the manipulation of existing valves to induce high transient shear stresses that, in turn, resuspend materials adhering to the pipe walls. Through the use of valves strategically positioned along the water distribution system, the systems are subjected to consecutive controlled transient pressure waves. It is possible to capitalize on the interference properties of the controlled transient waves at various points in the system. It is in this condition that various pressure waves can merge within the system, causing high pressures and relatively high shear stresses. However, it is vital to keep the pressure confined within the allowed range to ensure the integrity of the system. A Lagrangian with unsteady friction method was implemented and applied for modeling the transient behavior in two water networks. [ABSTRACT FROM AUTHOR]

Applying mechanistic water quality analysis models that are proficient at simulating the dynamics of heterotrophic bacteria within distribution pipes is a pragmatic approach to maintaining biological stability during drinking water distribution systems (DWDS) operation. Accurate interpretation of hydrodynamics and the uncertainties associated with the multifaceted exchanges within the distribution pipes is crucial to the reliability of these models' predictions. However, knowledge about most exchanges within DWDS is still inadequate. Therefore, state-of-the-art mechanistic models exist merely as theoretical frameworks to understand the causes and effects of microbiological quality fluctuations in DWDS, and they lack general applicability. Advancing the applicability and reliability of the mechanistic models necessitates adequate consideration of epistemic and aleatory uncertainties. This study developed mechanistic models to realize the degree of complexity that needs to be integrated into the modeling framework to accurately describe the water quality dynamics in a real-world DWDS. Under the test conditions considered, the simplest single-phase models that ignore the complex exchanges associated with the pipe biofilm layers were found to make similar microbiological quality predictions as the relatively complicated two-phase models. The results indicate that the knowledge uncertainty associated with mechanisms concerning heterotrophic bacterial regrowth in the bulk phase and biofilm detachment in the wall phase is critical in controlling the reliability of the mechanistic water quality models. [ABSTRACT FROM AUTHOR]

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal. [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]

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]

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]

A mechanistic simulation model predicting the response of water distribution systems (WDSs) operated with or without disinfectant residual toward accidental arsenic contamination is developed in this paper. The impacts of chlorination, chloramination, and organic loading to control the oxidation of arsenous acid [As(III)] and the adsorption/desorption of arsenic acid [As(V)] on/from iron pipe walls were simulated by applying the model to two real-world WDSs. The model predicted that during any As(III) contamination event, the arsenic spread in WDSs would engage conservatively in the absence of a residual disinfectant. Due to the swift reactions between chlorine and As(III), maintaining residual chlorine was recognized as an effective strategy to control the soluble As(III) levels. Chloramine was predicted to be less effective than chlorine in causing As(III) oxidation and subsequent As(V) adsorption onto the pipe wall. Besides, under the test conditions considered, the required chloramine dose in the source water had to be 10 times higher to produce equivalent effects in terms of As(III) depletion as the chlorine dose of 1 mg/L. The results presented that chlorine formation in chloraminated WDSs via the monochloramine hydrolysis mechanism contributes to >99% As(III) depletion inside the distribution pipes. Therefore, the paper recommends maintaining additional chloramine residual in chlorinated WDSs to control the As(III) spread during arsenic contamination events in the downstream sections. [ABSTRACT FROM AUTHOR]

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]

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]

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]

Past water distribution systems (WDS) management studies derived operation protocols to maximize WDS reliability by using residual chlorine as the sole surrogate parameter for water quality reliability. Albeit the advancement in mechanistic modeling to examine the WDS water quality, emerging water quality parameters of concern are not yet involved in solving WDS management problems. This paper attempts to overcome this limitation by developing a flexible decision-making framework -integrating EPANET-C, a mechanistic modeling tool for WDS water quality, with Analytic Hierarchy Process (AHP), a multi-criteria decision-making method - to rank the possible water quality parameter-based operating alternatives (organic matter and residual chlorine levels at the source points) for WDS. The uncertainty analysis was incorporated into the mechanistic modeling using the Monte Carlo method to realize insufficient knowledge about the complex biological and physicochemical interactions inside WDS. Six cases, each ranking the alternatives diversely, were applied to reflect the expert judgment impressions on the AHP outcomes. The consistency of the proposed decision-making framework was verified by deriving the operation protocol for two test networks by making trade-offs between the multiple and conflicting microbiological, chemical, and organoleptic quality criteria. The disinfection by-products formation control and taste and odor problems control emerged as the most critical water quality criteria determining the WDS performance under the operating alternatives examined. Altogether, the obtained results suggested the practicality of adopting a flexible operation protocol to maintain the water quality benchmarks over various plausible WDS performance scenarios, ranging from worst to best. [Display omitted] • General methodology to derive operation protocols for water distribution systems. • All-inclusive water distribution systems quality modeling considering uncertainty. • Flexible decision-making framework based on analytic hierarchy process. • Chemical and organoleptic quality criteria control the hierarchization process. • Flexible operation protocol minimizes contamination risks during operation. [ABSTRACT FROM AUTHOR]

A graph theory-based algorithm is demonstrated for optimal pump scheduling of two example application water networks. The hydraulic part of the problem is solved using a dedicated and efficient hydraulic solver. The pump scheduling part of the problem is solved using a skeletonized operational graph, representing only the basic logic operational relations existing in the network required for pump selection: the pumping units (with nominal operating costs), water tanks and clustered demand nodes. The hydraulic solver advances one time step at a time. After each time step advance, the nodes of the model are checked to see if satisfy minimum service pressure and minimum water tank level. For nodes not satisfying the service constraints, the Dijkstra's shortest path algorithm is applied to the skeletonized graph to determine the optimal pumping unit to be activated and then updating the pumps operation pattern in the model. The hydraulic solver is then reinitialized to resolve and recheck the time steps one by one. The algorithm ends when the solver reaches the last time step with all nodes meeting service constraints. The algorithm returns an optimal minimal cost pump-scheduling pattern under greater-than constraints over the examined time period, such as (1) minimal consumer service pressure, and (2) water balance closure at the water tanks. The algorithm returns discrete pump operation scheduling with minimal pump switching and minimal water age in the tanks, demonstrating short solution times (28 s to schedule 11 pumps over a 168-hour period). The algorithm may be applicable for real-time pump scheduling. Future research may include water quality constraints and variable frequency drive pump scheduling. Pump selection is based on the assumption that the optimal pump operation order is not affected by changes in the network's hydraulic conditions, such as water tank levels and location along the pump efficiency curve (assuming constant efficiency). If hydraulic conditions change the optimal activation order, then the pumps' working points must by updated after each time step solution, which is not addressed in the current work. [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]

Event detection is one of the current most challenging topics in water distribution systems analysis: how regular on-line hydraulic (e.g., pressure, flow) and water quality (e.g., pH, residual chlorine, turbidity) measurements at different network locations can be efficiently utilized to detect water quality contamination events. This study describes an integrated event detection model which combines multiple sensor stations data with network hydraulics. To date event detection modelling is likely limited to single sensor station location and dataset. Single sensor station models are detached from network hydraulics insights and as a result might be significantly exposed to false positive alarms. This work is aimed at decreasing this limitation through integrating local and spatial hydraulic data understanding into an event detection model. The spatial analysis complements the local event detection effort through discovering events with lower signatures by exploring the sensors mutual hydraulic influences. The unique contribution of this study is in incorporating hydraulic simulation information into the overall event detection process of spatially distributed sensors. The methodology is demonstrated on two example applications using base runs and sensitivity analyses. Results show a clear advantage of the suggested model over single-sensor event detection schemes. [ABSTRACT FROM AUTHOR]

• Analogy between systems biology and water supply engineering for the first time. • Novel strategy for analyzing the changes in delivered drinking water quality. • System-level understanding of the reaction mechanisms within water pipes. • Multiscale-based modeling inspired by the flux balance analysis concepts. • Achieving higher water quality and resource efficiency in water supply management. The complexity of modeling water quality variations in water distribution systems (WDS), studied for decades, stems from multiple constraints and variables involved and the complexity of the system behavior. The conventional macroscale-based WDS water quality models are founded on continuum mechanics. In attempts to provide a broad picture of the multi-species interactions, these models overlook the stochasticity corresponding to the reaction mechanisms within the WDS domain. Furthermore, owing to the black-box type modeling adopted in simulating the multi-species interactions, the existing state-of-the-art models have limitations in representing intermediates and/or by-products formation. Accordingly, they remain ineffective in describing the water chemistry-stoichiometric interactions within the WDS domain. Only a radically new modeling approach could overcome the limitations of the macroscale-based approaches and enables analyzing the stochastic WDS mechanisms by keeping the true nature of the system behavior. Stimulated by the metabolic network modeling principles in systems biology, this article outlines the prospect of developing an innovative 'water'bolic network modeling approach to provide a new outlook to the existing WDS water quality modeling research. [Display omitted] [ABSTRACT FROM AUTHOR]

The problem of contamination event detection in water distribution systems has become one of the most challenging research topics in water distribution systems analysis. Current attempts for event detection utilize a variety of approaches including statistical, heuristics, machine learning, and optimization methods. Several existing event detection systems share a common feature in which alarms are obtained separately for each of the water quality indicators. Unifying those single alarms from different indicators is usually performed by means of simple heuristics. A salient feature of the current developed approach is using a statistically oriented model for discrete choice prediction which is estimated using the maximum likelihood method for integrating the single alarms. The discrete choice model is jointly calibrated with other components of the event detection system framework in a training data set using genetic algorithms. The fusing process of each indicator probabilities, which is left out of focus in many existing event detection system models, is confirmed to be a crucial part of the system which could be modelled by exploiting a discrete choice model for improving its performance. The developed methodology is tested on real water quality data, showing improved performances in decreasing the number of false positive alarms and in its ability to detect events with higher probabilities, compared to previous studies. [ABSTRACT FROM AUTHOR]

Abstract: This study describes a new methodology for the disinfection booster design, placement, and operation problem in water distribution systems. Disinfectant residuals, which are in most cases chlorine residuals, are assumed to be sufficient to prevent growth of pathogenic bacteria, yet low enough to avoid taste and odor problems. Commonly, large quantities of disinfectants are released at the sources outlets for preserving minimum residual disinfectant concentrations throughout the network. Such an approach can cause taste and odor problems near the disinfectant injection locations, but more important hazardous excessive disinfectant by-product formations (DBPs) at the far network ends, of which some may be carcinogenic. To cope with these deficiencies booster chlorination stations were suggested to be placed at the distribution system itself and not just at the sources, motivating considerable research in recent years on placement, design, and operation of booster chlorination stations in water distribution systems. The model formulated and solved herein is aimed at setting the required chlorination dose of the boosters for delivering water at acceptable residual chlorine and TTHM concentrations for minimizing the overall cost of booster placement, construction, and operation under extended period hydraulic simulation conditions through utilizing a multi-species approach. The developed methodology links a genetic algorithm with EPANET-MSX, and is demonstrated through base runs and sensitivity analyses on a network example application. Two approaches are suggested for dealing with water quality initial conditions and species periodicity: (1) repetitive cyclical simulation (RCS), and (2) cyclical constrained species (CCS). RCS was found to be more robust but with longer computational time. [Copyright &y& Elsevier]

Abstract: The presented study features an event detection model alerting for contamination events in water distribution systems. The developed model comprises a minimum volume ellipsoid (MVE) classifier, detecting outlier measurements, and a following sequence analysis utilizing the MVE binary output, for the classification of events. The model is updated continuously and exploits a constantly growing data base. The MVE enables simultaneous analysis of the water quality parameters. The multivariate analysis explores the relations between water quality parameters and detects changes in their common patterns. The suggested model applied an un-supervised classification method, eliminates the need for simulated events examples in the classifier construction. In the absent of satisfying information regarding the influence of contamination event on the parameter measurements, eliminating the use of any assumption contributes to the model reliability and generality. The model was trained on a real water utility data, and tested on randomly simulated events that were superimposed on the original data base. The model showed high accuracy and detection ability compared to previous studies. [Copyright &y& Elsevier]

Abstract: This study describes a decision support system, alerts for contamination events in water distribution systems. The developed model comprises a weighted support vector machine (SVM) for the detection of outliers, and a following sequence analysis for the classification of contamination events. The contribution of this study is an improvement of contamination events detection ability and a multi-dimensional analysis of the data, differing from the parallel one-dimensional analysis conducted so far. The multivariate analysis examines the relationships between water quality parameters and detects changes in their mutual patterns. The weights of the SVM model accomplish two goals: blurring the difference between sizes of the two classes' data sets (as there are much more normal/regular than event time measurements), and adhering the time factor attribute by a time decay coefficient, ascribing higher importance to recent observations when classifying a time step measurement. All model parameters were determined by data driven optimization so the calibration of the model was completely autonomic. The model was trained and tested on a real water distribution system (WDS) data set with randomly simulated events superimposed on the original measurements. The model is prominent in its ability to detect events that were only partly expressed in the data (i.e., affecting only some of the measured parameters). The model showed high accuracy and better detection ability as compared to previous modeling attempts of contamination event detection. [Copyright &y& Elsevier]

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]

Abstract: The deployment of fixed online water quality sensors in water distribution systems has been recognized as one of the key components of contamination warning systems for securing public health. This study proposes to explore how the inclusion of mobile sensors for inline monitoring of various water quality parameters (e.g., residual chlorine, pH) can enhance water distribution system security. Mobile sensors equipped with sampling, sensing, data acquisition, wireless transmission and power generation systems are being designed, fabricated, and tested, and prototypes are expected to be released in the very near future. This study initiates the development of a theoretical framework for modeling mobile sensor movement in water distribution systems and integrating the sensory data collected from stationary and non-stationary sensor nodes to increase system security. The methodology is applied and demonstrated on two benchmark networks. Performance of different sensor network designs are compared for fixed and combined fixed and mobile sensor networks. Results indicate that complementing online sensor networks with inline monitoring can increase detection likelihood and decrease mean time to detection. [Copyright &y& Elsevier]

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]

A multi-objective methodology utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) linked to EPANET for trading-off pumping costs, water quality, and tanks sizing of water distribution systems is developed and demonstrated. The model integrates variable speed pumps for modeling the pumps operation, two water quality objectives (one based on chlorine disinfectant concentrations and one on water age), and tanks sizing cost which are assumed to vary with location and diameter. The water distribution system is subject to extended period simulations, variable energy tariffs, Kirchhoff’s laws 1 and 2 for continuity of flow and pressure, tanks water level closure constraints, and storage-reliability requirements. EPANET Example 3 is employed for demonstrating the methodology on two multi-objective models, which differ in the imposed water quality objective (i.e., either with disinfectant or water age considerations). Three-fold Pareto optimal fronts are presented. Sensitivity analysis on the storage-reliability constraint, its influence on pumping cost, water quality, and tank sizing are explored. The contribution of this study is in tailoring design (tank sizing), pumps operational costs, water quality of two types, and reliability through residual storage requirements, in a single multi-objective framework. The model was found to be stable in generating multi-objective three-fold Pareto fronts, while producing explainable engineering outcomes. The model can be used as a decision tool for both pumps operation, water quality, required storage for reliability considerations, and tank sizing decision-making. [Copyright &y& Elsevier]

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 study presents a methodology for the inclusion of hydraulics uncertainty in contamination source identification. Current research normally considers the system hydraulics as deterministic and the water quality sensors as ideal. In reality however only a small portion of the hydraulic data is known and most likely only Boolean sensor information of a contamination existence. There is a need to incorporate these considerations in contamination source identification models and to explore their influence on the modelling ability to correctly detect the characteristics of a contamination intrusion. This problem is addressed in this manuscript. The proposed method is based on a previous contamination source detection model developed by the authors which is further embedded in a statistical framework for quantifying the uncertainty of a contamination source detection outcome. The methodology is demonstrated on three example applications of increasing complexity through base runs and sensitivity analyses. [ABSTRACT FROM AUTHOR]

This study presents a new method for selecting monitoring wells for optimal evaluation of groundwater quality. The basic approach of this work is motivated by difficulties in interpolating groundwater quality from information collected for only few sampled wells. The well selection relies on other existing data relevant to contaminant distribution in the sampling domain, e.g. predictions of models which rely on past measurements. The objective of this study is to develop a method of selecting the optimal wells, from which measurements could best serve some external model, e.g. a kriging system for characterizing the entire plume distribution, a flow-and-transport model for predicting a future distribution, or an inverse model for locating contaminant sources or estimating aquifer parameters. The decision variable at each sampling round determines the specific wells to be sampled. The study objective is accomplished through a spatially-continuous utility density function (UDF) which describes the utility of sampling at every point. The entire methodology which utilizes the UDF in conjunction with a sampling algorithm is entitled the UDF method. By applying calculations in steady and unsteady state sampling domains the effectiveness of the UDF method is demonstrated. [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]

• Distribution network as an indirect PFASs contamination and exposure risks source. • Mechanistic model for predicting PFOA fate and transport in the distribution system. • PFASs exposure risks by comparing the simulated PFOA levels with guideline values. • Child population group was found susceptible to PFOA exposure beyond 3 ng/kg/day. • Reducing chlorine dose decreased the PFOA exposure risks in the distribution network. Recent studies identified fluoroalkyl amides (FAs) transformation to perfluorooctanoic acid (PFOA) during disinfection as an indirect source of PFASs contamination of drinking water. This paper discerns the position of water disinfection systems (WDSs) as a PFOA exposure pathway. A new mechanistic model incorporating the derived knowledge about the zwitterionic/cationic FAs transformation to PFOA with the unsteady-state hydraulic characteristics of WDSs was developed. The simulation outputs from model application to a WDS from the USA established the significant role of delivery via distribution network in the PFOA formation in drinking water. PFOA exposure risk assessment studies predicted >95% of the system nodes to be at high risk when the existing stringent health-based guideline values are adopted. The 1 to 3 years and 4 to 8 years old age groups were found susceptible to PFOA exposure through drinking water beyond the tolerable limit of 3 ng/kg/day. The model predicted that reducing the chlorine dose from 2±0.2 to 1±0.1 mg/L at the treatment units drops the share of 1 to 3 years old and 4 to 8 years old consumers falling to PFOA exposure from 4.32 to 0.45% and 0.32 to <0.01%, respectively. Besides, 24.9% more, including ∼x223C10% of the consumers of 1 to 3 years old age group, were found exposed to PFOA risks when the organic loading of water was reduced by 60%. [Display omitted] [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]

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 optimal design problem of a water distribution system is to find the water distribution system component characteristics (e.g. pipe diameters, pump heads and maximum power, reservoir storage volumes, etc.) which minimize the system's capital and operational costs such that the system hydraulic laws are maintained (i.e. Kirchhoff's first and second laws), and constraints on quantities and pressures at the consumer nodes are fulfilled. In this study, an adaptive stochastic algorithm for water distribution systems optimal design based on the heuristic cross-entropy method for combinatorial optimization is presented. The algorithm is demonstrated using two well-known benchmark examples from the water distribution systems research literature for single loading gravitational systems, and an example of multiple loadings, pumping, and storage. The results show the cross-entropy dominance over previously published methods. [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]

This article extends previous work on optimal booster chlorination injection design and operation in water distribution systems by solving the scheduling problem of pumping units in conjunction with the design and operation problem of booster chlorination stations. Two models are formulated and solved using a genetic algorithm scheme tailor-made to EPANET: Min Cost—for minimizing the costs of pumping and the chlorine booster design and operation, and Max Protection—for maximizing the system protection by maximizing the injected chlorine dose. An example application is explored through a base run and sensitivity analysis showing that the algorithm proposed is robust and reliable, and that the pump and chlorine injection scheduling are mutually connected. [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]

Drinking water utilities around the world are vulnerable to various types of terrorist attacks including warfare contamination and bioterrorism. A distribution system comprises water tanks, pipes, pumps, and other components that deliver treated water from treatment plants to consumers. Particularly among large utilities, distribution systems may contain thousands of kilometers of pipes and numerous delivery points, which can be highly vulnerable to a terrorist deliberate contamination injection. This paper extends previous work on optimal early warning monitoring system layout for water networks security by addressing the monitoring stations detection sensitivities and response delays, and the consumer demands and contaminant injected flow rates randomness. The methodology developed is demonstrated on two example applications. [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]

An application of stochastic simulation for the reliability analysis of single and multiquality water distribution systems (MWDS) is formulated and demonstrated. MWDS refers to systems in which waters of different qualities are taken from sources, possibly treated, mixed in the system, and supplied as a blend. The stochastic simulation framework was cast in a reliability analysis program (RAP), based on EPANET, and quantifying three reliability measures: the fraction of delivered volume (FDV), the fraction of delivered demand (FDD), and the fraction of delivered quality (FDQ). RAP is demonstrated on two example applications: a simple illustrative, and a more “real-life” complex one. The results quantify the reliability of the systems and provide lower bounds for the reliability measures adopted. [Copyright &y& Elsevier]

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]