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Incorporating a system of monitoring stations to insure high quality water is being delivered to consumers has been acknowledged a crucial component required by any public water distribution system (WDS). Extensive studies have acknowledged the risk posed to large populations by an accidental or intentional contamination intrusion within a WDS; failure of an early warning system (EWS) to report a contamination event carries profound economic and public health consequences. Dynamic, stochastic conditions exist in municipal WDSs and a monitoring system needs to be designed according to a robust protocol that incorporates the inherent uncertainty in WDS operation, including: demand variability, and contamination event characteristic variability. This work composes the problem of locating the best junctions within a WDS to place fixed monitoring stations, and the best junctions to input innovative inline mobile sensors, in a multi-objective framework that incorporates uncertainty in the network's demands and EWS operation. Mobile sensors are carried by flow within pipes sampling and monitoring water quality in real time, and wirelessly uploading data to fixed transceiver beacons, providing an implicit preference towards demand dense regions. A multi-objective noisy messy genetic algorithm is structured to the problem at hand and employed on a small, medium, and large-scale model WDS to calculate near-optimal solutions from the large solutions space. This multi-objective framework provides high performing trade off (Pareto) sets comparing an EWS's system cost to numerous performance objectives incorporating non-deterministic objective functions to provide a high performing and resilient EWS. Results show a large trade off surface between the cost and the respective system's performance, with large diminishing returns. Although implementing a more expensive solution may provide little to no benefit from a traditional performance standpoint, implementing a system of higher cost can increase the systems resiliency, highlighting the importance of incorporating proper objective measures in optimization procedure. [ABSTRACT FROM AUTHOR]

Abstract: Decision-making processes often involve uncertainty. A common approach for modeling uncertain scenario-based decision-making progressions is through multi-stage stochastic programming. The size of optimization problems derived from multi-stage stochastic programs is frequently too large to be addressed by a direct solution technique. This is due to the size of the optimization problems, which grows exponentially as the number of scenarios and stages increases. To cope up with this computational difficulty, solution schemes turn to decomposition methods for defining smaller and easier to solve equivalent sub-problems, or through using scenario-reduction techniques. In our study a new methodology is proposed, titled Limited Multi-stage Stochastic Programming (LMSP), in which the number of decision variables at each stage remains constant and thus the total number of decision variables increases only linearly as the number of scenarios and stages grows. The LMSP employs a decision-clustering framework, which utilizes the optimal decisions obtained by solving a set of deterministic optimization problems to identify decision nodes, which have similar decisions. These nodes are clustered into a preselected number of clusters, where decisions are made for each cluster instead of for each individual decision node. The methodology is demonstrated on a multi-stage water supply system operation problem, which is optimized for flow and salinity decisions. LMSP performance is compared to that of classical multi-stage stochastic programming (MSP) method. [Copyright &y& Elsevier]

Abstract: A seasonal multi-year model for management of water quantities and salinities in regional water supply systems (WSS) was developed and implemented. Water is taken from sources which include aquifers, reservoirs, and desalination plants, and conveyed through a distribution system to consumers who require quantities of water under salinity constraints. The year is partitioned into seasons, and the operation is subject to technological, administrative, and environmental constraints such as water levels and salinities in the aquifers, capacities of the pumping, distribution system, and the desalination plants, and the desalination plants maximum removal ratios. The objective is to operate the system at minimum total cost. The objective function and some of the constraints are nonlinear, leading to a nonlinear optimization problem. The nonlinear optimization problem is solved efficiently by adapting (1) a set of manipulations that reduce the problem size and (2) a novel finite difference scheme for calculating the derivatives required by the optimization solver, entitled the Time-Chained-Method (TCM). The model is demonstrated on a small illustrative example and on a real sized regional water supply system in Israel. [Copyright &y& Elsevier]

• A pragmatic solution to regulating water pressures and leakage by introducing PRVs. • Optimal selection of positions and setpoints of PRVs in a distribution network. • Graph theory-based algorithm employing the depth-first search method. • Pipes leading to the most extended downstream networks are ideal locations for PRVs. • The algorithm maintains the minimum pressure at the minimum required service level. Optimal pressure management is a standard strategy for water loss minimization in water distribution systems (WDS). A pragmatic solution to regulating water pressures and leakage is introducing pressure-reducing valves (PRVs). This paper presents a valve positioning algorithm for optimally deciding the positions and setpoints of PRVs in a WDS. The algorithm derives the hydraulic solution of a WDS as a directed graph, established on the flow directions, using EPANET 2.2 and develops the downstream network supplied by water flowing out of every pipe in the network by applying the depth-first search method. The algorithm later recognizes the pipes leading to the most extended downstream networks, with pressures above the minimum required service pressure, and prioritizes them as the ideal locations for PRV placement. In this way, the proposed algorithm overcomes the limitations of the state-of-the-art in realistically conceptualizing the leakage reduction for optimally positioning the PRVs in WDS. Four studies with varying complexities were selected to demonstrate the algorithm's applicability for deriving pressure management solutions. The solution time for PRV positioning was in seconds for the first three networks and several minutes for the extensive fourth case study. The results corroborate the algorithm's ability to pinpoint the critical nodes with the most increased potential for downstream pressure control and for maintaining the pressure at the least required service pressure level through optimally allocating the PRVs, with acceptable setpoint values, within the pipe network. [Display omitted]. [ABSTRACT FROM AUTHOR]

Optimal sensor placement for detecting contamination events in water distribution systems is a well explored problem in water distribution systems security. We study herein the problem of sensor placement in water networks to minimize the consumption of contaminated water prior to contamination detection. For any sensor placement, the average consumption of contaminated water prior to event detection amongst all simulated events is employed as the sensing performance metric. A branch and bound sensor placement algorithm is proposed based on greedy heuristics and convex relaxation. Compared to the state of the art results of the battle of the water sensor networks (BWSN) study, the proposed methodology demonstrated a significant performance enhancement, in particular by applying greedy heuristics to repeated sampling of random subsets of events. [ABSTRACT FROM AUTHOR]