Water Supply (Mar 2021)
S-PLACE GA for optimal water quality sensor locations in water distribution network for dual purpose: regular monitoring and early contamination detection – a software tool for academia and practitioner
Abstract
Security concerns about water distribution networks (WDNs) have led to increased interest in optimizing sensor locations in WDNs achieved through a calibrated hydraulic model. This paper presents a methodology, which consists of two stages. The first stage consists of calibration of a hydraulic model using a genetic algorithm (GA). A real-life network of one of the hydraulic zones of Nagpur city, India, is considered, which optimizes the settings of a throttled controlled valve at different timings for calibration. In this stage, a detailed case study, GA calibration model, methodology and results of calibrated models are discussed. The second stage consists of identifying optimal sensor locations using a newly developed software tool named ‘S-PLACE GA’ and its efficiency and effectiveness are discussed. It can be used for the dual purpose of routine monitoring of water quality and for early detection of contamination. The optimal locations are obtained considering two objective metrics, ‘Demand Coverage’ and ‘Time-Constrained Detection Likelihood’. These two objectives are combined into a single objective by using weights. Key features, input data required for the software and their applications on (1) BWSN network 1 and result comparison with others and (2) calibrated model of the first stage are discussed. Results showed the effectiveness of S-PLACE GA for practical applications. HIGHLIGHTS Paper highlights the calibration of hydraulic model of water distribution system (WDS) using genetic algorithm performing the modelling of throttled control valve.; The new software tool ‘S-PLACE GA’ is developed for dual purpose of routine monitoring of water quality in WDS and for early detection of contamination in case of accidental or intentional contamination simultaneously.; The new formulation is suggested considering weighted objective function comprising two objective metrics, ‘Demand Coverage’ and ‘Time-Constrained Detection Likelihood’. Application and result comparison are shown on benchmark problem.;
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