Abstract
Contamination in water distribution networks (WDNs) can occur at any time and location. One protection measure in WDNs is the placement of water quality sensors (WQSs) to detect contamination and provide information for locating the potential contamination source. The placement of WQSs in WDNs must be optimally planned. Therefore, a robust sensor-placement strategy (SPS) is vital. The SPS should have clear objectives regarding what needs to be achieved by the sensor configuration. Here, the objectives of the SPS were set to cover the contamination event stages of detection, consumption, and source localization. As contamination events occur in any form of intrusion, at any location and time, the objectives had to be tested against many possible scenarios, and they needed to reach a fair value considering all scenarios. In this study, the particle swarm optimization (PSO) algorithm was selected as the optimizer. The SPS was further reinforced using a databasing method to improve its computational efficiency. The performance of the proposed method was examined by comparing it with a benchmark SPS example and applying it to DMA-sized, real WDNs. The proposed optimization approach improved the overall fitness of the configuration by 23.1% and showed a stable placement behavior with the increase in sensors.
Highlights
Implementing an efficient monitoring system is critical for water distribution networks (WDNs)
Water quality sensor (WQS) placement is essential for maintaining WDN functionality and plays an integral part in preventing contamination events and ensuring the safety of distributed water to protect the health of users
A new sensor placement strategy (SPS) method, termed sensor placement swarm optimizer (SPSO), which accounted for possible contamination scenarios using particle swarm optimization (PSO), is proposed in this study
Summary
Implementing an efficient monitoring system is critical for water distribution networks (WDNs). Besides considering single or multiple objectives in SPS, other circumstances in the approach can include the network hydraulic or sensor characteristics. The use of GA or its variant NSGA-II as the optimization algorithm has been favored by many researchers [6,10,11,13] It has some weaknesses in requiring more computational power along with the increase in problem complexity and sensitivity to chosen parameters. The objectives considered the stages of contamination detection, contamination consumption, and source identification They were applied to many scenarios with different contaminant source locations and times of intrusion combination. The method proposed in the study was named the sensor placement swarm optimizer (SPSO) method. Some limitations of this method need to be noted. MataetreirailaslsananddMMetehthodods s TThheeSSPPSSOOmmeeththooddppeerrffoorrmmss SSPPSS ooppttiimmiizzaattiioonn uussiinnggtthheePPSSOOaassththeeopotpimtimiziaztaiotinonalalggoorriitthhmm..AAs sththeennamame esusguggegsetsst,st,htehepapratirctliecleorodr edceisciiosnionvavraiarbialebloefotfhethaelgaolgriotrhimthmis itshe thseensseonrsoprlapcleamceemntenctoncofingfiugrautriaotnio(nlo(claotciaotnioonfosfesnesnosrosrisninthteheWWDDNN). )T. hTehemmetehtohdodaiamims sto topprorovvidide ethtehebebsetsstesnesnosropr lpalcaecmemenetnbtabsaesdeodnonnentwetowrkordkadtaataanadnddedfienfiednecdonctoanmtaimnaitnioantiosncescneanraioriso.sT. hTehoevoevrearlal lplrporcoecsessosfotfhtihsims metehtohdodisips rperseesnetnetdedininFiFgiugruere1.1
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