An Analysis of Multiple Contaminant Warning System Design Objectives for Sensor Placement Optimization in Water Distribution Networks

Author(s):  
Jean-Paul Watson ◽  
William E. Hart ◽  
Harvey J. Greenberg ◽  
Cynthia A. Phillips
2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Symeon E. Christodoulou

Presented herein is a proposed greedy-search sensor placement optimization heuristic for the detection of water leaks in water distribution networks (WDN). The proposed method is based on entropy, a measure of uncertainty about the source of information, and its main mathematical properties of maximality, subadditivity and equivocation. The method proposes an entropic metric which is subsequently utilized in selecting nodal locations and in heuristically searching for the locations that maximize the total entropy in the WDN, relating maximal entropy with maximal sensing coverage.


2013 ◽  
Vol 27 (13) ◽  
pp. 4443-4468 ◽  
Author(s):  
Symeon E. Christodoulou ◽  
Anastasis Gagatsis ◽  
Savvas Xanthos ◽  
Sofia Kranioti ◽  
Agathoklis Agathokleous ◽  
...  

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 1999
Author(s):  
Malvin S. Marlim ◽  
Doosun Kang

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.


2018 ◽  
Vol 108 ◽  
pp. 152-162 ◽  
Author(s):  
Adrià Soldevila ◽  
Joaquim Blesa ◽  
Sebastian Tornil-Sin ◽  
Rosa M. Fernandez-Canti ◽  
Vicenç Puig

2017 ◽  
Vol 20 (6) ◽  
pp. 1286-1295 ◽  
Author(s):  
Xiang Xie ◽  
Quan Zhou ◽  
Dibo Hou ◽  
Hongjian Zhang

Abstract The performance of model-based leak detection and localization techniques heavily depends on the configuration of a limited number of sensors. This paper presents a sensor placement optimization strategy that guarantees sufficient diagnosability while satisfying the budget constraint. Based on the theory of compressed sensing, the leak localization problem could be transformed into acquiring the sparse leak-induced demands from the available measurements, and the average mutual coherence is devised as a diagnosability criterion for evaluating whether the measurements contain enough information for identifying the potential leaks. The optimal sensor placement problem is then reformulated as a {0, 1} quadratic knapsack problem, seeking an optimal sensor placement scheme by minimizing the average mutual coherence to maximize the degree of diagnosability. To effectively handle the complicated real-life water distribution networks, a validated binary version of artificial bee colony algorithm enhanced by genetic operators, including crossover and swap, is introduced to solve the binary knapsack problem. The proposed strategy is illustrated and validated through a real-life water distribution network with synthetically generated field data.


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