Multi-Objective Optimization of Locating the Pollution Detection Sensor in the Urban Water Distribution System Using the Multi-Objective Optimization Algorithm of Coordinated Search

Subject Areas : Article frome a thesis

hamed mazandarani zadeh ¹ , matin hendoopor ²

1 - Department of Water Sciences and Engineering, Imam Khomeini International University, Qazvin, Iran
2 - Department of Water Sciences and Engineering, Imam Khomeini International University, Qazvin, Iran

Received: 2019-09-07 Accepted : 2021-04-26 Published : 2021-04-21

Keywords: EPANET, Contaminate Warning System, Biological Attacks, Water Supply Network,

Abstract :

Introduction:Following the terrorist attacks of September 11, 2001 in the United States, concern about the security of water distribution systems has increased. Water distribution systems are highly vulnerable to intentional or accidental contamination, due to their physical and geographical characteristics. Such threats can affect public trust in the distribution system. Putting a contaminant warning system up, including sensors in the water distribution network, is a hopeful approach to disconnect water supply networks at the time of the occurrence of pollution. Due to the high cost of purchasing, installation and maintenance of such sensors, their location should be calculated by using optimization algorithms. Materials and Methods:In this research, a multi-objective optimization model has been developed using harmony search algorithm to calculate the optimal location of detection sensors in water distribution systems based on two criteria for minimizing the time detection of pollution and maximizing the coverage of sensors. The simulation of events in different scenarios is done by connecting the EPANET hydraulic model toolbox and the MATLAB software, and the second example of EPANET software is as the case study. Findings:The results show that the optimal detection time is 19296 seconds and the optimal coverage is 26494 gallons per minute. By increasing the importance of optimal detection time to the increasing the sensor coverage, the detection time decreased by 32%, and the sensor coverage, by contrast, decreased by 26%. Conclusion: It seems that in situations where two criteria are of equal importance, one of the sensors is located in a crowded place to reduce the time of contamination detection and the other in the middle of the network to increase network coverage.

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