Abstract

As the urban population increases, the consumption of water resources is also increasing. Safely and effectively supplying water to cities has become an issue that urgently needs to be addressed. The purpose of this research is to substantially reduce the number of contaminants in water distribution networks (WDNs) by using valve control, ensuring that the water infrastructure is not impacted by the adverse effects of wastewater. In addition, an improved parallel binary gannet algorithm (IPBGOA) is proposed and combined with this approach to solve the optimization problem of WDN contamination efficiently. The proposed method is compared with the gannet optimization algorithm (GOA), particle swarm optimization (PSO), differential evolution (DE), the grey wolf optimization (GWO), and the genetic algorithm (GA) on synthetic benchmark networks in simulation experiments. The evidence from the study indicates that the algorithm proposed in this paper is significantly more efficient and reliable than the comparison methods.

Details

Title
Optimization of valve switch control for contamination detection in water distribution network
Author
Pan, Jeng-Shyang 1 ; Shu, Hao 2   VIAFID ORCID Logo  ; Yang, Qingyong 3 ; Huang, Yu-Chung 4 ; Chu, Shu-Chuan 5   VIAFID ORCID Logo 

 Nanjing University of Information Science and Technology, School of Artificial Intelligence, Nanjing, China (GRID:grid.260478.f) (ISNI:0000 0000 9249 2313); Chaoyang University of Technology, Department of Information Management, Taichung, Taiwan (GRID:grid.411218.f) (ISNI:0000 0004 0638 5829) 
 Nanjing University of Information Science and Technology, School of Computer Science, Nanjing, China (GRID:grid.260478.f) (ISNI:0000 0000 9249 2313) 
 Shandong University of Science and Technology, College of Computer Science and Engineering, Qingdao, China (GRID:grid.412508.a) (ISNI:0000 0004 1799 3811) 
 LTD, Taiwan Smarter Water Co., Taiwan, China (GRID:grid.412508.a) 
 Nanjing University of Information Science and Technology, School of Artificial Intelligence, Nanjing, China (GRID:grid.260478.f) (ISNI:0000 0000 9249 2313); Shandong University of Science and Technology, College of Computer Science and Engineering, Qingdao, China (GRID:grid.412508.a) (ISNI:0000 0004 1799 3811) 
Pages
113
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20597037
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3127422944
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.