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  5. Bi-objective design-for-control for improving the pressure management and resilience of water distribution networks.
 
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Bi-objective design-for-control for improving the pressure management and resilience of water distribution networks.
File(s)
1-s2.0-S0043135422008612-main.pdf (2.42 MB)
Published version
OA Location
https://doi.org/10.1016/j.watres.2022.118914
Author(s)
Ulusoy, Aly-Joy
Mahmoud, Herman A
Pecci, Filippo
Keedwell, Edward C
Stoianov, Ivan
Type
Journal Article
Abstract
This paper investigates control and design-for-control strategies to improve the resilience of sectorized water distribution networks (WDN), while minimizing pressure induced pipe stress and leakage. Both evolutionary algorithms (EA) and gradient-based mathematical optimization approaches are investigated for the solution of the resulting large-scale non-linear (NLP) and bi-objective mixed-integer non-linear programs (BOMINLP). While EAs have been successfully applied to solve discrete network design problems for large-scale WDNs, gradient-based mathematical optimization methods are more computationally efficient when dealing with large search spaces associated with continuous variables in optimal network control problems. Considering the advantages of each method, we propose a sequential hybrid method for the optimal design-for-control of large-scale WDNs, where a refinement stage relying on gradient-based mathematical optimization is used to solve continuous optimal control problems corresponding to design solutions returned by an initial EA search. The proposed method is applied to compute the Pareto front of a bi-objective design-for-control problem for the operational network BWPnet, where we consider reopening closed connections between isolated supply areas. The results show that the considered design-for-control strategy increases the resilience of BWPnet while minimizing pressure induced leakage. Moreover, the refinement stage of the proposed hybrid method efficiently improves the coarse approximation computed by the initial EA search, returning a continuous and even Pareto front approximation.
Date Issued
2022-07-27
Date Acceptance
2022-07-26
Citation
Water Research, 2022, 222, pp.118914-118914
URI
http://hdl.handle.net/10044/1/98966
URL
https://www.sciencedirect.com/science/article/pii/S0043135422008612?via%3Dihub
DOI
https://www.dx.doi.org/10.1016/j.watres.2022.118914
ISSN
0043-1354
Publisher
IWA Publishing
Start Page
118914
End Page
118914
Journal / Book Title
Water Research
Volume
222
Copyright Statement
© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
http://creativecommons.org/licenses/by/4.0/
Sponsor
Engineering & Physical Science Research Council (E
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/35933815
PII: S0043-1354(22)00861-2
Grant Number
EP/P004229/1
Subjects
Bi-objective optimization
Design-for-control
Evolutionary algorithms
Mixed-integer non-linear programming
Pressure management
Resilience
Publication Status
Published online
Coverage Spatial
England
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