An Efficient Constraint-Based Pruning Method to Improve Chlorine Dosage Optimization
Publication: Journal of Water Resources Planning and Management
Volume 148, Issue 8
Abstract
Urban water system optimization, e.g., chlorine dosage optimization, requires repeatedly running hydraulic and water quality models, which leads to significant computational and time costs for large-scale real networks. In the process of optimization, the search space of decision variables is adjusted downward to obtain the lowest-cost scheduling plans, which lead to a large number of negative samples and low optimization efficiency. To address this problem, this study proposes an efficient constraint-based pruning method (CBPM) that uses accumulated data during the optimization calculation to determine whether a sample meets the constraints before running simulation models, thereby pruning negative samples and improving optimization efficiency. An example network and a large-scale real network were used as case studies to demonstrate the performance of the proposed CBPM. The results show that the proposed CBPM significantly can improve optimization results using the same calculations as the simulation model. For example, application in a real water distribution network showed that to obtain the same total chlorine dosage solutions, the model using the proposed CBPM saved 57.9% of the calculations compared with the original optimization model.
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Data Availability Statement
Some data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The Net3 network data and the water demand uncertainty data can be provided upon request.
Acknowledgments
This work was supported by the National Key Research and Development Program of China for International Science & Innovation Cooperation Major Projects—the Water Major Program (2017ZX07201002). Thanks are given to Lei Xiao, Chunfang Chen from Changzhou CGE Water Co. for supporting the data and network model.
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Journal of Water Resources Planning and Management
Volume 148 • Issue 8 • August 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jun 6, 2021
Accepted: Mar 23, 2022
Published online: May 24, 2022
Published in print: Aug 1, 2022
Discussion open until: Oct 24, 2022
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