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Operational Optimization of Large-Scale Parallel-Unit SWRO Desalination Plant Using Differential Evolution Algorithm

A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO) units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP) of...

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Detalles Bibliográficos
Autores principales: Wang, Jian, Wang, Xiaolong, Jiang, Aipeng, Jiangzhou, Shu, Li, Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3948477/
https://www.ncbi.nlm.nih.gov/pubmed/24701180
http://dx.doi.org/10.1155/2014/584068
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author Wang, Jian
Wang, Xiaolong
Jiang, Aipeng
Jiangzhou, Shu
Li, Ping
author_facet Wang, Jian
Wang, Xiaolong
Jiang, Aipeng
Jiangzhou, Shu
Li, Ping
author_sort Wang, Jian
collection PubMed
description A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO) units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP) of the plant is to find out a scheduling of operation to minimize the total running cost when the change happens. In this paper, the OOP is modelled as a mixed-integer nonlinear programming problem. A two-stage differential evolution algorithm is proposed to solve this OOP. Experimental results show that the proposed method is satisfactory in solution quality.
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spelling pubmed-39484772014-04-03 Operational Optimization of Large-Scale Parallel-Unit SWRO Desalination Plant Using Differential Evolution Algorithm Wang, Jian Wang, Xiaolong Jiang, Aipeng Jiangzhou, Shu Li, Ping ScientificWorldJournal Research Article A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO) units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP) of the plant is to find out a scheduling of operation to minimize the total running cost when the change happens. In this paper, the OOP is modelled as a mixed-integer nonlinear programming problem. A two-stage differential evolution algorithm is proposed to solve this OOP. Experimental results show that the proposed method is satisfactory in solution quality. Hindawi Publishing Corporation 2014-02-17 /pmc/articles/PMC3948477/ /pubmed/24701180 http://dx.doi.org/10.1155/2014/584068 Text en Copyright © 2014 Jian Wang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Jian
Wang, Xiaolong
Jiang, Aipeng
Jiangzhou, Shu
Li, Ping
Operational Optimization of Large-Scale Parallel-Unit SWRO Desalination Plant Using Differential Evolution Algorithm
title Operational Optimization of Large-Scale Parallel-Unit SWRO Desalination Plant Using Differential Evolution Algorithm
title_full Operational Optimization of Large-Scale Parallel-Unit SWRO Desalination Plant Using Differential Evolution Algorithm
title_fullStr Operational Optimization of Large-Scale Parallel-Unit SWRO Desalination Plant Using Differential Evolution Algorithm
title_full_unstemmed Operational Optimization of Large-Scale Parallel-Unit SWRO Desalination Plant Using Differential Evolution Algorithm
title_short Operational Optimization of Large-Scale Parallel-Unit SWRO Desalination Plant Using Differential Evolution Algorithm
title_sort operational optimization of large-scale parallel-unit swro desalination plant using differential evolution algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3948477/
https://www.ncbi.nlm.nih.gov/pubmed/24701180
http://dx.doi.org/10.1155/2014/584068
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