Optimisation of water treatment works performance using genetic algorithms

Roger Swan; Mark Sterling; Jonathan Bridgeman

doi:10.2166/hydro.2017.083

Optimisation of water treatment works performance using genetic algorithms

Roger Swan, Mark Sterling, Jonathan Bridgeman

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

314 Downloads (Pure)

Abstract

Verified static and dynamic models of an operational works were used alongside Monte-Carlo conditions and Non-Dominated Sorting Genetic Algorithm II (NGSAII) to optimise operational regimes. Static models were found to be more suitable for whole WTW optimisation modelling and offered the additional advantage of reduced computational burden. Static models were shown to predict solutions of comparable cost when applied to optimisation problems whilst being faster to simulate than dynamic models.

Original language	English
Journal	Journal of Hydroinformatics
Early online date	9 Jun 2017
DOIs	https://doi.org/10.2166/hydro.2017.083
Publication status	E-pub ahead of print - 9 Jun 2017

Keywords

Genetic Algorithms
Water Treatment Works
Optimisation

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10.2166/hydro.2017.083

Swan_et_al_Optimisation_Water_Treatment_Works_Journal_Hydroinformatics
Final Version of record available at: http://dx.doi.org/10.2166/hydro.2017.083 Checked 30/5/2017
Accepted author manuscript, 1.18 MBLicence: None: All rights reserved

Cite this

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KW - Genetic Algorithms

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KW - Optimisation

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M3 - Article

SN - 1464-7141

JO - Journal of Hydroinformatics

JF - Journal of Hydroinformatics

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Optimisation of water treatment works performance using genetic algorithms

Abstract

Keywords

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