Optimisation of water treatment works performance using genetic algorithms

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Optimisation of water treatment works performance using genetic algorithms. / Swan, Roger; Sterling, Mark; Bridgeman, Jonathan.

In: Journal of Hydroinformatics, 09.06.2017.

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@article{339bf374ead343c68914aa923f5d664e,
title = "Optimisation of water treatment works performance using genetic algorithms",
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.",
keywords = "Genetic Algorithms , Water Treatment Works, Optimisation",
author = "Roger Swan and Mark Sterling and Jonathan Bridgeman",
year = "2017",
month = jun,
day = "9",
doi = "10.2166/hydro.2017.083",
language = "English",
journal = "Journal of Hydroinformatics",
issn = "1464-7141",
publisher = "IWA Publishing",

}

RIS

TY - JOUR

T1 - Optimisation of water treatment works performance using genetic algorithms

AU - Swan, Roger

AU - Sterling, Mark

AU - Bridgeman, Jonathan

PY - 2017/6/9

Y1 - 2017/6/9

N2 - 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.

AB - 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.

KW - Genetic Algorithms

KW - Water Treatment Works

KW - Optimisation

U2 - 10.2166/hydro.2017.083

DO - 10.2166/hydro.2017.083

M3 - Article

JO - Journal of Hydroinformatics

JF - Journal of Hydroinformatics

SN - 1464-7141

ER -