Optimisation of water treatment works using static and dynamic models with an NSGAII genetic algorithm

Roger Swan; Jonathan Bridgeman; Mark Sterling

Optimisation of water treatment works using static and dynamic models with an NSGAII genetic algorithm

Roger Swan, Jonathan Bridgeman, Mark Sterling

Civil Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper applies a genetic algorithm to static and dynamic models of a case study water treatment works to find near optimal designs. The mechanisms of these models, their calibration and accuracy are described. The models were used with stochastic data representative of conditions observed at the works and the NSGAII genetic algorithm was applied to minimise the size of the works and the failure likelihood. The dynamic model was found to predict more conservative designs than the static model. The genetic algorithm was found to require greater calibration to identify near-optimal solutions efficiently.

Original language	English
Title of host publication	Machine Learning in Water Systems - AISB Convention 2013
Pages	20-24
Number of pages	5
Publication status	Published - 1 Dec 2013
Event	Machine Learning in Water Systems, Held at the AISB Convention 2013 - Exeter, United Kingdom Duration: 3 Apr 2013 → 5 Apr 2013

Conference

Conference	Machine Learning in Water Systems, Held at the AISB Convention 2013
Country/Territory	United Kingdom
City	Exeter
Period	3/04/13 → 5/04/13

ASJC Scopus subject areas

Human-Computer Interaction

Cite this

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title = "Optimisation of water treatment works using static and dynamic models with an NSGAII genetic algorithm",

abstract = "This paper applies a genetic algorithm to static and dynamic models of a case study water treatment works to find near optimal designs. The mechanisms of these models, their calibration and accuracy are described. The models were used with stochastic data representative of conditions observed at the works and the NSGAII genetic algorithm was applied to minimise the size of the works and the failure likelihood. The dynamic model was found to predict more conservative designs than the static model. The genetic algorithm was found to require greater calibration to identify near-optimal solutions efficiently.",

author = "Roger Swan and Jonathan Bridgeman and Mark Sterling",

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N2 - This paper applies a genetic algorithm to static and dynamic models of a case study water treatment works to find near optimal designs. The mechanisms of these models, their calibration and accuracy are described. The models were used with stochastic data representative of conditions observed at the works and the NSGAII genetic algorithm was applied to minimise the size of the works and the failure likelihood. The dynamic model was found to predict more conservative designs than the static model. The genetic algorithm was found to require greater calibration to identify near-optimal solutions efficiently.

AB - This paper applies a genetic algorithm to static and dynamic models of a case study water treatment works to find near optimal designs. The mechanisms of these models, their calibration and accuracy are described. The models were used with stochastic data representative of conditions observed at the works and the NSGAII genetic algorithm was applied to minimise the size of the works and the failure likelihood. The dynamic model was found to predict more conservative designs than the static model. The genetic algorithm was found to require greater calibration to identify near-optimal solutions efficiently.

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M3 - Conference contribution

AN - SCOPUS:84894108462

SN - 9781908187338

SP - 20

EP - 24

BT - Machine Learning in Water Systems - AISB Convention 2013

T2 - Machine Learning in Water Systems, Held at the AISB Convention 2013

Y2 - 3 April 2013 through 5 April 2013

ER -

Optimisation of water treatment works using static and dynamic models with an NSGAII genetic algorithm

Abstract

Conference

ASJC Scopus subject areas

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