Wastewater Treatment Plant Design: Optimizing Multiple Objectives

Roman Denysiuk, Isabel Espírito Santo, Lino Costa

Abstract

The high costs associated with the design of wastewater treatment plants (WWTPs) motivate the research in the area of modelling their construction and the water treatment process optimization. This work addresses different methodologies, which are based on defining and simultaneously optimizing several conflicting objectives, for finding the optimal values of the state variables in the plant design. We use an evolutionary many-objective optimization algorithm with clustering-based selection that proved effective in handling challenging optimization problems with a large number of objectives. The obtained results are promising and with physical meaning. It is shown that the overall WWTP design can be improved by coming up with appropriate formulation of the optimization problem and solving approach.

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Paper Citation


in Harvard Style

Denysiuk R., Espírito Santo I. and Costa L. (2018). Wastewater Treatment Plant Design: Optimizing Multiple Objectives.In Proceedings of the 7th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-285-1, pages 327-334. DOI: 10.5220/0006660303270334


in Bibtex Style

@conference{icores18,
author={Roman Denysiuk and Isabel Espírito Santo and Lino Costa},
title={Wastewater Treatment Plant Design: Optimizing Multiple Objectives},
booktitle={Proceedings of the 7th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2018},
pages={327-334},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006660303270334},
isbn={978-989-758-285-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Wastewater Treatment Plant Design: Optimizing Multiple Objectives
SN - 978-989-758-285-1
AU - Denysiuk R.
AU - Espírito Santo I.
AU - Costa L.
PY - 2018
SP - 327
EP - 334
DO - 10.5220/0006660303270334