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Multiobjective Genetic Algorithms for the Optimisation of Wastewater Treatment Processes

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Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control

Part of the book series: Studies in Computational Intelligence ((SCI,volume 218))

Abstract

The combination of multiobjective genetic algorithms with wastewater treatment plant (WWTP) models provides an efficient framework for the evaluation, optimisation and comparison of WWTP control laws. This chapter presents a methodology developed for this efficient combination. Existing models and simulation software are used. They are combined with NSGA-II, a multiobjective genetic algorithm capable of finding the best tradeoffs (Pareto front) among multiple opposed objectives. Long term evaluations of the optimized solutions are proposed to check their robustness. An application of the methodology on the Benchmark Simulation Model 1 is presented and illustrates the benefits of the methodology.

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Author information

Authors and Affiliations

  1. Anjou Recherche, Chemin de la Digue, BP 76, 78603, Maisons Laffitte, France

    Benoît Beraud & Cyrille Lemoine

  2. INRA, UR50, Laboratoire de Biotechnologie de l’Environnement, Avenue des Etangs, 11000, Narbonne, France

    Jean-Philippe Steyer

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  1. Benoît Beraud
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  2. Cyrille Lemoine
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  3. Jean-Philippe Steyer
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Editors and Affiliations

  1. CS Department, Universidade Federal de S. Carlos, Rod.Washington Luiz, km235, Caixa Postal 676, 13565-905, S. Carlos, SP, Brazil

    Maria do Carmo Nicoletti

  2. School of Electrical and Information Engineering, University of South Australia, Mawson Lakes Campus, 5095, Adelaide, SA, Australia

    Lakhmi C. Jain

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Beraud, B., Lemoine, C., Steyer, JP. (2009). Multiobjective Genetic Algorithms for the Optimisation of Wastewater Treatment Processes. In: do Carmo Nicoletti, M., Jain, L.C. (eds) Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control. Studies in Computational Intelligence, vol 218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01888-6_6

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  • DOI: https://doi.org/10.1007/978-3-642-01888-6_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01887-9

  • Online ISBN: 978-3-642-01888-6

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