A Heuristic Method to Optimize High-Dimensional Expensive Problems: Application to the Dynamic Optimization of a Waste Water Treatment Plant

  • Alberto Garre
  • Pablo S. Fernandez
  • Julio R. Banga
  • Jose A. Egea
Conference paper
Part of the Mathematics in Industry book series (MATHINDUSTRY, volume 26)

Abstract

The mathematical description of industrial processes usually requires the use of models consisting of large systems of differential and algebraic equations. The numerical simulations of such models may lead to high computation times, therefore, making optimization unaffordable using classical optimization methods.

This contribution describes an evolutionary approach for the optimization of computationally expensive, highly dimensional problems. The performance of the algorithm has been compared against well known surrogate based optimization methods using classical benchmark functions. The results show that our method outperforms the reference methods, specially for the high dimensional case.

The proposed algorithm has been applied to the optimization of the operation parameters of a waste water treatment plant, using dynamic profiles. The algorithm has been able to produce better solutions than those obtained previously using static profiles.

Notes

Acknowledgements

The financial support of this research work was provided by the Ministry of Economy and Competitiveness (MINECO) of the Spanish Government and European Regional Development Fund (ERDF) through projects AGL2013-48993-C2-1-R and DPI2011-28112-C04-(03, 04). Alberto Garre (BES-2014-070946) is grateful to the MINECO for awarding him a pre-doctoral grant.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2017

Authors and Affiliations

  • Alberto Garre
    • 1
  • Pablo S. Fernandez
    • 1
  • Julio R. Banga
    • 2
  • Jose A. Egea
    • 3
  1. 1.Departamento de Ingeniería de Alimentos y del Equipamiento Agrícola, Instituto de Biotecnología VegetalUniversidad Politécnica de Cartagena (ETSIA)CartagenaSpain
  2. 2.BioProcess Engineering Group, IIM-CSICVigoSpain
  3. 3.Departamento de Matemática Aplicada y EstadísticaUniversidad Politécnica de CartagenaCartagenaSpain

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