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An approach to parameters estimation of a chromatography model using a clustering genetic algorithm based inverse model

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Abstract

Genetic algorithms are tools for searching in complex spaces and they have been used successfully in the system identification solution that is an inverse problem. Chromatography models are represented by systems of partial differential equations with non-linear parameters which are, in general, difficult to estimate many times. In this work a genetic algorithm is used to solve the inverse problem of parameters estimation in a model of protein adsorption by batch chromatography process. Each population individual represents a supposed condition to the direct solution of the partial differential equation system, so the computation of the fitness can be time consuming if the population is large. To avoid this difficulty, the implemented genetic algorithm divides the population into clusters, whose representatives are evaluated, while the fitness of the remaining individuals is calculated in function of their distances from the representatives. Simulation and practical studies illustrate the computational time saving of the proposed genetic algorithm and show that it is an effective solution method for this type of application.

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Acknowledgments

The authors acknowledge the financial support provided by CAPES/Brasil, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; MES/Cuba, Ministerio de Educación Superior; CNP/Brasil, Conselho Nacional de Desenvolvimento Científico e Tecnológico and FAPERJ/Brasil, Fundação Carlos Chagas Filho de Amparo à Pesquisa do estado do Rio de Janeiro. Also, the authors want to thank the suggestions of the colleagues and friends Dr. David Pelta and Dr. José L. Verdegay from Granada University in Spain.

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Authors and Affiliations

  1. Department of Automation and Computers, Technical University of Havana (ISPJAE), Ciudad de La Habana, Cuba

    Mirtha Irizar Mesa & Orestes Llanes-Santiago

  2. Department of Automation and Computational Systems, Central University of Las Villas (UCLV), Villa Clara, Cuba

    Francisco Herrera Fernández

  3. Center of Molecular Immunology, Ciudad de la Habana, Cuba

    David Curbelo Rodríguez

  4. Departamento de Engenharia Mecânica e Energia, DEMEC, IPRJ-UERJ, Nova Friburgo, Brazil

    Antônio José Da Silva Neto & Leôncio Diógenes T. Câmara

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  1. Mirtha Irizar Mesa
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  2. Orestes Llanes-Santiago
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  3. Francisco Herrera Fernández
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  4. David Curbelo Rodríguez
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  5. Antônio José Da Silva Neto
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  6. Leôncio Diógenes T. Câmara
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Correspondence to Mirtha Irizar Mesa.

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Irizar Mesa, M., Llanes-Santiago, O., Herrera Fernández, F. et al. An approach to parameters estimation of a chromatography model using a clustering genetic algorithm based inverse model. Soft Comput 15, 963–973 (2011). https://doi.org/10.1007/s00500-010-0638-3

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  • DOI: https://doi.org/10.1007/s00500-010-0638-3

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