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Identifiability Analysis and Prediction Error Identification of Anaerobic Batch Bioreactors

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Abstract

This paper presents the identifiability analysis of a nonlinear model for a batch bioreactor and the estimation of the identifiable parameters within the prediction error framework. The output data of the experiment are the measurements of the methane gas generated by the process, during 37 days, and knowledge of the initial conditions is limited to the initial quantity of chemical oxygen demand. It is shown by the identifiability analysis that only three out of the eight model parameters can be identified with the available measurements and that identification of the remaining parameters would require further knowledge of the initial conditions. A prediction error algorithm is implemented for the estimation of the identifiable parameters. This algorithm is iterative, relies on the gradient of the prediction error, whose calculation is implemented recursively, and consists of a combination of two classic optimization methods: the conjugated gradient method and the Gauss–Newton method.

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Notes

  1. The data were filtered with the same Butterworth filter used in the previous case.

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Acknowledgments

The authors are grateful to Prof. Odorico Konrad from Centro Universitário UNIVATES for supplying the methane measurement samples and to Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and CNPq/Brazil for financial support.

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Authors and Affiliations

  1. Department of Electrical Engineering, Universidade Federal do Rio Grande do Sul, Av. Osvaldo Aranha, 103, Porto Alegre, RS, Brazil

    L. Campestrini, R. Rui & A. S. Bazanella

  2. Department of Applied and Pure Mathematics, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500 - Building 43-111, Porto Alegre, RS, Brazil

    D. Eckhard

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  1. L. Campestrini
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  2. D. Eckhard
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  3. R. Rui
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  4. A. S. Bazanella
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Correspondence to L. Campestrini.

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Campestrini, L., Eckhard, D., Rui, R. et al. Identifiability Analysis and Prediction Error Identification of Anaerobic Batch Bioreactors. J Control Autom Electr Syst 25, 438–447 (2014). https://doi.org/10.1007/s40313-014-0129-3

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  • DOI: https://doi.org/10.1007/s40313-014-0129-3

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