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Soft-Sensors for Lipid Fermentation Variables Based on PSO Support Vector Machine (PSO-SVM)

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Distributed Computing and Artificial Intelligence, 13th International Conference

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 474))

Abstract

On-line monitoring fermentation variables (e.g. biomass) can improve the performance of bio-processes, as well as the quality of the targeted products. However, on-line estimation could be a challenging task when an accurate model is not available. Over the existing methods for state estimation, the support vector machine (SVM) is an attractive method for its fast convergence and generalization of the approximated function. In this paper, a soft-sensor based on SVM and coupled to Particle Swarm Optimization (PSO) algorithm is presented and applied to estimate the concentrations of lipid fermentation variables: lipids, biomass, and citric acid. The soft-sensor was trained with one data set, and validated with an independent data set of fed-batch fermentations. The PSO-SVM was compared with the SVM algorithm. In general, the results show that the PSO-SVM is an efficient alternative for monitoring fermentations.

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Author information

Authors and Affiliations

  1. Université de Toulouse, UPS, INSA, INP, LISBP, 31077, Toulouse, France

    Carlos Eduardo Robles-Rodriguez, Carine Bideaux, Carole Molina-Jouve & Cesar Arturo Aceves-Lara

  2. INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, Toulouse, France

    Carlos Eduardo Robles-Rodriguez, Carine Bideaux, Carole Molina-Jouve & Cesar Arturo Aceves-Lara

  3. CNRS, UMR5504, 135 Avenue de Rangueil, 31077, Toulouse Cedex, France

    Carlos Eduardo Robles-Rodriguez, Carine Bideaux, Carole Molina-Jouve & Cesar Arturo Aceves-Lara

  4. LAAS-CNRS, Université de Toulouse, CNRS, UPS, Toulouse, France

    Gilles Roux

Authors
  1. Carlos Eduardo Robles-Rodriguez
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  2. Carine Bideaux
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  3. Gilles Roux
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  4. Carole Molina-Jouve
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  5. Cesar Arturo Aceves-Lara
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Corresponding author

Correspondence to Cesar Arturo Aceves-Lara .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, Osaka Institute of Technology Faculty of Engineering, Osaka, Osaka, Japan

    Sigeru Omatu

  2. Faculty of Computer Science & Informatio, Universiti Teknologi Malaysia (UTM) Faculty of Computer Science & Informatio, Baharu, Malaysia

    Ali Semalat

  3. Department of Electronics and Compu, Koszalin University of Technology Department of Electronics and Compu, Koszalin, Poland

    Grzegorz Bocewicz

  4. Faculty of Electrical Engineering and Cs, Kielce University of Technology Faculty of Electrical Engineering and Cs, Kielce, Poland

    Paweł Sitek

  5. Faculty of Engineering and Science, Aalborg University Faculty of Engineering and Science, Aalborg, Denmark

    Izabela E. Nielsen

  6. ETS Ingeniería Informática, University of Sevilla ETS Ingeniería Informática, Sevilla, Spain

    Julián A. García García

  7. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid Departamento de Inteligencia Artificial, Madrid, Spain

    Javier Bajo

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© 2016 Springer International Publishing Switzerland

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Robles-Rodriguez, C.E., Bideaux, C., Roux, G., Molina-Jouve, C., Aceves-Lara, C.A. (2016). Soft-Sensors for Lipid Fermentation Variables Based on PSO Support Vector Machine (PSO-SVM). In: Omatu, S., et al. Distributed Computing and Artificial Intelligence, 13th International Conference. Advances in Intelligent Systems and Computing, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-319-40162-1_19

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  • DOI: https://doi.org/10.1007/978-3-319-40162-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40161-4

  • Online ISBN: 978-3-319-40162-1

  • eBook Packages: EngineeringEngineering (R0)

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