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Data Reconciliation Using Neural Networks for the Determination of KLa

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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))

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Abstract

The oxygen mass transfer coefficient (KLa) is of paramount importance in conducting aerobic fermentation. KLa also serves to compare the efficiency of bioreactors and their mixing devices as well as being an important scale-up factor. In submerged fermentations, four methods are available to estimate the overall oxygen mass transfer coefficient (KLa): the dynamic method, the stationary method based on a previous determination of the oxygen uptake rate (QO2X), the gaseous oxygen balance and the carbon dioxide balance. Each method provides a distinct estimation of the value of KLa. Data reconciliation can be used to obtain the most probable value of KLa by minimizing an objective function that includes measurement terms and oxygen conservation models, each being weighted according to their level of confidence. Another alternative, for a more rapid determination of KLa, is using a neural network which has been previously trained to predict KLa from the series of oxygen conservation models. Results obtained with this new approach show that KLa can be predicted rapidly and gives values that are equivalent to those obtained with the conventional data reconciliation algorithm.

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Authors and Affiliations

  1. Department of Chemical and Biological Engineering, University of Ottawa, Ottawa (Ontario), Canada, K1N 6N5

    Nilesh Patel & Jules Thibault

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  1. Nilesh Patel
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  2. Jules Thibault
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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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Patel, N., Thibault, J. (2009). Data Reconciliation Using Neural Networks for the Determination of KLa. 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_7

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  • DOI: https://doi.org/10.1007/978-3-642-01888-6_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01887-9

  • Online ISBN: 978-3-642-01888-6

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