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Artificial intelligence and the supervision of bioprocesses (real-time knowledge-based systems and neural networks)

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Bioprocess Design and Control

Part of the book series: Advances in Biochemical Engineering/Biotechnology ((ABE,volume 48))

Abstract

The ability to supervise and control a highly non-linear and time variant bioprocess is of considerable importance to the biotechnological industries which are continually striving to obtain higher yields and improved uniformity of production. Two AI methodologies aimed at contributing to the overall intelligent monitoring and control of bioprocess operations are discussed.

The development and application of a real-time knowledge-based system to provide supervisory control of fed-batch bioprocesses is reviewed. The system performs sensor validation, fault detection and diagnosis and incorporates relevant expertise and experience drawn from both bioprocess engineering and control engineering domains. A complementary approach, that of artificial neural networks is also addressed. The development of neural network modelling tools for use in bioprocess state estimation and inferential control are reviewed. An attractive characteristic of neural networks is that with the appropriate topology any non-linear functional relationship can be modelled, hence significantly reducing model-process mismatch. Results from industrial applications are presented.

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Abbreviations

AI:

artificial intelligence

CER:

carbon dioxide evolution rate

ICP:

intelligent communication protocol

KBS:

knowledge-based system

OUR:

oxygen uptake rate

RQ:

respiratory quotient

RTKBS:

real-time knowledge-based system

SQL:

Structural query language

TCS:

Turnbull control system

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Authors and Affiliations

  1. Department of Chemical and Process Engineering, University of Newcastle, NE1 7RU, Newcastle upon Tyne, England

    M. Aynsley, A. Hofland, A. J. Morris, G. A. Montague & C. Di Massimo

Authors
  1. M. Aynsley
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  2. A. Hofland
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  3. A. J. Morris
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  4. G. A. Montague
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  5. C. Di Massimo
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Additional information

Dedicated to Prof. Karl Schügerl on the occasion of his 65th birthday

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© 1993 Springer-Verlag

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Aynsley, M., Hofland, A., Morris, A.J., Montague, G.A., Di Massimo, C. (1993). Artificial intelligence and the supervision of bioprocesses (real-time knowledge-based systems and neural networks). In: Bioprocess Design and Control. Advances in Biochemical Engineering/Biotechnology, vol 48. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0007194

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  • DOI: https://doi.org/10.1007/BFb0007194

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56315-0

  • Online ISBN: 978-3-540-47517-0

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