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HYBRID MODELLING FOR ON-LINE PENICILLIN FERMENTATION OPTIMISATION
M. Ignova, G.C. Paul+, C.A. Kent+, C.R. Thomas+, G.A. Montague, J. Glassey, and
A.C. Ward*
Department of Chemical and Process Engineering, University of Newcastle,
Newcastle upon Tyne, NE1 7RU, UK
* Department of Agriculture and Environmental Science,
University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 7RU, UK
+ BBSRC Centre for Biochemical Engineering, School of Chemical Engineering,
University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
This paper describes the procedures that are necessary to arrive at a model that is sufficiently accurate to be used in an on-line penicillin fermentation optimisation scheme. A structured mechanistic model was available but it failed to account for the effects of low levels of dissolved oxygen. When moving towards optimising the fermentation, it becomes the parameter limiting process productivity. Terms predicting dissolved oxygen changes and describing its effect when at low levels were included. On line correction of model coefficients via an observer approach provided the accuracy required for optimisation purposes.
Keywords: Fed-batch fermentation, hybrid modelling, artificial neural networks, penicillin fermentation
Session slot T-Mo-M09: Modeling, Analysis, and Control of Complex Bioprocesses/Area code 7d : Control of Biotechnological Processes
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