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Optimal control of a fed-batch fermentation process

Industrial Control Centre. University of Westminster, 115 New Cavendish Street, London W1M 8JS, UK

B.S. Zhang

Industrial Control Centre. University of Westminster, 115 New Cavendish Street, London W1M 8JS, UK

J.R. Leigh

Control Engineering Group, Dept of Electrical Engineering and Electronics, Brunel University, Uxbridge, UB8 3PH, UK

Optimisation of fed-batch fermentation processes usually employs the calculus of variations to determine optimal feed-rate profiles that will maximise a given objective function. This results in a two-point boundary-value problem and because of the nonlinear nature of the processes, the optimal solution usually falls out as an open-loop control algorithm. One advantage of this approach is that it does not need measurements of state variables which are often difficult to obtain on-line. Instead it assumes that the state variables are proceeding along known paths a-priori determined by models. However, the disadvantage of such an approach is that the performance will severely deteriorate in the presence of process disturbances or plant-model mismatch. Recently, on-line estimation of state variables has been successfully developed and used in the industry and therefore a method which operates the fed-batch fermentation in a closed-loop control scheme using state feedback is proposed. This is achieved by a two-step method. First, the optimal substrate concentration profile which governs the biochemical reactions in the fermentation process is determined. Then a controller is designed in closed-loop form to track this desired profile. Simulation studies for both primary and secondary metabolite production processes show that better performance is obtained by this closed-loop aptifaaal control method than by the open-loop optimal feed rate profile method. This is due to the self-correcting property of the proposed method, which proves to be advantageous when there are disturbances or plant-model mismatch.

Key Words: Fed-batch fermentation • optimal control • process optimisation • calculus of variations • primary metabolite • secondary metabolite.

Transactions of the Institute of Measurement and Control, Vol. 19, No. 5, 240-251 (1997)
DOI: 10.1177/014233129701900504


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