Dynamic Re-Optimization of a Fed-Batch Fermentor using Adaptive Critic Designs

Donald C. Wunsch, Missouri University of Science and Technology
M. S. Iyer

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Traditionally, fed-batch biochemical process optimization and control uses complicated off-line optimizers, with no online model adaptation or re-optimization. This study demonstrates the applicability of a class of adaptive critic designs for online re-optimization and control of an aerobic fed-batch fermentor. Specifically, the performance of an entire class of adaptive critic designs, viz., heuristic dynamic programming, dual heuristic programming and generalized dual heuristic programming, was demonstrated to be superior to that of a heuristic random optimizer, on optimization of a fed-batch fermentor operation producing monoclonal antibodies