TOWARDS IMPROVED PENICILLIN FERMENTATION VIA ARTIFICIAL NEURAL NETWORKS

Despite extensive research efforts in biosensor technology, effective supervision and control of bioprocess systems still remains a formidable task. However, the inference of important biological process variables from existing on-line measurements provides one means of easing the burden. Inferencing techniques are primarily based upon sound process models, which for non-linear bioprocess systems can be problematic to derive. The ability of artificial neural networks to learn essential process non-linearities from plant data may provide a means by which to assist in estimator development. This paper investigates this prospect by considering the construction of artificial network-based biomass and penicillin estimators for on-line application to an industrial fermentation. Results from on-line industrial site tests are presented.