ADAPTIVE OPTIMIZATION OF CONTINUOUS BIOREACTOR USING NEURAL-NETWORK MODEL

An adaptive optimization algorithm using backpropogation neural networ k model for dynamic identification is developed. The algorithm is appl ied to maximize the cellular productivity of a continuous culture of b aker's yeast. The robustness of the algorithm is demonstrated in deter mining and maintaining the optimal dilution rate of the continuous bio reactor in presence of disturbances in environmental conditions and mi crobial culture characteristics. The simulation results show that a si gnificant reduction in time required to reach optimal operating levels can be achieved using neural network model compared with the traditio nal dynamic linear input-output model. The extension of the algorithm for multivariable adaptive optimization of continuous bioreactor is br iefly discussed.