On-line adaptation of neural networks for bioprocess control

A recurrent neural network with intra-connections within the output layer is developed to track the dynamics of fed-batch yeast fermentation. The neural network is adapted on-line using only the dissolved oxygen measurement to account for varying operating conditions. The other states of the system, namely the substrate, ethanol and biomass concentrations are not measured but predicted by the adapted network. A neural network having a 10-8-4 architecture with output layer feed back and intra-connections between the nodes of the output layer has been studied in detail. A comparative study of its performance with and without online adaptation of weights is presented. Predictions based on online adaptation of weights were found to be superior compared to that without adaptation. The network was implemented as an online state-estimator facilitating the control of a yeast fermentation process. The results demonstrate that with on-line adaptation of weights, it is possible to implement neural networks to control processes in a wide region outside its training domain.