Comparison of feed-forward and recurrent neural networks for bioprocess state estimation

Abstract

The application of artificial neural networks to the estimation of bioprocess variables will be discussed. In fermentation processes, direct on-line measurements of primary process variables usually are unavailable. The state of the cultivation, therefore, has to be inferred from measurements of secondary variables and any previous knowledge of process dynamics. This research investigates the learning, recall and generalization characteristics of neural networks trained to model the nonlinear behavior of a fermentation process. Two different neural network methodologies are discussed, namely, feed-forward and recurrent neural networks, which differ in their treatment of time dependence. The neural networks are trained by backpropagation using a conjugate gradient technique, which provides a dramatic improvement in the convergence speed. The objective is to use environmental and physiological information available from on-line sensors to estimate concentrations of species in a bioreactor. Results of the neural network estimators are presented, based on experimental data available from the ethanol production by Zymomonas mobilis fermentation. The feed-forward and recurrent neural network methodologies are demonstrated to perform suitably as unmeasurable state estimators. Both networks offer comparable abilities of recall, but recurrent networks perform better than feed-forward networks in generalization.