The effect of a dynamical layer in neural network prediction of biomass in a fermentation process

In this paper, computational intelligence has been considered as a tool (software sensor) for state-estimation and prediction of biomass concentration in a simulated fermentation process. Two different paradigms of an artificial neural networks have been introduced as possible computational engines. Inclusion of process dynamics is inherent within the second paradigm, as a pre-processing layer. The constructed computational engines ‘infer’ the production of biomass from easily measured on-line variables. First and second-order non-linear optimisation methods are used to train the neural networks. It is shown that the use of the pre-processing layer which contains dynamical elements, produces better results and shows significant improvement in the convergence rate of the neural networks.