Neuroidentification of a biotechnological process: Issues and application

Abstract Fermentations are highly non-linear time-varying and inaccurately modelled processes. Our work towards the generation of a neural network model of an industrial secondary metabolic fermentation process is presented in this paper. The construction methodology and the various issues arising during the design, training and testing stages are also discussed. Such issues addressed in this paper include the treatment of dynamics and the determination of the number of hidden nodes. The usefulness of the neural network model with regard to the on-line estimation of fundamental measurements is demonstrated and its part in a control system is highlighted.

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