Abstract
An improved technique of hybrid modelling biochemical production processes is described, composed of a set of dynamical differential equations, an artificial neural network and a fuzzy expert system. The applicability of a hybrid model for state estimation, prediction, feed rate optimization, and process control is explained. Its performance is demonstrated by means of an the example of a fed-batch baker's yeast production process. The identification procedure is described in detail. Emphasis is placed on the most difficult part of it, the training of the neural network component of the hybrid model. New approaches to process control are developed based on neural networks.
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