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The problem of chemostat dynamics modelling for the purpose of control is considered. The``memory'' of the culture is explicitly taken into account. Two possibilities for improving the quality of the proposed modelling approaches are discussed. A general model that accounts for the culture`memory' by means of different`memory' functions in the expressions of the speci®c growth rate and of the speci®c consumption rate and a polynomial function of the substrate concentration for the yield factor is proposed. The case where the maintenance energy is taken into account is also discussed. Two modi®cations of the general model (l-type and S-type) are presented. A zero-order`memory' function and a d-function with delay are applied in order to describe the`memory' effects. Continuous growth of the strain Saccharomyces cerevisiae on a glucose limited medium is considered as a case study. Detailed investigations of the variety of models, derived from the general model by applying different`memory' functions and different assumptions are carried out. The results are compared with those previously reported for the same process. It is shown that a signi®cant improvement in predicting the substrate dynamics (not accompanied by any decrease in the quality of the model with respect to the biomass concentration) could be achieved, involving a ®rst-or second-order polynomial function for the yield factor. It is also shown that the quality of the model mainly depends on the way that`memory' function is incorporated. The detailed investigations give priority to the l-type models. In this case past values of both biomass and substrate variables are considered. The time delay models with pure (constant) delay and those which account for the culture`memory' by zero-order`memory' function (adaptability parameter) are compared with respect to their utilization for the purpose of modelbased control.

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In the present paper the problem of chemostat modelling using the neural networks techniques is considered. A feedforward neural network with time delay feedback connections from and to the output neurons, which take into account the culture memory is proposed. A model of the growth of a strain Saccharomyces cerevisiae on a glucose limited medium is developed. Simulation investigations are carried out. The results are discussed.

This paper deals with an application of neural networks for chemostat modelling. A feedforward neural network, taking into account culture memory is proposed for the speci®c growth rate approximation within the framework of the classical unstructured model. The investigations are carried out for different network topologies on the example of the growth of a strain Saccharomyces cerevisiae on a glucose limited medium and a model suitable for control synthesis is proposed.