Hybrid modelling and self-learning system for dextrose crystallization process

This paper deals with the modelling of a continuous cooling column crystallizer. An accurate model of this system is needed for complex process control. The investigated system consists on dextrose monohydrate in aqueous solution. An adaptive hybrid model is presented. The model consists of two parts: the phenomenological model, expressed by a set of differential algebraic equations, and a neural network, based on historical data, developed by the fuzzy ARMAP technique. The empirical part of the hybrid model is aimed at eliminating the deviations of the prediction of the phenomenological model caused mainly by incrustations over the surface of the cooling coils located along the column crystallizer. The model is adaptive since neural network parameters are updated by a self-learning system (SLS) based on the acquired process data storage of the DCS of an industrial plant. Firstly, the hybrid model was implemented by using the data of a three-month campaign of the crystallizer, then the self-learning technique was checked on site in a subsequent campaign.