Abstract
In order to apply a model based control strategy such as flexible recipes control to an industrial batch process, a model of the process dynamics is needed. This paper presents the modelling procedure for two such models: a semi-empirical and an artificial neural network model. Both models predict precipitation rates of TiO2 particles in an industrial hydrolysis process. Model properties and their prediction accuracy is compared. The artificial neural network is trained using the augmented training data set approach. A simulator has been designed to study the application Of flexible recipe instructions.
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