Abstract
This chapter describes a study of the influence of particle size and the settings of tableting parameters on tablet capping tendency. Artificial neural network (ANN) and fuzzy models were used for modelling the effect of particle size and tableting machine settings on the capping coefficient. The basic principles of the tableting process are described, followed by a description of principal components analysis, then ANN and fuzzy models. Finally, a real tableting case is presented. The suitability of routinely measured quantities for prediction of the tablet quality was tested. Results showed that model-based expert systems developed on the contemporary routinely measured quantities can significantly improve manual trial-and-error procedures; however, they cannot replace them. The results also suggest that where it is not possible to obtain a sufficient number of measurements to uniquely identify the model, it is beneficial to use several modelling techniques to estimate the quality of model prediction.
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