Abstract
The use of artificial neural networks (ANNs) in modelling a fluidized bed granulation process is reported. The granules were made in a fully instrumented laboratory-scale granulator (Glatt WSG 5, Glatt GmbH Germany). The independent input variables were inlet air temperature atomizing air pressure and binder solution amount. The input variables varied in three levels. The responses used were mean granule size and granule friability. Neural computing was carried out using a commercial NeuDesk software (Neural Computer Sciences U.K.) in a 486 microcomputer with a specific accelerator card NeuSprint (Neural Computer Sciences U.K.). In total, 36 different ANN models were tested. The results were also compared with a statistical method (multilinear stepwise regression analysis). The results showed clearly that the best networks were able to predict the experimental responses more accurately than the multilinear stepwise regression analysis. On the other hand it also became evident that several different structures should be trained with different training end points to generate a proper model.
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