An enhanced neural network model for predictive control of granule quality characteristics

An integrated approach is presented for predicting granule particle size using Partial Correlation (PC) analysis and Artificial Neural Networks (ANNs). In this approach, the proposed model is an abstract form from the ANN model, which intends to reduce model complexity via reducing the dimension of the input set and consequently improving the generalization capability of the model. This study involves comparing the capability of the proposed model in predicting granule particle size with those obtained from ANN and Multi Linear Regression models, with respect to some indicators. The numerical results confirm the superiority of the proposed model over the others in the prediction of granule particle size. In order to develop a predictive-control strategy, by employing the proposed model, several scenarios are developed to identify the most suitable process settings with respect to the desired process response. Utilization of these scenarios paves the way for decisions about spray drying to be made consistently and correctly without any need for judgmental speculations or expensive trial-and-error tests.