DYNAMIC AND HYBRID NEURAL MODEL OF THERMAL DRYING IN A FLUIDIZED-BED

A preliminary study aimed at comparing Classical Dynamic Neural Modell ing (CDNM) and Hybrid Neural Modelling (HNM) to describe thermal dewat ering process in a fluidized bed is presented. Two schemes of HN model ling were developed to find the most efficient way of combining a clas sical mathematical model of the process and Artificial Neural Network (ANN). CDN model was developed using ''moving window'' technique. In t he first scheme of HNM a feed-forward ANN was trained to predict evapo ration rate and heat flux in the drying process. In the second scheme of the HN model, ANN was used to determine heat transfer coefficient o nly. Excellent prediction of drying process by HNM is proved.