Dynamic and Hybrid Neural Model of Thermal Drying in a Flutdized Bed

A preliminary study aimed at comparing Classical Dynamic Neural Modelling (CDNM) and Hybrid Neural Modelling (HNM) to describe thermal dewatering process in a fluidized bed is presented Two schemes of HN modelling were developed to find the most efficient way of combining a classical mathematical model of the process and Artificial Neural Network (ANN). CDN model was developed using “moving window” technique. In the first scheme of HNM a feed-forward ANN was trained to predict evaporation rate and heat flux in the drying process. In the second scheme of the HN model, ANN was used to determine heat transfer coefficient only. Excellent prediction of drying process by HNM is proved.