Modelling of dust removal in rotating packed bed using artificial neural networks (ANN)

Abstract

Artificial neural network (ANN) models, including the Cascade-forward back propagation neural network (CFBPNN), feed-forward back propagation neural network (FFBPNN) and Elman-forward back propagation neural network (EFBPNN), were proposed to predict the dust removal efficiency in rotating packed bed (RPB) to speed up its development. Total 326 data sets for separation grade efficiency had been collected from literatures for training and verifying the model. Gas Reynolds number (ReG), liquid Reynolds number (ReL), rotational Reynolds number (Reω), M (d02ρL/dp2/ρp) and Csi/ρG were used as input data. While, the variable η (separation grade efficiency) was taken as output data for each model. Various of hidden neurons were compared based on the mean square error (E2), coefficient of determination (R2) and residual for each model. The separation grade efficiency in RPB was also compared with other existed dust removal equipments.