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

Abstract

Rotating packed bed (RPB) exhibits good performance for absorption, distillation and extraction. However, the development of RPB was restricted by complicated liquid flow pattern on activated carbon in adsorption. Therefore, a model of artificial neural network (ANN) was adopted to predict the adsorption in RPB. The experimental data were classified into two groups, and they were training ones for establishing the model and testing ones for validating the model. Different types of ANN models, including Cascade-forward back propagation neural network (CFBPNN), Elman-forward back propagation neural network (EFBPNN) and Feed-forward back propagation neural network (FFBPNN), were investigated in this study. High gravity factor, liquid Reynolds number, adsorption time to the maximum adsorption time and packing density to liquid concentration were used as input data. While, the adsorption amount to the maximum adsorption amount was taken as output data for each model. Optimal hidden neurons for FFBPNN, EBPNN, CFBPNN were 9, 12 and 8, respectively. Experimental adsorption amount data were in good agreement with the predicted data indicating that the ANN models had a superior performance. Besides, the ANN models exhibited a more accurate prediction and had more generalization ability than multiple nonlinear regression model (MNR). The proposed FFBPNN model of absorption in RPB will provide an optimization tool to maximize the adsorption amount.