VOC removal in rotating packed bed: ANN model vs empirical model

Abstract

The discharged volatile organic compounds (VOCs) have aroused more and more attention because of increasing serious air pollution. Accurate prediction of the emission is very important for the industry. Therefore, two kinds of model were developed to predict the VOCs removal efficiency in the RPB, including empirical model and artificial neural network (ANN) models. The empirical model was mainly including gas and liquid mass transfer, effective contact area and liquid holdup. The ANN models were including Cascade-forward back propagation neural network, Feed-forward distributed time delay neural network, Feed-forward back propagation neural network and Elman-forward back propagation neural network. The input parameters were dimensionless numbers, such as high gravity factor, liquid Reynolds number, gas Reynolds number and dimensionless Henry coefficient. The output parameter was removal efficiency. The outlet concentrations of different VOCs predicted by the ANN model were much better than those of the empirical model. And the disadvantages and the advantages were also analyzed. The effects of high gravity factor, gas flow rate and liquid flow rate on the removal efficiency in the RPB were simulated by ANN model with high precision. And then, operation conditions could be timely adjusted to meet the emission standards.