The prediction of SO2 removal using statistical methods and artificial neural network

Abstract

Artificial neural network and a statistical model have been applied in a laboratory scale trickle bed reactor (TBR) to investigate the SO2 removal efficiency of activated carbon. The performance of artificial neural network (ANN) model has been compared with the statistical model based on central composite experimental design. Two independent variables, which affect the amount of SO2 removal by the liquid phase in the TBR, were selected; namely liquid flow rate and gas flow rate. Amount of SO2 removal was chosen as the dependent variable (target data). A second order statistical model has been considered to show the dependence of the amount of SO2 removal on the operating parameters. A back-propagation ANN has been used to develop a model relating to the amount of SO2 removal. A series of experiments have been conducted on the basis of the statistics-based design of experimental method. It is observed that a neural network architecture having one input layer with two neurons, one hidden layer with three neurons, one output layer with one neuron and an epoch size of 20 gives better prediction. The predictions are more accurate than those obtained from regression models.