Application of response surface methodology and artificial neural network modeling to assess non-thermal plasma efficiency in simultaneous removal of BTEX from waste gases: Effect of operating parameters and prediction performance

Abstract

Abstract This study aimed to assess the prediction efficiencies of response surface methodology (RSM) and artificial neural network (ANN)-based models in terms of benzene, toluene, ethylbenzene, and xylenes (BTEX) removal from a polluted airstream using non-thermal plasma (NTP). The effect that key elements of the NTP process, including temperature, BTEX concentration, voltage and flow rate, had on the BTEX elimination efficiency was investigated using a central composite RSM design along with three ANN models including Feed-Forward Back Propagation Neural Network (FFBPNN), Cascade-Forward Back Propagation Neural Network (CFBPNN) and Elman-Forward Back Propagation Neural Network (EFBPNN) with the topology of 4-h-1. The RSM and ANN models were statistically compared using some indicators including Sum of Squared Errors (SSE), adjusted R2, determination coefficient (R2), Root Mean Squared Error (RMSE), Absolute Average Deviation (AAD). According to the RSM output, voltage was the most efficient variable with a coefficient proportion of 8.28. Besides, FFBPNN was the best model among the considered ANN models. Also, the R2 achieved for ANN (FFBPNN) and RSM models were 0.9736 and 0.9656 correspondingly. Therefore, it was concluded that the ANN (FFBPNN) represents a powerful tool for modeling the BTEX removal.