Prediction of variation of oxides of nitrogen in plasma-based diesel exhaust treatment using artificial neural network

Abstract

Diesel exhaust treatment in plasma environment is a complex phenomenon mainly involving oxidation of several gaseous pollutants. With the help of artificial neural network, an attempt has been made in this paper to predict the variation of nitric oxide/nitrogen dioxide when the exhaust is subjected to discharge plasma. Electrical (power and frequency) and physical (engine load and flow rate) parameters have been considered as inputs of a three-layered artificial neural network model to track the performance of the treatment. Two different backpropagation algorithms named Bayesian regularization and Levenberg–Marquardt have been applied to compare the prediction performance. Bayesian regularization training algorithm shows better agreement with the experimental data than Levenberg–Marquardt in terms of root-mean-square error and correlation coefficient. Further, sensitivity analysis has been carried out to obtain an insight about the relative importance of input parameters on output parameters. This investigation shows that the applied input power is the most influential among the four input parameters from the point of variation of nitric oxide/nitrogen dioxide.