Feasibility of Artificial Neural Networks and Fuzzy Logic Models for Prediction of NO$_{X}$ Concentrations in Nonthermal Plasma-Treated Diesel Exhaust

Abstract

High-voltage discharge-based nonthermal plasma (NTP) treatment for diesel exhaust is a laboratorial proven efficient technique. A prophecy of the treatment results based on the knowledge of its parameters would be a step forward toward bringing it into real-time applications of pollution control. In this paper, artificial neural networks (ANNs) and fuzzy logic model (FLM) have been used to model the NO $_{ X }$ (sum of NO and NO2) concentrations as a function of parameters of the NTP process. A data set of 4032 input–output pairs has been collected by conducting experiments, in which 70% of the data are used for the training of the models derived. The performances of all the considered models have been evaluated by testing them for the remaining 30% of the data, which is novel for the models. Furthermore, a comparison of the models has been made based on the root-mean-square error (RMSE) and mean relative error (MRE), where the FLM has been found to be the better compared to the ANN-based models, i.e., ANN, multilayer perceptrons (MLP), and functional link ANN (FLANN). The RMSE of FLM is 2.53 ppm for a test data of 1210 sets. It can be said from these results that the NO $_{ X }$ concentrations can be predicted using FLM with a good accuracy.