Prediction of NO and NO2 Concentrations in NTP Treated Diesel Exhaust Using Multilayer Perceptrons

Abstract

Non-thermal plasma technique (NTP) for mitigation of NOX from diesel engine exhaust is a laboratory proven technique with a good number of experimental studies. However, there are constraints which are preventing this technique to appear in practical applications. Prior prediction of exhaust characteristics with the NTP treatment according to the variations in its operating parameters can be a step ahead to make this technique applicable in real time. In this present study, experiments are conducted by varying parameters voltage, flow rate, temperature, discharge gap and initial sum of NO and NO2 concentrations. Six hundred number of datasets are collected to train and test the predictive models which are made using multilayer perceptrons (MLP). The objective of these models is to predict the sum of NO and NO2 concentrations at the downstream of the reactor during NTP based diesel exhaust treatment. As a part of the study, optimum number of neurons in the hidden layers is also found out. The root mean square error (RMSE) of an MLP with two hidden layers, each of 20 neurons, is found to be 3.29 ppm. Results assure that the prediction of sum of NO and NO2 concentrations can be achieved with a good accuracy using MLP.