Response surface methodology-based model for prediction of NO and NO2 concentrations in nonthermal plasma-treated diesel exhaust

Abstract

Nonthermal plasma (NTP) technique for diesel exhaust emission control has been in the interest of the researchers from the last two decades, and it almost became a laboratory-proven technique for its efficiency over conventional techniques. A prior prediction of effectiveness of the process may lead to overcome the constraints in bringing this technique to real-time applications of diesel exhaust pollution control. In this present study, an attempt is made to find out the most dominating parameters and to predict the sum of NO and NO 2 concentrations in NTP-treated diesel exhaust with respect to variations in operating parameter values using response surface methodology (RSM). Experiments have been conducted following 3 N full factorial design and collected the data by varying voltage, flow rate, temperature, discharge gap and initial (NO + NO 2 ) concentrations. The regression coefficients of the RSM-based mathematical model have been obtained by training it using these experimental data. The root-mean-square error (RMSE) is 4.7 ppm during the training. When this model is tested for a data other than that given during training (test data), the RMSE is 5.6 ppm. Further, the results are also compared with the model derived using the only available method in the literature, i.e., dimensional analysis, and found to be performing better.

Forecasting Ambient Air Pollutants by Box-Jenkins Stochastic Models in Tehran

“…There are also other researches in the field of forecasting air pollutants which use hybrid models such as MLR and ANN (Vlachogianni et al [22]), Support Vector Machine and ANN (Muñoz et al [23]), and Artificial Fuzzy Neural Networks (AFNN) (Niska et al [18]). In addition, some researches have used response surface modelling (RSM)-based approach for prediction of air pollutants (Allamsetty and Mohapatro [24]).…”

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