Regression-based models for prediction of oxides of nitrogen in diesel exhaust with electric discharge-based treatment
A prior prediction of oxides of nitrogen, i.e., NOX (sum of NO and NO2), in diesel exhaust while treating with electric discharge-based nonthermal plasma (NTP) technique, would assist the researchers in planning the resources required for the treatment. In this present study, the performance of different regression-based models, i.e., linear, support vector regression and Gaussian process regression (GPR), has been analyzed for predicting the NOX concentrations based on the values of five dominating parameters of the NTP treatment. Experiments have been conducted and collected a dataset of 4032 number of input–output pairs to be used for training and testing of the regression models. The performances of these models have been assessed while testing them for the unseen set of data. A comparison of root-mean-square error (RMSE) has been made, where Matern 3/2 type of GPR model has been found to be the best among all the considered models with an RMSE of 1.86 ppm for a test data of 1210 sets. The model is shown to perform consistently well even when the test data are increased to 50% of total data. Regression analysis shows that the NOX can be predicted with very good accuracy using the Matern 3/2 type of GPR model.
KeywordsAir quality Diesel exhaust Nonthermal plasma NOX removal Prediction of NOX Regression analysis Support vector regression Gaussian process regression
This research did not receive any specific Grant from funding agencies in the public, commercial, or not-for-profit sectors.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Allamsetty S, Mohapatro S (2018) Prediction of NO and NO2 concentrations in NTP treated diesel exhaust using multilayer perceptrons. In: 10th International conference on applied energy, Hong KongGoogle Scholar
- Allamsetty S, Mohapatro S (2018) Prediction of NOX concentrations in diesel exhaust with NTP treatment using different types of FLANNs. In: International symposium non-thermal/thermal plasma pollution control technology & sustainable energy, ItalyGoogle Scholar
- Bhattacharyya A, Rajanikanth BS (2013) Performance of helical and straight-wire corona electrodes for NOx abatement under AC/Pulse energizations. Int J Plasma Environ Sci Technol 7:148–156Google Scholar
- Flagan RC, Seinfeld JH (1988) Fundamentals of air pollution engineering. Prentice Hall, New JerseyGoogle Scholar
- Jasmin K, Bhattacharyya A, Rajanikanth BS (2015) Prediction of NOX removal efficiency in plasma treated exhaust: a dimensional analysis approach. In: 3rd ISNPEDADM, new electrical technology environmentGoogle Scholar
- Li X, Li X, Yuan D, Guo Y (2017) Using least squares support vector machine to predict the maximum ground surface settlement caused by shield tunneling the electron. J Geotech Eng 22:613–626Google Scholar
- Rasmussen CE, Williams CKI (2006) Regression. Gaussian processes for machine learning, vol 2. The MIT Press, Cambridge, pp 7–32Google Scholar
- Roslan NA, Buntat Z, Sidik MAB (2013) Application of dimensional analysis for prediction of NOX removal. In: IEEE 7th international power engineering and optimization conference, Langkawi, Malasiya, pp 218–222Google Scholar
- Samui P, Sitharam TG (2009) Application of least squares support vector machine in seismic attenuation prediction. ISET J Earthq Technol Tech Note 46:147–155Google Scholar
- Tang Z, Zhang H (2018) Modeling NOx emission of coal-fired boiler with differential evolution optimized least square support vector machine. In: Chinese control decision conference, pp 3364–3367Google Scholar