Prediction of variation of oxides of nitrogen in plasma-based diesel exhaust treatment using artificial neural network
- 23 Downloads
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.
KeywordsArtificial neural network Diesel exhaust Nitrogen oxides Nonthermal plasma Sensitivity analysis
This work is part of in-house research and not supported by any external funding agency.
Compliance with ethical standards
Conflict of interest
The authors declare that there is no conflict of interest whatsoever with anyone related directly/indirectly with this work.
- Alsmadi MKS, Omar KB, Noah SA et al (2009) Back propagation algorithm: the best algorithm among the multi-layer perceptron algorithm. Int J Comput Sci Netw Secur 9:378–383Google Scholar
- Bui DT et al (2012) Landslide susceptibility assessment in the Hoa Binh province of Vietnam: a comparison of the Levenberg–Marquardt and Bayesian regularized neural networks. Geomorphology 171:12–29Google Scholar
- Demuth H, Beale M (2000) Neural network toolbox user’s guideGoogle Scholar
- Eskander GS, et al. (2007) Round trip time prediction using the symbolic function network approach. Information Technology Convergence. In: International symposium on IEEE, pp 3–7Google Scholar
- Foresee FD, Hagan MT (1997) Gauss–Newton approximation to Bayesian learning. In: Proceedings of the 1997 international joint conference on neutral networks, vol 3, pp 1930–1935Google Scholar
- Garson GD (1991) Interpreting neural-network connection weights. AI Expert 6:46–51Google Scholar
- Hagan MT, Demuth HB, Beale MH, De Jesús O (1996) Neural network design, vol 20. PWS Pub, BostonGoogle Scholar
- Ibrahim OM (2013) A comparison of methods for assessing the relative importance of input variables in artificial neural networks. J Appl Sci Res 9:5692–5700Google Scholar
- Kayri M (2016) Predictive abilities of bayesian regularization and Levenberg–Marquardt algorithms in artificial neural networks: a comparative empirical study on social data. Math Comput Appl 21:20Google Scholar
- Lourakis MIA (2005) A brief description of the Levenberg–Marquardt algorithm implemented by levmar. Found Res Technol 4:1–6Google Scholar
- Mohammadhassani J, Khalilarya S, Solimanpur M, Dadvand A (2012) Prediction of NOx emissions from a direct injection diesel engine using artificial neural network. Model Simul Eng 2012:12Google Scholar
- Montgomery DC (2017) Design and analysis of experiments. Wiley, HobokenGoogle Scholar
- Ning Y, Liu Y, Zhang H, Ji Q (2010) Comparison of different BP neural network models for short-term load forecasting. In: IEEE international conference intelligent computing and intelligent systems (ICIS) 2010, vol 3, pp 435–438Google Scholar
- Srinivasan AD, Rajanikanth BS, Mahapatro S (2009) Corona treatment for NOx reduction from stationary diesel engine exhaust impact of nature of energization and exhaust composition. In: Proceedings electrostatics joint conference, pp 1–7Google Scholar
- Yamamoto T, Okubo M, Hayakawa K, Kitaura K (1999) Towards ideal NOx/control technology using plasma-chemical hybrid process. In: Industry applications conference. 34th IAS annual meeting, 1999. Conference record of the 1999 IEEE, vol 3, pp 1495–1502Google Scholar