ARTIFICIAL NEURAL NETWORKS APPROACH IN PREDICTING ENGINE-OUT EMISSIONS AND PERFORMANCE PARAMETERS OF A TURBO CHARGED DIESEL ENGINE

Abstract

This study details the artificial neural network (ANN) modelling of a diesel ngine to predict the torque, power, brake-specific fuel consumption and ollutant emissions, including carbon dioxide, carbon monoxide, nitrogen xides, total hydrocarbons and filter smoke number. To collect data for raining and testing the neural network, experiments were performed on a our cylinder, four stroke compression ignition engine. A total of 108 test oints were run on a dynamometer. For the first part of this work, a arameter packet was used as the inputs for the neural network, and atisfactory regression was found with the outputs (over ~95%), excluding otal hydrocarbons. The second stage of this work addressed developing new etworks with additional inputs for predicting the total hydrocarbons, and he regression was raised from 75 % to 90 %. This study shows that the ANN pproach can be used for accurately predicting characteristic values of an nternal combustion engine and that the neural network performance can be ncreased using additional related input data.

Keywords

Dates

  • Submission Date2012-03-21
  • Revision Date2012-09-14
  • Acceptance Date1970-01-01

DOI Reference

10.2298/TSCI120321220O

References

  1. ACEA, EU Economic Report, in, European Automobile Manufacturers' Association 2010.
  2. ACEA, Europan Vehicle Fleet, in, European Automobile Manufacturers' Association 2010.
  3. Bargezar, R., Shafee, S., Khalılarya, S., CFD simulatıon of the combustion process, emission formation and the flow field in an in-direct injection diesel engine, Thermal Science, (2012), doi:10.2298/TSCI111218108B.
  4. Sudeshkumar, M.P., Devaradjane, G., Development of a simulation model for compression ignition engine running with ignition improved blend, Thermal Science, 15 (2011) pp. 1131-1144.
  5. Moldovanu, D., Burnete, D., CFD simulation of a single cylinder research engine working with biodiesel, Thermal Science, (2012), doi:10.2298/TSCI111004021M.
  6. Ganapathy T., Gakkhar R.P., Murugesan K., Artificial Neural Network Modeling of Jatropha Oil Fueled Diesel Engine for Emission Predictions. , Thermal Science, 13 (2009) pp. 91-102.
  7. He, Y., Rutland, C.J., Application of artificial neural networks in engine modelling, International Journal of Engine Research, 5 (2004) pp. 281-296.
  8. Zhou, H., Cen, K., Fan, J., Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks, Energy, 29 (2004) pp. 167-183.
  9. Karonis, D., Lois, E., Zannikos, F., Alexandridis, A., Sarimveis, H., A Neural Network Approach for the Correlation of Exhaust Emissions from a Diesel Engine with Diesel Fuel Properties, Energy & Fuels, 17 (2003) pp. 1259-1265.
  10. Haykin, S., Neural Networks: A Comprehensive Foundation, Prentice Hall, 1998.
  11. Oğuz, H., Sarıtas, I., Baydan, H.E., Prediction of diesel engine performance using biofuels with artificial neural network, Expert Systems with Applications, 37 (2010) pp. 6579-6586.
  12. Moskwa, J.J., Chen, S.X., Dynamic engine modeling for diagnostics and simulation., Inc.PPT Group., 1995.
  13. Munns, S.A., Computer simulation of powertrain components with methodologies for generalized system modeling., in: Depaetment of Mechanical Engineering, University of Wisconsin-Madison, Wisconsin, 1996.
  14. Uzun, A., A parametric study for specific fuel consumption of an intercooled diesel engine using a neural network, Fuel, 93 (2012) pp. 189-199.
  15. Yuanwang, D., Meilin, Z., Dong, X., Xiaobei, C., An analysis for effect of cetane number on exhaust emissions from engine with the neural network, Fuel, 81 (2002) pp. 1963-1970.
  16. de Lucas, A., Durán, A., Carmona, M., Lapuerta, M., Modeling diesel particulate emissions with neural networks, Fuel, 80 (2001) pp. 539-548.
  17. Canakci, M., Ozsezen, A.N., Arcaklioglu, E., Erdil, A., Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oil, Expert Systems with Applications, 36 (2009) pp. 9268-9280.
  18. Parlak, A., Islamoglu, Y., Yasar, H., Egrisogut, A., Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine, Applied Thermal Engineering, 26 (2006) pp. 824-828.
  19. Technologies, A., ATI Vision, www.accuratetechnologies.com/content/view/240/237/lang.en/, (2012)
  20. Öztemel, E., Artificial Neural Networks, Papatya Publishing, 2003.
  21. Beale, H.M., Hagan, T.M., Demuth, H.B., Matlab Neural Network Toolbox User Guide, in, TheMathworks Inc., 2010.
  22. Heywood, J., Internal Combustion Engine Fundamentals, McGraw-Hill, 1988.
  23. Borman, L.G., K., R.W., Combustion Engineering, McGraw-Hill Book Co., 1998.
  24. Hagan, M.T., Demuth, H.B., Beale, M.H., Neural Network Design PWS Pub. Co., 1995.
  25. Manjunatha, R., Narayana, P.B., Reddy, K.H.C., Application of Artificial Neural Networks for Emission Modelling of Biodiesels for a C.I Engine under Varying Operating Conditions, Modern Applied Science, 4 (2010) pp. 77-89.
Volume 17, Issue 1, Pages153 -166