Table of Contents Author Guidelines Submit a Manuscript
Journal of Combustion
Volume 2012, Article ID 854393, 11 pages
http://dx.doi.org/10.1155/2012/854393
Research Article

HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm

1Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309-0427, USA
2Department of Mechanical Engineering, University of California, Berkeley, CA 94720-1740, USA
3Department of Aerospace Engineering, Sharif University of Technology, Tehran 8639-11365, Iran

Received 18 December 2011; Accepted 27 February 2012

Academic Editor: Constantine D. Rakopoulos

Copyright © 2012 AbdoulAhad Validi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. ABS, “Guidance Notes on the Prevention of Air Pollution from Ships,” USA, 1999.
  2. US-EPA, “Air quality criteria for oxides of nitrogen,” Rep. No. EPA-600/8-82-026, 1982.
  3. K. Epping, S. Aceves, and J. Dec, “The Potential of HCCI Combustion for High Efficiency and Low Emission,” SAE paper, 2002.
  4. M. Sjöberg and J. E. Dec, “EGR and Intake Boost for Managing HCCI Low-Temperature Heat Release Over Wide Ranges of Engine Speed,” SAE paper, 2007.
  5. Y. Choi and J. Y. Chen, “Fast prediction of start-of-combustion in hcci with combined artificial neural networks and ignition delay model,” in 30th International Symposium on Combustion, pp. 2711–2718, July 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. A. A. Validi, M. A. Soroudi, and A. Ghafourian, “Charged Mixture and EGR Effects on HCCI Engines Fuelled by n-Heptane,” in Proceedings of the 17th Propulsion Symposium of the Canadian Aeronautics & Space Institute (Aero '09), 2009.
  7. A. A. Validi, M. A. Soroudi, and A. Ghafourian, “Fast prediction of ignition delay time in HCCI with ANN,” in Proceedings of the 2nd Combustion Conference of Iran, 2008.
  8. S. A. Kalogirou, “Applications of artificial neural networks in energy systems. A review,” Energy Conversion and Management, vol. 40, no. 10, pp. 1073–1087, 1999. View at Publisher · View at Google Scholar · View at Scopus
  9. H. C. Krijnsen, J. C. M. Van Leeuwen, R. Bakker, C. M. Van Den Bleek, and H. P. A. Calis, “Optimum nox abatement in diesel exhaust using inferential feedforward reductant control,” Fuel, vol. 80, no. 7, pp. 1001–1008, 2001. View at Publisher · View at Google Scholar · View at Scopus
  10. D. Yuanwang, Z. Meilin, X. Dong, and C. Xiaobei, “An analysis for effect of cetane number on exhaust emissions from engine with the neural network,” Fuel, vol. 81, no. 15, pp. 1963–1970, 2002. View at Publisher · View at Google Scholar · View at Scopus
  11. A. De Lucas, A. Durán, M. Carmona, and M. Lapuerta, “Modeling diesel particulate emissions with neural networks,” Fuel, vol. 80, no. 4, pp. 539–548, 2001. View at Publisher · View at Google Scholar · View at Scopus
  12. F. C. Christo, A. R. Masri, and E. M. Nebot, “An integrated PDF-neural network approach for simulating turbulent reacting systems,” Combustion & Flame, vol. 106, pp. 406–427, 1996. View at Google Scholar
  13. M. Gen and R. Cheng, Genetic Algorithms and Engineering Design, John Wiley & Sons, London, UK, 1997.
  14. A. Homaifar, H. Y. Lai, and E. McCormick, “System optimization of turbofan engines using genetic algorithms,” Applied Mathematical Modelling, vol. 18, no. 2, pp. 72–83, 1994. View at Google Scholar · View at Scopus
  15. D. A. Manolas, C. A. Frangopoulos, T. P. Gialamas, and D. T. Tsahalis, “Operation optimization of an industrial cogeneration system by a genetic algorithm,” Energy Conversion and Management, vol. 38, no. 15-17, pp. 1625–1636, 1997. View at Google Scholar · View at Scopus
  16. Z. Hao, C. Kefa, and M. Jianbo, “Combining neural network and genetic algorithms to optimize low nox pulverized coal combustion,” Fuel, vol. 80, no. 15, pp. 2163–2169, 2001. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Ogaji, S. Sampath, R. Singh, and D. Probert, “Novel approach for improving power-plant availability using advanced engine diagnostics,” Applied Energy, vol. 72, no. 1, pp. 389–407, 2002. View at Publisher · View at Google Scholar · View at Scopus
  18. R. J. Kee, F. M. Rupley, J. A. Miller et al., Chemkin Collection, Release 3.7.1, Reaction Design, San Diego, Calif, USA, 2003.
  19. J. B. Heywood, Internal Combustion Engine Fundamentals, McGraw-Hill, New York, NY, USA, 1988.
  20. C. T. Bowman, R. K. Hanson, D. F. Davidson et al., “GRI-Mech home page,” 2000, http://www.me.berkeley.edu/gri-mech/.
  21. J. A. Blasco, N. Fueyo, J. C. Larroya, C. Dopazo, and Y. J. Chen, “A single-step time-integrator of a methane-air chemical system using artificial neural networks,” Computers and Chemical Engineering, vol. 23, no. 9, pp. 1127–1133, 1999. View at Publisher · View at Google Scholar · View at Scopus
  22. D. P. Sun, Q. Y. Fang, H. J. Wang, and H. C. Zhou, “A compact optimization strategy for combustion in a 125 mw tangentially anthracite-fired boiler by an artificial neural network,” Asia-pacific Journal of Chemical Engineering, vol. 3, no. 4, pp. 432–439, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Y. Chen, J. A. Blasco, N. Fueyo, and C. Dopazo, “An economical strategy for storage of chemical kinetics: fitting in situ adaptive tabulation with artificial neural networks,” Symposium (international) on Combustion, vol. 28, no. 1, pp. 115–121, 2000. View at Google Scholar · View at Scopus
  24. M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural Network Design, PWS, Boston, Mass, USA, 1996.
  25. S. Lu and B. W. Hogg, “Dynamic nonlinear modelling of power plant by physical principles and neural networks,” International Journal of Electrical Power and Energy System, vol. 22, no. 1, pp. 67–78, 2000. View at Publisher · View at Google Scholar · View at Scopus
  26. A. A. Validi, M. A. Soroudi, and A. Ghafourian, “Pre-Ignition Heat Release Effects on Auto-ignition Time in HCCI Engines,” ISME1797, 2009.
  27. Y. Yamasaki and N. Iida, “Numerical analysis of autoignition and combustion of n-butane and air mixture in homogeneous-charge compression-ignition engine using elementary reactions,” Jsme International Journal B, vol. 46, no. 1, pp. 52–59, 2003. View at Publisher · View at Google Scholar · View at Scopus
  28. R. Homma and R. Homma, “Combustion process optimization by genetic algorithms: reduction of NO2 emission via optimal post-flame process,” in 28th International Symposium on Combustion, p. 120, August 2000. View at Scopus
  29. A. A. Validi, Modeling and optimizing of Homogeneous Charge Compression Ignition (HCCI) engines by artificial intelligence, M.S. thesis, Dept. of Aerospace Engineering, Sharif University of Technology, 2008.