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CD-ARES 2012: Multidisciplinary Research and Practice for Information Systems pp 31–43Cite as

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Artificial Neural Networks Approach for the Prediction of Thermal Balance of SI Engine Using Ethanol-Gasoline Blends

Artificial Neural Networks Approach for the Prediction of Thermal Balance of SI Engine Using Ethanol-Gasoline Blends

  • Mostafa Kiani Deh Kiani21,
  • Barat Ghobadian21,
  • Fathollah Ommi21,
  • Gholamhassan Najafi21 &
  • …
  • Talal Yusaf22 
  • Conference paper
  • 2246 Accesses

  • 8 Citations

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7465)

Abstract

This study deals with artificial neural network (ANN) modeling of a spark ignition engine to predict engine thermal balance. To acquire data for training and testing of ANN, a four-cylinder, four-stroke test engine was fuelled with ethanol-gasoline blended fuels with various percentages of ethanol and operated at different engine speeds and loads. The performance of the ANN was validated by comparing the prediction data set with the experimental results. Results showed that the ANN provided the best accuracy in modeling the thermal balance with correlation coefficient equal to 0.997, 0.998, 0.996 and 0.992 for useful work, heat lost through exhaust, heat lost to the cooling water and unaccounted losses respectively. The experimental results showed as the percentage of ethanol in the ethanol-gasoline blends is increased, the percentage of useful work is increased, while the heat lost to cooling water and exhaust are decreased compared to neat gasoline fuel operation.

Keywords

  • SI engine
  • thermal balance
  • ethanol-gasoline blends
  • artificial neural network

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Author information

Authors and Affiliations

  1. Tarbiat Modares University, Tehran, Iran, P.O. Box 14115-111

    Mostafa Kiani Deh Kiani, Barat Ghobadian, Fathollah Ommi & Gholamhassan Najafi

  2. Faculty of Engineering and Surveying Mechanical and Mechatronic Engineering, University of Southern Queensland (USQ) Australia, Toowoomba Campus, Australia

    Talal Yusaf

Authors
  1. Mostafa Kiani Deh Kiani
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  2. Barat Ghobadian
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  3. Fathollah Ommi
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  4. Gholamhassan Najafi
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  5. Talal Yusaf
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Editor information

Editors and Affiliations

  1. Department of IT, Engineering and Environment, University of South Australia, Mawson Lakes Campus, 5001, Adelaide, SA, Australia

    Gerald Quirchmayr

  2. Department of Information Technologies, University of Economics, W. Churchill Sq. 4, 130 67, Prague 3, Czech Republic

    Josef Basl

  3. School of Information Science, Korean Bible University, 16 Danghyun 2-gil, Nowon-gu, 139-791, Seoul, Korea

    Ilsun You

  4. Information Technology and Decision Sciences, Old Dominion University, 2076 Constant Hall, 23529, Norfolk, VA, USA

    Lida Xu

  5. Institute of Software Technology and Interactive Systems, Vienna University of Technology and SBA Research, Favoritenstrsse 9-11, 1040, Vienna, Austria

    Edgar Weippl

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© 2012 IFIP International Federation for Information Processing

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Cite this paper

Kiani Deh Kiani, M., Ghobadian, B., Ommi, F., Najafi, G., Yusaf, T. (2012). Artificial Neural Networks Approach for the Prediction of Thermal Balance of SI Engine Using Ethanol-Gasoline Blends. In: Quirchmayr, G., Basl, J., You, I., Xu, L., Weippl, E. (eds) Multidisciplinary Research and Practice for Information Systems. CD-ARES 2012. Lecture Notes in Computer Science, vol 7465. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32498-7_3

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  • DOI: https://doi.org/10.1007/978-3-642-32498-7_3

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