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Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network

Author

Listed:
  • Najafi, G.
  • Ghobadian, B.
  • Tavakoli, T.
  • Buttsworth, D.R.
  • Yusaf, T.F.
  • Faizollahnejad, M.

Abstract

The purpose of this study is to experimentally analyse the performance and the pollutant emissions of a four-stroke SI engine operating on ethanol-gasoline blends of 0%, 5%, 10%, 15% and 20% with the aid of artificial neural network (ANN). The properties of bioethanol were measured based on American Society for Testing and Materials (ASTM) standards. The experimental results revealed that using ethanol-gasoline blended fuels increased the power and torque output of the engine marginally. For ethanol blends it was found that the brake specific fuel consumption (bsfc) was decreased while the brake thermal efficiency ([eta]b.th.) and the volumetric efficiency ([eta]v) were increased. The concentration of CO and HC emissions in the exhaust pipe were measured and found to be decreased when ethanol blends were introduced. This was due to the high oxygen percentage in the ethanol. In contrast, the concentration of CO2 and NOx was found to be increased when ethanol is introduced. An ANN model was developed to predict a correlation between brake power, torque, brake specific fuel consumption, brake thermal efficiency, volumetric efficiency and emission components using different gasoline-ethanol blends and speeds as inputs data. About 70% of the total experimental data were used for training purposes, while the 30% were used for testing. A standard Back-Propagation algorithm for the engine was used in this model. A multi layer perception network (MLP) was used for nonlinear mapping between the input and the output parameters. It was observed that the ANN model can predict engine performance and exhaust emissions with correlation coefficient (R) in the range of 0.97-1. Mean relative errors (MRE) values were in the range of 0.46-5.57%, while root mean square errors (RMSE) were found to be very low. This study demonstrates that ANN approach can be used to accurately predict the SI engine performance and emissions.

Suggested Citation

  • Najafi, G. & Ghobadian, B. & Tavakoli, T. & Buttsworth, D.R. & Yusaf, T.F. & Faizollahnejad, M., 2009. "Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network," Applied Energy, Elsevier, vol. 86(5), pages 630-639, May.
    • Handle: RePEc:eee:appene:v:86:y:2009:i:5:p:630-639
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    References listed on IDEAS

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    1. Gölcü, Mustafa & Sekmen, Yakup & ErduranlI, Perihan & Sahir Salman, M., 2005. "Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine," Applied Energy, Elsevier, vol. 81(2), pages 187-197, June.
    2. Bayraktar, Hakan, 2005. "Experimental and theoretical investigation of using gasoline–ethanol blends in spark-ignition engines," Renewable Energy, Elsevier, vol. 30(11), pages 1733-1747.
    3. Topgül, Tolga & Yücesu, Hüseyin Serdar & Çinar, Can & Koca, Atilla, 2006. "The effects of ethanol–unleaded gasoline blends and ignition timing on engine performance and exhaust emissions," Renewable Energy, Elsevier, vol. 31(15), pages 2534-2542.
    4. Huang, Yun-Hsun & Wu, Jung-Hua, 2008. "Analysis of biodiesel promotion in Taiwan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(4), pages 1176-1186, May.
    5. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    6. Canakci, Mustafa & Erdil, Ahmet & Arcaklioglu, Erol, 2006. "Performance and exhaust emissions of a biodiesel engine," Applied Energy, Elsevier, vol. 83(6), pages 594-605, June.
    7. Çelik, Veli & Arcaklioglu, Erol, 2005. "Performance maps of a diesel engine," Applied Energy, Elsevier, vol. 81(3), pages 247-259, July.
    8. Prieto, M.M & Montañés, E & Menéndez, O, 2001. "Power plant condenser performance forecasting using a non-fully connected artificial neural network," Energy, Elsevier, vol. 26(1), pages 65-79.
    9. Arcaklioglu, Erol & Çelikten, Ismet, 2005. "A diesel engine's performance and exhaust emissions," Applied Energy, Elsevier, vol. 80(1), pages 11-22, January.
    Full references (including those not matched with items on IDEAS)

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