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A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel

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  • Yusri, I.M.
  • Abdul Majeed, A.P.P.
  • Mamat, R.
  • Ghazali, M.F.
  • Awad, Omar I.
  • Azmi, W.H.

Abstract

Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy.

Suggested Citation

  • Yusri, I.M. & Abdul Majeed, A.P.P. & Mamat, R. & Ghazali, M.F. & Awad, Omar I. & Azmi, W.H., 2018. "A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 665-686.
    • Handle: RePEc:eee:rensus:v:90:y:2018:i:c:p:665-686
    DOI: 10.1016/j.rser.2018.03.095
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    1. 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.
    2. Kshirsagar, Charudatta M. & Anand, Ramanathan, 2017. "Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses," Applied Energy, Elsevier, vol. 189(C), pages 555-567.
    3. Shahbaz, Muhammad & Yusup, Suzana & Inayat, Abrar & Patrick, David Onoja & Pratama, Angga, 2016. "Application of response surface methodology to investigate the effect of different variables on conversion of palm kernel shell in steam gasification using coal bottom ash," Applied Energy, Elsevier, vol. 184(C), pages 1306-1315.
    4. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    5. Ghobadian, B. & Rahimi, H. & Nikbakht, A.M. & Najafi, G. & Yusaf, T.F., 2009. "Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network," Renewable Energy, Elsevier, vol. 34(4), pages 976-982.
    6. Poompipatpong, Chedthawut & Kengpol, Athakorn, 2015. "Design of a decision support methodology using response surface for torque comparison: An empirical study on an engine fueled with waste plastic pyrolysis oil," Energy, Elsevier, vol. 82(C), pages 850-856.
    7. Mohamed Ismail, Harun & Ng, Hoon Kiat & Queck, Cheen Wei & Gan, Suyin, 2012. "Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends," Applied Energy, Elsevier, vol. 92(C), pages 769-777.
    8. Hasan, M.M. & Rahman, M.M., 2017. "Performance and emission characteristics of biodiesel–diesel blend and environmental and economic impacts of biodiesel production: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 938-948.
    9. Channapattana, S.V. & Pawar, Abhay A. & Kamble, Prashant G., 2017. "Optimisation of operating parameters of DI-CI engine fueled with second generation Bio-fuel and development of ANN based prediction model," Applied Energy, Elsevier, vol. 187(C), pages 84-95.
    10. Nina Hall, 2017. "What is adaptation to climate change? Epistemic ambiguity in the climate finance system," International Environmental Agreements: Politics, Law and Economics, Springer, vol. 17(1), pages 37-53, February.
    11. Hosmath, R.S. & Banapurmath, N.R. & Khandal, S.V. & Gaitonde, V.N. & Basavarajappa, Y.H. & Yaliwal, V.S., 2016. "Effect of compression ratio, CNG flow rate and injection timing on the performance of dual fuel engine operated on honge oil methyl ester (HOME) and compressed natural gas (CNG)," Renewable Energy, Elsevier, vol. 93(C), pages 579-590.
    12. Pandian, M. & Sivapirakasam, S.P. & Udayakumar, M., 2011. "Investigation on the effect of injection system parameters on performance and emission characteristics of a twin cylinder compression ignition direct injection engine fuelled with pongamia biodiesel-d," Applied Energy, Elsevier, vol. 88(8), pages 2663-2676, August.
    13. Atmanli, Alpaslan & Ileri, Erol & Yilmaz, Nadir, 2016. "Optimization of diesel–butanol–vegetable oil blend ratios based on engine operating parameters," Energy, Elsevier, vol. 96(C), pages 569-580.
    14. Shivakumar & Srinivasa Pai, P. & Shrinivasa Rao, B.R., 2011. "Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings," Applied Energy, Elsevier, vol. 88(7), pages 2344-2354, July.
    15. Yusri, I.M. & Mamat, R. & Najafi, G. & Razman, A. & Awad, Omar I. & Azmi, W.H. & Ishak, W.F.W. & Shaiful, A.I.M., 2017. "Alcohol based automotive fuels from first four alcohol family in compression and spark ignition engine: A review on engine performance and exhaust emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 169-181.
    16. Hasan, M.M. & Rahman, M.M., 2016. "Homogeneous charge compression ignition combustion: Advantages over compression ignition combustion, challenges and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 282-291.
    17. Almonacid, Florencia & Fernandez, Eduardo F. & Mellit, Adel & Kalogirou, Soteris, 2017. "Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 938-953.
    18. Azad, A.K. & Rasul, M.G. & Khan, M.M.K. & Sharma, Subhash C. & Hazrat, M.A., 2015. "Prospect of biofuels as an alternative transport fuel in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 331-351.
    19. Yusaf, Talal F. & Buttsworth, D.R. & Saleh, Khalid H. & Yousif, B.F., 2010. "CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network," Applied Energy, Elsevier, vol. 87(5), pages 1661-1669, May.
    20. Najafi, Gholamhassan & Ghobadian, Barat & Yusaf, Talal & Safieddin Ardebili, Seyed Mohammad & Mamat, Rizalman, 2015. "Optimization of performance and exhaust emission parameters of a SI (spark ignition) engine with gasoline–ethanol blended fuels using response surface methodology," Energy, Elsevier, vol. 90(P2), pages 1815-1829.
    21. Çay, Yusuf & Korkmaz, Ibrahim & Çiçek, Adem & Kara, Fuat, 2013. "Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network," Energy, Elsevier, vol. 50(C), pages 177-186.
    22. Sehatpour, Mohammad-Hadi & Kazemi, Aliyeh & Sehatpour, Hesam-eddin, 2017. "Evaluation of alternative fuels for light-duty vehicles in Iran using a multi-criteria approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 295-310.
    23. Rezaei, Javad & Shahbakhti, Mahdi & Bahri, Bahram & Aziz, Azhar Abdul, 2015. "Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks," Applied Energy, Elsevier, vol. 138(C), pages 460-473.
    24. Mahmudul, H.M. & Hagos, F.Y. & Mamat, R. & Adam, A. Abdul & Ishak, W.F.W. & Alenezi, R., 2017. "Production, characterization and performance of biodiesel as an alternative fuel in diesel engines – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 497-509.
    25. Božnar, Marija Zlata & Grašič, Boštjan & Oliveira, Amauri Pereira de & Soares, Jacyra & Mlakar, Primož, 2017. "Spatially transferable regional model for half-hourly values of diffuse solar radiation for general sky conditions based on perceptron artificial neural networks," Renewable Energy, Elsevier, vol. 103(C), pages 794-810.
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