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Optimisation of operating parameters of DI-CI engine fueled with second generation Bio-fuel and development of ANN based prediction model

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  • Channapattana, S.V.
  • Pawar, Abhay A.
  • Kamble, Prashant G.

Abstract

Honne oil methyl ester which is derived from non-edible Honneoil was blended with petroleum diesel fuel and tested on the DI-CI engine. The experiments were conducted at different levels of operating parameters, viz. compression ratio, static injection timing, fuel injection pressure, load and blend. This study aims to determine optimal combination of engine operating parameters with objective of attaining better performance and lower emission. The multi-objective optimisation based on Genetic algorithm is performed which lead to multi pareto optimal solution.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:187:y:2017:i:c:p:84-95
    DOI: 10.1016/j.apenergy.2016.11.030
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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. Chauhan, Bhupendra Singh & Kumar, Naveen & Cho, Haeng Muk, 2012. "A study on the performance and emission of a diesel engine fueled with Jatropha biodiesel oil and its blends," Energy, Elsevier, vol. 37(1), pages 616-622.
    3. 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.
    4. Lin, Yuan-Chung & Hsu, Kuo-Hsiang & Chen, Chung-Bang, 2011. "Experimental investigation of the performance and emissions of a heavy-duty diesel engine fueled with waste cooking oil biodiesel/ultra-low sulfur diesel blends," Energy, Elsevier, vol. 36(1), pages 241-248.
    5. 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.
    6. 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.
    7. Wong, Ka In & Wong, Pak Kin & Cheung, Chun Shun & Vong, Chi Man, 2013. "Modeling and optimization of biodiesel engine performance using advanced machine learning methods," Energy, Elsevier, vol. 55(C), pages 519-528.
    8. Sanjid, A. & Masjuki, H.H. & Kalam, M.A. & Rahman, S.M. Ashrafur & Abedin, M.J. & Palash, S.M., 2013. "Impact of palm, mustard, waste cooking oil and Calophyllum inophyllum biofuels on performance and emission of CI engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 664-682.
    9. Kara Togun, Necla & Baysec, Sedat, 2010. "Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks," Applied Energy, Elsevier, vol. 87(1), pages 349-355, January.
    10. Jayed, M.H. & Masjuki, H.H. & Saidur, R. & Kalam, M.A. & Jahirul, M.I., 2009. "Environmental aspects and challenges of oilseed produced biodiesel in Southeast Asia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2452-2462, December.
    11. Mat Yasin, M.H. & Yusaf, Talal & Mamat, R. & Fitri Yusop, A., 2014. "Characterization of a diesel engine operating with a small proportion of methanol as a fuel additive in biodiesel blend," Applied Energy, Elsevier, vol. 114(C), pages 865-873.
    12. Sarin, Amit & Arora, Rajneesh & Singh, N.P. & Sarin, Rakesh & Malhotra, R.K. & Kundu, K., 2009. "Effect of blends of Palm-Jatropha-Pongamia biodiesels on cloud point and pour point," Energy, Elsevier, vol. 34(11), pages 2016-2021.
    13. Mohamed Ismail, Harun & Ng, Hoon Kiat & Gan, Suyin, 2012. "Evaluation of non-premixed combustion and fuel spray models for in-cylinder diesel engine simulation," Applied Energy, Elsevier, vol. 90(1), pages 271-279.
    14. Serrano, L. & Lopes, M. & Pires, N. & Ribeiro, I. & Cascão, P. & Tarelho, L. & Monteiro, A. & Nielsen, O. & da Silva, M. Gameiro & Borrego, C., 2015. "Evaluation on effects of using low biodiesel blends in a EURO 5 passenger vehicle equipped with a common-rail diesel engine," Applied Energy, Elsevier, vol. 146(C), pages 230-238.
    15. Mofijur, M. & Atabani, A.E. & Masjuki, H.H. & Kalam, M.A. & Masum, B.M., 2013. "A study on the effects of promising edible and non-edible biodiesel feedstocks on engine performance and emissions production: A comparative evaluation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 391-404.
    16. Shahabuddin, M. & Liaquat, A.M. & Masjuki, H.H. & Kalam, M.A. & Mofijur, M., 2013. "Ignition delay, combustion and emission characteristics of diesel engine fueled with biodiesel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 623-632.
    17. Vallinayagam, R. & Vedharaj, S. & Yang, W.M. & Lee, P.S. & Chua, K.J.E. & Chou, S.K., 2014. "Pine oil–biodiesel blends: A double biofuel strategy to completely eliminate the use of diesel in a diesel engine," Applied Energy, Elsevier, vol. 130(C), pages 466-473.
    18. Mohammadi, Pouya & Nikbakht, Ali M. & Tabatabaei, Meisam & Farhadi, Khalil & Mohebbi, Arash & Khatami far, Mehdi, 2012. "Experimental investigation of performance and emission characteristics of DI diesel engine fueled with polymer waste dissolved in biodiesel-blended diesel fuel," Energy, Elsevier, vol. 46(1), pages 596-605.
    19. Lešnik, Luka & Vajda, Blaž & Žunič, Zoran & Škerget, Leopold & Kegl, Breda, 2013. "The influence of biodiesel fuel on injection characteristics, diesel engine performance, and emission formation," Applied Energy, Elsevier, vol. 111(C), pages 558-570.
    20. Yusaf, T.F. & Yousif, B.F. & Elawad, M.M., 2011. "Crude palm oil fuel for diesel-engines: Experimental and ANN simulation approaches," Energy, Elsevier, vol. 36(8), pages 4871-4878.
    21. Tan, Pi-qiang & Hu, Zhi-yuan & Lou, Di-ming & Li, Zhi-jun, 2012. "Exhaust emissions from a light-duty diesel engine with Jatropha biodiesel fuel," Energy, Elsevier, vol. 39(1), pages 356-362.
    22. Mwangi, John Kennedy & Lee, Wen-Jhy & Chang, Yu-Cheng & Chen, Chia-Yang & Wang, Lin-Chi, 2015. "An overview: Energy saving and pollution reduction by using green fuel blends in diesel engines," Applied Energy, Elsevier, vol. 159(C), pages 214-236.
    23. 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.
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