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Evaluation of the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends using kernel-based extreme learning machine

Author

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  • Silitonga, A.S.
  • Masjuki, H.H.
  • Ong, Hwai Chyuan
  • Sebayang, A.H.
  • Dharma, S.
  • Kusumo, F.
  • Siswantoro, J.
  • Milano, Jassinnee
  • Daud, Khairil
  • Mahlia, T.M.I.
  • Chen, Wei-Hsin
  • Sugiyanto, Bambang

Abstract

It is known that biodiesel and bioethanol are viable alternative fuels to replace diesel for compression ignition engines. In this study, an experimental investigation is carried out to evaluate the performance and exhaust emissions of a single cylinder diesel engine fuelled with biodiesel-bioethanol-diesel blends. The engine performance parameters evaluated are the brake specific fuel consumption and brake thermal efficiency whereas the exhaust emission parameters evaluated are carbon monoxide, nitrogen oxide, and smoke opacity. Kernel-based extreme learning machine is used to predict the engine performance and exhaust emission parameters of the fuel blends at full throttle conditions. Based on the experimental results, the brake specific fuel consumption is lower while the brake thermal efficiency is higher for the biodiesel-bioethanol-diesel blends. The carbon monoxide emissions and smoke opacity are also lower for these fuel blends. The mean absolute percentage error of the brake specific fuel consumption, brake thermal efficiency, carbon monoxide, nitrogen oxide, and smoke opacity is 1.363, 1.482, 4.597, 2.224, and 2.090%, respectively. Thus, it can be concluded that K-ELM is a reliable method to estimate the engine performance and exhaust emission parameters of a single cylinder compression ignition engine fuelled with biodiesel-bioethanol-diesel blends to reduce fuel consumption and exhaust emissions.

Suggested Citation

  • Silitonga, A.S. & Masjuki, H.H. & Ong, Hwai Chyuan & Sebayang, A.H. & Dharma, S. & Kusumo, F. & Siswantoro, J. & Milano, Jassinnee & Daud, Khairil & Mahlia, T.M.I. & Chen, Wei-Hsin & Sugiyanto, Bamban, 2018. "Evaluation of the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends using kernel-based extreme learning machine," Energy, Elsevier, vol. 159(C), pages 1075-1087.
  • Handle: RePEc:eee:energy:v:159:y:2018:i:c:p:1075-1087
    DOI: 10.1016/j.energy.2018.06.202
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    References listed on IDEAS

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    1. 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.
    2. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
    3. Rahmani, R. & Rahnejat, H. & Fitzsimons, B. & Dowson, D., 2017. "The effect of cylinder liner operating temperature on frictional loss and engine emissions in piston ring conjunction," Applied Energy, Elsevier, vol. 191(C), pages 568-581.
    4. Subramani, Lingesan & Parthasarathy, M. & Balasubramanian, Dhinesh & Ramalingam, KrishnaMoorthy, 2018. "Novel Garcinia gummi-gutta methyl ester (GGME) as a potential alternative feedstock for existing unmodified DI diesel engine," Renewable Energy, Elsevier, vol. 125(C), pages 568-577.
    5. Wong, Pak Kin & Wong, Ka In & Vong, Chi Man & Cheung, Chun Shun, 2015. "Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search," Renewable Energy, Elsevier, vol. 74(C), pages 640-647.
    6. Roy, Sumit & Banerjee, Rahul & Bose, Probir Kumar, 2014. "Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network," Applied Energy, Elsevier, vol. 119(C), pages 330-340.
    7. Hwang, Joonsik & Qi, Donghui & Jung, Yongjin & Bae, Choongsik, 2014. "Effect of injection parameters on the combustion and emission characteristics in a common-rail direct injection diesel engine fueled with waste cooking oil biodiesel," Renewable Energy, Elsevier, vol. 63(C), pages 9-17.
    8. 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.
    9. Hulwan, Dattatray Bapu & Joshi, Satishchandra V., 2011. "Performance, emission and combustion characteristic of a multicylinder DI diesel engine running on diesel–ethanol–biodiesel blends of high ethanol content," Applied Energy, Elsevier, vol. 88(12), pages 5042-5055.
    10. Shahir, S.A. & Masjuki, H.H. & Kalam, M.A. & Imran, A. & Fattah, I.M. Rizwanul & Sanjid, A., 2014. "Feasibility of diesel–biodiesel–ethanol/bioethanol blend as existing CI engine fuel: An assessment of properties, material compatibility, safety and combustion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 379-395.
    11. Kusumo, F. & Silitonga, A.S. & Masjuki, H.H. & Ong, Hwai Chyuan & Siswantoro, J. & Mahlia, T.M.I., 2017. "Optimization of transesterification process for Ceiba pentandra oil: A comparative study between kernel-based extreme learning machine and artificial neural networks," Energy, Elsevier, vol. 134(C), pages 24-34.
    12. 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.
    13. Aghbashlo, Mortaza & Shamshirband, Shahaboddin & Tabatabaei, Meisam & Yee, Por Lip & Larimi, Yaser Nabavi, 2016. "The use of ELM-WT (extreme learning machine with wavelet transform algorithm) to predict exergetic performance of a DI diesel engine running on diesel/biodiesel blends containing polymer waste," Energy, Elsevier, vol. 94(C), pages 443-456.
    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.
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