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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

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  • Aghbashlo, Mortaza
  • Shamshirband, Shahaboddin
  • Tabatabaei, Meisam
  • Yee, Por Lip
  • Larimi, Yaser Nabavi

Abstract

In this study, a novel method based on Extreme Learning Machine with wavelet transform algorithm (ELM-WT) was designed and adapted to estimate the exergetic performance of a DI diesel engine. The exergetic information was obtained by calculating mass, energy, and exergy balance equations for the experimental trials conducted at various engine speeds and loads as well as different biodiesel and expanded polystyrene contents. Furthermore, estimation capability of the ELM-WT model was compared with that of the ELM, GP (genetic programming) and ANN (artificial neural network) models. The experimental results showed that an improvement in the exergetic performance modelling of the DI diesel engine could be achieved by the ELM-WT approach in comparison with the ELM, GP, and ANN methods. Furthermore, the results showed that the applied algorithm could learn thousands of times faster than the conventional popular learning algorithms. Obviously, the developed ELM-WT model could be used with a high degree of confidence for further work on formulating novel model predictive strategy for investigating exergetic performance of DI diesel engines running on various renewable and non-renewable fuels.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:94:y:2016:i:c:p:443-456
    DOI: 10.1016/j.energy.2015.11.008
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    7. Geng, ZhiQiang & Qin, Lin & Han, YongMing & Zhu, QunXiong, 2017. "Energy saving and prediction modeling of petrochemical industries: A novel ELM based on FAHP," Energy, Elsevier, vol. 122(C), pages 350-362.
    8. Hajjari, Masoumeh & Tabatabaei, Meisam & Aghbashlo, Mortaza & Ghanavati, Hossein, 2017. "A review on the prospects of sustainable biodiesel production: A global scenario with an emphasis on waste-oil biodiesel utilization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 445-464.
    9. Bahman Najafi & Sina Faizollahzadeh Ardabili & Amir Mosavi & Shahaboddin Shamshirband & Timon Rabczuk, 2018. "An Intelligent Artificial Neural Network-Response Surface Methodology Method for Accessing the Optimum Biodiesel and Diesel Fuel Blending Conditions in a Diesel Engine from the Viewpoint of Exergy and," Energies, MDPI, vol. 11(4), pages 1-18, April.
    10. Sina Faizollahzadeh Ardabili & Bahman Najafi & Meysam Alizamir & Amir Mosavi & Shahaboddin Shamshirband & Timon Rabczuk, 2018. "Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters," Energies, MDPI, vol. 11(11), pages 1-19, October.
    11. 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.
    12. Escalante, Jamin & Chen, Wei-Hsin & Tabatabaei, Meisam & Hoang, Anh Tuan & Kwon, Eilhann E. & Andrew Lin, Kun-Yi & Saravanakumar, Ayyadurai, 2022. "Pyrolysis of lignocellulosic, algal, plastic, and other biomass wastes for biofuel production and circular bioeconomy: A review of thermogravimetric analysis (TGA) approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).

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