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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 Energy Analysis

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  • Bahman Najafi

    (Biosystem Engineering Department, University of Mohaghegh Ardabili, 56199-11367 Ardabil, Iran)

  • Sina Faizollahzadeh Ardabili

    (Biosystem Engineering Department, University of Mohaghegh Ardabili, 56199-11367 Ardabil, Iran)

  • Amir Mosavi

    (Institute of Structural Mechanics, Bauhaus University Weimar, 99423 Weimar, Germany
    Institute of Automation, Obuda University, 1431 Budapest, Hungary)

  • Shahaboddin Shamshirband

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Timon Rabczuk

    (Institute of Structural Mechanics, Bauhaus University Weimar, 99423 Weimar, Germany)

Abstract

Biodiesel, as the main alternative fuel to diesel fuel which is produced from renewable and available resources, improves the engine emissions during combustion in diesel engines. In this study, the biodiesel is produced initially from waste cooking oil (WCO). The fuel samples are applied in a diesel engine and the engine performance has been considered from the viewpoint of exergy and energy approaches. Engine tests are performed at a constant 1500 rpm speed with various loads and fuel samples. The obtained experimental data are also applied to develop an artificial neural network (ANN) model. Response surface methodology (RSM) is employed to optimize the exergy and energy efficiencies. Based on the results of the energy analysis, optimal engine performance is obtained at 80% of full load in presence of B10 and B20 fuels. However, based on the exergy analysis results, optimal engine performance is obtained at 80% of full load in presence of B90 and B100 fuels. The optimum values of exergy and energy efficiencies are in the range of 25–30% of full load, which is the same as the calculated range obtained from mathematical modeling.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:860-:d:139912
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    Cited by:

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    3. Nosratabadi, Saeed & Mosavi, Amir & Shamshirband, Shahaboddin & Zavadskas, Edmundas Kazimieras & Rakotonirainy, Andry & Chau, Kwok Wing, 2020. "Sustainable Business Models: A Review," OSF Preprints u4xw3, Center for Open Science.
    4. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    5. Yi Dong & Jianmin Liu & Yanbin Liu & Xinyong Qiao & Xiaoming Zhang & Ying Jin & Shaoliang Zhang & Tianqi Wang & Qi Kang, 2020. "A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment," Energies, MDPI, vol. 13(1), pages 1-24, January.
    6. Taghavifar, Hadi & Nemati, Arash & Salvador, F.J. & De la Morena, J., 2019. "Improved mixture quality by advanced dual-nozzle, included-angle split injection in HSDI engine: Exergetic exploration," Energy, Elsevier, vol. 167(C), pages 211-223.
    7. Nosratabadi, Saeed & Mosavi, Amir & Shamshirband, Shahaboddin & Zavadskas, Edmundas Kazimieras & Rakotonirainy, Andry & Chau, Kwok Wing, 2020. "Sustainable Business Models: A Review," OSF Preprints ts54m, Center for Open Science.
    8. Shahriyar Abedinnezhad & Mohammad Hossein Ahmadi & Seyed Mohsen Pourkiaei & Fathollah Pourfayaz & Amir Mosavi & Michel Feidt & Shahaboddin Shamshirband, 2019. "Thermodynamic Assessment and Multi-Objective Optimization of Performance of Irreversible Dual-Miller Cycle," Energies, MDPI, vol. 12(20), pages 1-25, October.
    9. Najafi, Bahman & Akbarian, Eivaz & Lashkarpour, S. Mehdi & Aghbashlo, Mortaza & Ghaziaskar, Hassan S. & Tabatabaei, Meisam, 2019. "Modeling of a dual fueled diesel engine operated by a novel fuel containing glycerol triacetate additive and biodiesel using artificial neural network tuned by genetic algorithm to reduce engine emiss," Energy, Elsevier, vol. 168(C), pages 1128-1137.
    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. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    12. Roberto Finesso & Gilles Hardy & Alessandro Mancarella & Omar Marello & Antonio Mittica & Ezio Spessa, 2019. "Real-Time Simulation of Torque and Nitrogen Oxide Emissions in an 11.0 L Heavy-Duty Diesel Engine for Model-Based Combustion Control," Energies, MDPI, vol. 12(3), pages 1-32, January.

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