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The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks

Author

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  • Mohammed I. Jahirul

    (Biofuel Engine Research Facility, Science and Engineering Faculty, Queensland University of Technology, Brisbane 4000, Australia)

  • Richard J. Brown

    (Biofuel Engine Research Facility, Science and Engineering Faculty, Queensland University of Technology, Brisbane 4000, Australia)

  • Wijitha Senadeera

    (Biofuel Engine Research Facility, Science and Engineering Faculty, Queensland University of Technology, Brisbane 4000, Australia)

  • Ian M. O'Hara

    (Centre for Tropical Crops and Biocommodities, Queensland University of Technology, Brisbane 4000, Australia)

  • Zoran D. Ristovski

    (Biofuel Engine Research Facility, Science and Engineering Faculty, Queensland University of Technology, Brisbane 4000, Australia)

Abstract

Over the past few decades, biodiesel produced from oilseed crops and animal fat is receiving much attention as a renewable and sustainable alternative for automobile engine fuels, and particularly petroleum diesel. However, current biodiesel production is heavily dependent on edible oil feedstocks which are unlikely to be sustainable in the longer term due to the rising food prices and the concerns about automobile engine durability. Therefore, there is an urgent need for researchers to identify and develop sustainable biodiesel feedstocks which overcome the disadvantages of current ones. On the other hand, artificial neural network (ANN) modeling has been successfully used in recent years to gain new knowledge in various disciplines. The main goal of this article is to review recent literatures and assess the state of the art on the use of ANN as a modeling tool for future generation biodiesel feedstocks. Biodiesel feedstocks, production processes, chemical compositions, standards, physio-chemical properties and in-use performance are discussed. Limitations of current biodiesel feedstocks over future generation biodiesel feedstock have been identified. The application of ANN in modeling key biodiesel quality parameters and combustion performance in automobile engines is also discussed. This review has determined that ANN modeling has a high potential to contribute to the development of renewable energy systems by accelerating biodiesel research.

Suggested Citation

  • Mohammed I. Jahirul & Richard J. Brown & Wijitha Senadeera & Ian M. O'Hara & Zoran D. Ristovski, 2013. "The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks," Energies, MDPI, vol. 6(8), pages 1-43, July.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:8:p:3764-3806:d:27558
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    4. Pomeroy, Brett & Grilc, Miha & Likozar, Blaž, 2022. "Artificial neural networks for bio-based chemical production or biorefining: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    5. Jahirul, M.I. & Rasul, M.G. & Brown, R.J. & Senadeera, W. & Hosen, M.A. & Haque, R. & Saha, S.C. & Mahlia, T.M.I., 2021. "Investigation of correlation between chemical composition and properties of biodiesel using principal component analysis (PCA) and artificial neural network (ANN)," Renewable Energy, Elsevier, vol. 168(C), pages 632-646.
    6. Md Sufi Ullah Siddik Bhuyan & Abul Hasnat Md Ashraful Alam & Younghwan Chu & Yong Chan Seo, 2017. "Biodiesel Production Potential from Littered Edible Oil Fraction Using Directly Synthesized S-TiO 2 /MCM-41 Catalyst in Esterification Process via Non-Catalytic Subcritical Hydrolysis," Energies, MDPI, vol. 10(9), pages 1-17, August.
    7. Mohammad I. Jahirul & Wenyong Koh & Richard J. Brown & Wijitha Senadeera & Ian O'Hara & Lalehvash Moghaddam, 2014. "Biodiesel Production from Non-Edible Beauty Leaf ( Calophyllum inophyllum ) Oil: Process Optimization Using Response Surface Methodology (RSM)," Energies, MDPI, vol. 7(8), pages 1-15, August.
    8. Teuku Meurah Indra Riayatsyah & Hwai Chyuan Ong & Wen Tong Chong & Lisa Aditya & Heri Hermansyah & Teuku Meurah Indra Mahlia, 2017. "Life Cycle Cost and Sensitivity Analysis of Reutealis trisperma as Non-Edible Feedstock for Future Biodiesel Production," Energies, MDPI, vol. 10(7), pages 1-21, June.
    9. M. Anwar & M. G. Rasul & N. M. S. Hassan & M. I. Jahirul & Rezwanul Haque & M. M. Hasan & A. G. M. B. Mustayen & R. Karami & D. Schaller, 2022. "Stone Fruit Seed: A Source of Renewable Fuel for Transport," Energies, MDPI, vol. 15(13), pages 1-21, June.
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