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A Comparative Assessment of Biodiesel Cetane Number Predictive Correlations Based on Fatty Acid Composition

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  • Evangelos G. Giakoumis

    (Internal Combustion Engines Laboratory, School of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece)

  • Christos K. Sarakatsanis

    (Internal Combustion Engines Laboratory, School of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece)

Abstract

Sixteen biodiesel cetane number (CN) predictive models developed since the early 1980s have been gathered and compared in order to assess their predictive capability, strengths and shortcomings. All are based on the fatty acid (FA) composition and/or the various metrics derived directly from it, namely, the degree of unsaturation, molecular weight, number of double bonds and chain length. The models were evaluated against a broad set of experimental data from the literature comprising 50 series of measured CNs and FA compositions. It was found that models based purely on compositional structure manifest the best predictive capability in the form of coefficient of determination R 2 . On the other hand, more complex models incorporating the effects of molecular weight, degree of unsaturation and chain length, although reliable in their predictions, exhibit lower accuracy. Average and maximum errors from each model’s predictions were also computed and assessed.

Suggested Citation

  • Evangelos G. Giakoumis & Christos K. Sarakatsanis, 2019. "A Comparative Assessment of Biodiesel Cetane Number Predictive Correlations Based on Fatty Acid Composition," Energies, MDPI, vol. 12(3), pages 1-30, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:422-:d:201695
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    References listed on IDEAS

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    4. Ramadhas, A.S. & Jayaraj, S. & Muraleedharan, C. & Padmakumari, K., 2006. "Artificial neural networks used for the prediction of the cetane number of biodiesel," Renewable Energy, Elsevier, vol. 31(15), pages 2524-2533.
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    2. Theodoros C. Zannis & Roussos G. Papagiannakis & Efthimios G. Pariotis & Marios I. Kourampas, 2019. "Experimental Study of DI Diesel Engine Operational and Environmental Behavior Using Blends of City Diesel with Glycol Ethers and RME," Energies, MDPI, vol. 12(8), pages 1-36, April.
    3. Cédric Decarpigny & Abdulhadi Aljawish & Cédric His & Bertrand Fertin & Muriel Bigan & Pascal Dhulster & Michel Millares & Rénato Froidevaux, 2022. "Bioprocesses for the Biodiesel Production from Waste Oils and Valorization of Glycerol," Energies, MDPI, vol. 15(9), pages 1-30, May.
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    5. Małgorzata Hawrot-Paw & Patryk Ratomski & Adam Koniuszy & Wojciech Golimowski & Mirosława Teleszko & Anna Grygier, 2021. "Fatty Acid Profile of Microalgal Oils as a Criterion for Selection of the Best Feedstock for Biodiesel Production," Energies, MDPI, vol. 14(21), pages 1-14, November.
    6. Abul Kalam Hossain & Abdul Hussain, 2019. "Impact of Nanoadditives on the Performance and Combustion Characteristics of Neat Jatropha Biodiesel," Energies, MDPI, vol. 12(5), pages 1-16, March.
    7. Petronela Lina Matei & Cristina Busuioc & Niculina Ionescu & Anicuta Stoica-Guzun & Nicoleta-Aurelia Chira, 2021. "Cnicus benedictus Oil as a Raw Material for Biodiesel: Extraction Optimization and Biodiesel Yield," Sustainability, MDPI, vol. 13(23), pages 1-16, November.

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