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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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    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. Díez Valbuena, G. & García Tuero, A. & Díez, J. & Rodríguez, E. & Hernández Battez, A., 2024. "Application of machine learning techniques to predict biodiesel iodine value," Energy, Elsevier, vol. 292(C).
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