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Analysis of oil mixing for improvement of biodiesel quality with the application of mixture design method

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  • Kumar, Sandeep
  • Singhal, Mukesh Kumar
  • Sharma, Mahendra P.

Abstract

The study aims to improve biodiesel's fuel quality using optimal oil mixture. Four oils, namely, Karanja, Jatropha, Palm and Waste cooking oil(WCO) were selected and their mixtures were prepared. A simplex lattice mixture design model for biodiesel properties (Kinematic viscosity(KV), Density, Cetane number(CN) and Oxidation stability(OS)) was developed and optimisation of oil mixtures was conducted. Biodiesel fuel properties of the optimum ratio of the mixture of Karanja:Palm oil (51.6:48.4% v/v) are found as KV(3.854 mm2/s), density (860 kg/m3), CN(56.189), and OS (9.565 h), thereby, indicating that the OS of the biodiesel of optimum mixture is improved by 21.47% and CN by 4.2% compared to Karanja biodiesel. Jatropha oil biodiesel does not meet the biodiesel standard (EN 14214), but when mixed with Palm oil in an optimal ratio of 34.8:65.2% v/v (Jatropha:Palm), biodiesel from this optimal mixture obtained are KV(3.822 mm2/s), density(869.95 kg/m3), CN(55.69) and OS(8.16 h) found. So mixing oils improve the biodiesel OS by 46.9% and CN by 11.28% and also satisfies biodiesel standards compared to individual Jatropha oil biodiesel. However, no optimum ratio for the mixture of WCO with Jatropha or Karanja was found suitable for converting biodiesel that can fulfil biodiesel standards.

Suggested Citation

  • Kumar, Sandeep & Singhal, Mukesh Kumar & Sharma, Mahendra P., 2023. "Analysis of oil mixing for improvement of biodiesel quality with the application of mixture design method," Renewable Energy, Elsevier, vol. 202(C), pages 809-821.
    • Handle: RePEc:eee:renene:v:202:y:2023:i:c:p:809-821
    DOI: 10.1016/j.renene.2022.11.097
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    2. Giwa, Solomon O. & Taziwa, Raymond T. & Sharifpur, Mohsen, 2023. "Dependence of composition-based approaches on hybrid biodiesel fuel properties prediction using artificial neural network and random tree algorithms," Renewable Energy, Elsevier, vol. 218(C).
    3. Moradkhani, Mohammad Amin & Hosseini, Seyyed Hossein & Song, Mengjie & Teimoori, Khalil, 2024. "Comprehensive data-driven methods for estimating the thermal conductivity of biodiesels and their blends with alcohols and fossil diesels," Renewable Energy, Elsevier, vol. 221(C).
    4. Guo, Jinrui & Li, Fashe & Zhang, Huicong & Duan, Yaozong & Wang, Shuang & Tan, Fangguan & Chen, Yong & Lu, Fengju & Luo, Linglin, 2023. "Effects of fuel components and combustion parameters on the formation mechanism and emission characteristics of aldehydes from biodiesel combustion," Renewable Energy, Elsevier, vol. 219(P1).

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