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Transesterification process and biofuel blending actions on performance of compression ignition engine under different loading conditions

  • Aruna Mahalingam , Nagabhooshanam Nagarajan , Pragati Gajbhiye , Shivakrishna Dasi , Angel Belle , Divakara Rao , Debabrata Barik , Murali Mohan EMAIL logo and Sathiyamurthy Subbarayan
Published/Copyright: August 4, 2025

Abstract

As the demand for sustainable energy and environmental protection grows, alternative biofuels like biodiesel derived from Jatropha Curcas show great potential. By utilizing advanced transesterification techniques with a potassium hydroxide (KOH) catalyst and methanol mixture, we can produce biodiesel and blend it with diesel fuel in varying proportions (1–5 %). These blends demonstrate favourable physical and thermal properties that meet ASTM standards, with kinematic viscosity ranging from 2.1 to 2.5 × 10−6 m2/s. An experimental evaluation of biodiesel/diesel blends in compression ignition (CI) engines with direct injection, tested under various load conditions (10–70 %), produces compelling results. Among the tested blends, the 5 % biodiesel mixture shows superior performance, exhibiting optimal in-cylinder pressure (32 bar), brake specific fuel consumption (BSFC) of 0.32 ± 0.02 kg/kWh, brake power (BP) of 5.2 ± 0.17 kW, and brake thermal efficiency (BTE) of 34 %. Additionally, this blend results in lower emissions of carbon monoxide (CO) at 0.40 %, hydrocarbons (HC) at 23 ± 1 ppm, carbon dioxide (CO2) at 5.8 ± 0.18 %, and nitrogen oxides (NOx) at 275 ± 6 ppm when compared to pure fossil diesel. These findings indicate that the recommended biodiesel blend is a viable option for use in CI engines, providing a sustainable and environmentally friendly energy solution for future applications.


Corresponding author: Murali Mohan, Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Chennai 602105, Tamil Nadu, India, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-06-11
Accepted: 2025-07-16
Published Online: 2025-08-04

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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