Revolutionizing green fuels through artificial intelligence-driven optimization: A life cycle assessment approach to minimize the environmental impacts of Baobab seeds in green diesel synthesis

Collins Chimezie Elendu, Liang Pei, Fan Yang, Rehemanjiang Wufuer, Jia Duo and Pei-Gao Duan

Renewable Energy, 2025, vol. 248, issue C

Abstract: This investigation aimed to assess the environmental consequences of using a blend of baobab seed oil green diesel (BSO-GD) (B20) and low-sulfur diesel (LSB0) in a 15-kW test bed under full-load conditions. The adaptive neuro-fuzzy inference system (ANFIS) approach was integrated to optimize the production parameters. The results revealed a 36.8 wt% BSO content, with an unsaturated fatty acid content of approximately 83.49 %. The ANFIS-AI model indicated that higher cetane numbers, purity, yield, and lower viscosity could be achieved at a methanol–oil ratio of 14:1–15:1, a reaction temperature of 60–62 °C, and a maximum catalyst dosage of 6 wt% during BSO-GD synthesis. BSO-GD (B20) demonstrated a 5.7 % improvement in fuel economy compared with LSB0, with an enhanced thermal efficiency of 39.1 % versus 35.9 % for LSB0. Emissions testing revealed a 59.2 % reduction in CO and a 28.5 % reduction in HC at engine speeds of 1400–1900 rpm. Life cycle assessment indicated that BSO-GD (B20) has fewer environmental impacts than LSB0 does, with effect drop values of 18.77 %, 29.1 %, 36.52 %, and 12 % for the ozone layer depletion potential, global warming potential, ecotoxicity potential, and eutrophication potential, respectively.

Keywords: Baobab seed; Extraction; Transesterification; Green diesel; Engine test; Life cycle assessment (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:248:y:2025:i:c:s0960148125006743

DOI: 10.1016/j.renene.2025.123012

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