Researchers Propose AI-Driven Optimization for Biodiesel Production from  Baobab Seed Oil

A study led by researchers from the Xinjiang Institute of Ecology and Geography (XIEG) of the Chinese Academy of Sciences demonstrates how artificial intelligence (AI) can optimize the synthesis of baobab seed oil green diesel (BSO-GD) while minimizing environmental impacts. Published in Renewable Energy, the research introduces an innovative Adaptive Neuro-Fuzzy Inference System (ANFIS) model to enhance biofuel production efficiency and sustainability.

The team developed an AI-driven approach to identify optimal reaction conditions, including methanol-to-oil ratios, catalyst dosage, and temperature parameters. Their analysis revealed that BSO-GD synthesis achieves peak performance when leveraging the oil's high unsaturated fatty acid content, particularly oleic acid. The optimized process significantly improves fuel quality while reducing viscosity and increasing cetane numbers.

Life cycle assessment (LCA) confirms the environmental advantages of BSO-GD, showing substantial reductions in global warming potential, ecotoxicity, and other impact categories. These findings position baobab seed oil as a promising feedstock for sustainable energy solutions, combining improved engine performance with climate benefits.

"Our ANFIS and LCA models demonstrate how AI and sustainability models can overcome the limitations of conventional biofuel production methods," said Dr. Collins Elendu,  first author of the study.

This study highlights BSO-GD's superior combustion efficiency and emission reduction potential compared to traditional low-sulfur diesel, with notable decreases in carbon monoxide and hydrocarbon emissions. It also provides a framework for AI-optimized green fuel production, supporting the transition toward cleaner energy systems in agricultural and transportation sectors.

Read the full article: https://doi.org/10.1016/j.renene.2025.123012

Contact

LONG Huaping

Xinjiang Institute of Ecology and Geography

E-mail: longhp@ms.xjb.ac.cn

Web: http://english.egi.cas.cn