Recent Developments in Biodiesel Synthesis from Agricultural Wastes: A Comprehensive Review of Feedstocks, Catalysts, and Machine Learning Approaches
Keywords:
Agricultural waste valorization, Artificial intelligence, Biodiesel production, Edible and non-edible oils, Heterogeneous catalysts, Lignocellulosic biomass, Microalgae biodieselAbstract
The growing demand for sustainable energy and increasing concerns about the environmental effects of fossil fuels have driven interest in biodiesel as a renewable alternative. This review integrates recent advances in biodiesel production, feedstock valorization, catalyst-based approaches, and the applications of artificial intelligence (AI) and machine learning (ML), providing a comprehensive framework that extends beyond prior studies focused on isolated aspects of biodiesel synthesis. Current literature highlights the potential of non-edible oils, agricultural residues, and waste-driven feedstocks, along with emerging microalgae-based systems, as economical and sustainable resources. Improvements in heterogeneous and nanocatalysts have enhanced operational efficiency, ecological metrics, and reusability. Moreover, AI- and ML-based simulations have demonstrated significant predictive capacity as well as optimized approaches achieving biodiesel productivity beyond 95%. Future research should emphasize scaling production of microalgae, improving biocatalyst recovery, and integrating AI/ML-based tools to optimize process pathways and biomass selection, which ultimately facilitates the transition toward eco-friendly and industrially scalable biodiesel production strategies.
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