State-of-the-Art Review on Applications of Various Machine Learning Models in Biodiesel Production

Abstract

Recent advances in Artificial Intelligence (AI) have significantly influenced biodiesel production as a renewable source of energy, primarily through the enhancement of transesterification reactions and yield optimization. This review summarizes key findings from multiple studies on optimization of biodiesel production from biomass using machine learning models. This review analyzes various machine learning models and optimization techniques used for biodiesel production. Several optimization strategies, including evolutionary algorithms and heuristic methods, are explored across different studies. Among the models evaluated, those employing advanced configurations and ensemble techniques demonstrated superior performance in accuracy and correlation with biodiesel datasets. Particularly, enhanced versions of neural networks, extreme learning models, and fuzzy systems emerged as top performers, offering robust solutions for biodiesel optimization. Findings suggest that machine learning not only augments traditional catalyst development and yield prediction methods but also offers a consolidated framework enhancing overall process efficiency. This work intends to offer an extensive examination of the present status and forthcoming prospects of Artificial Intelligence applications in biodiesel production, synthesizing a broad range of contemporary useful literature.