Authors: Shubhangi Baghel, Om Prakash Sondhiya
Abstract: Bioethanol has emerged as one of the most promising renewable energy sources for reduc-ing greenhouse gas emissions and decreasing dependence on fossil fuels. However, con-ventional bioethanol production systems face significant challenges, including low conver-sion efficiency, process instability, high operational costs, and limitations in feedstock utili-zation. Recent developments in artificial intelligence (AI) and machine learning (ML) have introduced advanced computational approaches capable of transforming industrial bioetha-nol production through predictive analytics, process automation, and intelligent optimiza-tion. This paper examines the role of AI and ML technologies in enhancing fermentation efficiency, optimizing biomass pretreatment, predicting ethanol yield, and improving overall sustainability in bioethanol production systems. The study also discusses key machine learning algorithms, including artificial neural networks, support vector machines, random forests, and deep learning frameworks, alongside their industrial applications. Furthermore, the paper evaluates challenges associated with data quality, computational complexity, scalability, and ethical considerations. The findings indicate that AI-driven systems signifi-cantly improve process accuracy, reduce waste generation, and enhance economic feasibil-ity. Future research directions involving digital twins, autonomous biorefineries, and ex-plainable AI are also explored.
DOI: http://doi.org/10.5281/zenodo.20225773
