Authors: Shubhangi Baghel, Om Prakash Sondhiya
Abstract: Bioethanol has emerged as one of the most promising renewable energy sources for reducing greenhouse gas emissions and decreasing dependence on fossil fuels. However, conventional bioethanol production systems face significant challenges, including low conversion efficiency, process instability, high operational costs, and limitations in feedstock utilization. Recent developments in artificial intelligence (AI) and machine learning (ML) have introduced advanced computational approaches capable of transforming industrial bioethanol production through predictive analytics, process automation, and intelligent optimization. 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 significantly improve process accuracy, reduce waste generation, and enhance economic feasibility. Future research directions involving digital twins, autonomous biorefineries, and explainable AI are also explored.
