Advancing biofuel production through algal biorefineries: Optimization of polysaccharide utilization, AI-driven innovations, and sustainability assessments

Abstract

The growing global energy demand and the need for sustainable alternatives to fossil fuels have accelerated the development of algal biorefineries for biofuel production. Algal polysaccharides, such as cellulose and hemicellulose, offer high-potential feedstocks due to their rich carbohydrate content. Recent advances in pretreatment methods, including deep eutectic solvents and physicochemical techniques, have improved enzymatic hydrolysis, resulting in higher conversion rates and enhanced fermentation efficiency. These improvements have significantly increased biofuel yields while supporting environmental sustainability. Artificial intelligence (AI) and machine learning (ML) are transforming algal bioprocess optimization. Predictive modeling, artificial neural networks, and evolutionary algorithms such as genetic algorithms, fuzzy logic, and particle swarm optimization contribute to enzyme design, fermentation control, cost reduction, and process scalability. In parallel, technoeconomic and environmental assessments, particularly life cycle assessment (LCA), are vital for evaluating resource efficiency, greenhouse gas emissions, and long-term sustainability. This review highlights advancements in polysaccharide utilization, AI-driven innovations, and sustainability frameworks, positioning algal biorefineries as key enablers of global energy security and the transition to a circular bioeconomy.