The state-of-the-art applications of machine learning technologies in liquid biofuel biorefineries

Xie D.; Shi Y.; Wang J.; Cárdenas-Oscanoa A.J.; Wang S.; Huang C. · Biomass and Bioenergy (2026) · DOI: 10.1016/j.biombioe.2025.108510

Resumen

Lignocellulosic biomass, algal biomass, and organic waste serve as promising feedstocks to produce liquid biofuels through integrated biorefining. The complexity of these production processes necessitates advanced modeling tools for systematic design, optimization, and control. Compared to traditional theoretical modeling, machine learning (ML) offers more accurate and reliable results and reliable predictions alongside actionable insights. This paper presents a comprehensive review of ML applications in liquid biofuel production, with a focus on two primary liquid biofuels of bioethanol and biodiesel. Firstly, ML applications in conventional analytical characterization techniques (e.g., infrared and Raman spectroscopy) are reviewed alongside its applications in bioethanol and biodiesel production processes. Furthermore, ML applications in life cycle assessment and techno-economic analysis are evaluated aimed at improving economic viability and environmental performance. Finally, future research directions focusing on developing robust interpretable ML models for industrial-scale biofuel production are proposed to address challenges in data scarcity model generalization and real-time monitoring and control.

Instituciones: Universidad Nacional Agraria La Molina