Smart Biorefineries: Machine Learning for Process Control, Resource Utilization, and Emission Monitoring

Bio-refineries are central to achieving a sustainable bioeconomy by converting renewable biomass into fuels, chemicals, and energy, directly supporting global climate and resource-efficiency targets. Their operation remains challenging due to nonlinear process behavior, feedstock variability, and the need to simultaneously address productivity and environmental compliance. While machine learning (ML) has been increasingly applied to individual bio-refinery operations, existing studies lack an integrated perspective that links process control, resource use optimization, and emission monitoring with sustainability objectives defined under the Sustainable Development Goals (SDGs). The objective of this review is to analyze and synthesize recent advances in ML applications for smart bio-refineries, with a focus on improving process efficiency (SDG 9: Industry, Innovation and Infrastructure), reducing energy and water consumption (SDG 7: Affordable and Clean Energy; SDG 6: Clean Water and Sanitation), and minimizing environmental emissions (SDG 12: Responsible Consumption and Production; SDG 13: Climate Action). A structured review methodology was adopted, covering peer-reviewed studies on supervised, unsupervised, and reinforcement learning methods applied to fermentation, thermochemical conversion, heat integration, and emission monitoring. The review shows that ML models, including artificial neural networks, ensemble methods, and hybrid ML–physics frameworks, enhance predictive accuracy, stabilize process operation, and enable proactive emission control. Reported case studies demonstrate measurable reductions in energy demand, improved resource utilization, and better compliance with emission limits. These findings underline the role of ML as a practical enabler of sustainable, low-emission bio-refineries. By linking operational performance with SDG-oriented outcomes, this review provides a clear framework for deploying ML technologies to support environmentally responsible and economically viable bio-refinery systems.