Machine Learning Strategies for Enhancing Syngas Quality, Biofuel Stability, and Air Pollution Control in Bio-Energy Plants

Bio-energy plants play a key role in the transition toward low-carbon energy systems, yet their large-scale deployment is constrained by variability in syngas quality, biofuel instability during storage, and fluctuating air pollutant emissions. This review examines how machine learning (ML) methods support improved decision-making across bio-energy value chains by linking multi-source data with predictive and adaptive control strategies. The study synthesizes recent advances in ML-based modelling for syngas composition prediction, biofuel stability assessment, and real-time emission monitoring and mitigation. Emphasis is placed on uncertainty-aware models and hybrid approaches that combine data-driven learning with process knowledge to address feedstock heterogeneity and dynamic operating conditions. The findings show that ML enhances operational efficiency, supports cleaner production, and improves system reliability by enabling proactive control rather than reactive adjustments. From a sustainability perspective, these outcomes directly contribute to SDG 7 (Affordable and Clean Energy) through higher efficiency and reliability of bio-energy systems, SDG 9 (Industry, Innovation, and Infrastructure) by promoting intelligent and resilient industrial processes, SDG 12 (Responsible Consumption and Production) via optimized resource use and reduced waste, and SDG 13 (Climate Action) through lower emissions and improved carbon performance. Overall, the review highlights ML as a practical decision-support tool for industry and policy stakeholders seeking resilient, data-driven pathways toward sustainable bio-energy deployment.