Data-driven optimization of biomass conversion pathways: integrating thermochemical processes

Biomass conversion technologies are integral to the realization of sustainable, low-carbon energy systems; however, their scalability is significantly hampered by pronounced sensitivity to the composition of feedstock and the temperature of the processes employed. This review synthesizes insights on how temperature regimes and lignocellulosic composition interact to influence energy yields and product quality across various methodologies, including torrefaction, pyrolysis, gasification, and hydrothermal liquefaction. Furthermore, it elucidates how machine learning (ML) presents revolutionary prospects for mitigating variability, facilitating feedstock-agnostic forecasting of higher heating value, yields of bio-oil/char/biogas, syngas H₂/CO ratios, and tar propensity; enabling adaptive closed-loop control of operational parameters; and promoting multi-objective optimization that incorporates techno-economic and life cycle considerations. A comprehensive, data-driven roadmap is proposed to expedite deployment, comprising: (i) process matching and operational set-points that are cognizant of composition; (ii) hybrid models informed by physics for enhanced interpretability; (iii) frameworks for federated and active learning to bolster generalization across diverse regions and feedstocks; and (iv) optimization integrated with techno-economic analysis (TEA) and life cycle assessment (LCA) to guarantee economic feasibility and environmental sustainability. This roadmap not only amalgamates disparate insights into a cohesive strategy but also furnishes practical guidance for stabilizing the quality of outputs, minimizing operational expenses, and promoting decentralized, intelligent bioenergy infrastructures. Subsequent research endeavors should focus on establishing standardized biomass datasets, integrating robust sensors, and developing explainable artificial intelligence frameworks to ensure the scalable, reliable, and sustainable deployment of these systems.