A Comprehensive Review on Biomass Valorization Through Thermochemical Pathways: Product Properties and Usage of Artificial Intelligence

Biomass valorization plays a vital role in achieving carbon neutrality and circular economy frameworks. Owing to its carbon-rich structure, biomass represents a promising feedstock to produce bio-based hydrocarbons via biological and thermochemical pathways. While biological conversion routes have been extensively studied, their deployment at commercial scale is constrained by high capital costs and low product yields. In contrast, thermochemical conversion technologies are increasingly being explored as viable large-scale biomass valorization routes. This review presents a comprehensive assessment of thermochemical pathways, with particular emphasis on hydrothermal liquefaction (HTL). The review identifies hydrothermal liquefaction (HTL) as a strategically advantageous route for wet and heterogeneous biomass valorization, due to simultaneous yields of liquid biocrude, and solid hydrochar. The review emphasizes the application of biocrude upgradation processes like hydrodeoxygenation under biphasic solvent systems using sulfided NiMo and CoMo catalysts. Further, the review also establishes hydrochar as a tunable functional material rather than a mere byproduct for applications in fields of energy production, soil amendment, and heterogeneous catalysis. The review article examines technology readiness levels of different biomass valorization techniques, and suggests that while combustion, anaerobic digestion, torrefaction, and transesterification are commercially mature, HTL and carbon capture utilization and storage (CCUS)-integrated fuel synthesis pathways remain at intermediate readiness. Additionally, the review carries out an in-depth study on artificial intelligence and machine learning (AI and ML) applications in biomass valorization, where it observes that Tree-based ensemble models, particularly Random Forest and XGBoost, show strong performance for several HTL prediction tasks, while Gaussian Process Regression and neural network–Bayesian optimization approaches provide additional advantages for uncertainty estimation and process-level optimization. Finally, the future research opportunities in biomass valorization and AI/ML application in HTL-process optimization have been identified for improving the bio-based fuel production techniques.