Machine Learning in Biomass Valorization for Energy and Fuels: A Review and Perspectives on Feature Representation and Process Decision-Making

Abstract

Machine learning (ML) is increasingly used to support biomass valorization for energy and fuels by linking feedstock properties, process conditions, and product outcomes. However, current studies differ substantially in feature definition, algorithm selection, validation design, uncertainty treatment, and the use of predictions for engineering decisions. This Review synthesizes representative ML studies across thermochemical, hydrothermal, biochemical, pretreatment, and porous-carbon production pathways, with an emphasis on feature representation, model–task alignment, and decision-ready process modeling. The reviewed literature shows that hybrid feedstock-process descriptors often improve predictive performance because they represent both biomass identity and conversion environment, but they also increase the risks of overfitting, inconsistent reporting, multicollinearity, and leakage when feature timing is unclear. Model selection should therefore be evaluated according to data set size, descriptor structure, target definition, output coupling, validation strength, uncertainty requirement, and intended decision use, rather than by algorithm ranking alone. Beyond response prediction, ML can support feedstock screening, operating-window optimization, pathway comparison, and multiobjective trade-off analysis only when predictions are checked for physical feasibility, applicability domain, uncertainty, and process-level relevance. This Review highlights the need to move biomass valorization ML from accuracy-centered prediction toward transparent, transferable, uncertainty-aware, and decision-ready modeling.