Computational and Data-Driven Strategies for Lignocellulosic Biomass Valorization: From Multiscale Modeling to...

Lignocellulosic biomass is a major renewable carbon resource for sustainable production of fuels, chemicals, and functional materials. Yet its valorization remains constrained by structural recalcitrance, feedstock heterogeneity, and the coupling of molecular and process phenomena. This review critically examines computational and data-driven strategies for predictive biorefinery development, with emphasis on how experimentally anchored information can be transferred across scales. Structural determinants of deconstructability, including cellulose organization, lignin chemistry, lignin-carbohydrate interactions, porosity, and accessibility, are assessed together with density functional theory, molecular dynamics, reactive simulations, thermodynamic and kinetic modeling, and machine learning. Particular attention is given to computational solvent design, catalytic upgrading, pyrolysis reaction networks, reactor-scale coupling, and the limitations imposed by model simplification, dataset heterogeneity, extrapolation, and uncertainty. Representative multiscale case studies illustrate validated information handoffs from detailed kinetics to CFD, molecular screening to experiment, and pretreatment to life-cycle assessment. FAIR data infrastructure, workflow automation, and digital twins are discussed as enabling layers. Future progress depends on experimentally validated scale transfer, multicycle durability of solvents and catalysts, uncertainty-aware modeling, and interoperable data ecosystems for practical biorefinery design.