A Review of Lignocellulosic Biomass Alkaline Delignification: Feedstock Classification, Process Types, Modeling Approaches, and Applications

Alkaline delignification is a keystone pretreatment that governs carbohydrate accessibility, energy use, and yields across pulp and biorefinery value chains, yet its kinetic understanding remains fragmented and largely confined to bench-scale studies. This review provides an integrated assessment of the evolution and current state of kinetic approaches applied to alkaline delignification of lignocellulosic biomass, aiming to bridge academic research and industrial application. A systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta Analyses) guidelines identified 74 peer-reviewed articles and 359 patents published between 1995 and 2025. Kinetic models were classified into conventional (nth-order and pseudo-first-order) and emerging categories (Avrami/Š–B, diffusion-based, mechanistic multistep, isoconversional, and ML/statistical). The results show that pseudo-first-order kinetics and batch-scale studies dominate the literature, while pilot-scale validation and hybrid mechanistic data-driven frameworks remain limited. Patent analysis revealed technological convergence within D21C and C08B IPC domains, reflecting growing industrial interest in alkaline pulping and cellulose valorization. Unlike previous reviews, this work uniquely integrates conventional and emerging kinetic models with a patent-based technological perspective, providing a unified view of academic and industrial progress. The insights presented here provide a foundation for advancing future research, particularly by encouraging the development of standardized experimental protocols and the validation of kinetic models across multiple scales. Moreover, this review provides a consolidated reference for both academic researchers and industrial practitioners seeking to enhance delignification efficiency, reduce reagent consumption, and improve the sustainability of biorefinery processes.