Integrating alkaline pretreatment and machine learning for value-added products in lignocellulosic biorefineries

Lignocellulosic biomass (LCB)-based biorefineries have expanded the scope for the sustainable production of biofuels and platform chemicals. The complex structure of lignocellulosic feedstock poses a significant challenge, where alkaline pretreatments (AP) are extensively used to improve the accessibility of the cellulosic fraction of LCB. However, the impact of AP on subsequent conversion into value-added products is limited. This article provides a comprehensive assessment of AP-mediated changes in biomass characteristics, i.e., composition, chemical functionality, crystallinity, and the generation of inhibitory compounds. The application of machine learning (ML) to model LCB pretreatment and optimize process conditions has been discussed. Advances in the production of biofuels, nanoparticles, and platform chemicals from AP of biomass over the last five years (2019–2024) have been reviewed. Finally, challenges in the commercial production of value-added products and scale-up in LCB biorefineries have been reviewed.