Lignocellulosic Pretreatment in Integrated Biofuel Systems: Linking Biomass Chemistry, Microbial Dynamics, Reactor Engineering, and AI-Driven Optimization for Bioethanol and Biomethane Production

Abstract

Global efforts to decarbonise energy systems have intensified interest in lignocellulosic biofuels such as bioethanol and biomethane as complementary components of a diversified renewable energy portfolio. However, the recalcitrant architecture of lignocellulosic biomass, dominated by lignin, cellulose and hemicellulose networks remains a fundamental barrier to efficient hydrolysis and microbial conversion. Although numerous pretreatment technologies have been developed to address this challenge, existing reviews largely examine these strategies in isolation from downstream bioconversion processes and reactor engineeringconstraints. he present review provides an integrated synthesis of lignocellulosic pretreatment within the broader biofuel production system, linking biomass chemistry, microbial community dynamics, process engineering and emerging computational optimisation approaches. It evaluates physicochemical, biological and hybrid pretreatment strategies and analyses their impacts on hydrolysis efficiency, biomethane and bioethanol yields and process stability across diverse feedstocks. Particular attention is given to recent advances in co-digestion strategies, microbial community management, artificial intelligence-assisted process optimisation and innovative reactor configurations that enable improved conversion performance. By integrating biochemical mechanisms with engineering and data-driven perspectives, the review identifies critical technological bottlenecks. It proposes a systems-oriented framework for enhancing process efficiency, stability and techno-economic feasibility. These insights provide strategic directions for the development of next-generation lignocellulosic biorefineries capable of supporting scalable and sustainable biofuel production.