关键词: 木质纤维素, 厌氧消化, 甲烷, 预处理, 生物强化, 机器学习

Abstract:

Lignocellulosic biomass is the most abundant renewable organic carbon source, and anaerobic digestion can convert it into biogas (mainly methane), offering both energy recovery and emission reduction benefits. However, its dense and complex “cellulose-hemicellulose-lignin” structure, together with the formation of inhibitory by-products during degradation, leads to hydrolysis limitations and microbial instability, which become the bottleneck to efficiency. Conventional pretreatment technologies, including physical, chemical, physicochemical, and biological methods, can improve substrate degradability to some extent but still suffer from high energy consumption, high cost, and the generation of undesirable by-products. In recent years, research has increasingly focused on green and efficient emerging approaches, such as pulsed electric fields, electron beam irradiation, deep eutectic solvents (DES), and nanozymes, which have shown potential in enhancing substrate hydrolysis and promoting methane production. Meanwhile, multidimensional process intensification strategies, such as co-digestion, conductive materials, external magnetic fields, and bioelectrochemical interventions, continue to drive improvements in degradation efficiency and methane yield. With the advancement of artificial intelligence and data science, modeling has gradually shifted from traditional mechanistic frameworks such as Anaerobic Digestion Model No. 1 (ADM1) toward hybrid “mechanism + data” approaches, integrating methods such as artificial neural networks (ANN), support vector machines (SVM), random forests (RF), and extreme gradient boosting (XGBoost) to enable performance prediction and process optimization. This paper systematically reviews the structural characteristics and digestion bottlenecks of lignocellulose, summarizes the development and application prospects of emerging pretreatment and intensification strategies, and discusses the evolution of intelligent modeling for process optimization and risk prediction.

Key words: lignocellulose, anaerobic digestion, methane, pretreatment, biological enhancement, machine learning