Anaerobic digestion of lignocellulosic biomass: Process intensification and artificial intelligence

Jing Wang, Sitong Liu, Kun Feng, Yu Lou, Jun Ma and Defeng Xing

Renewable and Sustainable Energy Reviews, 2025, vol. 210, issue C

Abstract: Utilizing abundant and eco-friendly biomass is an effective strategy to realize the ‘carbon neutrality’ goal, aligning with contemporary demands for environmental sustainability, energy saving, and a low-carbon economy. Anaerobic digestion stands out as an energy-efficient option to mitigate greenhouse gas emissions and the recovery of biofuels from lignocellulosic biowaste. However, the individual fermentation of nitrogen-deficient lignocellulosic biomass might cause process inhibition. Furthermore, the unbalanced microbial metabolic activity and insufficient electron transport can result in the accumulation of inhibitors, reducing the efficiency of anaerobic digestion. Although there have been significant developments in revitalizing strategies for the anaerobic digestion of lignocellulosic biomass, existing studies often focus on isolated aspects rather than the entire process. Addressing this gap, this work provides a comprehensive overview of the whole-process of anaerobic digestion from design, implementation, and operations management. The comprehensively summarized anaerobic co-digestion feedstock options offer technical guidance for the scheme design of practical anaerobic digestion systems. Several recommendations are provided for the better management of lignocellulosic biomass by coupling anaerobic digestion with conductive materials, micro-aeration, and microbial electrochemical technology. The future research priorities for the optimization of process stability and product yield are discussed from new perspectives of conventional anaerobic digestion model No. 1 and emerging machine learning approaches. This work outlines the latest development in techno-economic analysis and life cycle assessment of anaerobic digestion systems to support waste management decisions and improve operational processes along the solid waste production chain.

Keywords: Anaerobic co-digestion; Lignocellulosic biomass; Livestock wastes; Waste activated sludge; Municipal solid waste; Conductive materials; Micro-aeration; Bioelectrochemical anaerobic digestion; Electro-fermentation; Anaerobic digestion model no. 1; Machine learning; Artificial neural network; Support vector machine; Decision trees; Random forest; Extreme gradient boosting; Genetic algorithm; Particle swarm optimization; Techno-economic analysis; Life cycle assessment (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.rser.2024.115264

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