Can wood waste be a feedstock for anaerobic digestion? A machine learning assisted meta-analysis

作者：Zhenghui Gao, Tianyi Cui, Hang Qian, Devin Sapsford, Peter John Cleall, Michael Harbottle · 发表于：Chemical Engineering Journal · 年份：2024 · DOI：10.1016/j.cej.2024.150496 · 被引用次数：16 · 研究领域：Municipal Solid Waste Management、Recycling and Waste Management Techniques

Anaerobic digestion is widely employed to process various organic wastes while generating renewable energy and nutrient-rich digestate. However, lignocellulosic wastes, especially wood waste, suffer from the recalcitrance associated with high lignin content, thereby adversely impacting on biogas production. It remains unclear whether wood waste is suitable as a feedstock for anaerobic digestion and to what extent pretreatment techniques could affect its biochemical methane potential. In this paper, 769 datasets on methane production from wood waste were collected for meta-analysis. The results showed an average 146 % increase in methane production for other organic wastes compared to wood waste when pretreatment techniques were not applied, but this gap could be mitigated to 99 % when pretreatment techniques were considered, indicating that pretreatment techniques could be more effective for wood waste. A further analysis of different pretreatment techniques showed that pretreatment significantly increased the methane production of wood waste by 113 % and that a combination of pretreatment techniques was more effective than a single method. Finally, three machine learning algorithms were applied to explore the relationship between methane production and selected variables. The results showed that the random forest method yielded better predictive performance for methane production (R2 = 0.9643) than artificial neural networks and support vector regression. Feature importance ...