Review of explainable machine learning for anaerobic digestion

作者：Rohit Gupta, Le Zhang, Jiayi Hou, Zhikai Zhang, Hongtao Liu, Siming You, Yong Sik Ok, Wangliang Li · 发表于：Bioresource Technology · 年份：2022 · DOI：10.1016/j.biortech.2022.128468 · 被引用次数：126 · 研究领域：Water-Energy-Food Nexus Studies、Water resources management and optimization、Energy Load and Power Forecasting

Anaerobic digestion (AD) is a promising technology for recovering value-added resources from organic waste, thus achieving sustainable waste management. The performance of AD is dictated by a variety of factors including system design and operating conditions. This necessitates developing suitable modelling and optimization tools to quantify its off-design performance, where the application of machine learning (ML) and soft computing approaches have received increasing attention. Here, we succinctly reviewed the latest progress in black-box ML approaches for AD modelling with a thrust on global and local model interpretability metrics (e.g., Shapley values, partial dependence analysis, permutation feature importance). Categorical applications of the ML and soft computing approaches such as what-if scenario analysis, fault detection in AD systems, long-term operation prediction, and integration of ML with life cycle assessment are discussed. Finally, the research gaps and scopes for future work are summarized.