Gupta, Rohit; Zhang, Le; Hou, Jiayi; Zhang, Zhikai; Liu, Hongtao; You, Siming; Sik Ok, Yong; Gupta, Rohit; Zhang, Le; Hou, Jiayi; Zhang, Zhikai; Liu, Hongtao; You, Siming; Sik Ok, Yong; Li, Wangliang; - view fewer (2023) Review of Explainable Machine Learning for Anaerobic Digestion. Bioresource Technology , 369 , Article 128468. 10.1016/j.biortech.2022.128468.

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Abstract

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.

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