Review of explainable machine learning for anaerobic digestion

[Display omitted] •Popularly used ML-based AD models are ANN, SVM, RF, and XGBOOST.•Predicted variables are biogas yield, process stability, and effluent characteristics.•Global and local model-agnostic explainability approaches are reviewed.•Potential applications are process parameter optimization, fault detection, and LCA.•It is necessary to inform ML models with biokinetic equations to improve accuracy. 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.