Review of explainable machine learning for anaerobic digestion. (February 2023)

• Record Type: Journal Article
• Authors: Gupta, Rohit; Zhang, Le; Hou, Jiayi; Zhang, Zhikai; Liu, Hongtao; You, Siming; Sik Ok, Yong; Li, Wangliang
• Is Part Of: Bioresource technology. Volume 369(2023)
• Publication Date: 2023-02
• Subjects: Data-driven modelling -- Sustainable waste management -- Renewable energy -- Bioenergy -- Artificial intelligence; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.128468
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: 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. 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.