Review of explainable machine learning for anaerobic digestion

Rohit Gupta; Le Zhang; Jiayi Hou; Zhikai Zhang; Hongtao Liu; Siming You; Yong Sik Ok; Wangliang Li

doi:10.1016/j.biortech.2022.128468

Review of explainable machine learning for anaerobic digestion

Bioresour Technol. 2023 Feb:369:128468. doi: 10.1016/j.biortech.2022.128468. Epub 2022 Dec 9.

Authors

Rohit Gupta¹, Le Zhang², Jiayi Hou³, Zhikai Zhang⁴, Hongtao Liu³, Siming You⁵, Yong Sik Ok⁶, Wangliang Li⁷

Affiliations

¹ James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; Nanoengineered Systems Laboratory, UCL Mechanical Engineering, University College London, London WC1E 7JE, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TS, UK.
² Department of Resources and Environment, School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
³ Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
⁴ CAS Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China; School of Water Resources and Environment, Hebei GEO University, Shijiazhuang 050031, Hebei, China.
⁵ James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.
⁶ Korea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South Korea.
⁷ CAS Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China. Electronic address: wlli@ipe.ac.cn.

PMID: 36503098
DOI: 10.1016/j.biortech.2022.128468

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.

Keywords: Artificial intelligence; Bioenergy; Data-driven modelling; Renewable energy; Sustainable waste management.

Publication types

Review

MeSH terms

Anaerobiosis
Machine Learning
Technology
Waste Management*