Review of explainable machine learning for anaerobic digestion

Gupta, R. , Zhang, L., Hou, J., Zhang, Z., Liue, H., You, S. , Okh, Y. S. and Li, W. (2023) Review of explainable machine learning for anaerobic digestion. Bioresource Technology, 369, 128468. (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.

• Item Type: Articles
• Additional Information: Wangliang Li would like to thank the financial support from the National Natural Science Foundation of China (No. 21878313). Siming You would like to acknowledge the financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) Programme Grant (EP/V030515/1), Supergen Bioenergy Hub Rapid Response Funding (RR 2022_10), and Royal Society Research Grant (RGS\R1\211358). Rohit Gupta gratefully acknowledges the Royal Society Newton International Fellowship (NIF\R1\211013). Yong Sik Ok acknowledges the support of the Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01475801) from Rural Development Administration, the Republic of Korea. This work was also partly supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1A6A1A10045235).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: You, Dr Siming and Gupta, Dr Rohit

Creator Roles

Gupta, R.: Conceptualization, Methodology, Data curation, Writing – original draft, Writing – review and editing
You, S.: Writing – review and editing, Funding acquisition, Supervision

• Authors: Gupta, R., Zhang, L., Hou, J., Zhang, Z., Liue, H., You, S., Okh, Y. S., and Li, W.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Bioresource Technology
• Publisher: Elsevier
• ISSN: 0960-8524
• ISSN (Online): 1873-2976
• Published Online: 09 December 2022
• Copyright Holders: Copyright © 2022 Elsevier Ltd.
• First Published: First published in Bioresource Technology 369: 128468
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
309846		Decentralised water technologies	William Sloan	Engineering and Physical Sciences Research Council (EPSRC)	EP/V030515/1	ENG - Infrastructure & Environment