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

Research output: Contribution to journalReview articlepeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Article number128468
JournalBioresource technology
Publication statusPublished - 2023 Feb

Bibliographical note

Funding 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).

Publisher Copyright:
© 2022 Elsevier Ltd


  • Artificial intelligence
  • Bioenergy
  • Data-driven modelling
  • Renewable energy
  • Sustainable waste management

ASJC Scopus subject areas

  • Bioengineering
  • Environmental Engineering
  • Renewable Energy, Sustainability and the Environment
  • Waste Management and Disposal


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