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

26 Citations (Scopus)

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.

Original languageEnglish
Article number128468
JournalBioresource technology
Volume369
DOIs
Publication statusPublished - 2023 Feb

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

Keywords

  • 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

Fingerprint

Dive into the research topics of 'Review of explainable machine learning for anaerobic digestion'. Together they form a unique fingerprint.

Cite this