Tree-based machine learning model for visualizing complex relationships between biochar properties and anaerobic digestion

Zhang Y 1 , Feng Y 1 , Ren Z 1 , Zuo R 1 , Zhang T 1 , Li Y 2 Show all authors , Wang Y 3 , Liu Z 4 , Sun Z 1 , Han Y 5 , Feng L 6 , Aghbashlo M 7 , Tabatabaei M 8 , Pan J 3

Affiliations 

  • 1 State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China
  • 2 State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China. Electronic address: liyeqingcup@126.com
  • 3 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China
  • 4 College of Science, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China
  • 5 College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
  • 6 NIBIO, Norwegian Institute of Bioeconomy Research, PO Box 115, N-1431 Ås, Norway
  • 7 Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
  • 8 Higher Institution Centre of Excellence (HICoE), Institute of Tropical Aquaculture and Fisheries (AKUATROP), Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia; Department of Biomaterials, Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences, Chennai 600 077, India
Bioresour Technol, 2023 Apr;374:128746.
PMID: 36813050 DOI: 10.1016/j.biortech.2023.128746

Abstract

The ideal conditions for anaerobic digestion experiments with biochar addition are challenging to thoroughly study due to different experimental purposes. Therefore, three tree-based machine learning models were developed to depict the intricate connection between biochar properties and anaerobic digestion. For the methane yield and maximum methane production rate, the gradient boosting decision tree produced R2 values of 0.84 and 0.69, respectively. According to feature analysis, digestion time and particle size had a substantial impact on the methane yield and production rate, respectively. When particle sizes were in the range of 0.3-0.5 mm and the specific surface area was approximately 290 m2/g, corresponding to a range of O content (>31%) and biochar addition (>20 g/L), the maximum promotion of methane yield and maximum methane production rate were attained. Therefore, this study presents new insights into the effects of biochar on anaerobic digestion through tree-based machine learning.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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