1. Tree-based machine learning model for visualizing complex relationships between biochar properties and anaerobic digestion.
- Author
-
Zhang Y, Feng Y, Ren Z, Zuo R, Zhang T, Li Y, Wang Y, Liu Z, Sun Z, Han Y, Feng L, Aghbashlo M, Tabatabaei M, and Pan J
- Subjects
- Anaerobiosis, Methane, Machine Learning, Bioreactors, Charcoal
- 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 R
2 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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)- Published
- 2023
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