文献类型： 外文期刊
作者： Zhao, Chenxi 1 ; Yue, Wenjing 1 ; Xia, Qi 1 ; Yang, Hang 1 ; Chen, Aihui 2 ; Liu, Xiaogang 1 ;
作者机构： 1.Harbin Univ Sci & Technol, Sch Mech & Power Engn, Harbin 150080, Peoples R China
2.Heilongjiang Acad Agr Sci, Heilongjiang Acad Agr Machinery Sci, Harbin 150086, Peoples R China
关键词： Biochar; CO2 adsorption; Machine learning; Activation conditions; SHAP explanations
期刊名称：BIOMASS & BIOENERGY （ 影响因子：5.8； 五年影响因子：5.7 ）
ISSN： 0961-9534
年卷期： 2025 年 201 卷
页码：
收录情况： SCI
摘要： Machine learning shows great potential in high-dimensional data processing and complex problem analysis, and is a promising approach for biochar adsorption CO2 modeling research. In this study, we innovatively predicted the CO2 adsorption by biochar from the activation conditions of biochar using deep neural network (DNN), random forest (RF), gradient boosted decision tree (GBDT), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM) algorithms. A comparison of seven different combinations of input features revealed that the best prediction accuracy was found for the combination that retained all the activation condition features and that the introduction of the activation condition had an effect on the other features as well. The LGBM model had a higher prediction accuracy and prediction performance for the dataset (R2 of 0.956, MAE of 0.245, and RMSE of 0.350). The importance of the features under this model was also analyzed, and the results showed that the activation temperature and the flow rate of the protective gas were more important than the type of activator and the activation ratio for CO2 adsorption, and activation time had the least effect. This study provides new perspectives and methods for the prediction study of CO2 adsorption by biochar and also provides a reference for a deeper understanding of the related mechanism of action.
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