Research on specific capacitance prediction of biomass carbon-based supercapacitors based on machine learning
文献类型： 外文期刊
作者： Zhao, Chenxi 1 ; Lu, Xueying 1 ; Tu, Huanyu 1 ; Yang, Yulong 1 ; Wang, Siyu 1 ; Chen, Aihui 2 ; Zhang, Haibin 2 ;
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 150081, Peoples R China
关键词： Machine learning; Biomass; Porous carbon; Supercapacitor; Capacitance
期刊名称：JOURNAL OF ENERGY STORAGE （ 影响因子：8.9； 五年影响因子：9.0 ）
ISSN： 2352-152X
年卷期： 2024 年 97 卷
页码：
收录情况： SCI
摘要： With the development of computer technology, a variety of machine learning models have been used to predict the electrochemical performance of energy storage devices. In this study, the elemental analysis, industrial analysis and structural composition, activation conditions, and current density of biomass are innovatively selected as input conditions from the biomass raw material characteristics perspective and predict specific capacitance based on the light gradient boosting machine (LightGBM) and deep neural network (DNN) algorithm. Meanwhile, the prediction effects of the contrast capacitance under seven different input combinations are compared. The results show that the combination prediction effect of retaining all features is the best, and the industrial analysis and structural composition of biomass have a greater impact on the model than elemental analysis, this conclusion is also verified by SHapley Additive exPlanations (SHAP) value analysis, indicating that they are essential for the model. It is also found that the Light GBM model has more advantages, with R2 of 0.951, mean absolute error (MAE) of 11.090, and Root-mean-square error (RMSE) of 14.756. This study builds a key bridge between the basic composition information of biomass raw materials and the electrochemical performance. Also, it helps to understand the related formation mechanism of biomass-derived carbon materials.
- 相关文献
作者其他论文 更多>>
-
Machine learning prediction of biochar-specific surface area based on plant characterization information
作者：Jiang, Zihao;Xia, Qi;Lu, Xueying;Yue, Wenjing;Liu, Xiaogang;Chen, Juhui;Zhao, Chenxi;Chen, Aihui
关键词：Machine learning; Biomass; Pyrolysis; Biochar; Specific surface area; Plant organs
-
Research on prediction of energy density and power density of biomass carbon-based supercapacitors based on machine learning
作者：Lu, Xueying;Zhao, Chenxi;Tu, Huanyu;Wang, Siyu;Chen, Aihui;Zhang, Haibin
关键词：Supercapacitor; Machine learning; Biomass carbon; Energy density; Power density
-
A study on machine learning prediction of bio-oil yield from biomass and plastic Co-pyrolysis
作者：Zhao, Chenxi;Xia, Qi;Wang, Siyu;Lu, Xueying;Yue, Wenjing;Chen, Juhui;Chen, Aihui
关键词：Biomass; Bio-oil; Co-pyrolysis; Machine learning; Plastic; Yield
-
Prediction of CO2 adsorption performance of biochar based on machine learning
作者：Zhao, Chenxi;Yue, Wenjing;Xia, Qi;Yang, Hang;Liu, Xiaogang;Chen, Aihui
关键词：Biochar; CO2 adsorption; Machine learning; Activation conditions; SHAP explanations
-
Prediction of Soil pH Improvement Through Biochar: A Machine Learning Based Solution
作者：Zhao, Chenxi;Yang, Hang;Zhang, Yiming;Xia, Qi;Yue, Wenjing;Liu, Xiaogang;Chen, Aihui
关键词：biochar; DNN; LightGBM; machine learning; pH
-
Impacts of farming activities on carbon deposition based on fine soil subtype classification
作者：Wang, Qiuju;Zhang, Haibin;Zhang, Dongdong;Jiao, Feng;Guo, Zhenhua
关键词：carbon sink; Sanjiang Plain; soil organic carbon; soil bacterial community; land-use model
-
Impacts of Farming Activities on Nitrogen Degradability under a Temperate Continental Monsoon Climate
作者：Wang, Qiuju;Zou, Jiahe;Liu, Yanxia;Li, Jingyang;Liu, Xin;Zhang, Haibin;Meng, Qingying;Jiao, Feng;Guo, Zhenhua
关键词：ground vegetation; nitrogen deposition; plants; soil organic nitrogen
