Machine-learning-aided prediction and engineering of nitrogen-containing functional groups of biochar derived from biomass pyrolysis

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成果类型：

期刊论文

作者：

Leng, Lijian;Lei, Xinni;Al-Dhabi, Naif Abdullah;Wu, Zhibin;Yang, Zequn;...

通讯作者：

Ting Xiong<&wdkj&>Hailong Li

作者机构：

[Lei, Xinni; Yang, Zequn; Li, Tanghao; Zhang, Weijin; Zhan, Hao; Peng, Haoyi; Li, Hailong] School of Energy Science and Engineering, Central South University, Changsha 410083, China

Xiangjiang Laboratory, Changsha 410205, China

[Xiong, Ting] School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha 410205, China

[Al-Dhabi, Naif Abdullah] Department of Botany and Microbiology, College of Science, King Saud University, P. O. Box 2455, Riyadh 11451, Saudi Arabia

[Wu, Zhibin] College of Resources and Environment, Hunan Agricultural University, Changsha, Hunan 410128, China

通讯机构：

[Ting Xiong; Hailong Li] S

School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha 410205, China<&wdkj&>School of Energy Science and Engineering, Central South University, Changsha 410083, China

语种：

英文

期刊：

Chemical Engineering Journal

ISSN：

1385-8947

年：

2024

卷：

485

页码：

149862

DOI：

10.1016/j.cej.2024.149862

基金类别：

CRediT authorship contribution statement Lijian Leng: Conceptualization, acquisition, Project administration, Writing – original draft, Writing – review & editing. Xinni Lei: Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. Naif Abdullah Al-Dhabi: Methodology, Writing – review & editing. Zhibin Wu: Writing – review & editing. Zequn Yang: Validation. Tanghao Li: Writing – review & editing. Weijin Zhang: Writing – review & editing. Wei Liu: Writing – review & editing. Hao

机构署名：

本校为其他机构

院系归属：

资源环境学院

摘要：

Biochar, derived through biomass pyrolysis, is an emerging carbonaceous material with significant potential in various applications. The performance of biochar in applications depends heavily on its surface characteristics, including its content of N-containing functional groups. In this study, machine learning (ML) was used for the prediction and engineering of biochar N-containing functional groups, specifically amine-N (N-A), pyrrolic-N (N-5), and pyridinic-N (N-6). The single-target random forest model accurately predicted N recovery in bio...

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Machine-learning-aided prediction and engineering of nitrogen-containing functional groups of biochar derived from biomass pyrolysis

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