Machine-learning-aided hydrochar production through hydrothermal carbonization of biomass by engineering operating parameters and/or biomass mixture recipes

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成果类型：

期刊论文

作者：

Leng, Lijian;Zhou, Junhui;Zhang, Weijin;Chen, Jiefeng;Wu, Zhibin;...

通讯作者：

Yang, Zequn;Li, HL

作者机构：

[Zhou, Junhui; Peng, Haoyi; Li, HL; Yang, Zequn; Zhan, Hao; Li, Hailong; Yang, ZQ; Leng, Lijian; Chen, Jiefeng; Zhang, Weijin] Cent South Univ, Sch Energy Sci & Engn, Changsha 410083, Hunan, Peoples R China.

[Yuan, Xingzhong; Leng, Lijian] Xiangjiang Lab, Changsha 410205, Hunan, Peoples R China.

[Wu, Zhibin] Hunan Agr Univ, Coll Resources & Environm, Changsha 410128, Hunan, Peoples R China.

[Xu, Donghai] Xi An Jiao Tong Univ, Sch Energy & Power Engn, Xian 710049, Shaanxi, Peoples R China.

[Yuan, Xingzhong] Hunan Univ, Coll Environm Sci & Engn, Changsha 410082, Hunan, Peoples R China.

通讯机构：

[Yang, ZQ; Li, HL ] C

Cent South Univ, Sch Energy Sci & Engn, Changsha 410083, Hunan, Peoples R China.

语种：

英文

关键词：

Hydrochar;Hydrothermal carbonization;Machine learning;Wet torrefaction;Biomass;Optimization

期刊：

Energy

ISSN：

0360-5442

年：

2024

卷：

288

DOI：

10.1016/j.energy.2023.129854

基金类别：

National Key Research and Development Program of China [2021YFE0104900]; Open Project of Xiangjiang Laboratory [22xj03003]; Hunan Provincial Natural Science Foundation of China [2022JJ20064]; Science and Technology Innovation Program of Hunan Province [2021RC4005]; National Natural Science Foundation of China [51906247, 51808278]

机构署名：

本校为其他机构

院系归属：

资源环境学院

摘要：

Hydrochar serves not only as a fuel source but also as a versatile carbon material that has found extensive application across various domains. The application performance of hydrochar, e.g., energy recovery and carbon stability, is substantially influenced by its mass yield, higher heating value (HHV), and compositions (C, H, O, N, S, and ash), so the prediction and engineering of these properties is promising. In this study, two machine learning algorithms, namely gradient boosting regression (GBR) and random forest (RF), were used to predict the hydrochar properties mentioned above. The GBR...

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Machine-learning-aided hydrochar production through hydrothermal carbonization of biomass by engineering operating parameters and/or biomass mixture recipes

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