Machine learning-driven optimization of Ni-based catalysts for catalytic steam reforming of biomass tar
文献类型：期刊论文
| 作者 | Wang, Nantao1; He, Hongyuan1; Wang, Yaolin1; Xu, Bin2; Harding, Jonathan1; Yin, Xiuli2; Tu, Xin1 |
| 刊名 | ENERGY CONVERSION AND MANAGEMENT
 |
| 出版日期 | 2024-01-15 |
| 卷号 | 300页码:11 |
| 关键词 | Machine learning Biomass gasification Tar reforming Syngas Toluene Catalytic reforming |
| ISSN号 | 0196-8904 |
| DOI | 10.1016/j.enconman.2023.117879 |
| 通讯作者 | Tu, Xin(xin.tu@liverpool.ac.uk) |
| 英文摘要 | Biomass gasification is a promising process for producing syngas, which is widely used in various industrial processes. However, the presence of tar in syngas poses a significant challenge to biomass gasification due to the difficulties in its removal and potential downstream issues, such as clogging, slagging, and corrosion. Extensive efforts have been made to address this challenge through catalytic tar removal using various catalysts, generating a vast amount of experimental data. Processing this large dataset and gaining new insights into process optimization requires the development of efficient data analysis methods. In this study, a comprehensive database was built, encompassing a total of 584 data points and 14 input parameters collected from literature published between 2005 and 2020. Machine learning algorithms were then trained using this dataset to predict and optimize the catalytic steam reforming of biomass tar. The predicted results were found to agree well with the experimental data. The results show that the reaction temperature is the most important process parameter, with the highest relative importance of 0.24, followed by the support (0.16), additive (0.12), nickel (Ni) loading (0.08), and calcination temperature (0.07), among the 14 input parameters. This work has proposed optimal ranges for the reaction temperature (600-700 degrees C), Ni loading (5-15 wt%), and calcination temperature (500-650 degrees C). Furthermore, it was found that a larger specific surface area and higher Ni dispersion are two critical factors for selecting additives and supports. This study provides insights into key parameters for optimizing the catalytic steam reforming of biomass tar, enabling enhanced efficiency and effectiveness in biomass gasification processes. |
| WOS关键词 | HYDROGEN-PRODUCTION ; MODEL-COMPOUND ; TOLUENE ; NAPHTHALENE ; SURROGATE ; GASIFICATION ; PERFORMANCE ; REMOVAL ; CE |
| 资助项目 | European Union[823745] ; University of Liverpool ; Chinese Scholarship Council |
| WOS研究方向 | Thermodynamics ; Energy & Fuels ; Mechanics |
| 语种 | 英语 |
| WOS记录号 | WOS:001139041600001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | European Union ; University of Liverpool ; Chinese Scholarship Council |
| 源URL | [http://ir.giec.ac.cn/handle/344007/40674]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Tu, Xin |
| 作者单位 | 1.Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3GJ, England 2.Chinese Acad Sci, Guangzhou Inst Energy Convers, CAS Key Lab Renewable Energy, Guangdong Prov Key Lab New & Renewable Energy Res, Guangzhou 510640, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Nantao,He, Hongyuan,Wang, Yaolin,et al. Machine learning-driven optimization of Ni-based catalysts for catalytic steam reforming of biomass tar[J]. ENERGY CONVERSION AND MANAGEMENT,2024,300:11. |
| APA | Wang, Nantao.,He, Hongyuan.,Wang, Yaolin.,Xu, Bin.,Harding, Jonathan.,...&Tu, Xin.(2024).Machine learning-driven optimization of Ni-based catalysts for catalytic steam reforming of biomass tar.ENERGY CONVERSION AND MANAGEMENT,300,11. |
| MLA | Wang, Nantao,et al."Machine learning-driven optimization of Ni-based catalysts for catalytic steam reforming of biomass tar".ENERGY CONVERSION AND MANAGEMENT 300(2024):11. |
入库方式： OAI收割
来源：广州能源研究所
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