Lattice Boltzmann Method and Back-Propagation Artificial Neural Network-Based Coke Mapping of Solid Acid Catalyst in Fructose Conversion
文献类型：期刊论文
| 作者 | Liu, Siwei4,5,6,7; Wei, Xiangqian3; Liu, Qiying2; Sun, Weitao1; Ma, Longlong3; Chen, Lungang3; Wang, Chenguang5,6,7 |
| 刊名 | ENERGY & FUELS
 |
| 出版日期 | 2024-05-17 |
| 页码 | 17 |
| ISSN号 | 0887-0624 |
| DOI | 10.1021/acs.energyfuels.4c01410 |
| 通讯作者 | Wang, Chenguang(wangcg@ms.giec.ac.cn) |
| 英文摘要 | Mapping and understanding humin coking during carbohydrate conversion is crucial for improving solid catalyst systems. However, models for coke mapping are still in need of development. In this study, a lattice Boltzmann method-based back-propagation artificial neural network reduced-order model (ROM) is developed to map the humin distribution during the conversion process. The ROM reveals three configurations of intraparticle coking distribution (surface focus, middle-layer focus, and central focus coking). The experimental feature of surface-focus configuration is the timely decreasing trend in the macroscopic coke accumulation rate, especially under extreme conditions (surface humins/central humins >10). Catalyst load, pellet size, substrate concentration (LSC), and temperature collectively influence the coking configurations. As the temperature increases (100-160 degrees C), the configuration with the highest occupancy in the LSC coordinate space changes from central to middle-layer and finally to surface configuration. Increasing the catalyst loading, reducing the particle size, and lowering the substrate concentration under a threshold number (phi(LSC)) in LSC space helps prevent the catalyst from working under the extreme surface coking configuration status. |
| WOS关键词 | PORE-SCALE SIMULATION ; LEVULINIC ACID ; HYDROTHERMAL CARBONIZATION ; LIGNOCELLULOSIC BIOMASS ; MOLECULAR-STRUCTURE ; GLUCOSE CONVERSION ; BRONSTED ACIDS ; FE/HY ZEOLITE ; DECOMPOSITION ; GROWTH |
| 资助项目 | National Natural Science Foundation of China[52276220] ; National Natural Science Foundation of China[2020B1111570001] ; Key Technologies R&D Program of Guangdong Province |
| WOS研究方向 | Energy & Fuels ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001227266500001 |
| 出版者 | AMER CHEMICAL SOC |
| 资助机构 | National Natural Science Foundation of China ; National Natural Science Foundation of China ; Key Technologies R&D Program of Guangdong Province |
| 源URL | [http://ir.giec.ac.cn/handle/344007/41840]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Wang, Chenguang |
| 作者单位 | 1.Univ Sci & Technol China, Dept Thermal Sci & Energy Engn, Lab Basic Res Biomass Convers & Utilizat, Hefei 230026, Peoples R China 2.Nanjing Forestry Univ, Coll Chem Engn, Nanjing 210037, Peoples R China 3.Southeast Univ, Sch Energy & Environm, Key Lab Energy Thermal Convers & Control, Minist Educ, Nanjing 210096, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Guangdong Key Lab New & Renewable Energy Res & Dev, Guangzhou 510640, Peoples R China 6.Chinese Acad Sci, Key Lab Renewable Energy, Guangzhou 510640, Peoples R China 7.Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou 510640, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Siwei,Wei, Xiangqian,Liu, Qiying,et al. Lattice Boltzmann Method and Back-Propagation Artificial Neural Network-Based Coke Mapping of Solid Acid Catalyst in Fructose Conversion[J]. ENERGY & FUELS,2024:17. |
| APA | Liu, Siwei.,Wei, Xiangqian.,Liu, Qiying.,Sun, Weitao.,Ma, Longlong.,...&Wang, Chenguang.(2024).Lattice Boltzmann Method and Back-Propagation Artificial Neural Network-Based Coke Mapping of Solid Acid Catalyst in Fructose Conversion.ENERGY & FUELS,17. |
| MLA | Liu, Siwei,et al."Lattice Boltzmann Method and Back-Propagation Artificial Neural Network-Based Coke Mapping of Solid Acid Catalyst in Fructose Conversion".ENERGY & FUELS (2024):17. |
入库方式： OAI收割
来源：广州能源研究所
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