Optimal design of large-scale solar-aided hydrogen production process via machine learning based optimisation framework
文献类型：期刊论文
| 作者 | Wang, Wanrong2; Ma, Yingjie2; Maroufmashat, Azadeh1; Zhang, Nan2; Li, Jie2; Xiao, Xin3 |
| 刊名 | APPLIED ENERGY
 |
| 出版日期 | 2022 |
| 卷号 | 305页码:18 |
| 关键词 | Solar energy Hydrogen Machine learning Hybrid optimization algorithm Surrogate model |
| ISSN号 | 0306-2619 |
| DOI | 10.1016/j.apenergy.2021.117751 |
| 英文摘要 | Hydrogen is an important energy carrier in the transportation sector and an essential industrial feedstock for petroleum refineries, methanol, and ammonia production. Renewable energy sources, especially solar energy have been investigated for large-scale hydrogen production in thermochemical, electrochemical, or photochemical manners due to considerable greenhouse gas emissions from the conventional steam reforming of natural gas and oil-based feedstock. The solar steam methane reforming using molten salt (SSMR-MS) is superior due to its unlimited operation hours and lower total annualized cost (TAC). In this work, we extend the existing optimisation framework for optimal design of SSMR-MS in which machine learning techniques are employed to describe the relationship between solar-related cost and molten salt heat duty and establish relationships of TAC, hydrogen production rate and molten salt heat duty with independent input variables in the whole flowsheet based on 18,619 sample points generated using the Latin hypercube sampling technique. A hybrid global optimisation algorithm is adopted to optimise the developed model and generate the optimal design, which is validated in SAM and Aspen Plus V8.8. The computational results demonstrate that a significant reduction in TAC by 14.9 % similar to 15.1 %, and CO2 emissions by 4.4 % similar to 5.2 % can be achieved compared to the existing SSMR-MS. The lowest Levelized cost of Hydrogen Production is 2.4 $ kg(-1) which is reduced by around 17.2 % compared to the existing process with levelized cost of 2.9 $ kg(-1). |
| WOS关键词 | WATER-GAS SHIFT ; NATURAL-GAS ; OIL |
| 资助项目 | China Scholar-ship Council - The University of Manchester Joint Scholarship[201809120005] |
| WOS研究方向 | Energy & Fuels ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000707902400002 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | China Scholar-ship Council - The University of Manchester Joint Scholarship |
| 源URL | [http://ir.ipe.ac.cn/handle/122111/50578]  |
| 专题 | 中国科学院过程工程研究所 |
| 通讯作者 | Li, Jie |
| 作者单位 | 1.HEC Montreal, Dept Decis Sci, GERAD, Montreal, PQ, Canada 2.Univ Manchester, Ctr Proc Integrat, Dept Chem Engn & Analyt Sci, Manchester M13 9PL, Lancs, England 3.Chinese Acad Sci, Inst Proc Engn, Beijing 100191, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Wanrong,Ma, Yingjie,Maroufmashat, Azadeh,et al. Optimal design of large-scale solar-aided hydrogen production process via machine learning based optimisation framework[J]. APPLIED ENERGY,2022,305:18. |
| APA | Wang, Wanrong,Ma, Yingjie,Maroufmashat, Azadeh,Zhang, Nan,Li, Jie,&Xiao, Xin.(2022).Optimal design of large-scale solar-aided hydrogen production process via machine learning based optimisation framework.APPLIED ENERGY,305,18. |
| MLA | Wang, Wanrong,et al."Optimal design of large-scale solar-aided hydrogen production process via machine learning based optimisation framework".APPLIED ENERGY 305(2022):18. |
入库方式： OAI收割
来源：过程工程研究所
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