CAS OpenIR
Optimal design of large-scale solar-aided hydrogen production process via machine learning based optimisation framework
Wang, Wanrong1; Ma, Yingjie1; Maroufmashat, Azadeh2; Zhang, Nan1; Li, Jie1; Xiao, Xin3
2022
Source PublicationAPPLIED ENERGY
ISSN0306-2619
Volume305Pages:18
AbstractHydrogen 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).
KeywordSolar energy Hydrogen Machine learning Hybrid optimization algorithm Surrogate model
DOI10.1016/j.apenergy.2021.117751
Language英语
WOS KeywordWATER-GAS SHIFT ; NATURAL-GAS ; OIL
Funding ProjectChina Scholar-ship Council - The University of Manchester Joint Scholarship[201809120005]
WOS Research AreaEnergy & Fuels ; Engineering
WOS SubjectEnergy & Fuels ; Engineering, Chemical
Funding OrganizationChina Scholar-ship Council - The University of Manchester Joint Scholarship
WOS IDWOS:000707902400002
PublisherELSEVIER SCI LTD
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Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/50579
Collection中国科学院过程工程研究所
Corresponding AuthorLi, Jie
Affiliation1.Univ Manchester, Ctr Proc Integrat, Dept Chem Engn & Analyt Sci, Manchester M13 9PL, Lancs, England
2.HEC Montreal, Dept Decis Sci, GERAD, Montreal, PQ, Canada
3.Chinese Acad Sci, Inst Proc Engn, Beijing 100191, Peoples R China
Recommended Citation
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.
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