Optimal design of large-scale solar-aided hydrogen production process via machine learning based optimisation framework

doi:10.1016/j.apenergy.2021.117751

CAS OpenIR

	Optimal design of large-scale solar-aided hydrogen production process via machine learning based optimisation framework
	Wang, Wanrong 1; Ma, Yingjie 1; Maroufmashat, Azadeh 2; Zhang, Nan 1; Li, Jie 1; Xiao, Xin 3
	2022
Source Publication	APPLIED ENERGY
ISSN	0306-2619
Volume	305 Pages:18
Abstract	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).
Keyword	Solar energy Hydrogen Machine learning Hybrid optimization algorithm Surrogate model
DOI	10.1016/j.apenergy.2021.117751
Language	英语
WOS Keyword	WATER-GAS SHIFT ; NATURAL-GAS ; OIL
Funding Project	China Scholar-ship Council - The University of Manchester Joint Scholarship[201809120005]
WOS Research Area	Energy & Fuels ; Engineering
WOS Subject	Energy & Fuels ; Engineering, Chemical
Funding Organization	China Scholar-ship Council - The University of Manchester Joint Scholarship
WOS ID	WOS:000707902400002
Publisher	ELSEVIER SCI LTD
Citation statistics
Document Type	期刊论文
Identifier	http://ir.ipe.ac.cn/handle/122111/50579
Collection	中国科学院过程工程研究所
Corresponding Author	Li, Jie
Affiliation	1.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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