Optimal design of large-scale solar-aided hydrogen production process via machine learning based optimisation framework
•Four solar steam methane reforming alternatives are investigated.•Machine learning based optimisation framework is proposed to achieve optimal design.•Total annualised cost is reduced by 14.9 % ~ 15.1% in comparison to existing work.•CO2 emission decreases by 80.0 kt yr−1 than conventional steam me...
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| Published in: | Applied energy Vol. 305; p. 117751 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.01.2022
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| ISSN: | 0306-2619, 1872-9118 |
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| Abstract | •Four solar steam methane reforming alternatives are investigated.•Machine learning based optimisation framework is proposed to achieve optimal design.•Total annualised cost is reduced by 14.9 % ~ 15.1% in comparison to existing work.•CO2 emission decreases by 80.0 kt yr−1 than conventional steam methane reforming.•Levelized cost of H2 production using solar is reduced from 2.9 to 2.4 $ kg−1.
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 % ~ 15.1 %, and CO2 emissions by 4.4 % ~ 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. |
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| AbstractList | 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 % ~ 15.1 %, and CO₂ emissions by 4.4 % ~ 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. •Four solar steam methane reforming alternatives are investigated.•Machine learning based optimisation framework is proposed to achieve optimal design.•Total annualised cost is reduced by 14.9 % ~ 15.1% in comparison to existing work.•CO2 emission decreases by 80.0 kt yr−1 than conventional steam methane reforming.•Levelized cost of H2 production using solar is reduced from 2.9 to 2.4 $ kg−1. 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 % ~ 15.1 %, and CO2 emissions by 4.4 % ~ 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. |
| ArticleNumber | 117751 |
| Author | Ma, Yingjie Xiao, Xin Wang, Wanrong Maroufmashat, Azadeh Zhang, Nan Li, Jie |
| Author_xml | – sequence: 1 givenname: Wanrong surname: Wang fullname: Wang, Wanrong organization: Centre for Process Integration, Department of Chemical Engineering and Analytical Science, The University of Manchester, Manchester M13 9PL, UK – sequence: 2 givenname: Yingjie surname: Ma fullname: Ma, Yingjie organization: Centre for Process Integration, Department of Chemical Engineering and Analytical Science, The University of Manchester, Manchester M13 9PL, UK – sequence: 3 givenname: Azadeh surname: Maroufmashat fullname: Maroufmashat, Azadeh organization: GERAD, Department of Decision Sciences, HEC Montréal, Montréal, Canada – sequence: 4 givenname: Nan surname: Zhang fullname: Zhang, Nan organization: Centre for Process Integration, Department of Chemical Engineering and Analytical Science, The University of Manchester, Manchester M13 9PL, UK – sequence: 5 givenname: Jie surname: Li fullname: Li, Jie email: jie.li-2@manchester.ac.uk organization: Centre for Process Integration, Department of Chemical Engineering and Analytical Science, The University of Manchester, Manchester M13 9PL, UK – sequence: 6 givenname: Xin surname: Xiao fullname: Xiao, Xin organization: Institute of Process Engineering, Chinese Academy of Science, Beijing 100191, China |
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| Snippet | •Four solar steam methane reforming alternatives are investigated.•Machine learning based optimisation framework is proposed to achieve optimal design.•Total... Hydrogen is an important energy carrier in the transportation sector and an essential industrial feedstock for petroleum refineries, methanol, and ammonia... |
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| SubjectTerms | algorithms ammonia carbon dioxide electrochemistry energy feedstocks heat Hybrid optimization algorithm Hydrogen hydrogen production Machine learning methane methanol natural gas petroleum photochemistry Solar energy steam Surrogate model transportation industry |
| Title | Optimal design of large-scale solar-aided hydrogen production process via machine learning based optimisation framework |
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