Optimal Design of Solar-Aided Hydrogen Production Process Using Molten Salt
In this work, a machine-learning based optimisation framework is proposed for optimal design of solar steam methane reforming using molten salt (SSMR-MS) through machine learning techniques. The artificial neural network (ANN) is employed to establish relationships between total annualised cost (TAC...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
AIDIC Servizi S.r.l.
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/678d275149424a1eb261d8dafaa6f2f9 |
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| Sumario: | In this work, a machine-learning based optimisation framework is proposed for optimal design of solar steam methane reforming using molten salt (SSMR-MS) through machine learning techniques. The artificial neural network (ANN) is employed to establish relationships between total annualised cost (TAC), hydrogen production rate and molten salt duty, and independent input variables in SSMR-MS. A hybrid global optimisation algorithm is adopted to solve the developed surrogate model and generate the optimal design. The computational results demonstrate that a significant reduction in TAC by around 15 % can be achieved than the existing SSMR-MS. The lowest Levelised cost of Hydrogen Production (LCHP) is 2.43 $ kg-1 which is reduced by around 15 % compared to the existing process with LCHP of 2.85 $ kg-1. |
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