Details
Originalsprache | Englisch |
---|---|
Aufsatznummer | 100829 |
Fachzeitschrift | Current Opinion in Chemical Engineering |
Jahrgang | 36 |
Frühes Online-Datum | 19 Mai 2022 |
Publikationsstatus | Veröffentlicht - Juni 2022 |
Abstract
The development of proton exchange membrane water electrolysis for hydrogen production to satisfy the industrial demand regarding scale, performance and lifetime is challenging. The objectives with the highest priority are a reduction of the power-specific cost and increases in efficiency, reliability, and durability. The main drivers for technology development are usually ex situ and in situ experiments with new or customised materials at various operating conditions. However, modelling was already able to support technology development in the past and will even gain more importance in future. This article looks at the importance of modelling for further development from an engineering perspective and therefore focuses on the macroscopic and microscopic levels. It is stated that the advances in computational engineering and digitalisation in combination with the trend to machine learning/artificial neural networks may even lead to more intensive use of models.
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in: Current Opinion in Chemical Engineering, Jahrgang 36, 100829, 06.2022.
Publikation: Beitrag in Fachzeitschrift › Übersichtsarbeit › Forschung › Peer-Review
}
TY - JOUR
T1 - An engineering perspective on the future role of modelling in proton exchange membrane water electrolysis development
AU - Bensmann, Boris
AU - Rex, Alexander
AU - Hanke-Rauschenbach, Richard
N1 - Funding Information: The authors gratefully acknowledge the financial support by the Federal Ministry of Education and Research ,Germany, in the framework of HyThroughGen (project number 03HY108C ). The authors are responsible for the content of the article.
PY - 2022/6
Y1 - 2022/6
N2 - The development of proton exchange membrane water electrolysis for hydrogen production to satisfy the industrial demand regarding scale, performance and lifetime is challenging. The objectives with the highest priority are a reduction of the power-specific cost and increases in efficiency, reliability, and durability. The main drivers for technology development are usually ex situ and in situ experiments with new or customised materials at various operating conditions. However, modelling was already able to support technology development in the past and will even gain more importance in future. This article looks at the importance of modelling for further development from an engineering perspective and therefore focuses on the macroscopic and microscopic levels. It is stated that the advances in computational engineering and digitalisation in combination with the trend to machine learning/artificial neural networks may even lead to more intensive use of models.
AB - The development of proton exchange membrane water electrolysis for hydrogen production to satisfy the industrial demand regarding scale, performance and lifetime is challenging. The objectives with the highest priority are a reduction of the power-specific cost and increases in efficiency, reliability, and durability. The main drivers for technology development are usually ex situ and in situ experiments with new or customised materials at various operating conditions. However, modelling was already able to support technology development in the past and will even gain more importance in future. This article looks at the importance of modelling for further development from an engineering perspective and therefore focuses on the macroscopic and microscopic levels. It is stated that the advances in computational engineering and digitalisation in combination with the trend to machine learning/artificial neural networks may even lead to more intensive use of models.
UR - http://www.scopus.com/inward/record.url?scp=85130841321&partnerID=8YFLogxK
U2 - 10.1016/j.coche.2022.100829
DO - 10.1016/j.coche.2022.100829
M3 - Review article
AN - SCOPUS:85130841321
VL - 36
JO - Current Opinion in Chemical Engineering
JF - Current Opinion in Chemical Engineering
SN - 2211-3398
M1 - 100829
ER -