A deep-learning-boosted surrogate model of a metal foam based protonic ceramic electrolysis cell stack for uncertainty quantification-Reference-Cited by-同舟云学术

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A deep-learning-boosted surrogate model of a metal foam based protonic ceramic electrolysis cell stack for uncertainty quantification

Published:2024-10 Issue: Volume:318 Page:118886
ISSN:0196-8904
Container-title:Energy Conversion and Management
language:en
Short-container-title:Energy Conversion and Management

Author:

Li Zheng^ORCID,Wang Chen,Chen Xi,Yu Na,Xing Lei,Xuan Jin,Han Minfang,Ni Meng^ORCID

Funder

University Grants Committee Research Grants Council

Publisher

Elsevier BV

Reference62 articles.

1. Life cycle assessment of H2 generation with high temperature electrolysis;Patyk;Int J Hydrogen Energy,2013

2. Engineering advanced noble-metal-free electrocatalysts for energy-saving hydrogen production from alkaline water via urea electrolysis;Yu;J Colloid Interface Sci,2024

3. Morphology control and electronic tailoring of CoxAy (A = P, S, Se) electrocatalysts for water splitting;Yu;Chem Eng J,2023

4. Advancing the multiscale understanding on solid oxide electrolysis cells via modelling approaches: A review;Li;Renew Sustain Energy Rev,2021

5. Recent developments on hydrogen production technologies: state-of-the-art review with a focus on green-electrolysis;Vidas;Appl Sci,2021

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