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Material Science and Artificial Intelligence for Green Hydrogen Production and...
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Material Science and Artificial Intelligence for Green Hydrogen Production and Storage

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 8896

Special Issue Editors

Dr. Mourad Nachtane

E-Mail Website
Guest Editor
CNRS, Arts et Metiers, Institute of Technology, University of Bordeaux, Bordeaux INP, INRAE, I2M Bordeaux, F-33400 Talence, France
Interests: composites materials; finite element method; damage modeling; impact and dynamic response; marine energy; hydrogen storage; data-driven modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Youssef Naimi

E-Mail Website
Guest Editor Assistant
Faculté des Sciences Ben M’sik, Université Hassan II de Casablanca, Casablanca 20670, Morocco
Interests: proton exchange membrane fuel cell (PEMFC); diffusion in gases; electrode; polymer solar cells; polymers; organic photovoltaics

Special Issue Information

Dear Colleagues,

ICRI'23 is one of the major conferences on research and innovation in the Maghreb area. The conference examines key research topics in the stated domains of interest, as well as their applications in commercial markets and technical and industrial areas. The conference draws on the world's leading research, with topics ranging from resource evaluation to market and policy changes. Materials, renewable energies, green hydrogen, and artificial intelligence were selected as the topics for this edition. This conference also focuses on cutting-edge research that meets the scientific needs of academic researchers, industry, and professionals in order to uncover new areas of knowledge in the fields of materials and energy applications, which are linked.

Over the next few years, the market for green hydrogen is expected to grow considerably. This is because there is a growing need for clean energy sources and the government is increasing its efforts to create a sustainable environment. Hydrogen might play a crucial role in a future sustainable energy system since it can help decarbonize the transportation sector. Advancements in artificial intelligence (AI) and material science have led to the development of new science and technology, such as green hydrogen technologies. These technologies attempt to reduce carbon dioxide emissions in order to combat climate change and the energy problem. Therefore, AI and the advancement of material science may offer a solution to achieving a sustainable environment.

This Special Issue will publish selected papers from ICRI'23, which will take place on 20–22 February at FSBM, Casablanca, Morocco.

We encourage contributions of significant and original works on material science and artificial intelligence and their applications for green hydrogen technologies.

Dr. Mourad Nachtane
Guest Editor
Prof. Dr. Youssef Naimi
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

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17 pages, 2486 KiB  
Article
Inversion of Interlayer Pressure in High-Vacuum Multilayer Insulation Structures for Cryogen Storage Using Extreme Learning Machine
by Hao Wu and Hongbo Tan
Appl. Sci. 2023, 13(9), 5779; https://doi.org/10.3390/app13095779 - 8 May 2023
Cited by 1 | Viewed by 1151
Abstract
Revealing the interlayer pressure distribution in multilayer insulation (MLI) for cryogen (e.g., liquid hydrogen) containers is very important to improve the insulation-performance-predicting quality. This paper proposed an inversion method to reconstruct the interlayer pressure of multilayer insulations on the basis of experimentally measuring [...] Read more.
Revealing the interlayer pressure distribution in multilayer insulation (MLI) for cryogen (e.g., liquid hydrogen) containers is very important to improve the insulation-performance-predicting quality. This paper proposed an inversion method to reconstruct the interlayer pressure of multilayer insulations on the basis of experimentally measuring the reflectors’ temperatures. The layer-by-layer (LBL) model was modified by considering the interlayer pressure distribution in MLIs to calculate the reflectors’ temperatures. Groups of pre-given interlayer pressure distributions and the corresponding temperature distributions calculated by the LBL model were used to train an extreme learning machine (ELM) algorithm. Finally, the interlayer pressure distribution of the MLI was reconstructed by the trained ELM algorithm based on the measured reflectors’ temperatures. The method was validated by four additional testing cases. The results showed that the proposed algorithm was accurate in reconstructing the interlayer pressures. Published experimentally measured temperature distributions of a 60-layer MLI were used as input data. The abovementioned inversion method was adopted, and a reasonable interlayer pressure distribution was obtained. Moreover, the thermal insulation performance of the MLI was calculated by the LBL model considering the reconstructed interlayer pressure distribution. We found that the predicted heat flux of the MLI deviated from the experimental results by only 2.77%, while the error of the classical LBL model ignoring the non-ideal vacuum condition was as high as 89%. Meanwhile, the predicted corresponding temperature distribution deviated from the tested value by less than 1.13 K. The proposed method can be applied to assess the interlayer pressure distribution of industrial cryogen containers and precisely predict the thermal insulation performance of a practical multilayer insulation structure. Full article
(This article belongs to the Special Issue Material Science and Artificial Intelligence for Green Hydrogen Production and Storage)
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Review

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43 pages, 10301 KiB  
Review
How Green Hydrogen and Ammonia Are Revolutionizing the Future of Energy Production: A Comprehensive Review of the Latest Developments and Future Prospects
by Khaoula Adeli, Mourad Nachtane, Abdessamad Faik, Dennoun Saifaoui and Abdelkader Boulezhar
Appl. Sci. 2023, 13(15), 8711; https://doi.org/10.3390/app13158711 - 28 Jul 2023
Cited by 9 | Viewed by 7211
Abstract
As the need for clean and sustainable energy sources grows rapidly, green hydrogen and ammonia have become promising sources of low-carbon energy and important key players in the transition to green energy. However, production and storage problems make it hard to use them [...] Read more.
As the need for clean and sustainable energy sources grows rapidly, green hydrogen and ammonia have become promising sources of low-carbon energy and important key players in the transition to green energy. However, production and storage problems make it hard to use them widely. The goal of this review paper is to give a complete overview of the latest technology for the manufacture and storage of hydrogen and ammonia. This paper deals with hydrogen and ammonia synthesis and storage. It examines the most recent technological breakthroughs in areas such as electrolysis, reforming, C-ZEROS, HYSATA, DAE, sulfide, and SRBW, as well as novel storage techniques, such as solid-state storage, plasma kinetics, and POWERPASTE. This article examines the history of ammonia production and discusses some of the newer and more sustainable techniques for producing ammonia, such as electrochemical and biological approaches. This study also looks at how artificial intelligence (AI) and additive manufacturing (AM) could be used to revolutionize the way green hydrogen and ammonia are produced, with an emphasis on recent breakthroughs in AI-assisted catalyst design and 3D-printed reactors, as well as considering major investments in the shift to green energy, such as Moroccan government programs, and how they may affect future hydrogen and ammonia production. Full article
(This article belongs to the Special Issue Material Science and Artificial Intelligence for Green Hydrogen Production and Storage)
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