Liquid organic hydrogen carrier hydrogenation–dehydrogenation : from ab initio catalysis to reaction micro-kinetics modelling

Rakić, Emilija (author), Grilc, Miha (author), Likozar, Blaž (author)

The continued selective focus on the exploitation of fossil fuel chemicals as one of the main environment-depleting sources of energy is one of the reasons for the carbon dioxide emissions, severe air pollution and market crisis of today. The related easy transition to efficient renewable resources also brings challenges, such as the storing of performance generated year round. Consistent application option is to convert the formed transferred electricity produced into the hydrogen through electrolysis, store gaseous H$_2$, and reversibly proceed with reforming or cracking. This routine way is also referred to in literature as evolving green H$_2$. A relatively new method of storage is liquid organic carriers (LOHCs). These are molecules that are in a (l) state at room temperature measurements, contain unsaturated covalent bonds, and can be hydrogenated/dehydrogenated in the many loading cycles without catalytic decomposition products. Paper presents possible structure systems that have been previously investigated as an alternative to conventional. The process of catalysis, reduction and coking, catalysts, and the most commonly used elementary groups, interactions, and reaction condition analyses are listed, while an overview of studies that have assessed technical transfer phenomena is also provided. Reports, dealing with derived micro-kinetic modelling/computational fluid dynamics (CFD), which is a direction for further research activities, are few. As for multiscale, review ranges from the density functional theory (DFT) to CFD. The review paper also addresses the latest studies on LOHCs in the field of artificial intelligence (AI), machine learning (ML), and artificial neural networks (ANN). The progress within the area with approaches is highlighted. Mesoscale surface–selectivity relationships, the robustness towards deactivation and techno-economics are dominant in linking the digital twin design to operation.

• Language: English
• Keywords: liquid organic hydrogen carrier molecules, LOHC, multiscale modelling chemical reaction kinetics, density functional theory, DFT, computational fluid dynamics, CFD, machine learning, ML, catalysis, H$_2$ storage
• Work type: Article
• Typology: 1.02 - Review Article
• Organization: FKKT - Faculty of Chemistry and Chemical Technology
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: 23 str.
• Numbering: Vol. 472, art. 144836
• PID: 20.500.12556/RUL-152906
• UDC: 544.3/.4
• ISSN on article: 1873-3212
• DOI: 10.1016/j.cej.2023.144836
• COBISS.SI-ID: 165967619
• Publication date in RUL: 11.12.2023

Record is a part of a journal

• Title: Chemical engineering journal
• Publisher: Elsevier
• ISSN: 1873-3212
• COBISS.SI-ID: 23123973

Licences

• License: CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
• Link: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Description: The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

• Language: Slovenian
• Keywords: kataliza, katalizatorji, fosilna goriva, dinamika fluidov, CFD

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0152
• Name: Kemijsko reakcijsko inženirstvo

• Funder: ARRS - Slovenian Research Agency
• Project number: J2-2492
• Name: Razvoj procesov valorizacije lignina kot vira aromatskih gradnikov za proizvodnjo bioosnovanih polimerov