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

脱氢 能量载体 工艺工程 可再生能源 化学 催化作用 化石燃料 有机化学 工程类 电气工程
作者
Emilija Rakić,Miha Grilc,Blaž Likozar
出处
期刊:Chemical Engineering Journal [Elsevier BV]
卷期号:472: 144836-144836 被引量:36
标识
DOI:10.1016/j.cej.2023.144836
摘要

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 H2, and reversibly proceed with reforming or cracking. This routine way is also referred to in literature as evolving green H2. 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

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