Artificial intelligence-enhanced solubility predictions of greenhouse gases in ionic liquids: A review

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10257201" target="_blank" >RIV/61989100:27240/25:10257201 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2590123024020942?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2590123024020942?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.rineng.2024.103851" target="_blank" >10.1016/j.rineng.2024.103851</a>

Result language
angličtina
Original language name
Artificial intelligence-enhanced solubility predictions of greenhouse gases in ionic liquids: A review
Original language description
Greenhouse gas emissions from human activities pose a significant threat to the ecosystem, causing climate change and ecological disruptions. Ionic liquids (ILs) show promise for gas separation and carbon capture, but predicting gas solubility in ILs is challenging due to limited data and complex thermodynamics. Artificial intelligence (AI) offers an innovative approach to improve the efficiency and accuracy of solubility predictions. This review analyzes recent advancements in AI-enabled solubility predictions, focusing on methodologies, models, and applications in gas separation and carbon capture. It examines artificial neural networks, deep learning models, and support vector machines for predicting solubility in ILs, and presents valuable results demonstrating the potential of these techniques. The study highlights AI's transformative power in understanding gas-IL interactions and inspiring environmentally friendly separation processes. It also discusses integrating AI-driven predictions with process modeling tools like Aspen Hysys and Aspen Plus, aiming to stimulate further research in gas separation technologies and pave the way for practical implementation. © 2025
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20704 - Energy and fuels

Project
<a href="/en/project/SS02030008" target="_blank" >SS02030008: Centre of environmental research: Waste management, circular economy and environmetal secutiry</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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