Artificial intelligence-enhanced solubility predictions of greenhouse gases in ionic liquids: A review
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 th...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024020942 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841560151151083520 |
|---|---|
| author | Bilal Kazmi Syed Ali Ammar Taqvi Dagmar Juchelkov Guoxuan Li Salman Raza Naqvi |
| author_facet | Bilal Kazmi Syed Ali Ammar Taqvi Dagmar Juchelkov Guoxuan Li Salman Raza Naqvi |
| author_sort | Bilal Kazmi |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-ed2c97dcaf024bca9e4d9750551622d3 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-ed2c97dcaf024bca9e4d9750551622d32025-01-05T04:28:35ZengElsevierResults in Engineering2590-12302025-03-0125103851Artificial intelligence-enhanced solubility predictions of greenhouse gases in ionic liquids: A reviewBilal Kazmi0Syed Ali Ammar Taqvi1Dagmar Juchelkov2Guoxuan Li3Salman Raza Naqvi4Department of Applied Chemistry and Chemical Technology, University of Karachi, Pakistan; Department of Electronics, Faculty of Electrical Engineering and Computer Science, VŠB – Technical University of Ostrava, 17. Listopadu 15/2172, Ostrava-Poruba 708 00, Czech RepublicDepartment of Chemical Engineering, NED University of Engineering and Technology, Karachi, PakistanDepartment of Electronics, Faculty of Electrical Engineering and Computer Science, VŠB – Technical University of Ostrava, 17. Listopadu 15/2172, Ostrava-Poruba 708 00, Czech RepublicCollege of Chemical Engineering, Qingdao University of Science and Technology, Zhengzhou Road No. 53, Qingdao 266042, ChinaDepartment of Engineering and Chemical Sciences, Karlstad University, Sweden; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2590123024020942Artificial intelligenceIonic liquidNeural networkDeep learningAcid gas captureSolubility prediction |
| spellingShingle | Bilal Kazmi Syed Ali Ammar Taqvi Dagmar Juchelkov Guoxuan Li Salman Raza Naqvi Artificial intelligence-enhanced solubility predictions of greenhouse gases in ionic liquids: A review Results in Engineering Artificial intelligence Ionic liquid Neural network Deep learning Acid gas capture Solubility prediction |
| title | Artificial intelligence-enhanced solubility predictions of greenhouse gases in ionic liquids: A review |
| title_full | Artificial intelligence-enhanced solubility predictions of greenhouse gases in ionic liquids: A review |
| title_fullStr | Artificial intelligence-enhanced solubility predictions of greenhouse gases in ionic liquids: A review |
| title_full_unstemmed | Artificial intelligence-enhanced solubility predictions of greenhouse gases in ionic liquids: A review |
| title_short | Artificial intelligence-enhanced solubility predictions of greenhouse gases in ionic liquids: A review |
| title_sort | artificial intelligence enhanced solubility predictions of greenhouse gases in ionic liquids a review |
| topic | Artificial intelligence Ionic liquid Neural network Deep learning Acid gas capture Solubility prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024020942 |
| work_keys_str_mv | AT bilalkazmi artificialintelligenceenhancedsolubilitypredictionsofgreenhousegasesinionicliquidsareview AT syedaliammartaqvi artificialintelligenceenhancedsolubilitypredictionsofgreenhousegasesinionicliquidsareview AT dagmarjuchelkov artificialintelligenceenhancedsolubilitypredictionsofgreenhousegasesinionicliquidsareview AT guoxuanli artificialintelligenceenhancedsolubilitypredictionsofgreenhousegasesinionicliquidsareview AT salmanrazanaqvi artificialintelligenceenhancedsolubilitypredictionsofgreenhousegasesinionicliquidsareview |