Machine learning for CO2 capture and conversion: A review
Coupled electrochemical systems for the direct capture and conversion of CO2 have garnered significant attention owing to their potential to enhance energy- and cost-efficiency by circumventing the amine regeneration step. However, optimizing the coupled system is more challenging than handling sepa...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-05-01
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| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824000272 |
| _version_ | 1797214798760378368 |
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| author | Sung Eun Jerng Yang Jeong Park Ju Li |
| author_facet | Sung Eun Jerng Yang Jeong Park Ju Li |
| author_sort | Sung Eun Jerng |
| collection | DOAJ |
| description | Coupled electrochemical systems for the direct capture and conversion of CO2 have garnered significant attention owing to their potential to enhance energy- and cost-efficiency by circumventing the amine regeneration step. However, optimizing the coupled system is more challenging than handling separated systems because of its complexity, caused by the incorporation of solvent and heterogeneous catalysts. Nevertheless, the deployment of machine learning can be immensely beneficial, reducing both time and cost owing to its ability to simulate and describe complex systems with numerous parameters involved. In this review, we summarized the machine learning techniques employed in the development of CO2 capture solvents such as amine and ionic liquids, as well as electrochemical CO2 conversion catalysts. To optimize a coupled electrochemical system, these two separately developed systems will need to be combined via machine learning techniques in the future. |
| first_indexed | 2024-04-24T11:19:54Z |
| format | Article |
| id | doaj.art-0fdf8b94169b40adb98ea8765f58550c |
| institution | Directory Open Access Journal |
| issn | 2666-5468 |
| language | English |
| last_indexed | 2024-04-24T11:19:54Z |
| publishDate | 2024-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj.art-0fdf8b94169b40adb98ea8765f58550c2024-04-11T04:41:59ZengElsevierEnergy and AI2666-54682024-05-0116100361Machine learning for CO2 capture and conversion: A reviewSung Eun Jerng0Yang Jeong Park1Ju Li2Department of Environmental and Energy Engineering, The University of Suwon, 17, Wauan-gil, Bongdam-eup, Hwaseong-si, 18323, Gyeonggi-do, Republic of KoreaDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States of America; Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States of America; MIT-IBM Watson AI Lab, 75 Binney Street, Cambridge, 02142, MA, United States of AmericaDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States of America; Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States of America; MIT-IBM Watson AI Lab, 75 Binney Street, Cambridge, 02142, MA, United States of America; Corresponding author at: Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States of America.Coupled electrochemical systems for the direct capture and conversion of CO2 have garnered significant attention owing to their potential to enhance energy- and cost-efficiency by circumventing the amine regeneration step. However, optimizing the coupled system is more challenging than handling separated systems because of its complexity, caused by the incorporation of solvent and heterogeneous catalysts. Nevertheless, the deployment of machine learning can be immensely beneficial, reducing both time and cost owing to its ability to simulate and describe complex systems with numerous parameters involved. In this review, we summarized the machine learning techniques employed in the development of CO2 capture solvents such as amine and ionic liquids, as well as electrochemical CO2 conversion catalysts. To optimize a coupled electrochemical system, these two separately developed systems will need to be combined via machine learning techniques in the future.http://www.sciencedirect.com/science/article/pii/S2666546824000272Machine learningCO2 conversionCO2 captureAmineIonic liquidsSingle-atom alloys |
| spellingShingle | Sung Eun Jerng Yang Jeong Park Ju Li Machine learning for CO2 capture and conversion: A review Energy and AI Machine learning CO2 conversion CO2 capture Amine Ionic liquids Single-atom alloys |
| title | Machine learning for CO2 capture and conversion: A review |
| title_full | Machine learning for CO2 capture and conversion: A review |
| title_fullStr | Machine learning for CO2 capture and conversion: A review |
| title_full_unstemmed | Machine learning for CO2 capture and conversion: A review |
| title_short | Machine learning for CO2 capture and conversion: A review |
| title_sort | machine learning for co2 capture and conversion a review |
| topic | Machine learning CO2 conversion CO2 capture Amine Ionic liquids Single-atom alloys |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824000272 |
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