Machine Learning in CO2 Sequestration
CO2 capture and sequestration is a prominent field of study with high research demands. It involves capturing CO2 from various large point sources and storing it to prevent its emission. Various conventional CO2 sequestration techniques currently in practice involve CO2 storage in geological formati...
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| Format: | Book |
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Springer Nature
2023
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| Online Access: | http://scholars.utp.edu.my/id/eprint/38051/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174755732&partnerID=40&md5=1fbcbf1b5a531e814534a473281c3b76 |
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| Summary: | CO2 capture and sequestration is a prominent field of study with high research demands. It involves capturing CO2 from various large point sources and storing it to prevent its emission. Various conventional CO2 sequestration techniques currently in practice involve CO2 storage in geological formations such as depleted oil and gas reservoirs, saline aquifers, and enhanced oil recovery (EOR) applicaÂtions. Another emerging technique is to store CO2 in the hydrate form in marine sediÂments owing to its large storage capacity. Gas hydrates are crystalline solid strucÂtures formed by the physical combination of gas (such as methane, carbon dioxide, propane, etc.) and water molecules at high-pressure and low-temperature condiÂtions. This chapter briefly describes the conventional CO2 sequestration techniques with the challenges encountered in their application. Further, the chapter discusses the use of machine learning in gas hydrate related studies particularly concerning hydrate-based CO2 capture and sequestration. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. |
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