Correction to “Gas Hydrate in Oil-Dominant Systems: A Review”

Almashwali, Abdulrab Abdulwahab; Bavoh, Cornelius B.; Lal, Bhajan; Khor, Siak Foo; Jin, Quah Chong; Zaini, Dzulkarnain

doi:10.1021/acsomega.2c04996

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2024

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“…Hydrate formation kinetics modeling needs to be studied just like its thermodynamic behavior. There is extensive experimental work − available in literature for predicting the thermodynamic conditions of the hydrate formation with and without inhibitors. In contrast, there are no sufficient studies on kinetic gas hydrate models in literature.…”

Section: Introductionmentioning

confidence: 99%

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Osei,

Bavoh,

Lal

2024

ACS Omega

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The complex modeling accuracy of gas hydrate models has been recently improved owing to the existence of data for machine learning tools. In this review, we discuss most of the machine learning tools used in various hydrate-related areas such as phase behavior predictions, hydrate kinetics, CO 2 capture, and gas hydrate natural distribution and saturation. The performance comparison between machine learning and conventional gas hydrate models is also discussed in detail. This review shows that machine learning methods have improved hydrate phase property predictions and could be adopted in current and new gas hydrate simulation software for better and more accurate results.

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