TY  - JOUR
TI  - Research Advances in Machine Learning Techniques in Gas Hydrate Applications
SP  - 4210
ID  - scholars19960
N2  - 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, CO2 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. © 2024 The Authors. Published by American Chemical Society.
N1  - cited By 0
IS  - 4
AV  - none
EP  - 4228
VL  - 9
JF  - ACS Omega
A1  - Osei, H.
A1  - Bavoh, C.B.
A1  - Lal, B.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183033490&doi=10.1021%2facsomega.3c04825&partnerID=40&md5=8d1e5e7ac1af28ff804828999b846f85
Y1  - 2024///
ER  -