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Cited article:

Application of machine learning on hydrate formation prediction of pure components with water and inhibitors solution

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The Canadian Journal of Chemical Engineering (2024)
https://doi.org/10.1002/cjce.25291

Heat transfer study in the exploitation of argillaceous‐silt natural gas hydrate reservoirs

Shuxian Jiang, Jinze Song, Chang Liu, Youming Xiong, Weixin Pang and Yufa He
Energy Science & Engineering 11 (1) 241 (2023)
https://doi.org/10.1002/ese3.1328

A multi-layer perceptron neural network model for predicting the hydrate equilibrium conditions in multi-component hydrocarbon systems

Qazi Nasir, Humbul Suleman, Israf Ud Din and Yasir Elsheikh Elfadol
Neural Computing and Applications 34 (18) 15863 (2022)
https://doi.org/10.1007/s00521-022-07284-4

Prediction of natural gas hydrates formation using a combination of thermodynamic and neural network modeling

Noura Rebai, Ahmed Hadjadj, Abdelbaki Benmounah, Abdallah S. Berrouk and Salim M. Boualleg
Journal of Petroleum Science and Engineering 182 106270 (2019)
https://doi.org/10.1016/j.petrol.2019.106270

PVTx measurements of mixed clathrate hydrates in batch conditions under different crystallization rates: Influence on equilibrium

S. Maghsoodloo Babakhani, B. Bouillot, J. Douzet, S. Ho-Van and J.M. Herri
The Journal of Chemical Thermodynamics 122 73 (2018)
https://doi.org/10.1016/j.jct.2018.03.006

Specific features of the growth, composition, and content of natural gas hydrates synthesized in inverted oil emulsions

V. V. Koryakina, I. K. Ivanova, M. E. Semenov, et al.
Russian Journal of Applied Chemistry 90 (8) 1258 (2017)
https://doi.org/10.1134/S1070427217080110

The Chemistry and Technology of Petroleum, Fifth Edition

Chemical Industries, The Chemistry and Technology of Petroleum, Fifth Edition 691 (2014)
https://doi.org/10.1201/b16559-29