Table 2. Summary of Machine Learning Techniques Used for Gas Hydrate Kinetics Studies.

Gas	Machine learning model	Inputs	Outputs	R2	AARD %	RMSE	Remarks	ref
Methane	ANN	T and P	Rhg	NS	13.86	NS	ANN modeled hydrate growth rate with high sensitivity to temperature difference driving force	(79)
Natural gasa	ANFIS	P, T, and Ic	ST	0.9977	1.1998	NS	ANFIS prediction of the interfacial tension of SDS surfactant-based systems near the ethylene hydrate formation region was accurate	(80)

^a(SDS/ethylene).