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Prediction of Ionic Liquids Methane Hydrate Average Depression Temperature via COSMO-RS

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COSMO-RS is a tool used to pre-screen and pair ionic liquids for gas hydrate mitigation. However, the ionic liquids properties estimated from COSMO-RS lack direct connectivity to the determination of the safety margin (hydrate depression temperature) the inhibitor can alter on the hydrate phase boundary conditions. In this study, a parametric function that relates COSMO-RS estimated ionic liquid surface area and hydrogen bonding energies to the methane hydrate average depression temperature (∆T) have been presented. The statistical model in this study was developed using multiple linear regression techniques on about 90 data samples at 10 wt.% ionic liquids. The developed model in this work accurately predicts the methane hydrate average depression temperature with standard error of 3.14 and average deviation error of 0.92. Also, the ionic liquid surface area and hydrogen bonding energy contribute to hydrate inhibition by 23% and 77%, respectively. The model could predict the methane hydrate average depression temperature in the similar range as reported in literature. The findings in this work provide a simple and fast method of pre-screening and developing efficient ionic liquids gas hydrate inhibitors for use in the oil and gas industry.
Title: Prediction of Ionic Liquids Methane Hydrate Average Depression Temperature via COSMO-RS
Description:
COSMO-RS is a tool used to pre-screen and pair ionic liquids for gas hydrate mitigation.
However, the ionic liquids properties estimated from COSMO-RS lack direct connectivity to the determination of the safety margin (hydrate depression temperature) the inhibitor can alter on the hydrate phase boundary conditions.
In this study, a parametric function that relates COSMO-RS estimated ionic liquid surface area and hydrogen bonding energies to the methane hydrate average depression temperature (∆T) have been presented.
The statistical model in this study was developed using multiple linear regression techniques on about 90 data samples at 10 wt.
% ionic liquids.
The developed model in this work accurately predicts the methane hydrate average depression temperature with standard error of 3.
14 and average deviation error of 0.
92.
Also, the ionic liquid surface area and hydrogen bonding energy contribute to hydrate inhibition by 23% and 77%, respectively.
The model could predict the methane hydrate average depression temperature in the similar range as reported in literature.
The findings in this work provide a simple and fast method of pre-screening and developing efficient ionic liquids gas hydrate inhibitors for use in the oil and gas industry.

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