Demulsification of crude oil emulsions using ionic liquids: A computational intelligence approach

Abstract

Ionic liquids have enormous applications in various areas of technology. Among their usage is for the destabilization of crude oil emulsions produced in oilfields. Herein, we propose a support vector regression (SVR) based model for smart screening and prediction water/oil separation driven by ionic liquids demulsifiers. The proposed SVR model applies attributes such as crude oil-water volumes, asphaltenes/resins content, emulsification speed/time, demulsification temperature, ionic liquid concentration, ionic liquid molecular weight, and demulsification time to predict the extent of demulsification performance of representative ionic liquids. Accordingly, the predicted demulsification efficiencies of the assessed ionic liquids exhibited significant matches with the experimental results. The developed approach demonstrated excellent accuracy as indicated by the root mean square error (RSME) values: 4.0123 and 19.6478 for the training and testing datasets, respectively. Additionally, the model demonstrated a considerable correlation coefficient (R2): 97.86 % and 75.97 % for the demulsification efficiency of tested ionic liquids in the training and testing datasets, respectively, thereby consolidating an appreciable agreement between the measured and the predicted results. It is envisaged that the SVR model employed in this study would greatly enhance the smart screening of ionic liquids used for demulsification activities in the petroleum and other related industries.