Abstract
Solubility data is one of the essential basic data for CO2 capture by ionic liquids. A selective ensemble modeling method, proposed to overcome the shortcomings of current methods, was developed and applied to the prediction of the solubility of CO2 in imidazolium ionic liquids. Firstly, multiple different sub–models were established based on the diversities of data, structural, and parameter design philosophy. Secondly, the fuzzy C–means algorithm was used to cluster the sub–models, and the collinearity detection method was adopted to eliminate the sub–models with high collinearity. Finally, the information entropy method integrated the sub–models into the selective ensemble model. The validation of the CO2 solubility predictions against experimental data showed that the proposed ensemble model had better performance than its previous alternative, because more effective information was extracted from different angles, and the diversity and accuracy among the sub–models were fully integrated. This work not only provided an effective modeling method for the prediction of the solubility of CO2 in ionic liquids, but also provided an effective method for the discrimination of ionic liquids for CO2 capture.
Highlights
IntroductionRoom–temperature ionic liquids, which are relatively new compounds, have gained much attention in recent years, and had the potential to be considered as an alternative to conventional volatile organic solvents in the reaction and separation processes
With the increase of energy consumption in industrial production, reducing CO2 emissions and increasing CO2 absorption have become an essential means to alleviate environmental degradation [1].Room–temperature ionic liquids, which are relatively new compounds, have gained much attention in recent years, and had the potential to be considered as an alternative to conventional volatile organic solvents in the reaction and separation processes
The modeling prediction methods have become an important way to obtain the solubility data of CO2 in ionic liquids, which is divided into the mechanism modeling method and the data–driven modeling method
Summary
Room–temperature ionic liquids, which are relatively new compounds, have gained much attention in recent years, and had the potential to be considered as an alternative to conventional volatile organic solvents in the reaction and separation processes. Information about the solubility and the rate of solubility is a crucial factor for consideration of ionic liquids in potential industrial processes [2,3]. Due to some difficulties associated with experimental measurements and the cost of ionic liquids, it is more advantageous to develop predictive methods for prediction of the phase behavior of such systems [4,5,6]. The modeling prediction methods have become an important way to obtain the solubility data of CO2 in ionic liquids, which is divided into the mechanism modeling method and the data–driven modeling method
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