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Journal articles
Artificial Neural Network modeling of solubility of supercritical carbon dioxide in 24 commonly used ionic liquids
Abstract : Application of supercritical CO2 for separation of ionic liquids from their organic solvents or extraction of various solutes from ionic liquid solvents have found great interest during recent years. Knowledge of phase behaviors of the mixtures of supercritical CO2+ionic liquids is therefore drastic in order to efficiently design such separation processes. In this communication, Artificial Neural Network procedure has been applied to represent the solubility of supercritical CO2 in 24 mostly used ionic liquids. An optimized Three-Layer Feed Forward Neural Network using critical properties of ionic liquids and operational temperature and pressure has been developed. Application of this model for 1128 data points of 24 ionic liquids show squared correlation coefficients of 0.993 and average absolute deviation of 3.6% from experimental values for calculated/estimated solubilities. The aforementioned deviations show the prediction capability of the presented model.
Keywords :
Artificial Neural Network
Model
Solubility
Supercritical carbon dioxide
Artificial neural network modeling
Average absolute deviation
Critical properties
Data points
Experimental values
Ionic liquid solvents
Operational temperature
Prediction
Prediction capability
Separation process
Squared correlation coefficients
Supercritical carbon dioxides
Supercritical CO
Three-layer
Carbon dioxide
Extraction
Forecasting
Ions
Supercritical fluid extraction
https://hal-mines-paristech.archives-ouvertes.fr/hal-00651416
Contributor : Severine Dumigron <>
Submitted on : Tuesday, December 13, 2011 - 3:32:48 PM
Last modification on : Wednesday, October 14, 2020 - 3:45:43 AM
Contributor : Severine Dumigron <>
Submitted on : Tuesday, December 13, 2011 - 3:32:48 PM
Last modification on : Wednesday, October 14, 2020 - 3:45:43 AM