Application of neural network molecular modeling for correlating and predicting Henry's law constants of gases in [bmim][PF6] at low pressures

Ionic liquids, due to their unique properties, have aroused great interests within chemical engineering, chemistry and environmental sciences. Solubility of gases in ionic liquids has been investigated experimentally by several researchers and different modeling techniques have been employed to correlate obtained experimental data. Almost all proposed modeling methods require tuned adjustable parameters which have been optimized based on experimental data. Without experimental data and tuned adjustable parameters, none of recommended modeling methods can be used confidently for estimating solubility of gases in ionic liquids.In this manuscript, Henry's law constants of carbon dioxide, carbon monoxide, argon, oxygen, nitrogen, methane and ethane in 1-butyl-3-methylimidazolium hexafluorophosphate has been modeled by neural network technique. Gas molecular weight, gas acentric factor (sphericity of gas molecule), reduced temperature and absolute pressure have been employed as network inputs, and Henry's law constant has been correlated accurately. In addition to precise modeling, the new method has the capability of predicting the Henry's law constant of a specific gas based on experimental data points of other gases.

► Reviewing major modeling methods for correlating solubility and Henry's law constant of gases in ionic liquids.
► Application of neural network molecular modeling for correlating Henry's law constants of gases in [bmim][PF6].
► Application of proposed method for predicting solubility of gases based on experimental data points of other systems.
► Comparison of proposed method to other modeling methods.