Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction

Abstract

Being a new generation of green solvents and high-tech reaction media of the future, ionic liquids have increasingly attracted much attention. Of particular interest in this context are room temperature ionic liquids (in short as ILs in this paper). Due to the relatively high viscosity, ILs is expected to be used in the form of solvent diluted mixture with reduced viscosity in industrial application, where predicting the viscosity of IL mixture has been an important research issue. Different IL mixture and many modelling approaches have been investigated. The objective of this study is to provide an alternative model approach using soft computing technique, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of ILs [C n -mim][NTf 2 ] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0-328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity taking account of IL alkyl chain length, as well as temperature and compositions simultaneously, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. This illustrates the potential application of ANN in the case that the physical and thermodynamic properties are highly non-linear or too complex.

A quantitative prediction of the viscosity of ionic liquids using Sσ-profilemolecular descriptors

“…Currently, many models [5][6][7][8][9][10][11][12][13][14][15][16][17] have been developed to predict the viscosity of ILs, and some have been well reviewed by Coutinho et al 18 Among these, Group Contribution (GC) and Quantitative Structure-Property Relationship (QSPR) methods are most commonly applied. GC is a very important method to be utilized to predict a variety of physical and thermodynamic properties.…”

Prediction of Dynamic Viscosity and Sensitivity Study of Potassium Amino-Acid Salt Aqueous Solutions by an Artificial Neural Network According to the Structure

“…To correctly model a nonlinear property, an activation function must be used to take this nonlinearity into account. The main function used in the literature ,,,,, is the hyperbolic tangent noted as tanh (eq ). Hyperbolic tangent has the necessary property to be continuous over the entire range of relative numbers and it can be derived throughout the interval tanh ( n ) = exp ( n ) − exp ( − n ) exp ( n ) + exp ( − n ) = 2 1 + exp ( − 2 · n ) − 1 …”

Viscosity of Ionic Liquids: Theories and Models

“…The STD in compositional viscosity for the binary [C n mim][Tf 2 N]−methanol and [C n mim][Tf 2 N]−ethanol systems is 0.0895−0.2433. The proposed ANN model successfully predicted the viscosity for each mixture S T D = 1 N ∑ i = 1 N ( η i cal − η i exp ) 2 where N is the total number of experimental points, η cal is the calculated viscosity, and η exp is the measured viscosity.…”