Abstract
Accurate determination of the physicochemical characteristics of ionic liquids (ILs), especially viscosity, at widespread operating conditions is of a vital role for various fields. In this study, the viscosity of pure ILs is modeled using three approaches: (I) a simple group contribution method based on temperature, pressure, boiling temperature, acentric factor, molecular weight, critical temperature, critical pressure, and critical volume; (II) a model based on thermodynamic properties, pressure, and temperature; and (III) a model based on chemical structure, pressure, and temperature. Furthermore, Eyring’s absolute rate theory is used to predict viscosity based on boiling temperature and temperature. To develop Model (I), a simple correlation was applied, while for Models (II) and (III), smart approaches such as multilayer perceptron networks optimized by a Levenberg–Marquardt algorithm (MLP-LMA) and Bayesian Regularization (MLP-BR), decision tree (DT), and least square support vector machine optimized by bat algorithm (BAT-LSSVM) were utilized to establish robust and accurate predictive paradigms. These approaches were implemented using a large database consisting of 2813 experimental viscosity points from 45 different ILs under an extensive range of pressure and temperature. Afterward, the four most accurate models were selected to construct a committee machine intelligent system (CMIS). Eyring’s theory’s results to predict the viscosity demonstrated that although the theory is not precise, its simplicity is still beneficial. The proposed CMIS model provides the most precise responses with an absolute average relative deviation (AARD) of less than 4% for predicting the viscosity of ILs based on Model (II) and (III). Lastly, the applicability domain of the CMIS model and the quality of experimental data were assessed through the Leverage statistical method. It is concluded that intelligent-based predictive models are powerful alternatives for time-consuming and expensive experimental processes of the ILs viscosity measurement.
Highlights
The attention in green chemical technologies has resulted in the growth of a new class of highly tunable and special compounds named ionic liquids (ILs) [1]
The first decision tree (DT) was the model first developed by Morgan and Sonquist [70], Automatic Interaction Detection (AID) that was introduced by Morgan and Sonquist [70]
In order to evaluate the validity of the obtained models, the mathematical formula for statistical assessment parameters including average relative deviation (ARD%), determination coefficient (R2 ), standard deviation (SD), root mean square error (RMSE), and average absolute relative deviation (AARD%) were used
Summary
The attention in green chemical technologies has resulted in the growth of a new class of highly tunable and special compounds named ionic liquids (ILs) [1]. Various computational methods such as group contribution methods (GCM), quantitative structure−property relationships (QSPR), and intelligent approaches (IA) can be used to predict the viscosity of ILs [35,36] To this end, Gardas and Coutinho [37] performed a modeling investigation of viscosity of ILs by applying GCM for 500 data points from 29 ILs (based on imidazolium, pyrrolidinium, and pyridinium) in a wide range of temperature (293–393 K). Zhao et al [41] proposed nonlinear (support vector machine) and linear (multiple linear regression) QSPR models to model 1502 experimental data points (89 ILs) in a wide range of temperature (253.15–395.2 K) and pressure (0.1–300 MPa), where AARD for linear and nonlinear models were obtained 10.68% and 6.58%, respectively. It should be noted that various graphical and statistical criteria were considered to investigate the reliability of the proposed approaches in order to obtain the most accurate approach
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