Prediction of viscosity of imidazolium-based ionic liquids using MLR and SVM algorithms

In this work, two models, one integrating the fragment contribution-corresponding states (FC-CS) method with multiple linear regression (MLR) algorithm and another. With support vector machine (SVM) algorithm, are proposed to predict the viscosity of imidazolium-based ionic liquids (ILs). The FC-CS method is applied to calculate the pseudo-critical volume and compressibility factor (Vc and Zc) as well as the boiling point temperature (Tb) which are employed to predict the viscosity with the MLR and SVM algorithms. A large data set of 1079 experimental data points of 45 imidazolium-based ILs covering a wide range of pressure and temperature is applied to validate the two models. The average absolute relative deviation (AARD) of the entire data set of the MLR and SVM is 24.2% and 3.95%, respectively. The nonlinear model developed by the SVM algorithm is much better than the linear model built by the MLR, which indicates the SVM algorithm is more reliable in the prediction of the viscosity of imidazolium-based ILs.

[1]  João A. P. Coutinho,et al.  Group Contribution Methods for the Prediction of Thermophysical and Transport Properties of Ionic Liquids , 2009 .

[2]  Xiangping Zhang,et al.  A quantitative prediction of the viscosity of ionic liquids using S(σ-profile) molecular descriptors. , 2015, Physical chemistry chemical physics : PCCP.

[3]  K. R. Seddon,et al.  Ionic liquids: a taste of the future. , 2003, Nature materials.

[4]  G Marcou,et al.  In silico design of new ionic liquids based on quantitative structure-property relationship models of ionic liquid viscosity. , 2011, The journal of physical chemistry. B.

[5]  Haifeng Dong,et al.  A new fragment contribution‐corresponding states method for physicochemical properties prediction of ionic liquids , 2013 .

[6]  Jens Sadowski,et al.  Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification , 2003, J. Chem. Inf. Comput. Sci..

[7]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[8]  Farhad Gharagheizi,et al.  Predictive Quantitative Structure–Property Relationship Model for the Estimation of Ionic Liquid Viscosity , 2012 .

[9]  K. Yamuna Rani,et al.  REPRESENTATION OF IONIC LIQUID VISCOSITY-TEMPERATURE DATA BY GENERALIZED CORRELATIONS AND AN ARTIFICIAL NEURAL NETWORK (ANN) MODEL , 2013 .

[10]  David Rooney,et al.  Thermophysical Properties of Ionic Liquids , 2009 .

[11]  Robin D. Rogers,et al.  Ionic Liquids--Solvents of the Future? , 2003, Science.

[12]  Haifeng Dong,et al.  Carbon capture with ionic liquids: overview and progress , 2012 .

[13]  R. Reid,et al.  The Properties of Gases and Liquids , 1977 .

[14]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[15]  J. Coutinho,et al.  A group contribution method for viscosity estimation of ionic liquids , 2008 .

[16]  Haifeng Dong,et al.  Thermodynamic Modeling and Assessment of Ionic Liquid-Based CO2 Capture Processes , 2014 .

[17]  Farhad Gharagheizi,et al.  Development of a group contribution method for determination of viscosity of ionic liquids at atmospheric pressure , 2012 .

[18]  Cinzia Chiappe,et al.  QSPR correlation for conductivities and viscosities of low‐temperature melting ionic liquids , 2008 .

[19]  Ulf Norinder,et al.  Support vector machine models in drug design: applications to drug transport processes and QSAR using simplex optimisations and variable selection , 2003, Neurocomputing.

[20]  Haifeng Dong,et al.  New models for predicting thermophysical properties of ionic liquid mixtures. , 2015, Physical chemistry chemical physics : PCCP.

[21]  H. -. Wang,et al.  A high correlate and simplified QSPR for viscosity of imidazolium-based ionic liquids , 2013 .

[22]  Mancang Liu,et al.  Prediction of ozone tropospheric degradation rate constants by projection pursuit regression. , 2007, Analytica chimica acta.

[23]  Guangren Yu,et al.  Data and QSPR study for viscosity of imidazolium-based ionic liquids , 2011 .

[24]  Thorsten Joachims,et al.  Learning to classify text using support vector machines - methods, theory and algorithms , 2002, The Kluwer international series in engineering and computer science.

[25]  John Fox,et al.  Applied Regression Analysis and Generalized Linear Models , 2008 .

[26]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[27]  Hiroshi Yamamoto,et al.  Computer-aided reverse design for ionic liquids by QSPR using descriptors of group contribution type for ionic conductivities and viscosities , 2007 .

[28]  T. Welton.  Room-Temperature Ionic Liquids. Solvents for Synthesis and Catalysis. , 1999, Chemical reviews.

[29]  Xiangping Zhang,et al.  Toxicity of ionic liquids: database and prediction via quantitative structure-activity relationship method. , 2014, Journal of hazardous materials.

[30]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[31]  Peisheng Ma,et al.  Estimation of liquid viscosity of pure compounds at different temperatures by a corresponding-states group-contribution method , 2002 .