New Method Based on the UNIFAC−VISCO Model for the Estimation of Ionic Liquids Viscosity Using the Experimental Data Recommended by Mathematical Gnostics.

0466492 - ÚCHP 2017 RIV US eng J - Journal Article
Zhao, N. - Oozeerally, R. - Degirmenci, V. - Wagner, Zdeněk - Bendová, Magdalena - Jacquemin, J.
New Method Based on the UNIFAC−VISCO Model for the Estimation of Ionic Liquids Viscosity Using the Experimental Data Recommended by Mathematical Gnostics.
Journal of Chemical and Engineering Data. Roč. 61, č. 111 (2016), s. 3908-3921. ISSN 0021-9568. E-ISSN 1520-5134
Grant - others:CEAR(GB) 4600261677/P6E3; EPSRC(GB) EP/M021785/1
Institutional support: RVO:67985858
Keywords : ionic liquids * mathematical gnostics * UNIFAC-VISCO
Subject RIV: CF - Physical ; Theoretical Chemistry
Impact factor: 2.323, year: 2016

The viscosity of ionic liquids (ILs) has been modeled as a function of temperature and at atmospheric pressure using a new method based on the UNIFAC−VISCO method. This model extends the calculations previously reported by our group which used 154 experimental viscositydata points of 25 ionic liquids for regression of a set of binary interaction parameters and ion Vogel−Fulcher−Tammann(VFT) parameters. Discrepancies in the experimental data ofthe same IL affect the quality of the correlation and thus the development of the predictive method. In this work, mathe-matical gnostics was used to analyze the experimental data from different sources and recommend one set of reliable data foreach IL. These recommended data (totally 819 data points) for 70 ILs were correlated using this model to obtain an extended set of binary interaction parameters and ion VFT parameters, with a regression accuracy of 1.4%. In addition, 966 experimental viscosity data points for 11 binary mixtures of ILs were collected from literature to establish this model. All the binary data consist of 128 training data points used for the optimization of Binary interaction parameters and 838 test data points used for the comparison of the pure evaluated values. The relative average absolute deviation (RAAD) for training and test is 2.9% and 3.9%, respectively.
Permanent Link: http://hdl.handle.net/11104/0269440