Modeling of surface tension for ionic liquids using group method of data handling

被引:41
|
作者
Atashrouz, Saeid [1 ]
Amini, Ershad [2 ]
Pazuki, Gholamreza [1 ]
机构
[1] Amirkabir Univ Technol, Dept Chem Engn, Tehran Polytech, Tehran, Iran
[2] Univ Tehran, Sch Chem Engn, Coll Engn, Tehran, Iran
关键词
Surface tension; Ionic liquids; GMDH; Neural network; Modeling; ARTIFICIAL NEURAL-NETWORK; PHYSICAL-PROPERTIES; BINARY-MIXTURES; ELECTROCHEMICAL PROPERTIES; THERMODYNAMIC PROPERTIES; TEMPERATURE-DEPENDENCE; DENSITY; TETRAFLUOROBORATE; VISCOSITY; HEXAFLUOROPHOSPHATE;
D O I
10.1007/s11581-014-1347-1
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
The group method of data handling (GMDH) was utilized to estimate the surface tension of 59 ionic liquids. In this regard, an extensive experimental data was selected from literature over the range 268.3 to 532.4 K. The GMDH model can predict the surface tension of ionic liquids by a grand polynomial correlation function of molar density, reduced boiling temperature, reduced temperature and pressure, acentric factor, and critical compressibility factor. The values of the GMDH model showed a very good regression with the experimental data. The average absolute relative deviation (AARD%) of the GMDH model for all ionic liquids was 4.59 % which indicated a good precision in comparison with those obtained from the generalized dimensionless equation, Mousazadeh-Faramarzi equation, and Parachor equation with AARDs% of 10.31, 13.02, and 11.89, respectively.
引用
收藏
页码:1595 / 1603
页数:9
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