Use of Artificial Neural Network-Group Contribution Method to Determine Surface Tension of Pure Compounds

被引：87

作者：

Gharagheizi, Farhad ^{[2
]}

Eslamimanesh, Ali ^{[1
]}

Mohammadi, Arnir H. ^{[1
,3
]}

Richon, Dominique ^{[1
]}

机构：

[1] MINES ParisTech, CEP, TEP, F-77305 Fontainebleau, France

[2] Saman Energy Giti, Tehran 3331619636, Iran

[3] Univ KwaZulu Natal, Sch Chem Engn, Thermodynam Res Unit, ZA-4041 Durban, South Africa

来源：

JOURNAL OF CHEMICAL AND ENGINEERING DATA | 2011年 / 56卷 / 05期

关键词：

CORRESPONDING STATES TECHNIQUES; LOWER FLAMMABILITY LIMIT; HYDROGEN PLUS WATER; POINT TEMPERATURE; THERMODYNAMIC PROPERTIES; DISSOCIATION CONDITIONS; MOLECULAR DIFFUSIVITY; STANDARD ENTHALPY; FREE-ENERGY; PREDICTION;

D O I：

10.1021/je2001045

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

This work aims at applying an artificial neural network-group contribution method to represent/predict the surface tension of pure chemical compounds at different temperatures and atmospheric pressure. To propose a comprehensive, reliable, and predictive tool, about 4700 data belonging to experimental surface tension values of 752 chemical compounds at different temperatures and atmospheric pressure have been studied. The investigated compounds belong to 78 chemical families containing 151 functional groups (group contributions), which include organic and inorganic liquids. Using this dedicated strategy, we obtain satisfactory results quantified by the following statistical parameters: absolute average deviations of the represented/predicted properties from existing experimental values, 1.7 %, and squared correlation coefficient, 0.997.

引用

页码：2587 / 2601

页数：15

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