Representation/Prediction of Solubilities of Pure Compounds in Water Using Artificial Neural Network-Group Contribution Method

被引：47

作者：

Gharagheizi, Farhad ^{[2
]}

Eslamimanesh, All ^{[1
]}

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

Richon, Dominique ^{[1
]}

机构：

[1] MINES ParisTech, CEP TEP Ctr Energet & Proc, F-77305 Fontainebleau, France

[2] Saman Energy Giti Co, 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 / 04期

关键词：

CORRESPONDING STATES TECHNIQUES; LOWER FLAMMABILITY LIMIT; HYDROGEN PLUS WATER; EQUATION-OF-STATE; AQUEOUS SOLUBILITY; ORGANIC-COMPOUNDS; DRUG DISCOVERY; DISSOCIATION CONDITIONS; MOLECULAR DIFFUSIVITY; STANDARD ENTHALPY;

D O I：

10.1021/je101061t

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

In this work, the artificial neural network group contribution (ANN-GC) method has been applied to represent/predict the solubilities of pure chemical compounds in water over the (293 to 298) K temperature range at atmospheric pressure. A set of 3585 pure compounds from various chemical families has been investigated to propose a comprehensive and predictive method. The obtained results show a squared correlation coefficient (R-2) value of 0.96 and a root-mean-square error of 0.4 for the calculated/predicted properties with respect to existing experimental values, demonstrating the reliability of the proposed model.

引用

页码：720 / 726

页数：7

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