Determination of Parachor of Various Compounds Using an Artificial Neural Network-Group Contribution Method

被引：35

作者：

Gharagheizi, Farhad ^{[2
]}

Eslamimanesh, Ali ^{[1
]}

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

Richon, Dominique ^{[1
]}

机构：

[1] MINES Paris Tech, 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：

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH | 2011年 / 50卷 / 09期

关键词：

CORRESPONDING STATES TECHNIQUES; LOWER FLAMMABILITY LIMIT; HYDROGEN PLUS WATER; DISSOCIATION CONDITIONS; MOLECULAR DIFFUSIVITY; STANDARD ENTHALPY; POINT TEMPERATURE; SURFACE-TENSION; PREDICTION; MODEL;

D O I：

10.1021/ie102464t

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

In this communication, an Artificial Neural Network-Group Contribution algorithm is applied to represent/predict the parachor of pure chemical compounds. To propose a reliable and predictive tool, 227 pure chemical compounds are investigated. Using the developed method, we obtain satisfactory results that are quantified by the following statistical parameters: absolute average deviations of the represented/predicted parachor values from existing experimental ones, %AAD = 1.2%; and squared correlation coefficient, R-2 = 0.997.

引用

页码：5815 / 5823

页数：9

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