A new neural network-group contribution method for estimation of flash point temperature of pure components

被引：86
|
作者：
Gharagheizi, Farhad [1 ]
Alamdari, Reza Fareghi [2 ]
Angaji, Mahmood Torabi [1 ]
机构：
[1] Univ Tehran, Fac Engn, Dept Chem Engn, Tehran, Iran
[2] Malek Ashtar Univ Technol, Fac Mat & Chem Engn, Dept Chem, Tehran, Iran
来源：
ENERGY & FUELS | 2008年 / 22卷 / 03期
关键词：
D O I：
10.1021/ef700753t
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
In the present study, a new collection of 79 functional groups are used to correlate flash point temperature (FP) of pure components. These functional groups construct an accurate neural network-group contribution correlation to estimate flash point of pure components. For developing the model, 1378 pure components of various chemical families are used. Therefore, the model can be utilized to estimate the FP of pure components without any basic limitations.
引用
收藏
页码：1628 / 1635
页数：8
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