Properties Estimation: Critical Volume (Vc) estimation
2011.6.15
Lecture note of Dr. Hiroshi Yamamoto
The program that Pirika provide.
Pirika neural network method (JAVA version 2004.11.14)
Joback method (JAVA version 2004.11.14)
Joback method（HTML5 version 2011.4.16, with Other Properties）
PirikaLight（JAVA version 2009.9.15, with Other Properties）
Temperature-Vapor Pressure relationship (HTML5 version 2011.6.13）
YMB simulator (HTML5 version 2011.6.10, need pass code to use full function)
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Critical temperature ( Tc ), critical pressure ( Pc ), and critical volume ( Vc ) represent three widely used pure component constants. These critical constants are very important properties in chemical engineering field because almost all other thermo chemical properties are predictable from boiling point and critical constants with using corresponding state theory. So precise prediction of critical constants are very needed.
There are several methods to predict critical constants.
- Ambrose
- Lydersen, JOBACK
- Fedors
- Riedel
- Vetere
- Klincewicz
Experimental Critical volume or Critical density ( molecular weight / critical volume ) data can not be available so much compare to Tc and Pc. Some data book listed not experimental values but estimated values. Actually, this property is not so sensitive to its structure, but uncertainty of experimental/estimated problem is so serious when applying Vc to estimate liquid density. Our neural network method introduce correction factor from absolute molecular volume calculated from optimized structure by Molecular Orbital.
Estimation Method
There are many methods to estimate Critical volume. The most popular method is Joback method. This method calculate Critical volume with this scheme.(Please refer to Wiki page)
Vc = 17.5+ΣΔVc
ΔVc:right parameters
I have checked the popular thermo-chemical Dipper 801 database. Experimental Critical volume is so limited.
So, estimation accuracy is very bad.
Vc is calculated from molar refraction [RD]
