Properties Estimation: Thermal conductivity of liquid
Lecture note of Dr. Hiroshi Yamamoto
The program that Pirika provide.
Thermal conductivity, ll, is defined as the quantity of heat which will traverse a medium of unit thickness and cross-sectional area per unit time, under the influence of an applied temperature gradient. Values of ll are usually in the range of 250-400 X 10-6 cal/cm s K, but some liquids with a high degree of association, such as may occur with hydrogen bonding, have higher conductivities. The thermal conductivity of organic liquids is generally estimated by means of equations that utilize other known properties of the material and, to a lesser extent, structural considerations. Sato - Riedel and Robbins - Kingrea Method is very popular. Thermal conductivity is only weakly a function of temperature, usually decreasing as temperature increases. At ambient conditions, any temperature correction to ll would likely be less than other uncertainties in the calculation.
The most popular method for liquid Thermal conductivity is Sato-Riedel method. This method is one of the Corresponding state theory and estimate with following scheme.
With using this scheme, I validated the data listed in ”Chemical properties handbook, Yaws". The accuracy is very low.
I also build Neural Network method with JAVA and put it on Pirika site. The accuracy of this version become like below.
YNU-simulator estimate density at certain temperature and build QSPR model to estimate temperature dependency of thermal conductivity of liquids.
Like Glycerine, Ethylene Glycol, such compounds have 3 dimensional hydrogen bonding have large error. And ring compounds have large error.
I show the example with Acetone.
From only chemical structure, I can estimate temperature dependency of thermal conductivity of liquids.