Gharagheizi F., Eslamimanesh A., Sattari M., Tirandazi B., Mohammadi A.H., Richon D.
Department of Chemical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran; MINES ParisTech., CEP/TEP, Centre Énergétique et Procédés, 35 Rue Saint Honoré, 77305 Fontainebleau, France; Saman Energy Giti Co., 3331619636 Tehran, Iran; Department of Chemical Engineering, Iran University of Science and Technology, Tehran, Iran; Thermodynamics Research Unit, School of Chemical Engineering, University of KwaZulu-Natal, King George V Avenue, Durban 4041, South Africa
Gharagheizi, F., Department of Chemical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran; Eslamimanesh, A., MINES ParisTech., CEP/TEP, Centre Énergétique et Procédés, 35 Rue Saint Honoré, 77305 Fontainebleau, France; Sattari, M., Saman Energy Giti Co., 3331619636 Tehran, Iran; Tirandazi, B., Department of Chemical Engineering, Iran University of Science and Technology, Tehran, Iran; Mohammadi, A.H., MINES ParisTech., CEP/TEP, Centre Énergétique et Procédés, 35 Rue Saint Honoré, 77305 Fontainebleau, France, Thermodynamics Research Unit, School of Chemical Engineering, University of KwaZulu-Natal, King George V Avenue, Durban 4041, South Africa; Richon, D., MINES ParisTech., CEP/TEP, Centre Énergétique et Procédés, 35 Rue Saint Honoré, 77305 Fontainebleau, France, Thermodynamics Research Unit, School of Chemical Engineering, University of KwaZulu-Natal, King George V Avenue, Durban 4041, South Africa
In the present communication, we propose a corresponding states method for calculation/estimation of the vapor thermal conductivity of chemical compounds (mostly organic), applying the gene expression programming (GEP) algorithm. Around 16000 thermal conductivity data of gases at various temperatures from 100 to 1500 K and atmospheric pressure related to about 1600 chemical compounds (mostly organic) are used for development and validation of the method. The quantities used in the corresponding states correlation include temperature, critical pressure, molecular weight, acentric factor, and normal boiling point. More than 14000 thermal conductivity data are randomly selected for developing (training + optimizing) the correlation, and about 1600 data are used for checking its prediction capability. The obtained statistical parameters including average absolute relative deviation of the results from the applied data (about 8%) show acceptable accuracy of the presented method. The most important features of the developed model are its simplicity, its wide range of applicability, and its validity based on the Leverage value test. © 2012 American Chemical Society.