Genetic programming (GP) approach for prediction of supercritical CO2 thermal conductivity

Gas thermal conductivity is one of the thermophysical properties that inevitably enters into mathematical models of real systems used in the design of chemical engineering processes or in the gas industry. In this study, via implementing a powerful and newly applied equation generator algorithm known as, genetic programming (GP) mathematical strategy, a novel correlation for estimation of supercritical CO2 thermal conductivity is established. The proposed correlation is developed and validated based on a comprehensive databank of 752 thermal conductivity datasets from open literature. By using various statistical quality measures, the result of the proposed GP model was compared with commonly used literature models. As a result, the proposed GP model gives the best fit and satisfactory agreement with the target data with an average absolute relative error of 2.31% and R2 = 0.997. A parametric sensitivity analysis showed that pressure and density of the CO2 gas stream have considerable impact on the thermal conductivity at supercritical condition. The results of this study can be of enormous practical worth for scientist and expertise in order to efficiently compute the thermal conductivity in any supercritical industry involving CO2.