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Abstract Gas thermal conductivity is one of the thermophysicalthermo physical 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 programingprogramming (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 the 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 in a cross plot Figure 7 Comprehensive error analysis of SC CO2 thermal conductivity calculation by the developed GP model in comparison with Amooey [31] correlation at different temperature ranges from 293.65 to 412.55 K when pressure varies from 0.1 to 127.8 MPa Figure 8 Comprehensive error analysis of SC CO2 thermal conductivity calculation by the developed GP model in comparison with Amooey [31] correlation at different temperature ranges from 427.13 to 961.05 K when pressure varies from 0.1 to 127.5 MPa Figure 9 Experimental SC CO2 thermal conductivity [21] and model predictions of GP Amooey [31] Jarrahian and Heidaryan [30] and Bahadori and Vuthaluru [3] versus pressure at constant temperate of 347.38 K Figure 10 Cumulative frequency versus absolute relative deviation percent of the GP model in comparison with other commonly used empirical correlations Figure 11 Relative importance of the independent variables on the SC CO2 thermal conductivity [...]
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Subject Area: Computer Science
Document Type: Research Paper
