Linking Thermal\nConductivity to Equations of State\nUsing the Residual Entropy Scaling Theory

In recent years, the application of the residual entropy\nscaling\n(RES) method for modeling transport properties has become increasingly\nprominent. Based on Yang et al. (<i>Ind. Eng. Chem. Res</i>. <b>2021</b>, <i>60</i>, 13052) in modeling the\nthermal conductivity of refrigerants, we present here an RES model\nthat extends Yang et al.’s approach to a wider range of pure\nfluids and their mixtures. All fluids available in the REFPROP 10.0\nsoftware, i.e., those with reference equations of state (EoS), were\nstudied. A total of 71,554 experimental data of 125 pure fluids and\n16,702 experimental data of 164 mixtures were collected from approximately\n647 references, mainly based on the NIST ThermoData Engine (TDE) database\n10.1. As a result, over 68.2% (corresponding to the standard deviation\nof a normal distribution) of the well-screened experimental data agree\nwith the developed RES model within 3.1% and 4.6% for pure fluids\nand mixtures, respectively. Comparative analysis against the various\nmodels implemented in the REFPROP 10.0 (one of the state-of-the-art\nsoftware packages for thermophysical property calculations) reveals\nthat our RES model demonstrates analogous statistical agreement with\nexperimental data yet with much fewer parameters. Regarding the average\nabsolute value of the relative deviation (AARD) from experimental\nvalues to model predictions, the developed RES model shows a smaller\nor equal AARD for 74 pure fluids out of 125 and 76 mixtures out of\n164. Besides, a detailed examination of the impact of the critical\nenhancement term on mixture calculations was conducted. To use our\nmodel easily, a software package written in Python is provided in\nthe Supporting Information.