Chrastil’s semi-empirical model has been widely used, since its introduction in 1982, to fit solubility data in supercritical carbon dioxide. However, it has been used with different sources for carbon dioxide’s density, with different objective functions and different numerical algorithms to determine the three Chrastil coefficients. These differences lead to an unnecessary variance in fitting performance and Chrastil coefficient values. The result is a reduction in the perceived quality of Chrastil’s model. Also, calculated solubilities based on published Chrastil coefficients using a different source for carbon dioxide’s density risks introducing considerable incremental error. This work uses Span and Wagner’s Fundamental Equation of State and a two-pass Nelder Mead algorithm to minimize the absolute average relative deviation (AARD) for 701 binary data sets. The median AARD is just 8.8% while just 5.2% of the 701 systems have an AARD greater than 30%.

自 1982 年推出以来，Chrastil 的半经验模型已被广泛用于拟合超临界二氧化碳中的溶解度数据。然而，它已被用于二氧化碳密度的不同来源、不同的目标函数和不同的数值算法来确定三个 Chrastil 系数。这些差异导致拟合性能和 Chrastil 系数值出现不必要的差异。结果是降低了 Chrastil 模型的感知质量。此外，基于公布的 Chrastil 系数计算的溶解度，使用不同来源的二氧化碳密度风险引入相当大的增量误差。这项工作使用 Span 和 Wagner 的基本状态方程以及两次通过的 Nelder Mead 算法来最小化 701 个二进制数据集的绝对平均相对偏差 (AARD)。AARD 的中位数仅为 8.8%，而 701 系统中只有 5.2% 的 AARD 大于 30%。