The lack of a transparent and universal parameterization method is one of the main problems in developing SAFT-type equations of state and it's an obstacle in their way of becoming an industrial equation of state. Usually, vapor-pressure and saturated liquid density data with a local optimization algorithm are used to parameterize SAFT-type equations of state. In addition to the ambiguity in the data selection process, using a local optimization method reduces the accuracy of the estimated parameters. Therefore, in this paper, a new method for calculating the adjustable parameters of SAFT or any equations of state with several parameters is presented. By providing a clear and explicit data selection procedure, including data categorization, comparison, and validation, integrated with the use of a combination of global and local optimization methods, the new methodology achieves the best possible parameters set for the equation of state. The proposed computational program for fitting parameters is based on the use of the Differential evolution algorithm as the main process and the Levenberg-Marquardt algorithm as the post-process along with applying one (or two) hyperparameter. Afterward, a wide range of pressure-temperature-density data has been used to estimate the parameters of 60 non-associating and associating pure compounds. Then, the SAFT equation of state with the new parameters, so-called PρT-SAFT-HR, is applied to predict pressure ($P$), temperature ($T$), density ($\rho$), vapor-pressure ($P^{sat}$), saturated vapor density (${\rho }_{vap}^{sat}$), saturated liquid density (${\rho }_{liq}^{sat}$), critical point, isochoric heat capacity ($c_v$), isobaric heat capacity ($c_P$), speed of sound ($u$), isothermal compressibility (${\kappa }_T$), and isobaric thermal expansivity (${\alpha }_P$). All properties are calculated with the PρT-SAFT-HR equation of state, and the results are compared with experimental data and SAFT-HR and the average absolute percentage deviation is reported for all of them. In total, 19460 experimental and pseudo-experimental data were used to reparameterization the SAFT equation of state, and more than 74,000 data were validated to calculate thermodynamic properties. The results showed a significant improvement in predicting second-order thermodynamic derivative properties. In general, PρT-SAFT-HR performs better than SAFT-HR, and in some cases, such as pressure, speed of sound, isothermal compressibility, and isobaric thermal expansivity, the results have been significantly improved, and the average absolute deviation of PρT-SAFT-HR has been much less than SAFT-HR.

缺乏透明且通用的参数化方法是开发SAFT型状态方程的主要问题之一，并且成为其成为工业状态方程的障碍。通常，使用局部优化算法的蒸气压和饱和液体密度数据来参数化SAFT型状态方程。除了数据选择过程中的不确定性之外，使用局部优化方法还会降低估计参数的准确性。因此，本文提出了一种计算SAFT可调参数或带有多个参数的状态方程的新方法。通过提供清晰明确的数据选择程序，包括数据分类，比较和验证，通过结合使用全局和局部优化方法，该新方法可以为状态方程实现最佳的参数设置。拟议的用于拟合参数的计算程序是基于使用差分演化算法作为主要过程，使用Levenberg-Marquardt算法作为后过程以及应用一个（或两个）超参数的。之后，广泛的压力-温度-密度数据已用于估算60种非缔合和缔合纯化合物的参数。然后，使用具有新参数的SAFT状态方程，即所谓的PρT-SAFT-HR，来预测压力（拟议的用于拟合参数的计算程序是基于使用差分演化算法作为主要过程，使用Levenberg-Marquardt算法作为后过程以及应用一个（或两个）超参数的。之后，广泛的压力-温度-密度数据已用于估算60种非缔合和缔合纯化合物的参数。然后，使用具有新参数的SAFT状态方程，即所谓的PρT-SAFT-HR，来预测压力（拟议的用于拟合参数的计算程序是基于使用差分演化算法作为主要过程，使用Levenberg-Marquardt算法作为后过程以及应用一个（或两个）超参数的。之后，广泛的压力-温度-密度数据已用于估算60种非缔合和缔合纯化合物的参数。然后，使用具有新参数的SAFT状态方程，即所谓的PρT-SAFT-HR，来预测压力（$P$）， 温度 （$Ť$）， 密度 （$\rho$）， 蒸汽压力 （$P^{s\mathrm{一个}Ť}$），饱和蒸气密度（${\rho }_{v\mathrm{一个}p}^{s\mathrm{一个}Ť}$），饱和液体密度（${\rho }_{升\mathrm{一世}q}^{s\mathrm{一个}Ť}$），临界点，等容热容量（$C_v$），等压热容（$C_P$）， 声音的速度 （$ü$），等温压缩率（${\kappa }_{Ť}$）和等压热膨胀系数（${\alpha }_P$）。所有性质均用状态PρT-SAFT-HR方程计算，并将结果与​​实验数据和SAFT-HR进行比较，并报告所有性质的平均绝对百分比偏差。总共使用了19460个实验和伪实验数据对SAFT状态方程进行重新参数化，并验证了74,000多个数据以计算热力学性质。结果表明，在预测二阶热力学导数性质方面有显着改善。通常，PρT-SAFT-HR的性能优于SAFT-HR，并且在某些情况下，例如压力，声速，等温可压缩性和等压热膨胀系数，结果得到了显着改善，并且PρT的平均绝对偏差-SAFT-HR远小于SAFT-HR。