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PR状态方程+基团贡献模型预测CO2+HFC二元混合物的气液相平衡性质

Predicting vapor-liquid equilibria of CO2+HFC binary mixtures by the PR EOS combined with a group contribution model

  • 摘要: 由于良好的性能和环保性,CO2+HFC二元混合物被认为是冷电联合循环系统中良好的替代工作流体。气液相平衡特性是计算混合物焓和熵的关键,这冷电联合循环系统的热力学分析至关重要。为了准确预测CO2和HFC (R23、R32、R41、R125、R134a、R143a、R152a、R161、R227ea) 二元混合物的气液平衡性质,本文建立了基于吉布斯自由能混合规则的群贡献模型(PR+MHV1+UNIFAC和PR+LCVM+UNIFAC)。通过CO2和HFC制冷剂的气液相平衡实验获得了—CO2、—烷烃、—CHF和—CHF3等基团之间的相互作用参数,这些基团参数对于预测其气液相平衡性质 (压力和气相摩尔分数) 至关重要。PR+LCVM+UNIFAC模型计算的AARDp值为5.53%,AADy1值为0.0132,PR+MHV1+UNIFAC模型的AARDp值和AADy1值分别为7.40%和0.0229。然而,对于CO2+R32系统,PR+MHV+UNIFAC预测模型的预测精度较高,AARDp和AADy1的值分别为1.53%和0.0045。综上所述,对于CO2+HFC二元混合物,PR+LCVM+UNIFAC预测模型预测精度较高,但对于CO2+R32二元混合物,PR+MHV1+UNIFAC模型也具有独特的优势。根据基团贡献模型的预测结果,与之前系统使用的PR+MHV1+UNIFAC模型相比,PR+LCVM+UNIFAC模型的计算进度显著提高。

     

    Abstract: CO2+HFC binary mixtures have good performance and environmental friendliness and are considered good alternative working fluids in cooling and power cycle systems. The vapor-liquid phase equilibrium properties are key to the calculation of the enthalpy and entropy of mixtures, which is critical for the analysis of cooling and power cycle systems. To accurately predict the vapor-liquid equilibrium of CO2 and HFC (R23, R32, R41, R125, R134a, R143a, R152a, R161, and R227ea) binary mixtures, a group contribution model based on the excess free energy (GE) mixing rules (PR+MHV1+UNIFAC and PR+LCVM+UNIFAC) is established in this paper. The interaction parameters between groups such as -CO2, -Alkane, -CHF, and -CHF3 are obtained by the vapor-liquid phase equilibrium experiment of CO2 and HFC refrigerants, and these group parameters are critical for predicting their vapor-liquid phase equilibrium properties (the pressures and vapor phase molar fractions). The AARDp value calculated by the PR+LCVM+UNIFAC model is 5.53%, the value of AADy1 is 0.0132, and the AARDp and AADy1 values of the PR+MHV1+UNIFAC model are 7.40% and 0.0229, respectively. However, for the CO2+R32 system, the PR+MHV+UNIFAC prediction model can reproduce the experimental data with lower deviations, and the values of AARDp and AADy1 are 1.53% and 0.0045, respectively. In summary, for CO2+HFC binary mixtures, the PR+LCVM+UNIFAC group contribution model can reproduce the experimental data with lower deviations, but for individual CO2 binary mixtures (such as CO2+R32), the PR+MHV1+UNIFAC model also has unique advantages. According to the prediction results of the group contribution model, the PR+LCVM+UNIFAC model has significantly improved the calculation progress compared with the PR+MHV1+UNIFAC model used in the previous system.

     

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