The non-randomness two-liquid model (NRTL) has been used successfully in chemical engineering to calculate phase equilibria. Different forms of the NRTL model have been developed, including original NRTL, electrolyte NRTL (e-NRTL), polymer NRTL and segment based NRTL (NRTL-SAC). The mentioned forms of the NRTL model are correlative so that they require experimental data to regress the NRTL interaction parameters, which in turn, limit the application of the NRTL models. In the current study, therefore, the NRTL functional activity coefficient (NRTL-FAC) model was developed based on the high-quality experimental vapor-liquid equilibrium (VLE) data. The quality of experimental data were checked using different thermodynamic consistency tests, including Herington test, Van Ness test, point test, infinite dilution test, EOS test and endpoint test. Of the available VLE data, 2938 isothermal/isobaric data sets including 46,651 data points were used in the development of the model. Besides, 137 molecules with 50 main groups were used to develop the NRTL-FAC model. The Stavermann-Guggenheim term was also used to account the combinatorial contribution of the NRTL-FAC model. The good agreement between the predicted results by the NRTL-FAC and the experimental VLE data is encouraging. The present model performs superior to the previous versions of the UNIFAC model.

非随机两液模型（NRTL）已成功用于化学工程中，以计算相平衡。已经开发了不同形式的NRTL模型，包括原始NRTL，电解质NRTL（e-NRTL），聚合物NRTL和基于片段的NRTL（NRTL-SAC）。提到的NRTL模型形式是相关的，因此它们需要实验数据来回归NRTL交互参数，从而限制了NRTL模型的应用。因此，在当前的研究中，基于高质量的实验气液平衡（VLE）数据开发了NRTL功能活性系数（NRTL-FAC）模型。使用不同的热力学一致性测试（包括Herington测试，Van Ness测试，点测试，无限稀释测试，EOS测试和终点测试）来检查实验数据的质量。在可用的VLE数据中，该模型的开发过程中使用了2938个等温/等压数据集，包括46,651个数据点。此外，使用具有50个主要基团的137个分子建立了NRTL-FAC模型。Stavermann-Guggenheim术语也用于说明NRTL-FAC模型的组合贡献。NRTL-FAC的预测结果与VLE实验数据之间的良好一致性令人鼓舞。本模型的性能优于UNIFAC模型的先前版本。NRTL-FAC的预测结果与VLE实验数据之间的良好一致性令人鼓舞。本模型的性能优于UNIFAC模型的先前版本。NRTL-FAC的预测结果与VLE实验数据之间的良好一致性令人鼓舞。本模型的性能优于UNIFAC模型的先前版本。