It is necessary to develop general and efficient models for the representation of vapor-liquid equilibria (VLE) for the binary mixtures containing R1234ze(E) which will be new alternatives to conventional refrigerants. This work investigates the applicability of four machine learning models, including K-nearest neighbor (KNN), support vector regression (SVR), random forests (RF), and multi-layer perceptron (MLP), in representing VLE for 10 binary mixtures. The accuracy, stability, computational complexity and extrapolation ability of these machine learning models are analyzed and compared with two thermodynamic models including Soave–Redlich–Kwong (SRK) and Peng–Robinson (PR). The results show the KNN, RF and MLP models are not suitable to accurately represent the experimental pressure and vapor-phase mole fraction. The SVR model is the most accurate machine learning model in representing the experimental pressure with a mean absolute relative error of 0.71%, basically as accurate as the PR and SVR models. Meanwhile, the SVR model describes the experimental vapor-phase mole fraction more accurately with a mean absolute error of 0.0049 compared to the PR and SVR models. In addition, the SVR model has a low running-time cost and a certain extent of extrapolation ability.

必须开发通用有效的模型来表示含R1234ze（E）的二元混合物的气液平衡（VLE），这将是常规制冷剂的新替代品。这项工作调查了四种机器学习模型的适用性，其中包括K近邻（KNN），支持向量回归（SVR），随机森林（RF）和多层感知器（MLP），它们代表10种二元混合物的VLE。分析了这些机器学习模型的准确性，稳定性，计算复杂性和外推能力，并将其与包括Soave-Redlich-Kwong（SRK）和Peng-Robinson（PR）在内的两个热力学模型进行了比较。结果表明，KNN，RF和MLP模型不适合准确表示实验压力和气相摩尔分数。SVR模型是代表实验压力的最准确的机器学习模型，平均绝对相对误差为0.71％，基本上与PR和SVR模型一样准确。同时，与PR和SVR模型相比，SVR模型可以更准确地描述实验气相摩尔分数，平均绝对误差为0.0049。另外，SVR模型具有较低的运行时间成本和一定程度的外推能力。