Representation of Vapor-Liquid Equilibria Properties for Binary Mixtures Containing R1234ze(E) using Machine Learning Models

Abstract

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.