Supercritical thermophysical properties prediction of multi-component hydrocarbon fuels based on artificial neural network models

A good understanding of the thermophysical properties of hydrocarbon fuels at supercritical pressure is important to research on experiment and numerical simulation of fuel supercritical spray. Experimental measurements are difficult to conduct directly because of the extremely high pressure and high temperature. In this study, back propagation (BP) neural network, BP optimized by mind evolution algorithm (MEA-BP) and BP neural network optimized by genetic algorithm (GA-BP) are established to determine the nonlinear temperature-dependent thermophysical properties of density, viscosity, and isobaric specific heat (Cp) of hydrocarbon fuels at supercritical pressure Meanwhile, approximate formulas for these properties prediction are primarily proposed using polynomial fitting In this paper, models that can predict three types of physical properties of three kinds of hydrocarbon fuels and their mixtures in a wide temperature range under supercritical pressure are established In the prediction of density and Cp, BP neural network has a good prediction effect. The results show that the MAPE is lower than 2% in the prediction of density and Cp, but the MAPE of viscosity prediction is slightly higher than 5% using BP. Furthermore, MEA and GA are used to optimize the prediction of viscosity. The optimization effect and computation of the MEA is better than that of GA because MEA does not have the local optimization and prematurity problems. The present work offers an efficient tool to predict the thermophysical properties of hydrocarbon fuels over a wide range of temperatures under supercritical pressure which can be easily extended to other fuels of interest. It will be beneficial to the experiment and numerical simulation studies of supercritical sprays.