PREDICTION OF PHYSICAL PROPERTIES OF WORKING FLUID BASED ON ARTIFICIAL NEURAL NETWORK

Abstract

Abstract: Traditional working fluids in thermodynamic cycles, such as refrigeration and waste heat power generation, have been associated with issues such as ozone layer depletion and global warming. The development of efficient and environmentally friendly novel working fluids has become a research focus. Hydrocarbons and halogenated hydrocarbons are ideal candidates, but their large number and many unknown thermophysical properties make it crucial to establish accurate models for predicting these properties in order to screen new working fluids effectively. In this study, the basic thermophysical parameters of more than 2 500 hydrocarbons and halogenated hydrocarbons containing C, H, F, and Cl atoms were collected from various public databases, including normal boiling point(T_b) , critical temperature(T_c) , critical pressure (p_c) and acentric factor (ω), and furtherly, by improving the method of group contribution-artificial neural network (GC-ANN), a neural network model for predicting T_b,T_c,p_c,and ω of hydrocarbons and halogenated hydrocarbons containing C, H, F, and Cl atoms was established by adding relative molecular mass, T_b and approximate wiener index to the input parameters of the model. The prediction errors of the models developed in this study were smaller than those of the traditional GC-ANN.

Key words: new working fluid, property prediction, group contribution method, BP neural network

References

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