A new comprehensive model of thermal conductivity for hydrofluoroolefins refrigerants using feed-forward back-propagation neural networks

• Published in: Thermophysics and aeromechanics 2023-03, Vol.30 (2), p.367-380
• Main Authors: Ghalem, N., Hanini, S., Amrane, A., Hamadache, M., Laidi, M., Naceur, M. W.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Back propagation networks; Correlation coefficients; Data points; Dipole moments; Heat conductivity; Heat transfer; Neural networks; Neurons; Physics; Physics and Astronomy; Refrigerants; Thermal conductivity; Thermodynamics; Transfer functions
• ISSN: 1531-8699; 0869-8643; 1531-8699
• DOI: 10.1134/S086986432302018X

In this work, the thermal conductivity of refrigerants systems from three different hydrofluoroolefins including R1234yf, R1234ze(E), and R1233zd(E) were studied using artificial neural network. A total of 4395 data points of liquid and vapor thermal conductivity at several temperatures (241.92 to 344.46 K) and pressures (0.068 to 21.73 MPa) were used to train and test the model. Five neurons were used in the input layer, fifteen neurons at hidden layer and one was used in the output layer. Bayesian Regulation back propagation algorithm, logarithmic sigmoid transfer function, and linear transfer function were used at the hidden and output layer, respectively. Temperature, pressure, applied heating power; acentric factor, and dipole moment were considered as input variables of the networks. The optimal parameters were obtained through the weights searching method. The average absolute relative deviations and correlation coefficient were 1.48 and 0.9998, respectively. This study shows, therefore, that the artificial neural network model represents an excellent alternative to estimate the thermal conductivity of different refrigerant systems with a good accuracy.