An artificial neural network for the residual isobaric heat capacity of liquid HFC and HFO refrigerants

摘要

In this work, a feed-forward artificial neural network (ANN) was developed for the calculation of isobaric heat capacity of pure HFC and HFO refrigerants in liquid phase. First of all, a total of 1142 available experimental data points for different pure HFC or HFO refrigerants were collected and evaluated before being used to train and test the network. By a trial-and-error method, optimum structural of the network was found out to be an input layer using four dimensionless input parameters, one hidden layer with 34 neurons, and an output layer with reduced residual heat capacity as output. The ANN was applied for 12 different refrigerants, and the predicted isobaric heat capacity showed satisfactory agreement with experimental data. The overall average absolute deviation (AAD %) and maximum absolute deviation (MAD %) were 0.383% and 5.92% respectively. (C) 2018 Elsevier Ltd and IIR. All rights reserved.