Investigation of thermodynamic properties of refrigerant/absorbent couples using artificial neural networks

Abstract This paper presents a new approach to determine the properties of liquid and two phase boiling and condensing of two alternative refrigerant/absorbent couples (methanol–LiBr and methanol–LiCl), which do not cause ozone depletion for absorption thermal systems (ATSs) using artificial neural networks (ANNs). The back-propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. In input layer, there are temperatures in the range of 298–498 K (with 25 K increase), pressures (0.1–40 MPa) and concentrations of 2, 7, and 12% of the couples; specific volume is in output layer. After training, it is found that maximum error is less than 3%, average error is about 1% and R 2 values are 99.999%. As seen from the results obtained the thermodynamic properties have been obviously predicted within acceptable errors. This paper shows that values predicted with ANN can be used to define the thermodynamic properties instead of approximate and complex analytic equations.