Calculation for the thermodynamic properties of an alternative refrigerant (R508b) using artificial neural network


APPLIED THERMAL ENGINEERING, vol.27, pp.551-559, 2007 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 27
  • Publication Date: 2007
  • Doi Number: 10.1016/j.applthermaleng.2006.06.003
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.551-559
  • Keywords: R508b, thermodynamic properties, ozone safe refrigerant, artificial neural networks, PERFORMANCE, SYSTEM, CYCLE, FORMULATION, PREDICTION
  • Gazi University Affiliated: Yes


This study proposes a alternative approach based on artificial neural networks (ANNs) to determine the thermodynamic properties - specific volume, enthalpy and entropy - of an alternative refrigerant (R508b) for both saturated liquid-vapor region (wet vapor) and superheated vapor region. In the ANN, the back-propagation learning algorithm with two different variants, namely scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM), and Logistic Sigmoid transfer function were used to determine the best approach. The most suitable algorithm and with appropriate number of neurons (i.e. 7) in the hidden layer is found to be the LM algorithm which has provided the minimum error. For wet vapor region, R-2 values - which are errors known as absolute fraction of variance - are 0.983495, 0.969027, 0.999984, 0.999963, 0.999981, and 0.999975, for specific volume, enthalpy and entropy for training and testing, respectively. Similarly, for superheated vapor, they are: 0.995346, 0.996947, 0.999996, 0.999997, 0.999974, and 0.999975, for training and testing, respectively. According to the regression analysis results, R-2 values are 0.9312, 0.9708, 0.9428, 0.9343, 0.967 and 0.9546 for specific volume, enthalpy and entropy for wet vapor region and superheated vapor, respectively. The comparisons of the results suggest that, ANN provided results comfortably within the acceptable range. This study, deals with the potential application of the ANNs to represent PVTx (pressure-specific volume-temperature-vapor quality) data. Therefore, reducing the risk of experimental uncertainties and also removing the need for complex analytic equations requiring long computational time and efforts. (c) 2006 Elsevier Ltd. All rights reserved.