A new approach to thermodynamic analysis of ejector–absorption cycle: artificial neural networks

Abstract

Thermodynamic analysis of absorption thermal systems is too complex because of analytic functions calculating the thermodynamic properties of fluid couples involving the solution of complex differential equations. To simplify this complex process, the use of artificial neural networks (ANNs) has been proposed for the analysis of ejector–absorption refrigeration systems (EARSs). ANNs approach was used to determine the properties of liquid and two phase boiling and condensing of an alternative working fluid couple (methanol/LiBr), which does not cause ozone depletion for EARS. The back-propagation learning algorithm with three different variants and logistic sigmoid transfer function was used in the network. In addition, this paper presents a comparative performance study of the EARS using both analytic functions and prediction of ANN for properties of the fluid couple. After training, it was found that average error is less than 1.3% and R 2 values are about 0.9999. Additionally, when the results of analytic equations obtained by using experimental data and by means of ANN were compared, deviations in coefficient of performance (COP), exergetic coefficient of performance (ECOP) and circulation ratio ( F) for all working temperatures were found to be less than 1.8%, 4%, 0.2%, respectively. Deviations for COP, ECOP and F at a generator temperature of ∼90 °C for which the COP of the system is maximum are 1%, 2%, 0.1%, respectively, for other working temperatures. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable uncertainties.