Energy analysis of single-stage LiBr–water vapour absorption refrigeration system using artificial neural network approach

Abstract

In this study, artificial neural network (ANN) has been used as a new approach for carrying out the energy analysis of a single-stage absorption refrigeration cycle with water–lithium bromide as the working fluid pair. Energy analysis of an absorption system is a very complicated process mainly because of the limited experimental data and analytical functions required for calculating the thermodynamic properties of fluid pairs, which usually involves the solution of complex differential equations. Instead of complex differential equation and limited experimental data, faster and simpler solutions were obtained by using equations derived from the ANN model. As seen from the results obtained, the calculated thermodynamic properties are within acceptable results. Thermodynamic properties of each point in the cycle are calculated using related equations of the state. Heat flow rate of each component in the cycle and some performance parameters are calculated from the first law analysis. The results show that a high coefficient of performance value is obtained at high generator and evaporator temperatures and also at low condenser and absorber temperatures.