Artificial intelligent methods for thermodynamic evaluation of ammonia–water refrigeration systems

In this paper, Linear Regression and M5’Rules models within Data Mining Process and Artificial Neural Network (ANN) model for thermodynamic evaluation of ammonia–water absorption refrigeration systems was carried out. A new formulation based on ANN model is presented for the analysis of ammonia–water absorption refrigeration systems (AWRS) because the optimal result was obtained by using ANN Model. Thermodynamic analysis of the AWRS is very complex because of analytic functions used for calculating the properties of fluid couples and simulation programs. Therefore, it is extremely difficult to perform analysis of this system. COP and f are estimated depending on the temperatures of system component and concentration values. Using the weights obtained from the trained network a new formulation is presented for the calculation of the COP and f; the use of ANN is proliferating with high speed in simulation. The R2-values obtained when unknown data were used to the networks was 0.9996 and 0.9873 for the circulation ratio and COP respectively which is very satisfactory. The use of this new formulation, which can be employed with any programming language or spreadsheet program for the estimation of the circulation ratio and COP of AWRS, as described in this paper, may make the use of dedicated ANN software unnecessary.