An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower

Abstract

This paper investigates the applicability of adaptive neuro-fuzzy inference system (ANFIS) to predict the performance of an R134a vapor-compression refrigeration system using a cooling tower for heat rejection. For this aim, an experimental system was developed and tested at steady state conditions while varying the evaporator load, dry bulb temperature and relative humidity of the air entering the tower, and the flow rates of air and water streams. Then, utilizing some of the experimental data for training, an ANFIS model for the system was developed. This model was used for predicting various performance parameters of the system including the evaporating temperature, compressor power and coefficient of performance. It was found that the predictions usually agreed well with the experimental data with correlation coefficients in the range of 0.807-0.999 and mean relative errors in the range of 0.83-6.24%. The results suggest that the ANFIS approach can be used successfully for predicting the performance of refrigeration systems with cooling towers.