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

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.

► We propose an ANFIS model for predicting the performance of a vapor-compression refrigeration system with a cooling tower. ► Based on experimental data acquired in steady-state tests, the ANFIS model was developed and used for predicting various performance parameters of the system including the evaporating temperature, compressor power and coefficient of performance. ► The predictions usually agreed well with the experimental data. ► The results show that refrigeration systems with cooling towers can be modeled accurately using the ANFIS approach. ► Engineers relying on the ANFIS technique for determining the performance of refrigeration systems can save both time and funds.