Performance analysis of vapor compression refrigeration system using an adaptive neuro-fuzzy inference system

In this work, an experimental investigation is carried out with R134a and LPG refrigerant mixture (composed of R134a and LPG in the ratio of 28:72 by weight) as an alternative to R134a in a vapor compression refrigeration system. Performance tests were performed with different evaporator temperatures under controlled ambient conditions. The results showed that the R134a/LPG mixture has a higher coefficient of performance (COP) than R134a by about 15.28% in the studied range. The applicability of adaptive neuro-fuzzy inference system (ANFIS) to predict the COP of R134a/LPG system was also investigated. An ANFIS model for the system was developed. The comparison of statistical analysis of mathematical and ANFIS model predictions respectively in terms of the absolute fraction of variance (0.982 and 0.994), the root mean square error (0.0056 and 0.0050) and the mean absolute percentage error (0.286% and 0.217%) showed that ANFIS model gave the better statistical prediction efficiency.