Adaptive neuro-fuzzy inference system (ANFIS) approach for the irreversibility analysis of a domestic refrigerator system using LPG/TiO2 nanolubricant

Abstract

This work presents an adaptive neuro-fuzzy inference system (ANFIS) artificial intelligence methodology of predicting the 2nd law efficiency and total irreversibility of a refrigeration system running on LPG/TiO2–nano-refrigerants. For this purpose, substractive clustering and grid partition approaches were utilized to train the ANFIS models required in estimating the 2nd law efficiency and total irreversibility using some experimental data. Furthermore, predictions of ANFIS models with subtractive clustering approach was found to be more accurate than ANFIS models predictions with grid partition approach. The predictions of ANFIS models with subtractive clustering approach were also compared with experimental results that were not included in the model training and predictions of already existing ANN models of authors previous publication. The comparison of variance, root mean square error (RMSE), mean absolute percentage error (MAPE) were 0.996–0.999, 0.0296–0.1726 W and 0.108–0.176 % marginal variability values. These results indicate that the ANFIS model with subtractive clustering approach having cluster radii 0.7 and 0.5 can predict the 2nd law efficiency and total irreversibility respectively, with higher accuracy than authors’ previous publication ANN models.