Adaptive neuro-fuzzy inference system approach to predict the mass flow rate of R-134a/LPG refrigerant for straight and helical coiled adiabatic capillary tubes in the vapor compression refrigeration system. (June 2017)

• Record Type: Journal Article
• Authors: Gill, Jatinder; Singh, Jagdev
• Is Part Of: International journal of refrigeration. Volume 78(2017)
• Page Start: 166
• Page End: 175
• Publication Date: 2017-06
• Subjects: VCRS -- Dimensionless correlation -- Adaptive Neuro-Fuzzy Inference System (ANFIS) -- Mass flow rate; VCRS -- Corrélation adimensionnelle -- Système d'inférence neuro-flou adaptatif -- Système ANFIS -- Débit massique; Refrigeration and refrigerating machinery -- Periodicals; 621.56
• DOI: 10.1016/j.ijrefrig.2017.02.004
• Languages: English
• ISSNs: 0140-7007

Abstract

Highlights: Experimental data of mass flow rate through adiabatic capillary tubes are presented. Effect of dimensionless parameters on refrigerant mass flow rate is studied. Dimensionless correlation and ANFIS models for mass flow rate are developed. Statistical prediction performances of both models are measured. ANFIS model predictions show better statistical performance. Abstract: This study deals with predicting the mass flow rate of R-134a/LPG as refrigerant inside a straight and helical coiled adiabatic capillary tube of vapor compression refrigeration system by combining dimensionless analysis and Adaptive Neuro-Fuzzy Inference System techniques. For this purpose the experimental system was designed and tested under steady state conditions, by changing the length of the capillary tube, the inner diameter of the capillary tube, the coil diameter and the degree of subcooling of the refrigerant at the capillary tube inlet. Dimensional analysis was utilized to provide generalized dimensionless parameters and to reduce the number of input parameters, while Adaptive Neuro-Fuzzy Inference System was applied as a generalized approximator of the nonlinear multi-input and single-output function. The comparison of the absolute fraction of variance (R 2 ) (0.998 and 0.961), the root mean square error (RMSE) (0.105 kg/h and 0.489 kg/h) and the mean absolute percentage error (MAPE) (0.954% and 4.75%) demonstrated the result for combination of dimensional analysis and Adaptive Neuro-Fuzzy Inference System and dimensionless correlation model predictions respectively. The results indicated that the combination of dimensional analysis and Adaptive Neuro-Fuzzy Inference System gave the best statistical prediction efficiency.