Prediction of Refrigerant Mass Flow Rates through Capillary Tubes Using Adaptive Neuro-fuzzy Inference System

A capillary tube is a common expansion device widely used in small-scale refrigeration and air conditioning systems. Generalized correlation method for refrigerant flow rate through adiabatic capillary tubes is developed by combining dimensional analysis and adaptive neuron-fuzzy inference system (ANFIS).Dimensional analysis is utilized to provide the generalized dimensionless parameters and reduce the number of input parameters, while a five-layer feedforward ANFIS is served as a universal approximator of the nonlinear multi-input and single output function. For ANFIS training and test,measured data for R134a, R22, R290, R407C, R410A,and R600a in the open literature are employed. The most suitable membership function and number of membership functions are found as Gauss and two,respectively, for the ANFIS correlation. The statistical data can be considered as very promising. This paper shows the appropriateness of ANFIS for the prediction of refrigerant mass flow rates through capillary tubes.