Optimization of carbon dioxide refrigerant charge in capillary radiation air conditioning system based on genetic algorithm

Abstract

The traditional capillary radiant air conditioning system based on back propagation (BP) neural network optimization model of carbon dioxide refrigerant filling, using neural network algorithm to optimize the carbon dioxide refrigerant filling, there is a big error. Aiming at this problem, a carbon dioxide refrigerant charging and filling optimization model in capillary radiant air conditioning system based on genetic algorithm (GA) is constructed. Based on the idea of solving multiobjective optimization problem with genetic algorithm, an adiabatic capillary steady‐state distribution model is constructed to analyze the change of refrigerant flow rate and critical temperature in capillary tube. Corresponding data samples, the GA‐BP neural network model is calculated based on the sample data, and the optimization of carbon dioxide refrigerant filling capacity is realized through the interpolation and extrapolation test model of condenser heat transfer and refrigerant outlet temperature. The experimental results show that the model has a significant application in optimizing the charging capacity of carbon dioxide refrigerant. The error of optimizing the charging capacity of carbon dioxide is small, and the error range is between (−0.03, 0.03).