Modeling the Energy Consumption of R600a Gas in a Refrigeration System with New Explainable Artificial Intelligence Methods Based on Hybrid Optimization

Abstract

Refrigerant gases, an essential cooling system component, are used in different processes according to their thermophysical properties and energy consumption values. The low global warming potential and energy consumption values of refrigerant gases are primarily preferred in terms of use. Recently, studies on modeling properties such as compressor energy consumption, efficiency coefficient, exergy, and thermophysical properties of refrigerants in refrigeration systems with artificial intelligence methods has become increasingly common. In this study, a hybrid-optimization-based artificial intelligence classification method is applied for the first time to produce explainable, interpretable, and transparent models of compressor energy consumption in a vapor compression refrigeration system operating with R600a refrigerant gas. This methodological innovation obtains models that determine the energy consumption values of R600a gas according to the operating parameters. From these models, the operating conditions with the lowest energy consumption are automatically revealed. The innovative artificial intelligence method applied for the energy consumption value determines the system's energy consumption according to the operating temperatures and pressures of the evaporator and condenser unit. When the obtained energy consumption model results were compared with the experimental results, it was seen that it had an accuracy of 84.4%. From this explainable artificial intelligence method, which is applied for the first time in the field of refrigerant gas, the most suitable operating conditions that can be achieved based on the minimum, medium, and maximum energy consumption ranges of different refrigerant gases can be determined.