Abstract
Abstract In this study, the development of a physical model for a miniature refrigeration system is presented. The model is based on physical foundations and the incorporation of information provided in the manufacturers’ catalogs. The model is able to adequately predict the energy behavior of the refrigeration system, showing a maximum error of 10% in comparison with the experimental information found in the literature. Thus, the model is used to perform a multi-objective optimization by comparing two algorithms, nondominated storing genetic algorithm II and multi-objective particle swarm optimization algorithm. For this purpose, three scenarios are proposed whose objective functions involve parameters such as coefficient of performance, compressor discharge temperature, energy consumption, and refrigerant mass flowrate. The decision variables are the evaporation and condensation temperatures, the rotational speed of the compressor, and the degrees of subcooling and superheating. Thus, an overview of the optimal operating conditions that represent the best energy performance of the refrigeration system is provided.
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