Exergy assessment of a refrigeration plant using computational intelligence based on hybrid learning methods
Abstract In this study, a method to model the exergetic behavior of a refrigeration system using some techniques from computational intelligence is proposed. The input parameters of the model are: the compressor rotation speed, the volumetric flow rates and the temperatures of the secondary fluids. The artificial neural network was trained using a hybrid learning method based on Simulated Annealing and Levenberg Marquardt method. Two independent neural networks were designed to visualize and analyze the exergy destruction and exergy efficiency for each component of a vapor compression system. The relative errors produced during the validation of the model were within ±10%. From the application simulation, it was concluded that the major exergy destruction is located at the compressor and at the condenser. Additionally, it was observed that the parameters that most influence the exergetic behavior of the system are: the compressor rotation speed and the inlet temperatures of the secondary fluids.