Étude De Remplacement Des Fluides Frigorigènes Polluants Via Une Technique Basée Sur Propriétés Thermo-physiques (approche Réseaux De Neurones Et Optimisation Par Algorithme Ant Colony)

Résumé: Hydrofluoroolefins (HFOs) currently consider the new generation of refrigerants. The precise knowledge of the thermodynamic and thermo-physical properties of these pure and mixed refrigerants, in particular, the properties related to the refrigeration cycle, is necessary for the development and calculations of refrigeration and air-conditioning systems. The objectives of this work are, apply artificial neural networks (ANN) for the prediction of the thermo-physical properties of these refrigerants on the one hand. On the other hand, these refrigerants will be studied in a refrigeration system coupled with a renewable energy source to reduce the impact on the environment and the costs of the installation. The studied fluids are R1234yf, R1234ze (E), R1234ze (Z), R1234zf and R1216. The system in which these fluids have been applied is the compression-absorption cascade refrigeration system. In another part, we applied the ant colony optimization algorithm (ACO) to optimize the ANN structure and a simple group contribution method to generalize the proposed ANN model. The present study demonstrated that the error and deviation indicators in all cases of estimates the properties were good and the correlations resulting from the implementation of the ANNs show a significant agreement with the experimental data. The results of the simulation also showed a decrease from 51.36 to 54.16% in electricity consumption when using these refrigerants in the proposed system compared to conventional vapor compression cycles. The COP values for all considered combinations were (0.184-0.595). The results obtained during this study show that the use of HFO has advantages, from an economic and energy point of view.

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