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Modeling of rotary vane compressor applying artificial neural network / Sepehr Sanaye in International journal of refrigeration, Vol. 34 N° 3 (Mai 2011) 

Public ISBD [article]  in International journal of refrigeration > Vol. 34 N° 3 (Mai 2011) . - pp. 764-772 Titre : Modeling of rotary vane compressor applying artificial neural network Titre original : Modélisation d'un compresseur rotatif à l'aide d'un réseau neuronal Type de document : texte imprimé Auteurs : Sepehr Sanaye, Auteur ; Masoud Dehghandokht, Auteur ; Hassan Mohammadbeigi, Auteur Année de publication : 2011 Article en page(s) : pp. 764-772 Note générale : Génie Mécanique Langues : Anglais (eng) Mots-clés : Neural  network  Rotary  compressor  Automobile  Air  conditioning Index. décimale : 621.5 Energie pneumatique. Machinerie et outils. Réfrigération Résumé : The thermal modeling of rotary vane compressor (RVC) was performed in this paper by applying Artificial Neural Network (ANN) method. In the first step, appropriate tests were designed and experimental data were collected during steady state operating condition of RVC in the experimental setup. Then parameters including refrigerant suction temperature and pressure, compressor rotating speed as well as refrigerant discharge pressure were adjusted.With these input values, the operating output parameters such as refrigerant mass flow rate and refrigerant discharge temperature were measured. In the second step, the experimental results were used to train ANN model for predicting RVC operating parameters such as refrigerant mass flow rate and compressor power consumption. These predicted operating parameters by ANN model agreed well with the experimental values with correlation coefficient in the range of 0.962–0.998, mean relative errors in the range of 2.79–7.36% as well as root mean square error (RMSE) 10.59 kg h−1 and 12 K for refrigerant mass flow rate and refrigerant discharge temperature, respectively. Results showed closer predictions with experimental results for ANN model in comparison with nolinear regression model. DEWEY : 621.5 ISSN : 0140-7007 En ligne : http://www.sciencedirect.com/science/article/pii/S0140700710002811 [article] Modeling of rotary vane compressor applying artificial neural network = Modélisation d'un compresseur rotatif à l'aide d'un réseau neuronal [texte imprimé] / Sepehr Sanaye, Auteur ; Masoud Dehghandokht, Auteur ; Hassan Mohammadbeigi, Auteur . - 2011 . - pp. 764-772. Génie Mécanique Langues : Anglais (eng) in International journal of refrigeration > Vol. 34 N° 3 (Mai 2011) . - pp. 764-772 Mots-clés : Neural  network  Rotary  compressor  Automobile  Air  conditioning Index. décimale : 621.5 Energie pneumatique. Machinerie et outils. Réfrigération Résumé : The thermal modeling of rotary vane compressor (RVC) was performed in this paper by applying Artificial Neural Network (ANN) method. In the first step, appropriate tests were designed and experimental data were collected during steady state operating condition of RVC in the experimental setup. Then parameters including refrigerant suction temperature and pressure, compressor rotating speed as well as refrigerant discharge pressure were adjusted.With these input values, the operating output parameters such as refrigerant mass flow rate and refrigerant discharge temperature were measured. In the second step, the experimental results were used to train ANN model for predicting RVC operating parameters such as refrigerant mass flow rate and compressor power consumption. These predicted operating parameters by ANN model agreed well with the experimental values with correlation coefficient in the range of 0.962–0.998, mean relative errors in the range of 2.79–7.36% as well as root mean square error (RMSE) 10.59 kg h−1 and 12 K for refrigerant mass flow rate and refrigerant discharge temperature, respectively. Results showed closer predictions with experimental results for ANN model in comparison with nolinear regression model. DEWEY : 621.5 ISSN : 0140-7007 En ligne : http://www.sciencedirect.com/science/article/pii/S0140700710002811