Partager

Document IIF

Méthode unifiée de contribution des groupes de réseaux neuronaux artificiels aux prévisions du point d'ébullition normal et de la température critique des frigorigènes et des composés apparentés.

Unified artificial neural network-group contribution method for predictions of normal boiling point and critical temperature of refrigerants and related compounds.

Auteurs : DEVOTTA S., CHELANI A.

Type d'article : Article de la RIF

Résumé

In this study, both normal boiling point and critical temperature of refrigerants and related compounds are predicted only from their molecular structures using a simple and unified Artificial Neural Network - Group Contribution Method. Identical 32 (including molecular mass) groups and methodologies have been used with 251 experimental data for TB and 132 experimental data for TC. In spite of its simplicity, the agreements between experimental and ANN predicted data for TB and TC are very good, better than most of the existing models. The percentage errors for training and test data sets are 2.4% and 3.7% and 2.8% and 5.7% for TB and TC respectively. The overall percentage errors for TB and TC are 2.8% and 3.7% respectively. A comparison of the proposed models with other models shows that for the class of compounds considered i.e., refrigerants and related compounds, this model predicts most accurately. These models can be conveniently used for any preliminary screening of compounds as alternative refrigerants or working fluids or for any other applications.

Documents disponibles

Format PDF

Pages : 112-124

Disponible

  • Prix public

    20 €

  • Prix membre*

    Gratuit

* meilleur tarif applicable selon le type d'adhésion (voir le détail des avantages des adhésions individuelles et collectives)

Détails

  • Titre original : Unified artificial neural network-group contribution method for predictions of normal boiling point and critical temperature of refrigerants and related compounds.
  • Identifiant de la fiche : 30029908
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 140
  • Date d'édition : 08/2022
  • DOI : http://dx.doi.org/10.1016/j.ijrefrig.2022.04.020
  • Document disponible en consultation à la bibliothèque du siège de l'IIF uniquement.

Liens


Voir d'autres articles du même numéro (18)
Voir la source

Indexation