Nonlinear predictive control based on artificial neural network model for industrial crystallization - Université de La Réunion Accéder directement au contenu
Article Dans Une Revue Journal of Food Engineering Année : 2010

Nonlinear predictive control based on artificial neural network model for industrial crystallization

Cédric Damour
Connectez-vous pour contacter l'auteur
Michel Benne
Connectez-vous pour contacter l'auteur
Brigitte Grondin-Perez
  • Fonction : Auteur correspondant
  • PersonId : 970429

Connectez-vous pour contacter l'auteur
Jean-Pierre Chabriat
Connectez-vous pour contacter l'auteur

Résumé

This paper illustrates the benefits of a nonlinear model based predictive control (NMPC) strategy for setpoint tracking control of an industrial crystallization process. A neural networks model is used as internal model to predict process outputs. An optimization problem is solved to compute future control actions taking into account real-time control objectives. Furthermore, a more suitable output variable is used for process control: the mass of crystals in the solution is used instead of the traditional electrical conductivity. The performance of the NMPC implementation is assessed via simulation results based on industrial data.
Fichier non déposé

Dates et versions

hal-01202296 , version 1 (19-09-2015)

Identifiants

Citer

Cédric Damour, Michel Benne, Brigitte Grondin-Perez, Jean-Pierre Chabriat. Nonlinear predictive control based on artificial neural network model for industrial crystallization. Journal of Food Engineering, 2010, 99 (2), pp.225--231. ⟨10.1016/j.jfoodeng.2010.02.027⟩. ⟨hal-01202296⟩
88 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More