Application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration)

Abstract : The aim of this work was to elaborate a predictive model of the mass transfer (water loss and solute gain) that occurs during dewatering and soaking by using neural network modelling. Two separate feedforward networks with one hidden layer were used (for water loss and solute gain respectively). Model validation was carried out on results obtained previously, which dealt with agar gel soaked in sucrose solution over a wide experimental range (temperature, 30-70 °C; solu tion concentration, 30-70 g sucrose/100 g solution; time 0-500 min; agar concentration, 2-8%). The best results were obtained with three hidden neurons, which made it possible to predict mass transfer, with an accuracy at least as good as the experimental error, over the whole experimental range. The technological interest of such a model is related to a rapidity in simulation compa rable to that of a traditional transfer function, a limited number of parameters and experimental data, and the fact that no preliminary assumption on the underlying mechanisms was needed
Type de document :
Article dans une revue
Food Science and Technology International, SAGE Publications, 1997, 3 (6), pp.459-465. 〈10.1177/108201329700300608〉
Liste complète des métadonnées

https://hal-agroparistech.archives-ouvertes.fr/hal-01537210
Contributeur : Ioan-Cristian Trelea <>
Soumis le : mercredi 14 juin 2017 - 14:59:59
Dernière modification le : mercredi 10 octobre 2018 - 14:28:06
Document(s) archivé(s) le : mardi 12 décembre 2017 - 11:51:55

Fichier

Postprint Trelea Raoult-Wack 1...
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Ioan-Cristian Trelea, Anne-Lucie Raoult-Wack, Gilles Trystram. Application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration). Food Science and Technology International, SAGE Publications, 1997, 3 (6), pp.459-465. 〈10.1177/108201329700300608〉. 〈hal-01537210〉

Partager

Métriques

Consultations de la notice

151

Téléchargements de fichiers

82