Note : application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration)
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; solution concentration, 30-70 g sucrose/100g 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 comparable 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.
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Subjects: | Q02 - Traitement et conservation des produits alimentaires, U30 - Méthodes de recherche, séchage osmotique, Immersion, transfert de masse, solute, teneur en eau, modèle de simulation, modélisation, http://aims.fao.org/aos/agrovoc/c_36939, http://aims.fao.org/aos/agrovoc/c_2316, http://aims.fao.org/aos/agrovoc/c_28601, http://aims.fao.org/aos/agrovoc/c_24133, http://aims.fao.org/aos/agrovoc/c_4886, http://aims.fao.org/aos/agrovoc/c_24242, http://aims.fao.org/aos/agrovoc/c_230ab86c, |
Online Access: | http://agritrop.cirad.fr/390026/ http://agritrop.cirad.fr/390026/1/390026.pdf |
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dig-cirad-fr-3900262024-01-27T23:11:48Z http://agritrop.cirad.fr/390026/ http://agritrop.cirad.fr/390026/ Note : application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration). Tréléa I.C., Raoult-Wack Anne-Lucie, Trystram Gilles. 1997. Food Science and Technology International, 3 (6) : 459-465.https://doi.org/10.1177/108201329700300608 <https://doi.org/10.1177/108201329700300608> Note : application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration) Tréléa, I.C. Raoult-Wack, Anne-Lucie Trystram, Gilles eng 1997 Food Science and Technology International Q02 - Traitement et conservation des produits alimentaires U30 - Méthodes de recherche séchage osmotique Immersion transfert de masse solute teneur en eau modèle de simulation modélisation http://aims.fao.org/aos/agrovoc/c_36939 http://aims.fao.org/aos/agrovoc/c_2316 http://aims.fao.org/aos/agrovoc/c_28601 http://aims.fao.org/aos/agrovoc/c_24133 http://aims.fao.org/aos/agrovoc/c_4886 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_230ab86c 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; solution concentration, 30-70 g sucrose/100g 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 comparable 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/390026/1/390026.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1177/108201329700300608 10.1177/108201329700300608 http://catalogue-bibliotheques.cirad.fr/cgi-bin/koha/opac-detail.pl?biblionumber=89897 info:eu-repo/semantics/altIdentifier/doi/10.1177/108201329700300608 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1177/108201329700300608 |
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Q02 - Traitement et conservation des produits alimentaires U30 - Méthodes de recherche séchage osmotique Immersion transfert de masse solute teneur en eau modèle de simulation modélisation http://aims.fao.org/aos/agrovoc/c_36939 http://aims.fao.org/aos/agrovoc/c_2316 http://aims.fao.org/aos/agrovoc/c_28601 http://aims.fao.org/aos/agrovoc/c_24133 http://aims.fao.org/aos/agrovoc/c_4886 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_230ab86c Q02 - Traitement et conservation des produits alimentaires U30 - Méthodes de recherche séchage osmotique Immersion transfert de masse solute teneur en eau modèle de simulation modélisation http://aims.fao.org/aos/agrovoc/c_36939 http://aims.fao.org/aos/agrovoc/c_2316 http://aims.fao.org/aos/agrovoc/c_28601 http://aims.fao.org/aos/agrovoc/c_24133 http://aims.fao.org/aos/agrovoc/c_4886 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_230ab86c |
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Q02 - Traitement et conservation des produits alimentaires U30 - Méthodes de recherche séchage osmotique Immersion transfert de masse solute teneur en eau modèle de simulation modélisation http://aims.fao.org/aos/agrovoc/c_36939 http://aims.fao.org/aos/agrovoc/c_2316 http://aims.fao.org/aos/agrovoc/c_28601 http://aims.fao.org/aos/agrovoc/c_24133 http://aims.fao.org/aos/agrovoc/c_4886 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_230ab86c Q02 - Traitement et conservation des produits alimentaires U30 - Méthodes de recherche séchage osmotique Immersion transfert de masse solute teneur en eau modèle de simulation modélisation http://aims.fao.org/aos/agrovoc/c_36939 http://aims.fao.org/aos/agrovoc/c_2316 http://aims.fao.org/aos/agrovoc/c_28601 http://aims.fao.org/aos/agrovoc/c_24133 http://aims.fao.org/aos/agrovoc/c_4886 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_230ab86c Tréléa, I.C. Raoult-Wack, Anne-Lucie Trystram, Gilles Note : application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration) |
description |
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; solution concentration, 30-70 g sucrose/100g 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 comparable 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. |
format |
article |
topic_facet |
Q02 - Traitement et conservation des produits alimentaires U30 - Méthodes de recherche séchage osmotique Immersion transfert de masse solute teneur en eau modèle de simulation modélisation http://aims.fao.org/aos/agrovoc/c_36939 http://aims.fao.org/aos/agrovoc/c_2316 http://aims.fao.org/aos/agrovoc/c_28601 http://aims.fao.org/aos/agrovoc/c_24133 http://aims.fao.org/aos/agrovoc/c_4886 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_230ab86c |
author |
Tréléa, I.C. Raoult-Wack, Anne-Lucie Trystram, Gilles |
author_facet |
Tréléa, I.C. Raoult-Wack, Anne-Lucie Trystram, Gilles |
author_sort |
Tréléa, I.C. |
title |
Note : application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration) |
title_short |
Note : application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration) |
title_full |
Note : application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration) |
title_fullStr |
Note : application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration) |
title_full_unstemmed |
Note : application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration) |
title_sort |
note : application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration) |
url |
http://agritrop.cirad.fr/390026/ http://agritrop.cirad.fr/390026/1/390026.pdf |
work_keys_str_mv |
AT treleaic noteapplicationofneuralnetworkmodellingforthecontrolofdewateringandimpregnationsoakingprocessosmoticdehydration AT raoultwackannelucie noteapplicationofneuralnetworkmodellingforthecontrolofdewateringandimpregnationsoakingprocessosmoticdehydration AT trystramgilles noteapplicationofneuralnetworkmodellingforthecontrolofdewateringandimpregnationsoakingprocessosmoticdehydration |
_version_ |
1792491277756923904 |