Using hybrid neural models to describe supercritical fluid extraction processes

Using hybrid neural models to describe supercritical fluid extraction processes

This work presents the results of a hybrid neural model (HNM) technique as applied to modeling supercritical fluid extraction (SCFE) curves obtained from two Brazilian vegetable matrices. The serial HNM employed uses a neural network to estimate parameters of a phenomenological model. A small set of SCFE data for each vegetable was used to generate a semi-empirical extended data set, large enough for efficient network training, using three different approaches. Afterwards, other sets of experimental data, not used during the training procedure, were used to validate each approach. The HNM correlates well withthe experimental data, and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.

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Main Authors: FONSECA,A. P., STUART,G., OLIVEIRA,J. V., LIMA,E.
Format: Digital revista
Language:English
Published: Brazilian Society of Chemical Engineering 1999
Online Access:http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66321999000300005
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spelling oai:scielo:S0104-663219990003000051999-12-16Using hybrid neural models to describe supercritical fluid extraction processesFONSECA,A. P.STUART,G.OLIVEIRA,J. V.LIMA,E. Supercritical fluid extraction Modeling Artificial neural network Brazilian rosemary oil pepper oil This work presents the results of a hybrid neural model (HNM) technique as applied to modeling supercritical fluid extraction (SCFE) curves obtained from two Brazilian vegetable matrices. The serial HNM employed uses a neural network to estimate parameters of a phenomenological model. A small set of SCFE data for each vegetable was used to generate a semi-empirical extended data set, large enough for efficient network training, using three different approaches. Afterwards, other sets of experimental data, not used during the training procedure, were used to validate each approach. The HNM correlates well withthe experimental data, and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.info:eu-repo/semantics/openAccessBrazilian Society of Chemical EngineeringBrazilian Journal of Chemical Engineering v.16 n.3 19991999-09-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66321999000300005en10.1590/S0104-66321999000300005
institution SCIELO
collection OJS
country Brasil
countrycode BR
component Revista
access En linea
databasecode rev-scielo-br
tag revista
region America del Sur
libraryname SciELO
language English
format Digital
author FONSECA,A. P.
STUART,G.
OLIVEIRA,J. V.
LIMA,E.
spellingShingle FONSECA,A. P.
STUART,G.
OLIVEIRA,J. V.
LIMA,E.
Using hybrid neural models to describe supercritical fluid extraction processes
author_facet FONSECA,A. P.
STUART,G.
OLIVEIRA,J. V.
LIMA,E.
author_sort FONSECA,A. P.
title Using hybrid neural models to describe supercritical fluid extraction processes
title_short Using hybrid neural models to describe supercritical fluid extraction processes
title_full Using hybrid neural models to describe supercritical fluid extraction processes
title_fullStr Using hybrid neural models to describe supercritical fluid extraction processes
title_full_unstemmed Using hybrid neural models to describe supercritical fluid extraction processes
title_sort using hybrid neural models to describe supercritical fluid extraction processes
description This work presents the results of a hybrid neural model (HNM) technique as applied to modeling supercritical fluid extraction (SCFE) curves obtained from two Brazilian vegetable matrices. The serial HNM employed uses a neural network to estimate parameters of a phenomenological model. A small set of SCFE data for each vegetable was used to generate a semi-empirical extended data set, large enough for efficient network training, using three different approaches. Afterwards, other sets of experimental data, not used during the training procedure, were used to validate each approach. The HNM correlates well withthe experimental data, and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.
publisher Brazilian Society of Chemical Engineering
publishDate 1999
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66321999000300005
work_keys_str_mv AT fonsecaap usinghybridneuralmodelstodescribesupercriticalfluidextractionprocesses
AT stuartg usinghybridneuralmodelstodescribesupercriticalfluidextractionprocesses
AT oliveirajv usinghybridneuralmodelstodescribesupercriticalfluidextractionprocesses
AT limae usinghybridneuralmodelstodescribesupercriticalfluidextractionprocesses
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