Using hybrid neural models to describe supercritical fluid extraction processes

• Autor(a) principal: FONSECA,A. P.
• Data de Publicação: 1999
• Outros Autores: STUART,G., OLIVEIRA,J. V., LIMA,E.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Chemical Engineering
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66321999000300005
• topic: 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.