Using hybrid neural models to describe supercritical fluid extraction processes



Título del documento: Using hybrid neural models to describe supercritical fluid extraction processes
Revista: Brazilian journal of chemical engineering
Base de datos: PERIÓDICA
Número de sistema: 000308619
ISSN: 0104-6632
Autores: 1


Instituciones: 1Universidade Federal do Rio de Janeiro, Instituto Alberto Luiz Coimbra de Pos-Graduacao e Pesquisa de Engenharia, Rio de Janeiro. Brasil
Año:
Periodo: Sep
Volumen: 16
Número: 3
Paginación: 267-278
País: Brasil
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Experimental, aplicado
Resumen en inglés 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
Disciplinas: Química,
Ingeniería
Palabras clave: Ingeniería química,
Extracción supercrítica de fluídos,
Modelación,
Redes neuronales artificiales,
Aceite de romero,
Aceite de pimienta
Keyword: Chemistry,
Engineering,
Chemical engineering,
Supercritical fluid extraction,
Modeling,
Artificial neural networks,
Rosemary oil,
Pepper oil
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