| Revista: | Brazilian journal of chemical engineering |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000308619 |
| ISSN: | 0104-6632 |
| Autors: | Fonseca, A.P1 Stuart, G Oliveira, J.V Lima, E |
| Institucions: | 1Universidade Federal do Rio de Janeiro, Instituto Alberto Luiz Coimbra de Pos-Graduacao e Pesquisa de Engenharia, Rio de Janeiro. Brasil |
| Any: | 1999 |
| Període: | Sep |
| Volum: | 16 |
| Número: | 3 |
| Paginació: | 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 |
| Disciplines | Química, Ingeniería |
| Paraules clau: | 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 |
| Text complet: | Texto completo (Ver HTML) |
