| Revista: | Brazilian journal of chemical engineering |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000308685 |
| ISSN: | 0104-6632 |
| Autores: | Fonseca, A.P1 Oliveira, J.V Lima, E.L |
| Instituciones: | 1Universidade Federal do Rio de Janeiro, Instituto Alberto Luiz Coimbra de Pos-Graduacao e Pesquisa de Engenharia, Rio de Janeiro. Brasil |
| Año: | 2000 |
| Periodo: | Dic |
| Volumen: | 17 |
| Número: | 4-7 |
| Paginación: | 517-524 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | Neural networks have been investigated for predicting mass transfer coefficients from supercritical Carbon Dioxide/Ethanol/Water system. To avoid the difficulties associated with reduce experimental data set available for supercritical extraction in question, it was chosen to use a technique to generate new semi-empirical data. It combines experimental mass transfer coefficient with those obtained from correlation available in literature, producing an extended data set enough for efficient neural network identification. With respect to available experimental data, the results obtained to benefit neural networks in comparing with empirical correlations for predicting mass transfer parameters |
| Disciplinas: | Química |
| Palabras clave: | Ingeniería química, Extracción supercrítica de gas, Coeficiente de transferencia de masa, Etanol, Dióxido de carbono, Redes neuronales |
| Keyword: | Chemistry, Chemical engineering, Supercritical gas extraction, Mass transfer coefficient, Ethanol, Carbon dioxide, Neural networks |
| Texto completo: | Texto completo (Ver HTML) |
