| Revista: | Brazilian journal of chemical engineering |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000308823 |
| ISSN: | 0104-6632 |
| Autores: | Migliavacca, S.C.P1 Rodrigues, C Nascimento, C.A.O2 |
| Instituciones: | 1Instituto de Pesquisa Energetica e Nucleares, Sao Paulo. Brasil 2Universidade de Sao Paulo, Escola Politecnica, Sao Paulo. Brasil |
| Año: | 2002 |
| Periodo: | Jul |
| Volumen: | 19 |
| Número: | 3 |
| Paginación: | 299-306 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental |
| Resumen en inglés | Neural networks are an attractive alternative for modeling complex problems with too many difficulties to be solved by a phenomenological model. A feed-forward neural network was used to model a gas-centrifugal separation of uranium isotopes. The prediction showed good agreement with the experimental data. An optimization study was carried out. The optimal operational condition was tested by a new experiment and a difference of less than 1% was found |
| Disciplinas: | Química |
| Palabras clave: | Ingeniería química, Separación isotópica, Centrifugación de gas, Uranio, Redes neuronales |
| Keyword: | Chemistry, Chemical engineering, Isotopic separation, Gas centrifugation, Uranium, Neural networks |
| Texto completo: | Texto completo (Ver HTML) |
