Journal: | Brazilian journal of chemical engineering |
Database: | PERIÓDICA |
System number: | 000308823 |
ISSN: | 0104-6632 |
Authors: | Migliavacca, S.C.P1 Rodrigues, C Nascimento, C.A.O2 |
Institutions: | 1Instituto de Pesquisa Energetica e Nucleares, Sao Paulo. Brasil 2Universidade de Sao Paulo, Escola Politecnica, Sao Paulo. Brasil |
Year: | 2002 |
Season: | Jul |
Volumen: | 19 |
Number: | 3 |
Pages: | 299-306 |
Country: | Brasil |
Language: | Inglés |
Document type: | Artículo |
Approach: | Experimental |
English abstract | 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 |
Disciplines: | Química |
Keyword: | Ingeniería química, Separación isotópica, Centrifugación de gas, Uranio, Redes neuronales |
Keyword: | Chemistry, Chemical engineering, Isotopic separation, Gas centrifugation, Uranium, Neural networks |
Full text: | Texto completo (Ver HTML) |