Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks
AUTOR(ES)
Migliavacca, S.C.P., Rodrigues, C., Nascimento, C.A.O.
FONTE
Brazilian Journal of Chemical Engineering
DATA DE PUBLICAÇÃO
2002-07
RESUMO
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.
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