Analysis and Optimization of Gas- Centrifugal Separation of Uranium Isotopes by Neural Networks

نویسندگان

  • S. C. P. Migliavacca
  • C. Rodrigues
  • C. A. O. Nascimento
چکیده

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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تاریخ انتشار 2002