Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks
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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| Main Authors: | , , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Brazilian Society of Chemical Engineering
2002
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| Online Access: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322002000300005 |
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