Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks

Brazilian Journal of Chemical Engineering. 2002;19(3):299-306

 

Journal Homepage

Journal Title: Brazilian Journal of Chemical Engineering

ISSN: 0104-6632 (Print); 1678-4383 (Online)

Publisher: Brazilian Society of Chemical Engineering

LCC Subject Category: Technology: Chemical technology: Chemical engineering

Country of publisher: Brazil

Language of fulltext: English

Full-text formats available: PDF, HTML, XML

 

AUTHORS

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

EDITORIAL INFORMATION

Peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 12 weeks

 

Abstract | Full Text

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.