Multivariate analysis with covariance matrix applied to separative power modeling of a gas centrifuge

  • Migliavacca E
  • Andrade D
  • 4


    Mendeley users who have this article in their library.
  • 0


    Citations of this article.


In this work, the least-squares methodology with covariance matrix is applied to determine a data curve fitting to obtain a performance function for the separative power δU of an ultracentrifuge as a function of variables that are experimentally controlled. The experimental data refer to 460 experiments on the ultracentrifugation process for uranium isotope separation. The experimental uncertainties related to these independent variables are considered in the calculation of the experimental separative power values, determining an experimental data input covariance matrix. The process variables, which significantly influence the δU values, are chosen to give information on the ultracentrifuge behaviour when submitted to several levels of feed flow rate F, cut θ and pressure in the product line, Pp. After the model goodness-of-fit validation, a residual analysis is carried out to verify the assumed basis concerning its randomness and independence and mainly the existence of residual heteroscedasticity with any explained regression model variable. © 2007 Elsevier Ltd. All rights reserved.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Elder Migliavacca

  • Delvonei A. Andrade

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free