Controlling the properties
of PuO
2
through processing
is of vital importance to environmental transport and fate, production
of nuclear fuels, nuclear forensic analyses, stockpile stewardship,
and storage of nuclear wastes applications. A number of processing
conditions have been identified to control final product properties,
including specific surface area (SSA), residual carbon content, adsorption
of volatile species, morphology, and particle size. In this paper,
a novel approach is developed for the prediction of PuO
2
SSA via the synthetic route of Pu(IV) oxalate precipitation followed
by calcination. The proposed model utilizes multivariate regression
methodology and leave one out formalism to link Savannah River Site
(SRS) precipitation and calcination production data to the SSA of
the final product. A comparison among the models provides insight
into the accuracy and ability to identify variations amongst the processing
data. Additionally, the models may also be used to fit new data outside
of the parameters explored in a production facility. Finally, the
trained model was compared to a similarly trained conventional model
form to illustrate the influence of precipitation parameters on the
prediction of the final SSA. The models presented here attempt to
provide new methods for more accurate prediction of the PuO
2
product properties in a production scale environment for key environmental
and nuclear applications.