Abstract
Inferring occurrences of dissolutions at a radiochemical processing facility using measurements from an independent monitoring system has potential for verifying operator declarations and compliance with nuclear technology use agreements. We consider the identification of such dissolution events using measurements of radioactive effluents collected at the facility’s off-gas stack. By using simple models for the source term based on radioactive decay-chains and sensor measurements of transported effluents, we formulate a simplified on/off classification problem. There are many available machine learning classification methods applicable to this problem, but they are subject to potential over-fitting. We mitigate this effect by fusing four promising but disparate classifiers: the ensemble of trees, support vector machines, naive Bayes, and k-nearest neighbors. Each classifier is trained to distinguish using effluents measurements between dissolutions of irradiated 237Np/238Pu and Cm/Cf targets from other (or no) radiochemical processing activities. We present Chow and ensemble of trees methods to fuse the classifier outputs to produce a single classification output, and thereby incorporate their diversity of design. We study the performance of these classifiers and fusers using measurements of87Kr,135Xe,138Xe, and138Cs isotopes,and present their training accuracy estimates using individual isotope measurements and all four isotopes taken together.
