Use of Bayesian inference to estimate diversion likelihood in a PUREX facility

Abstract

Nuclear Fuel reprocessing is done today with the PUREX process, which has been demonstrated to work at industrial scales at several facilities around the world. Use of the PUREX process results in the creation of a stream of pure plutonium, which allows the process to be potentially used by a proliferator. Safeguards have been put in place by the IAEA and other agencies to guard against the possibility of diversion and misuse, but the cost of these safeguards and the intrusion into a facility they represent could cause a fuel reprocessing facility operator to consider foregoing standard safeguards in favor of diversion detection that is less intrusive. Use of subjective expertise in a Bayesian network offers a unique opportunity to monitor a fuel reprocessing facility while collecting limited information compared to traditional safeguards. This work focuses on the preliminary creation of a proof of concept Bayesian network and its application to a model nuclear fuel reprocessing facility.