Limitations for Data-driven Safeguards at Enrichment Facilities

Effective safeguards measures for gaseous centrifuge enrichment plants (GCEPs) have already been implemented by the International Atomic Energy Agency at several facilities. However, the constituent signals used in current approaches are often considered piecemeal. For example, the OnLine Enrichment Monitor (OLEM) provides the current enrichment level at a given location, however, it cannot guard against excess production. This work hypothesized that better utilization of existing safeguards signals through machine learning could lead to a more effective safeguards system. However, both real-world limitations and data scarcity prohibit the development of such a system. This work will discuss why these barriers ultimately prevent the development of a more effective, data-driven safeguards system. *Notice*:SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525