Abstract
The aim of nuclear safeguards is to ensure that special nuclear material is used for peaceful purposes. Historically, nuclear material accounting (NMA) has provided the quantitative basis for monitoring for nuclear material loss or diversion, and process monitoring (PM) data is collected by the operator to monitor the process. PM data typically support NMA in various ways, often by providing a basis to estimate some of the in-process nuclear material inventory. We develop options for combining PM residuals and NMA residuals (residual = measurement − prediction), using a hybrid of period-driven and data-driven hypothesis testing. The modified statistical tests can be used on time series of NMA residuals (the NMA residual is the familiar material balance), or on a combination of PM and NMA residuals. The PM residuals can be generated on a fixed time schedule or as events occur.
Highlights
Introduction and BackgroundNuclear material accounting (NMA) is a component of nuclear safeguards, which are designed to detect illicit diversion of special nuclear material (SNM) from the peaceful fuel cycle to a potential weapons application
The requirement for high-quality predictions leads to technical challenges in safeguarding either aqueous or electrochemical reprocessing facilities
There is ongoing work aimed at high-quality modeling of the electrorefiner in an electrochemical facility [17]
Summary
Nuclear material accounting (NMA) is a component of nuclear safeguards, which are designed to detect illicit diversion of special nuclear material (SNM) from the peaceful fuel cycle to a potential weapons application. Both PM and NMA measurements are available at aqueous and electrochemical reprocessing facilities. One key assumption is that the safeguards approach includes model-based predictions that can be compared to corresponding measurements, resulting in time series of residuals. The requirement for high-quality predictions leads to technical challenges in safeguarding either aqueous or electrochemical reprocessing facilities. The following sections include a description of NMA and of PM, pattern recognition, model-based prediction, discussion of simulated data, extensions to include additional PM residuals, example of combining PM and NMA data, and a summary. Reference [1] reviews related work in the nuclear safeguards and statistics communities
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