Hindawi Publishing Corporation Science and Technology of Nuclear Installations Volume 2013, Article ID 961360, 15 pages http://dx.doi.org/10.1155/2013/961360 Research Article Revisiting Statistical Aspects of Nuclear Material Accounting T. Burr and M. S. Hamada Los Alamos National Laboratory, Statistical Sciences Group, NM 87545, USA Correspondence should be addressed to T. Burr; tburr@lanl.gov Received 4 December 2012; Accepted 18 February 2013 Academic Editor: Michael F. Simpson Copyright © 2013 T. Burr and M. S. Hamada. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Nuclear material accounting (NMA) is the only safeguards system whose benefts are routinely quantifed. Process monitoring (PM) is another safeguards system that is increasingly used, and one challenge is how to quantify its beneft. Tis paper considers PM in the role of enabling frequent NMA, which is referred to as near-real-time accounting (NRTA). We quantify NRTA benefts using period-driven and data-driven testing. Period-driven testing makes a decision to alarm or not at fxed periods. Data-driven testing decides as the data arrives whether to alarm or continue testing. Te diference between period-driven and datad-riven viewpoints is illustrated by using one-year and two-year periods. For both one-year and two-year periods, period-driven NMA using once-per-year cumulative material unaccounted for (CUMUF) testing is compared to more frequent Shewhart and joint sequential cusum testing using either MUF or standardized, independently transformed MUF (SITMUF) data. We show that the data-driven viewpoint is appropriate for NRTA and that it can be used to compare safeguards efectiveness. In addition to providing period-driven and data-driven viewpoints, new features include assessing the impact of uncertainty in the estimated covariance matrix of the MUF sequence and the impact of both random and systematic measurement errors. 1. Introduction One challenge in modern safeguards at declared facilities that process special nuclear material (SNM) is how to quantify the beneft of process monitoring (PM) [1–4]. Tere are many examples of PM data used in safeguards, such as neutron- based counting of a waste stream (the “hulls”) exiting the dissolver in an aqueous reprocessing facility, or in-tank bulk mass measurements of solutions in tanks. Although there is no standard defnition of PM data, it is generally collected very frequently (in-line), is ofen an indirect measurement of SNM, and is ofen collected by the operator for process control. Recent eforts to quantify the benefts of PM data are described, for example, in Burr et al. [1, 2]. Because PM can have several roles, it is necessary to consider the quantitative benefts of each possible role. For example, PM can have a “front-line” role in monitoring for indicators of facility misuse, such as a shif in nitric acid con- centration to direct excess Pu to a waste stream from a sep- arations area in an aqueous reprocessing facility [5]. Alter- natively, in the role this paper considers, PM can enable in-process inventory estimation by using empirical modeling and measurements of fows in and out of a separations area [4]. In a pyroreprocessing facility, several PM options are being studied, such as monitoring voltage and current in the electrorefner, which holds most of the in-process inventory [6–9]. Electrorefner voltage and current are among the mea- sured quantities that can, using a model such as the one in Zhang [10], predict the SNM inventory in the electrorefner in real time. Traditionally, nuclear material accounting (NMA) con- sists of relatively infrequent material balance closures (such as once per year), with the material balance (MB) defned as MB = begin +− end , where  is an inventory and  is a transfer. And, assuming that the MB has approximately a normal distribution (the assumption is justifed because many measurements enter an MB calculation, so the central limit theorem is in efect), the measurement error standard deviation of the MB,  MB , determines the probability to detect a specifed amount of SNM for a given false alarm probability. Terefore, for a single balance period,  MB is the main quantitative measure of safeguards efectiveness, with PM in