CUA is a material control and accountability methodology that takes into account the system logic and statistical characteristics of a plant process through the formulation of closure equations. The study evaluated CUA methodology to meet performance oriented regulations. The criterion is defined as the detection of a material loss of two kilograms of SNM with 97.5% confidence. Specifically investigated were the timeliness of detection, the ability to localize material loss, process coverage, cost/benefits, and compatibility with other safeguards techniques such as diversion path analysis and data filtering. The feasibility of performance-oriented regulations is demonstrated. To fully use the system of closure equations, a procedure was developed to formally integrate the effect of both short-term and long-term closure equations into an overall systems criterion of performance. Both single and multiple diversion strategies are examined in order to show how the CUA method can protect against either strategy. Quantitative results show that combined closure equations improve the detection sensitivity to material loss, and that multiple diversions provide only diminishing returns

Ciramella, A. F.

Fushimi, F. C.

Rogers, D. R.

Seabaugh, P. W.

Woltermann, H. A.

UNT Digital Library

JtfJ.111 • ilS'S-,(oJO) I 4£A-Slit · ~1Vi"c C.,o r\l F- 1i I ~o1 - -- '~ IAEA International Symp o sium on Nuclear Material Safeguards October 2-6, 1978 Vienna, Austria IAEA-SM-231/80 ..----NOT ICE------, This report was prepared as an account of work sponsored by the United Sutes Government. Neither the United States nor the United Statu Deputment of Energy , nor any of their employees, noi any of their contractors, subcontracton, or their employees, makes any warranty, express or implied , or assumes any legal liability or responsibility for the accuracy, completeness or usefulness of any information, appuatus, product or process disclosed, or represents that its u1e would not infringe privately owned rights. An Application of the Controllable Unit Approach (CUA) to Analyzing Safeguards Measurement Systems ABSTRACT P. W. Seabaugh, D. R. Rogers, H. A. Woltermann , F. C. Fushimi, and A. F. Ciramella Mound Faci l ity * Miamisburg, Ohio 45342 The controllable unit approach (CUA) is a material control and accountability methodology that takes into account the system logic and statistical characteristics of a plant process through the formulation of closure equations. The methodology is adaptable to plant processes of varying degrees of design and operational complexity. No alteration or modification of a pro-cess is required to apply the methodology . Cost/benefits of re-finements in, or changes to , the proposed measurement system are obtained as incremental cost . To eucourage improved safeguards 80.0.nun t abi l ity, the U. S. Nuclear Regulatory Commission (NRC) has been considering the use of performance oriente d regulations to supplement those currently used. The study, sponsored by NRC/Office of Standards Develop-ment, evaluated CUA methodology to meet performance oriented regulations. For this study, the criterion is defined as the detection of a material loss of two kilograms of SNM with 97.5% confidence. Specifically investigated were: the timeliness of detection, the ability to localize material loss , process cover-age, cost/benefits, and compatibiltiy wi th other safeguards techniques such as diversion path analysis and data filtering. The feasibility of performance-oriented regulations is demon-strated. cv V DISTruBUTI~ or '1'IlIS nocUMENT IS~-*Mound Facility is operated by Monsanto Research Corporation for the U.S. Department of Energy under Contract No. EY - 76 - C-04 - 0053. DISCLAIMER This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency Thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. DISCLAIMER Portions of this document may be illegible in electronic image products. Images are produced from the best available original document. To fully use the system of closure equations, a procedure was developed to formally integrate the effect of both short-term and long-term closure equations into an overall systems criterion of performance. Both single and multiple diversion strategies are examined in order to show how the CUA method can protect against either strategy. Quantitative results show that combined closure equations improve the detection sensitivity to material loss, and that multiple diversions provide only dimin-ishing returns. INTRODUCTION The controllable unit approach [l] (CUA) is a material con-trol and accountability methodology that takes into account the system logic and statistical characteristics of a plant process through the formulation of closure equations. These material balance equations model inputs, outputs, inventories, holdups and possible losses or diversions. They depend upon fixed mea-surement points in the process. The methodology is adaptable to plant processes of varying degrees of design and operational complexity, exemplary of present and future facilities. Applica-tion of the method does not require alteration or modification of an applicant's process. Because base-case calculations are a natural first step in the evaluation scheme, the cost/benefits of refinements in, or changes to, the proposed measurement system for purely safeguards purposes are easily obtained as incremental cost. To encourage improved safeguards accountability, the Nuclear Regulatory Commission (NRC) has been considering the use of per-formance oriented regulations to supplement those currently used. In the area of material control and accountability, for instance, one such performance oriented criterion could be the assurance that a given loss of material be detect...ed within a specit'ic time frame. The present sLudy, sponsored by NRC/Office of 8tandards Development, evaluated CUA methodology to meet performance ori-ented regulations. For purposes of this study, the criterion is defined as the detection of a material loss of two kilograms of SNM with 97.5% confidence. Specifically invAstigated were: the timeliness of detection, the ability to localize material loss, process coverage, cost/benefits, and compatibility with other safeguards techniques such as DPA (diversion path analysis) and data filtering. In addition, this study was undertaken as a first step in providing the NRC with the methodolo gy and in-formation to: 1. Support development of safeguards re g ulations that emphasize performance requirements, 2. Assess license applications, and 3. Inspect processes. Like many successful management systems, the CUA methodology iteratively compares the actual situation to the need. In this . , study, the performance of the proposed or existing measurement system is compared to the material control criterion. Then, additions or refinements to the measurement system or process are iteratively compared to the criterion until it has been met. This systematic comparison can efficiently ensure that a com-plicated process measurement system will perform to the level as specified by the need. Furthermore, because the existing or proposed system is mathematically modeled with the CUA method, modifications to the process for any reason can be tested quickly for their effect on material control before implementation. A summary flow diagram of the CUA methodology is shown in Figure 1. PROCESS MODEL I A process model [l) was developed to provide a severe test of controllable unit meth~dology. The process model was based primarily· on a commercial high:throughput (200 MT) mixed-oxide fuel fabrication plant similar to that proposed by Westinghouse [2) and further described by Science Applications Inc. [31. Modeling techniques were developed to include as much realism into the model process as possible. Simultaneous operation modes, randomly varied process streams, and flow, time, and event-dependent hold-up functions are examples of the realistic features of the process model. EFFECTIVENESS OF MULTIPLE CLOSURE EQUATIONS AND VULNERABILITY ASSESSMENT To fully use the system of closure equations generated to describe and control the process, a procedure was developed to formally integrate the effect of both short-term and long-term closure equations into an overall systems criterion of perform-ance. The objective is to maximize the detection sensitivity within a given detection time period. In this assessment of the value of using multiple closure equations, the following situa-tions were accounted for: 1. The combination of independent non-overlapping closure equations to obtain an overall performance criterion; ~. Possible overlap between several closure equations; 3. Possible correlated variables between different closure equations. Each closure equation rAquires an associated hypothesis test procedure. For explanatory purposes assume a closure equa-tion of the simple form Ml = xl - x2, with the dJ8trihutions of errors in measurements X1 and X2 normal with zero means and variances 0 1 2 and 0 2 2 respectively. Then M1 is N(O, 0 12 + 0 2 2 ) assuming no losses in this control area. Next form a one-sided hypothesis test: rt0: Null Hypothesis (No Diversion) M1 < L 1 H1: Alternative Hypothesis (Diversion has occurred at the Q1 level) M1 = Q1 A threshold C1 is obtained to effect the hypothesis test. H0 is accepted if M1 < C1 , and rejected otherwise. Let M1 be the measured CEI (closure equation imbalance), · whereas CEI 1 is the actual material diverted; then Type-1 (a 1) and Type-2 (B 1) errors are defined as: P(M1 > C1/CEI1 = L1) = a1 P(M1 > C1/CEii = Q1) = (1 - B1); where a 1 is the probability given no diversion has occurred that the measured CEI (M 1) exceeds the threshold C 1 , and is sometimes called the "false-alarm" error probability; and (1 - B1) is the probability given a diversion at the Q1 level that the measured CEI (M1) exceeds the threshold C1 , and is referred to as the "power" of the test. The probability that a diversion has oc-curred but the hypothesis test has failed to detect it is given by Bi. Assume for each control region that a, L, Q, and measure-ment error variances are given, so that the error B and the re-quired threshold C can be obtained from the two equations pre-sented above. It will be assumed for the analyses that each control region hypothesis test is designed independently, and thus all of these parameters are determined before computing overall error probabilities. For the case of n non-overlapping independent closure equa-tions (situation one), the overall probability that a diversion at a given level has occurred, but has not been detP.ctP.d by any of the closure equations, can be obtained as n Overall Probability = n Prob of nondetection i=l = nondetection in the ith closure equation The product of the Bi follows from the observation that all n potential independent diversions must go undetected for overall nondetection. A schematic for overlapping closure equations is shown in Figure 2. If a diversion D2 should occur in area 2, it could be detected by either closure equation. Yet closure equations I and II may or may not be correlated deµer1ding upon their specific form. For example, if they are of the form: 1V!odel A Closure Equation I II .. I .. it is clear that they are uncorrelated, in that they do not have any measurement values in common. But the.closure equations in Figure 2 might also appear as: Model B Closure Equation . I. II in which case M1 and M2 are clearly correlated when they share measurements X2 and X3 in common. In such cases, if the under-lying measurement ·errors are Gaussian the resulting closure equation imbalances Mj can be described by multivariate Gaussian distributions. Probability calculations are considerably more complicated, but can be evaluated numerically. On the other hand, whether the closure equations are cor-related or not, the effect of overlap can be handled. simply. In Figure 2, assume diversions D1 , D2 and D3 have occurred in areas 1., 2, and 3 respectively. Then since it is assumed that all measurement errors have zero mean Gaussian distributions, the distributions for M1 and M2 have means of (D 1 + D2 ) and (D2 + D3) respectively·. Thus the controlling effect of the overlap shows up in a relatively simple way in the imbalances, and a diversion in area 2 shows up in the means of both M1 and M2 . Because this diversion D2 occurs in both means, it is much more liable to be detected. As an example consider the determination of the overall probability for Model A given diversions D1 , D2, and Da in areas 1, 2, and 3 respectively shown in Figure 2. A ~6rst case analysis reqµires examination of all possible partitions of Q into D1 , D2, and D3 where and D 2 + D 3 > Qi . If eitner of these inequalities is not valid, then we will con-sider that no diversion has occurred in the corresponding region at all, and~a much simpler problem results .. With these assumptions the maximization of the probability of nondetection is given by D+D +D=Q which rewritten in·terms of the usual normal distribution nota-tion using normalized variables becomes z = ~(C1-(Q-Q2)) ~(C2-Q2) crI crII where for brevity crI 2 = cr1 2 + CT3 2 ~{ = cr2 2 + a~ 2 A numeric example is selected using data from the first stage of the mixed oxide process, controlling this process from the initial weighing of incoming nuclear material until material enters the M0 2 subblend silo. Only short term closure equations [l] S-1, S-3, S-4, S-5, S-9, and S-10 are considered. For the specific data, see Table 4.8 and Table I.l in Reference 1. These results are shown in Figure 3 where it is clear that multiple diversions provide only diminishing returns for the potential divertor even without the increased risk and logistic difficulty taken into account. RESULTS Comparative results for CUA [l] and MUF/LEMUF (4) as applied to the mixed-oxide process are given which show that CUA provides an improvement factor of 3 for detection sensitivity and a greater improvement for timeliness of detection. Results to date indicate that the methodology will be highly effective in timely detection of SNM material loss and in materi-al control. Specifically through the CUA methodology account-ability and process data have been used effectively to meet the following principle objectives of this study. • Demonstration that the detection capability for material loss of SNM in the mixed-oxide process is 2 kg at a de-tection probability of 97.5% with a false alarm rate of three per year. This applies,to either a single-event material loss or to random accumulative material ·1osses up to a two-month period. • Identification of the area and approximate time (generally within a shift) of the suspected diversions. These results were accomplished without modification of the plant process or operations from the original model. Furthermore, the application of this concept provides the user with the added benefit of estimating the cost and effectiveness of additional measurements or measurement points anywhere in the process. Cur-rently, CUA is being applied to an existing high-throughput op-erating plant. REFERENCES 1. SEABAUGH, P. W., ROGERS, D. R., WOLTERMANN, H. A., FUSHIMI, F. C., CIRAMELLA, A. F., The Controllable Unit Approach to Material Control: Application to a High Through-put Mixed-Oxide Process, Mound Facility Rep. MLM-NUREG-2532 (1978). 2. "Westinghouse License Application for the Recycle Fuels Plant at Anderson, S. C.," USAEC Docket No. 70-1432 (July 1973). 3. ''An Evaluatio~ of Real-Time Material Control and Account-ability in a Model M02 Fuel Plant," Science Applications, Inc .. , SAI-75-648-lJ (September 15, 1975). 4. GLANCY, J. E., "Safeguards implementation practices for a mixed-oxide recycle fuel fabrication facility," Science Applica-tions, lnc., La Jolla, California. BNL-21409, Brookhaven National Laboratory, Upton, N.Y. (May 1976), 109 pp. - YES @)CALCULATE SYSTEM VARIABILITY CD MODEL PROCESS @EXAMINE MEASUREMENT INFORMATION AND FORMULATE CLOSURE EQUATIONS ©IDENTIFY DOMINATING ERRORS (J) DEFINE CQNTRQ!J-ABLE UNITS ANALYSIS OF DIVERSION PATHS IMPLEMENT SYSTEM YES @MODEL SPECIFIC SYSTEM REFINEMENTS YES FIGURE 1 ~ CUA methodology is a systematic approach to material control. MODIFY PROCESS .. ""' ~-CONTROL REGION I ... -4 CONTROL REGION 11 x, • • AREA 1 m, l FIG~RE 2 - A schematic for the overlapping but uncorrelated closure equation model shows the control regions and diversion area. 1.3 TOTAL MATEHIAL LOSS lkyl AT 97.5''., 1rv1:1 0.8 0 1 2 3 4 5 6 NUMBER OF DIVERSIONS FIGURE 3 - Computations show that multiple diversions reach a pbint of diminishing returns. FIGURE CAPTIONS Figure 1. CUA methodology is a systematic approach to material control. Figure 2. A schemati~ for the overlapp~ng but uncorrelated closure equation model shows the control regions and diversion areas. Figure 3. Computations show that multiple diversions reach a point of diminishing returns. 

Application of the Controllable Unit Approach (CUA) to analyzing safeguards measurement systems

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Application of the Controllable Unit Approach (CUA) to analyzing safeguards measurement systems

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