Information-Driven Inspections

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1 Information-Driven Inspections IAEA-CN-184/44 Presentation for the 2010 IAEA Symposium on International Safeguards Vienna, Austria 1 5 November 2010 Michael Whitaker, ORNL Mark Laughter, ORNL Dunbar Lockwood, DOE/NNSA

2 Overview Model Safeguards Approach for Gas Centrifuge Enrichment Plants (GCEPs) Next Generation Safeguards for GCEPs Information-Driven Inspections Definition Example: Information-Driven Inspection Schedules Comparison with Remote Inspections Using Unattended Monitoring Data Information Sensitivity Conclusion 2 Managed by UT-Battelle

3 Model Safeguards Approach for GCEPs Hexapartite Safeguards Project (HSP) Based Safeguards Approach (early 1980s) Annual and interim physical inventory verification (PIV) with destructive and non-destructive assay (DA and NDA) Limited-frequency, unannounced access (LFUA) with visual observation and NDA (header pipe enrichment measurements [CHEM]) Design information verification (DIV) Supplemented with containment and surveillance measures Post-HSP Continuous enrichment measurement (CEMO) of cascade product header pipes Environmental sampling Source: J. Cooley, et al., Model safeguards approach and innovative techniques implemented by the IAEA at gas centrifuge enrichment plants, 2007 INMM Annual Meeting, Tucson, AZ, July Managed by UT-Battelle

4 Model Safeguards Approach for GCEPs Additional Safeguards Measures Short-notice random inspections (SNRIs) w/ mailbox declaration system Header pipe flow and enrichment monitoring Load cell monitoring (with proper authentication) Laser identification of UF 6 cylinders (L2IS) coupled with video surveillance Integrated safeguards (with Additional Protocol) Complementary access and open-source information analysis Source: J. Cooley, et al., Model safeguards approach and innovative techniques implemented by the IAEA at gas centrifuge enrichment plants, 2007 INMM Annual Meeting, Tucson, AZ, July Managed by UT-Battelle

5 Next Generation Safeguards for GCEPs Safeguards-by-Design Automated cylinder identification and tracking Universal ID labeling, RF tagging Automated cylinder NDA (cylinder portal monitor) Remote, continuous attribute monitoring Authenticated accountability scales w/ video surveillance Continuous process load cell monitoring Continuous enrichment and flow monitoring of unit product header pipes Integrated surveillance (event-triggered) Centralized data acquisition and analysis system (GCEP Attribute Monitoring System [GAMS]) Unannounced, information-driven inspections Optimized DIV (laser-based, automated) Onsite DA and environmental sampling (ES) analysis/screening 5 Managed by UT-Battelle

6 Information-Driven Inspections Informal definition: Using unattended monitoring data to optimize inspection schedules and activities Not using unattended monitoring data to draw safeguards conclusions A realization of Information-Driven Safeguards Continuing to make inspections more effective and efficient: Inspection evolution: Scheduled inspections SNRIs Information-Driven inspections Fewer inspections Maintaining the deterrent effect of inspector presence Meeting timeliness goals Random from operator s perspective 6 Managed by UT-Battelle

7 Example #1 Time of Day Real data from an accountancy scale in an operating facility* 7 Managed by UT-Battelle * M. Laughter et al., Interpretation of field test data from UF 6 cylinder accountancy scales, 2010 INMM Annual Meeting, Baltimore, United States, July 2010.

8 Example #2 Day of Week Real data from an accountancy scale in an operating facility* 8 Managed by UT-Battelle * M. Laughter et al., Interpretation of field test data from UF 6 cylinder accountancy scales, 2010 INMM Annual Meeting, Baltimore, United States, July 2010.

9 Information-Driven Inspection Schedule A refinement of scheduled and randomized: SNRI+ NOT an indicator of off-normal activity An inspection would take place eventually anyway if no threshold is reached Scheduled Inspections Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec. Achieves safeguards timeliness goals Predictable May be inefficient for IAEA 9 Managed by UT-Battelle

10 Information-Driven Inspection Schedule A refinement of scheduled and randomized: SNRI+ NOT an indicator of off-normal activity An inspection would take place eventually anyway if no threshold is reached Short-Notice Random Inspections Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec. Effective deterrent Fewer inspections = more efficient for IAEA May not make use of all available data 10 Managed by UT-Battelle

11 Information-Driven Inspection Schedule A refinement of scheduled and randomized: SNRI+ NOT an indicator of off-normal activity An inspection would take place eventually anyway if no threshold is reached Information-Driven Inspections Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec. 11 Managed by UT-Battelle Even fewer inspections to meet timeliness goals Still Random from the operator s point-of-view Effective deterrent, more efficient use of inspectors

12 Comparison to Remote Inspections Remote Safeguards Inspections transferring routine inspection work to IAEA HQ to make better use of time in the field* Neither concept eliminates inspector presence in the field Both are complemented by unannounced inspections, complementary access, novel technologies, etc. Primary difference: potential to phase information-driven inspections on top of existing randomized scheme over time, whereas approach with remote inspections must be fully formed for the portion of activities being replaced Technical details of implementation would be very similar Remote transmission, information protection, analysis tools, etc. Any unattended data source used for remote inspections could also be used for an information-driven approach 12 Managed by UT-Battelle * M. Zendel and N. Khlebnikov, Optimizing and joining future safeguards efforts by remote inspections, 2 nd Japan-IAEA Workshop on Advanced Safeguards Technology for the Future Nuclear Fuel Cycle, Tokai-mura, Japan, November 2009.

13 Unattended Monitoring: 3 Options 1. Remote Transmission and Remote Analysis 2. Onsite Analysis: Single-system Go/No-Go 3. Onsite Analysis: Aggregate Go/No-Go or Risk Score Variables: Information Sensitivity Analysis Automation Note: also applicable to remote inspections 13 Managed by UT-Battelle

14 Option #1 Remote Transmission / Remote Analysis 1. Transmission of all transmissible unattended monitoring data to IAEA HQ 2. Manual or software analysis of data at HQ to determine optimal inspection schedule Load Cells Facility IAEA HQ Cylinder Tracking CEMO/ FEMO Data Aggregator Data Analysis Inspection Schedule Other SG Systems 14 Managed by UT-Battelle

15 Option #2 Single-system Go/No-Go 1. Each safeguards system generates its own go/no-go signal (similar to CEMO, except thresholds can be milder) 2. All go/no-go signals transmitted to IAEA HQ 3. Set of signals viewed as a whole to determine optimal inspection schedule Cylinder Tracking CEMO/ FEMO Load Cells Analysis Facility Aggregator Facility Status IAEA HQ Inspection Schedule Other SG Systems 15 Managed by UT-Battelle

16 Option #3 Aggregate Go/No-Go 1. All unattended monitoring systems deliver data to onsite aggregator for automated analysis 2. Aggregator/analyzer generates whole-plant go/no-go or risk score for transmission to HQ 3. Go/no-go or risk score observed over time to determine optimal inspection schedule Load Cells Facility IAEA HQ Cylinder Tracking CEMO/ FEMO Other SG Systems Data Aggregator/ Analyzer Facility Status Inspection Schedule 16 Managed by UT-Battelle

17 Information Sensitivity Where to locate the information barrier for different sources and types of data Not available, at station, on-site, or inspectorate HQ Raw data vs. filtered data Small-scale field testing required to determine what can be derived through analysis and display at various locations Different Levels of Information Protection 1. Publically releasable or inherently obvious 2. Not public but made available to the IAEA (safeguards-relevant) 3. Restricted even from the IAEA 17 Managed by UT-Battelle

18 Conclusions Information-driven inspections using unattended monitoring data are the logical evolution of inspection planning Information-driven inspections are an intermediate, phase-able step towards remote inspections (as applicable) Continuous monitoring systems can be used for information-driven inspections while meeting operator requirements and reducing operator burden: fewer, better inspections Any such system can be designed to effectively protect sensitive information 18 Managed by UT-Battelle

19 19 Managed by UT-Battelle Thank You

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