New Approaches in Process Monitoring for Fuel Cycle Facilities

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Melvyn Cook
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New Approaches in Process Monitoring for Fuel Cycle Facilities P R E S E

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1 New Approaches in Process Monitoring for Fuel Cycle Facilities P R E S E N T E D B Y Ben Cipiti & Nathan Shoman SAND C Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy s National Nuclear Security Administration under contract DE-NA

2 2 Overview We are examining how process monitoring and NDA measurements used with machine learning can reduce the reliance on DA for material accountancy and move international safeguards more toward unattended monitoring. IAEA has a strong interest in eliminating the need for an on-site laboratory in future facilities, and shipping samples is becoming more problematic. This work presents the approach and some modeling results that examine the concept.

3 3 Traditional Safeguards Approach IAT (HKED, laboratory analysis,~one sample per day) Inventory Change (HKED, ~20 per month) U Output (KED, laboratory analysis, ~one sample per day) Pu Output (KED, laboratory analysis, ~one sample per 5 days) Electromanometers for Bulk Mass Waste (NDA) Traditional approach requires a lot of samples HKED is the workhorse of materials accountancy to provide rapid analysis with uncertainties < 1%. DA is used for calibration IAEA uses various statistical techniques to minimize number of samples as much as possible.

4 4 Alternative Safeguards Approach IAT (Bulk Mass, Spectroscopy) Inventory Change (Spectroscopy) U Output (Bulk Mass, Spectroscopy) Pu Output (Bulk Mass, Spectroscopy) Electromanometers for Bulk Mass Waste (NDA) Alternative approach is more dependent on bulk balances Bulk mass balance will detect any direct material loss. On-line spectroscopy can measure changes in concentration. Effectively will detect any substitution diversion, can achieve 0.5-1% measurement uncertainties.

5 5 Alternative Approach The alternative approach focuses less on a Pu balance and more on IAEA s goals: Timely detection of diversion of declared nuclear material Timely detection of undeclared production or processing of nuclear material Requires taking a deeper look at various diversion scenarios Bulk mass measurements have low uncertainty; we know they can be installed as an unattended measurement, and can be used to detect any direct material loss. We also need to be able to detect substitution loss, which is where process monitoring plays a role. We don t necessarily need to be able to quantify Pu to low uncertainty as long as we can detect a signature of a substitution.

6 6 Pyro Initial Safeguards Approach Oxide Reduction Salt Voltammetry/Actinide Sensor Input SNF (KMP1) Sampling & DA/NDA Current/Voltage Monitoring ER Salt (KMP2) Sampling & DA U Product (KMP3) NDA or Melt Sampling & DA Metal Waste Form NDA C/S (similar to aqueous) U/TRU Drawdown Salt Voltammetry/Actinide Sensor Confirmatory Gamma/Neutron U/TRU Product (KMP4) Melt Sampling & DA Bulk Mass Throughout (not shown) FP Waste NDA (Gamma/Neutron)

7 7 Substitution Diversion Scenarios For pyroprocessing, adversary may remove ER salt and replace it with a surrogate (LiCl with UCl 3 ). Removal of Cm will alter a neutron measurement. Removal of fission products will alter a gamma spectra. For Pu quantification neutron and gamma measurements may have ~5% uncertainty, but we might be able to detect a loss of Cm or loss of fission products to <1%. An indication of substitution loss could be verified with DA. Adversary may remove U/TRU product and replace with U metal. A neutron measurement can detect this change. There is less of a need to quantify Pu loss; instead just provide detection of substitution.

8 8 Machine Learning The complication is that neutron and gamma emissions vary depending on fuel characteristics (IE, BU, CT), and reprocessing plants will process a mixture of SNF. Simply detecting a gross change in neutron emission or gamma peaks will not by itself indicate a substitution loss. The gross change must be correlated with other areas of the plant. For example, a drop-off in the neutron emission rate from a U/TRU product without a drop-off in neutron emission from the ER salt might indicate a problem. Machine Learning techniques are being examined to automate detection for such a system. A One-Class Support Vector Machine (OCSVM) is being used this is an unsupervised technique, chosen because it only needs to be trained using normal training data. (It will be very difficult to generate abnormal training data in an actual facility.)

9 9 Approach is Being Testing Using The SSPM

10 10 Coupling with GADRAS

11 11 Example Substitution Diversion Results Batch B (Diversion) Batch A (No Diversion) The SSPM tracks full elemental and isotopic compositions coupling with GADRAS allows us to simulate gamma spectra for a sample from a particular location. This plot shows the effect of diversion on a particular peak.

12 12 Substitution Diversion from U/TRU Product

13 13 Correlation Between ER Salt and U/TRU Product Indicator of Diversion

14 14 Machine Learning Algorithm Machine Learning helps us to automate the analysis of all this data: Bulk measurements and bulk balance NDA gamma and/or neutron measurements Possibly current, voltage, voltammetry, etc. An actinide balance can still be performed, but the overall uncertainty will be higher. Can still be used for book values, but the overall approach has much more reliance on other indicators.

15 15 Challenges The approach becomes dependent on a very thorough diversion scenario analysis. Need to have a good understanding of the facility isotopics as a function of time, since it can have a huge impact on what can be detected with NDA. There are many realities to consider with getting uniform and representative NDA measurements. Even if we can fully prove the approach, it will require IAEA buy-in. But, all could be worth it if we can eliminate the need for an on-site laboratory.

16 16 Conclusions The Machine Learning Approach described here can be considered as an alternative approach to provide similar levels of detection at much reduced cost to the IAEA. This approach would probably be difficult to retrofit onto existing facilities, and is more of a Safeguards by Design approach for new facilities. This type of approach may need to be considered in the case of pyroprocessing if there are difficulties achieving low measurement uncertainties (even for DA).

Validation Metrics. Kathryn Maupin. Laura Swiler. June 28, 2017

Validation Metrics. Kathryn Maupin. Laura Swiler. June 28, 2017 Validation Metrics Kathryn Maupin Laura Swiler June 28, 2017 Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC,