Inspector interfaces to facilitate subjective reasoning about quantities in trends

Howell, John; Bevan, Geraint; Burr, Tom

doi:10.1016/j.compchemeng.2012.07.010

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…International safeguards has the same challenge as domestic safeguards, but the possibility of data falsification by the operator to conceal SNM diversion (a type of loss) is an added complication. Howell et al (2013) present a semi-quantitative approach for domestic safeguards to simultaneously monitor for changing sensor health and SNM loss. The approach assumes that some sensor redundancy is available, and that neighboring sensors will not simultaneously malfunction.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Updating of Material Balances Covariance Matrices Using Training Data

Burr

Hamada

2020

IJPHM

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The main quantitative measure of nuclear safeguards effectiveness is nuclear material accounting (NMA), which consists of sequences of measured material balances that should be close to zero if there is no loss of special nuclear material such as Pu. NMA is essentially “accounting with measurement errors,” which arise from good, but imperfect, measurements. The covariance matrix MB of a sequence of material balances is the key quantity that determines the probability to detect loss. There is a recent push to include process monitoring (PM) data along with material balances from NMA in new schemes to monitor for material loss. PM data includes near-real-time measurements by the operator to monitor and control process operations. One concern regarding PM data is the need to estimate normal behavior of PM residuals, which requires a training period prior to ongoing testing for material loss. Assuming that a training period is used for PM data prior to its use in statistical testing for loss, that same training period could also be used for improving the estimate of MB that is used in NMA. We consider updating MB using training data with a Bayesian approach. A simulation study assesses the improvement gained with larger amounts of training data.

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Section: Introductionmentioning

confidence: 99%

Bayesian Updating of Material Balances Covariance Matrices Using Training Data

Burr

Hamada

2020

IJPHM

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“…However, estimation of the residual vector's pdf requires a combination of modeling and data analysis as illustrated by example in [1]. The approach in [1] does not distinguish sensor faults from SNM loss, but assuming no more than one sensor malfunctions within small time windows, Howell et al [38] and Hines et al [39] illustrate options that are also based on monitoring…”

Section: Remarkmentioning

confidence: 99%

Hybrid Statistical Testing for Nuclear Material Accounting Data and/or Process Monitoring Data in Nuclear Safeguards

et al. 2015

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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.

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Additive requirement ratio prediction using trend distribution features for hydrometallurgical purification processes

Zhang

Yang

et al. 2016

Control Engineering Practice

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Inspector interfaces to facilitate subjective reasoning about quantities in trends

Cited by 4 publications

References 12 publications

Bayesian Updating of Material Balances Covariance Matrices Using Training Data

Bayesian Updating of Material Balances Covariance Matrices Using Training Data

Hybrid Statistical Testing for Nuclear Material Accounting Data and/or Process Monitoring Data in Nuclear Safeguards

Additive requirement ratio prediction using trend distribution features for hydrometallurgical purification processes

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