Proof of concept simulations of the Multi-Isotope Process monitor: An online, nondestructive, near-real-time safeguards monitor for nuclear fuel reprocessing facilities

Orton, Christopher R.; Fraga, Carlos G.; Christensen, Richard N.; Schwantes, Jon M.

doi:10.1016/j.nima.2010.10.024

Cited by 30 publications

(11 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Making detection of covert diversion easier and more definitive would help overcome the single biggest obstacle to closing the fuel cycle and minimizing the volume of high level wastes that need to be isolated and stored in waste repositories. The example considered here is from a recent paper by Orton et al (2011), which focuses on the monitoring of process variables solely within a declared facility. Not considered here is the more challenging problem of assessing the processing intent of hidden or denied facilities based on limited, discontinuous external observations or indicators.…”

Section: Example 5: Fuel Reprocessing Facility Misusementioning

confidence: 99%

“…Widely used and accepted reprocessing methods like PUREX rely on solvent extraction between two immiscible phases (aqueous and organic) to achieve separation between the elemental constituents of the dissolved spent fuel elements (Godfrey et al, 2000). In the situation described by Orton et al (2011), the available data include, in addition to traditional NMA and PM, gamma emission spectra of the aqueous and organic phases in the reprocessing plant as well. Of particular importance in the NMA is the history of the spent fuel inventory.…”

Section: Example 5: Fuel Reprocessing Facility Misusementioning

confidence: 99%

See 1 more Smart Citation

Integration of facility modeling capabilities for nuclear nonproliferation analysis

García¹,

Burr²,

Coles³

et al. 2012

Progress in Nuclear Energy

View full text Add to dashboard Cite

Developing automated methods for data collection and analysis that can facilitate nuclear nonproliferation assessment is an important research area with significant consequences for the effective global deployment of nuclear energy. Facility modeling that can integrate and interpret observations collected from monitored facilities in order to ascertain their functional details will be a critical element of these methods. Although improvements are continually sought, existing facility modeling tools can characterize all aspects of reactor operations and the majority of nuclear fuel cycle processing steps, and include algorithms for data processing and interpretation. Assessing nonproliferation status is challenging because observations can come from many sources, including local and remote sensors that monitor facility operations, as well as open sources that provide specific business information about the monitored facilities, and can be of many different types. Although many current facility models are capable of analyzing large amounts of information, they have not been integrated in an analyst-friendly manner. This paper addresses some of these facility modeling capabilities and illustrates how they could be integrated and utilized for nonproliferation analysis. The inverse problem of inferring facility conditions based on collected observations is described, along with a proposed architecture and computer framework for utilizing facility modeling tools. After considering a representative sampling of key facility modeling capabilities, the proposed integration framework is illustrated with several examples. Highlights: We consider integration of facility modeling into a tool for proliferation analysis.  We describe the inverse problem of inferring the facility based on observables.  We propose architecture for integrating facility models into a suitable framework.  We illustrate the proposed integration framework with several examples.

show abstract

Section: Example 5: Fuel Reprocessing Facility Misusementioning

confidence: 99%

Section: Example 5: Fuel Reprocessing Facility Misusementioning

confidence: 99%

Integration of facility modeling capabilities for nuclear nonproliferation analysis

García¹,

Burr²,

Coles³

et al. 2012

Progress in Nuclear Energy

View full text Add to dashboard Cite

show abstract

“…Multivariate analysis methods common in chemometrics, such as principal component analysis (PCA) and partial least squares regression (PLS), act as pattern recognition techniques, which can detect small deviations from the expected, nominal condition. In past research, a database of training data covering "normal" conditions was used to train these multivariate methods (Orton et al 2011(Orton et al , 2012. This approach can identify gross changes in process parameters, but small deviations may be obfuscated by the range of training data.…”

Section: Task 2: Enhanced Multivariate Techniques and Approachesmentioning

confidence: 99%

The Multi-Isotope Process (MIP) Monitor Project: FY12 Progress and Accomplishments

Coble¹,

Orton²,

Jordan³

et al. 2012

Self Cite

View full text Add to dashboard Cite

“…In past research, a database of training spectra covering normal operating conditions across a variety of used nuclear fuel types was used to train these multivariate methods [1,2]. This approach can identify gross changes in process parameters, but small deviations may be obfuscated by the large number of conditions lumped into a single model.…”

Section: Introductionmentioning

confidence: 99%

“…Multivariate analysis of gamma spectra is used to automatically and autonomously process the spectra from these detectors in real time, continuously monitoring for indications of changing stream conditions. Initial simulations and bench-scale experiments reported in prior work have documented the performance and potential of the MIP Monitor [1,2]. This approach can identify gross changes in process parameters, but small deviations may be obfuscated by the range of normal conditions, resulting in part from the variety of used nuclear fuel that can be reprocessed in a given facility.…”

Section: Introductionmentioning

confidence: 99%

Multivariate analysis of gamma spectra to characterize used nuclear fuel

Coble

Orton

Schwantes

2017

Nuclear Instruments and Methods in Physics Research Section A:

View full text Add to dashboard Cite

The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gamma spectra were used to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. This approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to

show abstract

Proof of concept simulations of the Multi-Isotope Process monitor: An online, nondestructive, near-real-time safeguards monitor for nuclear fuel reprocessing facilities

Cited by 30 publications

References 12 publications

Integration of facility modeling capabilities for nuclear nonproliferation analysis

Integration of facility modeling capabilities for nuclear nonproliferation analysis

The Multi-Isotope Process (MIP) Monitor Project: FY12 Progress and Accomplishments

Multivariate analysis of gamma spectra to characterize used nuclear fuel

Contact Info

Product

Resources

About