Automated Characterization of Spent Fuel through the Multi-Isotope Process (MIP) Monitor

Technical Report · Tue Jul 31 00:00:00 EDT 2012

DOI:https://doi.org/10.2172/1186985· OSTI ID:1186985

Coble, Jamie B. ^[1]; Orton, Christopher R. ^[1]; Schwantes, Jon M. ^[1]

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1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

This research developed an algorithm for characterizing spent nuclear fuel (SNF) samples based on simulated gamma spectra. The gamma spectra for a variety of light water reactor fuels typical of those found in the United States were simulated. Fuel nuclide concentrations were simulated in ORIGEN-ARP for 1296 fuel samples with a variety of reactor designs, initial enrichments, burn ups, and cooling times. The results of the ORIGEN-ARP simulation were then input to SYNTH to simulate the gamma spectrum for each sample. These spectra were evaluated with partial least squares (PLS)-based multivariate analysis methods to characterize the fuel according to reactor type (pressurized or boiling water reactor), enrichment, burn up, and cooling time. Characterizing some of the features in series by using previously estimated features in the prediction greatly improves the performance. By first classifying the spent fuel reactor type and then using type-specific models, the prediction error for enrichment, burn up, and cooling time improved by a factor of two to four. For some features, the prediction was further improved by including additional information, such as including the predicted burn up in the estimation of cooling time. The optimal prediction flow was determined based on the simulated data. A PLS discriminate analysis model was developed which perfectly classified SNF reactor type. Burn up was predicted within 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment within approximately 2% RMSPE.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1186985
• Report Number(s): PNNL-21599; AF5835000
• Country of Publication: United States
• Language: English