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Title: Multivariate analysis of gamma spectra to characterize used nuclear fuel

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 in this paper 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. Finally, this approach to automated fuelmore » characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.« less
Authors:
 [1] ;  [2] ;  [2]
  1. Univ. of Tennessee, Knoxville, TN (United States). Dept. of Nuclear Engineering
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Grant/Contract Number:
AC05-76RL01830
Type:
Accepted Manuscript
Journal Name:
Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment
Additional Journal Information:
Journal Volume: 850; Journal ID: ISSN 0168-9002
Publisher:
Elsevier
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Univ. of Tennessee, Knoxville, TN (United States)
Sponsoring Org:
USDOE Office of Nuclear Energy (NE), Fuel Cycle Technologies (NE-5)
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; Used nuclear fuel; Fuel characterization; Multivariate analysis; Gamma spectroscopy
OSTI Identifier:
1342225

Coble, Jamie, Orton, Christopher, and Schwantes, Jon. Multivariate analysis of gamma spectra to characterize used nuclear fuel. United States: N. p., Web. doi:10.1016/J.NIMA.2017.01.030.
Coble, Jamie, Orton, Christopher, & Schwantes, Jon. Multivariate analysis of gamma spectra to characterize used nuclear fuel. United States. doi:10.1016/J.NIMA.2017.01.030.
Coble, Jamie, Orton, Christopher, and Schwantes, Jon. 2017. "Multivariate analysis of gamma spectra to characterize used nuclear fuel". United States. doi:10.1016/J.NIMA.2017.01.030. https://www.osti.gov/servlets/purl/1342225.
@article{osti_1342225,
title = {Multivariate analysis of gamma spectra to characterize used nuclear fuel},
author = {Coble, Jamie and Orton, Christopher and Schwantes, Jon},
abstractNote = {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 in this paper 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. Finally, this approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.},
doi = {10.1016/J.NIMA.2017.01.030},
journal = {Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment},
number = ,
volume = 850,
place = {United States},
year = {2017},
month = {1}
}