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Development of a data-mining methodology for spent nuclear fuel forensics

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Abstract

The purpose of this study is to categorize the type of spent nuclear fuels using simulation data-based classification methods. Considering the practical conditions making the full analysis of radioactive nuclides difficult, the classification methods were designed to be robust to noise and missing information. The strength and weakness of three classifiers, linear discriminant analysis, quadratic discriminant analysis and support vector classification were compared, which is developed by the history information such as burnup, enrichment, and cooling type generated from ORIGEN-ARP upon fuel assembly types. Auto-Associative Kernel Regression improved outlier management as a pre-processing technique.

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References

  1. Kim DY (2015) iBEST: a program for burnup history estimation of spent fuels based on ORIGEN-S. Nucl Eng Technol 47:596–607

    Article  CAS  Google Scholar 

  2. Scott MR (2005) Nuclear forensics: attributing the source of spent fuel used in an RDD Event. A&M University, Texas

    Book  Google Scholar 

  3. Jones AE (2014) Classification of Spent Fuel for nuclear forensics. Anal Chem 86:5399–5405

    Article  CAS  Google Scholar 

  4. Robel M. (2009) Nuclear forensic inferences using iterative multidimensional statistics. institute of nuclear materials management 50th annual meeting

  5. Parker DR, Mills RW, Little P, Hassall CM (2002) BNFL Res Technol 1–115

  6. OECD/NEA SFCOMPO. http://www.oecd-nea.org/sfcompo/Ver.2/Eng/

  7. Nicolaou G (2006) Determination of the origin of unknown irradiated nuclear fuel. J Environ Radioact 86:313–318

    Article  CAS  Google Scholar 

  8. Nicolaou G (2009) Identification of unknown irradiated nuclear fuel through its fission product content. J Radioanal Chem 279:503–508

    Article  CAS  Google Scholar 

  9. Wallenius M (2001) Origin determination of reactor produced plutonium by mass spectrometric techniques: application to nuclear forensic science and safeguards. Faculty of Science of the University of Helsinki, Helsinki

    Google Scholar 

  10. Han J, Kamber M (2011) Data mining: concepts and techniques. Morgan Kaufmann, Burlington

    Google Scholar 

  11. Rapidminer internet homepage, https://rapidminer.com/

  12. ** KH (2017) Development of nuclear forensic models using kernel regression. Prog Nucl Energy 94:55–63

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the Nuclear Safety Research Program through the Korea Foundation of Nuclear Safety (KoFONS), granted financial resources from the Nuclear Safety and Security Commission (NSSC), Republic of Korea (No. 1305016), and the “Human Resources Program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), and granted financial resources from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 20164030200990).

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Authors and Affiliations

  1. Kyunghee University, Deogyeong-daero, Giheung-gu, Yongin-Si, Gyeonggi-do, 17104, Korea

    Sanghwa Lee, Kyungho ** & Gyunyoung Heo

  2. Korea Institute of Nuclear Nonproliferation and Control, Yuseong-daero, Yuseong-gu, Daejeon-Si, 34054, Korea

    Jaekwang Kim

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  1. Sanghwa Lee
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  2. Kyungho **
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  3. Jaekwang Kim
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  4. Gyunyoung Heo
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Correspondence to Gyunyoung Heo.

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Lee, S., **, K., Kim, J. et al. Development of a data-mining methodology for spent nuclear fuel forensics. J Radioanal Nucl Chem 312, 495–505 (2017). https://doi.org/10.1007/s10967-017-5250-x

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  • DOI: https://doi.org/10.1007/s10967-017-5250-x

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