Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Dynamic data validation and reconciliation for improving the detection of sodium leakage in a sodium-cooled fast reactor

  • Received : 2022.10.14
  • Accepted : 2023.02.09
  • Published : 2023.04.25
Download PDF
⟨ Previous Next ⟩

Abstract

Since the leakage of sodium in an SFR (sodium-cooled fast reactor) causes an explosion upon reaction with air and water, sodium leakages represent an important safety issue. In this study, a novel technique for improving the reliability of sodium leakage detection applying DDVR (dynamic data validation and reconciliation) is proposed and verified to resolve this technical issue. DDVR is an approach that aims to improve the accuracy of a target system in a dynamic state by minimizing random errors, such as from the uncertainty of instruments and the surrounding environment, and by eliminating gross errors, such as instrument failure, miscalibration, or aging, using the spatial redundancy of measurements in a physical model and the reliability information of the instruments. DDVR also makes it possible to estimate the state of unmeasured points. To validate this approach for supporting sodium leakage detection, this study applies experimental data from a sodium leakage detection experiment performed by the Korea Atomic Energy Research Institute. The validation results show that the reliability of sodium leakage detection is improved by cooperation between DDVR and hardware measurements. Based on these findings, technology integrating software and hardware approaches is suggested to improve the reliability of sodium leakage detection by presenting the expected true state of the system.

Keywords

Acknowledgement

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea Government (No. 2021M2E2A2081063, 2022M2E2A2079840).

References

  1. K. Aoto, P. Dufour, Y. Hongyi, J.P. Glatz, Y.I. Kim, Y. Ashurko, R. Hill, N. Uto, A summary of sodium-cooled fast reactor development, Prog. Nucl. Energy 77 (2014) 247-265. https://doi.org/10.1016/j.pnucene.2014.05.008
  2. J. Yoo, J. Chang, J.Y. Lim, J.S. Cheon, T.H. Lee, S.K. Kim, H.K. Joo, Overall system description and safety characteristics of prototype Gen IV sodium cooled fast reactor in Korea, Nucl. Eng. Technol. 48 (2016) 1059-1070. https://doi.org/10.1016/j.net.2016.08.004
  3. G. Manzini, F. Parozzi, F. Polidoro, Fast reactor nuclear power plant safety a review of sodium release fire scenarios, in: 65th International Seminar on Fire and Explosion, April, 2010.
  4. S.W. Seo, S.G. Park, J.Y. Lee, S. J Kim, Safety issues of sodium-water reaction in Gen-IV SFR, in: Transaction of the Korean Nuclear Society Autumn Meeting, Gyeongju, Korea, October 26-27, 2017.
  5. T.J. Kim, V.S. Yugay, J.Y. Jeong, J.M. Kim, B.H. Kim, T.H. Lee, D. Hahn, Acoustic leak detection technology for water/steam small leaks and microleaks into sodium to protect an SFR steam generator, Nucl. Technol. 170 (2) (2010) 360-369. https://doi.org/10.13182/NT10-A9489
  6. S. Armiroli, R. Cotillard, G. Laffont, M. Girard, D. Cambet, J.P. Jeannot, S. Lusso, Optical fiber distributed sensing based on Rayleigh scatter frequency domain reflectometry for Sodium leakage detection within SFR reactors, in: EPJ Web of Conferences vol. 170, EDP Sciences, 2018, 03001.
  7. H. Yamamoto, Y. Sato, E. Ibe, A. Suzuoki, Development of fluctuation monitor type sodium ionization detector, J. Nucl. Sci. Technol. 23 (7) (1986) 573-581. https://doi.org/10.1080/18811248.1986.9735025
  8. B.K. Guepie, E. Grall-Maes, P. Beauseroy, I. Nikiforov, F. Michel, Reliable leak detection in a heat exchanger of a sodium-cooled fast reactor, Ann. Nucl. Energy 142 (2020), 107357.
  9. L.M. Elshenawy, M.A. Halawa, T.A. Mahmoud, H.A. Awad, M.I. Abdo, Unsupervised machine learning techniques for fault detection and diagnosis in nuclear power plants, Prog. Nucl. Energy 142 (2021), 103990.
  10. M.C. dos Santos, V.H.C. Pinheiro, F.S.M. do Desterro, R.K. de Avellar, R. Schirru, A. dos Santos Nicolau, A.M.M. de Lima, Deep rectifier neural network applied to the accident identification problem in a PWR nuclear power plant, Ann. Nucl. Energy 133 (2019) 400-408. https://doi.org/10.1016/j.anucene.2019.05.039
  11. H.A. Saeed, M.J. Peng, H. Wang, B.W. Zhang, Novel fault diagnosis scheme utilizing deep learning networks, Prog. Nucl. Energy 118 (2020), 103066.
  12. G. Lee, S.J. Lee, C. Lee, A convolutional neural network model for abnormality diagnosis in a nuclear power plant, Appl. Soft Comput. 99 (2021), 106874.
  13. X. Li, T. Huang, K. Cheng, Z. Qiu, T. Sichao, Research on anomaly detection method of nuclear power plant operation state based on unsupervised deep generative model, Ann. Nucl. Energy 167 (2022), 108785.
  14. S. Narasimhan, C. Jordache, Data Reconciliation and Gross Error Detection; an Intelligent Use of Process Data, Gulf Publishing Company, Houston, 2000.
  15. C. M Crowe, Data reconciliation -progress and challenges, J. Process Control 6 (1996) 89-98. https://doi.org/10.1016/0959-1524(96)00012-1
  16. C. M Crowe, Formulation of linear data reconciliation using information theory, Chem. Eng. Sci. 51 (1996) 3359-3366. https://doi.org/10.1016/0009-2509(95)00369-X
  17. D.R. Kuehn, Computer control, Chem. Eng. Prog. 57 (6) (1961) 44-47.
  18. J.A. Romagnoli, M.C. Sanchez, Data Processing and Reconciliation for Chemical Process Operations, Elsevier, 1999.
  19. J. Placido, A.A. Campos, D.F. Monteiro, Data reconciliation practice at a petroleum refinery company in Brazil, In Computer Aided Chemical Engineering 27 (2009) 777-782. https://doi.org/10.1016/S1570-7946(09)70350-5
  20. X. Jiang, P. Liu, Z. Li, Data reconciliation and gross error detection for operational data in power plant, Inside Energy 75 (2014) 14-23.
  21. M. Langenstein, Power recapture and power uprate in NPPS with process data reconciliation in accordance with VDI 2048, in: International Conference on Nuclear Engineering, vol. 42428, 2006, pp. 7-14. January.
  22. S. Park, K. Oh, G. Heo, Data Reconciliation to Validate Thermal Power Mismatch in Nuclear Power Plants, Korea Society for Prognostics and Health Management, Jeju, Korea, 2021. Sep 9-11.
  23. K. Oh, S. Park, G. Heo, Application of Reactor PHM Using Data Reconciliation Algorithm, Korea Society for Prognostics and Health Management", Jeju, Korea, 2021. Sep 9-11.
  24. S. Park, G. Heo, Detection of leakage using data reconciliation, in: Transactions of the Korean Nuclear Society Spring Meeting, Pyeongchang, Korea, May, 27-28, 2010.
  25. K.M. Kim, D.T. Gam, K.E. Nam, J. Yoon, J. Yoon, J.H. Lee, Y.I. Cho, Development of Advanced Sodium-Leak Detection System, Korea Atomic Energy Research Institute, 2020. KAERI/TR-8473.
  26. G. Welch, G. Bishop, An Introduction to the Kalman Filter, 1995.
  27. S. Bai, J. Thibault, D.D. McLean, Dynamic data reconciliation: alternative to Kalman filter, J. Process Control 16 (5) (2006) 485-498. https://doi.org/10.1016/j.jprocont.2005.08.002