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The Application of Artificial Intelligence to Nuclear Power Plant Safety

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Artificial Intelligence for Knowledge Management, Energy, and Sustainability (AI4KMES 2021)

Abstract

Through application of artificial intelligence (AI), the burden of analytical computational load for analysis of any given problem where countless variables have to be taken into account, is virtually eliminated. Since for engagements in real life operations and instantaneous actions are of paramount importance and vital, AI can be a strong alternative to overcome the complex problem solving in short time frames. As such, in this study a brief review of AI basics is given and literature for AI applications in nuclear field such as defect detection in nuclear fuel assembly, dose prediction in nuclear emergencies, fuel and component failure detection, core monitoring for reactor transients, core fuel optimization models, gamma spectroscopy analysis and specifically nuclear reactor safety in operation are assessed. Afterwards, an AI model for analyzing transients in VVER type nuclear power plants that is being built in Turkey is proposed. This model must keep up with instantaneous data flow and giving actionable feedback to the operator both for the cause and the solution. A semi-autonomous AI control system that help the operator decision making is a significant contributor to the safety of a reactor.

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References

  1. Krawczak, M.: Multilayer Neural Networks a Generalized Net Perspective, Springer Cham (2013)

    Google Scholar 

  2. da Silva, I.N.: Artificial Neural Networks: A Practical Course, Springer International Publishing, Cham (2017)

    Google Scholar 

  3. Santosh, T.V.: Application of artificial neural networks to nuclear power plant transient diagnosis. Reliab. Eng. Syst. Saf. 92, 1468–1472 (2007)

    Article  Google Scholar 

  4. Liu, W.: A survey of deep neural network architectures and their applications. Neurocomputing 234, 11–26 (2017)

    Article  Google Scholar 

  5. Abiodun, O.I.: State-of-the-art in artificial neural network applications. Heliyon 4, e00938 (2018)

    Google Scholar 

  6. Guo, Z.: Defect detection of nuclear fuel assembly based on deep neural network, Ann. Nucl. Energy 137, 107078 (2020)

    Google Scholar 

  7. Ling, Y.: Nuclear accident source term estimation using kernel principal component analysis, particle swarm optimization, and backpropagation neural networks. Ann. Nucl. Energy 136, 107031 (2020)

    Google Scholar 

  8. Desterro, F.S.M.: Development of a deep rectifier neural network for dose prediction in nuclear emergencies with radioactive material releases. Prog. Nucl. Energy 118, 103110 (2020)

    Google Scholar 

  9. Dong, B.: Detection of fuel failure in pressurized water reactor with artificial neural network, Ann. Nucl. Energy 140, 107104 (2020)

    Google Scholar 

  10. Xia, H.: Research on intelligent monitor for 3D power distribution of reactor core. Ann. Nucl. Energy 73, 446–454 (2014)

    Article  Google Scholar 

  11. Saeed, A.: Development of core monitoring system for a nuclear power plant using artificial neural network technique, Ann. Nucl. Energy 144, 107513 (2020)

    Google Scholar 

  12. Nissan, E.: An overview of AI methods for in-core fuel management: tools for the automatic design of nuclear reactor core configurations for fuel reload, (re)arranging new and partly spent fuel. Designs 3, 37 (2019)

    Article  Google Scholar 

  13. Pirouzmand, A.: Estimation of relative power distribution and power peaking factor in a VVER-1000 reactor core using artificial neural networks. Prog. Nucl. Energy 85, 17–27 (2015)

    Article  Google Scholar 

  14. Rose Mary, G.P.: Neural network correlation for power peak factor estimation. Ann. Nucl. Energy 33, 594–608 (2006)

    Article  Google Scholar 

  15. Babazadeh, D.: Optimization of fuel core loading pattern design in a VVER nuclear power reactors using Particle Swarm Optimization (PSO). Ann. Nucl. Energy 36, 923–930 (2009)

    Article  Google Scholar 

  16. Sahiner, H.: Gamma spectroscopy by artificial neural network coupled with MCNP. Doctoral dissertations. p. 2598 (2017)

    Google Scholar 

  17. U.S. Nuclear Regulatory Commission. https://www.nrc.gov/reading-rm/basic-ref/glossary/transient.html. Accessed 31 Nov 2021

  18. de Oliveira, M.V.: Application of artificial intelligence techniques in modeling and control of a nuclear power plant pressurizer system. Prog. Nucl. Energy 63, 71–85 (2013)

    Google Scholar 

  19. Joyce, M.: Nuclear Engineering, Nuclear Safety and Regulation, Butterworth-Heinemann, New York (2018)

    Google Scholar 

  20. Mogahed, E.A.: Loss of Coolant Accident and Loss of Flow Accident Analysis of the Aries-at Design, Fusion Technology Institute University of Wisconsin-Madison (2010)

    Google Scholar 

  21. Mokhov, V.A.: Advanced Designs of VVER Reactor Plant, VVER-2010 Experience & Perspectives 01–03 November 2010, Prague Czech Republic (2010)

    Google Scholar 

  22. Tian, D.: A constraint-based genetic algorithm for optimizing neural network architectures for detection of loss of coolant accidents of nuclear power plants. Neurocomputing 322, 102–119 (2018)

    Article  Google Scholar 

  23. Ivanov, B.: VVER-1000 Coolant Transient Benchmark. US Department of Energy, Nuclear Energy Agency Organization For Economic Co-operation and Development (2002)

    Google Scholar 

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Correspondence to Senem Şentürk Lüle .

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Yavuz, C., Şentürk Lüle, S. (2022). The Application of Artificial Intelligence to Nuclear Power Plant Safety. In: Mercier-Laurent, E., Kayakutlu, G. (eds) Artificial Intelligence for Knowledge Management, Energy, and Sustainability. AI4KMES 2021. IFIP Advances in Information and Communication Technology, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-030-96592-1_9

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  • DOI: https://doi.org/10.1007/978-3-030-96592-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96591-4

  • Online ISBN: 978-3-030-96592-1

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