IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i16p6048-d893501.html
   My bibliography  Save this article

Applications of Machine Learning to Consequence Analysis of Hypothetical Accidents at Barakah Nuclear Power Plant Unit 1

Author

Listed:
  • Mohannad Khameis Almteiri

    (Department of NPP Engineering, KEPCO International Nuclear Graduate School, 658-91 Haemaji-ro, Seosaeng-myeon, Ulju-gun, Ulsan 45014, Korea)

  • Juyoul Kim

    (Department of NPP Engineering, KEPCO International Nuclear Graduate School, 658-91 Haemaji-ro, Seosaeng-myeon, Ulju-gun, Ulsan 45014, Korea)

Abstract

The United Arab Emirates (UAE) built four nuclear power plants at the Barakah site to supply 25% of the region’s electricity. Among the Barakah Nuclear Power Plants, (BNPPs), their main objectives are to achieve the highest possible safety for the environment, operators, and community members; quality nuclear reactors and energy; and power production efficiency. To meet these objectives, decision-makers must access large amounts of data in the case of a nuclear accident to prevent the release of radioactive materials. Machine learning offers a feasible solution to propose early warnings and help contain accidents. Thus, our study aimed at developing and testing a machine learning model to classify nuclear accidents using the associated release of radioactive materials. We used Radiological Assessment System for Consequence Analysis (RASCAL) software to estimate the concentration of released radioactive materials in the four seasons of the year 2020. We applied these concentrations as predictors in a classification tree model to classify three types of severe accidents at Unit 1 of BNPPs each season. The average accuracy of the classification models in the four seasons was 97.3% for the training data and 96.5% for the test data, indicating a high efficacy. Thus, the generated classification models can distinguish between the three simulated accidents in any season.

Suggested Citation

  • Mohannad Khameis Almteiri & Juyoul Kim, 2022. "Applications of Machine Learning to Consequence Analysis of Hypothetical Accidents at Barakah Nuclear Power Plant Unit 1," Energies, MDPI, vol. 15(16), pages 1-11, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:6048-:d:893501
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/16/6048/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/16/6048/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmed Abd El-Hameed & Juyoul Kim, 2021. "Machine Learning-Based Classification and Regression Approach for Sustainable Disaster Management: The Case Study of APR1400 in Korea," Sustainability, MDPI, vol. 13(17), pages 1-14, August.
    2. Przemysław Stanisz & Mikołaj Oettingen & Jerzy Cetnar, 2022. "Development of a Trajectory Period Folding Method for Burnup Calculations," Energies, MDPI, vol. 15(6), pages 1-15, March.
    3. Andrea Di Ronco & Francesca Giacobbo & Antonio Cammi, 2020. "A Kalman Filter-Based Approach for Online Source-Term Estimation in Accidental Radioactive Dispersion Events," Sustainability, MDPI, vol. 12(23), pages 1-19, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maithah M. Alaleeli & Saeed A. Alameri & Mohammad Alrwashdeh, 2022. "Neutronic Analysis of SiC/SiC Sandwich Cladding Design in APR-1400 under Normal Operation Conditions," Energies, MDPI, vol. 15(14), pages 1-20, July.
    2. Ruslan Irkimbekov & Alexander Vurim & Galina Vityuk & Olzhas Zhanbolatov & Zamanbek Kozhabayev & Artur Surayev, 2023. "Modeling of Dynamic Operation Modes of IVG.1M Reactor," Energies, MDPI, vol. 16(2), pages 1-20, January.
    3. Mohammad Alrwashdeh & Saeed A. Alameri, 2022. "Chromium-Coated Zirconium Cladding Neutronics Impact for APR-1400 Reactor Core," Energies, MDPI, vol. 15(21), pages 1-16, October.
    4. Mikołaj Oettingen & Juyoul Kim, 2023. "Detection of Numerical Power Shift Anomalies in Burnup Modeling of a PWR Reactor," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    5. Nianbiao Deng & Chao Xie & Cheng Hou & Zhulun Li & Jinsen Xie & Tao Yu, 2023. "The Influence Mechanism of Neutron Kinetics of the Accelerator-Driven Subcritical Reactor Based on the Fast/Thermal Neutron Spectra by Monte Carlo Homogenization Method," Energies, MDPI, vol. 16(8), pages 1-15, April.
    6. Brielle Lillywhite & Gregor Wolbring, 2022. "Emergency and Disaster Management, Preparedness, and Planning (EDMPP) and the ‘Social’: A Scoping Review," Sustainability, MDPI, vol. 14(20), pages 1-50, October.
    7. Mikołaj Oettingen, 2022. "The Application of Radiochemical Measurements of PWR Spent Fuel for the Validation of Burnup Codes," Energies, MDPI, vol. 15(9), pages 1-15, April.
    8. Minyu Peng & Yafen Liu & Yang Zou & Ye Dai, 2023. "Preliminary Design and Study of a Small Modular Chlorine Salt Fast Reactor Cooled by Supercritical Carbon Dioxide," Energies, MDPI, vol. 16(13), pages 1-18, June.
    9. Nailia Rakhimova, 2022. "Recent Advances in Alternative Cementitious Materials for Nuclear Waste Immobilization: A Review," Sustainability, MDPI, vol. 15(1), pages 1-25, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:6048-:d:893501. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.