A Bayesian Network Approach to Predicting Severity Status in Nuclear Reactor Accidents with Resilience to Missing Data

Kaiyu Li, Ling Chen, Xinxin Cai, Cai Xu, Yuncheng Lu, Shengfeng Luo, Wenlin Wang, Lizhi Jiang () and Guohua Wu ()
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Kaiyu Li: College of Nuclear Science and Technology, Naval University of Engineering of PLA, Wuhan 430033, China
Ling Chen: College of Nuclear Science and Technology, Naval University of Engineering of PLA, Wuhan 430033, China
Xinxin Cai: Wuhan Second Ship Design and Research Institute, Wuhan 430000, China
Cai Xu: Third Military Representative Office Stationed in Wuhan, Wuhan 430000, China
Yuncheng Lu: Wuhan Second Ship Design and Research Institute, Wuhan 430000, China
Shengfeng Luo: Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
Wenlin Wang: Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
Lizhi Jiang: College of Nuclear Science and Technology, Naval University of Engineering of PLA, Wuhan 430033, China
Guohua Wu: Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China

Energies, 2025, vol. 18, issue 11, 1-20

Abstract: Nuclear energy is a cornerstone of the global energy mix, delivering reliable, low-carbon power essential for sustainable energy systems. However, the safety of nuclear reactors is critical to maintaining operational reliability and public trust, particularly during accidents like a Loss of Coolant Accident (LOCA) or a Steam Line Break Inside Containment (SLBIC). This study introduces a Bayesian Network (BN) framework used to enhance nuclear energy safety by predicting accident severity and identifying key factors that ensure energy production stability. With the integration of simulation data and physical knowledge, the BN enables dynamic inference and remains robust under missing-data conditions—common in real-time energy monitoring. Its hierarchical structure organizes variables across layers, capturing initial conditions, intermediate dynamics, and system responses vital to energy safety management. Conditional Probability Tables (CPTs), trained via Maximum Likelihood Estimation, ensure accurate modeling of relationships. The model’s resilience to missing data, achieved through marginalization, sustains predictive reliability when critical energy system variables are unavailable. Achieving R 2 values of 0.98 and 0.96 for the LOCA and SLBIC, respectively, the BN demonstrates high accuracy, directly supporting safer nuclear energy production. Sensitivity analysis using mutual information pinpointed critical variables—such as high-pressure injection flow (WHPI) and pressurizer level (LVPZ)—that influence accident outcomes and energy system resilience. These findings offer actionable insights for the optimization of monitoring and intervention in nuclear power plants. This study positions Bayesian Networks as a robust tool for real-time energy safety assessment, advancing the reliability and sustainability of nuclear energy production.

Keywords: nuclear energy safety; accident prediction; Bayesian networks; energy system reliability; missing-data resilience (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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