Nuclear emergency scenario analysis for spent fuel reprocessing based on dynamic Bayesian network

doi:10.16265/j.cnki.issn1003-3033.2024.10.1738

Abstract

Abstract:

To improve the emergency preparedness and response capabilities of spent fuel reprocessing nuclear accidents, a spent fuel reprocessing nuclear accident emergency scenario based on knowledge meta was proposed to address the uncertainty of the nuclear accident emergency evolution process, the importance of scenario analysis in emergency response decision-making, the complexity of the evolution process, and the difficulty of organization and implementation. The disaster event, causative agent, causal agent, and emergency response were determined, and then a dynamic scenario model for spent fuel reprocessing nuclear emergencies based on a DBN was developed to calculate the occurrence probability of key scenarios, evaluate the development trend of the scenarios, and analyze the evolution laws and paths. Taking the explosion of the high-release liquid storage tank of the Mayak spent fuel reprocessing plant as an example, the process of deduction of the scenario analysis method of the spent fuel reprocessing nuclear accident based on the knowledge meta theory and DBN was performed, and the results were further analyzed. The results showed that: the loss probability of emergency cooling water supply was 73%, the probability of explosion of the high-release waste liquid storage tank was 86%, the probability of radioactive nuclides transferred to animal and plant products and drinking water through multiple pathways was 87%, the probability of long-lived radioactive nuclides deposition in part of the area was 89%, and the probability of the event calming down and dying out was 72%. The probability of the accident contaminating the air, soil, and river was 89%, 85%, and 81%, respectively. The probability of affecting public health and safety was 86%. The scenario evolution process is consistent with the emergency response development of the reprocessing storage tank explosion accident and its impact on the public and the environment, validating the model performance.

Key words: dynamic Bayesian network(DBN), spent fuel reprocessing, nuclear emergency response, scenario projection, knowledge element

CLC Number:

X945

SUN Meilan, ZOU Shuliang, XU Shoulong, CHEN Jiahua. Nuclear emergency scenario analysis for spent fuel reprocessing based on dynamic Bayesian network[J]. China Safety Science Journal, 2024, 34(10): 214-220.

Figures/Tables 6

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情景阶段	情景要素表示
发生情景	事件E:冷却与应急冷却供水丧失E₀; 致灾体D:冷却系统失控D₀; 承灾体H:贮槽H₁; 应急响应R:将设备室集水坑的溶液转送到适当的容器中R₀;去污设备室地面以及设备室集水坑R₁;更换损坏的部件R₂
发展情景	事件E:高放废液贮槽爆炸E₁; 致灾体D:温度升高,贮槽内物质蒸干D₁; 承灾体H:设备室H₂;建(构)筑物H₃; 应急响应R:辐射环境监测与后果评价R₃;非应急工作人员与公众撤离R₄;人员去污R₅
蔓延情景	事件E:放射性核素通过多种途径转移到动植物产品和饮用水E₂; 致灾体D:放射性核素D₂; 承灾体H:空气H₄;土壤H₅;河流H₆ 应急响应R:食物和饮水控制R₆;公众沟通R₇
消退情景	事件E:部分地区有长寿命放射性核素沉积E₃; 承灾体H:公众健康与安全H₇; 应急响应R:地区去污R₈;临时避迁或永久再定居R₉
消失情景	事件E:事件平息与消亡E₄

节点	节点变量概率			先验概率
	条件	概率
	条件	T	F
E₀	D₀=G, R₀=T, R₁=T, R₂=T	0.70	0.30	P(D₀=G)=0.95 P(D₀=N)=0.05 P(R₀=T)=0.75 P(R₀=F)=0.25 P(R₁=T)=0.70 P(R₁=F)=0.30 P(R₂=T)=0.90 P(R₂=F)=0.10
	D₀= G, R₀=T, R₁=T, R₂=F	0.85	0.15
	D₀= G, R₀=T, R₁= F, R₂=T	0.85	0.15
	D₀=G, R₀=T, R₁= F, R₂= F	0.90	0.10
	D₀= G, R₀= F, R₁= T, R₂=T	0.80	0.20
	D₀= G, R₀= F, R₁= T, R₂= F	0.90	0.10
	D₀= G, R₀= F, R₁= F, R₂=T	0.75	0.25
	D₀= G, R₀= F, R₁= F, R₂= F	0.95	0.05
	D₀=N, R₀=T, R₁=T, R₂=T	0.05	0.95
	D₀= N, R₀=T, R₁=T, R₂=F	0.10	0.90
	D₀=N, R₀=T, R₁= F, R₂=T	0.05	0.95
	D₀ =N, R₀=T, R₁= F, R₂= F	0.10	0.90
	D₀=N, R₀= F, R₁= T, R₂=T	0.05	0.95
	D₀=N, R₀= F, R₁= T, R₂= F	0.10	0.90
	D₀=N, R₀= F, R₁= F, R₂=T	0.05	0.95
	D₀=N, R₀= F, R₁= F, R₂= F	0.10	0.90