基于DBT-DBN模型的气化炉超温动态风险分析

doi:10.16265/j.cnki.issn1003-3033.2021.03.011

中国安全科学学报 ›› 2021, Vol. 31 ›› Issue (3): 73-81.doi: 10.16265/j.cnki.issn1003-3033.2021.03.011

基于DBT-DBN模型的气化炉超温动态风险分析

高涵¹, 多依丽^**1,2 副教授, 孙铁¹ 教授, 王志荣³ 教授, 郭品坤³ 副教授

1 辽宁石油化工大学机械工程学院,辽宁抚顺 113001;
2 中国石油大学华东储运与建筑工程学院,山东青岛 266580;
3 南京工业大学安全科学与工程学院,江苏南京 211816

收稿日期:2020-12-14 修回日期:2021-02-20 出版日期:2021-03-28 发布日期:2021-12-20
通讯作者: **多依丽(1985—),女,辽宁沈阳人,硕士,副教授,主要从事安全工程方面的研究。E-mail: lnpuduoyili@163.com。
作者简介:高涵 (1996—),男,辽宁锦州人,硕士研究生,主要研究方向为煤气化装置安全风险评估。E-mail: gaoh1298090424@163.com。
基金资助:
国家重点研发计划项目(2018YFC0808500)。

Dynamic risk analysis of gasifier overtemperature scenario based on DBT-DBN

GAO Han¹, DUO Yili^1,2, SUN Tie¹, WANG Zhirong³, GUO Pinkun³

1 School of Mechanical Engineering, Liaoning Shihua University, Fushun Liaoning 113001, China;
2 College of Pipeline and Civil Engineering, China University of Petroleum, Qingdao Shandong 266580, China;
3 College of Safety Science and Engineering, Nanjing Tech University, Nanjing Jiangsu 211816, China

Received:2020-12-14 Revised:2021-02-20 Online:2021-03-28 Published:2021-12-20

摘要/Abstract

摘要： 为加强煤气化装置核心设备气化炉的安全风险管理,利用动态领结(DBT)模型和动态贝叶斯网络(DBN)相结合的风险分析方法,构建气化炉超温事故风险分析模型。首先,分析设备故障的时序性,建立超温事故的DBT模型,结合模糊评价确定设备故障的发生概率;然后,将DBT映射到DBN中,将故障维修的动态特征定义为转移概率,双向推理气化炉超温的风险因素;最后,预测气化炉发生超温及其后果的动态趋势,并依据诊断推理辨识导致气化炉超温的主要因素。研究结果表明:在考虑维修因素下气化炉运行一年后发生超温的概率为64.4%;气化炉超温的风险因素中,操作失误在生产周期内的影响较大,设备故障集中在磨煤制浆工段。

关键词: 气化炉超温, 动态风险分析, 动态领结(DBT)模型, 动态贝叶斯网络(DBN), 设备故障

Abstract: In order to strengthen safety risk management of gasifier in coal gasification units, a dynamic risk analysis approach based on DBT and DBN was applied to construct an analysis model of gasifiers' overtemperature risks. Firstly, DBT model was established by analyzing sequential dependency of failure events, and their probability was determined considering fuzzy evaluation. Then, DBT model was mapped into DBN, linguistic variables were converted into prior probabilities, and risk factors were obtained through bidirectional reasoning. Finally, dynamic risk trend of gasifier overtemperature and consequences was predicted, and critical factors were identified by diagnosis reasoning. The results show that probability of gasifier overtemperature is about 64.4% after one year with maintenance factor taken into consideration. Among all key factors, operation error in production cycle has a greater impact, and equipment faults mainly occur in coal grinding and pulping section.

Key words: gasifier overtemperature, dynamic risk analysis, dynamic bow-tie (DBT)model, dynamic Bayesian network (DBN), equipment fault

中图分类号:

X937

高涵, 多依丽, 孙铁, 王志荣, 郭品坤. 基于DBT-DBN模型的气化炉超温动态风险分析[J]. 中国安全科学学报, 2021, 31(3): 73-81.

GAO Han, DUO Yili, SUN Tie, WANG Zhirong, GUO Pinkun. Dynamic risk analysis of gasifier overtemperature scenario based on DBT-DBN[J]. China Safety Science Journal, 2021, 31(3): 73-81.

参考文献

[1] 杨芊, 杨帅, 张绍强. 煤炭深加工产业“十四五”发展思路浅析[J]. 中国煤炭, 2020,46(3): 67-73.
YANG Qian, YANG Shuai,ZHANG Shaoqiang.Analysis of the "14^th five-year plan" development strategies of coal deep processing industry[J]. China Coal, 2020,46(3): 67-73.
[2] 叶莲祥. 风险管控行动模型在煤气化装置上的应用[J]. 石油石化绿色低碳, 2019,4(4): 65-69.
YE Lianxiang. Application and risk control and management process in coal gasification plant[J]. Green Petroleum & Petrochemicals, 2019,4(4): 65-69.
[3] 陈全, 张丽婷. HAZOP-FTA综合分析方法在煤化工装置中的应用[J]. 中国安全生产科学技术, 2013,9(7): 134-138.
CHEN Quan, ZHANG Liting. Application of HAZOP-FTA comprehensive analytical method in coal chemical plant[J]. Journal of Safety Science and Technology, 2013,9(7): 134-138.
[4] 张贝克, 许欣, 马昕, 等. 基于SDG-HAZOP方法的煤气化关键设备安全评价[J]. 煤炭学报, 2008,33(7): 816-821.
ZHANG Beike, XU Xin, MA Xin, et al. Safety assessment on the key equipment of coal gasification based on SDG-HAZOP method[J]. Journal of China Coal Society, 2008,33(7): 816-821.
[5] 王文和, 董传富, 刘林精, 等. 基于FTA-BN模型的页岩气井口装置失效概率分析[J]. 中国安全科学学报, 2019,29(4): 89-95.
WANG Wenhe, DONG Chuanfu, LIU Linjing, et al. Failure probability analysis of shale gas wellhead devices based on FTA-BN model[J]. China Safety Science Journal, 2019,29(4): 89-95.
[6] 王健, 张明广, 黄馨, 等. 易燃易爆危险化学品道路运输事故情景分析[J]. 中国安全科学学报, 2019,29(5): 171-177.
WANG Jian, ZHANG Mingguang, HUANG Xin, et al. Scenario analysis of road transportation accidents of inflammable and explosive hazardous chemicals[J]. China Safety Science Journal, 2019,29(5): 171-177.
[7] SUN Feng, XU Wei, WANG Guangjian, et al. A technique to control major hazards of the coal gasification process developed from critical events and safety barriers[J]. Process Safety Progress, 2017,36(4): 382-391.
[8] KHAKZAD N, KHAN F, AMYOTTE P, et al. Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network[J]. Process Safety and Environmental Protection, 2013,91(1): 46-53.
[9] 王好一, 赵东风, 孟亦飞, 等. 基于前导事件和贝叶斯网络的储罐溢油动态风险评价[J]. 安全与环境学报, 2018,18(2): 446-450.
WANG Haoyi, ZHAO Dongfeng, MENG Yifei, et al. Dynamic risk analysis model for the oil-container overflow scenario based on the Bayesian network and precursor events[J]. Journal of Safety and Environment, 2018,18(2): 446-450.
[10] 陈志煌, 刘国恒, 王莹莹, 等. 基于动态贝叶斯网络的水下连接器故障诊断[J]. 中国安全科学学报, 2020,30(5): 81-87.
CHEN Zhihuang,LIU Guoheng,WANG Yingying,et al. Fault diagnosis of subsea collet connector based on dynamic Bayesian network[J]. China Safety Science Journal, 2020,30(5): 81-87.
[11] SIMON C, MECHRI W, CAPIZZI G, et al. Assessment of Safety Integrity Level by simulation of dynamic Bayesian networks considering test duration[J]. Journal of Loss Prevention in the Process Industries, 2019,57: 101-113.
[12] 陈洁, 陈国明, 李新宏, 等. 基于DBN的深水井控人机界面系统可靠性分析[J]. 中国安全科学学报, 2018,28(1): 124-129.
CHEN Jie, CHEN Guoming, LI Xinhong, et al. Impact analysis of human-organizational factors on pilotage risks at harbor[J]. China Safety Science Journal, 2018,28(1): 124-129.
[13] 古莹奎, 邱光琦. DTBN在复杂系统动态概率安全评价中的应用[J]. 中国安全科学学报, 2014,24(4): 74-79.
GU Yingkui, QIU Guangqi. Application of DTBN to complex system dynamic probabilistic safety assessment[J]. China Safety Science Journal, 2014,24(4): 74-79.
[14] WU Shengnan, ZHANG Laibin, ZHENG Wenpei, et al. A DBN-based risk assessment model for prediction and diagnosis of offshore drilling incidents[J]. Journal of Natural Gas Science and Engineering, 2016,34: 139-158.
[15] CHANG Yuanjiang, ZHANG Changshuai, SHI Jihao, et al. Dynamic Bayesian network based approach for risk analysis of hydrogen generation unit leakage[J]. International Journal of Hydrogen Energy, 2019,44(48): 26 665-26 678.
[16] DELVOSALLE C, FIEVEZI C, PIOART A, et al. ARAMIS project: a comprehensive methodology for the identification of reference accident scenarios in process industries[J]. Journal of Hazardous Materials, 2006,130(3): 200-219.
[17] ZAIRE E, KHAKZAD N, COZZANI V, et al. Safety analysis of process systems using fuzzy bayesian network (FBN)[J].Journal of Loss Prevention in the Process Industries, 2019,57:7-16.
[18] ONISAWA T. An application of fuzzy concepts to modelling of reliability analysis[J]. Fuzzy Sets and Systems, 1990, 37(3): 267-286.

基于DBT-DBN模型的气化炉超温动态风险分析

Dynamic risk analysis of gasifier overtemperature scenario based on DBT-DBN

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