复杂风速风向与事件树下储罐区多米诺事故分析

doi:10.16085/j.issn.1000-6613.2024-0146

化工进展 ›› 2025, Vol. 44 ›› Issue (2): 1170-1182.DOI: 10.16085/j.issn.1000-6613.2024-0146

• 化工园区 • 上一篇

复杂风速风向与事件树下储罐区多米诺事故分析

张迁¹^,²(), 刘鑫³, 王冰³, 徐晶¹^,², 曹晨熙³()

^1.华东理工大学化工学院，上海 200237
^2.绿色化工与工业催化全国重点实验室，上海 201208
^3.华东理工大学能源化工过程智能制造教育部重点实验室，上海 200237

收稿日期:2024-01-17 修回日期:2024-04-11 出版日期:2025-02-25 发布日期:2025-03-10
通讯作者: 曹晨熙
作者简介:张迁（1999—），男，硕士研究生，研究方向为化工园区风险评估。E-mail：714545037@qq.com。
基金资助:
国家自然科学基金基础科学中心项目(61988101);国家自然科学基金面上项目(62373153);国家自然科学基金青年科学基金(62303186);中央高校基本科研业务费专项资金

Quantitative analysis of domino effects in large tank farms under various wind conditions and accident scenarios

ZHANG Qian¹^,²(), LIU Xin³, WANG Bing³, XU Jing¹^,², CAO Chenxi³()

^1.School of Chemical Engineering, East China University of Science and Technology, Shanghai 200237, China
^2.State Key Laboratory of Green Chemical Engineering and Industrial Catalysts, Shanghai 201208, China
^3.Key Laboratory of Smart Manufacturing in Energy Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2024-01-17 Revised:2024-04-11 Online:2025-02-25 Published:2025-03-10
Contact: CAO Chenxi

摘要/Abstract

摘要：

针对大型危险化学品储罐区多级多米诺事故，提出一种基于概率图分解与综合的贝叶斯网络（BN）高效构建与分析方法。在综合考虑多种风速风向组合以及定量风险评价标准规范涵盖的常见事故模式时，能够处理10²个储罐和10⁶个独立场景以上规模的多米诺事故，实现自动因果推理与诊断推理分析。基于上海某大型化工企业实际布局构建示例罐区，进行多米诺事故案例分析。结果表明：较多常压液体储罐的区域以及位于压力球罐下风向的储罐区受多米诺事故影响较大；由于蒸气云爆炸等事故范围受风速风向影响巨大，全罐区多米诺事故风险空间分布呈现显著的季节性变化。通过BN自动推理，不仅绘制出各罐区最可能的事故路径，还可以根据实际事故发展推测泄漏孔径等难以直接观测的情境参数，为事故预防、应急处置和事故调查提供了有力的分析工具。

关键词: 化学品罐区, 多米诺效应, 贝叶斯网络, 安全, 过程系统, 计算机模拟

Abstract:

An efficient Bayesian network (BN) construction and analysis method is proposed for multi-level domino accidents within large chemical tank farms. Leveraging probabilistic graphical model decomposition and integration, the method addresses various wind conditions and common accident modes adhering to quantitative risk assessment guidelines. It is capable of managing domino accidents involving over 10² tanks and 10⁶ possible scenarios, facilitating automated causal and diagnostic inference. The method was applied to an example tank area derived from a realistic large chemical logistics facility in Shanghai. It revealed that domino accidents significantly contribute to the overall tank failure risk, particularly in facilities comprising numerous atmospheric-pressure liquid tanks or those situated downwind from pressurized spherical tanks. Seasonality plays a crucial role in the spatial distribution of domino accident risks due to the impact of wind on incidents like vapor cloud explosions. BN’s automatic inference capabilities were employed to identify the most probable accident sequences and infer unobserved parameters, such as leak sizes, based on actual accident progression. The approach proves invaluable for pre-accident prevention, emergency response, and post-accident investigations.

Key words: chemical tank farm, domino effect, Bayesian network, safety, process systems, computer simulation

中图分类号:

X937

张迁, 刘鑫, 王冰, 徐晶, 曹晨熙. 复杂风速风向与事件树下储罐区多米诺事故分析[J]. 化工进展, 2025, 44(2): 1170-1182.

ZHANG Qian, LIU Xin, WANG Bing, XU Jing, CAO Chenxi. Quantitative analysis of domino effects in large tank farms under various wind conditions and accident scenarios[J]. Chemical Industry and Engineering Progress, 2025, 44(2): 1170-1182.

图/表 26

参考文献 41

1	ZHOU Jianfeng, RENIERS Genserik. Probabilistic analysis of domino effects by using a matrix-based simulation approach[J]. Risk Analysis, 2020, 40(10): 1913-1927.
2	国务院天津港“ 8·12”瑞海公司危险品仓库特别重大火灾爆炸事故调查组. 天津港“8·12”瑞海公司危险品仓库特别重大火灾爆炸事故调查报告[EB/OL]. (2016-02-05) [2023-11-24]. .
3	魏思佳. 如何切实提高风险隐患排查整改质量——也说聊城鲁西化工“5·1”爆炸着火事故[J]. 中国应急管理, 2023(5): 90-93.
	WEI Sijia. How to improve the quality of investigation and rectification of potential risks—Also on the “5.1” explosion and fire accident of Luxi chemical industry in Liaocheng[J]. China Emergency Management, 2023(5): 90-93.
4	AMENDOLA A, CASSIDY K. Special issue：The Seveso Ⅱ directive (96/82/EC) on the control of major accident hazards involving dangerous substances[J]. Journal of Hazardous Materials, 1999, 65(1/2): 9-11.
5	WITTER Ray E. Guidelines for hazard evaluation procedures: Second edition[J]. Plant/Operations Progress, 1992, 11(2): 50-52.
6	ANTONIONI Giacomo, SPADONI Gigliola, COZZANI Valerio. Application of domino effect quantitative risk assessment to an extended industrial area[J]. Journal of Loss Prevention in the Process Industries, 2009, 22(5): 614-624.
7	LANDUCCI Gabriele, ANTONIONI Giacomo, TUGNOLI Alessandro, et al. Probabilistic assessment of domino effect triggered by fire: Implementation in quantitative risk assessment[J]. Chemical Engineering Transactions, 2012, 26: 195-200.
8	XU Yuanyuan, RENIERS Genserik, YANG Ming, et al. Uncertainties and their treatment in the quantitative risk assessment of domino effects: Classification and review[J]. Process Safety and Environmental Protection, 2023, 172: 971-985.
9	贾梅生, 陈国华, 胡昆. 化工园区多米诺事故风险评价与防控技术综述[J]. 化工进展, 2017, 36(4): 1534-1543.
	JIA Meisheng, CHEN Guohua, HU Kun. Review of risk assessment and pre-control of domino effect in chemical industry park[J]. Chemical Industry and Engineering Progress, 2017, 36(4): 1534-1543.
10	ZENG Chuchu, QIAN Yu, LIU Yongsheng. Domino effect index for chemical plants based on fishbone analysis[M]//Materials in Environmental Engineering. Berlin: De Gruyter, 2017: 837-846.
11	ZHOU Jianfeng, RENIERS Genserik, COZZANI Valerio. A Petri-net approach for firefighting force allocation analysis of fire emergency response with backups[J]. Reliability Engineering & System Safety, 2023, 229: 108847.
12	SU Mingqing, WEI Lijun, ZHOU Shennan, et al. Study on dynamic probability and quantitative risk calculation method of domino accident in pool fire in chemical storage tank area[J]. International Journal of Environmental Research and Public Health, 2022, 19(24): 16483.
13	赵景斌, 王彦富, 王涛, 等. 基于蒙特卡洛模拟和动态事件树的储罐脆弱性评估[J]. 化工进展, 2023, 42(5): 2751-2759.
	ZHAO Jingbin, WANG Yanfu, WANG Tao, et al. Vulnerability assessment of storage tanks based on Monte Carlo simulation and dynamic event tree[J]. Chemical Industry and Engineering Progress, 2023, 42(5): 2751-2759.
14	KABIR Golam, SUDA Haruki, CRUZ Ana Maria, et al. Earthquake-related Natech risk assessment using a Bayesian belief network model[J]. Structure and Infrastructure Engineering, 2019, 15(6): 725-739.
15	KAMIL Mohammad Zaid, Mohammed TALEB-BERROUANE, KHAN Faisal, et al. Dynamic domino effect risk assessment using Petri-nets[J]. Process Safety and Environmental Protection, 2019, 124: 308-316.
16	HUANG Kongxing, CHEN Guohua, KHAN Faisal, et al. Dynamic analysis for fire-induced domino effects in chemical process industries[J]. Process Safety and Environmental Protection, 2021, 148: 686-697.
17	KHAKZAD Nima, KHAN Faisal, AMYOTTE Paul, et al. Domino effect analysis using Bayesian networks[J]. Risk Analysis, 2013, 33(2): 292-306.
18	KHAKZAD Nima. Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures[J]. Reliability Engineering & System Safety, 2015, 138: 263-272.
19	王贝贝, 李丽, 关忠慧, 等. 化工装置多级多米诺效应定量风险评价模型的建立及应用[J]. 工业安全与环保, 2019, 45(12): 42-45, 54.
	WANG Beibei, LI Li, GUAN Zhonghui, et al. Establishment and application of multi-stage domino effect quantitative risk assessment model for chemical plant[J]. Industrial Safety and Environmental Protection, 2019, 45(12): 42-45, 54.
20	ZENG Tao, CHEN Guohua, YANG Yunfeng, et al. Developing an advanced dynamic risk analysis method for fire-related domino effects[J]. Process Safety and Environmental Protection, 2020, 134: 149-160.
21	CUI Xingmin, ZHANG Mingguang, PAN Wenjie. Dynamic probability analysis on accident chain of atmospheric tank farm based on Bayesian network[J]. Process Safety and Environmental Protection, 2022, 158: 146-158.
22	COZZANI Valerio, GUBINELLI Gianfilippo, ANTONIONI Giacomo, et al. The assessment of risk caused by domino effect in quantitative area risk analysis[J]. Journal of Hazardous Materials, 2005, 127(1/2/3): 14-30.
23	LI Xiaofeng, CHEN Guohua, AMYOTTE Paul, et al. Modeling and analysis of domino effect in petrochemical storage tank farms under the synergistic effect of explosion and fire[J]. Process Safety and Environmental Protection, 2023, 176: 706-715.
24	DING Long, JI Jie, KHAN Faisal. Combining uncertainty reasoning and deterministic modeling for risk analysis of fire-induced domino effects[J]. Safety Science, 2020, 129: 104802.
25	MALIK Asher Ahmed, NASIF Mohammad Shakir, MOKHTAR Ainul Akmar, et al. Numerical investigation of the effect of weather conditions on the escalation and propagation of fire-induced domino effect[J]. Process Safety Progress, 2021, 40(4): 296-308.
26	潘世豪, 窦站, 李杨, 等. 基于图模型的储罐区火灾事故多米诺效应评定[J]. 中国安全科学学报, 2019, 29(6): 83-89.
	PAN Shihao, DOU Zhan, LI Yang, et al. Evaluation of domino effect in fire accidents in storage tank area based on graph model[J]. China Safety Science Journal, 2019, 29(6): 83-89.
27	ALILECHE Nassim, OLIVIER Damien, ESTEL Lionel, et al. Analysis of domino effect in the process industry using the event tree method[J]. Safety Science, 2017, 97: 10-19.
28	Kemal ÖZFIRAT M, Erkan ÖZKAN, KAHRAMAN Bayram, et al. Integration of risk matrix and event tree analysis: A natural stone plant case[J]. Sādhanā, 2017, 42(10): 1741-1749.
29	RAMZALI Nahid, LAVASANI Mohammad Reza Miri, GHODOUSI Jamal. Safety barriers analysis of offshore drilling system by employing fuzzy event tree analysis[J]. Safety Science, 2015, 78: 49-59.
30	中华人民共和国国家市场监督管理总局, 中国国家标准化管理委员会. 危险化学品生产装置和储存设施外部安全防护距离确定方法: [S]. 北京: 中国标准出版社, 2019.
	State Administration for Market Regulation, Standardization Administration of the People’s Republic of China. Determination method of external safety distance for hazardous chemicals production units and storage installations: [S]. Beijing: Standards Press of China, 2019.
31	中华人民共和国应急管理部化工企业定量风险评价导则: A [S]. 北京: 煤炭工业出版社, 2013.
	State Administration of Work Safety. Guidelines for chemical process safety management: A [S]. Beijing: China Coal Industry Publishing House, 2013.
32	UIJT DE HAAG P A M, ALE B J M. Guidelines for quantitative risk assessment[M]//The Hague: Committee for the Prevention of Disasters, 2005.
33	WANG Jingdong, ZHANG Ting, SONG Jingkuan, et al. A survey on learning to hash[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 769-790.
34	COZZANI Valerio, ANTONIONI Giacomo, SPADONI Gigliola. Quantitative assessment of domino scenarios by a GIS-based software tool[J]. Journal of Loss Prevention in the Process Industries, 2006, 19(5): 463-477.
35	LI Yuntao, YU Lin, JING Qi. Dynamic risk assessment method for urban hydrogen refueling stations: A novel dynamic Bayesian network incorporating multiple equipment states and accident cascade effects[J]. International Journal of Hydrogen Energy, 2024, 54: 1367-1385.
36	党文义, 刘昌华. 压缩气体容器物理爆炸计算模型[J]. 大庆石油学院学报, 2010, 34(2): 104-107, 130-131.
	DANG Wenyi, LIU Changhua. Physical explosion calculation models of the pressurized gas vessels[J]. Journal of Daqing Petroleum Institute, 2010, 34(2): 104-107, 130-131.
37	ZHANG Mingguang, ZHENG Feng, CHEN Fuzhen, et al. Propagation probability of domino effect based on analysis of accident chain in storage tank area[J]. Journal of Loss Prevention in the Process Industries, 2019, 62: 103962.
38	MORAL Serafín, CANO Andrés, Manuel GÓMEZ-OLMEDO. Computation of kullback-leibler divergence in Bayesian networks[J]. Entropy, 2021, 23(9): 1122.
39	夏晨曦, 韩辉, 李伟敏. 基于贝叶斯网络的粉尘爆炸多米诺效应分析[J]. 化工进展, 2016, 35(S2): 110-115.
	XIA Chenxi, HAN Hui, LI Weimin. Domino effect analysis of dust explosions using Bayesian networks[J]. Chemical Industry and Engineering Progress, 2016, 35(S2): 110-115.
40	WANG Yuhan, YANG Dan, PENG Xin, et al. Causal network structure learning based on partial least squares and causal inference of nonoptimal performance in the wastewater treatment process[J]. Processes, 2022, 10(5): 909.
41	XU Qingqing, LIU Hao, SONG Zhenhua, et al. Dynamic risk assessment for underground gas storage facilities based on Bayesian network[J]. Journal of Loss Prevention in the Process Industries, 2023, 82: 104961.

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次数	5	25
比例	17%	83%

泄漏状态	泄漏孔径/mm	最长泄漏时间/min
大孔泄漏	100	10
中孔泄漏	25	20
小孔泄漏	5	30
完全破裂	—	5

泄漏状态	泄漏孔径/mm	最长泄漏时间/min
大孔泄漏	100	10
中孔泄漏	25	20
小孔泄漏	5	30
完全破裂	—	5

物质分类	连续释放	瞬时释放	立即点燃概率
类别0 （中/高活性）	<10kg/s	<1000kg	0.2
	10~100kg/s	1000~10000kg	0.5
	>100kg/s	>10000kg	0.7
类别0 （低活性）	<10kg/s	<1000kg	0.02
	10~100kg/s	1000~10000kg	0.04
	>100kg/s	>10000kg	0.09
类别1	任意速率	任意量	0.065
类别2	任意速率	任意量	0.01
类别3或类别4	任意速率	任意量	0

物质分类	连续释放	瞬时释放	立即点燃概率
类别0 （中/高活性）	<10kg/s	<1000kg	0.2
	10~100kg/s	1000~10000kg	0.5
	>100kg/s	>10000kg	0.7
类别0 （低活性）	<10kg/s	<1000kg	0.02
	10~100kg/s	1000~10000kg	0.04
	>100kg/s	>10000kg	0.09
类别1	任意速率	任意量	0.065
类别2	任意速率	任意量	0.01
类别3或类别4	任意速率	任意量	0

物质类别	燃烧性	条件
类别0	极度易燃	闪点<0℃、沸点≤35℃的液体或暴露于空气中，在正常温度和压力下可以点燃的气体
类别1	高可燃性	闪点<21℃的液体，但不是极度易燃的
类别2	可燃	21℃≤闪点≤55℃的液体
类别3	可燃	55℃<闪点≤100℃的液体
类别4	可燃	闪点>100℃的液体

复杂风速风向与事件树下储罐区多米诺事故分析

Quantitative analysis of domino effects in large tank farms under various wind conditions and accident scenarios

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图/表 26

参考文献 41

相关文章 15

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事故类型	物理效应	所用模型
池火	热辐射	源项模型、液池燃烧模型
闪火	—	源项模型、高斯扩散模型、闪火后果模型
火球	热辐射	火球后果模型
喷射火	热辐射	源项模型、喷射火后果模型
蒸气云爆炸	超压	源项模型、高斯扩散模型、TNT当量模型^[36]

扩展向量类型	目标储罐类型	阈值	概率模型
热辐射	常压储罐	15kW/m²	γ = 12.54 - 1.847 ln(ttf) ln(ttf) = -1.128 lnQ - 2.667×10 ^-5V +9.887
热辐射	压力储罐	50kW/m²	γ= 12.54 - 1.847 ln(ttf) ln (ttf) = -0.947 lnQ + 8.835V^0.032
超压	常压储罐	15kPa	γ= -18.96 + 2.44 ln p
超压	压力储罐	32kPa	γ= -42.44 + 4.33 lnp

罐区编号	储存物质	储罐种类	储存温度/℃	储存压力/Pa	储罐高度/m	储罐半径/m	物料体积/m³	罐区围堰面积/m²
1号罐区	聚二苯基甲烷二异氰酸酯	拱顶罐	293.15	101325	14	5	950	5760
2号罐区	丙酮	内浮顶罐	298.15	101325	21	9.2	4750	7220
3号罐区	甲苯二异氰酸酯	拱顶罐	298.15	101325	16	6.7	1900	2573
4号罐区	甲基丙烯酸甲酯	拱顶罐	298.15	101325	16	7.4	2508	5690
5号罐区	68%硝酸	拱顶罐	298.15	101325	20	9.45	4750	2337
6号罐区	氨	全容罐	240.15	101325	32.2	22.75	45000	4058
7号罐区	苯酚	拱顶罐	298.15	101325	20	12.98	9500	6079
8号罐区	异丙基苯	内浮顶罐	298.15	101325	21.2	11.5	7600	5880
9号罐区	丙烯	压力球罐	298.15	1000000	18	9	503	4120

风向	受影响储罐编号	升级概率/a^-1
风向	受影响储罐编号	BN方法	QRA方法
E	FD02TK02	0.035	0.035
ESE	FD03TK10	0.1	0.1
SSE	FD06TK02	0.15	0.15
S	FD08TK02	0.026	0.026
SSW	FD08TK03	0.049	0.049
SW	FD08TK06	0.025	0.025
WSW	FD08TK07	0.0000031	0.0000031

风向	失效概率/a^-1		计算时间/s
风向	传统方法	本文方法	传统方法	本文方法
SE	5.548×10^-16	5.544×10^-16	2.389×10^-2	9.956×10^-4
SSE	6.613×10^-12	6.605×10^-12	7.965×10^-3	9.959×10^-4
S	5.057×10^-12	5.057×10^-12	4.978×10^-3	9.956×10^-4
SSW	1.204×10^-18	1.204×10^-18	1.991×10^-3	9.956×10^-4
SW	5.795×10^-16	5.795×10^-16	1.992×10^-3	9.956×10^-4
综合风向	1.167×10^-11	1.166×10^-11	2.688×10^-2	2.994×10^-3

风向	风速/m·s^-1	风速风向组合频率	风向角度/(°)
N	3.8	0.111	0
NNE	3.5	0.143	22.5
NE	3.2	0.108	45
ENE	2.7	0.076	67.5
E	2.1	0.045	90
ESE	2.6	0.064	112.5
SE	1.9	0.029	135
SSE	2.4	0.015	157.5
S	3.9	0.0226	180
SSW	5.5	0.012	202.5
SW	2.9	0.026	225
WSW	2.3	0.044	247.5
W	2.7	0.067	270
WNW	3.6	0.055	292.5
NW	4.4	0.058	315
NNW	3.9	0.12	337.5

风向	风速/m·s^-1	风速风向组合频率	风向角度/(°)
N	2.7	0.035	0
NNE	3.3	0.072	22.5
NE	3.8	0.088	45
ENE	3.5	0.1	67.5
E	3.4	0.099	90
ESE	3.4	0.1189	112.5
SE	2.5	0.0877	135
SSE	3	0.078	157.5
S	3.8	0.077	180
SSW	3.8	0.051	202.5
SW	3.9	0.055	225
WSW	3	0.046	247.5
W	2.5	0.022	270
WNW	3	0.021	292.5
NW	2.5	0.017	315
NNW	2.8	0.033	337.5

事故类型	泄漏状态	一级多米诺节点		二级多米诺节点
事故类型	泄漏状态	FD08TK03	FD08TK04	FD08TK01	FD08TK05	FD08TK06	FD08TK02
池火	大孔泄漏	3.11×10^-1	2.03×10^-4	7.90×10^-5	7.30×10^-5	6.60×10^-5	2.02×10^-7
	中孔泄漏	2.00×10^-6	3.23×10^-7	7.89×10^-4	7.29×10^-4	6.59×10^-4	2.00×10^-6
	小孔泄漏	5.91×10^-8	3.18×10^-8	3.16×10^-4	2.92×10^-4	2.64×10^-4	8.07×10^-7
	完全破裂	6.23×10^-1	4.01×10^-4	1.56×10^-4	1.44×10^-4	1.31×10^-4	3.99×10^-7
闪火	大孔泄漏	8.36×10^-7	4.64×10^-7	4.70×10^-3	4.34×10^-3	3.93×10^-3	1.20×10^-5
	中孔泄漏	8.00×10^-6	5.00×10^-6	4.70×10^-2	4.34×10^-2	3.92×10^-2	1.20×10^-4
	小孔泄漏	3.00×10^-6	2.00×10^-6	1.86×10^-2	1.72×10^-2	1.56×10^-2	4.80×10^-5
	完全破裂	2.00×10^-6	9.31×10^-7	9.43×10^-3	8.72×10^-3	7.88×10^-3	2.40×10^-5
VCE	大孔泄漏	1.20×10^-4	3.33×10^-7	3.10×10^-3	2.87×10^-3	2.59×10^-3	8.00×10^-6
	中孔泄漏	1.20×10^-3	3.00×10^-6	3.10×10^-2	2.87×10^-2	2.59×10^-2	7.90×10^-5
	小孔泄漏	4.84×10^-4	1.00×10^-6	1.25×10^-2	1.15×10^-2	1.04×10^-2	3.20×10^-5
	完全破裂	2.44×10^-4	6.75×10^-7	6.29×10^-3	5.82×10^-3	5.26×10^-3	1.60×10^-5

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罐区编号	储存物质	储存温度/℃	管道压力/Pa	围堰内管道长度/m	管道上阀门直径/m	泵房面积/m²
1号罐区	聚二苯基甲烷二异氰酸酯	293.15	340000	8.96	152.4	294
2号罐区	丙酮	298.15	470000	10.45	152.4	306
3号罐区	甲苯二异氰酸酯	298.15	937000	10.41	152.4	324
4号罐区	甲基丙烯酸甲酯	298.15	580000	12.56	101.6	209.44
5号罐区	68%硝酸	298.15	1300000	9.01	203.2	179.7
6号罐区	氨	240.15	1880000	56	152.4	179.7
7号罐区	苯酚	328.15	800000	18.375	254	203.49
8号罐区	异丙基苯	298.15	1690000	18.375	203.2	361.56
9号罐区	丙烯	298.15	1900000	16.15	152.4	166.69

罐区编号	储存物质	储存温度/℃	管道压力/Pa	围堰内管道长度/m	管道上阀门直径/m	泵房面积/m²
1号罐区	聚二苯基甲烷二异氰酸酯	293.15	340000	8.96	152.4	294
2号罐区	丙酮	298.15	470000	10.45	152.4	306
3号罐区	甲苯二异氰酸酯	298.15	937000	10.41	152.4	324
4号罐区	甲基丙烯酸甲酯	298.15	580000	12.56	101.6	209.44
5号罐区	68%硝酸	298.15	1300000	9.01	203.2	179.7
6号罐区	氨	240.15	1880000	56	152.4	179.7
7号罐区	苯酚	328.15	800000	18.375	254	203.49
8号罐区	异丙基苯	298.15	1690000	18.375	203.2	361.56
9号罐区	丙烯	298.15	1900000	16.15	152.4	166.69