Study on Location of Fire Stations in Chemical Industry Parks from a Public Safety Perspective: Considering the Domino Effect and the Identification of Major Hazard Installations for Hazardous Chemicals
Abstract
:1. Introduction
2. Risk Assessment Considering Domino Effect and the Identification of Major Hazardous Installations for Hazardous Chemicals
2.1. Risk Analysis and Assessment
2.2. Estimation of Accident Probability of Hazard Installations for Hazardous Chemicals Considering Domino Effect
3. Mathematical Model for Locating Fire Stations
3.1. Description of the Problem
3.2. Construction of the Mathematical Model
- This study did not consider the effect of road traffic conditions and vehicle travel status on time;
- Each candidate fire station met the safety requirements to provide emergency rescue to the point of need;
- The total risk covered by the emergency response facilities in providing emergency services to the demand points was expressed as the sum of the risk values of the grid they pass through on the line segment connected by two, denoted as . Meanwhile, the decay function is used to express the quality difference of the emergency services [25], as shown in Equation (5):
- Among all the emergency resources required for hazardous chemical accidents, the amount of foam required to handle hazardous chemical accidents in the normal mission fire station, special mission fire station, or enterprise fire station was selected as a representative emergency resource for calculation.
3.3. Methods and Steps for Solving the Mathematical Model
Algorithm 1. Methodology and detailed steps for solving the optimal solution of fire station location model |
Augmented-Constraint Method to Find the Pareto Solutions—TOPSIS Method to Optimize the Pareto Solutions |
Step 1 Input data and relevant parameters. |
Step 2 The optimal and inferior values of the objective functions and are solved with lexicographic optimization [29]: |
2.1 , s.t. Equations (9)–(12), output |
2.2 , s.t. Equations (9)–(12), output |
2.3 Perform step 2.1 for |
2.4 Obtain the optimal and inferior values of the objective functions and |
Step 3 Make and set the value of |
Step 4 Execute the following loop to generate the Pareto solution of the model. |
4.1 When , |
Execute: |
s.t. Equations (9)–(12) and |
End |
4.2 Output all Pareto solutions |
Step 5 The Pareto solutions derived from step 4 are preferred using the TOPSIS method. |
Step 6 The preferred solution is output according to the ranking result of TOPSIS, i.e., the best fire stations locating solution. |
4. Analysis of the Case
4.1. Information about the Case
4.2. Results of the Risk Assessment and Its Analysis
4.3. Results and Analyses of the Location Model for Fire Stations
5. Conclusions
- In the location problem of fire stations, the domino effect and the classification of major hazard installations for hazardous chemicals could have a comprehensive impact on the location decision of fire stations.
- The improved risk calculation model that integrates the domino effect and the classification of the major hazard installations for hazardous chemicals could highlight the differences in risk levels and their distribution in the regions with different hazard installations.
- At the level of focusing on the impact of the risk results on locating decisions, the location optimization model developed in this study could make the location results of fire stations pay more attention to high-risk regions. In the results of the case study, an increase of only 2.7% in total cost resulted in a 242.86% increase in total risk covered.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Zhou, Y.; Liu, M. Risk Assessment of Major Hazards and Its Application in Urban Planning: A Case Study: Risk Assessment of Major Hazards. Risk Anal. 2012, 32, 566–577. [Google Scholar] [CrossRef] [PubMed]
- Yang, M. Integrating Safety and Security Management to Protect Chemical Industrial Areas from Domino Effects. Reliab. Eng. Syst. Saf. 2022, 191, 106470. [Google Scholar]
- Kourniotis, S.P.; Kiranoudis, C.T.; Markatos, N.C. Statistical Analysis of Domino Chemical Accidents. J. Hazard. Mater. 2000, 71, 239–252. [Google Scholar] [CrossRef] [PubMed]
- Zhang, N.; Shen, S.; Zhou, A.; Chen, J. A Brief Report on the March 21, 2019 Explosions at a Chemical Factory in Xiangshui, China. Proc. Saf. Prog. 2019, 38, e12060. [Google Scholar] [CrossRef]
- Zhang, J.-H.; Sun, X.-Q.; Zhu, R.; Li, M.; Miao, W. Solving an Emergenc-y Rescue Materials Problem under the Joint Reserves Mode of Government and Framework Agreement Suppliers. PLoS ONE 2017, 12, e0186747. [Google Scholar] [CrossRef]
- Zhao, M.; Chen, Q. Risk-Based Optimization of Emergency Rescue Facilities Locations for Large-Scale Environmental Accidents to Improve Urban Public Safety. Nat. Hazards 2015, 75, 163–189. [Google Scholar] [CrossRef]
- Wang, J. Discussion on Fire Prevention Code for Petrochemical Enterprise Design (GB 50160). Pet. Refin. Eng. 2005, 35, 59. [Google Scholar]
- Cozzani, V.; Reniers, G. Dynamic Risk Assessment and Management of Domino Effects and Cascading Events in the Process Industry; Elsevier: Amsterdam, The Netherlands, 2021. [Google Scholar]
- de Lira-Flores, J.A.; Gutiérrez-Antonio, C.; Vázquez-Román, R. A MILP Approach for Optimal Storage Vessels Layout Based on the Quantitative Risk Analysis Methodology. Process Saf. Environ. Prot. 2018, 120, 1–13. [Google Scholar] [CrossRef]
- Men, J.; Jiang, P.; Zheng, S.; Kong, Y.; Zhao, Y.; Sheng, G.; Su, N.; Zheng, S. A Multi-Objective Emergency Rescue Facilities Location Model for Catastrophic Interlocking Chemical Accidents in Chemical Parks. IEEE Trans. Intell. Transport. Syst. 2020, 21, 4749–4761. [Google Scholar] [CrossRef]
- Du, Y.; Xiao, H.; Sun, J.; Duan, Q.; Qi, K.; Chai, H.; Liew, K.M. Hierarchical Pre-Positioning of Emergency Resources for a Chemical Industrial Parks Concentrated Area. J. Loss Prev. Process Ind. 2020, 66, 104130. [Google Scholar] [CrossRef]
- Zhao, J.; Ke, G.Y. Optimizing Emergency Logistics for the Offsite Hazardous Waste Management. J. Syst. Sci. Syst. Eng. 2019, 28, 747–765. [Google Scholar] [CrossRef]
- Zahiri, B.; Suresh, N.C. Hub Network Design for Hazardous-Materials Transportation under Uncertainty. Transp. Res. Part E Logist. Transp. Rev. 2021, 152, 102424. [Google Scholar] [CrossRef]
- Freeman, R.A. CCPS Guidelines for Chemical Process Quantitative Risk Analysis. Plant Oper. Prog. 1990, 9, 231–235. [Google Scholar] [CrossRef]
- Lees, F. Lees’ Loss Prevention in the Process Industries: Hazard Identification, Assessment and Control; Butterworth-Heinemann: Oxford, UK, 2012. [Google Scholar]
- Rui, W.; Mingguang, Z.; Yinting, C.; Chengjiang, Q. Study on Safety Capacity of Chemical Industrial Park in Operation Stage. Procedia Eng. 2014, 84, 213–222. [Google Scholar] [CrossRef]
- Cong, L.; Ke, X.; Qian, L.; Yunsheng, Z. Discrimination of Relevant Concepts of Safety Risk Classification Control. China Saf. Sci. J. 2019, 29, 43. [Google Scholar]
- Basheer, A.; Tauseef, S.M.; Abbasi, T.; Abbasi, S.A. A Template for Quantitative Risk Assessment of Facilities Storing Hazardous Chemicals. Int. J. Syst. Assur. Eng. Manag. 2019, 10, 1158–1172. [Google Scholar] [CrossRef]
- Pak, S.; Kang, C. Increased Risk to People around Major Hazardous Installations and the Necessity of Land Use Planning in South Korea. Process Saf. Environ. Prot. 2021, 149, 325–333. [Google Scholar] [CrossRef]
- Sebos, I.; Progiou, A.; Symeonidis, P.; Ziomas, I. Land-Use Planning in the Vicinity of Major Accident Hazard Installations in Greece. J. Hazard. Mater. 2010, 179, 901–910. [Google Scholar] [CrossRef]
- Xu, X.; Zhu, H. Fire Station Planning Method Based on Identification of Major Hazards in Chemical Parks. In Proceedings of the Annual Scientific and Technical Conference of the China Fire Protection Association, 2020, Beijing, China, 23 September 2020. (In Chinese). [Google Scholar]
- Abdolhamidzadeh, B.; Abbasi, T.; Rashtchian, D.; Abbasi, S.A. A New Method for Assessing Domino Effect in Chemical Process Industry. J. Hazard. Mater. 2010, 182, 416–426. [Google Scholar] [CrossRef]
- Vose, D. Risk Analysis: A Quantitative Guide; John Wiley & Sons: Hoboken, NJ, USA, 2008. [Google Scholar]
- Kai, K.; Tao, C.; Hongyong, Y. Cooperative Scheduling Model for Multi-Level Emergency Response Teams. J. Tsinghua Univ. (Sci. Technol.) 2016, 56, 830–835. [Google Scholar]
- Berman, O.; Krass, D. The Generalized Maximal Covering Location Problem. Comput. Oper. Res. 2002, 29, 563–581. [Google Scholar] [CrossRef]
- Zhao, J.; Wu, B.; Ke, G.Y. A Bi-Objective Robust Optimization Approach for the Management of Infectious Wastes with Demand Uncertainty during a Pandemic. J. Clean. Prod. 2021, 314, 127922. [Google Scholar] [CrossRef] [PubMed]
- Li, X.; Wang, K.; Liu, L.; Xin, J.; Yang, H.; Gao, C. Application of the Entropy Weight and TOPSIS Method in Safety Evaluation of Coal Mines. Procedia Eng. 2011, 26, 2085–2091. [Google Scholar] [CrossRef]
- Nan, S.; Li, K.; Li, P.; Tang, F.; Baolati, J.; Zou, Y.; Tu, J.; Jin, Y. A Novel Method for Priority Assessment of Electrical Fire Risk in Typical Underwater Equipment Cabins in China. Fire Technol. 2022, 58, 2441–2462. [Google Scholar] [CrossRef]
- Mavrotas, G. Effective Implementation of the ε-Constraint Method in Multi-Objective Mathematical Programming Problems. Appl. Math. Comput. 2009, 213, 455–465. [Google Scholar] [CrossRef]
- He, Z.; Weng, W. A Dynamic and Simulation-Based Method for Quantitative Risk Assessment of the Domino Accident in Chemical Industry. Process Saf. Environ. Prot. 2020, 144, 79–92. [Google Scholar] [CrossRef]
Level | |
---|---|
Level 1 () | 4 |
Levle 2 () | 3 |
Level 3 () | 2 |
Level 4 () | 1 |
No. | Substances | Types of Accidents | Reserves (ton) | Types of Storage Tanks | Gridding Coordinates | |||
---|---|---|---|---|---|---|---|---|
A | LNG | UVCE | 175 | Atmospheric | 3 | (5, 2) | 452 | 5 |
B | Liquid ammonia | UVCE | 170 | Pressure | 4 | (25, 3) | 314 | 3 |
C | Chlormethane | UVCE | 160 | Atmospheric | 2 | (9, 5) | 200 | 2 |
D | Ethane | UVCE | 265 | Atmospheric | 2 | (15, 14) | 615 | 6 |
E | Oxirane | BLEVE | 130 | Atmospheric | 3 | (28, 18) | 200 | 2 |
F | HFO | BLEVE | 135 | Atmospheric | 1 | (3, 20) | 200 | 2 |
G | Methylal | BLEVE | 170 | Pressure | 3 | (12, 14) | 379 | 4 |
H | LPG | BLEVE | 235 | Atmospheric | 3 | (19, 27) | 452 | 5 |
No. | Construction Cost (RMB) | Maximum Capacity of Emergency Resources | Gridding Coordinates |
---|---|---|---|
1 | 950 | 9 | (13, 3) |
2 | 900 | 8 | (20, 5) |
3 | 900 | 8 | (4, 9) |
4 | 950 | 9 | (14, 10) |
5 | 900 | 8 | (23, 12) |
6 | 950 | 9 | (10, 19) |
7 | 900 | 8 | (19, 21) |
8 | 1000 | 10 | (26, 24) |
9 | 1000 | 10 | (12, 25) |
10 | 900 | 8 | (26, 24) |
Risk Models | TOPSIS Rank | Total Covered Risk | Total Cost | Optimal Solution |
---|---|---|---|---|
5 | 0.0122 | 9350 | 1, 2, 3, 4, 6, 7, 8, 9 | |
4 | 0.0113 | 8400 | ||
1 | 0.0096 | 7550 | ||
3 | 0.0101 | 7450 | ||
2 | 0.0086 | 7350 | ||
5 | 0.0038 | 9350 | 1, 2, 3, 4, 5, 6, 7, 10 | |
4 | 0.0035 | 8400 | ||
3 | 0.0032 | 7500 | ||
2 | 0.0031 | 7400 | ||
1 | 0.0028 | 7350 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Jiang, J.; Zhang, X.; Wei, R.; Huang, S.; Zhang, X. Study on Location of Fire Stations in Chemical Industry Parks from a Public Safety Perspective: Considering the Domino Effect and the Identification of Major Hazard Installations for Hazardous Chemicals. Fire 2023, 6, 218. https://doi.org/10.3390/fire6060218
Jiang J, Zhang X, Wei R, Huang S, Zhang X. Study on Location of Fire Stations in Chemical Industry Parks from a Public Safety Perspective: Considering the Domino Effect and the Identification of Major Hazard Installations for Hazardous Chemicals. Fire. 2023; 6(6):218. https://doi.org/10.3390/fire6060218
Chicago/Turabian StyleJiang, Junhao, Xiaochun Zhang, Ruichao Wei, Shenshi Huang, and Xiaolei Zhang. 2023. "Study on Location of Fire Stations in Chemical Industry Parks from a Public Safety Perspective: Considering the Domino Effect and the Identification of Major Hazard Installations for Hazardous Chemicals" Fire 6, no. 6: 218. https://doi.org/10.3390/fire6060218