Volume 22, Issue 1 (2025)
ioh 2025, 22(1): 215-226 |
Back to browse issues page
Ethics code: IR.IUMS.REC.1403.028
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Keyvani Brojeni M, Kheirandish Sarabi S, Vosoughi S, Souri B, Akbarzadeh Miandoab F, Moradi Hanifi S. QUANTITATIVE RISK ASSESSMENT OF A CARBON DIOXIDE STORAGE TANK BY USING CONSEQUENCE MODELING AND FUZZY BAYESIAN NETWORKS. ioh 2025; 22 (1) :215-226
URL: http://ioh.iums.ac.ir/article-1-3735-en.html
URL: http://ioh.iums.ac.ir/article-1-3735-en.html
Mohammadhosein Keyvani Brojeni 
, Somayeh Kheirandish Sarabi , Shahram Vosoughi , Behzad Souri , Fatemeh Akbarzadeh Miandoab , Saber Moradi Hanifi *
, Somayeh Kheirandish Sarabi , Shahram Vosoughi , Behzad Souri , Fatemeh Akbarzadeh Miandoab , Saber Moradi Hanifi *
Occupational Health Research Center Department of Occupational Health Engineering, School of Public Health, Iran University of Medical Sciences , moradi.sab@iums.ac.ir
Abstract: (1141 Views)
ABSTRACT
BACKGROUND AND AIMS: Given the asphyxiant and odorless nature of carbon dioxide gas, which, while non-toxic, can pose a serious threat to human life at high concentrations, this study aims to conduct a more precise risk assessment. Since the conventional Bowtie method, by itself, is incapable of dynamic analysis and does not adequately account for uncertainties, this research integrates the causal framework of the Bowtie method with Bayesian Networks, together with consequence modeling and risk assessment using SAFETI 8.4 software. This integrated approach provides a practical framework for enhanced hazard identification, more accurate evaluation of CO₂ leak consequences, and improved preparedness against potential accidents.
METHODS: In this study, after hazard identification using the HAZOP method, leakage scenarios were defined and analyzed through an integrated Bowtie–Bayesian Network approach implemented in GeNIe 5.0. Event probabilities were estimated using a fuzzy logic-based method, and the consequences of leakages were modeled with PHAST 8.4. Finally, risk assessment was conducted using the probit equation in SAFETI 8.4.
RESULTS: In the BOWTIE approach, a total of 17 basic events and 13 intermediate events were identified. The probability of the final consequence resulting from CO₂ leakage, calculated using the Bowtie method and the integrated Bowtie–Bayesian Network approach, was 7.832726×10⁻⁶ using the BOWTIE approach and 7.305449×10⁻⁶ when combined with the Bayesian Network. The most critical basic events were identified as: (1) failure in equipment maintenance, (2) failure in connections and clamp No. 1, and (3) poor quality of purchased equipment. The highest concentration in the event of catastrophic rupture was determined to be 106 ppm at a distance of 5 meters from the tank. The risk level was less than 10-4, which is within the acceptable level.
BACKGROUND AND AIMS: Given the asphyxiant and odorless nature of carbon dioxide gas, which, while non-toxic, can pose a serious threat to human life at high concentrations, this study aims to conduct a more precise risk assessment. Since the conventional Bowtie method, by itself, is incapable of dynamic analysis and does not adequately account for uncertainties, this research integrates the causal framework of the Bowtie method with Bayesian Networks, together with consequence modeling and risk assessment using SAFETI 8.4 software. This integrated approach provides a practical framework for enhanced hazard identification, more accurate evaluation of CO₂ leak consequences, and improved preparedness against potential accidents.
METHODS: In this study, after hazard identification using the HAZOP method, leakage scenarios were defined and analyzed through an integrated Bowtie–Bayesian Network approach implemented in GeNIe 5.0. Event probabilities were estimated using a fuzzy logic-based method, and the consequences of leakages were modeled with PHAST 8.4. Finally, risk assessment was conducted using the probit equation in SAFETI 8.4.
RESULTS: In the BOWTIE approach, a total of 17 basic events and 13 intermediate events were identified. The probability of the final consequence resulting from CO₂ leakage, calculated using the Bowtie method and the integrated Bowtie–Bayesian Network approach, was 7.832726×10⁻⁶ using the BOWTIE approach and 7.305449×10⁻⁶ when combined with the Bayesian Network. The most critical basic events were identified as: (1) failure in equipment maintenance, (2) failure in connections and clamp No. 1, and (3) poor quality of purchased equipment. The highest concentration in the event of catastrophic rupture was determined to be 106 ppm at a distance of 5 meters from the tank. The risk level was less than 10-4, which is within the acceptable level.
CONCLUSION: The social risk curve showed that the risk associated with the carbon dioxide tank is at an acceptable level, indicating a low level of risk. Complex models, such as Fuzzy Bayesian Networks (FBN), can significantly contribute to optimal decision-making in safety and crisis management, particularly when modeling high-risk accidents and assessing associated risks.
Type of Study: Applicable |
Subject:
Safety
Received: 2025/03/6 | Accepted: 2025/07/8 | Published: 2025/03/30
Received: 2025/03/6 | Accepted: 2025/07/8 | Published: 2025/03/30
References
1. Heinrich JJ, Herzog HJ, Reiner DM, editors. Environmental assessment of geologic storage of CO2. second national conference on carbon sequestration; 2003.
2. Veritas DN. Mapping of potential HSE issues related to large-scale capture, transport and storage of CO2. DNV Re port. 2008(1993).
3. Benson SM, Hepple R, Apps J, Tsang C-F, Lippmann M. Lessons learned from natural and industrial analogues for storage of carbon dioxide in deep geological formations. 2002. [DOI:10.2172/805134]
4. Department ILOPI. The social and decent work dimensions of a new Agreement on Climate Change. 2009.
5. Agency USEP. "Carbon Dioxide as a Fire Suppressant: Examining the Risks,. 2025.
6. Haldane JS, Smith JL. The physiological effects of air vitiated by respiration1892. [DOI:10.1002/path.1700010205]
7. Metz B, Davidson O, De Coninck H, Loos M, Meyer L. IPCC special report on carbon dioxide capture and storage: Cambridge: Cambridge University Press; 2005.
8. Hedlund FH. The extreme carbon dioxide outburst at the Menzengraben potash mine 7 July 1953. Safety science. 2012;50(3):537-53. [DOI:10.1016/j.ssci.2011.10.004]
9. Xi D, Lu H, Fu Y, Dong S, Jiang X, Matthews J. Carbon dioxide pipelines: A statistical analysis of historical accidents. Journal of Loss Prevention in the Process Industries. 2023;84:105129. [DOI:10.1016/j.jlp.2023.105129]
10. Askaripoor T, Kazemi E, Aghaei H, Marzban M. Evaluating and comparison of fuzzy logic and analytical hierarchy process in ranking and quantitative safety risk analysis (case study: a combined cycle power plant). Safety promotion and injury prevention (Tehran). 2015;3(3):169-74.
11. Zarei E, Jafari M, Dormohammadi A, Sarsangi V. The role of modeling and consequence evaluation in improving safety level of industrial hazardous installations: A case study: Hydrogen production unit. Iran Occupational Health. 2013;10(6):54-69. [Persian]
12. Khoshakhlagh AH, Sulaie SA, Yazdanirad S, Park J. Examining the effect of safety climate on accident risk through job stress: a path analysis. BMC psychology. 2023;11(1):89. [DOI:10.1186/s40359-023-01133-2] [PMID] []
13. Bartolozzi V, Castiglione L, Picciotto A, Galluzzo M. Qualitative models of equipment units and their use in automatic HAZOP analysis. Reliability Engineering & System Safety. 2000;70(1):49-57. [DOI:10.1016/S0951-8320(00)00042-9]
14. Fabbrocino G, Iervolino I, Orlando F, Salzano E. Quantitative risk analysis of oil storage facilities in seismic areas. Journal of hazardous materials. 2005;123(1-3):61-9. [DOI:10.1016/j.jhazmat.2005.04.015] [PMID]
15. Markowski AS, Mannan MS, Bigoszewska A. Fuzzy logic for process safety analysis. Journal of loss prevention in the process industries. 2009;22(6):695-702. [DOI:10.1016/j.jlp.2008.11.011]
16. Alizadeh SS, Moshashaei P. The Bowtie method in safety management system: A literature review. Scientific Journal of Review. 2015;4(9):133-8.
17. Kazemi M, Abbasi A, Kazemi M, Jamshidzadeh N, Rashidi MA. Identification of Hazards and Risk Assessment among Various Units of Ilam Gas Refinery using the Integrated Approach of Bow-tie and FMEA Methods. Journal of Ilam University of Medical Sciences. 2021;29(2):1-12. [Persian] [DOI:10.52547/sjimu.29.2.1]
18. Hatch D, McCulloch P, Travers I. Enhancing PHAs: The power of bowties. Chemical Engineering Progress. 2019;115:20-6.
19. Mohammadi H, Laal F, Mohammadian F, Yari P, Kangavari M, Hanifi SM. Dynamic risk assessment of storage tank using consequence modeling and fuzzy Bayesian network. Heliyon. 2023;9(8). [DOI:10.1016/j.heliyon.2023.e18842] [PMID] []
20. Tseng M-L, Wu W-W, Lin Y-H, Liao C-H. An exploration of relationships between environmental practice and manufacturing performance using the PLS path modeling. WSEAS transactions on environment and development. 2008;4(6):487-502.
21. Nazari S, Karami N, Moghadam H, Nasiri P. Consequence analysis of BLEVE scenario in the propane tank: ACase study at Bandar Abbas gas condensate refinery of Iran. International Journal of Scientific Engineering and Technology. 2015;4(9):472-5. [DOI:10.17950/ijset/v4s9/904]
22. Hanna S, Britter R, Leung J, Hansen O, Sykes I, Drivas P, editors. Source emissions and transport and dispersion models for toxic industrial chemicals (tics) released in cities. Eighth Symposium on the Urban Environment, Room, Italy; 2009.
23. Hanna S, Dharmavaram S, Zhang J, Sykes I, Witlox H, Khajehnajafi S, et al. Comparison of six widely‐used dense gas dispersion models for three recent chlorine railcar accidents. Process Safety Progress. 2008;27(3):248-59. [DOI:10.1002/prs.10257]
24. Kwak H, Kim M, Min M, Park B, Jung S. Assessing the Quantitative Risk of Urban Hydrogen Refueling Station in Seoul, South Korea, Using SAFETI Model. Energies. 2024;17(4):867. [DOI:10.3390/en17040867]
25. Leoni L, De Carlo F, Paltrinieri N, Sgarbossa F, BahooToroody A. On risk-based maintenance: A comprehensive review of three approaches to track the impact of consequence modelling for predicting maintenance actions. Journal of Loss Prevention in the Process Industries. 2021;72:104555. [DOI:10.1016/j.jlp.2021.104555]
26. Mohammadfam I. Safety Engineering.second edition. Tehran: Fanavaran; 2011.
27. Macdonald D. Practical hazops, trips and alarms: Elsevier; 2004.
28. Aliabadi MM, Ramezani H, Kalatpour O. Quantitative Risk Assessment of Condensate Storage Tank, Considering Domino Effects. Journal of Health & Safety at Work. 2022;12(1).
29. King R. Safety in the process industries: Elsevier; 2013. [Persian]
30. Badri N, Nourai F, Rashtchian D. The role of quantitative risk assessment in improving hazardous installations siting: a case study. 2011.
31. Markowski AS, Kotynia A. "Bow-tie" model in layer of protection analysis. Process Safety and Environmental Protection. 2011;89(4):205-13. [DOI:10.1016/j.psep.2011.04.005]
32. Grossel SS. Guidelines for Chemical Process Quantitative Risk Analysis: ; By Center for Chemical Process Safety; American Institute of Chemical Engineers, New York, NY, 2000, pp. 750. Journal of Loss Prevention in the Process Industries. 2001;14(5):438-9. [DOI:10.1016/S0950-4230(01)00002-X]
33. Fazli Z, Laal F, Keighobadi E, Ebrahimi H, Medvari RF, Hanifi SM. Quantitative Risk assessment of Gasoline Storage Tank Farm Unit using by Fuzzy Set Theory and Consequence modeling. Iran Occupational Health. 2024;20(2):0-. [Persian] [DOI:10.61186/ioh.20.2.18]
34. Jafari MJ. The application of Fuzzy logic to determine the failure probability in Fault Tree Risk Analysis. Journal of Safety Promotion and Injury Prevention. 2014;Vol. 2:113-23. [Persian]
35. Cooke RM, ElSaadany S, Huang X. On the performance of social network and likelihood-based expert weighting schemes. Reliability Engineering & System Safety. 2008;93(5):745-56. [DOI:10.1016/j.ress.2007.03.017]
36. Rajakarunakaran S, Kumar AM, Prabhu VA. Applications of fuzzy faulty tree analysis and expert elicitation for evaluation of risks in LPG refuelling station. Journal of Loss Prevention in the Process Industries. 2015;33:109-23. [DOI:10.1016/j.jlp.2014.11.016]
37. Lavasani SM, Yang Z, Finlay J, Wang J. Fuzzy risk assessment of oil and gas offshore wells. Process Safety and Environmental Protection. 2011;89(5):277-94. [DOI:10.1016/j.psep.2011.06.006]
38. Saaty TL, Ozdemir MS. Why the magic number seven plus or minus two. Mathematical and computer modelling. 2003;38(3-4):233-44. [DOI:10.1016/S0895-7177(03)90083-5]
39. Abbasi Kharajou B, Ahmadi H, Rafiei M, Moradi Hanifi S. Quantitative risk estimation of CNG station by using fuzzy bayesian networks and consequence modeling. Scientific Reports. 2024;14(1):4266. [DOI:10.1038/s41598-024-54842-y] [PMID] []
40. Yazdi M, Kabir S. A fuzzy Bayesian network approach for risk analysis in process industries. Process safety and environmental protection. 2017;111:507-19. [DOI:10.1016/j.psep.2017.08.015]
41. Akkurt S, Tayfur G, Can S. Fuzzy logic model for the prediction of cement compressive strength. Cement and Concrete Research. 2004;34(8):1429-33. [DOI:10.1016/j.cemconres.2004.01.020]
42. Shi L, Shuai J, Xu K. Fuzzy fault tree assessment based on improved AHP for fire and explosion accidents for steel oil storage tanks. Journal of hazardous materials. 2014;278:529-38. [DOI:10.1016/j.jhazmat.2014.06.034] [PMID]
43. Ross TJ. Fuzzy logic with engineering applications: Wiley Online Library; 2004.
44. Sharma RK, Kumar D, Kumar P. Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling. International Journal of Quality & Reliability Management. 2005. [DOI:10.1108/02656710510625248]
45. Renjith V, Madhu G, Nayagam VLG, Bhasi A. Two-dimensional fuzzy fault tree analysis for chlorine release from a chlor-alkali industry using expert elicitation. Journal of hazardous materials. 2010;183(1-3):103-10. [DOI:10.1016/j.jhazmat.2010.06.116] [PMID]
46. Mandali H, Ghasemi F, Farshad AA, Moradi Hanifi S, Abedi K, Ghorbani M, et al. Risk and Reliability Assessment of Metal Lathe Machining Operation with DBN‐FFTA Hybrid Approach. Mathematical Problems in Engineering. 2023;2023(1):8873531. [DOI:10.1155/2023/8873531]
47. Koller D, Friedman N. Probabilistic graphical models: principles and techniques: MIT press; 2009.
48. Abimbola M, Khan F, Khakzad N, Butt S. Safety and risk analysis of managed pressure drilling operation using Bayesian network. Safety science. 2015;76:133-44. [DOI:10.1016/j.ssci.2015.01.010]
49. Zarei E, Azadeh A, Khakzad N, Aliabadi MM, Mohammadfam I. Dynamic safety assessment of natural gas stations using Bayesian network. Journal of hazardous materials. 2017;321:830-40. [DOI:10.1016/j.jhazmat.2016.09.074] [PMID]
50. van den Bosch CJH, Weterings RAPM. Methods for the Calculation of Physical Effects: Due to Releases of Hazardous Materials, Liquids and Gases : Yellow Book: Ministerie van Volkshuisvesting en Ruimtelijke Ordening (VROM); 2005.
51. Kvien K, Flach T, Solomon S, Napoles OM, Hulsbosch-Dam C, Spruijt M. An integrated approach for risk assessment of CO2 infrastructure in the COCATE project. Energy Procedia. 2013;37:2932-40. [DOI:10.1016/j.egypro.2013.06.179]
52. Yuan S, Cai J, Reniers G, Yang M, Chen C, Wu J. Safety barrier performance assessment by integrating computational fluid dynamics and evacuation modeling for toxic gas leakage scenarios. Reliability Engineering & System Safety. 2022;226:108719. [DOI:10.1016/j.ress.2022.108719]
53. Eskandari T, Mirzaei M, Mohammadfam I. Dynamic Safety Analysis in CNG Stations Using a Hybrid Fault Tree Approach and Bayesian Network Techniques. 2024. [Persian]
54. Munahar S, Purnomo BC, Ferdiansyah N, Widodo EM, Aman M, Rusdjijati R, et al. Risk-Based Leak Analysis of an LPG Storage Tank: A Case Study. Indonesian Journal of Science and Technology. 2022;7(1):37-64. [DOI:10.17509/ijost.v7i1.42916]
55. (NIOSH) TNIfOSaH. Carbon dioxide. 2025.
56. Hsieh K-J, Lien F-S, Yee E. Dense gas dispersion modeling of CO2 released from carbon capture and storage infrastructure into a complex environment. International Journal of Greenhouse Gas Control. 2013;17:127-39. [DOI:10.1016/j.ijggc.2013.05.003]
57. Zhao Z, He Q, Lu Z, Zhao Q, Wang J. Analysis of atmospheric CO2 and CO at Akedala Atmospheric Background Observation Station, a regional station in Northwestern China. International Journal of Environmental Research and Public Health. 2022;19(11):6948. [DOI:10.3390/ijerph19116948] [PMID] []
58. Yarandi MS, Mahdinia M, Barazadeh J, Soltanzadeh A. The modeling of toxic consequence of Ammonia release in industrial refrigerators. J Health Field. 2020;8(3):18-31. [Persian]
59. Pasman H, Reniers G. Past, present and future of Quantitative Risk Assessment (QRA) and the incentive it obtained from Land-Use Planning (LUP). Journal of loss prevention in the process industries. 2014;28:2-9. [DOI:10.1016/j.jlp.2013.03.004]
60. Frank W, Jones D. Choosing appropriate quantitative safety risk criteria: Applications from the new CCPS guidelines. Process Safety Progress. 2010;29(4):293-8. [DOI:10.1002/prs.10404]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
