Volume 20, Issue 2 (2024)
ioh 2024, 20(2): 280-295 |
Back to browse issues page
Ethics code: IR.MUBAM.REC.1402.084
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Fazli Z, laal F, keighobadi E, Ebrahimi H, Falah Medvari R, moradi hanifi S. Quantitative Risk assessment of Gasoline Storage Tank Farm Unit using by Fuzzy Set Theory and Consequence modeling. ioh 2024; 20 (2) :280-295
URL: http://ioh.iums.ac.ir/article-1-3510-en.html
URL: http://ioh.iums.ac.ir/article-1-3510-en.html
Zohreh Fazli 
, Fereydoon Laal , Elham Keighobadi , Hossein Ebrahimi , Rohullah Falah Medvari , Saber Moradi hanifi *
, Fereydoon Laal , Elham Keighobadi , Hossein Ebrahimi , Rohullah Falah Medvari , Saber Moradi hanifi *
Iran University of Medical Sciences , sabermoradi22@yahoo.com
Abstract: (768 Views)
Introduction: Storage tanks are always one of the equipment that can cause serious risks for industries and nearby units due to the complex process conditions. Releasing the substances of these tanks can lead to consequences such as fiery explosions and the dispersion of toxic substances. Therefore, identifying the causes and modeling their consequences is considered essential.
Material and Methods: In this study, the bow tie diagram for identifying the cause-and-effect diagram of diesel tanks, and also fuzzy theory for determining the failure rate of basic events, were used. In order to determine the probability of basic events, fuzzy theory and experts' opinions were considered, and finally, phast8.2 software was used for modeling possible scenarios.
Results: The results of the bow tie analysis showed that a total of 45 basic events and 4 consequences, including pool fire, eruption fire, sudden fire and explosion were identified. The result of consequence modeling showed that the maximum intensity of the thermal radiation caused by the pool fire for the diesel tank was equal to 23 kW / m2 and the maximum increase in the blast wave was estimated to be 19.7 bar. The results of risk evaluation for the consequences of pool fire, eruptive fire and steam explosion showed that the estimated risk number is more than 104 and are in the unacceptable range.
Conclusion: Using the bow tie method in combination with consequence modeling can provide experts with a more open view of the accidents along with causes and consequences which happen for storage tanks
Material and Methods: In this study, the bow tie diagram for identifying the cause-and-effect diagram of diesel tanks, and also fuzzy theory for determining the failure rate of basic events, were used. In order to determine the probability of basic events, fuzzy theory and experts' opinions were considered, and finally, phast8.2 software was used for modeling possible scenarios.
Results: The results of the bow tie analysis showed that a total of 45 basic events and 4 consequences, including pool fire, eruption fire, sudden fire and explosion were identified. The result of consequence modeling showed that the maximum intensity of the thermal radiation caused by the pool fire for the diesel tank was equal to 23 kW / m2 and the maximum increase in the blast wave was estimated to be 19.7 bar. The results of risk evaluation for the consequences of pool fire, eruptive fire and steam explosion showed that the estimated risk number is more than 104 and are in the unacceptable range.
Conclusion: Using the bow tie method in combination with consequence modeling can provide experts with a more open view of the accidents along with causes and consequences which happen for storage tanks
Type of Study: Research |
Subject:
Safety
Received: 2023/04/24 | Accepted: 2024/01/14 | Published: 2023/12/31
Received: 2023/04/24 | Accepted: 2024/01/14 | Published: 2023/12/31
References
1. 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.
2. Moradi Hanifi S, Omidi L, Moradi G. Risk calculation and consequences simulation of natural gas leakage accident using ALOHA software. Journal of Health and Safety at Work. 2019;9(1):13-20.
3. Veritas DN. OREDA-offshore reliability data handbook. OREDA Participants, Norway. 2002.
4. CCPS. Guidelines for Process Equipment Reliability Data, with Data Tables: Wiley-AIChE; 1989.
5. Less FP. Loss prevention in the process industries: hazard identification, assessment and control: Butterworth-Heinemann; 1980.
6. 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]
7. 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.
8. Jafari MJ, Pouyakian M, Mozaffari P, Laal F, Mohamadi H, Pour MT, et al. A new approach to chemicals warehouse risk analysis using computational fluid dynamics simulation and fuzzy Bayesian network. Heliyon. 2022;8(12). [DOI:10.1016/j.heliyon.2022.e12520]
9. 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]
10. 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]
11. 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]
12. Assael MJ, Kakosimos KE. Fires, explosions, and toxic gas dispersions: effects calculation and risk analysis: CRC Press; 2010. [DOI:10.1201/9781439826768]
13. Hort M, Robbins A. The dispersion of fugitive emissions from storage tanks. Journal of Wind Engineering and Industrial Aerodynamics. 2002;90(11):1321-48. [DOI:10.1016/S0167-6105(02)00252-0]
14. Jones D, Berger S, editors. How to Select Appropriate Quantitative Safety Risk Criteria-Applications from the Center for Chemical Process Safety (CCPS) Guidelines on Quantitative Safety Risk Criteria. SPE International Conference on Health, Safety and Environment in Oil and Gas Exploration and Production; 2010: OnePetro. [DOI:10.2523/127012-MS]
15. Ghamari F, Mohammadfam I, Mohammadbeigi A, Ebrahimi H, Khodayari M. Determination of Effective Risk Factors in Incidence of Occupational Accidents in One of the Large Metal Industries, Arak (2005-2007). Iran Occupational Health. 2013;9(4).
16. Khoshakhlagh AH, Yazdanirad S, Kashani MM, Khatooni E, Hatamnegad Y, Kabir S. A Bayesian network based study on determining the relationship between job stress and safety climate factors in occurrence of accidents. BMC public health. 2021;21(1):1-12. [DOI:10.1186/s12889-021-12298-z]
17. Heidari A, Shafiei Karaji F, Mousavi M. Evaluation the Factors that Affecting the Performance of HSE Unit in Pars Service Port. Iranian Journal of Marine Science and Technology. 2022;26(101):41-50.
18. 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. 2014;10(6):29-41.
19. 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):1-9. [DOI:10.1186/s40359-023-01133-2]
20. 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]
21. Tixier J, Dusserre G, Salvi O, Gaston D. Review of 62 risk analysis methodologies of industrial plants. Journal of Loss Prevention in the process industries. 2002;15(4):291-303. [DOI:10.1016/S0950-4230(02)00008-6]
22. 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).
23. Marhavilas P-K, Koulouriotis D, Gemeni V. Risk analysis and assessment methodologies in the work sites: On a review, classification and comparative study of the scientific literature of the period 2000-2009. Journal of Loss Prevention in the Process Industries. 2011;24(5):477-523. [DOI:10.1016/j.jlp.2011.03.004]
24. Moradi Hanifi S, Omidi L, Moradi G. Risk calculation and consequences simulation of natural gas leakage accident using ALOHA software. Health and Safety at Work. 2019;9(1):13-20.
25. 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]
26. 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]
27. SINTEF O. Offshore Reliability Data Handbook. Høvik: Det Norske Veritas. 2002.
28. CCPS C. Guidelines for Process Equipment Reliability Data, with Data Tables. AIChE New York; 1989.
29. Association NOI. Application of IEC 61508 and IEC 61511 in the Norwegian Petroleum Industry. Norway: The Norwegian Oil Industry Association. 2004.
30. Jianxing Y, Haicheng C, Yang Y, Zhenglong Y. A weakest t-norm based fuzzy fault tree approach for leakage risk assessment of submarine pipeline. Journal of Loss Prevention in the Process Industries. 2019;62:103968. [DOI:10.1016/j.jlp.2019.103968]
31. Hosseini N, Givehchi S, Maknoon R. Cost-based fire risk assessment in natural gas industry by means of fuzzy FTA and ETA. Journal of Loss Prevention in the Process Industries. 2020;63:104025. [DOI:10.1016/j.jlp.2019.104025]
32. Jafari M, Pouyakian M, Hanifi S. Development of a framework for dynamic risk assessment of environmental impacts in chemicals warehouse using CFD-BN. International Journal of Environmental Science and Technology. 2021:1-16. [DOI:10.1007/s13762-020-03040-0]
33. Hanna S, Britter R, Leung J, Hansen O, Sykes I, Drivas P. Source emissions and transport and dispersion models for toxic industrial chemicals (tics) released in cities. J14. 2009;1.
34. Murphy MJ, Taylor JD, McCormick RL. Compendium of experimental cetane number data. 2004. [DOI:10.2172/1086353]
35. King R. Safety in the process industries: Elsevier; 2013.
36. Badri N, Nourai F, Rashtchian D. The role of quantitative risk assessment in improving hazardous installations siting: a case study. Iranian Journal of Chemistry and Chemical Engineering (IJCCE). 2011;30(4):113-9.
37. Jahangiri M, norozi ma, sareban zadeh k. Risk assessment and management: Fanavaran; 1392.
38. 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]
39. Ouache R, Adham A. Reliability quantitative risk assessment in engineering system using fuzzy bow-tie. Int J Curr Eng Technol. 2014;4(2):1117-23.
40. de Ruijter A, Guldenmund F. The bowtie method: A review. Safety science. 2016;88:211-8. [DOI:10.1016/j.ssci.2016.03.001]
41. Safety CfCP. Guidelines for chemical process quantitative risk analysis: Center for Chemical Process Safety/AIChE; 2000.
42. Participants O. OREDA Offshore Reliability Data Handbook. DNV, PO Box; 2002.
43. Khosravirad F, Zarei E, Mohammadfam I, Shoja E, Majidi Daryani M. Explosion risk analysis on Town Border Stations (TBS) of natural gas using Failure Mode & Effect Analysis (FMEA (and Fault Tree Analyses (FTA (methods. Iran Occupational Health. 2016;12(6):16-27.
44. rahimi r, Mohammadfam I, varshosaz k. Fuel oil tank explosion risk assessment using fault tree analysis. the 2nd conference on environmental planing and management2012.
45. Tan Q, Chen G, Zhang L, Fu J, Li Z. Dynamic accident modeling for high-sulfur natural gas gathering station. Process Safety and Environmental Protection. 2014;92(6):565-76. [DOI:10.1016/j.psep.2013.03.004]
46. (RIVM) NIoPHatE, Safety CfE. Reference Manual Bevi Risk Assessments Introduction. Version 3.2 ed2009.
47. Jafari mj, Mirza S, Omidvari M, Lavasani SMRM. The application of Fuzzy logic to determine the failure probability in Fault Tree Risk Analysis. Safety promotion and injury prevention (Tehran). 2014;2(2):113-23.
48. Ishikawa A, Amagasa M, Shiga T, Tomizawa G, Tatsuta R, Mieno H. The max-min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy sets and systems. 1993;55(3):241-53. [DOI:10.1016/0165-0114(93)90251-C]
49. 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]
50. 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]
51. 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]
52. Omidvari M, Lavasani S, Mirza S. Presenting of failure probability assessment pattern by FTA in Fuzzy logic (case study: Distillation tower unit of oil refinery process). Journal of Chemical Health and Safety. 2014;21(6):14-22. [DOI:10.1016/j.jchas.2014.06.003]
53. Chen S-J, Hwang C-L. Fuzzy multiple attribute decision making methods. Fuzzy multiple attribute decision making: Springer; 1992. p. 289-486. [DOI:10.1007/978-3-642-46768-4_5]
54. 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]
55. 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]
56. Lees F. Lees' Loss prevention in the process industries: Hazard identification, assessment and control: Butterworth-Heinemann; 2012.
57. Tixier J, Dusserre G, Rault-Doumax S, Ollivier J, Bourely C. OSIRIS: software for the consequence evaluation of transportation of dangerous goods accidents. Environmental modelling & software. 2002;17(7):627-37. [DOI:10.1016/S1364-8152(02)00025-7]
58. Moradi Hanifi S, Laal F, Panjali Z, Khoubi J. Health risk Assessment of exposure to harmful chemical agents in a refinery. Archives of Occupational Health. 2019;3(1):299-306. [DOI:10.18502/aoh.v3i1.349]
59. Cozzani V, Salzano E. The quantitative assessment of domino effects caused by overpressure: Part I. Probit models. Journal of Hazardous Materials. 2004;107(3):67-80. [DOI:10.1016/j.jhazmat.2003.09.013]
60. Jones D, Berger S, editors. How to Select Appropriate Quantitative Safety Risk Criteria Applications from the Center for Chemical Process Safety (CCPS) Guidelines on Quantitative Safety Risk Criteria. SPE International Conference on Health, Safety and Environment in Oil and Gas Exploration and Production; 2010: Society of Petroleum Engineers. [DOI:10.2523/127012-MS]
61. sekhavati A, norozi H, shojaie A. application of fault tree analysis in a gas compressor unite. journal of explosion and production oil and gas. 2013.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
