Volume 8, Issue 2 (Summer 2021)
johe 2021, 8(2): 16-23 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Mohammadfam I, Gholamizadeh K. Assessment of Security Risks by FEMA and Fuzzy FEMA Methods, A Case Study: Combined Cycle Power Plant. johe 2021; 8 (2) :16-23
URL: http://johe.umsha.ac.ir/article-1-625-en.html
URL: http://johe.umsha.ac.ir/article-1-625-en.html
1- Occupational Health and Safety Research Center, Department of Occupational Health and Safety, School of Public Health, Hamadan University of Medical Sciences
2- Occupational Health and Safety Research Center, Department of Occupational Health and Safety, School of Public Health, Hamadan University of Medical Sciences ,ka.gholamizadeh@edu.umsha.ac.ir
2- Occupational Health and Safety Research Center, Department of Occupational Health and Safety, School of Public Health, Hamadan University of Medical Sciences ,
Abstract: (3566 Views)
Background and Objective: In today's world, intentional accidents occur in many organizations due to numerous reasons. These intentional accidents usually aim to cause substantial damages to industries. To minimize the risk of these threats, it is essential to design and implement risk identification and risk assessment programs. The present study aimed to assess the risk associated with conscious threats with Federal Emergency Management Agency (FEMA) and fuzzy FEMA and compare the results of these two methods.
Materials and Methods: In the present study, FMEA and fuzzy FMEA methods were used to identify and assess terrorist threats in a combined cycle power plant. The risks were identified using FEMA checklist. Risk assessment was performed through field observation, the examination of documents, and expert opinion. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was used for prioritizing and selecting the optimum approach. Data were analyzed in SPSS software (version 21).
Results: Based on the results, although the fuzzy FEMA method requires more time, as well as higher cost of implementation and educational needs, this method allows a more accurate estimation of risk levels due to the high level of accuracy of the results, and therefore, it prioritizes the units more efficiently. Therefore, the fuzzy FEMA was introduced as the preferred method.
Conclusion: As evidenced by the results of the current study, the fuzzy FEMA method could be applied to overcome the weakness of the traditional method of FEMA. Moreover, it reduces uncertainty and increases the efficiency of organizations.
Materials and Methods: In the present study, FMEA and fuzzy FMEA methods were used to identify and assess terrorist threats in a combined cycle power plant. The risks were identified using FEMA checklist. Risk assessment was performed through field observation, the examination of documents, and expert opinion. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was used for prioritizing and selecting the optimum approach. Data were analyzed in SPSS software (version 21).
Results: Based on the results, although the fuzzy FEMA method requires more time, as well as higher cost of implementation and educational needs, this method allows a more accurate estimation of risk levels due to the high level of accuracy of the results, and therefore, it prioritizes the units more efficiently. Therefore, the fuzzy FEMA was introduced as the preferred method.
Conclusion: As evidenced by the results of the current study, the fuzzy FEMA method could be applied to overcome the weakness of the traditional method of FEMA. Moreover, it reduces uncertainty and increases the efficiency of organizations.
Type of Study: Research Article |
Subject:
Safety
Extended Abstract [HTML 33 KB] (63 Download)
References
1. Argenti F, Landucci G, Spadoni G, Cozzani V. The assessment of the attractiveness of process facilities to terrorist attacks. Saf Sci. 2015;77:169-81. DOI: 10.1016/j.ssci.2015.02.013 [DOI]
2. Guzman NC, Kufoalor DKM, Kozin I, Lundteigen MA. Combined safety and security risk analysis using the UFoI-E method: A case study of an autonomous surface vessel. 29th European Safety and Reliability Conference, Lower Saxony, Germany; 2019.
3. Landucci G, Reniers G, Cozzani V, Salzano E. Vulnerability of industrial facilities to attacks with improvised explosive devices aimed at triggering domino scenarios. Reliabil Eng Syst Saf. 2015;143:53-62. DOI: 10.1016/j.ress.2015.03.004 [DOI]
4. Howie L. Terrorism, the worker and the city: simulations and security in a time of terror. London: Routledge; 2017.
5. Hosseinnia B, Khakzad N, Reniers G. An emergency response decision matrix against terrorist attacks with improvised device in chemical clusters. Saf Secur Stud. 2018;1:187-99.
6. Reniers G, Van Lerberghe P, Van Gulijk C. Security risk assessment and protection in the chemical and process industry. Proc Saf Progr. 2015;34(1):72-83. DOI: 10.1002/prs.11683 [DOI]
7. Baybutt P. Issues for security risk assessment in the process industries. J Loss Prev Proc Ind. 2017;49:509-18. DOI: 10.1016/j.jlp.2017.05.023 [DOI]
8. Abdo H, Kaouk M, Flaus J-M, Masse F. A safety/security risk analysis approach of industrial control systems: a cyber bowtie–combining new version of attack tree with bowtie analysis. Comput Secur. 2018;72:175-95. DOI: 10.1016/j.cose.2017.09.004 [DOI]
9. Fatemi F, Ardalan A, Aguirre B, Mansouri N, Mohammadfam I. Social vulnerability indicators in disasters: Findings from a systematic review. Int J Disaster Risk Red. 2017;22:219-27. DOI: 10.1016/j.ijdrr.2016.09.006 [DOI]
10. Fatemi F, Ardalan A, Aguirre B, Mansouri N, Mohammadfam I. Areal location of hazardous atmospheres simulation on toxic chemical release: a scenario-based case study from Ray, Iran. Electron Physician. 2017;9(10):5638-45. PMID: 29238509 DOI: 10.19082/5638 [DOI] [PubMed]
11. McClintock JA, Stathakopoulos GN. Security risk response impact analysis. Washington, D.C: United States Patent; 2019.
12. Ouffroukh LA, Chaib R, Ion V, Khochmane L. Analysis of risk and the strengthening of the safety technical barriers: application of Skikda (Algeria) oil refining complex. World J Eng. 2018;15(1):99-109. DOI: 10.1108/WJE-02-2017-0031 [DOI]
13. De Silva CW. Intelligent control: fuzzy logic applications. Florida: CRC Press; 2018.
14. Roudneshin M, Azadeh A. A novel multi-objective fuzzy model for optimization of oil sludge management by considering Health, Safety and Environment (HSE) and resiliency indicators in a gas refinery. J Cleaner Product. 2019;206:559-71. DOI: 10.1016./j.jclepro.2018.09.142 [DOI]
15. John A, Yang Z, Riahi R, Wang J. A decision support system for the assessment of seaports’ security under fuzzy environment. Modeling, computing and data handling methodologies for maritime transportation. Cham: Springer; 2018. P. 145-77.
16. Mohammadfam I, Mansouri N, Nikoomaram H. Systemic accident analysis methods: a comparison of tripod-β, RCA and ECFC. Jundishapur J Health Sci. 2014;6(2):327-33.
17. Mohammadfam I, Mansouri N, Nikoomaram H, Ghasemi F. Comparison of commonly used accident analysis techniques for manufacturing industries. Int J Occup Hyg. 2015;7(1):32-7.
18. Mohammadfam I, Nikoomaram H, Lotfi F, Mansouri N, Rajabi A, Mohammadfam F. Development of a decision-making model for selecting and prioritizing accident analysis techniques in process industries. J Sci Ind Res. 2014;73(8):517-20.
19. Mohammadfam I, Kalatpour O, Gholamizadeh K. Quantitative assessment of safety and health risks in HAZMAT road transport using a hybrid approach: a case study in Tehran. ACS Chem Health Saf. 2020;27(4):240-50. DOI: 10.1021/acs.chas.0c00018 [DOI]
20. Gholamizadeh K, Mohammadfam I, Kalatpour O. Evaluation of the health consequences in Chemicals road transport accidents: Using a fuzzy approach. J Occup Hyg Eng. 2019;6(3):1-8. DOI: 10.29252/johe.6.3.1 [DOI]
21. Torabifard M, Arjmandi R, Rashidi A, Nouri J, Mohammadfam I. Inherent health and environmental risk assessment of nanostructured metal oxide production processes. Environ Monit Assess. 2018;190(2):73. PMID: 29322356 DOI: 10.1007/s10661-017-6450-0 [DOI] [PubMed]
22. Mohammadfam I, Aliabadi MM, Soltanian AR, Tabibzadeh M, Mahdinia M. Investigating interactions among vital variables affecting situation awareness based on Fuzzy DEMATEL method. Int J Ind Ergon. 2019;74:102842. DOI: 10.1016/j.ergon.2019.102842 [DOI]
23. Abbassinia M, Kalatpour O, Soltanian AR, Mohammadfam I, Ganjipour M. Determination and score of effective criteria to prioritize emergency situations in a petrochemical industry. Occup Hyg Health Promot J. 2019;3(1):16-25. DOI: 10.18502/ohhp.v3i1.961 [DOI]
24. Sadeghi A, Jabbari M, Alidoosti A, Rezaeian M. Vulnerability and security risk assessment of a thermal power plant using SVA technique. J Integrated Security Sci. 2017;1(1):16-28. DOI: 10.18757/jiss.2017.1.1390 [DOI]
25. Mazlomi A, Yari M, Haghbin A. Studying the causes and risk factors of vulnerability with SVA model and resilience engineering approach in national Iranian gas company about one of city gate stations. Occup Med. 2018;10(3):73-87.
26. Mohammadfam I, Shokouhipour A, Zamanparvar A. A framework for assessment of intentional fires. J Occup Hyg Eng. 2014;1(1):16-25.
27. Bajpai S, Sachdeva A, Gupta J. Security risk assessment: applying the concepts of fuzzy logic. J Hazard Mater. 2010;173(1-3):258-64. PMID: 19744788 DOI: 10.1016/j.jhazmat.2009.08.078 [DOI] [PubMed]
28. Mohammadfam I. Introducing a risk assessment and management model for bazaar safety analysis case study: Hamadan Bazaar. J Health Safe Work. 2012;1(2):17-22.
29. Moore DA. Application of the API/NPRA SVA methodology to transportation security issues. J Hazard Mater. 2006;130(1-2):107-21. DOI: 10.1016/j.jhazmat.2005.07.042 [DOI]
30. Moore DA, Fuller B, Hazzan M, Jones JW. Development of a security vulnerability assessment process for the RAMCAP chemical sector. J Hazard Mater. 2007;142(3):689-94. PMID: 16920260 DOI: 10.1016/j.jhazmat.2006.06.133 [DOI] [PubMed]
31. Jamshidi A, Yazdani-Chamzini A, Yakhchali SH, Khaleghi S. Developing a new fuzzy inference system for pipeline risk assessment. J Loss Prev Proc Indust. 2013;26(1):197-208. DOI: 10.1016/j.jlp.2012.10.010 [DOI]
32. Markowski AS, Mannan MS, Kotynia A, Pawlak H. Application of fuzzy logic to explosion risk assessment. J Loss Prev Proc Indust. 2011;24(6):780-90. DOI: 10.1016/j.jlp.2011.06.002 [DOI]
33. Liu HT, Tsai YL. A fuzzy risk assessment approach for occupational hazards in the construction industry. Saf Sci. 2012;50(4):1067-78. DOI: 10.1016/j.ssci.2011.11.021 [DOI]
34. Beriha G, Patnaik B, Mahapatra S, Padhee S. Assessment of safety performance in Indian industries using fuzzy approach. Exp Syst Applicat. 2012;39(3):3311-23. DOI: 10.1016/j.eswa.2011.09.018 [DOI]
35. Aliabadi MM, Gholamizadeh K. Locating urban CNG stations using quantitative risk assessment: using the Bayesian network. InSafety and Reliability 2021 Jan 2 (Vol. 40, No. 1, pp. 48-64). Taylor & Francis
36. Mohammadfam I, Gholamizadeh K. Investigation of Causes of Plasco Building Accident in Iran Using Timed MTO and ACCIMAP Methods. Journal of Failure Analysis and Prevention. 2020 Dec;20(6):2087-96
37. Mohammadfam I, Gholamizadeh K. Developing a Comprehensive Technique for Investigating Hazmat Transport Accidents. Journal of Failure Analysis and Prevention. 2021 Jun 26:1-2
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Articles Copyright © The Author(s).
Owned by Hamadan University of Medical Sciences.
Published by UMSHA Press
Journal Tel: +9881 38380090
Publisher Tel: +988138380548
Website: http://johe.umsha.ac.ir
Email: johe[at]umsha.ac.ir
