Risk-based domino effect analysis for fire and explosion accidents considering uncertainty in processing facilities

Jie Ji, Qi Tong, Faisal Khan, Mohammad Dadashzadeh, Rouzbeh Abbassi

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Process facilities are vulnerable to catastrophic accidents due to the storage, transportation and processing of large amounts of flammable/explosive materials. Among a variety of accident scenarios, fire and explosion are the most frequent ones. Fire and explosion are interactive events and may cause a ‘chain of accidents’ (also known as the ‘domino effect’). Especially in processing facilities where units are located within a limited distance, fire or explosion occurring in one unit is likely to spread to other units. Currently, there is a lack of proper methodology that considers the effect of fire and explosion interaction. Ignoring this interaction provides uncertainty in the domino effect risk analysis. High complexity and uncertainty, due to the interaction of fire and explosion, thus make it challenging to analyze the domino effect propagation. Fuzzy Inference System (FIS) is known to be an efficient tool for handling uncertainty and imprecision. The current study has developed a new methodology by adopting FIS method to handle the data uncertainties in the dynamic Bayesian network (DBN) to conduct a robust domino effect analysis considering interactions of fire and explosion. Application of the proposed methodology demonstrates that the FIS acts as a quick semi-quantitative method involved in the domino effect analysis. Results obtained from FIS are consistent with those obtained using the DBN. Moreover, it illustrates that DBN is an effective technique to analyze the combination of a fire and explosion accident.
LanguageEnglish
Pages3990-4006
JournalIndustrial and Engineering Chemistry Research
Volume57
Issue number11
DOIs
Publication statusPublished - 1 Mar 2018

Fingerprint

Explosions
Accidents
Fires
Fuzzy inference
Processing
Bayesian networks
Risk analysis
Uncertainty

Keywords

  • Risk analysis
  • domino effect
  • fire and explosion
  • Fuzzy Inference System
  • dynamic Bayesian network

Cite this

Ji, Jie ; Tong, Qi ; Khan, Faisal ; Dadashzadeh, Mohammad ; Abbassi, Rouzbeh. / Risk-based domino effect analysis for fire and explosion accidents considering uncertainty in processing facilities. 2018 ; Vol. 57, No. 11. pp. 3990-4006.
@article{e4ffebba43da4ac7b80029959c4e329c,
title = "Risk-based domino effect analysis for fire and explosion accidents considering uncertainty in processing facilities",
abstract = "Process facilities are vulnerable to catastrophic accidents due to the storage, transportation and processing of large amounts of flammable/explosive materials. Among a variety of accident scenarios, fire and explosion are the most frequent ones. Fire and explosion are interactive events and may cause a ‘chain of accidents’ (also known as the ‘domino effect’). Especially in processing facilities where units are located within a limited distance, fire or explosion occurring in one unit is likely to spread to other units. Currently, there is a lack of proper methodology that considers the effect of fire and explosion interaction. Ignoring this interaction provides uncertainty in the domino effect risk analysis. High complexity and uncertainty, due to the interaction of fire and explosion, thus make it challenging to analyze the domino effect propagation. Fuzzy Inference System (FIS) is known to be an efficient tool for handling uncertainty and imprecision. The current study has developed a new methodology by adopting FIS method to handle the data uncertainties in the dynamic Bayesian network (DBN) to conduct a robust domino effect analysis considering interactions of fire and explosion. Application of the proposed methodology demonstrates that the FIS acts as a quick semi-quantitative method involved in the domino effect analysis. Results obtained from FIS are consistent with those obtained using the DBN. Moreover, it illustrates that DBN is an effective technique to analyze the combination of a fire and explosion accident.",
keywords = "Risk analysis, domino effect, fire and explosion, Fuzzy Inference System, dynamic Bayesian network",
author = "Jie Ji and Qi Tong and Faisal Khan and Mohammad Dadashzadeh and Rouzbeh Abbassi",
note = "Reference text: (1) Pat{\'e}‐Cornell, M. E. Learning from the piper alpha accident: A postmortem analysis of technical and organizational factors. Risk Anal. 1993, 13, 215-232. (2) Dole, E.; Scannell, G. Phillips 66 company Houston chemical complex explosion and fire. A report to the President. Occupational Safety and Health Administration, US Department of Labor, 1990. (3) Ouddai, R.; Chabane, H.; Boughaba, A.; Frah, M. The Skikda LNG accident: losses, lessons learned and safety climate assessment. Int. J. Global Energ. Issues 2012, 35, 518-533. (4) Kalantarnia, M.; Khan, F.; Hawboldt, K. Modelling of BP Texas City refinery accident using dynamic risk assessment approach. Process Saf. Environ. Prot. 2010, 88, 191-199. (5) Mishra, K. B.; Wehrstedt, K.-D.; Krebs, H. Lessons learned from recent fuel storage fires. Fuel Process. Technol. 2013, 107, 166-172. (6) Wan, H.; Ji, J.; Li, K.; Huang, X.; Sun, J.; Zhang, Y. Effect of air entrainment on the height of buoyant turbulent diffusion flames for two fires in open space. Proc. Combust. Inst. 2017, 36, 3003-3010. (7) Wan, H.; Gao, Z.; Ji, J.; Sun, J.; Zhang, Y.; Li, K. Predicting heat fluxes received by horizontal targets from two buoyant turbulent diffusion flames of propane burning in still air. Combust. Flame 2018, 190, 260-269. (8) Khan, F. I.; Abbasi, S. Techniques and methodologies for risk analysis in chemical process industries. J. Loss Prev. Process Ind. 1998, 11, 261-277. (9) Tixier, J.; Dusserre, G.; Salvi, O.; Gaston, D. Review of 62 risk analysis methodologies of industrial plants. J. Loss Prev. Process Ind. 2002, 15, 291-303. (10) Pietersen, C. Analysis of the LPG-disaster in Mexico City. J. Hazard. Mater. 1988, 20, 85-107. (11) Pritchard, D. A review of methods for predicting blast damage from vapour cloud explosions. J. Loss Prev. Process Ind. 1989, 2, 187-193. (12) Khan, F. I.; Abbasi, S. Models for domino effect analysis in chemical process industries. Process Saf. Prog. 1998, 17, 107-123. (13) Cozzani, V.; Gubinelli, G.; Antonioni, G.; Spadoni, G.; Zanelli, S. The assessment of risk caused by domino effect in quantitative area risk analysis. J. Hazard. Mater. 2005, 127, 14-30. (14) Cozzani, V.; Tugnoli, A.; Salzano, E. Prevention of domino effect: from active and passive strategies to inherently safer design. J. Hazard. Mater. 2007, 139, 209-219. (15) Cozzani, V.; Salzano, E. The quantitative assessment of domino effects caused by overpressure: Part I. Probit models. J. Hazard. Mater. 2004, 107, 67-80. (16) Cozzani, V.; Salzano, E. The quantitative assessment of domino effect caused by overpressure: Part II. Case studies. J. Hazard. Mater. 2004, 107, 81-94. (17) Cozzani, V.; Gubinelli, G.; Salzano, E. Escalation thresholds in the assessment of domino accidental events. J. Hazard. Mater. 2006, 129, 1-21. (18) Landucci, G.; Necci, A.; Antonioni, G.; Argenti, F.; Cozzani, V. Risk assessment of mitigated domino scenarios in process facilities. Reliab Eng. Syst. Saf. 2017, 160, 37-53. (19) Landucci, G.; Argenti, F.; Spadoni, G.; Cozzani, V. Domino effect frequency assessment: The role of safety barriers. J. Loss Prev. Process Ind. 2016, 44, 706-717. (20) Landucci, G.; Argenti, F.; Tugnoli, A.; Cozzani, V. Quantitative assessment of safety barrier performance in the prevention of domino scenarios triggered by fire. Reliab Eng. Syst. Saf. 2015, 143, 30-43. (21) Khakzad, N.; Khan, F.; Amyotte, P.; Cozzani, V. Domino effect analysis using Bayesian networks. Risk Anal. 2013, 33, 292-306. (22) Khakzad, N.; Khan, F.; Amyotte, P.; Cozzani, V. Risk management of domino effects considering dynamic consequence analysis. Risk Anal. 2014, 34, 1128-1138. (23) Khakzad, N.; Landucci, G.; Cozzani, V.; Reniers, G.; Pasman, H. Cost-effective fire protection of chemical plants against domino effects. Reliab Eng. Syst. Saf. 2018, 169, 412-421. (24) Khakzad, N. Which Fire to Extinguish First? A Risk‐Informed Approach to Emergency Response in Oil Terminals. Risk Anal. 2017. (25) Khakzad, N. Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures. Reliab Eng. Syst. Saf. 2015, 138, 263-272. (26) Khakzad, N.; Reniers, G. Using graph theory to analyze the vulnerability of process plants in the context of cascading effects. Reliab Eng. Syst. Saf. 2015, 143, 63-73. (27) Khakzad, N.; Landucci, G.; Reniers, G. Application of Graph Theory to Cost‐Effective Fire Protection of Chemical Plants During Domino Effects. Risk Anal. 2017, 37, 1652-1667. (28) Markowski, A. S.; Mannan, M. S.; Bigoszewska, A. Fuzzy logic for process safety analysis. J. Loss Prev. Process Ind. 2009, 22, 695-702. (29) Assael, M. J.; Kakosimos, K. E. Fires, explosions, and toxic gas dispersions: effects calculation and risk analysis; CRC Press, 2010. (30) Khan, F. I.; Abbasi, S. Risk analysis of a typical chemical industry using ORA procedure. J. Loss Prev. Process Ind. 2001, 14, 43-59. (31) Landucci, G.; Gubinelli, G.; Antonioni, G.; Cozzani, V. The assessment of the damage probability of storage tanks in domino events triggered by fire. Accid. Anal. Prev. 2009, 41, 1206-1215. (32) Eisenberg, N. A.; Lynch, C. J.; Breeding, R. J. Vulnerability model. A simulation system for assessing damage resulting from marine spills; Enviro control inc rockville md,1975. (33) Finney, D. J.; Tattersfield, F. Probit analysis; Cambridge University Press, Cambridge, 1952. (34) Khakzad, N.; Landucci, G.; Reniers, G. Application of dynamic Bayesian network to performance assessment of fire protection systems during domino effects. Reliab Eng. Syst. Saf. 2017. (35) Zadeh, L. A. Fuzzy sets. Inf. Control 1965, 8, 338-353. (36) Bellman, R. E.; Zadeh, L. A. Decision-making in a fuzzy environment. Manage. Sci. 1970, 17, B-141-B-164. (37) Markowski, A. S.; Mannan, M. S.; Kotynia, A.; Pawlak, H. Application of fuzzy logic to explosion risk assessment. J. Loss Prev. Process Ind. 2011, 24, 780-790. (38) Yang, M.; Khan, F. I.; Sadiq, R. Prioritization of environmental issues in offshore oil and gas operations: A hybrid approach using fuzzy inference system and fuzzy analytic hierarchy process. Process Saf. Environ. Prot. 2011, 89, 22-34. (39) Markowski, A. S.; Mannan, M. S. Fuzzy risk matrix. J. Hazard. Mater. 2008, 159, 152-157. (40) Sivanandam, S.; Sumathi, S.; Deepa, S. Introduction to fuzzy logic using MATLAB; Springer, 2007; Vol. 1. (41) Markowski, A. S.; Mannan, M. S. Fuzzy logic for piping risk assessment (pfLOPA). J. Loss Prev. Process Ind. 2009, 22, 921-927. (42) Mur{\`e}, S.; Demichela, M. Fuzzy Application Procedure (FAP) for the risk assessment of occupational accidents. J. Loss Prev. Process Ind. 2009, 22, 593-599. (43) Markowski, A. S.; Mannan, M. S.; Kotynia, A.; Siuta, D. Uncertainty aspects in process safety analysis. J. Loss Prev. Process Ind. 2010, 23, 446-454. (44) Elsayed, T. Fuzzy inference system for the risk assessment of liquefied natural gas carriers during loading/offloading at terminals. Appl. Ocean Res. 2009, 31, 179-185. (45) Khakzad, N.; Khan, F.; Amyotte, P. Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches. Reliab Eng. Syst. Saf. 2011, 96, 925-932. (46) Yuan, Z.; Khakzad, N.; Khan, F.; Amyotte, P. Risk analysis of dust explosion scenarios using Bayesian networks. Risk Anal. 2015, 35, 278-291. (47) Yuan, Z.; Khakzad, N.; Khan, F.; Amyotte, P. Domino effect analysis of dust explosions using Bayesian networks. Process Saf. Environ. Prot. 2016, 100, 108-116. (48) Yang, Y.; Khan, F.; Thodi, P.; Abbassi, R. Corrosion induced failure analysis of subsea pipelines. Reliab Eng. Syst. Saf. 2017, 159, 214-222. (49) Khakzad, N.; Khan, F.; Amyotte, P. Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network. Process Saf. Environ. Prot. 2013, 91, 46-53. (50) Kohda, T.; Cui, W. Risk-based reconfiguration of safety monitoring system using dynamic Bayesian network. Reliab Eng. Syst. Saf. 2007, 92, 1716-1723. (51) Codetta-Raiteri, D.; Bobbio, A.; Montani, S.; Portinale, L. A dynamic Bayesian network based framework to evaluate cascading effects in a power grid. Eng. Appl. Artif. Intel. 2012, 25, 683-697. (52) Khakzad, N.; Khan, F.; Amyotte, P. Risk-based design of process systems using discrete-time Bayesian networks. Reliab Eng. Syst. Saf. 2013, 109, 5-17. (53) Van Den Bosh, C.; Weterings, R. Methods for the calculation of physical effects (Yellow Book); Committee for the Prevention of Disasters, The Hague (NL) 1997. (54) Woodward, J. L.; Pitbaldo, R. LNG Risk Based Safety: modeling and consequence analysis; John Wiley & Sons, 2010. (55) Jujuly, M. M.; Rahman, A.; Ahmed, S.; Khan, F. LNG pool fire simulation for domino effect analysis. Reliab Eng. Syst. Saf. 2015, 143, 19-29. (56) De Haag, P. U.; Ale, B. Guidelines for Quantitative Risk Assessment: Purple Book; Ministerie van Volkshuisvesting en Ruimtelijke Ordening (VROM), 2005. (57) US Environmental Protection Agency, National Oceanic and Atmospheric Administration. ALOHA user manual. http://www.epa.gov/OEM/cameo/aloha.htm; 2014 (accessed on 21 June 2014). (58) R Development Core Team. R: a language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org; 2009. (59) GeNie. Decision Systems Laboratory, University of Pittsburg. http://www.genie.sis.pitt.edu. (60) Dadashzadeh, M.; Khan, F.; Hawboldt, K.; Amyotte, P. An integrated approach for fire and explosion consequence modelling. Fire Saf. J. 2013, 61, 324-337. (61) CSB, 2009, Caribbean Petroleum Tank Terminal Explosion and Multiple Tank Fires; Final Investigation Report. http://www.csb.gov/assets/1/19/CAPECO_Final_Report__10.21.2015.pdf.",
year = "2018",
month = "3",
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doi = "10.1021/acs.iecr.8b00103",
language = "English",
volume = "57",
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Risk-based domino effect analysis for fire and explosion accidents considering uncertainty in processing facilities. / Ji, Jie; Tong, Qi; Khan, Faisal; Dadashzadeh, Mohammad; Abbassi, Rouzbeh.

Vol. 57, No. 11, 01.03.2018, p. 3990-4006.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Risk-based domino effect analysis for fire and explosion accidents considering uncertainty in processing facilities

AU - Ji, Jie

AU - Tong, Qi

AU - Khan, Faisal

AU - Dadashzadeh, Mohammad

AU - Abbassi, Rouzbeh

N1 - Reference text: (1) Paté‐Cornell, M. E. Learning from the piper alpha accident: A postmortem analysis of technical and organizational factors. Risk Anal. 1993, 13, 215-232. (2) Dole, E.; Scannell, G. Phillips 66 company Houston chemical complex explosion and fire. A report to the President. Occupational Safety and Health Administration, US Department of Labor, 1990. (3) Ouddai, R.; Chabane, H.; Boughaba, A.; Frah, M. The Skikda LNG accident: losses, lessons learned and safety climate assessment. Int. J. Global Energ. Issues 2012, 35, 518-533. (4) Kalantarnia, M.; Khan, F.; Hawboldt, K. Modelling of BP Texas City refinery accident using dynamic risk assessment approach. Process Saf. Environ. Prot. 2010, 88, 191-199. (5) Mishra, K. B.; Wehrstedt, K.-D.; Krebs, H. Lessons learned from recent fuel storage fires. Fuel Process. Technol. 2013, 107, 166-172. (6) Wan, H.; Ji, J.; Li, K.; Huang, X.; Sun, J.; Zhang, Y. Effect of air entrainment on the height of buoyant turbulent diffusion flames for two fires in open space. Proc. Combust. Inst. 2017, 36, 3003-3010. (7) Wan, H.; Gao, Z.; Ji, J.; Sun, J.; Zhang, Y.; Li, K. Predicting heat fluxes received by horizontal targets from two buoyant turbulent diffusion flames of propane burning in still air. Combust. Flame 2018, 190, 260-269. (8) Khan, F. I.; Abbasi, S. Techniques and methodologies for risk analysis in chemical process industries. J. Loss Prev. Process Ind. 1998, 11, 261-277. (9) Tixier, J.; Dusserre, G.; Salvi, O.; Gaston, D. Review of 62 risk analysis methodologies of industrial plants. J. Loss Prev. Process Ind. 2002, 15, 291-303. (10) Pietersen, C. Analysis of the LPG-disaster in Mexico City. J. Hazard. Mater. 1988, 20, 85-107. (11) Pritchard, D. A review of methods for predicting blast damage from vapour cloud explosions. J. Loss Prev. Process Ind. 1989, 2, 187-193. (12) Khan, F. I.; Abbasi, S. Models for domino effect analysis in chemical process industries. Process Saf. Prog. 1998, 17, 107-123. (13) Cozzani, V.; Gubinelli, G.; Antonioni, G.; Spadoni, G.; Zanelli, S. The assessment of risk caused by domino effect in quantitative area risk analysis. J. Hazard. Mater. 2005, 127, 14-30. (14) Cozzani, V.; Tugnoli, A.; Salzano, E. Prevention of domino effect: from active and passive strategies to inherently safer design. J. Hazard. Mater. 2007, 139, 209-219. (15) Cozzani, V.; Salzano, E. The quantitative assessment of domino effects caused by overpressure: Part I. Probit models. J. Hazard. Mater. 2004, 107, 67-80. (16) Cozzani, V.; Salzano, E. The quantitative assessment of domino effect caused by overpressure: Part II. Case studies. J. Hazard. Mater. 2004, 107, 81-94. (17) Cozzani, V.; Gubinelli, G.; Salzano, E. Escalation thresholds in the assessment of domino accidental events. J. Hazard. Mater. 2006, 129, 1-21. (18) Landucci, G.; Necci, A.; Antonioni, G.; Argenti, F.; Cozzani, V. Risk assessment of mitigated domino scenarios in process facilities. Reliab Eng. Syst. Saf. 2017, 160, 37-53. (19) Landucci, G.; Argenti, F.; Spadoni, G.; Cozzani, V. Domino effect frequency assessment: The role of safety barriers. J. Loss Prev. Process Ind. 2016, 44, 706-717. (20) Landucci, G.; Argenti, F.; Tugnoli, A.; Cozzani, V. Quantitative assessment of safety barrier performance in the prevention of domino scenarios triggered by fire. Reliab Eng. Syst. Saf. 2015, 143, 30-43. (21) Khakzad, N.; Khan, F.; Amyotte, P.; Cozzani, V. Domino effect analysis using Bayesian networks. Risk Anal. 2013, 33, 292-306. (22) Khakzad, N.; Khan, F.; Amyotte, P.; Cozzani, V. Risk management of domino effects considering dynamic consequence analysis. Risk Anal. 2014, 34, 1128-1138. (23) Khakzad, N.; Landucci, G.; Cozzani, V.; Reniers, G.; Pasman, H. Cost-effective fire protection of chemical plants against domino effects. Reliab Eng. Syst. Saf. 2018, 169, 412-421. (24) Khakzad, N. Which Fire to Extinguish First? A Risk‐Informed Approach to Emergency Response in Oil Terminals. Risk Anal. 2017. (25) Khakzad, N. Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures. Reliab Eng. Syst. Saf. 2015, 138, 263-272. (26) Khakzad, N.; Reniers, G. Using graph theory to analyze the vulnerability of process plants in the context of cascading effects. Reliab Eng. Syst. Saf. 2015, 143, 63-73. (27) Khakzad, N.; Landucci, G.; Reniers, G. Application of Graph Theory to Cost‐Effective Fire Protection of Chemical Plants During Domino Effects. Risk Anal. 2017, 37, 1652-1667. (28) Markowski, A. S.; Mannan, M. S.; Bigoszewska, A. Fuzzy logic for process safety analysis. J. Loss Prev. Process Ind. 2009, 22, 695-702. (29) Assael, M. J.; Kakosimos, K. E. Fires, explosions, and toxic gas dispersions: effects calculation and risk analysis; CRC Press, 2010. (30) Khan, F. I.; Abbasi, S. Risk analysis of a typical chemical industry using ORA procedure. J. Loss Prev. Process Ind. 2001, 14, 43-59. (31) Landucci, G.; Gubinelli, G.; Antonioni, G.; Cozzani, V. The assessment of the damage probability of storage tanks in domino events triggered by fire. Accid. Anal. Prev. 2009, 41, 1206-1215. (32) Eisenberg, N. A.; Lynch, C. J.; Breeding, R. J. Vulnerability model. A simulation system for assessing damage resulting from marine spills; Enviro control inc rockville md,1975. (33) Finney, D. J.; Tattersfield, F. Probit analysis; Cambridge University Press, Cambridge, 1952. (34) Khakzad, N.; Landucci, G.; Reniers, G. Application of dynamic Bayesian network to performance assessment of fire protection systems during domino effects. Reliab Eng. Syst. Saf. 2017. (35) Zadeh, L. A. Fuzzy sets. Inf. Control 1965, 8, 338-353. (36) Bellman, R. E.; Zadeh, L. A. Decision-making in a fuzzy environment. Manage. Sci. 1970, 17, B-141-B-164. (37) Markowski, A. S.; Mannan, M. S.; Kotynia, A.; Pawlak, H. Application of fuzzy logic to explosion risk assessment. J. Loss Prev. Process Ind. 2011, 24, 780-790. (38) Yang, M.; Khan, F. I.; Sadiq, R. Prioritization of environmental issues in offshore oil and gas operations: A hybrid approach using fuzzy inference system and fuzzy analytic hierarchy process. Process Saf. Environ. Prot. 2011, 89, 22-34. (39) Markowski, A. S.; Mannan, M. S. Fuzzy risk matrix. J. Hazard. Mater. 2008, 159, 152-157. (40) Sivanandam, S.; Sumathi, S.; Deepa, S. Introduction to fuzzy logic using MATLAB; Springer, 2007; Vol. 1. (41) Markowski, A. S.; Mannan, M. S. Fuzzy logic for piping risk assessment (pfLOPA). J. Loss Prev. Process Ind. 2009, 22, 921-927. (42) Murè, S.; Demichela, M. Fuzzy Application Procedure (FAP) for the risk assessment of occupational accidents. J. Loss Prev. Process Ind. 2009, 22, 593-599. (43) Markowski, A. S.; Mannan, M. S.; Kotynia, A.; Siuta, D. Uncertainty aspects in process safety analysis. J. Loss Prev. Process Ind. 2010, 23, 446-454. (44) Elsayed, T. Fuzzy inference system for the risk assessment of liquefied natural gas carriers during loading/offloading at terminals. Appl. Ocean Res. 2009, 31, 179-185. (45) Khakzad, N.; Khan, F.; Amyotte, P. Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches. Reliab Eng. Syst. Saf. 2011, 96, 925-932. (46) Yuan, Z.; Khakzad, N.; Khan, F.; Amyotte, P. Risk analysis of dust explosion scenarios using Bayesian networks. Risk Anal. 2015, 35, 278-291. (47) Yuan, Z.; Khakzad, N.; Khan, F.; Amyotte, P. Domino effect analysis of dust explosions using Bayesian networks. Process Saf. Environ. Prot. 2016, 100, 108-116. (48) Yang, Y.; Khan, F.; Thodi, P.; Abbassi, R. Corrosion induced failure analysis of subsea pipelines. Reliab Eng. Syst. Saf. 2017, 159, 214-222. (49) Khakzad, N.; Khan, F.; Amyotte, P. Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network. Process Saf. Environ. Prot. 2013, 91, 46-53. (50) Kohda, T.; Cui, W. Risk-based reconfiguration of safety monitoring system using dynamic Bayesian network. Reliab Eng. Syst. Saf. 2007, 92, 1716-1723. (51) Codetta-Raiteri, D.; Bobbio, A.; Montani, S.; Portinale, L. A dynamic Bayesian network based framework to evaluate cascading effects in a power grid. Eng. Appl. Artif. Intel. 2012, 25, 683-697. (52) Khakzad, N.; Khan, F.; Amyotte, P. Risk-based design of process systems using discrete-time Bayesian networks. Reliab Eng. Syst. Saf. 2013, 109, 5-17. (53) Van Den Bosh, C.; Weterings, R. Methods for the calculation of physical effects (Yellow Book); Committee for the Prevention of Disasters, The Hague (NL) 1997. (54) Woodward, J. L.; Pitbaldo, R. LNG Risk Based Safety: modeling and consequence analysis; John Wiley & Sons, 2010. (55) Jujuly, M. M.; Rahman, A.; Ahmed, S.; Khan, F. LNG pool fire simulation for domino effect analysis. Reliab Eng. Syst. Saf. 2015, 143, 19-29. (56) De Haag, P. U.; Ale, B. Guidelines for Quantitative Risk Assessment: Purple Book; Ministerie van Volkshuisvesting en Ruimtelijke Ordening (VROM), 2005. (57) US Environmental Protection Agency, National Oceanic and Atmospheric Administration. ALOHA user manual. http://www.epa.gov/OEM/cameo/aloha.htm; 2014 (accessed on 21 June 2014). (58) R Development Core Team. R: a language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org; 2009. (59) GeNie. Decision Systems Laboratory, University of Pittsburg. http://www.genie.sis.pitt.edu. (60) Dadashzadeh, M.; Khan, F.; Hawboldt, K.; Amyotte, P. An integrated approach for fire and explosion consequence modelling. Fire Saf. J. 2013, 61, 324-337. (61) CSB, 2009, Caribbean Petroleum Tank Terminal Explosion and Multiple Tank Fires; Final Investigation Report. http://www.csb.gov/assets/1/19/CAPECO_Final_Report__10.21.2015.pdf.

PY - 2018/3/1

Y1 - 2018/3/1

N2 - Process facilities are vulnerable to catastrophic accidents due to the storage, transportation and processing of large amounts of flammable/explosive materials. Among a variety of accident scenarios, fire and explosion are the most frequent ones. Fire and explosion are interactive events and may cause a ‘chain of accidents’ (also known as the ‘domino effect’). Especially in processing facilities where units are located within a limited distance, fire or explosion occurring in one unit is likely to spread to other units. Currently, there is a lack of proper methodology that considers the effect of fire and explosion interaction. Ignoring this interaction provides uncertainty in the domino effect risk analysis. High complexity and uncertainty, due to the interaction of fire and explosion, thus make it challenging to analyze the domino effect propagation. Fuzzy Inference System (FIS) is known to be an efficient tool for handling uncertainty and imprecision. The current study has developed a new methodology by adopting FIS method to handle the data uncertainties in the dynamic Bayesian network (DBN) to conduct a robust domino effect analysis considering interactions of fire and explosion. Application of the proposed methodology demonstrates that the FIS acts as a quick semi-quantitative method involved in the domino effect analysis. Results obtained from FIS are consistent with those obtained using the DBN. Moreover, it illustrates that DBN is an effective technique to analyze the combination of a fire and explosion accident.

AB - Process facilities are vulnerable to catastrophic accidents due to the storage, transportation and processing of large amounts of flammable/explosive materials. Among a variety of accident scenarios, fire and explosion are the most frequent ones. Fire and explosion are interactive events and may cause a ‘chain of accidents’ (also known as the ‘domino effect’). Especially in processing facilities where units are located within a limited distance, fire or explosion occurring in one unit is likely to spread to other units. Currently, there is a lack of proper methodology that considers the effect of fire and explosion interaction. Ignoring this interaction provides uncertainty in the domino effect risk analysis. High complexity and uncertainty, due to the interaction of fire and explosion, thus make it challenging to analyze the domino effect propagation. Fuzzy Inference System (FIS) is known to be an efficient tool for handling uncertainty and imprecision. The current study has developed a new methodology by adopting FIS method to handle the data uncertainties in the dynamic Bayesian network (DBN) to conduct a robust domino effect analysis considering interactions of fire and explosion. Application of the proposed methodology demonstrates that the FIS acts as a quick semi-quantitative method involved in the domino effect analysis. Results obtained from FIS are consistent with those obtained using the DBN. Moreover, it illustrates that DBN is an effective technique to analyze the combination of a fire and explosion accident.

KW - Risk analysis

KW - domino effect

KW - fire and explosion

KW - Fuzzy Inference System

KW - dynamic Bayesian network

U2 - 10.1021/acs.iecr.8b00103

DO - 10.1021/acs.iecr.8b00103

M3 - Article

VL - 57

SP - 3990

EP - 4006

IS - 11

ER -