Which Fire to Extinguish First? A Risk‐Informed Approach to Emergency Response in Oil Terminals

Khakzad, Nima

doi:10.1111/risa.12946

Cited by 30 publications

(33 citation statements)

References 26 publications

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“…When a domino scenario occurs, the engineering measure can reduce hazards and determine the number of installations in danger and set the corresponding best emergency strategy for different units. Furthermore, in emergencies, the fire protection team can intervene according to the best plan planned [49].…”

Section: Discussionmentioning

confidence: 99%

Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network

Zhu

et al. 2019

Sustainability

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Many chemical plant explosion accidents occur along with the development of the chemical industry. Meanwhile, the interaction and influence of various factors significantly increase the uncertainty of the evolution process of such accidents. This paper presents a framework to dynamically evaluate chemical plant explosion accidents. We propose twelve nodes to represent accident evolution and establish a Bayesian network model for chemical plant explosion accidents, combining historical data with expert experience to support the prevention, management, and real-time warning. Hypothetical scenarios and catastrophic explosion scenarios were analyzed by setting different combinations of states for nodes. Moreover, the impacts of factors such as factory type, material form, accident equipment, the emergency response on casualty and property loss are evaluated. We find that sensitivity of property loss and casualties to factory type and ongoing work are less significant; the equipment factors result in more casualties than that from personnel factors; the impact of emergency response on the accident results is significant; equipment safety management and personnel safety training are the most important measures to prevent chemical plant explosion risks.

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Section: Discussionmentioning

confidence: 99%

Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network

Zhu

et al. 2019

Sustainability

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“…Firefighting plays a key role in preventing or delaying fire spread. The role of firefighting becomes even more important considering the possibility of damage or malfunction of add-on safety systems [29] or insufficient firefighting resources [19,30,31]. The effectiveness of firefighting strongly depends on the number, skills and the level of preparedness of firefighting crews as well as the amount and the type of firefighting equipment.…”

Section: Modeling Of Firefightingmentioning

confidence: 99%

A Tutorial on Fire Domino Effect Modeling Using Bayesian Networks

Khakzad

2021

Modelling

Self Cite

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High complexity and growing interdependencies of chemical and process facilities have made them increasingly vulnerable to domino effects. Domino effects, particularly fire dominoes, are spatial-temporal phenomena where not only the location of involved units, but also their temporal entailment in the accident chain matter. Spatial-temporal dependencies and uncertainties prevailing during domino effects, arising mainly from possible synergistic effects and randomness of potential events, restrict the use of conventional risk assessment techniques such as fault tree and event tree. Bayesian networks—a type of probabilistic network for reasoning under uncertainty—have proven to be a reliable and robust technique for the modeling and risk assessment of domino effects. In the present study, applications of Bayesian networks to modeling and safety assessment of domino effects in petroleum tank terminals has been demonstrated via some examples. The tutorial starts by illustrating the inefficacy of event tree analysis in domino effect modeling and then discusses the capabilities of Bayesian network and its derivatives such as dynamic Bayesian network and influence diagram. It is also discussed how noisy OR can be used to significantly reduce the complexity and number of conditional probabilities required for model establishment.

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“…Several works have also been performed for standby systems that use standby sparing components to continue the mission in the case of an online component malfunctioning. There are three standby modes: hot, cold, and warm (Khakzad, 2018; Levitin, Xing, & Dai, 2013; Xing, Levitin, & Wang, 2019). A hot standby component operates in parallel with the online component, providing immediate restoration of system functioning when the online component malfunctions.…”

Section: Introductionmentioning

confidence: 99%

Mission Abort Policy for Systems with Observable States of Standby Components

Levitin

Xing

2020

Risk Analysis

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For some critical applications, successfully accomplishing the mission or surviving the system through aborting the mission and performing a rescue procedure in the event of certain deterioration condition being satisfied are both pivotal. This has motivated considerable studies on mission abort policies (MAPs) to mitigate the risk of system loss in the past several years, especially for standby systems that use one or multiple standby sparing components to continue the mission when the online component fails, improving the mission success probability. The existing MAPs are mainly based on the number of failed online components ignoring the status of the standby components. This article makes contributions by modeling standby systems subject to MAPs that depend not only on the number of failed online components but also on the number of available standby components remaining. Further, dynamic MAPs considering another additional factor, the time elapsed from the mission beginning in the event of the mission abort decision making, are investigated. The solution methodology encompasses an event-transition based numerical algorithm for evaluating the mission success probability and system survival probability of standby systems subject to the considered MAPs. Examples are provided to demonstrate the benefit of considering the state of standby components and elapsed operation time in obtaining more flexible MAPs.

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Which Fire to Extinguish First? A Risk‐Informed Approach to Emergency Response in Oil Terminals

Cited by 30 publications

References 26 publications

Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network

Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network

A Tutorial on Fire Domino Effect Modeling Using Bayesian Networks

Mission Abort Policy for Systems with Observable States of Standby Components

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