A stochastic approach for risk analysis in vapor cloud explosion |
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| Institution: | 1. Instituto Tecnológico de Celaya, Departamento de Ingeniería Química, Av. Tecnológico y A.G. Cubas s/n, Celaya 38010, Gto., Mexico;2. Instituto Tecnológico de Roque, Departamento de Ingenierías, km 8 carretera, Celaya-Juventino Rosas CP 38110, Gto., Mexico;3. Mary Kay O''Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843-3122, USA;1. Center for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China;2. Tianjin University-Curtin University Joint Research Centre of Structure Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University, WA 6102, Australia;1. ExxonMobil Research Qatar Science and Technology Park, PO Box 22500, Doha, Qatar;2. Mary Kay O''Connor Process Safety Center at Qatar, Texas A&M University at Qatar, PO Box 27874, Doha, Qatar |
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| Abstract: | A stochastic approach for evaluating the risk of vapor cloud explosions is proposed in this work. The proposed methodology aims to incorporate the effect of uncertainty into the risk analysis to produce a better overall view for the risk. Some stochastic variables are used to estimate the probability of vapor cloud explosions: frequency of the release, the probability of not having an immediate ignition, the probability of delayed ignition and the probability of a vapor cloud explosion given a delayed ignition, as well as different possible meteorological conditions. These stochastic variables are represented with probability distribution curves. Different curves for the frequencies of releases from process equipment types (steel process pipes, flanges, manual valves, actuated valves, etc.), different equipment diameters and different leak sizes are also used in this analysis. Monte Carlo simulation is performed to obtain the risk as a probability distribution using the Analytic Solver Platform. Then the risk distribution curve obtained by Monte Carlo simulation is used to estimate the probability of satisfying the risk tolerance criterion. |
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| Keywords: | VCE Monte Carlo simulation Risk analysis Stochastic methodology Probability distributions |
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