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Vapor cloud explosions (VCEs) are serious hazards in refining and petrochemical industries. Statistics indicate that 75% of the total losses were caused by explosion so that considerable research effort has been focused on this subject. Consequences of an explosion are aerial overpressure and impulse, responsible for injury to humans and structures. Many studies were performed with fuel-air explosions but few works focused on fuel- oxygen mixtures explosion. In this study, experiments were performed on a large experimental field. In order to represent realistic conditions, the shape of the gas envelope was elongated like the dispersion shape from leakage with small wind. The flammable cloud was ignited with SEMTEX explosive charges put at the center of the gas volume envelopes. Overpressure values were collected at different positions on the field. Flame propagation was recorded by high speed camera and gave a constant velocity of 2384 m.s-1, indicating a detonation regime.

A possible event during a domino effect escalation is the projection and impact of a fragment on a storage. The fragment can be a consequence of a previous industrial accident, or may originate from a terrorist action. If this previous event (triggering event) is an explosion, the fragment impact can be combined with others effects such as blast or fireball. Many papers can be found about the impact of a fragment, a blast or heat fluxes on a tank during a domino effect. But very few work focused on a combination fragment + blast + fire ball. An experimental work was performed in order to study that topic. Fragments were launched against tanks containing LPG. A pyrotechnic setup was used to generate a blast effect and a fire ball. The influence of three effects (fragment, blast, heat) on the tank is be presented and discussed in order to better understand the domino effect occurrence in chemical plants.

Assessment of likely consequences of a potential accident is a major concern for loss prevention and safety promotion in process industry. Loss of confinement on a storage tank, vessel or piping on industrial sites could imply atmospheric dispersion of toxic or flammable gases. Gas dispersion forecasting is a difficult task since turbulence modeling at large scale involves expensive calculations. Therefore simpler models are used but remain inaccurate especially when turbulence is heterogeneous. The present work aims to study if Artificial Neural Networks coupled with Cellular Automata could be relevant to overcome these gaps. Two methods are reviewed and compared. An example database was designed from RANS k- e CFD model. Both methods were then applied. Their efficiencies are compared and discussed in terms of quality, real-time applicability and real-life plausibility.

Industrial activities are increasingly dependent on each other. Several recent events (e.g. Tsunami and earthquake in Japan in 2011) illustrate the consequences (e.g. humans, economic…) of interactions between industrial infrastructures. The state-of-the-art review focused mainly on the risk assessment and interdependencies between critical infrastructures. The aim of this paper is to present an approach able to determine an infrastructure vulnerability level and risk scenarios development based on Seveso regulations and critical infrastructures. The vulnerability infrastructure level is assessed with a multi-criteria analysis. The goal is to identify several criteria according to physic, functional, economic, social and environment context of the infrastructure in the territory. The risk scenarios are developed in order to estimate the consequences in a territory of a threat on one or several infrastructures.

Assessment of likely consequences of a potential accident is a major concern of loss prevention and safety promotion in process industry. Loss of confinement on a storage tank, vessel or piping on industrial sites may imply atmospheric dispersion of toxic or flammable gases. Gas dispersion forecasting is a difficult task since turbulence modeling at large scale involves expensive calculations. Therefore simpler models are used but remain inaccurate especially in near field of the gas source. The present work aims to study if Neural Networks and Cellular Automata could be relevant to overcome these gaps. These tools were investigated on steady state and dynamic state. A database was designed from RANS k-E CFD and Gaussian plume models. Both methods were then applied. Their efficiencies are compared and discussed in terms of quality, real-time applicability and real-life plausibility.