Design documentation, safety and security analysis, environmental studies, studies on organizational factors, product characterization, etc., constitute the knowledge base each process plant, with a higher or lower detail, uses for plant management. Most of this knowledge is often lost inside an accumulation of formal documents that are not made available for practical use, while it should be disclosed and exploited within a living model of the plant (updated in real time), to which the various actors should refer to make their decisions throughout the lifecycle of the installations. How to give a shared representation of the factory (state, history, behavior), in order to improve the reliability and flow of decision-making, investment, prevention, protection, crisis management? A Risk monitoring systems and knowledge management to be integrated in the architectures of the company IoT has been proposed, developed and tested in French national institute for industrial environment and risks (INERIS). The initial risk modelling embedded in the knowledge management systems, based on the bow-tie methodology to identify the barriers for critical sequences to the Major accidents and to assess their availability, to be used for decision making, has been here integrated with the Integrated Dynamic Decision Analysis in order to obtain the critical sequences of events, that include the operator contribution (in terms of errors and recovery), the barrier effectiveness and the plant behavior. The representation of the plant in the shape of sequences allow a more user-friendly management of the information and thus a simplified control of the coherence of the risk assessment modelling with the real plant behavior, and an enhanced decision-making support in the definition of plant control measures, both technical and operational. It also allows an easier integration of the data coming from the field, with traditional or new technologies, as virtual and augmented reality. The proposed solution is exemplified through the application to an ammonia storage plant.

Integrated monitoring of risks for Seveso plants / Baldissone, G.; Comberti, L.; Demichela, M.; Marcon, T.; Plot, E.; Leva, M. C.. - STAMPA. - (2018), pp. 1909-1914. ((Intervento presentato al convegno 28th International European Safety and Reliability Conference, ESREL 2018 tenutosi a Trondheim; Norway nel 17 June 2018 through 21 June 2018.

Integrated monitoring of risks for Seveso plants

Baldissone G.;Comberti L.;Demichela M.;
2018

Abstract

Design documentation, safety and security analysis, environmental studies, studies on organizational factors, product characterization, etc., constitute the knowledge base each process plant, with a higher or lower detail, uses for plant management. Most of this knowledge is often lost inside an accumulation of formal documents that are not made available for practical use, while it should be disclosed and exploited within a living model of the plant (updated in real time), to which the various actors should refer to make their decisions throughout the lifecycle of the installations. How to give a shared representation of the factory (state, history, behavior), in order to improve the reliability and flow of decision-making, investment, prevention, protection, crisis management? A Risk monitoring systems and knowledge management to be integrated in the architectures of the company IoT has been proposed, developed and tested in French national institute for industrial environment and risks (INERIS). The initial risk modelling embedded in the knowledge management systems, based on the bow-tie methodology to identify the barriers for critical sequences to the Major accidents and to assess their availability, to be used for decision making, has been here integrated with the Integrated Dynamic Decision Analysis in order to obtain the critical sequences of events, that include the operator contribution (in terms of errors and recovery), the barrier effectiveness and the plant behavior. The representation of the plant in the shape of sequences allow a more user-friendly management of the information and thus a simplified control of the coherence of the risk assessment modelling with the real plant behavior, and an enhanced decision-making support in the definition of plant control measures, both technical and operational. It also allows an easier integration of the data coming from the field, with traditional or new technologies, as virtual and augmented reality. The proposed solution is exemplified through the application to an ammonia storage plant.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2725041
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