This paper concerns the design and development of a dynamic risk assessment tool, and the determination of a dynamic indicator of the safety level. The DYN-RISK system is intended to be a predictive decision-making tool, which provides a constantly updated risk, based on the ongoing operations, for the whole plant and all the components. The aim of the DYN-RISK project is to overcome the limitations of the classical approaches to QRA, mainly related to their intrinsically static nature, which reflects on an inability to adequately catch interdependencies and likelihoods in fault trees and event trees. In accordance with the resilience engineering practice, the objective of this project is to enhance the operational control, focusing on the overall performance of the complex system constituted by plant and environment. The DYN-RISK system relies on a robust Machine Learning architecture providing the system the ability to automatically learn and improve from experience, without being explicitly programmed. The overall model includes Deep Neural Networks, for predicting the critical process variables values, Hidden Markov Models for inferring the most probable sequence of states for the system, and Hierarchical Bayesian Networks, for combining Boolean events, such malfunctions and failures of critical equipment, and system states predictions. The final outcome is a real time dynamic risk indicator. The DYN-RISK system is validated on a Chemical and Petrochemical storage terminal in northern Italy.

DYN-RISK- Design and Development of a Dynamic Risk Assessment Tool

Vairo T.;Pettinato M.;Fabiano B.
2022

Abstract

This paper concerns the design and development of a dynamic risk assessment tool, and the determination of a dynamic indicator of the safety level. The DYN-RISK system is intended to be a predictive decision-making tool, which provides a constantly updated risk, based on the ongoing operations, for the whole plant and all the components. The aim of the DYN-RISK project is to overcome the limitations of the classical approaches to QRA, mainly related to their intrinsically static nature, which reflects on an inability to adequately catch interdependencies and likelihoods in fault trees and event trees. In accordance with the resilience engineering practice, the objective of this project is to enhance the operational control, focusing on the overall performance of the complex system constituted by plant and environment. The DYN-RISK system relies on a robust Machine Learning architecture providing the system the ability to automatically learn and improve from experience, without being explicitly programmed. The overall model includes Deep Neural Networks, for predicting the critical process variables values, Hidden Markov Models for inferring the most probable sequence of states for the system, and Hierarchical Bayesian Networks, for combining Boolean events, such malfunctions and failures of critical equipment, and system states predictions. The final outcome is a real time dynamic risk indicator. The DYN-RISK system is validated on a Chemical and Petrochemical storage terminal in northern Italy.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/1089071
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