Abstract
Nowadays hazard identification and risk assessment play an established and fundamental role for the
prevention of major accidents in the process industries. Despite their proved effectiveness, many hazard
identification and risk assessment techniques lack the dynamic dimension, which is the ability to learn from
new risk notions, experience and early warnings. Nevertheless, recent major disasters have raised the need to
go beyond the limits of conventional static methods for hazard identification and risk assessment. The
necessity to address risk issues in a continuously evolving environment, coupled with improved information
and communication technologies, led in the last few years to the development of several advanced dynamic
techniques for hazard identification and risk assessment in process systems. Eventually dynamic approaches
to risk have proved to be capable of identifying and assessing emerging and increasing risks throughout the
lifetime of the process. Recent applications have shown the effectiveness of dynamic approaches to major
accidents, as well as to maintenance activities. Despite the relevant differences among the mentioned
approaches, all these dynamic methods aim at dealing with uncertainties, system complexity, real-time
changing environments and real-time information from different sources with enhanced flexibility, in respect to
conventional approaches. The present study addresses dynamic approaches to hazard identification and risk
assessment in the process industry. These novel methods will be inserted in the broader framework of
dynamic risk management. These techniques will be joined with representative applications based on real
events. The results of the mentioned applications are used to show how risk can be assessed by means of
continuous activities of monitoring and review, coupled with real time risk evaluation. The ability of such
dynamic approaches to capture general failures and risk management deficits demonstrate their effectiveness,
both in risk management and in the prevention of major accidents, providing a more robust decision-making
within the process industry context.
prevention of major accidents in the process industries. Despite their proved effectiveness, many hazard
identification and risk assessment techniques lack the dynamic dimension, which is the ability to learn from
new risk notions, experience and early warnings. Nevertheless, recent major disasters have raised the need to
go beyond the limits of conventional static methods for hazard identification and risk assessment. The
necessity to address risk issues in a continuously evolving environment, coupled with improved information
and communication technologies, led in the last few years to the development of several advanced dynamic
techniques for hazard identification and risk assessment in process systems. Eventually dynamic approaches
to risk have proved to be capable of identifying and assessing emerging and increasing risks throughout the
lifetime of the process. Recent applications have shown the effectiveness of dynamic approaches to major
accidents, as well as to maintenance activities. Despite the relevant differences among the mentioned
approaches, all these dynamic methods aim at dealing with uncertainties, system complexity, real-time
changing environments and real-time information from different sources with enhanced flexibility, in respect to
conventional approaches. The present study addresses dynamic approaches to hazard identification and risk
assessment in the process industry. These novel methods will be inserted in the broader framework of
dynamic risk management. These techniques will be joined with representative applications based on real
events. The results of the mentioned applications are used to show how risk can be assessed by means of
continuous activities of monitoring and review, coupled with real time risk evaluation. The ability of such
dynamic approaches to capture general failures and risk management deficits demonstrate their effectiveness,
both in risk management and in the prevention of major accidents, providing a more robust decision-making
within the process industry context.
