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.
