Abstract

n innovative risk analysis model has been developed in order to quantify and analyse safety risks related to loss-of- containment scenarios in the pipeline transport of CO2. The risk model integrates the identified failure modes, consequence estimates and emergency response, producing consistent risk profiles based on complete outcome spaces and for different system design choices. The method involves integration in a Bayesian Belief Network (BN) of analytical equations for gas dispersion combined with statistics and expert estimates of particularly uncertain variables. Future failure initiators, scenarios and impacts are captured in a graphical structure which represents and calculates the effects of common causes. The test case for the integrated risk model will be a large CO2 capture and transport network at the Le Havre industrial zone with export to Rotterdam. The primary relative advantages of the BN risk model approach are discussed.

Abstract

Runway excursion (RE) is recognized as one of the main Airport Safety Issues. It can be defined as an event in which an aircraft veers-off or overruns the runway surface during either takeoff or landing. In this paper an application of Bayesian Belief Network (BBN) method on RE events is presented. BBN belongs to quantitative class of causal methods. It estimates the risk of incident or accident according to estimation of probability of occurrence of each cause of an event. It might be restricted to pure statistical analysis based on the available data or combine these data with expert judgment (personal beliefs) on the accident causes. The aim of this paper is to identify potential causal factors, analyze and determine the probability of their realization. In order to illustrate BBN application on RE, two models were developed: one for landing and other for take-off. Based on the Ishikawa diagram method, causal factors have been identified, and then through a qualitative BBN model, their interdependencies are presented. The quantification of the model is accomplished by combining statistical data with the expert beliefs. Sensitivity analysis has shown what are the most critical causal factors whose knowledge allows developing certain measures to reduce the risk of RE. Document type: Article