1 research outputs found
Dynamic risk assessment of process facilities using advanced probabilistic approaches
A process accident can escalate into a chain of accidents, given the degree of congestion
and complex arrangement of process equipment and pipelines. To prevent a chain of
accidents, (called the domino effect), detailed assessments of risk and appropriate safety
measures are required. The present study investigates available techniques and develops
an integrated method to analyze evolving process accident scenarios, including the domino
effect. The work presented here comprises two main contributions: a) a predictive model
for process accident analysis using imprecise and incomplete information, and b) a
predictive model to assess the risk profile of domino effect occurrence. A brief description
of each is presented below.
In recent years the Bayesian network (BN) has been used to model accident causation and
its evolution. Though widely used, conventional BN suffers from two major uncertainties,
data and model uncertainties. The former deals with the used of evidence theory while the
latter uses canonical probabilistic models.
High interdependencies of chemical infrastructure makes it prone to the domino effect.
This demands an advanced approach to monitor and manage the risk posed by the domino
effect is much needed. Given the dynamic nature of the domino effect, the monitoring and
modelling methods need to be continuous time-dependent. A Generalized Stochastic Petrinet
(GSPN) framework was chosen to model the domino effect. It enables modelling of an
accident propagation pattern as the domino effect. It also enables probability analysis to
estimate risk profile, which is of vital importance to design effective safety measures