Process accident model considering dependency among contributory factors

Abstract With the increasing complexity of the hazardous process operation, potential accident modelling is becoming challenging. In process operation accidents, causation is a function of nonlinear interactions of various factors. Traditional accident models such as the fault tree represent cause and effect relationships without considering the dependency and nonlinear interaction of the causal factors. This paper presents a new non-sequential barrier-based process accident model. The model uses both fault and event tree analysis to study the cause–consequence relationship. The dependencies and nonlinear interaction among failure causes are modelled using a Bayesian network (BN) with various relaxation strategies. The proposed model considers six prevention barriers in the accident causation process: design error, operational failure, equipment failure, human failure and external factor prevention barriers. Each barrier is modelled using BN and the interactions within the barrier are also modelled using BN. The proposed model estimates the lower and upper bounds of prevention barriers failure probabilities, considering dependencies and non-linear interaction among causal factors. Based on these failure probabilities, the model predicts the lower and upper bounds of the process accident causation probability. The proposed accident model is tested on a real life case study.