Accident consequence modeling procedure

This model is wholly compatible with the Bowtie model of the accident process, leading from exposure to hazards, through a central loss of control event to accident consequences Figure 2.7.3). This model links the barriers, preventing the accident process from propagating, which may be hardware barriers or behavioural/procedural barriers, to the management system needed to put them in place and keep them functioning. 

Safety historical data has been highlighted as part of the conceptual plant safety model. Figure 3-11 showed the detailed data groups of safety historical data component where safety historical data is divided into two parts plant specifications, which covers all specification changes due to design, operation, or any other process throughout the plant lifecycle and accident/incident data, which includes plant, events, process, equipment, cause, consequence, and procedures in-place. 

The next part of the procedure involves risk assessment. This includes a deterrnination of the accident probabiUty and the consequence of the accident and is done for each of the scenarios identified in the previous step. The probabiUty is deterrnined using a number of statistical models generally used to represent failures. The consequence is deterrnined using mostiy fundamentally based models, called source models, to describe how material is ejected from process equipment. These source models are coupled with a suitable dispersion model and/or an explosion model to estimate the area affected and predict the damage. The consequence is thus determined. 

This model of accident causation is described further in Figure 1.3. This represents the defenses against accidents as a series of shutters (engineered safety systems, safety procedures, emergency training, etc.) When the gaps in these shutters come into coincidence then the results of earlier hardware or human failures will not be recovered and the consequences will occur. Inap-... 

The health consequences on the population must be estimated by taking into consideration the type of the accident and the population distribution in the area as a function of time. To calculate the variation of the spatial population density, we have developed a stochastic model that simulates the evacuation procedure. More precisely, we have adopted a Markovian type stochastic model to simulate the movement of the population (Georgiadou et al, 2006). 

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