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An integrated model for risk in a real-time environment for the hydrocarbon industry based on the CATS model for commercial aviation safety has been further developed. The approach described in earlier papers required Bayesian Belief Nets (BBN) to be developed for each process unit separately. A much more efficient method for developing the continuous BBNs is based on standard building blocks consisting of nodes for a piece of equipment and nodes for potential safety features. Elements of the BBN can be switched on or off as required. From the equipment inventory of a chemical plant a complete BBN can be built automatically. Humans are represented by multiple instances of the same BBN structure, but with different parameters. The resulting computer program calculates the frequency of Loss of Containment Accidents for an existing or a proposed plant including the effects of human behaviour and intervention, including rigorous handling of the uncertainties.

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The recent blow-out and subsequent environmental disaster in the Gulf of Mexico have highlighted a number of serious problems in scientific thinking about safety. Risk models have generally concentrated on technical failures, which are easier to model and for which there are more concrete data. However, many primary cause of the disasters, such as BP's Texas City and Deepwater Horizon, are rooted in management decisions and organizational. Therefore, there is a strong need to develop a risk management support tool for chemical process industries which incorporates human and organizational factors into quantified risk models. In this paper, we model two human performance model for oil and gas company Royal Dutch Shell. Interviews were conducted to obtain important human factors. As the quality and operation of the management actions have important influences on human factors (e.g. safety attitude, training), we have linked a safety management model with the human factors model and quantify the risk implications of different management changes to prevent accidents. The methodology of integrating organisational factors into a Bayesian Belief Networks (BBNs) model is discussed in Lin et al. (2012). In this paper the development and quantification of human and management factors for an international oil company is given. own personnel and the personnel hired through contractors are built. The structure of the human models and their development are described in more detail in section 3. The influences of management on human model are shown in section 4. This section also discusses the influence of management which follows with the conclusions.

The complexity of the cities’ layout and other public spaces, together with the large number of people involved leads to increased strain on the resources of emergency responders. An accident, such as a fire, remains a rare event so it is difficult for those in charge of preparing for an emergency and deciding on the acceptability of risk to get a picture of such an event. The interest of all emergency response agencies is to minimize the impact of disaster events on the entities of interest, which include first of all the human population. For this, there is need for a tool that helps the decision makers estimate the distribution of the fire outcome, given different information about the environment in which the fire takes place. This paper discusses the possibility of using continuous Bayesian belief nets for the study of the factors that influence the risk to which the people involved in a building fire are exposed, and how these factors influence the risk. The big advantage of Bayesian belief net approach is that it can model uncertain events. The distribution of the variables of interest can be easily updated given information about some of the other variables. Moreover, the intuitive visual representation of the problem at hand can help people to understand complex systems or processes, like a fire in a building. In this study, the approach is tested for a small example and the results are analyzed. The possibility of extending this method to a more complex model is discussed.