Dynamic safety and security risk management of hazardous operations

Song, Guozheng (2018) Dynamic safety and security risk management of hazardous operations. Doctoral (PhD) thesis, Memorial University of Newfoundland.

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Abstract

Hazardous operations, such as the operations in process plants, are confronted by three major risks: occupational, process and intentional damage risks. Previous works have studied these risks independently. Furthermore, these works failed to consider many important elements. For example: 1) Hazardous operations are expanding to remote areas in harsh environments, and thus harsh environmental factors need to be included in the assessment model to deal with this emerging challenge. 2) Scarce prior data can cause uncertainty of assessment results. Conventional assessment methods, such as fault trees, produce static outcomes which neither reduce the uncertainty caused by scarce data nor reflect the latest risks. 3) Variables in the models are considered to be discrete (normally binary). This approximation reduces the accuracy of assessment results. 4) Influence of intrusion scenarios on security risks is not considered. 5) Safety and security have interactions which can influence the real risk level and decision making. Existing works neither conduct a dynamic assessment of integrated risk considering such interaction in a robust framework, nor do they analyze the measure selection for the effective prevention of integrated risks. To overcome these limitations, this research establishes a dynamic model which includes harsh environmental factors to quantify the occupational risks and identify the critical causal factors. Moreover, a continuous Bayesian network is proposed to represent the continuous variables. Intrusion scenarios have been included in the dynamic assessment model for intrusion risk. The critical intrusion scenarios and weak links of the security system are identified. Then the interaction of safety and security is analyzed in an integrated framework. Its influence on risk level and decision making is studied using a Bayesian network and influence diagram. These methods applied in this research not only reduce the uncertainty of assessment results, but also explore a new area of integrated risk assessment and management.

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