Kalantarnia, Maryam (2009) Dynamic risk assessment using accident precursor data and Bayesian theory. Masters thesis, Memorial University of Newfoundland.
- Accepted Version
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To improve the safety of a process system, engineers use different methods to identify the potential hazards. Chemical processes involve handling of hazardous chemicals, which on release may potentially cause catastrophic consequences in terms of assets lost, human fatalities or injuries and loss of public confidence in the company. In spite of using endless end-of-the-pipe safety systems, tragic accidents such as BP Texas City refinery still occur. One of the main reasons of such rare but catastrophic event occurrences is lack of effective monitoring and modeling approaches that provide early warnings and help to prevent such events. Other reasons such as lack of criteria and/or assessment parameters and measures for detection of abnormal events may also have an important contribution. -- One of the most popular methods used in the industry today is quantitative risk assessment (QRA) which quantifies the risk associated with a particular process activity by determining the likelihood of occurrence of an unwanted event and the consequences involved. One of QRA's major disadvantages is its inability to update risk during the life of a process. As the process/system operates, abnormal events will result in incidents and near misses. These events are often called accident precursors. A conventional QRA process is unable to use the accident precursor information to revise the risk profile. -- Dynamic failure assessment is a new approach in process safety management, which enables the real time failure analysis of a process. Dynamic failure assessment has been used in the past by nuclear industries for accident likelihood estimation using accident precursors. Recently it has been successfully applied to process units to revise failure probabilities using incident and near miss data. In dynamic risk assessment, an extension of dynamic failure assessment, Bayesian and joint probability theories are used to develop a predictive failure model for a given process. As the process/system operates and generates incidents and near misses, the accident occurrence probability is predicted using accident precursors and later multiplied with consequences to quantify real time risk. -- In this thesis the dynamic risk assessment methodology is discussed in detail. First, potential accident scenarios are identified and represented in terms of an event tree, next, using the event tree and available failure data end state probabilities are estimated. Subsequently, using the available accident precursor data, safety system failure likelihood and event tree end state probabilities are revised. Finally, the updated probabilities are used in revising the risk profile of the process system. -- Application of this tool is demonstrated by two case studies. The first case study is the process facility of an offshore oil and gas platform where the risk profiles of process units are determined over time. In the second case study dynamic risk assessment is applied to the BP Texas City refinery in order to demonstrate the predictive abilities of the tool. Dynamic risk assessment demonstrates the importance of a learning and predictive tool in risk assessment by verifying significant changes in system failure frequency in comparison with the conventional QRA approach.
|Item Type:||Thesis (Masters)|
|Additional Information:||Includes bibliographical references (leaves 75-81)|
|Department(s):||Engineering and Applied Science, Faculty of|
|Library of Congress Subject Heading:||Bayesian statistical decision theory; Chemical processes--Risk assessment; Industrial safety--Mathematical models; Risk assessment--Mathematical models|
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