Oil/gas and petrochemical plants are complicated and dynamic in nature. Dynamic characteristics include ageing of equipment/components, season changes, stochastic processes, operator response times, inspection and testing time intervals, sequential dependencies of equipment/components and timing of safety system operations, all of which are time dependent criteria that can influence dynamic processes. The conventional risk assessment methodologies can quantify dynamic changes in processes with limited capacity. Therefore, it is important to develop method that can address time-dependent effects. The primary objective of this study is to propose a risk assessment methodology for dynamic systems. In this study, a new technique for dynamic operational risk assessment is developed based on the Bayesian networks, a structure optimal suitable to organize cause-effect relations. The Bayesian network graphically describes the dependencies of variables and the dynamic Bayesian network capture change of variables over time. This study proposes to develop dynamic fault tree for a chemical process system/sub-system and then to map it in Bayesian network so that the developed method can capture dynamic operational changes in process due to sequential dependency of one equipment/component on others. The developed Bayesian network is then extended to the dynamic Bayesian network to demonstrate dynamic operational risk assessment. A case study on a holdup tank problem is provided to illustrate the application of the method. A dryout scenario in the tank is quantified. It has been observed that the developed method is able to provide updated probability different equipment/component failure with time incorporating the sequential dependencies of event occurrence. Another objective of this study is to show parallelism of Bayesian network with other available risk assessment methods such as event tree, HAZOP, FMEA. In this research, an event tree mapping procedure in Bayesian network is described. A case study on a chemical reactor system is provided to illustrate the mapping procedure and to identify factors that have significant influence on an event occurrence. Therefore, this study provides a method for dynamic operational risk assessment capable of providing updated probability of event occurrences considering sequential dependencies with time and a model for mapping event tree in Bayesian network.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2012-08-11573 |
| Date | 2012 August 1900 |
| Creators | Barua, Shubharthi |
| Contributors | Mannan, M.Sam |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
Page generated in 0.0018 seconds