Generating cause-implication graphs for process systems via blended hazard identification methods
Causal knowledge in complex process systems is a powerful representational model that permits a range of important applications related to process risk management. These include the development of operator training systems, diagnosis tools, emergency response planning as well as implications on proc...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/46058 |
| _version_ | 1848757455221161984 |
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| author | Németh, E. Seligmann, Ben Hockings, K. Oakley, J. O'Brien, C. Hangos, K. Cameron, I. |
| author_facet | Németh, E. Seligmann, Ben Hockings, K. Oakley, J. O'Brien, C. Hangos, K. Cameron, I. |
| author_sort | Németh, E. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Causal knowledge in complex process systems is a powerful representational model that permits a range of important applications related to process risk management. These include the development of operator training systems, diagnosis tools, emergency response planning as well as implications on process and control system retrofit and design. Using a blended hazard identification approach we show how causal knowledge can be generated from design documentation and represented in a structured language, which is then amenable to display cause-implication graphs that explicitly show the links between failures, causes and implications. A case study illustrates the application of the methodology to a safety system in an industrial coke making plant. |
| first_indexed | 2025-11-14T09:28:22Z |
| format | Journal Article |
| id | curtin-20.500.11937-46058 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:28:22Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-460582017-09-13T15:15:02Z Generating cause-implication graphs for process systems via blended hazard identification methods Németh, E. Seligmann, Ben Hockings, K. Oakley, J. O'Brien, C. Hangos, K. Cameron, I. Causal knowledge in complex process systems is a powerful representational model that permits a range of important applications related to process risk management. These include the development of operator training systems, diagnosis tools, emergency response planning as well as implications on process and control system retrofit and design. Using a blended hazard identification approach we show how causal knowledge can be generated from design documentation and represented in a structured language, which is then amenable to display cause-implication graphs that explicitly show the links between failures, causes and implications. A case study illustrates the application of the methodology to a safety system in an industrial coke making plant. 2011 Journal Article http://hdl.handle.net/20.500.11937/46058 10.1016/B978-0-444-53711-9.50214-5 restricted |
| spellingShingle | Németh, E. Seligmann, Ben Hockings, K. Oakley, J. O'Brien, C. Hangos, K. Cameron, I. Generating cause-implication graphs for process systems via blended hazard identification methods |
| title | Generating cause-implication graphs for process systems via blended hazard identification methods |
| title_full | Generating cause-implication graphs for process systems via blended hazard identification methods |
| title_fullStr | Generating cause-implication graphs for process systems via blended hazard identification methods |
| title_full_unstemmed | Generating cause-implication graphs for process systems via blended hazard identification methods |
| title_short | Generating cause-implication graphs for process systems via blended hazard identification methods |
| title_sort | generating cause-implication graphs for process systems via blended hazard identification methods |
| url | http://hdl.handle.net/20.500.11937/46058 |