In *Methods in Chemical Process Safety*, volume 1, pages 201–254. Elsevier, January, 2017.

Paper doi abstract bibtex

Paper doi abstract bibtex

This chapter places recent advances in dynamic risk analyses in perspective. After some preliminary concepts such as alarms, near misses, and accidents, conventional risk analysis approaches are reviewed. Next, the use of Bayesian analysis in dynamic risk analysis without alarm data is discussed. This leads to dynamic risk analysis with extensive alarm data and a review of typical safety systems and upset states. Data compaction approaches needed to compact data associated with thousands of daily alarm events are presented. For a large fluidized-bed catalytic conversion unit, the use of Bayesian analysis with copulas to estimate safety system failure probabilities (and the probabilities of plant trips and accidents) is demonstrated. Next, using plant and operator data, methods for creating informed prior distributions for Bayesian analyses are covered. These methods are shown to improve the risk analyses for an industrial steam-methane reformer. Throughout, concepts are reviewed, covering the basics and referring the reader to more complete presentations in the original papers. This chapter is intended for process engineers and operators concerned with safety risks, who seek to learn and implement methods of quantitative dynamic risk analysis.

@incollection{seider_chapter_2017, title = {Chapter {Five} - {Introduction} to {Dynamic} {Risk} {Analyses}}, volume = {1}, url = {http://www.sciencedirect.com/science/article/pii/S2468651417300053}, abstract = {This chapter places recent advances in dynamic risk analyses in perspective. After some preliminary concepts such as alarms, near misses, and accidents, conventional risk analysis approaches are reviewed. Next, the use of Bayesian analysis in dynamic risk analysis without alarm data is discussed. This leads to dynamic risk analysis with extensive alarm data and a review of typical safety systems and upset states. Data compaction approaches needed to compact data associated with thousands of daily alarm events are presented. For a large fluidized-bed catalytic conversion unit, the use of Bayesian analysis with copulas to estimate safety system failure probabilities (and the probabilities of plant trips and accidents) is demonstrated. Next, using plant and operator data, methods for creating informed prior distributions for Bayesian analyses are covered. These methods are shown to improve the risk analyses for an industrial steam-methane reformer. Throughout, concepts are reviewed, covering the basics and referring the reader to more complete presentations in the original papers. This chapter is intended for process engineers and operators concerned with safety risks, who seek to learn and implement methods of quantitative dynamic risk analysis.}, language = {en}, urldate = {2020-07-02}, booktitle = {Methods in {Chemical} {Process} {Safety}}, publisher = {Elsevier}, author = {Seider, Warren D. and Pariyani, Ankur and Oktem, Ulku G. and Moskowitz, Ian and Arbogast, Jeffrey E. and Soroush, Masoud}, editor = {Khan, Faisal}, month = jan, year = {2017}, doi = {10.1016/bs.mcps.2017.02.001}, pages = {201--254}, }

Downloads: 0