Real time risk monitoring in a processing system using Bayesian networks

Butt, Emily (2019) Real time risk monitoring in a processing system using Bayesian networks. Masters thesis, Memorial University of Newfoundland.

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Safety and risk are essential components of process industries. The research objective of this thesis is to develop a method to measure and monitor safety in terms of real-time risk of a process system failure. The risk monitoring concept was developed using event trees and Bayesian networks. Process instrument data such as flowrate was used as a basis for the risk probability calculations. The risk monitoring methodology was developed and applied to the Williams Geismar reboiler rupture and fire in 2013. The risk level of the reboiler was examined based on the original design prior to failure and an updated design based on recommendations made by the CSB. The accident probability was decreased by 96% and risk level was reduced by 76.9%. By plotting the risk of a process overtime, future projections of risk can be predicted and action can be taken to prevent accidents before they could occur.

Item Type: Thesis (Masters)
Item ID: 13888
Additional Information: Includes bibliographical references (pages 66-68).
Keywords: Risk Monitoring, Bayesian Networks, Process Industries, Safety and Risk, Real Time Monitoring
Department(s): Engineering and Applied Science, Faculty of
Date: May 2019
Date Type: Submission
Library of Congress Subject Heading: Risk assessment--Mathematical models; System analysis; Bayesian statistical decision theory

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