Dynamic risk assessment of process operations

Adedigba, Sunday Adeshina (2017) Dynamic risk assessment of process operations. Doctoral (PhD) thesis, Memorial University of Newfoundland.

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Process engineering systems have become increasingly complex and more vulnerable to potential accidents. The risks posed by these systems are alarming and worrisome. The operation of these complex process engineering systems requires a high level of understanding both from the operational as well as the safety perspective. This study focuses on dynamic risk assessment and management of complex process engineering systems’ operations. To reduce risk posed by process systems, there is a need to develop process accident models capable of capturing system dynamics in real-time. This thesis presents a set of predictive process accident models developed over four years. It is prepared in manuscript style and consists of nine chapters, five of which are published in peer reviewed journals. A dynamic operational risk management tool for process systems is developed, considering evolving process conditions. The obvious advantage of the developed methodologies is that it dynamically captures the real time changes occurring in the process operations. The real time risk profile provided by the methodologies developed serve as performance indicator for operational decision making. The research has made contributions on the following topics: (a) process accident model considering dependency among contributory factors, (b) dynamic safety analysis of process systems using a nonlinear and non-sequential accident model, (c) dynamic failure analysis of process systems using principal component analysis and a Bayesian network, (d) dynamic failure analysis of process systems using a neural network and (e) an integrated approach for dynamic economic risk assessment of process systems.

Item Type: Thesis (Doctoral (PhD))
URI: http://research.library.mun.ca/id/eprint/13261
Item ID: 13261
Additional Information: Includes bibliographical references.
Keywords: Accident Modelling, Non Sequential accident model, Dynamic Risk, Incident Analysis, Accident Prediction
Department(s): Engineering and Applied Science, Faculty of
Date: November 2017
Date Type: Submission
Library of Congress Subject Heading: Production engineering -- Risk assessment -- Mathematical models; Industrial accidents -- Prevention -- Mathematical models

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