Dynamic risk assessment of process facilities using advanced probabilistic approaches

Kamil, Mohammad Zaid (2019) Dynamic risk assessment of process facilities using advanced probabilistic approaches. Masters thesis, Memorial University of Newfoundland.

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A process accident can escalate into a chain of accidents, given the degree of congestion and complex arrangement of process equipment and pipelines. To prevent a chain of accidents, (called the domino effect), detailed assessments of risk and appropriate safety measures are required. The present study investigates available techniques and develops an integrated method to analyze evolving process accident scenarios, including the domino effect. The work presented here comprises two main contributions: a) a predictive model for process accident analysis using imprecise and incomplete information, and b) a predictive model to assess the risk profile of domino effect occurrence. A brief description of each is presented below. In recent years the Bayesian network (BN) has been used to model accident causation and its evolution. Though widely used, conventional BN suffers from two major uncertainties, data and model uncertainties. The former deals with the used of evidence theory while the latter uses canonical probabilistic models. High interdependencies of chemical infrastructure makes it prone to the domino effect. This demands an advanced approach to monitor and manage the risk posed by the domino effect is much needed. Given the dynamic nature of the domino effect, the monitoring and modelling methods need to be continuous time-dependent. A Generalized Stochastic Petrinet (GSPN) framework was chosen to model the domino effect. It enables modelling of an accident propagation pattern as the domino effect. It also enables probability analysis to estimate risk profile, which is of vital importance to design effective safety measures.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/13767
Item ID: 13767
Additional Information: Includes bibliographical references.
Keywords: Dynamic Risk Assessment, Process Industries, Bayesian Network, Stochastic Petrinets, Chemical Industries
Department(s): Engineering and Applied Science, Faculty of
Date: May 2019
Date Type: Submission
Library of Congress Subject Heading: Production engineering--Risk assessment.

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