Show simple item record

dc.contributor.advisorMannan, Sam
dc.creatorPalaniappan, Visalatchi
dc.date.accessioned2019-01-18T16:43:38Z
dc.date.available2019-01-18T16:43:38Z
dc.date.created2018-08
dc.date.issued2018-08-03
dc.date.submittedAugust 2018
dc.identifier.urihttps://hdl.handle.net/1969.1/174151
dc.description.abstractPipelines are the most efficient mode of transportation for various chemicals and are considered as safe, yet pipeline incidents remain occurring. Corrosion is one of the main reasons for incidents especially in subsea pipelines due to the harsh corrosive environment that prevails. Corrosion can be attributed to 36% amongst all the causes of subsea pipeline failure. Internal corrosion being an incoherent process, one can never forecast exact occurrences inside a pipeline resulting in highly unpredictable risk. Therefore, this paper focuses on risk assessment of internal corrosion in subsea pipelines. Corrosion is time-dependent phenomena, and conventional risk assessment tools have limited capabilities of quantifying risk in terms of time dependency. Hence, this paper presents a Dynamic Bayesian Network (DBN) model to assess and manage the risk of internal corrosion in subsea. DBN possesses certain advantages such as representation of temporal dependence between variable, ability to handle missing data, ability to deal with continuous data, time- based risk update, observation of the change of variables with time and better representation of cause and effect relationship. This model aims to find the cause of internal corrosion and predict the consequence in case of pipeline failure given the reliability of safety barrier in place at each time step. It also demonstrates the variation of corrosion promoting agents, corrosion rate and safety barriers with time.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectDynamic Bayesian Networken
dc.subjectrisk assessmenten
dc.subjectsubsea pipelineen
dc.subjectinternal corrosionen
dc.subjectprobability distributionen
dc.subjectprobability of failure on demanden
dc.titlePipeline Risk Assessment Using Dynamic Bayesian Network (DBN) for Internal Corrosionen
dc.typeThesisen
thesis.degree.departmentChemical Engineeringen
thesis.degree.disciplineSafety Engineeringen
thesis.degree.grantorTexas A & M Universityen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelMastersen
dc.contributor.committeeMemberBanerjee, Debjyoti
dc.contributor.committeeMemberSchubert, Jerome
dc.type.materialtexten
dc.date.updated2019-01-18T16:43:38Z
local.etdauthor.orcid0000-0002-8896-196X


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record