A Dynamic Predictive Analysis on Gas Pipeline Failure Consequence Using Bayesian Network

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dc.contributor.advisorKabir, Golam
dc.contributor.authorAalirezaei, Armin
dc.contributor.committeememberHenni, Amr
dc.contributor.committeememberKhondoker, Mohammad
dc.contributor.externalexaminerShahriar, Nashid
dc.date.accessioned2022-08-05T16:04:31Z
dc.date.available2022-08-05T16:04:31Z
dc.date.issued2021-09
dc.descriptionA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Industrial Systems Engineering, University of Regina. xi, 114 p.en_US
dc.description.abstractNowadays, due to the economical, environmental, and social concerns which are the three pillars of sustainable development objectives, natural gas is considered as one of the most important and critical energies. Natural gas pipeline failure and related losses is a kind of devastating disaster because the threats of natural gas failure consequences may provide a huge extension that can simply lead to cascading disasters. Therefore, maintenance and repair of the deteriorating infrastructure like buried gas pipelines have been considered significant considerations amongst stakeholders and researchers in recent years. In this thesis, a dynamic Bayesian network (BN) was used to investigate natural gas pipeline network failure consequences in a probabilistic way. Seven parent nodes including, age, diameter, length, depth, population, time of occurrence, and land-use have been considered for the developed model. Twelve consequences factors also were recognized based on the literature review and expert’s opinion. Production loss, asset loss, environmental loss, and social and reputational loss were also measured as the overall losses due to the failure consequences. The proposed model can manage both static and dynamic systems employing quantitative and/or qualitative data. Methods of the extreme-condition test, scenario analysis, sensitivity analysis, and partial validation test were utilized to validate the model and to detect the key and leading parent nodes and their effect on the overall loss index. The developed static and dynamic BN models will help the decision-makers to manage and prioritize their asset effectively. To demonstrate the applicability and effectiveness of the developed model, the gas pipeline network of the City of Regina, SK, in Canada is studied. The results show that age and diameters are the most important and sensitive parameters amongst others which need to take into consideration for disaster reaction decision-making and loss prevention.en_US
dc.description.authorstatusStudenten
dc.description.peerreviewyesen
dc.identifier.tcnumberTC-SRU-14961
dc.identifier.thesisurlhttps://ourspace.uregina.ca/bitstream/handle/10294/14961/Aalirezaei_Armin_MASC_ISE_Spring2022.pdf
dc.identifier.urihttps://hdl.handle.net/10294/14961
dc.language.isoenen_US
dc.publisherFaculty of Graduate Studies and Research, University of Reginaen_US
dc.titleA Dynamic Predictive Analysis on Gas Pipeline Failure Consequence Using Bayesian Networken_US
dc.typeThesisen_US
thesis.degree.departmentFaculty of Engineering and Applied Scienceen_US
thesis.degree.disciplineEngineering - Industrial Systemsen_US
thesis.degree.grantorUniversity of Reginaen
thesis.degree.levelMaster'sen
thesis.degree.nameMaster of Applied Science (MASc)en_US
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