Quantitative risk analysis for explosion safety of oil and gas facilities

    Research output: ThesisDoctoral Thesis

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    Abstract

    This research carries out a comprehensive study on the gas explosion risk analysis approaches of different oil and gas facilities and aims at developing more accurate, detailed, efficient, or reliable risk analysis methods for vapour cloud explosions under different conditions. Various risk analysis methods such as a multi-level method, a grid-based method and a Bayesian network are implemented to estimate explosion risks of offshore and onshore process facilities with different sizes and properties. Furthermore, a confidence level-based approach is proposed to enable a more reliable risk evaluation by reducing the impact of subjective judgement-related uncertainties.
    Original languageEnglish
    QualificationDoctorate
    Awarding Institution
    • The University of Western Australia
    Thesis sponsors
    Award date10 Aug 2017
    DOIs
    Publication statusUnpublished - 2017

    Fingerprint

    Risk analysis
    Explosions
    Gases
    Bayesian networks
    Vapors
    Oils

    Cite this

    @phdthesis{9406790bbe5346228de3176321d90d42,
    title = "Quantitative risk analysis for explosion safety of oil and gas facilities",
    abstract = "This research carries out a comprehensive study on the gas explosion risk analysis approaches of different oil and gas facilities and aims at developing more accurate, detailed, efficient, or reliable risk analysis methods for vapour cloud explosions under different conditions. Various risk analysis methods such as a multi-level method, a grid-based method and a Bayesian network are implemented to estimate explosion risks of offshore and onshore process facilities with different sizes and properties. Furthermore, a confidence level-based approach is proposed to enable a more reliable risk evaluation by reducing the impact of subjective judgement-related uncertainties.",
    keywords = "explosion risk analysis, vapour cloud explosion, quantitative risk assessment, process safety, Bayesian network, multi-level risk analysis",
    author = "Yimiao Huang",
    year = "2017",
    doi = "10.4225/23/59a787d6bc7f2",
    language = "English",
    school = "The University of Western Australia",

    }

    TY - THES

    T1 - Quantitative risk analysis for explosion safety of oil and gas facilities

    AU - Huang, Yimiao

    PY - 2017

    Y1 - 2017

    N2 - This research carries out a comprehensive study on the gas explosion risk analysis approaches of different oil and gas facilities and aims at developing more accurate, detailed, efficient, or reliable risk analysis methods for vapour cloud explosions under different conditions. Various risk analysis methods such as a multi-level method, a grid-based method and a Bayesian network are implemented to estimate explosion risks of offshore and onshore process facilities with different sizes and properties. Furthermore, a confidence level-based approach is proposed to enable a more reliable risk evaluation by reducing the impact of subjective judgement-related uncertainties.

    AB - This research carries out a comprehensive study on the gas explosion risk analysis approaches of different oil and gas facilities and aims at developing more accurate, detailed, efficient, or reliable risk analysis methods for vapour cloud explosions under different conditions. Various risk analysis methods such as a multi-level method, a grid-based method and a Bayesian network are implemented to estimate explosion risks of offshore and onshore process facilities with different sizes and properties. Furthermore, a confidence level-based approach is proposed to enable a more reliable risk evaluation by reducing the impact of subjective judgement-related uncertainties.

    KW - explosion risk analysis

    KW - vapour cloud explosion

    KW - quantitative risk assessment

    KW - process safety

    KW - Bayesian network

    KW - multi-level risk analysis

    U2 - 10.4225/23/59a787d6bc7f2

    DO - 10.4225/23/59a787d6bc7f2

    M3 - Doctoral Thesis

    ER -