rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: The
    title: development of Dynamic Operational Risk Assessment in oil/gas and chemical industries
  name:
    name part: Yang, Xiaole.
    role:
      role term: creator
  name:
    name part: Mannan, Mahboobul Sam
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: In oil/gas and chemical industries, dynamics is one of the most essential characteristics of any process. Time-dependent response is involved in most steps of both the physical/engineering processes and the equipment performance. The conventional Quantitative Risk Assessment (QRA) is unable to address the time dependent effect in such dynamic processes. In this dissertation, a methodology of Dynamic Operational Risk Assessment (DORA) is developed for operational risk analysis in oil/gas and chemical industries. Given the assumption that the component performance state determines the value of parameters in process dynamics equations, the DORA probabilistic modeling integrates stochastic modeling and process dynamics modeling to evaluate operational risk. The stochastic system-state trajectory is modeled based on the abnormal behavior or failure of the components. For each of the possible system-state trajectories, a process dynamics evaluation is carried out to check whether process variables, e.g., level, flow rate, temperature, pressure, or chemical concentration, remain in their desirable regions. Monte Carlo simulations are performed to calculate the probability of process variable exceeding the safety boundaries. Component testing/inspection intervals and repair time are critical parameters to define the system-state configuration; and play an important role for evaluating the probability of operational failure. Sensitivity analysis is suggested to assist selecting the DORA probabilistic modeling inputs. In this study, probabilistic approach to characterize uncertainty associated with QRA is proposed to analyze data and experiment results in order to enhance the understanding of uncertainty and improve the accuracy of the risk estimation. Different scenarios on an oil/gas separation system were used to demonstrate the application of DORA method, and approaches are proposed for sensitivity and uncertainty analysis. Case study on a knockout drum in the distillatio…
  note: by Xiaole Yang.
  note: "Major Subject: Chemical Engineering"
  note: Title from author supplied metadata (automated record created 2011-08-09 15:09:44).
  note: Electronic resource.
  note: Doctor of Philosophy Texas A&M University 2010 http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-7755
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major chemical engineering
  subject:
    topic: Uncertainty
  subject:
    topic: Multiobjective Optimization
  subject:
    topic: Dynamic Operational Risk Assessment
  identifier: http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-7755
  record info:
    record content source: TXA
    record creation date: 110817
    record change date: 20151202142259.0
    record identifier: in00002706142