Fuzzy Risk Assessment and Management in Process Industries-Case Study: Gas Pipelines


Abstract:

Risk assessment is the heart of ISO and OHSAS auditions and also is vital demand for any industry to characterize hazards and their risks for personnel, environment and loss of money. Traditionally, Risk matrix was a very useful tool to estimate the risk of process or equipment or acts that helps decision-making processes. Establishment and development of soft set theory and its applications are used in recent years and are extended in combined forecasting, decision-making, information science and so on. Fuzzy modeling is one of the most powerful tools to estimate relation between input-output of nonlinear systems. In this method, fuzzy numbers are referred to variables that express uncertainties. A fuzzy number expresses relation between an imprecise or uncertain number, X and a membership function, ì that is between (0,1). In other words, the fuzzy logic tool provides a technique to deal with imprecision and information, the fuzzy theory provides a mechanism for representing linguistic constructs such as “many”, “low”, “medium”, “few”, etc. In the new view of analysts and decision makers, risk analysis is being done with great uncertainties because risk assessment requires detail information about damage frequency rate of particular processes and equipment components. This data usually are imprecise and uncertain. One of the advantages of fuzzy logic in the field of risk assessment is dealing with these problems.

Keywords: Fuzzy Logic, Risk Assessment, Uncertainties

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References:

  • Khaleghi S., Givehchi S., Hoveydi H., Introducing an analogue model for improvement and propagation of safety culture, 2nd national conference of environment, 15-16May 2012, Tehran, Iran.
  • Qureshi Z.H., A review of accident modeling approach for complex critical sociotechnical systems. Technical report DSTO-TR-2094, Defence Science and Technology organization, Edinburgh, Australia, 2008.
  • Markowski, A.S., Mannan, M.S., & Bigoszewska, A. (2009). Fuzzy logic for process safety analysis, Journal of loss prevention in the process industries, 22, 695-702.
  • CCPS. (2000).Guidelines for chemical process quantitative risk analysis. New York: Center for Chemical Process Safety Publication.
  • M. Rausand, Risk Assessment Theory, Methods, and Applications, John Wilwy ans Sons, Inc., Hoboken, New Jersey, 2011.
  • Lee, S., Yang, J., & Han, J., Development of a decision making system for selection of dental implant abutments based on the fuzzy cognitive map, Expert Systems with Applications, 39, 11564–11575, 2012.
  • I. Mohammadfam, H. Nikoomaram, FTA vs. Tripod-Beta, which seems better for the analysis of major accident in process industries?, Journal of loss prevention in the process Industries, DOI: 10.1016/j.jlp.2012.09.005, 2012.
  • Nadaf A., Rahimian A., Abasian V., Besharat A., Pipeline risk management, Gas Transmission Operation District 4, Iranian Gas Transmission Co., 2003, Iran.
  • Muhlbauer, W. K., Pipeline risk management manual (3nd Ed.).Houston: Gulf Professional Publishing, 2004.
  • Zadeh, L. A., Fuzzy set.Information and Control, 8, 338-353, 1965.
  • Yazdani-Chamzini, A., & Yakhchali, S. H., Tunnel Boring Machine (TBM)selection using fuzzy multicriteria decision making methods. Tunnelling and Underground Space Technology, 30,194-204, 2012.
  • Markowski, A.S., & Mannan, M.S., Fuzzy logic for piping risk assessment (pfLOPA), Journal of loss prevention in the process industries, 22, 921-927, 2009.
  • Jang, J. S. R., Sun, C. T., & Mizutani, E., Neural-fuzzy and soft computing.
  • Englewood Cliffs, NJ: Prentice-Hall, 1997.