Safety and Risk Analysis of an Operational Heater Using Bayesian Network
Tóm tắt
Industrials systems, including chemical industries, can be exposed to undesired events that may cause terrible accidents. These accidents must be controlled and reduced. To this end, numerous risk analysis management approaches have been aimed at reducing the risks to a tolerable level to avoid the catastrophic accident. This reduction is achieved by implementing several layers of protection, including organizational and technical barriers, this later known as safety-instrumented systems (SIS). The main objective assigned to a SIS is the detection of dangerous situations and implementation of a set of reactions necessary at a specific time to ensure that the equipment is under control. This function is typically name the safety-instrumented function and is characterized by a safety integrity level (SIL). A SIL is defined as a measure of the confidence to perform the safety function. This paper deals with the uncertainties of SIS using one of several robust probabilistic methods from a group of Bayesian networks (BN). A case study of an operational heater is used to illustrate the application. The results are then compared with the risk tolerance criteria and the safety of the improved process by updating the BN model.
Tài liệu tham khảo
International Electrotechnical Commission (2003) IEC 61511: functional safety—safety instrumented systems for the process industry. Part1: framework, definitions, system, hardware and software requirements
International Electrotechnical Commission (2010) IEC 61508: functional safety of electrical/ electronic/programmable electronic safety-related systems. Part1: General requirements. Edition 2.0, April 2010
Y. Liu, M. A. Lundteigen, Reliability Importance of the Channels in Safety Instrumented Systems, in 2nd International Conference on Industrial Engineering, Management Science and Applications (ICIMSA2015), Tokyo, Japan, 2015
Y. Dutuit, F. Innal, A. Rauzy, J.-P. Signoret, Probabilistic assessments in relationship with safety integrity levels by using Fault Trees. Reliab. Eng. Syst. Saf. 93(12), 1867–1876 (2008)
P.R. Kannan, Bayesian networks: application in safety instrumentation and risk reduction. ISA Trans. 46(2), 255–259 (2007)
N. Ouazraouia, R. Nait-Saida, M. Bourarechea, I. Sellami, Layers of protection analysis in the framework of possibility theory. J. Hazard. Mater. 262, 168–178 (2013)
J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufman, San Francisco, 1988)
W. Philippe, L. Jouffe, Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN). Reliab. Eng. Syst. Saf. 91(2), 149–162 (2006)
D. Hanea, B. Ale, Risk of human fatality in building fires: A decision tool using Bayesian networks. Fire Saf. J. 44(5), 704–710 (2009)
A. Bobbioa, L. Portinalea, M. Minichinob, E. Ciancamerlab, Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliab. Eng. Syst. Saf. 71(3), 249–260 (2001)
F.V. Jensen, T.D. Nielsen, Bayesian Networks and Decision Graphs, 2nd edn. (Springer, New York, 2007)
O. Doguc, J.E. Ramirez-Marquez, A generic method for estimating system reliability using Bayesian networks. Reliab. Eng. Syst. Saf. 94(2), 542–550 (2009)
IEC61882. Hazard and Operability Studies (HAZOP studies)—Application Guide, International Electrotechnical Commission (IEC), 2001
AgenaRisk software, version 6.1 (2015). <www.agenarisk.com>
Methodology for Layer of Protection Analysis, SONATRACH Company, Hassi-R’Mel, Rep. S-30-1240-140, 2007
OREDA, Offshore Reliability Data Handbook, 4th edn. (OREDA, Norway, 2002)
CCPS, Layer Of Protection Analysis, Simplified Process Assessment (Center for Chemical Process Safety of the American Institute for Chemical Engineers, New York, 2001)