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An integration of human factors into quantitative risk analysis using Bayesian Belief Networks towards developing a ‘QRA+’

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Author: Steijn, W.M.P. · Kampen, J.N. van · Beek, D. van der · Groeneweg, J. · Gelder, P.H.A.J.M. van
Type:article
Date:2020
Source:Safety Science, 122
Identifier: 869710
doi: doi:10.1016/j.ssci.2019.104514
Article number: 104514
Keywords: Atmospheric pressure · Bayesian networks · Chemical analysis · Chemical industry · Decision theory · Factor analysis · Human engineering · Offshore oil well production · Probability distributions · Reliability analysis · Risk assessment · Risk perception · Uncertainty analysis · Chemical storage tanks · Human reliability analysis · Human reliability assessments · Offshore exploration · Quantitative knowledge · Quantitative risk analysis · Seamless integration · Technological parameters · Risk analysis · Human · Interview · Probability · Quantitative analysis · Reliability · Uncertainty

Abstract

Quantitative Risk Analysis (QRA) is a standard tool in some high-risk industries (such as the on- and offshore exploration and production and chemical industry). Presently, existing knowledge concerning human error likelihood and human reliability assessment is insufficiently represented in QRAs. In this paper we attempt to implement the quantification of the human factors in a QRA, which we call QRA+. We analysed a specific incident scenario: the risk of overfilling chemical storage tanks that operate at atmospheric pressure. This scenario was chosen because it is a relevant example of a high-risk scenario in the chemical industry. We identified relevant technological and human parameters within this scenario through on-site visits and interviews with site-experts. The quantitative knowledge concerning the technological parameters was obtained from officially documented SIL statistics, whereas the Standardized Plant Analysis Risk-Human Reliability analysis (SPAR-H) was used to quantify the human factors. Beta distributions were used to model failure probability distributions to account for the uncertainty inherent in dealing with human reliability. For seamless integration of existing qualitative and quantitative knowledge, we made use of a Bayesian Belief Network. The resulting model provides an integrated and more accurate estimation of the failure probabilities for both technological and human factors and the uncertainty surrounding such probability estimates. Furthermore, it gives insight in where these failure probabilities originate and how they interact. This will allow companies to identify those parameters they need to influence to get optimal results concerning their management of risk. © 2019 Elsevier Ltd