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Major accident modelling using spare data


El-Ghariani, M and Khan, F and Chen, D and Abbassi, R, Major accident modelling using spare data, Process Safety and Environmental Protection, 106 pp. 52-59. ISSN 0957-5820 (2017) [Refereed Article]

Copyright Statement

Copyright 2016 Institution of Chemical Engineers

DOI: doi:10.1016/j.psep.2016.12.004


In the field of risk and reliability analysis, the information available to acquire probabilities is usually insufficient (i.e. scarce, little). Utilizing a variety of information sources introduces many uncertainties associated with risk estimation. This is an obstacle in the prediction of major accidents which have significant consequences for human life and the environment, in addition to incurring financial losses. In order to get reasonable results and to support decision making in a cost effective manner, there is a need to aggregate the relevant data from different regions, operational conditions and different sectors (e.g. chemical, nuclear or mining). In this paper, a methodology is developed considering Hierarchical Bayesian Analysis (HBA) as a robust technique for event frequency estimation. Here, HBA is able to treat source-to-source uncertainty among the aggregated data for each event and provide a precise value for the parameter of interest (e.g. failure rate, probability or time to failure). The estimated eventís parameter is reintegrated via probabilistic modelling techniques such as Bowtie analysis to estimate the probability of major accidents. The application of the proposed methodology to risk analysis is illustrated using a case study of an offshore major accident and its effectiveness is demonstrated over the traditional statistical estimators. The results illustrate that the developed methodology assists in making better estimates of the probabilities when dealing with sparse data. The ability to update the primary event and safety barrier probabilities as new data becomes available further enhances its usefulness.

Item Details

Item Type:Refereed Article
Keywords:data scarcity, probabilistic modelling, Hierarchical Bayesian Analysis, risk analysis, offshore, major accidents
Research Division:Engineering
Research Group:Environmental engineering
Research Field:Air pollution modelling and control
Objective Division:Mineral Resources (Excl. Energy Resources)
Objective Group:Environmentally sustainable mineral resource activities
Objective Field:Environmentally sustainable mineral resource activities not elsewhere classified
UTAS Author:Khan, F (Professor Faisal Khan)
UTAS Author:Abbassi, R (Dr Rouzbeh Abbassi)
ID Code:113497
Year Published:2017
Web of Science® Times Cited:44
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2017-01-04
Last Modified:2018-04-19

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