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Alternative prediction models for data scarce environment

Citation

Al-Shanini, A and Ahmad, A and Khan, FI and Oladokun, O and Nor, S, Alternative prediction models for data scarce environment, Proceedings of the 12th International Symposium of Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering, 31 May - 5 June 2015, Copenhagen, Denmark, pp. 665-670. ISBN 9780444634290 (2015) [Refereed Conference Paper]

Copyright Statement

Copyright 2015 Elsevier B.V

DOI: doi:10.1016/B978-0-444-63578-5.50106-7

Abstract

An accident modelling approach is used to assess the safety of a hydrogen station as part of a ground transportation network. The method incorporates prevention barriers associated to human factors, management and organizational failures in a risk assessment framework. Failure probabilities of these barriers and end-states events are predicted using Fault Tree Analysis and Event Tree Analysis respectively. Results from the case study considered revealed the capability of the proposed method in estimating the likelihood of various outcomes as well as predicting their future probabilities. In addition, the scheme offers an opportunity to provide dynamic adjustment by updating the failure probability with actual plant data. Results from the analysis can be used to plan maintenance and management of change as required by the plant condition. © 2014 Hydrogen Energy Publications, LLC. All rights reserved.

Item Details

Item Type:Refereed Conference Paper
Keywords:accident modelling, Bayesian updating, offsite hydrogen station, prediction model, SHIPP methodology, accidents, empowerment of personnel, fault tree analysis, hydrogen, probability, Bayesian updating, hydrogen station, management of change
Research Division:Engineering
Research Group:Interdisciplinary Engineering
Research Field:Risk Engineering (excl. Earthquake Engineering)
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Engineering
Author:Khan, FI (Professor Faisal Khan)
ID Code:121235
Year Published:2015
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2017-09-18
Last Modified:2018-04-05
Downloads:0

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