A blended hazard identification methodology to support process diagnosis

Seligmann, Benjamin J., Nemeth, Erzsébet, Hangos, Katalin M. and Cameron, Ian T. (2012) A blended hazard identification methodology to support process diagnosis. Journal of Loss Prevention in the Process Industries, 25 4: 746-759.


Author Seligmann, Benjamin J.
Nemeth, Erzsébet
Hangos, Katalin M.
Cameron, Ian T.
Total Author Count Override 4
Title A blended hazard identification methodology to support process diagnosis
Journal name Journal of Loss Prevention in the Process Industries   Check publisher's open access policy
ISSN 0950-4230
1873-3352
Publication date 2012-07
Sub-type Article (original research)
DOI 10.1016/j.jlp.2012.04.012
Volume 25
Issue 4
Start page 746
End page 759
Total pages 14
Place of publication Oxford, United Kingdom
Publisher Elsevier
Collection year 2013
Language eng
Abstract A novel hazard identification methodology applied to process systems is presented in this paper. This blended hazard identification (BLHAZID) methodology blends two different types of HAZID methods: the function-driven and component-driven approach. The BLHAZID method is based on a conceptual framework called the Functional Systems Framework, which describes structure-function-goal relationships in process systems.The goals of the BLHAZID methodology are to generate outcomes that contain a high coverage of hazards, describe detailed failure causality in process systems and express this knowledge in a structured form for effective reused in subsequent applications, such as fault diagnosis, operator training, design reviews, fault and event tree construction and hazard updates to satisfy major hazard facility requirements.Both the BLHAZID methodology and the Functional Systems Framework were developed with involvement and advice from two major industrial partners. An industrial case study of a benzene saturation unit is presented to illustrate how the BLHAZID methodology operates in practice.
Keyword Hazard identification
Failure
Function
Blending
Causality
Knowledge management
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Chemical Engineering Publications
Official 2013 Collection
 
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