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Accident contributor interconnection study as a basis for accident mechanism prediction

The accident rate in the chemical process industry (CPI) is not decreasing although the large majority of accident causes are known, foreseeable and could be prevented by existing knowledge. Continuous learning and improvement on experience feedback system is essential for process safety. In this pa...

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Main Authors: Kidam, Kamarizan, Kamarden, Hamidah, Hurme, Markku, Hashim, Mimi H., Md. Kasmani, Rafiziana
Format: Article
Published: Italian Association of Chemical Engineering - AIDIC 2014
Subjects:
TP Chemical technology
Online Access:http://eprints.utm.my/id/eprint/51704/
http://dx.doi.org/10.3303/CET1436005
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spelling my.utm.517042018-10-14T08:37:11Z http://eprints.utm.my/id/eprint/51704/ Accident contributor interconnection study as a basis for accident mechanism prediction Kidam, Kamarizan Kamarden, Hamidah Hurme, Markku Hashim, Mimi H. Md. Kasmani, Rafiziana TP Chemical technology The accident rate in the chemical process industry (CPI) is not decreasing although the large majority of accident causes are known, foreseeable and could be prevented by existing knowledge. Continuous learning and improvement on experience feedback system is essential for process safety. In this paper, the experience feedback system is used to generate accident knowledge and understanding on accident mechanism. Accident data such as accident contributors are analysed and their interconnection matrix is develop. The matrix reveals three main functional groups of contributor to accident. Therefore, the likelihood of chronological event of accident could be predicted Italian Association of Chemical Engineering - AIDIC 2014 Article PeerReviewed Kidam, Kamarizan and Kamarden, Hamidah and Hurme, Markku and Hashim, Mimi H. and Md. Kasmani, Rafiziana (2014) Accident contributor interconnection study as a basis for accident mechanism prediction. CISAP6: 6th International Conference on Safety & Environment in Process & Power Industry, 36 . pp. 25-30. ISSN 1974-9791 http://dx.doi.org/10.3303/CET1436005
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TP Chemical technology
spellingShingle TP Chemical technology
Kidam, Kamarizan
Kamarden, Hamidah
Hurme, Markku
Hashim, Mimi H.
Md. Kasmani, Rafiziana
Accident contributor interconnection study as a basis for accident mechanism prediction
description The accident rate in the chemical process industry (CPI) is not decreasing although the large majority of accident causes are known, foreseeable and could be prevented by existing knowledge. Continuous learning and improvement on experience feedback system is essential for process safety. In this paper, the experience feedback system is used to generate accident knowledge and understanding on accident mechanism. Accident data such as accident contributors are analysed and their interconnection matrix is develop. The matrix reveals three main functional groups of contributor to accident. Therefore, the likelihood of chronological event of accident could be predicted
format Article
author Kidam, Kamarizan
Kamarden, Hamidah
Hurme, Markku
Hashim, Mimi H.
Md. Kasmani, Rafiziana
author_facet Kidam, Kamarizan
Kamarden, Hamidah
Hurme, Markku
Hashim, Mimi H.
Md. Kasmani, Rafiziana
author_sort Kidam, Kamarizan
title Accident contributor interconnection study as a basis for accident mechanism prediction
title_short Accident contributor interconnection study as a basis for accident mechanism prediction
title_full Accident contributor interconnection study as a basis for accident mechanism prediction
title_fullStr Accident contributor interconnection study as a basis for accident mechanism prediction
title_full_unstemmed Accident contributor interconnection study as a basis for accident mechanism prediction
title_sort accident contributor interconnection study as a basis for accident mechanism prediction
publisher Italian Association of Chemical Engineering - AIDIC
publishDate 2014
url http://eprints.utm.my/id/eprint/51704/
http://dx.doi.org/10.3303/CET1436005
_version_ 1643653048788058112
score 13.211869

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