F
Faisal Khan
Researcher at Memorial University of Newfoundland
Publications - 785
Citations - 28657
Faisal Khan is an academic researcher from Memorial University of Newfoundland. The author has contributed to research in topics: Risk assessment & Risk analysis. The author has an hindex of 70, co-authored 705 publications receiving 21281 citations. Previous affiliations of Faisal Khan include Royal Hobart Hospital & Australian Maritime College.
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Analysis of pitting corrosion on steel under insulation in marine environments
TL;DR: In this article, the authors reviewed and analyzed six categories of pitting knowledge to assess the current depth and breadth of understanding and to identify knowledge gaps in each category, and they found that the depth of knowledge on pitting corrosion rate modeling and pitting mechanism is limited and requires further detailed study.
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Dynamic availability assessment of safety critical systems using a dynamic Bayesian network
TL;DR: A dynamic Bayesian network (DBN)-based dynamic availability assessment technique is proposed that offers much flexibility in representing different failure scenarios and the interdependence of failure causes.
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Dynamic process fault detection and diagnosis based on a combined approach of hidden Markov and Bayesian network model
TL;DR: This study introduces a novel methodology for fault detection and diagnosis (FDD), based on a combined approach of data and process knowledge driven techniques, which detects the abnormalities based on process history while the Bayesian Network diagnoses the root causes of faults.
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Moderation of dust explosions
TL;DR: Inherent safety is a proactive approach to process safety in which hazards are removed or minimized so as to reduce risk without engineered (add-on) or procedural intervention as mentioned in this paper, which can be achieved by processing a material under less severe operating conditions or by processing the material in a less hazardous form.
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Accident modelling and analysis in process industries
TL;DR: In this paper, the authors present a review of accident models that have been developed for the chemical process industry with in-depth analyses of a class of models known as dynamic sequential accident models (DSAMs).