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.
Papers
More filters
Journal ArticleDOI
Evolving extreme events caused by climate change: A tail based Bayesian approach for extreme event risk analysis:
TL;DR: In this article, the frequency and extent of natural hazards are of significant concern for engineering development in the offshore environment and climate change phenomena are making these concerns even greater, which is a concern for offshore development.
Journal ArticleDOI
Optimization and production of alpha-amylase using Bacillus subtilis from apple peel: Comparison with alternate feedstock
Singh,Sapna Langyan,Seema Sangwan,Priyank Gaur,Faisal Khan,Pranjal Yadava,Bharti Rohatgi,Manoj Shrivastava,Ashish Khandelwal,Sibananda Darjee,P. Sahu +10 more
TL;DR: In this paper , the authors explored the possibility of using apple peel as feedstock for production of alpha-amylase, a commercially important enzyme with role in food industry and bio-ethanol production that is critical to support nutrition and energy security.
Journal ArticleDOI
Why Risk-Based Multivariate Fault Detection and Diagnosis?
TL;DR: In this paper, a risk-based fault detection method has been developed, which provides a dynamic process risk indication based on the probability of happening a fault and its consequence, instead of generating an alarm based on residuals or signals an alarm is activated only when the calculated risk of operation exceeds the acceptable threshold.
Journal ArticleDOI
Dynamic risk management of assets susceptible to pitting corrosion
TL;DR: In this paper, a methodology to assess and dynamically update the risk of process components affected by pitting corrosion is presented, which considers the time-dependent growth of the process components.
Book ChapterDOI
Alternative prediction models for data scarce environment
TL;DR: The hybrid first-order grey model with Bayesian network BG(1,1) is most accurate, followed by the grey models G(1-1) and G(2,1), with the Poisson model trailing behind, which illustrated the potentials of grey modelling approach in dealing with scarce data conditions.