Machine learning-based models to prioritize scenarios in a Quantitative Risk Analysis: An application to an actual atmospheric distillation unit-Reference-Cited by-同舟云学术

Machine learning-based models to prioritize scenarios in a Quantitative Risk Analysis: An application to an actual atmospheric distillation unit

Published:2022-07 Issue: Volume:77 Page:104797
ISSN:0950-4230
Container-title:Journal of Loss Prevention in the Process Industries
language:en
Short-container-title:Journal of Loss Prevention in the Process Industries

Author:

Bias Macedo July^ORCID,das Chagas Moura Márcio José,Ramos Marília,Didier Lins Isis,Zio Enrico

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Energy Engineering and Power Technology,Safety, Risk, Reliability and Quality,General Chemical Engineering,Food Science,Control and Systems Engineering

Reference48 articles.

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4. Teaching children and youths about risk and risk analysis : what are the goals and the risk analytical foundation;Aven;J. Risk Res.,2020

5. On the completeness of scenario identification in process hazard analysis (PHA) Paul Baybutt. J. Loss Prev;Baybutt;Process Ind,2018

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