Detection of outliers in a gas centrifuge experimental data
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Brazilian Journal of Chemical Engineering
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Isotope separation with a gas centrifuge is a very complex process. Development and optimization of a gas centrifuge requires experimentation. These data contain experimental errors, and like other experimental data, there may be some gross errors, also known as outliers. The detection of outliers in gas centrifuge experimental data is quite complicated because there is not enough repetition for precise statistical determination and the physical equations may be applied only to control of the mass flow. Moreover, the concentrations are poorly predicted by phenomenological models. This paper presents the application of a three-layer feed-forward neural network to the detection of outliers in analysis of performed on a very extensive experiment.
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ANDRADE, M.C.V.; NASCIMENTO, C.A.O.; MIGLIAVACCA, S.C.P. Detection of outliers in a gas centrifuge experimental data. Brazilian Journal of Chemical Engineering, v. 22, n. 3, p. 389-400, 2005. DOI: 10.1590/S0104-66322005000300008. Disponível em: http://repositorio.ipen.br/handle/123456789/5486. Acesso em: 31 Dec 2025.
Esta referência é gerada automaticamente de acordo com as normas do estilo IPEN/SP (ABNT NBR 6023) e recomenda-se uma verificação final e ajustes caso necessário.