Detection of outliers in a gas centrifuge experimental data
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 i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Brazilian Society of Chemical Engineering
2005-09-01
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| Series: | Brazilian Journal of Chemical Engineering |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322005000300008 |
| Summary: | 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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| ISSN: | 0104-6632 1678-4383 |