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Type: | Artigo de periódico |
Title: | Application of an Artificial Intelligence Technique to Improve Purification in the Zone Refining Process |
Author: | Cheung, T Cheung, N Garcia, A |
Abstract: | A combined theoretical and experimental approach was undertaken to quantitatively determine the influence of a variable solute distribution coefficient, k, on impurity distribution in multipass purification by zone refining. Axial impurity profiles have been experimentally determined for a number of zone passes. It has been shown that the adoption of a variable-k approach in the simulation of impurity profiles during different zone passes is generally much closer to the experimental profiles than the usual adoption of a constant k. An artificial intelligence technique interacts with the numerical model to determine the best molten zone size in each pass in order to provide maximum purification. |
Subject: | Purification zone refining segregation pure materials simulation artificial intelligence |
Country: | EUA |
Editor: | Springer |
Citation: | Journal Of Electronic Materials. Springer, v. 39, n. 1, n. 49, n. 55, 2010. |
Rights: | fechado |
Identifier DOI: | 10.1007/s11664-009-0947-4 |
Date Issue: | 2010 |
Appears in Collections: | Unicamp - Artigos e Outros Documentos |
Files in This Item:
File | Description | Size | Format | |
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WOS000273402700009.pdf | 432.46 kB | Adobe PDF | View/Open |
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