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|Type:||Artigo de periódico|
|Title:||Application of an Artificial Intelligence Technique to Improve Purification in the Zone Refining Process|
|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.|
|Citation:||Journal Of Electronic Materials. Springer, v. 39, n. 1, n. 49, n. 55, 2010.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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