Application of an Artificial Intelligence Technique to Improve Purification in the Zone Refining Process

Authors: Cheung, Thais1; Cheung, Noé1; Garcia, Amauri2

Source: Journal of Electronic Materials, Volume 39, Number 1, January 2010 , pp. 49-55(7)

Publisher: Springer

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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.

Keywords: Purification; artificial intelligence; pure materials; segregation; simulation; zone refining

Document Type: Research Article

DOI: http://dx.doi.org/10.1007/s11664-009-0947-4

Affiliations: 1: Department of Materials Engineering, University of Campinas (UNICAMP), P.O. Box 6122, 13083-970, Campinas, SP, Brazil 2: Department of Materials Engineering, University of Campinas (UNICAMP), P.O. Box 6122, 13083-970, Campinas, SP, Brazil, Email: amaurig@fem.unicamp.br

Publication date: January 1, 2010

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