Application of an Artificial Intelligence Technique to Improve Purification in the Zone Refining Process
Source: Journal of Electronic Materials, Volume 39, Number 1, January 2010 , pp. 49-55(7)
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.
Document Type: Research Article
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: email@example.com
Publication date: January 1, 2010