Use of neural networks to optimize graphite content in magnesia-graphite refractories

Use of neural networks to optimize graphite content in magnesia-graphite refractories Neural networks are one of the newest tools for computation. They provide unique opportunities for understanding and studying nonlinear problems, which makes such networks well-suited for use in different fields of engineering and technology. Only a few studies employing this new tool have been conducted in the field of ceramics. In the study discussed in this article, a three-layer neural network was used to construct a model that can predict the amounts of synthetic resin and graphite which graphite-bearing magnesia-carbon refractories should contain in order to maximize their compressive strength and minimize their open porosity. The neural network that was created can successfully predict results, and it predicted that compressive strength will be maximal and porosity will be minimal when graphite content is within the range 10 – 17.5% and the content of synthetic resin is 3%. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Refractories and Industrial Ceramics Springer Journals

Use of neural networks to optimize graphite content in magnesia-graphite refractories

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Publisher
Springer US
Copyright
Copyright © 2012 by Springer Science+Business Media New York
Subject
Materials Science; Characterization and Evaluation of Materials; Materials Science, general; Ceramics, Glass, Composites, Natural Materials
ISSN
1083-4877
eISSN
1573-9139
D.O.I.
10.1007/s11148-012-9491-5
Publisher site
See Article on Publisher Site

Abstract

Neural networks are one of the newest tools for computation. They provide unique opportunities for understanding and studying nonlinear problems, which makes such networks well-suited for use in different fields of engineering and technology. Only a few studies employing this new tool have been conducted in the field of ceramics. In the study discussed in this article, a three-layer neural network was used to construct a model that can predict the amounts of synthetic resin and graphite which graphite-bearing magnesia-carbon refractories should contain in order to maximize their compressive strength and minimize their open porosity. The neural network that was created can successfully predict results, and it predicted that compressive strength will be maximal and porosity will be minimal when graphite content is within the range 10 – 17.5% and the content of synthetic resin is 3%.

Journal

Refractories and Industrial CeramicsSpringer Journals

Published: Nov 13, 2012

References

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