Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4712
Title: Prediction of the slag corrosion of MgO-C ladle refractories by the use of artificial neural networks
Authors: Akkurt, Sedat
Keywords: Artificial neural networks
Corrosion
MgO-C refractory
Magnesium compounds
Slag-corrosion systems
Issue Date: 2004
Publisher: Trans Tech Publications
Source: Akkurt, S. (2004). Prediction of the slag corrosion of MgO-C ladle refractories by the use of artificial neural networks. Key Engineering Materials, 264-268(III), 1727-1730. doi:10.4028/www.scientific.net/KEM.264-268.1727
Abstract: A multilayer feed-forward back-propagation learning algorithm was employed as an artificial neural network (ANN) tool to create a model to predict the corrosion of MgO-C ladle refractory bricks based on laboratory slag corrosion test data. The corrosion process occurred by immersion of the rectangular refractory specimens in molten slag-steel bath. An ANN model to predict the amount of corrosion was created by using the training data. The model was also tested with experimentally measured data and relatively low error levels were achieved. This model was then used to predict the response of the slag-corrosion system to different values of the factors affecting the corrosion of bricks at high temperatures. Exposure time, exposure temperature of slag-brick contact and CaO/SiO2 ratio of the slag were the factors used for modelling. Model results provided the potential for selection of the best conditions for avoiding the factor combinations that may accelerate corrosion.
Description: Proceedings of the 8th Conference and Exhibition of the European Ceramic Society; Istanbul; Turkey; 29 June 2003 through 3 July 2003
URI: http://doi.org/10.4028/www.scientific.net/KEM.264-268.1727
http://hdl.handle.net/11147/4712
ISSN: 1013-9826
1013-9826
Appears in Collections:Mechanical Engineering / Makina Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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