APPLICATION OF NEURAL NETWORKS AT PREDICTION OF HOT METAL COMPOSITION

1 KLIMCZYK Arkadiusz
Co-authors:
1 BERNASOWSKI Mikolaj 1 STACHURA Ryszard
Institution:
1 AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science, Mickiewicza Av. 30, 30-059 Krakow, Poland, EU, Arkadiusz.Klimczyk@agh.edu.pl
Conference:
25th Anniversary International Conference on Metallurgy and Materials, Hotel Voronez I, Brno, Czech Republic, EU, May 25th - 27th 2016
Proceedings:
Proceedings 25th Anniversary International Conference on Metallurgy and Materials
Pages:
121-126
ISBN:
978-80-87294-67-3
ISSN:
2694-9296
Published:
14th December 2016
Proceedings of the conference were published in Web of Science and Scopus.
Metrics:
124 views / 55 downloads
Abstract

Use of the control-steering systems in blast furnace technology contributes to the improvement of the quality of hot metal, which can be expressed by desirable and stable chemical composition and required temperature of hot metal at the tap. The paper presents the possibility of using artificial neural network as part of BF technology supporting system, and in particular its use to predict silicon, sulfur and phosphorus containing in hot metal. The models of neural networks have been created on the industrial operation basis of blast furnace No. 5 Arcelor Mittal Poland Krakow.

Keywords: Blast furnace, neural networks, prediction of hot metal composition

© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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