The use of artificial neural networks for the prediction of a chemical composition of hot metal produced in blast furnace

• Autorzy: Dobrzański L.A. , Gawron M. , Berliński M.
• Języki publikacji: EN

Abstrakty

EN

Purpose: The paper presents the possibilities of using neural networks for the prediction of chemical composition of hot metal produced in blast furnace. Design/methodology/approach: Three blast furnaces in ArcelorMittal, Unit in Dąbrowa Górnicza, provided the data for the model construction. The data reflect a number of variables, which describe the blast furnace process. Findings: The results obtained, based on input parameters, show that the construction of such neural networks is viable. There is a good correlation between expected and actual results. Practical implications: The model can be used as an auxiliary tool for blast furnace operators. Originality/value: Prediction of a chemical composition of hot metal at the stage of adjusting hot metal process parameters.

Słowa kluczowe

EN

Artificial Neural Networks; blast furnace; chemical composition

• Wydawca: International OCSCO World Press
• Czasopismo: Archives of Materials Science and Engineering
• Rocznik: 2014
• Tom: Vol. 67, nr 1
• Strony: 32--38
• Opis fizyczny: Bibliogr. 19 poz.

Twórcy

autor

Dobrzański L.A.

• Division of Materials Processing Technology, Management and Computer Techniques in Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland

autor

Gawron M.

• Arcelor Mittal Poland S.A., Al. Jozefa Pilsudskiego 92, 41-308 Dąbrowa Górnicza, Poland

autor

Berliński M.

• Arcelor Mittal Poland S.A., Al. Jozefa Pilsudskiego 92, 41-308 Dąbrowa Górnicza, Poland

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