The use of artificial neural networks for the prediction of sulphur content in hot metal produced in blast furnace

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

Abstrakty

EN

Purpose: The paper presents the possibilities of using artificial intelligence for the prediction of sulphur content in 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: Materials research performed with the use of data mining and neural networks is consistent with the results obtained during the real research in a real laboratory. The obtained results show that the construction of such neural networks is practical. There is a strong correlation between predicted value and real value. Practical implications: The presented model can be used in the industrial practice as an additional tool for blast furnace and steel plant operators. Originality/value: Prediction of sulphur content in hot metal at the stage of adjusting hot metal process parameters.

Słowa kluczowe

EN

artificial intelligence methods; blast furnace; sulphur; data mining

PL

metody sztucznej inteligencji; wielki piec; siarka; eksploracja danych

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2015
• Tom: Vol. 70, nr 2
• Strony: 86--92
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Dobrzański L. A.

• Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland

autor

Trzaska J.

• Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland

autor

Gawron M.

• ArcelorMittal Poland S.A., Al. Józefa Piłsudskiego 92, 41-308 Dąbrowa Górnicza, Poland

autor

Berliński M.

• ArcelorMittal Poland S.A., Al. Józefa Piłsudskiego 92, 41-308 Dąbrowa Górnicza, Poland

Bibliografia

• [1] M.W. Blicharski, Introduction to materials engineering. WNT, Warsaw, 1998 (in Polish).
• [2] L.A. Dobrzański, Metallic engineering materials, WNT, Warsaw, 2004 (in Polish).
• [3] T. Diaz de la Rubia, V.V. Bulatov, Materials Research by Means of Multiscale Computer Simulation, MRS Bulletin, 26.03.2001, 169-175.
• [4] R. Magali, G. Meireles, P.E.M. Almeida, A Comprehensive Review for Industrial Applicability of Artificial Neural Networks, IEEE Transactions on Industrial Electronics 50/3 (2003). 585-601
• [5] Z. Zhang, K. Friedrich, Artificial neural networks applied to polymer composites: a review, Composites Science and Technology 63 (2003) 2029-2044.
• [6] D. Mackall, S. Nelson, J. Schumann, Verification and Validation of Neural Networks of Aerospace Applications, Technical Report CR-211409, NASA, 2002.
• [7] A. Husseina, M. Addab, M. Atieha, W. Fahsb, Smart Home Design for Disabled People based on Neural Networks, Procedia Computer Science 37 (2014) 117-126.
• [8] N. Sowar, K. Gromley, A sharper view: Analytic in the global steel industry, Deloitte, 2011, 1-13.
• [9] L.A. Dobrzański, M. Gawron, M. Berliński, The use of artificial neural networks for the prediction of a chemical composition of hot metal produced in blast furnace, Journal of Achievements in Materials and Manufacturing Engineering 67/1 (2014) 32-38.
• [10] S. Maharana, B. Biswas, A. Ganguly, A. Kumar, Artificial Neural Network Prediction for Coke Strength after Reaction and Data Analysis, World Academy of Science, Engineering and Technology 45 (2010) 556-570.
• [11] L.A. Dobrzański, R. Honysz, Application of artificial neural networks in modelling of normalized structural steels mechanical properties, Journal of Achievements in Materials and Manufacturing Engineering 32/1 (2009) 37-45.
• [12] J.A. Burgo, The Manufacture of Pig Iron in the Blast Furnace, The AISE Steel Foundation, Pittsburgh, 1999, 699-740.
• [13] M. Geerdes, H. Toxopeus, C. van der Vliet, Modern blast furnace ironmaking, Amsterdam, 2009.
• [14] L. Król, Blast furnace, construction and equipment, Silesian University of Technology Press, 1989 (in Polish).
• [15] E. Mazanek, L. Król, Blast furnace, technology and practice, "Ślsk" Publishing House, 1969 (in Polish).
• [16] R. Benesch, J. Janowski, E. Mazanek, Blast furnace process, "Ślsk" Publishing House, 1972 (in Polish).
• [17] E. Mazanek, W. Sabela, Combustion and heat transfer processes in blast furnace, "Ślsk" Publishing House, 1970 (in Polish).
• [18] E. Davoodi, A.R. Khanteymoori, Horse Racing Prediction Using Artificial Neural Networks, Recent advances in neural networks, fuzzy systems and evolutionary computing, Romania, 2010, 155-160.
• [19] D.A. Wahab, L. Amelia, N.K. Hooi, C.H.C. Haron, C.H. Azhari, The application of artificial intelligence in optimisation of automotive components for reuse, Journal of Achievements in Materials and Manufacturing Engineering 31/2 (2008) 595-601.
• [20] Q.J. Zhang, K.C. Gupta, Neural Networks for RF and Microwave Design, IEEE transactions on microwave theory and techniques 51/4 (2003) 1339-1350.
• [21] P. Sibi, S. Allwyn Jones, P. Siddarth, Analysis of different activation functions using back propagation neural networks, Journal of Theoretical and Applied Information Technology 47/3 (2013) 1264-1268.
• [22] J. Konieczny, L.A. Dobrzański, B. Tomiczek, J. Trzaska, Application of the artificial neural networks for prediction of magnetic saturation of metallic amorphous alloys, Journal of Achievements in Materials and Manufacturing Engineering 30/2 (2008) 105-108.