Study of Optimizing Combined-Blowing in EAF Based on K-Medoids Clustering Algorithm

Ling Zhi Yang; Rong Zhu; Kai Dong; Wen Juan Liu; Guo Hong Ma

doi:10.4028/www.scientific.net/AMR.881-883.1540

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 881-883Study of Optimizing Combined-Blowing in EAF Based...

Study of Optimizing Combined-Blowing in EAF Based on K-Medoids Clustering Algorithm

Abstract:

The integrated technology of oxygen blowing and bottom blowing was the core of EAF combined-blowing. Today's process model can not exactly describe the control of combined-blowing. In this paper, the EAF steelmaking history data was analyzed by K-medoids clustering algorithm which is data mining technology. Different clustering results were obtained under the cost of steelmaking, oxygen consumption, and etc. Finally, the optimizing combined-blowing curve is found out by analyzing the classified results. In the early of smelting, the flow rate of No.1 oxygen lance and No.2 oxygen lance is increased to improve the strength of decarburization and the pace of smelting. In the late of smelting, door oxygen lance starts to be used for dynamics conditions of carbon-oxygen reaction. The two bottom blowing lance use argon instead of nitrogen after 21 minutes in order to protect the steel components.

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Periodical:

Advanced Materials Research (Volumes 881-883)

Pages:

1540-1544

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.881-883.1540

Citation:

Cite this paper

Online since:

January 2014

Authors:

Ling Zhi Yang*, Rong Zhu, Kai Dong, Wen Juan Liu, Guo Hong Ma

Keywords:

Clustering, Combined-Blowing, Electric Arc Furnace, Optimizing

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* - Corresponding Author

References

[1] Zhenzhou Wang, Rong Zhu Progress of oxygen lances in EAF steel-making. Steelmaking, 2008, 02: 59-62.

Google Scholar

[2] Shiqi Li, Jian Yu, Jingshe Li. Development of Electric Arc Furnace Steelmaking Technology. China Metallurgy, 2010, 20(4): 1-7.

Google Scholar

[3] M. Martín, M. Rendueles, M. Díaz. Steel-Slag Mass Transfer in Steel Converter, Bottom and Top/Bottom Combined Blowing Through Cold Model Experiments. Chemical Engineering Research and Design, 2005, 83(9): 1076-1084.

DOI: 10.1205/cherd.02156

Google Scholar

[4] Mahdavi M, Abolhassani H. Harmony k-means algorithm for document clustering. Data Mining and Knowledge Discovery, 2009, 18(3): 370-391.

DOI: 10.1007/s10618-008-0123-0

Google Scholar

[5] Haitao Zheng, Kang B Y, Kim H G. Exploiting noun phrases and semantic relationships for text document clustering. Information Sciences, 2009, 179(13): 2249-2262.

DOI: 10.1016/j.ins.2009.02.019

Google Scholar

[6] Sugiyama, A, Kotani, M. analysis of gene expression data by using self-organizing maps and K-means clustering. Proceeding of the 2002 international joint conference , 2002, 2: 1342-1345.

DOI: 10.1109/ijcnn.2002.1007690

Google Scholar

[7] S. Kalyani, K.S. Swarup. Particle swarm optimization based K-means clustering approach for security assessment in power systems. Expert Systems with Applications, 2011, 38(9): 10839-10846.

DOI: 10.1016/j.eswa.2011.02.086

Google Scholar

[8] C.T. Yiakopoulos, K.C. Gryllias, I.A. Antoniadis. Rolling element bearing fault detection in industrial environments based on a K-means clustering approach. Expert Systems with Applications, 2011, 38(3): 2888-2911.

DOI: 10.1016/j.eswa.2010.08.083

Google Scholar

[9] Damodar Reddy, Prasanta K. Jana. Initialization for K-means Clustering using Voronoi Diagram. Procedia Technology, 2012, 4: 395-400.

DOI: 10.1016/j.protcy.2012.05.061

Google Scholar

[10] Yubin Luo，Ranwen Niu. Impact Analysis of Sample Data Normalization to the Result of GPS Height Conversion. Bulletin of Surveying and Mapping, 2013, 08: 33-35.

Google Scholar