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End-point prediction of basic oxygen furnace (BOF) steelmaking based on improved twin support vector regression

C. Gao ; School of Materials and Metallurgy, University of Science and Technology Liaoning, Anshan, China
M. G. Shen ; School of Materials and Metallurgy, University of Science and Technology Liaoning, Anshan, China

Puni tekst: engleski, pdf (206 KB) str. 29-32 preuzimanja: 144* citiraj
APA 6th Edition
Gao, C. i Shen, M.G. (2019). End-point prediction of basic oxygen furnace (BOF) steelmaking based on improved twin support vector regression. Metalurgija, 58 (1-2), 29-32. Preuzeto s https://hrcak.srce.hr/206471
MLA 8th Edition
Gao, C. i M. G. Shen. "End-point prediction of basic oxygen furnace (BOF) steelmaking based on improved twin support vector regression." Metalurgija, vol. 58, br. 1-2, 2019, str. 29-32. https://hrcak.srce.hr/206471. Citirano 16.09.2019.
Chicago 17th Edition
Gao, C. i M. G. Shen. "End-point prediction of basic oxygen furnace (BOF) steelmaking based on improved twin support vector regression." Metalurgija 58, br. 1-2 (2019): 29-32. https://hrcak.srce.hr/206471
Harvard
Gao, C., i Shen, M.G. (2019). 'End-point prediction of basic oxygen furnace (BOF) steelmaking based on improved twin support vector regression', Metalurgija, 58(1-2), str. 29-32. Preuzeto s: https://hrcak.srce.hr/206471 (Datum pristupa: 16.09.2019.)
Vancouver
Gao C, Shen MG. End-point prediction of basic oxygen furnace (BOF) steelmaking based on improved twin support vector regression. Metalurgija [Internet]. 2019 [pristupljeno 16.09.2019.];58(1-2):29-32. Dostupno na: https://hrcak.srce.hr/206471
IEEE
C. Gao i M.G. Shen, "End-point prediction of basic oxygen furnace (BOF) steelmaking based on improved twin support vector regression", Metalurgija, vol.58, br. 1-2, str. 29-32, 2019. [Online]. Dostupno na: https://hrcak.srce.hr/206471. [Citirano: 16.09.2019.]

Sažetak
In this paper, a novel prediction method for low carbon steel is proposed based on an improved twin support vector regression algorithm. 300 qualified samples are collected by the sublance measurements from the real plant. The simulation results show that the prediction models can achieve a hit rate of 96 % for carbon content within the error bound of 0,005 % and 94 % for temperature within the error bound of 15 °C. The double hit rate reaches to 90 %. It indicates that the proposed method can provide a significant reference for real BOF applications, and also it can be extended to the prediction of other metallurgical industries.

Ključne riječi
steel; basic oxygen furnace; twin support vector regression; end-point prediction

Hrčak ID: 206471

URI
https://hrcak.srce.hr/206471

Posjeta: 211 *