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AOD furnace splash soft-sensor in the smelting process based on improved BP neural network
Author(s): Haitao Ma; Shanshan Wang; Libin Wu; Ying Yu
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Paper Abstract

In view of argon oxygen refining low carbon ferrochrome production process, in the splash of smelting process as the research object, based on splash mechanism analysis in the smelting process , using multi-sensor information fusion and BP neural network modeling techniques is proposed in this paper, using the vibration signal, the audio signal and the flame image signal in the furnace as the characteristic signal of splash, the vibration signal, the audio signal and the flame image signal in the furnace integration and modeling, and reconstruct splash signal, realize the splash soft measurement in the smelting process, the simulation results show that the method can accurately forecast splash type in the smelting process, provide a new method of measurement for forecast splash in the smelting process, provide more accurate information to control splash.

Paper Details

Date Published: 15 November 2017
PDF: 6 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106052S (15 November 2017); doi: 10.1117/12.2294038
Show Author Affiliations
Haitao Ma, Changchun Univ. of Technology (China)
Shanshan Wang, Changchun Univ. of Technology (China)
Libin Wu, Changchun Univ. of Technology (China)
Ying Yu, Aviation Univ. Air Force (China)


Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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