AOD furnace splash soft-sensor in the smelting process based on improved BP neural network

被引：0

作者：

Ma Haitao ^{[1
]}

Wang Shanshan ^{[1
]}

Wu Libin ^{[1
]}

Yu Ying ^{[2
]}

机构：

[1] Changchun Univ Technol, Inst Elect & Elect Engn, Changchun 130012, Peoples R China

[2] Aviat Univ Air Force, Inst Informat Confrontat, Changchun 130000, Peoples R China

来源：

LIDAR IMAGING DETECTION AND TARGET RECOGNITION 2017 | 2017年 / 10605卷

关键词：

multi-sensors; AOD furnace; splash; forecast; soft-sensor; low carbon ferrochrome; BP neural network; vibration signal; audio signal; flame image signal;

D O I：

10.1117/12.2294038

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

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.

引用

页数：6