|
|
A Compensation Model of Continuous Temperature Measurement for Molten Steel in Tundish |
ZU Ling-yu,MENG Hong-ji,XIE Zhi |
College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China |
|
|
Abstract A compensation model has been proposed to reduce errors caused by the immersion depth of the sensor and the time lag of continuous temperature measurement for molten steel in tundish, which is based on the limited data fitting method and data fusion technology. According to the heat transfer analysis of sensor, the thermal model has been bulit to determine the temperature variation function.The parameters of the compensation model are recognized by generic algorithm, which combines the determine function, the molten steel mass in the ladle and pouring time. The processing of error compensation is divided into three stages: tracking, holding and compensation. When the processing is stable, the measured temperature error is small, and the measured temperature is regarded as accurate value and tracked. For the end of pouring stage of the ladle, the temperature error is caused by the immersion depth of the sensor, and the measured temperature before sharp decreasing is considered as real temperature and held. For the temperature increasing stage after ladle changed, the measured temperature is compensated online.The application results show that the error between the compensation temperatures and the actual ones have been decreased to ��2 ��, and the time lag could be shortened from 3-5 min to 40 s by applying this model.
|
Received: 24 May 2011
Published: 25 July 2012
|
Corresponding Authors:
Ling-Yu ZU
E-mail: menghongji@ise.neu.edu.cn
|
|
|
|
[1] |
LI Xue-tao����JIANG She-ming��ZHANG Qi-fu ��TENG Hua-xiang��ZHAO Hai-feng��HUANG Ming-dong. Kinetics model of non-isothermal austenite phase transformation for hot stamping boron steel[J]. Chinese Journal of Iron and Steel, 2017, 52(8): 92-96. |
[2] |
BI Zhi-min,WANG Yan. Method of flatness pattern recognition based on improved genetic algorithm optimization Elman neural network[J]. Chinese Journal of Iron and Steel, 2017, 29(4): 305-311. |
[3] |
HE Chuan-lan��WANG Wei-xin. Error analysis of the linear expansion coefficient during the measurement process[J]. Chinese Journal of Iron and Steel, 2017, 35(1): 25-28. |
[4] |
REN Ting-zhi��MA Cai-sheng. Optimization of burden distribution process for blast furnace with bell-less top based on genetic algorithm[J]. Chinese Journal of Iron and Steel, 2016, 51(6): 26-33. |
[5] |
SHANG Fei��LI Hong-bo��ZHANG Jie��HU Chao��ZHANG Chao��CHEN Jian-fei. Asymmetric wear and wear prediction model of CVC work roll[J]. Chinese Journal of Iron and Steel, 2016, 51(6): 59-64. |
[6] |
HAN Shun-jie,QI Ji-fan,JIANG Yu-lian,YOU Wen. Splash prediction based on principal component analysis and genetic algorithm-support vector machine[J]. Chinese Journal of Iron and Steel, 2016, 28(12): 21-26. |
|
|
|
|