A New Spectral Analysis Technique Used in Converter Steelmaking BOF Endpoint Control

Abstract:

This article introduces an arithmetic approach to establish an optical system aiming to eliminate the shortcomings in the steel making technique. The current basic oxygen furnace technique is flawed because the level of carbon content in the molten iron is estimated by the workers observation through experience. It is hard to compare, measure and control. The proposed model, on the other hand, is much more computable: relevant data is collected from spectrum distribution during the process of basic oxygen furnace. It predicts the end-point of BOF relatively accurate because spectrum is quantifiable, and the changing process of the furnace flame is essentially the changing process of the spectrum. In this model, we can measure the temperature of the molten steel by the Flame emission spectroscopy theory principle. Further more, the result of the experiment conducted based on the model shows that the model meets the requirements of endpoint judgment online.

• Periodical: Advanced Materials Research (Volumes 139-141)
• Pages: 689-692
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.139-141.689
• Online since: October 2010
• Authors: Ling Fei Xu, Wu Sen Li, Shi Xue Xu, Jia Li, Yong Qing Wang
• Keywords: BOF, End-Point Controlling, Spectrum, Support Vector Machine (SVM)

References

[1] Feng Jie, Zhang Hongwen: BOF steelmaking. Beijing: Metallurgical Industry Press, 2006, pp.332-338.

[2] Sharan A.: Light sensors for BOF carbon control in low cabon heats. Steelmaking Conference Proceedings, Vol 81 (1998), pp.337-345.

[3] Hong-yuan Wen, Qi Zhao, Yan-ru Chen, Mu-chun Zhou, Meng Zhang, Ling-fei Xu: BasicOxygen-Furnace Endpoint Forecasting Model Based on Radiation and Modified Neural Network, ACTA Opitca sinica, Vol 28(2008) no. 11, pp.2131-2135.

DOI: 10.3788/aos20082811.2131

[4] Hong-yuan Wen, Qi Zhao, Yan-ru Chen, Mu-chun Zhou, Meng Zhang, Ling-fei Xu: TConerter End-point Prediction Model Using Spectrum Image Analysis and Improved Neural Network Algorithm, Optica Applicata, Vol 38(2008) no. 4, pp.693-704.

[5] C. Cortes, V. Vapnik: Support vector networks, Machine Learning, (1995) no. 20, pp.273-297.

DOI: 10.1007/bf00994018

[6] Nello Cristianini, John Shawe-Taylor: An Introduction to Support Vector Machines, London: Cambridge University Press, 2006, pp.54-59.

[7] Wang Weiwei: Time Series Prediction Based on SVM and GA, The Eighth International Conference on Electronic Measurement and Instruments, (2007) no. 2, pp.307-310.

DOI: 10.1109/icemi.2007.4350680