Applying Flame Spectrum on SVC-RVM Modeling for BOF Endpoint Prediction

Abstract:

A new non-contact method for predicting the basic oxygen furnace(BOF) end point carbon content is proposed in this study. A model applying the flame spectrum of the converter vessel mouth is constructed to carry out the prediction. This model consists two parts, viz. a classifier based on support vector classification to classify the whole period of one BOF heat into two main phases, and a relevance vector machine working at the posterior phase to predict the carbon content. Compared with current non-contact methods of end point carbon content prediction, the proposed method can make better use of the information of the flame of the converter mouth. Simulations on industrial data show that this method yields good results on the classification as well as end point carbon content prediction.

• Periodical: Advanced Materials Research (Volumes 631-632)
• Pages: 870-874
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.631-632.870
• Online since: January 2013
• Authors: Yan Ming Shao, Feng Ji, Shu An Zhao, Mu Chun Zhou, Yan Ru Chen, Qi Zhao
• Keywords: Basic Oxygen Furnace, Endpoint Prediction, Flame Spectrum, Relevance Vector Machine, Support Vector Classification

References

[1] Jia You, Shaochun Wang, Xisheng Li, Yiping Han. Estimate Blowing Final Point by Analyzing Texture Features of Top-blowing BOF Vessel Mouth Flame. Jouranl of University of Science and Technology Beijing. Vol22, No. 6, Dec. 2000, 524-528.

[2] Hongyuan Wen, Qi Zhao, Yanru Cheng, Muchun Zhou, Meng Zhang, L.F. Xu. Converter End-point Real-time Prediction Using Spectrum Image Analysis. Opto- Electronic Engineering. Vol, 35, No. 5, May, 2008, 693-704.

[3] M. Cardin, J. F. MacGregor, I. P. Miletic, M. J. Bruwer. Image Based Endpoint Carbon Prediction for a Basic Oxygen Furnace. Iron&Steel Technology. Vol. 8, No. 11, 2011, 79-85.

[4] Xiaomei Lin, Xin Wang, Yaoping Guo, Gongzheng Cao. Carbon Content Detection Online Based on Light Intensity of Furnace Mouth in Molten Bath. Ferro-Alloy. No. 6, 2009, 37-39.

[5] Lingfei Xu, Wusheng Li, Meng Zhang, Shixue Xu, Jia Li. A model of basic oxygen furnace (BOF) end-point prediction based on spectrum information of the furnace flame with support vector machine (SVM). optik. 122(2011), 594-598.

DOI: 10.1016/j.ijleo.2010.04.018

[6] Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin. A Practical Guide to Support Vector Classification. http: /www. csie. ntu. edu. tw/~cjlin/. April 15, (2010).

DOI: 10.1109/72.857780

[7] Tipping, M.E. Sparse Bayesian Learning and the Relevance Vector Machine. Journal of Machine Learning Research. 2000, 1(3), 311-244.