The Online Prediction of the Low Carbon Ferrochrome Terminal Composition in Smelting Process

Abstract:

The online prediction of the low carbon ferrochrome terminal composition in electro-silicothemic smelting process plays a key role in guiding the determining the tapping time, the smelting process of the power supply system, the production quality and the energy consumption and so on. By introducing the multi-scale wavelet kernel function in the support vector machine (SVM) algorithm, and according to the Bayesian classifier to certain different smelting conditions, we chose different decomposition scales. In this way, the accuracy of the terminal composition prediction during the smelting process is improved greatly. Experiments show the effectiveness of the proposed method.

• Periodical: Advanced Materials Research (Volume 645)
• Pages: 519-522
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.645.519
• Online since: January 2013
• Authors: Niao Na Zhang, Ying Ying Wang, Yong Jun Bai
• Keywords: Bayesian, Electro-Silicothermic, Multi-Scale, Support Vector Machine (SVM)

References

[1] Chai Tianyou. The challenge that the optimization control of manufacturing full process to the control and optimization of the theoretical approaches [J]. ACTA AUTOMATICA SINICA, 2009, 35(6): 641-648.

[2] I. J. Barker, Some considerations on future developments in ferro-alloy furnaces, Southern African Pyrometallurgy 2011, Edited by R.T. Jones & P. den Hoed, Southern African Institute of Mining and Metallurgy, Johannesburg, 6-9 March 2011-7-24.

[3] Opfer R. Tight frame expansions of multiscale reproducing kernels in Sobolev spaces [J]. Applied andComputational Harmonic Analysis, 2006, 20(1): 357-374.

DOI: 10.1016/j.acha.2005.05.003

[4] FuY, Chai T Y, Yue H. Intelligent control using multiple models and neural network. International Journal of Adaptive Control and Signal Processing, 2008, 22(5): 495-509.

DOI: 10.1002/acs.1007

[5] Mu Xiangyang. Zhang Taiyi, Zhou Yatong. The application of the least squares vector machine (SVM) scale nuclear function in the signal approximation[J]. Journal of Xi`an jiaotong University , 2008, 12: 1464-1467.

DOI: 10.1109/wcica.2008.4593650

[6] Rakotomamonjy A, CANU S. Frame, reproducing kernel, regularization and learning [J]. Journal of Machine Learning Research, 2005, 6 (12): 1485-1515.