JACIII Vol.20 No.2 pp. 279-286
doi: 10.20965/jaciii.2016.p0279


Subspace Modeling Method for Burn-Through Point

Yongyue Zhang*, Weihua Cao**, †, and Min Wu**

*School of Information Science and Engineering, Central South University
Changsha 410083, China

**School of Automation, China University of Geosciences
Wuhan 430074, China

Corresponding author

November 10, 2015
December 10, 2015
Online released:
March 18, 2016
March 20, 2016
burning-through point, data-driven, subspace modeling method, sintering process
In the study, for the iron sintering process which is strongly time-delayed and confounding, a data-driven modeling method called subspace modeling method is proposed to predict the burn-through point (BTP) in this paper. First, by analyzing the mechanism of the sintering process and processing the industrial data, the relationship between the exhaust gas temperature and bellows is confirmed. Then, based on the position of the BTP, a subspace modeling method is used to establish a temperature identification model. This model outputs, the temperature of the BTP and its inputs include the exhaust gas temperature of the front three bellows and the operating parameters. Finally, we compare the obtained result with those of other algorithms. The simulation experiment shows the effectiveness and veracity of the subspace model for BTP, which will provide a precise model for controller design.
Cite this article as:
Y. Zhang, W. Cao, and M. Wu, “Subspace Modeling Method for Burn-Through Point,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.2, pp. 279-286, 2016.
Data files:
  1. [1] H. D. Wang, G. Z. Qiu, and S. S. Huang, “Advances in control techniques for sintering process,” Mining and Metallurgical Engineering, Vol.19, No.3, pp. 3-6, 1999.
  2. [2] M. Wu, C. H. Xu, J. H. She, and W. H. Cao, “Neural-network-based integrated model for predicting burn-through point in lead-zinc sintering process,” J. of Process Control, Vol.22, pp. 68-77, 2012.
  3. [3] N. Tamura, M. Konishi, and T. Morita, “Mathematical approach for the optimization of the sintering process operation,” Automation in Mining, Mineral and Metal Processing, pp. 203-208, 1986.
  4. [4] H. Hibino, “Method to control manufacturing cell by driving simulation model,” Int. J. of Automation Technology, Vol.8, No.4, pp. 539-549, 2014.
  5. [5] H. G. Han, “Adaptive controller for T-S fuzzy model with modeling error,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.15, No.7, pp. 759-766, 2011.
  6. [6] P. Wu, “Subspace based identification and its application,” Hangzhou: Zhejiang University, School of Information, 2009.
  7. [7] A. G. Wills, G. Knagge, and B. Ninness, “Fast linear model predictive control via custom integrated circuit architecture,” IEEE Trans. on Control Systems Technology, Vol.20, No.1, pp. 59-71, 2010.
  8. [8] S. D. M. Borjas and C. Garcia, “Subspace identification for industrial processes,” Tema, Vol.12, No.3, pp. 183-194, 2011.
  9. [9] I. Houtzager, J.-W. van Wingerden, M. Verhaegen, “Recursive Predictor-Based Subspace Identification with Application to the Real-Time Closed-Loop Tracking of Flutter,” IEEE Trans. on Control Systems Technology, Vol.20, No.4, pp. 934-949, 2012.
  10. [10] A. Alenany, H. Shang, and M. Soliman, “Brief paper-Improved subspace identification with prior information using constrained least squares control,” Control Theory and Applications, Vol.5, No.13, pp. 1568-1576, 2011.
  11. [11] M. Wu, L. Ding, and W. H. Cao, “An integrated prediction model for burn-through-point in lead-zinc sintering process,” Control Theory and Applications, Vol.26, No.7, pp. 739-744, 2009.
  12. [12] M. H. Li and J. Wang, “The research for soft measuring technique of sintering burning through point,” IEEE Conf. on Industrial Electronics and Application, pp. 1-4, 2006.
  13. [13] S. J. Qin, “An overview of subspace identification,” Computer and Chemical Engineering, Vol.30, No.10-12, pp. 1502-1513, 2006.
  14. [14] D. Wei, T. Zhang, and N. Jiang, “System identification modeling of distillation column based on subspace method,” Computer Simulation, Vol.26, No.4, pp. 109-112, 2009.
  15. [15] T Hyakudome, H Baba, N Ikoma, and H Maeda, “State space model with time-varying mixing weight to estimate user type,” The 20th Fuzzy Systems Symp., pp. 541-544, 2004.
  16. [16] S. Becker and V Karri, “Implementation of neural network models for parameter estimation of a PEM-Electrolyzer,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.14, No.6, pp. 735-745, 2010.
  17. [17] Y. C. Wei and J. Watada, “Building a Type-2 fuzzy qualitative regression model,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.16, No.4, pp. 527-532, 2012.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 19, 2024