JACIII Vol.21 No.5 pp. 785-794
doi: 10.20965/jaciii.2017.p0785


A Cascade Prediction Model of CO/CO2 in the Sintering Process

Ben Xu, Xin Chen, Min Wu, and Weihua Cao

School of Automation, China University of Geosciences
Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
Wuhan 430074, China

Corresponding author

December 17, 2016
June 14, 2017
September 20, 2017
sintering process, CO/CO2, spearman’s rank correlation coefficient (SRCC), stepwise regression analysis (SRA), back-propagation neural network (BPNN)

Sintering is an important production process in iron and steel metallurgy. Carbon fuel consumption accounts for about 80% of the total energy consumption in the sintering process. To enhance the efficiency of carbon fuel consumption, we need to determine the factors affecting carbon efficiency and build a model of it. In this paper, the CO/CO2 is taken to be a measure of carbon efficiency, and a cascade predictive model is built to predict it. This model has two parts: the key state parameter submodel and the CO/CO2 submodel. The submodels are built using particle swarm optimization-based back propagation neural networks (PSO-BPNNs). Based on the mechanism analysis, spearman’s rank correlation coefficient (SRCC) and stepwise regression analysis (SRA) are used to determine the relationship between the process parameters, in order to determine the inputs of each submodel. Finally, the results of a simulation show the feasibility of the cascade model, which will serve as the basic model for the optimization and control of the carbon efficiency of the sintering process.

  1. [1] Z. H. Feng, H. Zhang, and Y. H. Wang, “Study on prediction and optimization of sintering process energy consumption,” Sintering and Pelletizing, Vol.37, No.6, pp. 13-17, 2012.
  2. [2] M. Pahlevaninezhad, M. D. Emami, and M. Panjepour, “The effects of kinetic parameters on combustion characteristics in a sintering bed,” Energy, Vol.73, pp. 160-176, 2014.
  3. [3] M. Z. Bian, X. Y. He, and G. S. Hou, “Analysis of solid fuel and measures for energy consumption,” Technology of Steel, Vol.28, No 3, pp. 19-22, 2002.
  4. [4] K. S. Kim, Y. J. Cho, and S. J. Jeong, “Simulation of CO2 emission reduction potential of the iron and steel industry using a system dynamics model,” Int. J. of Precision Engineering and Manufacturing, Vol.15, No.2, pp. 361-373, 2014.
  5. [5] C. Wang, M. Larsson, C. Ryman, et al., “A Model on CO2 emission reduction in integrated steelmaking by optimization methods,” Int. J. of Energy Research, Vol.32, pp. 1092-1106, 2008.
  6. [6] A. Ranzani da Costa, D. Wagner, and F. Patisson, “Modelling a new, low CO2 emissions, hydrogen steelmaking process,” J. of Cleaner Production, Vol.46, pp. 27-35, 2013.
  7. [7] J. P. Zhao, C. E. Loo, and R. D. Dukino, “Modeling fuel combustion in iron ore sintering,” Combustion and Flame, Vol.162, pp. 1019-1034, 2015.
  8. [8] H. Zhou, J. P. Zhao, C. E. Loo, B. G. Ellis, and K. F. Cen, “Numerical modeling of the iron ore sintering process,” ISIJ Int., Vol.52, No.9, pp. 1550-1558, 2012.
  9. [9] V. R. Radhakrishnan and A. R. Mohamed, “Neural networks for the identification and control of blast furnace hot metal quality,” J. of Process Control, Vol.10, pp. 509-524, 2000.
  10. [10] A. Kusiak, M. Li, and Z. Zhang, “A data-driven approach for steam load prediction in buildings,” Appl Energy, Vol.87, pp. 925-933, 2010.
  11. [11] H. Long, Z. J. Zhang, and Y. Su, “Analysis of daily solar power prediction with data-driven approaches,” Appl Energy, Vol.126, pp. 29-37, 2014.
  12. [12] C. H. Lu and X. F. Gu, “Quality prediction of batch process using the global-local discriminant analysis based gaussian process regression model,” J. of Southeast University, Vol.31, pp. 80-86, 2015.
  13. [13] H. Y. Jiang, S. L. Jing, and T. Y. Chai, “Sintering condition prediction based on SVM and PSO,” J. of Northeastern University, Vol.31, No.4, pp. 494-497, 2010.
  14. [14] S. Zhang and W. M. Gao, “Application of neural networks in the prediction of sinter quality,” Sintering and Pelletizing, Vol.26, No.4, pp. 6-10, 2001.
  15. [15] X. Chen, W. W. Wen, M. Wu, and W. H. Cao, “BP neural network model of coke consumption of sintering process based on chaotic PSO algorithm,” Computers and Applied Chemistry, Vol.30, No.10, pp. 111-114, 2013.
  16. [16] J. Hu, M. Wu, X. Chen, and W. H. Cao, “Prediction model of comprehensive coke ratio based on principal component analysis for sintering process,” Proc. of the 35th Chinese Control Conf., pp. 3612-3617, 2016.
  17. [17] X. X. Chen, J. H. She, X. Chen, and M. Wu, “Discrete wavelet transfer based BPNN for calculating carbon efficiency of sintering process,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.7, pp. 1070-1076, 2016.
  18. [18] H. Jan and K. Tomasz, “Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data,” Quaestiones Geographicae, Vol.30, No.2, pp. 87-93, 2011.
  19. [19] H. S. Zhao and H. Zhang, “Based on stepwise regression analysis of the rowing athlete selection potential research colleges and universities,” Bio Technology, Vol.10, No.10, pp. 4625-4633, 2014.
  20. [20] X. Q. Cheng and W. F. Lin, “Highway traffic incident detection based on BPNN,” Procedia Engineering, Vol.7, pp. 482-489, 2010.

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Last updated on Oct. 20, 2017