Multi-objective intelligent coordinating optimization blending system based on qualitative and quantitative synthetic model

  • Wang Ya-lin Email author
  • Ma Jie 
  • Gui Wei-hua 
  • Yang Chun-hua 
  • Zhang Chuan-fu 


A multi-objective intelligent coordinating optimization strategy based on qualitative and quantitative synthetic model for Pb-Zn sintering blending process was proposed to obtain optimal mixture ratio. The mechanism and neural network quantitative models for predicting compositions and rule models for expert reasoning were constructed based on statistical data and empirical knowledge. An expert reasoning method based on these models were proposed to solve blending optimization problem, including multi-objective optimization for the first blending process and area optimization for the second blending process, and to determine optimal mixture ratio which will meet the requirement of intelligent coordination. The results show that the qualified rates of agglomerate Pb, Zn and S compositions are increased by 7.1%, 6.5% and 6.9%, respectively, and the fluctuation of sintering permeability is reduced by 7.0%, which effectively stabilizes the agglomerate compositions and the permeability.

Key words

Pb-Zn sintering blending process qualitative and quantitative synthetic model multi-objective optimization area optimization intelligent coordination 

CLC number



Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    XIA De-hong, ZHANG Gang. Simulation of thermal process of sintering[J]. Energy for Metallurgical Industry, 2004, 23(4): 10–13. (in Chinese)Google Scholar
  2. [2]
    JIA Juan-yu, BAI Chen-guang, LAI Hong, et al. Optimization and realization for sinter and ore blending for blast furnace[J]. Journal of Chongqing University: Natural Science, 2002, 25(10): 68–71. (in Chinese)Google Scholar
  3. [3]
    YANG Dong-jin, CHEN Ji-guo, YU Zhong-nian, et al. Optimization of mix proportioning, sintering and pelletizing[J]. Sintering and Pelletizing, 2000, 25(1): 14–17. (in Chinese)Google Scholar
  4. [4]
    LIANG Zhong-yu, HU Lin, DENG Neng-yun, et al. Analysis of sinter charge proportioning by linear optimization[J]. Iron and Steel, 2001, 36(10): 12–13. (in Chinese)Google Scholar
  5. [5]
    Efsathiou J. Expert systems in process control[M]. Essex: Longman, 1989.Google Scholar
  6. [6]
    Hagan M T, Demuth H B, Beale M H. Neural network design[M]. Boston: PWS Publishing, 1996.Google Scholar
  7. [7]
    WU Min, Nakano M, SHE Jin-hua. A model-based expert control strategy using neural network for the coal blending process in an iron and steel plant[J]. Expert Systems with Application, 1999, 16(3): 271–281.CrossRefGoogle Scholar
  8. [8]
    YANG Chun-hua, SHEN De-yao, WU Min, et al. Synthesis of qualitative and quantitative methods in a coal blending expert system for coke oven[J]. Acta Automatica Sinica, 2000, 26(2): 226–232. (in Chinese)Google Scholar
  9. [9]
    WANG Ya-lin. Study on intelligent integrated modeling theory and its applications to optimal control of nonferrous metallurgical process [D]. Changsha: School of Information Science and Engineering, Central South University, 2001. (in Chinese)Google Scholar
  10. [10]
    WANG Yi-wen, GUI Wei-hua, WANG Ya-lin. Integrated model for predicting burning through point of sintering process based on optimal combination algorithm[J]. The Chinese Journal of Nonferrous Metals, 2002, 12(1): 191–195. (in Chinese)Google Scholar
  11. [11]
    CHEN Xiao-fang, GUI Wei-hua, WANG Ya-lin, et al. Soft-sensing model of sulfur content in agglomerate based on intelligent integrated strategy, Softsensing model of sulfur content in agglomerate based on intelligent integrated strategy[J]. Control Theory and Applications, 2004, 21(1): 75–80. (in Chinese)Google Scholar
  12. [12]
    WANG Ya-lin, GUI Wei-hua, YANG Chun-hua, et al. Prediction for composition of Pb-Zn agglomerate based on integrated modeling[J]. Journal of Central South University: Science and Technology, 2005, 36(1): 113–118. (in Chinese)Google Scholar

Copyright information

© Science Press 2001

Authors and Affiliations

  • Wang Ya-lin 
    • 1
    Email author
  • Ma Jie 
    • 1
  • Gui Wei-hua 
    • 1
  • Yang Chun-hua 
    • 1
  • Zhang Chuan-fu 
    • 2
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.School of Metallurgical Science and EngineeringCentral South UniversityChangshaChina

Personalised recommendations