Journal of Iron and Steel Research International

, Volume 26, Issue 11, pp 1154–1161 | Cite as

Coupling effect and characterization modeling of iron ore fines mixing and granulating at 0–1 mm

  • Dai-fei LiuEmail author
  • Xian-ju Shi
  • Chao-jun Tang
  • Hai-peng Cao
  • Jun Li
Original Paper


Characteristic of iron ore is the essential factor of granulating. Three ores, namely specularite, magnetite concentrate and limonite, were selected as adhesion powder to investigate granulating behavior and evolution process of agglomeration. Experiments and modeling were performed to represent granulating behavior on the basis of selectivity, ballability and adhesion rate. The mass fraction of water and particles size of adhesion and nucleation were set at (11 ± 1)%, 0–1 mm and 3–5 mm, respectively. Experimental results show that selectivity and ballability promote the evolution of granulation. The water absorption rate of specularite and the ballability of limonite are better. The coupling effects exist in two ores mixing and present positive effect when the proportion of magnetite concentrate is greater than that of specularite or specularite and limonite blend. During three ores mixing, the coupling effect presents a complex superposition state. A characterization model of adhesion rate of mixing granulation was established by random forest algorithms. Its output is adhesion rate, and its inputs include water absorption rate, balling index and mixing proportion. The model parameters are 957 trees and four branches, and the training and prediction errors of the model are 2.3% and 3.7%, respectively. Modeling indicates that the random forest model can be used to represent coupling effects of mixing granulation.


Iron ore Granulating Coupling effect Modeling Random forest algorithm 



This work was financially supported by the National Natural Science Foundation of China (No. 51674042) and Hunan Province 2011 Collaborative Innovation Center of Clean Energy and Smart Grid.


  1. [1]
    W.M. Gao, S.T. Wang, Q.D. Zhou, Iron and Steel 30 (1995) No. 4, 1–5.Google Scholar
  2. [2]
    K.V.S. Sastry, P. Dontula, C. Hosten, Powder Technol. 130 (2003) 231–237.CrossRefGoogle Scholar
  3. [3]
    C.Z. Li, T. Honeyands, D. O’Dea, R. Moreno-Atanasio, Powder Technol. 320 (2017) 257–272.CrossRefGoogle Scholar
  4. [4]
    H. Wei, X.J. Tang, Y. Ge, M. Li, H. Saxén, Y.W. Yu, Powder Technol. 353 (2019) 526–534.CrossRefGoogle Scholar
  5. [5]
    X.H. Fan, M. Gan, W.Q. Li, Q. Wang, L.B. Xie, L. Hu, X.L. Chen, L.S. Yuan, J. Univ. Sci. Technol. Beijing 34 (2012) 373–377.Google Scholar
  6. [6]
    C.Q. Zhou, C.G. Bai, X.W. Lv, H. Xie, T. Wang, H.Y. Xia, Sintering and Pelletizing 34 (2009) No. 3, 16–20.Google Scholar
  7. [7]
    S.H. Dai, F.M. Shen, Iron and Steel 45 (2010) No. 6, 20–25.Google Scholar
  8. [8]
    Y.W. Zhong, J.T. Gao, Z.C. Guo, Powder Technol. 335 (2018) 371–374.CrossRefGoogle Scholar
  9. [9]
    X.W. Lv, The construction of evaluation system of iron ore in iron-making process, Chongqing University, Chongqing, China, 2010.Google Scholar
  10. [10]
    T. Higuchi, L.M. Lu, E. Kasai, T. Yamamoto, H. Matsuno, Powder Technol. 339 (2018) 550–559.CrossRefGoogle Scholar
  11. [11]
    J.L. Zhang, D.H. Liu, Z.J. Liu, G.W. Wang, Y.Z. Wang, Q.F. Kang, Iron Steel Vanadium Titanium 38 (2017) No. 6, 91–97.Google Scholar
  12. [12]
    D.Q. Zhu, B.J. Shi, J. Pan, F. Zhang, Powder Technol. 331 (2018) 250–257.CrossRefGoogle Scholar
  13. [13]
    S.L. Wu, Y.F. Wang, J. Zhu, Iron and Steel 50 (2015) No. 5, 19–25.Google Scholar
  14. [14]
    J.P. Zhao, C.E. Loo, H. Zhou, J.L. Yuan, X.B. Li, Y.Y. Zhu, G.H. Yang, Combust. Flame 189 (2018) 257–274.CrossRefGoogle Scholar
  15. [15]
    A.M. Nyembwe, R.D. Cromarty, A.M. Garbers-Craig, Powder Technol. 295 (2016) 7–15.CrossRefGoogle Scholar
  16. [16]
    J.Q. Gan, A.B. Yu, Z.Y. Zhou, Chem. Eng. Sci. 156 (2016) 64–76.CrossRefGoogle Scholar
  17. [17]
    G.K.P. Barrios, R.M. de Carvalho, A. Kwade, L.M. Tavares, Powder Technol. 248 (2013) 84–93.CrossRefGoogle Scholar
  18. [18]
    Y. He, Z. Wang, T.J. Evans, A.B. Yu, R.Y. Yang, Int. J. Miner. Process. 142 (2015) 73–81.CrossRefGoogle Scholar
  19. [19]
    D. Wang, M. Servin, T. Berglund, K.O. Mickelsson, S. Rönnbäck, Powder Technol. 283 (2015) 475–487.CrossRefGoogle Scholar
  20. [20]
    D. Oliveira, S.L. Wu, Y.M. Dai, J. Xu, H. Chen, J. Iron Steel Res. Int. 19 (2012) No. 6, 1–5.CrossRefGoogle Scholar
  21. [21]
    G. Gustafsson, H.Å. Häggblad, M. Nishida, S. Larsson, P. Jonsén, Int. J. Impact Eng. 102 (2017) 180–186.CrossRefGoogle Scholar
  22. [22]
    E. Donskoi, T.D. Raynlyn, A. Poliakov, Miner. Eng. 120 (2018) 102–109.CrossRefGoogle Scholar
  23. [23]
    J.C.Á. Iglesias, K.S. Augusto, O. da F.M. Gomes, A.L.A. Domingues, M.B. Vieira, C. Casagrande, S. Paciornik, J. Mater. Res. Technol. 7 (2018) 376–380.Google Scholar
  24. [24]
    E. Donskoi, J. R. Manuel, P. Austin, A. Poliakov, M. J. Peterson, S. Hapugoda, J. Earth Sci. 122 (2013) 217–229.Google Scholar
  25. [25]
    J.Y. Fu, T. Jiang, D.Q. Zhu, Pelletization and agglomeration of sinter, CSUT Press, Changsha, China, 1997.Google Scholar
  26. [26]
    K.V.S. Sastry, D.W. Fuerstenau, Trans. Soc. Min. Eng. AIME 262 (1977) 43–46.Google Scholar
  27. [27]
    L. Breiman, Machine Learning 45 (2001) 5–32.CrossRefGoogle Scholar
  28. [28]
    A. Liaw, M. Wiener, R News 2 (2002) 18–22.Google Scholar

Copyright information

© China Iron and Steel Research Institute Group 2019

Authors and Affiliations

  1. 1.School of Energy and Power EngineeringChangsha University of Science and TechnologyChangshaChina
  2. 2.Ironmaking SectionWuhan Branch of Baosteel Central Research Institute (R & D Center of Wuhan Iron & Steel Co., Ltd.)WuhanChina

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