Skip to main content
Log in

Prediction of suitable water content in granulation of sintering mixture based on Litster’s model

  • Original Paper
  • Published:
Journal of Iron and Steel Research International Aims and scope Submit manuscript

Abstract

Suitable water content plays a decisive role in the granulation of sintering mixtures. Herein, a method was proposed to predict the suitable water content for effective granulation on the basis of Litster’s granulation model. The granulation effectiveness of a sintering mixture was predicted by the model, with the allowance error of ± 10%. The effects of the water absorption properties, particle size composition and content of adhesive particles on the suitable water content were studied. The results showed that the allowable error of prediction was within ± 0.5% compared to the experimentally determined suitable water content. With an increase in adhesive powder content of mixtures with higher water absorption, the suitable water content increased to achieve similar granulation effectiveness. Moreover, as the amount of concentrates increased, the suitable water content first increased and then remained steady. The influence of the water absorption characteristics of the adhesive particles on the suitable water content was less than that of their particle size composition in the mixture.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. S.S. Bureau, Steel statistical yearbook, World Steel Association, Belgium, Brussels, 2021.

    Google Scholar 

  2. L. Zhu, F. He, Appl. Math. Model. 103 (2022) 162–175.

    Article  MathSciNet  Google Scholar 

  3. J. Fu, T. Jiang, Sintering pelletology, Central South University of Technology Press, Changsha, China, 1996.

    Google Scholar 

  4. T. Liu, Q.Y. Zhang, J.H. Sheng, Y.W. Jiang, B.W. Wang, Y.L. Song, Sinter. Pelletiz. 45 (2020) No. 3, 17–21.

    CAS  Google Scholar 

  5. D. Fernández-González, I. Ruiz-Bustinza, J. Mochón, C. González-Gasca, L.F. Verdeja, Miner. Process. Extr. Metall. Rev. 38 (2017) 36–46.

    Article  Google Scholar 

  6. S.M. Iveson, J.D. Litster, K. Hapgood, B.J. Ennis, Powder Technol. 117 (2001) 3–39.

    Article  CAS  Google Scholar 

  7. X. Fan, Principle and technology of iron ore matching for sintering, Metallurgical Industry Press, Beijing, China, 2013.

    Google Scholar 

  8. H. Takasaki, E. Yonemochi, R. Messerschmid, M. Ito, K. Wada, K. Terada, Int. J. Pharm. 456 (2013) 58–64.

    Article  CAS  PubMed  Google Scholar 

  9. S. Kawachi, S. Kasama, Tetsu-to-Hagane 94 (2008) 475–482.

    Article  CAS  Google Scholar 

  10. M. Gan, X.H. Fan, Z.Y. Ji, X.L. Chen, L. Yin, T. Jiang, Z.Y. Yu, Y.S. Huang, Ironmak. Steelmak. 42 (2015) 351–357.

    Article  CAS  Google Scholar 

  11. T. Maeda, C. Fukumoto, T. Matsumura, K. Nishioka, M. Shimizu, ISIJ Int. 45 (2005) 477–484.

    Article  CAS  Google Scholar 

  12. J. Khosa, J. Manuel, ISIJ Int. 47 (2007) 965–972.

    Article  CAS  Google Scholar 

  13. M. Matsumura, T. Kawaguchi, Tetsu-to-Hagane 87 (2001) 290–297.

    Article  CAS  Google Scholar 

  14. R. Marín Rivera, A. Koltsov, B. Araya Lazcano, J.F. Douce, Int. J. Miner. Process. 162 (2017) 36–47.

    Article  Google Scholar 

  15. 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.

    CAS  Google Scholar 

  16. L.F. Chen, M. Wu, W.H. Cao, X.Z. Lai, Computers and Applied Chemistry 28 (2011) 816–820.

    Google Scholar 

  17. S.L. Wu, J.X. Fan, J. Zhu, J.C. Bei, Z.G. Que, J. Iron Steel Res. 27 (2015) No. 1, 27–34.

    CAS  Google Scholar 

  18. G.L. Zhang, S.L. Wu, J. Zhu, Y.Z. Wang, Int. J. Miner. Metall. Mater. 21 (2014) 122–130.

    Article  CAS  Google Scholar 

  19. X. Lv, C. Bai, G. Qiu, S. Zhang, M. Hu, ISIJ Int. 50 (2010) 695–701.

    Article  CAS  Google Scholar 

  20. L. Zhou, Shandong Metallurgy 43 (2021) No. 2, 30–32.

    Google Scholar 

  21. Y.Q. Ren, C.Q. Huang, Y.S. Jiang, Z.X. Wu, Metals 12 (2022) 1287.

    Article  CAS  Google Scholar 

  22. H.Y. Cai, Intelligent control and research of self-learning model in sinter mixture moisture, Northeastern University, Shenyang, China, 2017.

    Google Scholar 

  23. Y.M. Wu, H.Y. Nie, C.X. Wu, Metallurgical Industry Automation 45 (2021) No. 1, 27–33.

    Google Scholar 

  24. Y.S. Jiang, N. Yang, Q.Q. Yao, Z.X. Wu, W. Jin, Neurocomputing 396 (2020) 209–215.

    Article  Google Scholar 

  25. C. Yang, D. Zhu, J. Pan, L. Lu, ISIJ Int. 58 (2018) 1427–1436.

    Article  CAS  Google Scholar 

  26. J.D. Litster, A.G. Waters, Powder Technol. 55 (1988) 141–151.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant No. 51804347.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Xiao-hui Fan or Xiao-xian Huang.

Ethics declarations

Conflict of interest

Zhi-yun Ji is an youth editorial board member for Journal of Iron and Steel Research International and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests. The authors have no potential conflict of interest to report.

Appendix

Appendix

1.1 Calculation flow of average particle size of granules

For a given ore blending condition, \({x}_{0.5}\) can be obtained according to Eq. (6). Then, α(\(x\)) can be calculated by \({x}_{0.5}\) in Eq. (2). Based on the Litster’s granulation model, the calculation flow is as follows.

The ratio of layer mass to nuclei mass of granules in ith size, \({R}_{i}\), is shown in following formula:

$$R_{i} = \frac{{\sum\limits_{i = 1}^{n} {\sum\limits_{k = 1}^{m} {\left[ {\left( {1 - \alpha_{i} } \right)\delta_{k} \omega_{ik} } \right]} } }}{{\sum\limits_{i = 1}^{n} {\sum\limits_{k = 1}^{m} {\alpha_{i} \delta_{k} \omega_{ik} } } }}$$
(7)

where n is the total particle size number; αi is the partition coefficient of the particles with ith size; and ωik is the mass fraction of component k in ith size.

The mass fraction of granules in ith size, \({\omega }_{\mathrm{g}i}\) (%) is depicted in Eq. (8):

$$\omega_{ {\text g} {i}} = \alpha_{i} (1 + R_{i} )\sum\limits_{k = 1}^{m} {(\delta_{k} \omega_{ik} )}$$
(8)

The adhering layer thickness of granules in ith size (ddl grain size), \({\varDelta }_{i}\), in mm, is described in Eq. (9):

$$2\varDelta_{i} = \frac{{R_{i} dl}}{4}$$
(9)

The top size of granule in ith size, \({x}_{i}\), in mm, can be calculated as follows:

$$x_{{{\text{g}}i}} = dl + 2\varDelta_{i}$$
(10)

Finally, the average particle size of granules, \(\overline{d}\), in mm, can be obtained from Eq. (11):

$$\overline{d} = \sum\limits_{i = 1}^{n} {x_{{\text g}{i}} \cdot \omega_{{\text g}{i}} } /100$$
(11)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dai, Fl., Fan, Xh., Huang, Xx. et al. Prediction of suitable water content in granulation of sintering mixture based on Litster’s model. J. Iron Steel Res. Int. 31, 552–560 (2024). https://doi.org/10.1007/s42243-023-01089-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42243-023-01089-y

Keywords

Navigation