Sheng-li Wu, Juan Zhu, Ji-cheng Bei, Guo-liang Zhang,  and Xiao-bo Zhai, Effects of particle characteristics on the granulation ability of iron ores during the sintering process, Int. J. Miner. Metall. Mater., 22(2015), No. 9, pp. 907-916. https://doi.org/10.1007/s12613-015-1149-0
Cite this article as:
Sheng-li Wu, Juan Zhu, Ji-cheng Bei, Guo-liang Zhang,  and Xiao-bo Zhai, Effects of particle characteristics on the granulation ability of iron ores during the sintering process, Int. J. Miner. Metall. Mater., 22(2015), No. 9, pp. 907-916. https://doi.org/10.1007/s12613-015-1149-0

Effects of particle characteristics on the granulation ability of iron ores during the sintering process

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  • Corresponding author:

    Juan Zhu    E-mail: zhujuanustb@aliyun.com

  • Received: 10 June 2014Revised: 13 November 2014Accepted: 15 November 2014
  • The granulation behavior of iron ores is essential for subsequent parameter optimization and efficient granulation, especially under changing material conditions. In this study, the effects of surface properties and particle size were analyzed using a laboratory granulation method; an estimation of the granulation of sintering blends was subsequently conducted for the base ores. Circularity and porosity were observed to negatively affect the granulation of iron ores, whereas wettability positively affected the granulation and was the most influential factor, indicating that wetting of iron ores is desirable during granulation. When iron ores with complex size distributions were granulated, the equivalent surface area was the main influencing factor for coarse particles larger than 1 mm and the ratio of adhering fines to intermediates was the main factor for fine particles smaller than 1 mm. By combining the granulation of coarse and fine particles with their proportioning, we proposed a calculation method for estimating the granulation ability of sintering blends. Good verification was demonstrated with the designed schemes. The results suggest that the developed method is effective for predicting the granulation of iron ore mixtures.
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