A study of micro-particles in the dust and melt at different stages of iron and steelmaking

Sammanfattning: The dust particles generated due to mechanical wear of iron ore pellets and clusters formed in molten stainless steel alloyed with rare earth metals (REM) are considered in this study. Firstly, the influence of the characteristics of iron ore pellets, applied load on a pellet bed and partial reduction of the pellets on the size distribution of the generated dust was investigated. Secondly, REM clusters are investigated to evaluate the size distribution of the clusters. Also, an extreme value distribution (EVD) analysis has been applied for the observed REM clusters.The large sized pellets showed 10-20% higher wear rate than small sized pellets during wear in a planetary mill. Moreover, an increase of ~67% was observed in the friction and dust generation in the pellet bed as the applied load increased from 1 to 3 kg. Also, it was observed that a higher friction in the pellet bed can lead to an increased amount of airborne particles. The mechanical wear experiments of pellets reduced at 500 °C (P500) and 850 °C (P850) showed that P500 pellets exhibit ~16-35% higher wear rate than unreduced pellets. For the P850 pellets, the wear is inhibited by formation of a metallic layer at the outer surface of the pellets. The mechanism of dust generation has been explained using the obtained results.A reliable cluster size distribution of REM clusters was obtained by improving the observation method and it was used to explicate the formation and growth mechanism of REM clusters. The results show that the growth of clusters is governed by different types of collisions depending on the size of the clusters. For EVD analysis three different size parameters were considered. Moreover, using the maximum length of clusters results in a better correlation of EVD regression lines compared to other size parameters. Moreover, a comparison of predicted and observed maximum lengths of clusters showed that further work is required for the application of EVD analyses for REM clusters.