Effectiveness of Air Classification as a Pretreatment for Chromium Separation from Electric Arc Furnace Slag
Mikio Koide et al.
What the paper says
Electric arc furnace slag is a byproduct of the steel scrap refining process. It must be effectively used as a recycled resource because of its enormous production volume. Cement usage is a promising application; however, there is a risk that chromium (Cr) in the slag will be oxidized to hexavalent chromium during the cement manufacturing process and subsequently leach into the environment. Because chemical treatments, such as alkali roasting, are required to significantly reduce Cr in electric arc furnace slag, it is important to establish a chemical-free physical pretreatment to reduce the overall energy demand. In this study, we investigated the presence of Cr compounds in oxidizing and reducing slag and comparatively evaluated the effectiveness of physical pretreatment using air classification. The results showed that Cr mainly existed as a spinel phase bonded to other metals, such as Fe and Mg, and this feature was common in both slags. In air classification experiments using an elbow-jet air classifier, the Cr concentrations were similar for all classifications of the oxidizing slag. However, the Cr concentrations in the reducing slag were higher in the coarse powder fraction, whereas Ca tended to be concentrated in the fine powder fraction. Air classification using an elbow-jet air classifier was effective in reducing the Cr content in the coarse fraction of reducing slag, suggesting that it could be used as a pretreatment prior to chemical processing to reduce the overall energy demand. The contrasting classification behavior of oxidizing and reducing slags highlights the importance of slag-type-specific separation strategies.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.