Abstract
Global steelmaking largely follows either the integrated (blast furnace/BOF) route to produce steel or the electric arc furnace (EAF) route, which relies on scrap and, in some cases direct reduced iron (DRI). The industry is moving toward higher EAF and DRI adoption made by reacting iron ore pellets with reformed natural gas, with longer-term interest in hydrogen-based DRI to reduce CO2 emissions. This shift is expected to increase the production of EAF dust and reduce traditional recycling pathways for this dust, strengthening the need for circular-economy solutions that recover valuable metals while lowering the carbon footprint of dust processing.
This project investigates a low emission approach for processing EAF dust using carbonaceous reductants, with the longer-term objective of replacing fossil carbon with biochar. The work has developed a kinetic model which can be used to optimize zinc recovery while minimizing overall carbon consumption. Initial experiments used graphite as a reference reductant to establish baseline kinetics, with the model subsequently adapted to account for more rapid kinetics with biochar.
The kinetic framework represents carbon oxidation with CO2 as the source of CO and couples this to the reduction of zinc and iron oxides within reacting pellets. Carbon oxidation and oxide reduction are treated as sequential steps, where zinc oxide and iron oxide reduction proceed in parallel and compete for available CO depending on the evolving CO/CO₂ ratio within the pellet. Experimental data collected under a range of conditions were analyzed to determine rate constants for carbon oxidation, iron oxide reduction, and zinc oxide reduction. Using a single set of kinetic parameters, the model predicts reaction progress (measured by mass loss) across a wide range of conditions and reproduces the observed balance between zinc and iron reduction with a high degree of reliability. Additional experiments at multiple temperatures were also performed to obtain rate constants at distinct temperatures, enabling improved predictive capability.
Building on the graphite-based foundation, the next step is to parameterize the model for biochar and use it to identify the minimum biochar requirement needed to achieve strong reduction and high zinc recovery, supporting process optimization for lower-carbon, more sustainable EAF dust recycling.