Neslihan Dogan

This study investigates the growth and evolution of SiO₂-based inclusions in Si-killed steel under stagnant conditions and varying oxygen levels. Deoxidation experiments were conducted in a high-temperature furnace using commercial FeSi, with systematic variations in holding time and total oxygen content. Automated SEM–EDS analysis was employed to quantify inclusion size, number density, and chemical composition. Under stagnant conditions, SiO₂ inclusions were observed to grow and coarsen in the absence of melt agitation, following a t^1/3 scaling law. In high-oxygen melts, rapid inclusion growth was dominated by Stokes collision mechanisms, resulting in the formation of inclusions in the size range of 1–5 μm, which were subsequently removed by flotation. In contrast, low-oxygen melts exhibited slower growth kinetics governed primarily by Brownian motion and Ostwald ripening, producing smaller inclusions with characteristic sizes of 1–2 μm. These results demonstrate that the initial oxygen content plays a decisive role in controlling the dominant growth mechanisms and the extent of inclusion coarsening in non-agitated steel.

In the pre-reduction cyclone of the HIsarna process, both thermal decomposition and gas reduction of the injected iron ores occur simultaneously at gas temperatures of 1723–1773 K. In this study, the kinetics of the thermal decomposition of three iron ores (namely OreA, OreB and OreC) for HIsarna ironmaking were analysed as an isolated process with a symmetrical thermogravimetric analyser (TGA) under an inert atmosphere. Using various methods, the chemical and mineralogical composition, particle size distribution, morphology and phase distribution of the ores were analysed. The ores differ in their mineralogy and morphology, where OreA only contains hematite as iron-bearing phase and OreB and OreC include goethite and hematite. To obtain the kinetic parameters in non-isothermal conditions, the Coats–Redfern Integral Method was applied for heating rates of 1, 2 and 5 K/min and a maximum temperature of 1773 K. The TGA results indicate that goethite and hematite decomposition occur as a two-stage process in an inert atmosphere of Ar. The proposed reaction mechanism for the first stage of goethite decomposition is chemical reaction with an activation energy ranging from 46.55 to 60.38 kJ/mol for OreB and from 69.90 to 134.47 kJ/mol for OreC. The proposed reaction mechanism for the second stage of goethite decomposition is diffusion, showing an activation energy ranging between 24.43 and 44.76 kJ/mol for OreB and between 3.32 and 23.29 kJ/mol for OreC. In terms of hematite decomposition, only the first stage was analysed. The proposed reaction mechanism is chemical reaction control. OreA shows an activation energy of 545.47 to 670.50 kJ/mol, OreB one of 587.68 to 831.54 kJ/mol and OreC one of 424.31 to 592.32 kJ/mol.

In this work, the coarsening mechanisms of CaS inclusions in liquid steel were investigated by analyzing inclusions in experimental and industrial samples. A detailed particle size distribution evolution was reported. The observed CaS coarsening rate was compared with the theoretical coarsening rate calculated by using the models proposed in the literature. For both experimental and industrial data, it was observed that the coarsening mechanisms varied during different stages of Ca treatment. It was found that in the early stage (after Ca addition) of experiments and during Ca addition under industrial conditions, the coarsening of CaS was governed by diffusion-controlled growth. As the Ca dissolved in steel diminished, the coarsening was governed by collision-dependent mechanisms. For experimental conditions, the growth of CaS was controlled by the Brownian collisions, while the coarsening by turbulent collisions was the dominant mechanism under industrial conditions.

This study focused on the characteristics of complex MnS inclusions in advanced high strength steels. The effect of metal chemistry (Al and N) and the cooling rate of steel were evaluated by analyzing the inclusions present in five laboratory produced steels. The observed complex MnS inclusions contained Al₂O₃-MnS, AlN-MnS, and AlON-MnS. An increase in Al content from 0.5% to 6% increased the number of complex MnS inclusions by ~4 times. In comparison, a decrease of ~80% was observed due to the increased N content of steel from AlON > Al₂O₃. Moreover, it was determined that AlN particles could be easily engulfed at the solidification front relative to Al₂O₃, which resulted in a higher MnS precipitation ratio for Al₂O₃ under slow cooling conditions.

The effect of N content on the characteristics and formation of inclusions in the Fe-5Mn-3Al steels was investigated in this study. Two synthetic steel melts were produced by two different methods—N₂ gas purging and injecting—to introduce nitrogen into the melt. The N content of steel melt varied from 2 to 54 ppm. An increase in the N content to 47 ppm (for 533N-P) and 58 ppm (for 533N-I) increased the total amount of inclusions from 13 to 64 mm⁻² and from 21 to 101 mm⁻², respectively. The observed inclusions were Al₂O_3(pure), Al₂O₃-MnS, AlN_(pure), AlN-MnS, AlON, AlON-MnS, and MnS. When the N content was less than 10 ppm, AlN-MnS inclusions were the primary type of inclusions and they formed as solidification products. With an increase in the N content, AlN_(pure) inclusions became the dominant type of inclusions as AlN was stable in the liquid steel. These findings were confirmed by thermodynamic calculations. The influence of cooling rate on the types of inclusions was studied and a higher number of AlN-MnS inclusions were observed in samples with slow cooling rate.

Basic oxygen furnaces (BOFs) are widely used to produce steel from hot metal. The process typically has limited automation which leads to sub-optimal operation. Economically optimal operation can be potentially achieved by using a dynamic optimization framework to provide operators the best combination of input trajectories. In this paper, a first-principles based dynamic model for the BOF that can be used within the dynamic optimization routine is described. The model extends a previous work by incorporating a model for slag formation and energy balances. In this new version of the mathematical model, the submodel for the decarburization in the emulsion zone is also modified to account for recent findings, and an algebraic equation for the calculation of the calcium oxide saturation in slag is developed. The dynamic model is then used to simulate the operation of two distinct furnaces. It was found that the prediction accuracy of the developed model is significantly superior to its predecessor and the number of process variables that it is able to predict is also higher.