Pellet swelling has been widely studied, being simultaneous with reduction reactions and common in the operation of blast furnaces. A tube furnace equipped with a camera recording system was used here to study the dynamic and isothermal reduction swelling behaviour of olivine and acid pellets under simulated BF shaft conditions. The olivine pellets were magnetically separated into three fractions, containing low, medium and high amounts of magnetite in the core. The divalent iron (FeO) content of these fractions was 0.1 wt-%, 0.2 wt-% and 2.9 wt-%, respectively. Pellets with a large magnetite nucleus were observed to encompass numerous cracks, which was reflected in a poor LTD test value, while SiO2-rich reference pellets with a different slag chemistry had more restrained swelling and cracking behaviour in dynamic reduction. Swelling in the olivine pellets was associated with cracking at the boundary between the original magnetite nucleus and the hematite shell.The dynamic reduction swelling test results showed lower reduction swelling indices (max 17% in volume) than under isothermal conditions (max 51% in volume), in which case the pellets were suddenly exposed to a strongly reducing atmosphere. It is thus suggested that the reduction swelling behaviour of iron ore pellets should preferably be studied dynamically under simulated blast furnace conditions in order to achieve a realistic understanding of their swelling behaviour in a blast furnace.

The electric arc furnace is the main process unit in scrap‐based steelmaking. Owing to its importance, numerous mathematical models for predicting the course of the electric arc furnace process have been developed. This article reviews mathematical process models proposed in the literature, identifying the most common modeling approaches, and uses mathematical descriptions for the main phenomena. Furthermore, the validation of such models is discussed in detail. Finally, the article identifies gaps in the existing knowledge and provides suggestions for the further development of mathematical process models.

IDS (Inter-Dendritic Solidification) is a thermodynamic-kinetic software package that simulates phase changes, compound formation/dissolution, and solute distribution during solidification of steels as well as during their cooling/heating process after solidification. The software package also simulates solid-state phase transformations related to the austenite decomposition process at temperatures below 900/600 °C, and calculates thermophysical material properties from the liquid state down to room temperature. These data are needed in other models, such as heat transfer and thermal stress models, whose reliability heavily depends on the input data. The software package also features a database for thermodynamic, kinetic and microstructure data, as well as for several material properties. Owing to the short calculation times, the IDS tool is suitable for online applications. This paper presents IDS and its modules with the latest developments and validations, along with examples of modeling results.

Hot metal desulfurization serves as the main unit process for removing sulfur in blast‐furnace based steelmaking. The available body of literature on modeling hot metal desulfurization is reviewed to provide an in‐depth analysis of the approaches used and results obtained. The mathematical models for reaction kinetics have evolved from simplistic rate equations to more complex phenomenon‐based models that provide useful information on the effect of physico‐chemical properties and operating parameters on desulfurization efficiency. Data‐driven approaches with varying levels of phenomenological basis have also been proposed with the aim of achieving better predictive performance in industrial scale applications. Bath mixing has been studied using physical and numerical modeling to optimize mixing conditions in ladles and torpedo cars. The coupling of gas‐particle jets and their penetration into the liquid have been a focal point of physical and numerical modeling. In recent years, the fluid flow phenomena in mechanically stirred ladles has been studied extensively using physical and numerical modeling. These studies have focused on the fluid flow field, reagent dispersion, and bubble dispersion.

Sulfur is considered as one of the main impurities in hot metal and hot metal desulfurization is often carried out using injection of fine-grade desulfurization reagent. The selection of variables used for predicting the course of hot metal desulphurization requires expert knowledge. However, it is difficult to model the complex interactions in the process and to evaluate a high number of possible variable subsets with manual variable selection techniques. As the amount of data gathered from the process increases, manual variable selection becomes too time-consuming and might lead to a suboptimal prediction model. The objective of this work is to execute an automatic variable selection procedure for prediction of hot metal desulfurization based on an industrial scale data set. The variable selection problem is formulated as a constrained optimization problem, in which the objective function is formulated based on repeated leave-multiple-out cross-validation. The implemented solution strategy is a binary-coded genetic algorithm (GA). By making use of the developed model, the effect of the main production variables on the rate and efficiency of primary hot metal desulfurization is quantified. The variables related to properties of the reagent and the injection parameters were found to be of great importance.