In a steel-refining ladle, the properties of manufactured steel can be notably degraded due to the presence of excessive inclusions. Stirring via gas injection through a porous plug is often used as part of the steel-refining process to reduce these inclusions. In this paper, 3D computational fluid dynamics (CFD) modeling is used to analyze transient multiphase flow and inclusion removal in a gas-stirred ladle. The effects of gas stirring with bubble-inclusion interaction are analyzed using the Euler–Euler approach for multiphase flow modeling, while the effects of inclusions aggregation and removal are modeled via a population balance model (PBM).

This paper presents a study on the effectiveness of two turbulence models, the large eddy simulation (LES) model and the k-ε turbulence model, in predicting mixing time within a ladle furnace using the computational fluid dynamics (CFD) technique. The CFD model was developed based on a downscaled water ladle from an industrial ladle. Corresponding experiments were conducted to provide insights into the flow field, which were used for the validation of CFD simulations. The correlation between the flow structure and turbulence kinetic energy in relation to mixing time was investigated. Flow field results indicated that both turbulence models aligned well with time-averaged velocity data from the experiments. However, the LES model not only offered a closer match in magnitude but also provided a more detailed representation of turbulence eddies. With respect to predicting mixing time, increased flow rates resulted in extended mixing times in both turbulence models. However, the LES model consistently projected longer mixing times due to its capability to capture a more intricate distribution of turbulence eddies.

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features.

Industrial fusion of microalloying elements in steelmaking is imperative in defining and optimizing certain steel properties due to their strengthening and significant grain refinements effects at minute quantities. Copper, vanadium, and columbium are explored in this investigation to monitor their respective dissolution processes in a ladle metallurgy furnace (LMF), with concise parametric studies on effects of number of plugs and variations in argon gas flow rates for stirring. To track particle disintegration in the molten bath inside, intricate numerical processing was carried out with the use of mathematical models and to simulate the mixing process; turbulent multiphase computational fluid dynamics (CFD) models were combined with a user-defined function. The numerical findings highlight the connection between mixing time and gas blowing since the quantity of stirring plugs employed and the gas flow rates directly affect mixing effectiveness. The amount of particles to be injected and their total injection time were validated using industrial measurement; an average difference of 9.9% was achieved. In order to establish the need for an exceptionally high flow rate and inevitably reduce resource waste, extreme charging of flow rates for gas stirring were compared to lesser gas flow rates in both dual- and single-plug ladles. The results show that a single-plug ladle with a flow rate of 0.85 m³/min and a dual-plug ladle with a total flow rate of 1.13 m³/min have the same mixing time of 5.6 min, which was the shortest among all scenarios.

Optimizing the operational parameters of an RH degasser is essential for increasing the production of high-quality steel while reducing energy and resource consumption. This paper presents a study on the impact of different injection gas flow rates on the mixing characteristics of an industrial-scale RH degasser and evaluates the optimal flow rate for achieving the lowest mixing time. A 3D simulation model was developed using a VOF–DPM framework, with gas flow rates being varied from 18 to 72 SCFM to assess mixing time and associated flow behavior. The results indicate that the mixing time has a non-linear relationship with the gas flow rate, and increasing the flow rate does not always lead to a reduced mixing time. A flow rate of 45 SCFM (a 1.5-fold increase from 18 SCFM) provided the best mixing efficiency, reducing the mixing time by 52%. Additionally, beyond 36 SCFM, a saturation limit was observed in the circulation rate, where further increases in the gas flow rate resulted in a less than 5% improvement in steel flowing through the snorkels. These findings highlight the need for careful evaluation of injection gas flow rates in RH operations to identify the optimal value that maximizes mixing efficiency, minimizes resource consumption, and enhances productivity by enabling greater steel output in less time.