This article examines the low-temperature reducibility of four types of iron ore pellets in a pure hydrogen atmosphere, with the aim of understanding the thermodynamic aspects of the process. The research focuses on optimizing conditions for pellet reduction in order to reduce CO2 emissions and improve iron production efficiency. Experimental tests were conducted at temperatures of 600 °C and 800 °C, supplemented by thermodynamic simulations predicting the equilibrium composition and energy requirements. Chemical and microstructural analyses revealed that porosity, mineralogical composition, and phase distribution homogeneity significantly affect reduction efficiency. High-quality pellets with low SiO2 content demonstrated the best reduction ability, while fluxed pellets with the presence of calcium silicate ferrites and pellets with a higher content of SiO2 showed lower reduction potential due to the presence of hard-to-reduce phases such as calcium silicate ferrites and iron silicates. The results highlight the importance of controlling process conditions and optimizing pellet properties to enhance the reduction process and minimize environmental impacts. This study provides valuable insights for the application of hydrogen reduction in industrial conditions, contributing to the decarbonization of the metallurgical industry.

Controlling dissolved nitrogen is critical to meeting increasingly stringent steel quality targets, yet the variable kinetics of gas absorption and removal across production stages complicate real-time decision-making. Leveraging a total of 291 metal samples, the research applied ordinary least squares (OLS) regression, enhanced by cointegration diagnostics, to develop four stage-specific models covering pig iron after desulfurization, crude steel in the basic oxygen furnace (BOF) before tapping, steel at the beginning and end of secondary metallurgy processing. Predictor selection combined thermodynamic reasoning and correlation analysis to produce prediction equations that passed heteroscedasticity, normality, autocorrelation, collinearity, and graphical residual distribution tests. The k-fold cross-validation method was also used to evaluate models’ performance. The models achieved an adequate accuracy of 77.23–83.46% for their respective stages. These findings demonstrate that statistically robust and physically interpretable regressions can capture the complex interplay between kinetics and the various processes that govern nitrogen pick-up and removal. All data are from U. S. Steel Košice, Slovakia; thus, the models capture specific setup, raw materials, and production practices. After adaptation within the knowledge transfer, implementing these models in process control systems could enable proactive parameter optimization and reduce laboratory delays, ultimately minimizing excessive nitrogenation in finished steel.

Nitrogen control in steel production critically influences mechanical properties and product quality, yet traditional mechanistic models struggle to capture complex multivariable interactions across the complete steelmaking chain. This study developed and validated automated machine learning (AutoML) models using Microsoft Azure Machine Learning Studio to predict nitrogen content at four critical stages: desulfurization of pig iron (Stage 1), basic oxygen furnace prior to tapping (Stage 2), secondary steelmaking initiation (Stage 3), and secondary steelmaking finishing (Stage 4). Industrial data from 291 metal samples across 76 heats were collected and processed, with stage-specific models employing stack ensemble architectures combining 4–7 algorithms with feature sets ranging from 12 to 35 variables. The models achieved normalized root mean squared errors between 0.112–0.149, mean absolute percentage errors of 14.6–21.1%, and Spearman correlations of 0.310–0.587, with secondary steelmaking models demonstrating superior performance due to more controlled thermodynamic conditions. All models achieved sub-second prediction latencies suitable for real-time industrial implementation. This research demonstrates that AutoML effectively captures complex physicochemical relationships governing nitrogen behavior throughout the steelmaking process, providing practical solutions for Industry 4.0 applications in steelmaking process control and quality optimization.

The behavior of molten steel within a tundish plays a crucial role in achieving uniform temperature and chemical composition, enhancing the removal efficiency of non-metallic inclusions, and reducing the wear of refractory linings. These aspects are key for ensuring the production of steel with superior quality. In multi-strand delta-type tundishes, such as the six-strand configuration, flow dynamics become particularly challenging. Key considerations include strand-specific residence times, the uniform distribution of steel flow, and the mitigation of refractory degradation. This paper presents a detailed numerical analysis aimed at designing an optimally shaped impact pad. The effectiveness of each proposed design was assessed through a tracer-based visualization of flow behavior and the evaluation of residence time distribution (RTD) curves. RTD curves were created in isothermal conditions, while the calculations of the temperature fields of steel in the tundish were made in non-isothermal conditions. The results of the simulations were verified by a real plant trial test and indicate that the use of the “SPHERIC-K4” impact pad can greatly enhance the flow characteristics of liquid steel during the continuous casting process. These improvements include preventing the erosion of the tundish refractory lining, improving the distribution of residence times between individual casting strands, and adjusting the proportions of the mixing zones.

In this paper, the influence of the novel design of a ladle shroud (LS) on the liquid steel flow structure inside the working volume of a two-strand slab tundish was assessed, determining the best solutions for LS use to achieve the optimal level of active flow zones and protect the tundish lining. A 0.33 scale water model was used for physical experiments. Numerical simulations were carried out in the Ansys-Fluent 12.1 software for a 1:1 scale tundish. The effect of the influence of LS type, LS immersion depth, LS side ports position, LS misalignment and casting speed was examined. Finally, the use of the "umbrella" ladle shroud allows stable hydrodynamics to be maintained even with shroud misalignment. Moreover, the "umbrella" ladle shroud effectively decreases the average velocity of liquid steel inside the tundish and significantly decreases shear stresses and dynamic pressure at the tundish lining in the tundish pouring area.

The research focuses on the management of oxygen lance copper tip rotation to mitigate wear on the MgO–C refractory lining in the basic oxygen furnace (BOF). This study investigates the continuous increase in the consumption of gunning mixture throughout the BOF campaign, particularly in the trunnion area. Clear trends in refractory thickness reduction were observed, with two significant wear phases identified: between heats 1000–7000 and between heats 11,000–16,000. These phases correlate with increased gunning mixture consumption. The most significant wear was found between 4–5.2 m height, known as the trunnion area. The study proposes turning of the oxygen lance copper tip (jet) during its replacement to distribute refractory lining wear more evenly and reduce gunning mixture consumption. A detailed analysis of the gunning mixture consumption during whole campaign as well as laser measurements of the working lining profile confirmed localized wear in areas of the trunnions that were excessively exposed by the direct impact of the pure oxygen jet stream and the sprayed and spitted emulsion of molten metal and slag. This position management strategy, coupled with slag splashing and high-basic slag coating, can reduce trunnion area gunning mixture usage and promote uniform MgO–C lining wear.

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The aim of the presented research was to optimize a pig iron desulfurization process through data-driven machine learning methods. Utilizing historical data, chemical analysis of pig iron and slag, and the thermodynamics of the process including simulations of the chemical reactions between individual phases, a neural network was trained for the predictive modeling of desulfurization efficiency. The accuracy of the model was enhanced by integrating Physics-Informed Neural Networks (PINNs), which incorporate chemical reaction principles. The results show better performance of PINNs in comparison to the Feedforward Neural Network (FNN) in the generalization of the desulfurization process, bringing better reliability to the model.

To meet the increasing cleanliness requirements in conticast steel production, innovative solutions must be developed. The tundish, which serves as the final refractory-lined reactor, provides ample space to eliminate inclusions through optimization of the steel flow. The key component of the tundish is the impact pad, which determines the flow pattern of steel and is a crucial element of tundish metallurgy. To prevent the formation of dead zones and slag eyes in the slag layer surrounding the ladle shroud, the optimal steel flow within the tundish must facilitate the removal of inclusions through reactions at the steel-slag interface. Additionally, the flow must prevent excessive erosion of the tundish refractory lining. This study compares the standard impact pad with the spherical impact pad using physical modeling, evaluating the residence time and flow within the tundish at three different casting speeds.

The identification of the wear mechanism of the refractory lining of the basic oxygen furnace is a prerequisite for the optimization of the steelmaking process. Knowledge of the way in which a given factor acts on the amortization of the refractory lining is closely related to the development of technological procedures serving to limit their influence. The results of the analysis of the mechanism by which the thickness of basic oxygen furnace refractory lining is reduced during the campaign can be applied to the development of new refractories with more suitable chemical composition and physical properties. In this respect, the determination of a balanced consumption of gunning materials is also very important, since the expenditure on repair mixtures has a major portion of the total financial cost of the campaign. By determining the optimum consumption of gunning materials, it is possible to define the duration of the basic oxygen converter campaign during which the entire steelmaking process is financially viable for the producer. Based on the literature, the mechanism of wear of refractory lining has been developed. Temperatures from 1,300 degrees C to 1,700 degrees C, which can occur during the steelmaking process in the basic oxygen vessel, were considered. The wear process was divided into logical steps. In each step, the most probable course of the sub-processes and the products of the chemical reactions are described based on thermodynamic data.

In Reducing energy consumption and increasing energy efficiency have long been very important issues affecting all sectors of energy consumption, including the industrial sector. Industrial furnaces have a significant energy consumption that needs to be optimised, but before that an analysis of the energy situation is necessary. This article discusses the energy state analysis of a burner system, which was performed using CFD model. Specifically, the analysis of the combustion process in a radiant tube, for which a 3D model of this tube was created. The analysis also addressed the subsequent heat transfer from the surface of the radiant tube in the power range of 50-109 kW. Input parameters and model conditions were defined based on the parameters of the real burner system. The CFD modelling performed showed that as the fuel consumption increased, the radiant tube power increased and at the same time the flue gas temperature also increased. The heat flux from the tube surface was slightly different on both sides of the tube, which was due to the flame tilt inside the radiant tube.

Tundish is a flow reactors, hence there is a strong correlation between its flow control devices such as dam, subflux flow controller, gas permeable barrier or advanced ladle shroud equipped, its shape of the internal working volume and the number of outlets visible in the formation of individual hydrodynamic structures. During numerical and physical simulations, the process of continuous steel casting of two slabs with dimensions of 1.15 m x 0.225 m at a speed of 1 m/min was simulated. Two-strand tundish with and without subflux flow controller (SFC) was tested. Off-centered location of subflux flow controller and ladle shroud misalignment in the tundish pouring zone were investigated. Basis on the obtained results the non-standard interaction of the feed stream with the SFC working space revealed the occurrence of a favorable hydrodynamic structure in the tundish working space in the context of limiting the stagnation flow. This is show by the formed hydrodynamic structure consists of vertically circulating streams of liquid steel, effectively eliminating and limiting the impact of reverse streams.