Identification of the factors influencing refractory lining wear and its residual thickness in the basic oxygen furnace (BOF) is a prerequisite for optimizing the steelmaking process. In this study, the factors that contribute significantly to the wear of the refractory lining in the most stressed areas of the banded lining (i.e., the trunnion ring area and slag line area) are identified. Knowledge of the rate at which a given factor acts on refractory wear is closely related to the development of technological procedures aimed at limiting its influence. This research evaluates the technological causes and describes the lining wear mechanism and the thermodynamic parameters of the reactions between the MgO–C metal, slag, and gunning material phases. In researching the topic, real operational data were processed using statistical methods and data analysis, which were supported by thermodynamic modeling of chemical reactions. The results show that the combination of technological factors, mechanical action of the raw materials, blowing and free oxygen in the metal, silicon from the pig iron, and slag viscosity have the greatest influence on the residual thickness of the MgO–C refractory lining in BOFs. Refractory gunning material consumption, its effect on campaign length, and the cost-effectiveness of repair work were also analyzed.

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.

The metallurgical sector, a key component of European industries, plays a crucial role both in advanced economies and in the infrastructure development of emerging nations. As environmental concerns increasingly influence global trade policies, the introduction of Carbon Border Adjustment Mechanisms (CBAMs) becomes a pivotal factor, especially those affecting the steel industry. This study focuses on assessing the impact of CBAM implementation on the steel industry’s evolution until 2034, utilizing a combination of econometric models and scenario analysis.

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.

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.

This study presents the development of a novel material for a spherical impact pad for tundishes during steel production, focusing on improving steel cleanliness and flow optimization. Traditional low-carbon and ultra-low carbon concrete (LCC/ULCC) materials are replaced with a new cement-free mixture, utilizing a sol–gel method binder. This innovative approach leads to the creation of IPC TECAST BPV CST, a refractory concrete with enhanced resistance to corrosion and shape stability under extreme conditions. The material’s effectiveness is demonstrated through operational tests, showing remarkable durability and no erosion defects after extensive use in casting liquid metal. The sol–gel binder significantly reduces the carbon footprint and energy consumption during the drying process, compared to traditional concretes. This study concludes that the new material not only withstands the dynamic environment of liquid steel but also ensures consistent dynamic flow conditions throughout the steel casting process, marking a significant advancement in tundish impact pad technology.