This paper focuses on the study of current knowledge regarding the use of hydrogen as a reducing agent in the metallurgical processes of iron and steel production. This focus is driven by the need to introduce environmentally suitable energy sources and reducing agents in this sector. This theoretical study primarily examines laboratory research on the reduction of Fe-based, metal-bearing materials. The article presents a critical analysis of the reduction in iron oxides using hydrogen, highlighting the advantages and disadvantages of this method. Most experimental facilities worldwide employ their unique original methodologies, with techniques based on Thermogravimetric analysis (TGA) devices, fluidized beds, and reduction retorts being the most common. The analysis indicates that the mineralogical composition of the Fe ores used plays a crucial role in hydrogen reduction. Temperatures during hydrogen reduction typically range from 500 to 900 °C. The reaction rate and degree of reduction increase with higher temperatures, with the transformation of wüstite to iron being the slowest step. Furthermore, the analysis demonstrates that reduction of iron ore with hydrogen occurs more intensively and quickly than with carbon monoxide (CO) or a hydrogen/carbon monoxide (H₂/CO) mixture in the temperature range of 500 °C to 900 °C. The study establishes that hydrogen is a superior reducing agent for iron oxides, offering rapid reduction kinetics and a higher degree of reduction compared to traditional carbon-based methods across a broad temperature range. These findings underscore hydrogen’s potential to significantly reduce greenhouse gas emissions in the steel production industry, supporting a shift towards more sustainable manufacturing practices. However, the implementation of hydrogen as a primary reducing agent in industrial settings is constrained by current technological limitations and the need for substantial infrastructural developments to support large-scale hydrogen production and utilization.

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.

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.

This article is aimed to study theoretical knowledges related to deep steel desulfurization and their practical application in specific production conditions. The main goal of this study was to verify the efficiency of added CaSi to processed heats in two batches, i.e., first for deep desulfurization at the beginning of the secondary metallurgy and then in a second smaller batch at the end of that process for final modification. At the beginning of the secondary metallurgy process, it was crucial to prepare a suitable chemical composition of ladle slag with advantageous properties for good assimilation of sulfur-based inclusions from desulfurization reactions. Samples were taken of final basic oxygen furnace slag, ladle slag before the deep desulfurization process at the beginning of secondary metallurgy and ladle slag at the end of processing. Detailed cleanliness analyses of steel samples taken from mold during the casting were performed using the Automated Steel Cleanliness Analyses Tool. For better understanding and interpretation of the obtained cleanliness results and their relationship, the following defined parameters were used: ratio of “Ca/Al” oxide inclusions, percentage of “liquid” inclusions and area of CaS inclusions.

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.