Environmental aspects and the sustainable manufacturing of steels require producers to pay more and more attention to the efficient utilization of materials and waste products during steelmaking. This study is focused on the evaluation of possibilities for the recovery of metals (such as Fe, Ni and Cr) from waste products used for slag foaming in the Electric Arc Furnace (EAF) process. Two types of industrial briquettes were produced by mixing mill-scale from the hot rolling of stainless steels with anthracite and pet-coke, respectively. Thereafter, an assessment of the metal reduction processes in briquettes at high temperatures (1500 °C) was made by using laboratory thermo-gravimetric reduction experiments in an argon atmosphere. The amounts of metal, slag and gas obtained from the briquettes were estimated. In addition, the velocity and time for the removal of metal droplets from the liquid slag depending on the size of the metal droplets was estimated. It was found that up to 97% of metal droplets can be removed from the slag during the first 30 min. Moreover, results showed that most of the Cr, Ni and Fe (up to 93–100%) can be reduced from oxides of these metals in briquettes at 1500 °C. Moreover, the anthracite and pet-coke in the investigated briquettes have similar reduction capabilities. It was found that up to 330 kg of Fe, 28 kg of Ni and 66 kg of Cr per ton of added briquettes can be recovered from waste products by the industrial application of those briquettes for slag foaming in EAF.

Thin films of ice and water on soil particles play crucial roles in environmental and technological processes. Understanding the fundamental physical mechanisms underlying their formation is essential for advancing scientific knowledge and engineering practices. Herein, we focus on the role of the Casimir-Lifshitz force, also referred to as dispersion force, in the formation and behavior of thin films of ice and water on soil particles at 273.16 K, arising from quantum fluctuations of the electromagnetic field and depending on the dielectric properties of interacting materials. We employ the first-principles density functional theory (DFT) to compute the dielectric functions for two model materials, ${\mathrm{CaCO}}_3$ and $\mathrm{Al}{}_2\mathrm{O}{}_3$, essential constituents in various soils. These dielectric functions are used with the Kramers-Kronig relationship and different extrapolations to calculate the frequency-dependent quantities required for determining forces and free energies. Moreover, we assess the accuracy of the optical data based on the DFT to model dispersion forces effectively, such as those between soil particles. Our findings reveal that moisture can accumulate into almost micron-sized water layers on the surface of calcite (soil) particles, significantly impacting the average dielectric properties of soil particles. This research highlights the relevance of DFT-based data for understanding thin film formation in soil particles and offers valuable insights for environmental and engineering applications.

Vibration measurements were carried out using highly sensitive accelerometers in an experimental ladle integrated into the LIMMCAST (Liquid Metal Model for Steel Casting) facility at HZDR. The model is operated with liquid Sn–40 wt pctBi alloy at 200 °C, whose physical properties are close to those of molten steel. Three accelerometers were attached to the outer wall of the LIMMCAST vessel to record the vibrations caused by the argon bubble flow in the liquid metal at different process parameters. The results obtained at the liquid metal experiments differ from those reported for water models where the relationship between root mean square (RMS) value of the vibration amplitude and the gas flow rate follows different curve shapes. Furthermore, the results of vibration measurements in the LIMMCAST model are compared with vibration measurements in a steel plant during vacuum degassing. The comparison of the RMS data shows a fairly good agreement. This indicates that the vibrations in both the industrial process and the laboratory model are caused by the same physical mechanisms, and thus, the vibration behavior in an industrial steelmaking ladle can be reproduced quite well by suitable liquid metal models. These studies on bubble flows can help to improve the understanding of industrial stirring processes and thus contribute to a better process control.

A novel horizontal stirring has been introduced to investigate the effect of such a stirring on the ladle profile during the combined stirring process at the refining stage. The multiphaseInterFoam solver has been updated to consider the induction forces imposed on the liquid bath by the magnetic stirrer.During the combined stirring stage, the gas plume is affected by the rotational movement. The gas plume seems to be intact in the lower one-third of the domain, then breaks into clusters in the upper section while the rotational movement of clusters dissipates a large portion of upward momentum of the bubbles. This prevents large openings in the slag layer and respectively, prevents the exposure of steel. It also disperses the bubbles to various sections of the ladle. Hence, such a novel stirring strategy seems to have the potential of improving the cleanness of the liquid steel during the ladle refining process.

In recent years there is an attempt to control the gas stirring intensity in metal-making ladles with the aid of vibration measurements. Understanding better the induced vibrations in two-phase flows can substantially improve the existing models for gas stirring control. In this work, highly sensitive accelerometers were used for the vibration measurements in a liquid metal alloy; Sn–40 wt pctBi alloy at 200 °C and water at 20 °C. The examination of the liquids was conducted in the ladle mockup integrated into the Liquid Metal Model for Steel Casting facility at Helmholtz-Zentrum Dresden Rossendorf. Single bubbles were generated in the respective liquids by controlled argon injection at low flow rates in the range of 0.01 to 0.15 NL min⁻¹through a single nozzle installed at the bottom of the ladle. Obtained results demonstrate differences between the induced vibrations in the examined liquids in terms of the magnitude of the root mean square values of vibration amplitude and the shape of the resulting curves with increasing flow rate. Furthermore, continuous wavelet transform reveals variations in the duration and vibrational frequency of the evolved bubble phenomena. The findings suggest that differences in the physical properties of the examined liquids result in variations in the vibrations induced during bubble evolution.

Continuous casting of aluminum (Al) deoxidized steels demands careful inspection due to the occurrence of submerged entry nozzle (SEN) clogging, leading to unexpected production stops. Recognizing the castability of a specific “cast” by monitoring the condition of the SEN is essential for uninterrupted casting. With this information prior to casting, operators can take preventive action against possible clogging occurrences, thus reducing unplanned downtimes. In response to the severe implications of SEN clogging, this work introduces a novel way to forecast castability of Al‐killed steels. A hybrid model is proposed that integrates the adaptive neuro‐fuzzy inference system (ANFIS) and long short‐term memory (LSTM) networks. The output of the model helps to anticipate the event of clogging by analyzing both the past condition of the SEN and changes in the steel chemistry during the transport of the steel ladle from refining to the casting process. A comprehensive analysis of 150 casts helped to build the ANFIS algorithm for estimating the castability index (CI) parameter from steel chemistry. LSTM algorithm is used as a subsequent step to forecast castability in the next 20–25 min. Discrepancies between the predictive response and the actual conditions are reported. Although the real‐time implementation of the proposed model is the ultimate goal, the focus of this work was to present the methodology and demonstrate its potential.