The electric arc furnace operation at the Štore Steel company, one of the largest flat spring steel producers in Europe, consists of charging, melting, refining the chemical composition, adjusting the temperature, and tapping. Knowledge of the consumed energy within the individual electric arc operation steps is essential. The electric energy consumption during melting and refining was analyzed including the maintenance and technological delays. In modeling the electric energy consumption, 25 parameters were considered during melting (e.g., coke, dolomite, quantity), refining and tapping (e.g., injected oxygen, carbon, and limestone quantity) that were selected from 3248 consecutively produced batches in 2018. Two approaches were employed for the data analysis: linear regression and genetic programming model. The linear regression model was used in the first randomly generated generations of each of the 100 independent developed civilizations. More accurate models were subsequently obtained during the simulated evolution. The average relative deviation of the linear regression and the genetic programming model predictions from the experimental data were 3.60% and 3.31%, respectively. Both models were subsequently validated by using data from 278 batches produced in 2019, where the maintenance and the technological delays were below 20 minutes per batch. It was possible, based on the linear regression and the genetically developed model, to calculate that the average electric energy consumption could be reduced by up to 1.04% and 1.16%, respectively, in the case of maintenance and other technological delays.

In this work, a rolling simulation system for the hot rolling of steel is elaborated. The system is capable of simulating rolling of slabs and blooms, as well as round or square billets, in different symmetric or asymmetric forms in continuous, reversing, or combined rolling. Groove geometries are user-defined and an arbitrary number of rolling stands and distances between them may be used. A slice model assumption is considered, which allows the problem to be efficiently coped with. The related large-deformation thermomechanical problem is solved by the novel meshless Local Radial Basis Function Collocation Method. A compression test is used to compare the simulation results with the Finite Element Method. A user-friendly rolling simulation application has been created for the industrial use based on C# and .NET framework. Results of the simulation, directly taken from the system, are shown for each type of the rolling mill configurations.

Two binary biodegradable Mg-alloys and one ternary biodegradable Mg-alloy (Mg-0.3Ca, Mg-5Zn and Mg-5Zn-0.3Ca, all in wt%) were investigated. Surface-sensitive X-ray photoelectron spectroscopy analyses (XPS) of the alloy surfaces before and after immersion in simulated body fluid (SBF) were performed. The XPS analysis of the samples before the immersion in SBF revealed that the top layer of the alloy might have a non-homogeneous composition relative to the bulk. Degradation during the SBF immersion testing was monitored by measuring the evolution of H₂. It was possible to evaluate the thickness of the sample degradation layers after the SBF immersion based on scanning electron microscopy (SEM) of the tilted sample. The thickness was in the order of 10–100 µm. The typical bio-corrosion products of all of the investigated alloys consisted of Mg, Ca, P and O, which suggests the formation of apatite (calcium phosphate hydroxide), magnesium hydrogen phosphate hydrate and magnesium hydroxide. The bioapplicability of the analyzed alloys with regard to surface composition and degradation kinetics is discussed.