In terms of its chemical composition, biomass is a very complex type of fuel. Its combustion leads to the formation of materials such as alkaline ash and gases, and there is evidence of the corrosive effect this process has on refractory linings, thus shortening the service life of the combustion unit. This frequently encountered process is known as “alkaline oxidative bursting”. Corrosion is very complex, and it has not been completely described yet. Alkaline corrosion is the most common cause of furnace-lining degradation in aggregates that burn biomass. This article deals with an experiment investigating the corrosion resistance of 2 types of refractory materials in the Al₂O₃-SiO₂ binary system, for the following compositions: I. (53 wt.% SiO₂/42 wt.% Al₂O₃) and II. (28 wt.% SiO₂/46 wt.% Al₂O₃/12 wt.% SiC). These were exposed to seven types of ash obtained from one biomass combustion company in the Czech Republic. The chemical composition of the ash is a good indicator of the problematic nature of a type of biomass. The ashes were analyzed by X-ray diffraction and X-ray fluorescence. Analysis confirmed that ash composition varies. The experiment also included the calculation of the so-called “slagging/fouling index” (I/C, TA, Sr, B/A, Fu, etc.), which can be used to estimate the probability of slag formation in combustion units. The corrosive effect on refractory materials was evaluated according to the norm ČSN P CEN/TS 15418, and a static corrosion test was used to investigate sample corrosion.

The submitted article deals with the use of physical and numerical modelling to study the process of the steel flow in an asymmetric five-strand tundish that continuously casts steel. For the purposes of physical modelling, a 1:4-scale plexiglass model was used as the operating tundish, and for numerical modelling, the geometry of the operating tundish was created on a 1:1 scale. A model liquid (water) was used in the physical modelling of the melt flow process, while liquid steel was used as the standard flowing medium in the numerical modelling. We assessed the relevant operating parameters influencing the characteristics of the flow of the bath in the tundish—the shape of the turbulence inhibitor, the position of the ladle shroud in relation to the turbulence inhibitor and the distance between the ladle shroud orifice and the bottom of the turbulence inhibitor. The preliminary results show that optimal steel flow characteristic results are achieved by using the TI3-C configuration. The results from both modelling methods achieved the same characteristics, therefore verifying the results of each other and demonstrating that when taken together, the results of physical and numerical modelling can be considered sufficiently informative.

The main problem affecting the life of refractory linings in furnaces is alkaline corrosion formed during biomass combustion, especially in systems with SiO₂–Al₂O₃. This corrosion effect is very intensive compared to using conventional technologies designed for burning traditional fuels. This study focuses on the development of a new type of andalusite refractory material with a higher corrosion resistance to K₂CO₃ and fly ash after biomass combustion. The original andalusite refractory material is labeled A60PT0, with an oxide content of 60 wt.% Al₂O₃ and 37 wt.% SiO₂, a compressive strength parameter of 64 MPa, and an apparent porosity of 15%. In the experiment, four mixtures (labeled A60PT1–A60PT4) were modified primarily using the raw materials and granulometry. The fly ash was characterized by an X-ray diffraction analysis with the following phases: quartz, calcite, microcline, leucite, portlandite, and hematite. According to the X-ray fluorescence analysis, the samples contained the following oxides: 47 wt.% CaO, 12 wt.% K₂O, 4.6 wt.% SiO₂, 3.5 wt.% MgO, and some minority oxides such as P₂O₅, MgO, MnO, and Fe₂O₃ between 2 and 5 %. The tendency for slagging/fouling of the ash was determined with the help of the indexes B/A, TA, K_t, and Fu. The final material was a shaped andalusite refractory material labeled A60PT4 with a content of 65 wt.% Al₂O₃ and 36 wt.% SiO_2. The properties of the andalusite material were a compressive strength of 106.9 MPa, an apparent porosity of 13%, and the recommended temperature of use up to 1300 °C. For corrosion testing, a static crucible test was performed according to the norm ČSN CEN/TS 15418 and the company’s internal regulation. The exposure time of the samples was 2 h and 5 h at temperatures of 1100 °C and 1400 °C for K₂CO₃ and ash, respectively. For the evaluation of tested samples, an X-ray powder differential analysis, an X-ray fluorescence analysis, scanning electron microscopy, and energy-dispersive X-ray spectroscopy were used.

The article describes the possibilities of optimization of combustion processes at waste incineration, which require a specific approach in comparison with conventional fuels. The main cause is an instability of thermo-chemical properties of majority of waste. The work defines and evaluates criteria, which are critical for the waste incineration process. The numerical model “Combustion temperature” for optimization of thermal work of the waste incinerator was designed for support of implementation of other measures aimed at increasing the process energy efficiency, and it was verified in practice in real waste incineration plant.

This study is focused on the treatment of waste sludge from a zinc chloride production in order to prepare iron-rich pigments usable for a production of glazes. In galvanizing plants, yellow waste sludge containing significant amount of ZnO, Cl, and Fe₂O₃, is formed. This raw waste sludge cannot be used as a pigment in glaze. Therefore, three methods of treating this material were proposed: (a) washing with H₂O, (b) calcination at 180 °C and washing by H₂O, and (c) calcination at 900 °C and washing by H₂O. These methods helped to reduce Zn and Cl content up to 97%. According to X-ray fluorescence analysis percentage of Fe₂O₃ increased from ~41% to ~98%. X-ray power diffraction analysis confirmed the formation of α-Fe₂O₃ (hematite) in the pigment prepared. Scanning electron microscopy with Energy dispersive X-ray analysis showed clusters of rounded particles, and also the change in size of particles after calcination was observed. Particle size, specific surface area, and density measurements together with thermogravimetric and differential thermal analyses were performed. Pigments prepared from the waste sludge were added to transparent glaze in amounts of 1, 5, 10, and 15 wt.%. Pigment-containing glazes were applied by spraying on fired ceramic tiles and then fired at 1060 °C. Color of glazes was determined by (Commission Internationale de l’Eclairage) CIE L*a*b* coordinates as colorless, light brown shades, brown-red, brown-yellow, and deep red-brown. Comparison with colors of glazes prepared using commercial pigments was also performed. Waste sludge can be used to prepare pigments and glazes containing pigments as an alternative to commercial products.

When describing the behaviour and modelling of real systems, which are characterized by considerable complexity, great difficulty, and often the impossibility of their formal mathematical description, and whose operational monitoring and measurement are difficult, conventional analytical–statistical models run into the limits of their use. The application of these models leads to necessary simplifications, which cause insufficient adequacy of the resulting mathematical description. In such cases, it is appropriate for modelling to use the methods brought by a new scientific discipline—artificial intelligence. Artificial intelligence provides very promising tools for describing and controlling complex systems. The method of neural networks was chosen for the analysis of the lifetime of the teeming ladle. Artificial neural networks are mathematical models that approximate non-linear functions of an arbitrary waveform. The advantage of neural networks is their ability to generalize the dependencies between individual quantities by learning the presented patterns. This property of a neural network is referred to as generalization. Their use is suitable for processing complex problems where the dependencies between individual quantities are not exactly known.