Based on the operational measurement, of which content was to determine ladle thermal profile, there were analysed causes of possible damage of lining in steel ladles by steel breakout through the ladle shell. There exists connection between thermal state of ladle lining during the operation and its lifetime. There were reached to the conclusion that the cause of failure in the lining of ladle is except for high temperature of bath, also wide interval of temperature change during the tap operation, in consequence with possible insufficient pre-heating of ladle, discontinuous operation of aggregate and damage of insulating lining layer, respectively deformation of ladles shell.

Continuous casting comprises thermal, mechanical and chemical processes running in a complex system that contains a number of elements, such as a solidifying steel strand, a mould with an oscillation mechanism, a withdrawal mechanism, a water cooling subsystem with nozzles, several control subsystems , etc. An external observer might see the process as robust and stable, but in reality there are fluctuations in the internal thermal and mechanical quantities, reflected in the structure and quality of the product. The research on unsteady behaviour of the quantities such as a solidifying strand temperature field, solid shell thickness and metallurgical length was conducted using an industrial diagnostic system DGS complemented with special measurement equipment and a thermal numerical model. Selected results of the monitoring and simulation of the non-standard process states are shown and analysed in the paper. Methods for determining the boundary conditions for the numerical model are also presented. The effect of the Leidenfrost phenomenon on the heat-transfer coefficient during water cooling by nozzles is also discussed. Since the determination of precise and immediate boundary conditions has technical limits, the model provides only smoothed values in time and space. As knowledge of the instantaneous state of the fluctuating process is a prerequisite for achieving quality and defect-free production, it is appropriate to complement the thermal numerical model by on-line monitoring of the machine's internal state. The results of the simulations are closely linked to the real process data.

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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.

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.