Nowadays, digitalization and automation in both industrial and research activities is a driving force for innovations. In recent years, Machine Learning (ML) techniques - the subset of Artificial Intelligence - have been widely applied as a key tool to automize and digitalize numerous in-dustrial processes. ML models can predict specific features, like materials' lifetime in a heating device, based on recently trained algorithms with applied input data. The results of ML algorithms are easy to interpret and can significantly decrease the time of research and decision-making, thus, substituting the trial-error approach. This work presents the state of the art in the application of Machine Learning in the investigation of MgO-C refractories for the steel industry. Also, the work presents an overview of the most commonly used ML algorithms in refractory engineering. So far published results confirm the high performance of ML, e.g., for prediction of oxidation behaviour, optimum graphite content and wear rate of MgO-C refractories, or for clustering materials based on various fused and sintered MgO sources into groups with similar corrosion resistance, thus, permitting to reduce expensive fused raw materials content. Most of the presented works were positively validated via experiment.

Mortarless refractory masonry structures are widely used in the steel industry for the linings of many high-temperature industrial applications including steel ladle. The design and the optimization of these components require accurate numerical models that consider the presence of joints as well as joints closure and opening due to cyclic heating and cooling. The present work reports on the formulation, numerical implementation, validation, and application of homogenized numerical models for simulation of refractory masonry structures with dry joints. The validated constitutive model has been used to simulate a steel ladle and to analyze its transient thermomechanical behavior during a typical thermal cycle of steel ladle. 3D solution domain, enhanced thermal and mechanical boundary conditions have been used. Parametric studies to investigate the impact of joints thickness on the thermomechanical response of the ladle have been carried out. The results clearly demonstrate that the thermomechanical behavior of mortarless masonry is orthotropic nonlinear due to gradual closure and reopening of joints with the increase and decrease of temperature. Also, resulting thermal stresses increase with the increase of temperature and decrease with the increase of joints thickness.

In this paper, the phases in the Mg-Ti-O system using the 1:1 formulation of MgO:TiO2 mixing synthetic brucite of Mexican origin with TiO2 microparticles of high purity and with a heat treatment at 1100°C for 1 h were studied. The raw materials and formulation by XPS and DRX techniques were characterized. The results demonstrate the presence of different oxidation states in titania and the formation of different oxides in the Mg-Ti-O system when mixed and calcined at 1100°C; additionally, the formation of vacancies in the crystal lattice during the transformation from hexagonal brucite to magnesia with a cubic structure centered on the faces is estimated. With the results, its thermal behavior is warned based on the MgO-TiO2 phase diagram.