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The limestone, as an economically sustainable and easily available basic raw material, is frequently utilized in the building industry for resolving of the environmental protection issues. The limestone is incorporated in a cementitious system either by grinding with cement clinker, or by blending with the binder during concrete production. The employing of powdery limestone as partial cement replacement gives the construction composites with properties comparable to that of conventional concrete. The study of limestone thermal behavior and its chemistry is crucial for the prognosis of the designed composites properties. In this work, the instrumental techniques (atomic emission spectroscopy, differential thermal and thermo-gravimetric analysis, Fourier transform infrared spectroscopy) and the Principal component analysis were employed to discriminate and classify 22 limestone types. The PCA statistical method, as a means of spectra and experimental data fingerprinting, grouped the samples in a multi-dimensional factor space producing four graphical prognosis - one for each instrumental method. DTA/TG peak values varied the most in a short range between 830-870?C, while FTIR spectra showed the highest diversity in the 867-887 cm-1 and 1237-1647 cm-1 ranges. This research was governed by an idea to reveal whether it is possible to differentiate various limestone types and to predict the possibility of their employment in construction composites on the basis of the results of instrumental and mathematical analyses. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. ON 172057, III 45008, TR 31055, TR 34006, and TR 34013]

For the development of the Lost Foam refractory coatings with controlled rheologic properties, the influence of the mechanical activation process on the cordierite-based filler's properties change was examined. First of all, the test referred to the change of filler particles' size and shape, as well as to dispersion ability and stability of the coating suspension. Cordierite was obtained by synthesis in a solid state, out of the mass consisting of kaoline, alumina, quartz, sepiolite. For characterization purposes, the following methods were used: X-ray diffraction, differential thermal analysis, SEM and optical microscopy. Mechanical activation of filler was performed in a vibrating mill. The upper boundary of the grain size was 100%-30 x 10-6 m, the times were (min): 15; 30; 60. The new composition of Lost foam refractory coatings has been developed with a change of the coating production process, as well. These newly synthesized coatings proved to be effective in terms of a positive influence on a surface quality, structural and mechanical properties of aluminium castings. Test results may be useful to have the Lost Foam refractory coatings specified together with other process parameters used for the production of castings according to this casting method.

Mineral additives are extensively applied as cement replacement materials in both construction concrete and mortar. Fly ash is one of the most commonly utilized additives which improve rheological properties, as well as thermal and mechanical behavior of mortar, and as such it has been widely investigated. This industrial byproduct comprises heavy metals in its composition; therefore further research is needed to optimize its effective dosage. Moreover, certain sorptive clays, such as natural zeolite and bentonite, can prevent migration of toxic elements from fly ash by immobilizing them in their structure. Ten experimental mortars are prepared with Portland cement, river sand and addition of fly ash, zeolite and/or bentonite in accordance with chemometric experimental design rules. The aim of the study was to investigate the effect of mineral additives on thermal and mechanical performances of mortar. Thermal characteristics were monitored via dilatometric analysis and DTA method. Principal component analysis was used on the results of physico-mechanical testing (workability, bulk density, water absorption, shrinkage, compressive and flexural strength) to enable the divisions of the observed samples into groups in the factor space. The performance of Artificial Neural Network was compared with the experimental data in order to develop rapid and accurate method for prediction of mechanical parameters of mortar. The ANN model showed high overall prediction accuracy (r 2 = 0.989, during training cycle). The test results indicate that incorporation of the mineral additives gave cost effective mortars with sufficiently good properties. However, tools of analytical modeling highlighted mortar with zeolite and fly ash as the optimal composition regarding its mechanical performance.

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