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Based on the results of tests on feed coal from the Lublin Coal and Upper Silesian Coal Basin and its fly ash and slag carried out using X-ray diffraction and X-ray fluorescence analysis, atomic emission spectroscopy, and scanning electron microscopy, it was found that in feeds, coal Th is associated with phosphates and U with mineral matter. The highest Th content was found in anhedral grains of monazite and in Al-Si porous particles of fly ash of <0.05 mm size; whereas in the slag, Th is concentrated in the massive Al-Si grains and in ferrospheres. U is mainly concentrated in the Al-Si surface of porous grains, which form a part of fly ash of <0.05 mm size. In the slag, U is to be found in the Al-Si massive grains or in a dispersed form in non-magnetic and magnetic grains. Groups of mineral phase particles have been identified that have the greatest impact on the content of Th and U in whole fly ash and slag. The research results contained in this article may be important for predicting the efficiency of Th and U leaching from furnace waste storage sites and from falling dusts to soils and waters.

The study included 24 samples of coal with 7 cores, boreholes (7 coal seams), made by the Polish Geological Institute in Warsaw at the site of a Chelm field and 6 coal samples taken from 2 decks in the Lublin Coal mine „Bogdanka“ S.A. in LCB. Based on performed tests found generally low levels of Sb and Bi in coal. In the vertical profile of the LCB contents of Bi and Sb in coal generally increases from coal seams younger to older age. Content of Bi in coal from roof part coal seams is usually higher, and ash content in the coal content of Sb are generally lower than in the carbon of the middle part decks. The content of Bi in the lateral coal deposits is unlikely to vary, and the gap in the coal content of Bi between the sampling regions coal do not exceed 1.7 g / Mg. In contrast gap Sb content in coal on the extent LCB is from 1.7 g / Mg of 5.8 g / Mg. The biggest influence on the content of Bi and Sb in coal from the LCB is probably organic matter in which these elements are scattered and do not form their own minerals.

The purpose of this paper is to assess the content and distribution of some elements in coal from two bituminous coal basins and in fly ash and slag derived from combustion of the coals in six power plants in Poland. The petrographic composition and distribution of elements were characterized in the tested samples, using reflected light microscope, X-ray powder diffractometer, inductively coupled plasma atomic emission spectroscopy, and scanning electron microscope with energy dispersive X-ray. The highest content of elements in coal occurs in siderite. In Al-Si particles, as well as in magnetite with skeletal and dendritic structure crystallized on the surface of Al-Si microspheres or cenospheres included in fly ash size < 0.05 mm and in the magnetic fraction of slag, the highest content of elements was noted. Due to the content of elements, fly ash and slag were considered to be neutral for the soil environment. Correlations, which have not been described before, have been observed between the likely mode of binding of some elements in coal and their distribution in fly ash and slag. These correlations could be of particular value when predicting the content and distribution of elements in combustion residues and in the assessment of their environmental toxicity.

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The hemispherical temperature (HT) is the most important indicator representing ash fusion temperatures (AFTs) in the Polish industry to assess the suitability of coal for combustion as well as gasification purposes. It is important, for safe operation and energy saving, to know or to be able to predict value of this parameter. In this study a non-linear model predicting the HT value, based on ash oxides content for 360 coal samples from the Upper Silesian Coal Basin, was developed. The proposed model was established using the machine learning method—extreme gradient boosting (XGBoost) regressor. An important feature of models based on the XGBoost algorithm is the ability to determine the impact of individual input parameters on the predicted value using the feature importance (FI) technique. This method allowed the determination of ash oxides having the greatest impact on the projected HT. Then, the partial dependence plots (PDP) technique was used to visualize the effect of individual oxides on the predicted value. The results indicate that proposed model could estimate value of HT with high accuracy. The coefficient of determination (R2) of the prediction has reached satisfactory value of 0.88.

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