This work investigates the cost-efficient integration of renewable hydrogen into steelworks for the production of methane and methanol as an efficient way to decarbonize the steel industry. Three case studies that utilize a mixture of steelworks off-gases (blast furnace gas, coke oven gas, and basic oxygen furnace gas), which differ on the amount of used off-gases as well as on the end product (methane and/or methanol), are analyzed and evaluated in terms of their economic performance. The most influential cost factors are identified and sensitivity analyses are conducted for different operating and economic parameters. Renewable hydrogen produced by PEM electrolysis is the most expensive component in this scheme and responsible for over 80% of the total costs. Progress in the hydrogen economy (lower electrolyzer capital costs, improved electrolyzer efficiency, and lower electricity prices) is necessary to establish this technology in the future.

The study of the petrographic structure of medium- and high-rank coals is important from both a cognitive and a utilitarian point of view. The petrographic constituents and their individual characteristics and features are responsible for the properties of coal and the way it behaves in various technological processes. This paper considers the application of convolutional neural networks for coal petrographic images segmentation. The U-Net-based model for segmentation was proposed. The network was trained to segment inertinite, liptinite, and vitrinite. The segmentations prepared manually by a domain expert were used as the ground truth. The results show that inertinite and vitrinite can be successfully segmented with minimal difference from the ground truth. The liptinite turned out to be much more difficult to segment. After usage of transfer learning, moderate results were obtained. Nevertheless, the application of the U-Net-based network for petrographic image segmentation was successful. The results are good enough to consider the method as a supporting tool for domain experts in everyday work.

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 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.

It is supposed that the determination of the content and the mode of occurrence of ecotoxic elements (EE) in feed coal play the most significant role in forecasting distribution of EE in the soil and plants in the vicinity of power stations. Hence, the aim of the work was to analyze the properties of the feed coal, the combustion residues, and the topsoil which are reached by EE together with dust from power stations. The mineral and organic phases, which are the main hosts of EE, were identified by microscopy, X-ray powder diffraction, inductively coupled plasma atomic emission spectrometry, and scanning electron microscope with an energy dispersive X-ray methods. The highest content of elements was observed in the Oi and Oe subhorizons of the topsoil. Their hosts are various types of microspheres and char, emitted by power stations. In the areas of long-term industrial activity, there are also sharp-edged grains of magnetite emitted in the past by zinc, lead, and ironworks. The enrichment of the topsoil with these elements resulted in the increase in the content of EE, by between 0.2 times for Co; and 41.0 times for Cd in the roots of Scots pine, common oak and undergrowth, especially in the rhizodermis and the primary cortex and, more seldom, in the axle roller and cortex cells.