Studies on the quality of bituminous coal are mainly focused on physico-chemical analysis, examining the ash content, sulphur content, volatile matter content, moisture content, and the Net Calorific Value of coal. Until now, the above mentioned parameters form the basis of the Polish Standard PN-82/87002, on the basis of which individual types of bituminous coal are determined. In addition, an elemental analysis, providing information about the content of primary elements in the organic matter of solids, i.e. coal, hydrogen, nitrogen, oxygen, and sulphur, is carried out for the selected samples. This issue has been studied by many authors, which undoubtedly provide invaluable knowledge due to the huge amount of data, but, as the authors themselves indicate, the knowledge of the petrography of coal, coking properties (Probierz et al. 2012) and finally the coke obtained from individual coal types (based on tests carried out using the Karbotest installation or the so-called „box tests” performed in the coke oven battery) is still very limited. The article discusses the impact of petrographic composition on the quality of metallurgical coke. The analysis was performed using samples of coking coal from the following mines: Pniówek, Zofiówka, Borynia, and Krupiński. The mentioned coal types are used to produce coke mixtures used for the production of coke in the Przyjaźń and Radlin coking plants. Based on the rank of coal and physicochemical parameters, the mentioned coal types were classified according to the Polish classification and the UN/ECE International Classification of In-Seam Coals (UN/ECE 1995). The prediction of thermomechanical properties of coke (CSR and CRI) performed according to the original CCP method were compared with the results obtained using the classical method of Nippon Steel Corporation.

The petrographic composition of coal has a significant impact on its technological and sorption properties. That composition is most frequently determined by means of microscope quantitative analyses. Thus, aside from the purely scientific aspect, such measurements have an important practical application in the industrial usage of coal, as well as in issues related to the safety in underground mining facilities. The article discusses research aiming at analyzing the usefulness of selected parameters of a digital image description in the process of automatic identification of macerals of the inertinite group using neural networks. The description of the investigated images was based on statistical parameters determined on the basis of a histogram and co-occurrence matrix (Haralick parameters). Each of the studied macerals was described by means of a 20-element feature vector. An analysis of its principal components (PCA) was conducted, along with establishing the relationship between the number of the applied components and the effectiveness of the MLP network. Based on that, the optimum number of input variables for the investigated classification task was chosen, which resulted in reduction of the size of the network’s hidden layer. As part of the discussed research, the authors also analyzed the process of classification of macerals of the inertinite group using an algorithm based on a group of MLP networks, where each network possessed one output. As a result, average recognition effectiveness of 80.9% was obtained for a single MLP network, and of 93.6% for a group of neural networks. The obtained results indicate that it is possible to use the proposed methodology as a tool supporting microscopic analyses of coal.