VISUALIZATION OF MULTIDIMENSIONAL DATA IN PURPOSE OF QUALITATIVE CLASSIFICATION OF VARIOUS TYPES OF COAL / WIZUALIZACJA WIELOWYMIAROWYCH DANYCH W CELU KLASYFIKACJI JAKOŚCIOWEJ RÓŻNYCH TYPÓW WĘGLA

Abstract

Abstract Coal as energetic raw material features by many parameters determining its quality. In classification of coal types there are many of them with typical division of energetic, semi-coking and coking coal. The data concerning coal are usually treated as independent values while this kind of approach is not always right. Authors proposed new solutions in this aspect and performed the multidimensional analysis of three selected types of coal featuring by various properties which originated from three various hard coal mines located in Upper Silesia Region. The object of the research was so-called raw coal which was not processed before. For each type of coal the detailed statistical analysis of seven chosen properties of coal was performed. To perform adequate and complete statistical analysis it is necessary to analyze the chosen properties of coal together in multidimensional way. It was decided to apply new and modern visualizing methods of multidimensional data which were observational tunnels method and parallel coordinates method. The applied methods allowed to obtain visualization of seven-dimensional data describing coal. By means of these visualizations it was possible to observe the significant division of the features space between researched types of coal. These methods allowed to look at the investigated data from various perspectives and make possible to determine significant differences between researched materials. For the investigated coals such differences were determined clearly what proved that by means of these methods it is possible to successfully identify type of coal as well to analyze in details its individual properties and identify, for example, particle size fraction etc. The obtained results are innovative and are the basis for more detailed researches taking into consideration also other coal properties, including its structure and texture. This methodology can be also applied successfully for other types of raw materials, like ores.