The article characterizes geological formations occurring in the Polish lignite deposits having the characteristics of raw materials, i.e. accompanying minerals, giving their location, quality characteristics, estimated resources and potential applications. Attention has also been paid to the economic suitability, e.g. in infrastructure works and for the reclamation of many geological formations found in the overburden, classified as so-called earth or rock mass. There are also raw materials of sorption properties representing a huge potential source of minerals valuable for the economy and environmental protection. This refers to e.g.: beidellite clays from Bełchatów, Poznań clays from the region of Konin and Adamów, lacustrine chalk from Bełchatów, as well as Mesozoic limestone from the lignite bedding in Bełchatów. The reasons for the unsatisfactory use of accompanying minerals have been given. The authors described the methods used in the mining operation and processing of associated minerals, also applicable in Poland, as the legal basis for the extraction of these minerals and the economic and financial conditions. They stressed the need to protect mined not associated minerals used by the construction of anthropogenic deposits. This activity primarily requires regulating the legal status of these deposits and the development and application of an economic and financial system that stimulates the economy of these minerals. In summary, the necessary actions were taken to increase the use of the accompanying minerals and their contribution to the balance of mineral resources in the country.

Power electronic circuits (PECs) are prone to various failures, whose classification is of paramount importance. This paper presents a data-driven based fault diagnosis technique, which employs a support vector data description (SVDD) method to perform fault classification of PECs. In the presented method, fault signals (e.g. currents, voltages, etc.) are collected from accessible nodes of circuits, and then signal processing techniques (e.g. Fourier analysis, wavelet transform, etc.) are adopted to extract feature samples, which are subsequently used to perform offline machine learning. Finally, the SVDD classifier is used to implement fault classification task. However, in some cases, the conventional SVDD cannot achieve good classification performance, because this classifier may generate some so-called refusal areas (RAs), and in our design these RAs are resolved with the one-against-one support vector machine (SVM) classifier. The obtained experiment results from simulated and actual circuits demonstrate that the improved SVDD has a classification performance close to the conventional one-against-one SVM, and can be applied to fault classification of PECs in practice.