The article analyzes the dynamics of the development of the electromobility sector in Poland in the context of the European Union and due to the economic situation and development of the electromobility sector in the contexts of Switzerland and Norway. On the basis of obtained data, a forecast was made which foresees the most likely outlook of the electric car market in the coming years. The forecast was made using the creeping trend method, and extended up to 2030. As part of the analysis of the effect of the impact of electromobility, an original method was proposed for calculating the primary energy factor (PEF) primary energy ratio in the European Union and in its individual countries, which illustrates the conversion efficiency of primary energy into electricity and the overall efficiency of the power system. The original method was also verified, referring to the methods proposed by the Fraunhofer-Institut. On the basis of all previous actions and analyses, an assessment was made of the impact of the development of the electromobility sector on air quality in the countries studied. Carbon dioxide tank-to-wheels emission reductions which result from the conversion of the car fleet from conventional vehicles to electric motors were then calculated. In addition to reducing carbon dioxide emissions, other pollutant emissions were also calculated, such as carbon monoxide (CO), nitrogen oxides (NOx) and particulate matter (PM). The increase in the demand for electricity resulting from the needs of electric vehicles was also estimated. On this basis, and also on the basis of previously calculated primary energy coefficients, the emission reduction values have been adjusted for additional emissions resulting from the generation of electricity in power plants.

Maintenance of industrial reactors supported by deep learning driven ultrasound toMography eksploatacja reaktorów przeMysłowych ze wspoMaganieM toMografii ultradźwiękowej i algorytMów głębokiego uczeniaMonitoring of industrial processes is an important element ensuring the proper maintenance of equipment and high level of processes reliability. The presented research concerns the application of the deep learning method in the field of ultrasound tomography (UST). A novel algorithm that uses simultaneously multiple classification convolutional neural networks (CNNs) to generate monochrome 2D images was developed. In order to meet a compromise between the number of the networks and the number of all possible outcomes of a single network, it was proposed to divide the output image into 4-pixel clusters. Therefore, the number of required CNNs has been reduced fourfold and there are 16 distinct outcomes from single network. The new algorithm was first verified using simulation data and then tested on real data. The accuracy of image reconstruction exceeded 95%. The results obtained by using the new CNN clustered algorithm were compared with five popular machine learning algorithms: shallow Artificial Neural Network, Linear Support Vector Machine, Classification Tree, Medium k-Nearest Neighbor classification and Naive Bayes. Based on this comparison, it was found that the newly developed method of multiple convolutional neural networks (MCNN) generates the highest quality images.

The paper presents a method combining the processes of straightening and thermal treatment. Technological processes with axial strain were considered, for the case of heated material and without its heating. The essence of the process in the case of heated material consisted in the fact that if under tension all longitudinal forces in the first approximation are uniform -the same strains are generated. The presented technological approach, aimed at reducing the curvature of axial-symmetrical parts, is acceptable as the process of rough, preliminary machining, in the case of shafts with the ratio L/D≤100 (L -shaft length, d -shaft diameter) and without a tendency of strengthening. To improve the accuracy and stability of geometric form of low-rigidity parts, a method was developed that combines the processes of straightening and heat treatment. The method consists in that axial strain -tension, is applied to the shaft during heating, and during cooling the product is fixed in a fixture, the cooling rate of the shaft being several-fold greater than that of the fixture. A device is presented for the realisation of the method of controlling the process of plastic deformation of lowrigidity shafts. In the case of the presented device and the adopted calculation scheme, a method was developed that permits the determination of the length of shaft section and of the time of its cooling.

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The study determines the most suitable pattern of alignment and fixing of low-rigidity shafts; it also presents factors that determine such choice. The part is fixed both by force closing and kinematic closing. A new method for machining low rigidity shafts is developed to control the elastic-deformable state of these shafts in a technological system and to produce parts with the required accuracy during turning. To implement this new developed method for low rigidity shafts, an apparatus is designed. The apparatus allows to increase the rigidity of shafts during machining by the application of axial tensile force to the workpiece. Rational prime costs of preparing technological alignment centers at the stage of production preparation are determined; knowing these costs, it is possible to select a suitable machining technology for low rigidity shafts, to produce a technology-oriented design, and to reduce the costs of machining these shafts.