The authors of the paper emphasize that when the Nyquist-Shannon sampling theorem is used in practice, there arise several problems, which can be explained only through the use of new methodologies and mathematical models. The review of the researchers' works, as well as the authors' own practical research in the course of processing the statistical sample, which is described by a wave-like sine-cosine function, leads to the conclusion that it is necessary to take into account optimization criteria for high-tech processes and innovative indicators of building functions for the statistical sample, for example, when signals are transmitted and sampled using neural networks at production facilities. In the practice of economic calculations, for example, when making a graphic presentation of trend lines based on the functions built subject to the sampling conditions by the Nyquist theorem, the authors propose to use new methods for approximating piece linear functions, which allow for achieving a smaller error as compared to standard calculation methods. The work resulted in the creation of a neural network regulation algorithm, which will be trained based on the collected data and adapted to a specific type of a boiler unit. Besides, it was established that the task of neural network algorithms in the program is to find the optimal value of the weight coefficient for each argument of the resulting function to obtain the maximum number of predictions of the flare level and the particle burn-up time, which are consistent with reality. The use of these methods for the first time made it possible to significantly reduce the error, which is confirmed not only by calculations, but also by experimental data.
The aim of this scientific research is to experimentally determine the exergy losses of a ground heat pump and further optimization for more efficient use of operating modes and improvement of individual structural elements. In addition, it is proposed to use photovoltaic panels as a backup power source for the experimental installation under study. The exergetic losses are calculated, not only for the ground heat pump itself, with R407C refrigerant. The research methodology consists in a comprehensive assessment of exergetic flows, their optimization using new methods of approximation of piecewise linear functions, and the development of prerequisites for the use of anergy as one of the components of a new type of analysis of the efficiency of low-potential energy sources. As a result of processing the experimental data, the values of Coefficient of performance (COP) 4.136, exergetic temperature for the lower heat source 0.0253 and for the upper heat source 0.155, exergetic efficiency of the installation 0.62, and total loss of specific exergy of the heat pump 24.029 kJ/kg were obtained. Controllers with the Modbus protocol were used for data collection. Matlab Simulink was used to process the experimental data. When carrying out the procedure for optimizing the operating modes and selecting several modes with minimal exergetic losses, an important role is given to mathematical methods of processing statistical data. The method of increasing the efficiency of the heat pump is shown, first of all, based on the use of photovoltaic panels as a backup power source and optimization of exergetic losses due to exergo-anergetic evaluation of operating modes. The authors present the measurement errors of the heat pump plant parameters in the form of a 3D Gaussian curve, which becomes possible only when applying new approximation methods in the processing of measurements.
A method for evaluating the thermophysical characteristics of the torch is developed. Mathematically the temperature at the end of the zone of active combustion based on continuous distribution functions of particles of solid fuels, in particular coal dust. The particles have different average sizes, which are usually grouped and expressed as a fraction of the total mass of the fuel. The authors suggest taking into account the sequential nature of the entry into the chemical reactions of combustion of particles of different masses. In addition, for the application of the developed methodology, it is necessary to divide the furnace volume into zones and sections. In particular, the initial section of the torch, the zone of intense burning and the zone of afterburning. In this case, taking into account all the thermophysical characteristics of the torch, it is possible to make a thermal balance of the zone of intense burning. Then determines the rate of expiration of the fuel-air mixture, the time of combustion of particles of different masses and the temperature at the end of the zone of intensive combustion. The temperature of the torch, the speed of flame propagation, and the degree of particle burnout must be controlled. The authors propose an algorithm for controlling the thermophysical properties of the torch based on neural network algorithms. The system collects data for a certain time, transmits the information to the server. The data is processed and a forecast is made using neural network algorithms regarding the combustion modes. This allows to increase the reliability and efficiency of the combustion process. The authors present experimental data and compare them with the data of the analytical calculation. In addition, data for certain modes are given, taking into account the system’s operation based on neural network algorithms.
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