The article is concerned with energy conservation in the process of cleaning grain. The study aimed to find ways of reducing energy consumption when cleaning food grains by using energy-saving operating modes (ESOM) on the production lines of grain storage facilities. For this purpose, experimental tests were performed which involved studying the effects of physical and chemical properties of grain as well as the influence of various operating modes of the production lines on the specific energy consumption (SEC), which was found to be the most informative indicator for determining ESOM. The mathematical description of the target function was carried out by conducting a multifactorial experiment with the application of the orthogonal central composite design (OCCD) of the second order. Calculations produced a number of mathematical models describing the dependence of the response function on the input parameters. The proposed approach made it possible to obtain the minimum SEC for cleaning food grains and to develop practical recommendations for ways to reduce energy consumption, which resulted in the development of scientifically based SEC standards for a grain cleaning machine.
The object of this study is the process of functioning of the "man-robot" system. The task to coordinate parameters of the human operator and the robot is investigated. Aligning these parameters is based on the method of determining the dynamic parameters of the human operator using mathematical models that describe two types of relative errors. The first type includes relative errors in determining the dynamic parameters of the operator, which depend on the error in determining the signals characterizing his response to the test impact. The second type of relative errors is the methodical error, which is due to the approximation of partial derivatives. The formation of a test impact on the operator is carried out using an interactive whiteboard. The method is based on finding the roots of a linear system of algebraic equations, for the construction of which an approximation of partial derivatives from signals characterizing the operator's response to the test effect is used. The parameters of this system of algebraic equations depend on time parameters. Determination of time parameters is carried out using tolerance criteria and using nomograms. When justifying the main parameter of the test impact on the operator – the speed of movement of the fire front on the interactive whiteboard screen, the properties of the angular eye control system of the mobile fire installation operator are used. These properties are formalized as a mathematical model of dynamic error, which occurs in the process of tracking by the operator the image of a fire on the interactive whiteboard screen. To verify the obtained results, a test problem has been solved; it is shown that the error in determining the dynamic parameters of the operator does not exceed 1.0 %. The results reported here could be used for designing mobile fire installations of a new generation, the structure of which is based on the use of segways
The paper proposes the creation of a network of fully automatic monitoring stations for air pollution on the basis of networks of 3G / 4G base stations of mobile operators of Ukraine, which will provide data on concentrations of pollutants subject to mandatory real-time control at a specific point in space. with known coordinates. Substantiation of the choice and adaptation of the mathematical model for calculating the distribution of impurities of pollutants in the atmosphere (the necessary component of the proposed method) taking into account the engineering and technical means of automated measurements. A method for predicting the level of pollution and its distribution taking into account meteorological conditions based on the adaptation of the OND-86 model, as well as its supplementation by calculations based on the nonstationary Gaussian model, has been developed. The method differs from the existing ones by estimating the contribution of each source using the results of operational control, which allows to create automated air quality assurance systems.
One the effective technical methods of monitoring the condition electric motors is a means of measuring and controlling the amount leakage current, which characterizes the state of insulation of the electric motor. The use of more advanced devices that can not only record but also predict the achievement dangerous values leakage current, makes it possible to warn and inform in advance about the possible danger to staff, reduce downtime and allows maintenance, repair or replacement motors in the technological pause without waiting for their complete rejection. The neural networks used to predict the reliability electric motors have the form a mathematical model of parallel computing, which consists simple processor elements that interact with each other and are called artificial neurons.The purpose of the study is to synthesize the neural network on the basis selected technological parameters and check its technological acceptability for predicting the leakage current of the motor.The synthesized neural network according to the technological parameters should be the basis for building a system for predicting the leakage current of the electric motor according to the technological parameters. The prediction system based on the neural network on technological parameters also includes means of measuring technological parameters, parameters of motor operation and database. The key decision in such a system is made by man.
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