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1
Content available remote Increasing the efficiency of the functioning of industrial electrical networks thanks to the integration of vacuum reclosers
Kalinchyk Vasyl, Meita Olexandr, Pobigaylo Vitalii, Borychenko Olena, Kalinchyk Vitalii, Kuzovkin Sergii
Journal of New Technologies in Environmental Science
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2023
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Vol. 7, nr 1
28--33
EN
Topical matter of power supply for today is effective increase in the reliability of power supply in medium voltage overhead power systems by sectioning of lines with switching devices, such as disconnectors, controlled disconnectors or sectioning points. In such schemes, the manual approach to emergency management is used. This kind of schemes can be used where the overhead power lines are. Protective device on the outgoing feeder is switched off as soon as damage occurs in any area. As a result, all consumers of the line lose power for a long time. Remotely operated disconnectors or remote controlled sectioning points can also be installed instead of manual line disconnectors. This process of damage localization differs only in that all switching operations are performed remotely. Decision on switching is made by the dispatcher, constant communication with each controlled element of the network is necessary, otherwise it becomes virtually uncontrollable and the entire effect of remote control of disconnectors is eliminated.
2
Content available Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex
Kalinchyk Vasyl, Meita Olexandr, Pobigaylo Vitalii, Borychenko Olena, Kalinchyk Vitalii
Rocznik Ochrona Środowiska
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2022
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Tom 24
26--40
EN
This article investigates the application of neural network models to create automated control systems for industrial processes. We reviewed and analysed works on dispatch control and evaluation of equipment operating modes and the use of artificial neural networks to solve problems of this type. It is shown that the main requirements for identification models are the accuracy of estimation and ease of algorithm implementation. It is shown that artificial neural networks meet the requirements for accuracy of classification problems, ease of execution and speed. We considered the structures of neural networks that can be used to recognise the modes of operation of technological equipment. Application of the model and structure of networks with radial basis functions and multilayer perceptrons for identifying the mode of operation of equipment under given conditions is substantiated. The input conditions for constructing neural network models of two types with a given three-layer structure are offered. The results of training neural models on the model of a multilayer perceptron and a network with radial basis functions are presented. The estimation and comparative analysis of models depending on model parameters are made. It is shown that networks with radial basis functions offer greater accuracy in solving identification problems. The structural scheme of the automated process control system with mode identification based on artificial neural networks is offered.
3
Content available Neuromodel of the "Crusher mill" Mechatronic Complex
Kalinchyk Vasyl, Meita Olexandr, Pobigaylo Vitalii, Kalinchyk Vitalii, Filyanin Danylo
Rocznik Ochrona Środowiska
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2021
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Tom 23
470--483
EN
To create a mechatron model of vacuum tube vibration system for rock cutting by flat auger tools based on physical and mechanical properties of processed array and kinematic characteristics of the instrument. The analytical model of the fracture process of rock cutting by tool vibration with considering of plastic properties of the massif were developed. The main technological parameters of massif vibrating cutting with normal variations were modeled: dependences of normal and tangential pressure in the zone of working body interaction with the medium, normal and shear stresses in the zone of destruction, rock characteristics of the medium, vibration parameters dependences on the characteristics of mechatronic system geometry of the contact area of flat incisors treated with medium. The choice of the computational model of the vibration rock cutting with the normal to the direction of movement of the working body fluctuations with considering arising from processes at once or disorders: the occurrence of compressive and tensile stresses were backgrounded. Main stages and interconnection options in the simulation of vibration cutting were established. Scientific novelty lies in the development of a method of analysis of contact interaction of roller working body molding machine with an array taking into account the changes in the stabilization process of physical and mechanical properties of the treated medium, the aim of which is to predict the required voltage and depth of the formed layer. The theoretical basis of rock cutting by flat auger tool taking into account the normal component of the vibration with respect to the movement direction allowing for the deformation of the rock mass and the contact interaction with the working body were established, that allows to improve the technology of drilling wells by reducing the power consumption of the cutting process. The results enable us to determine the parameters of power and kinematic conditions for the occurrence of the vibration cutting.
4
Content available Forecasting the Energy Consumption of an Industrial Enterprise Based on the Neural Network Model
Kalinchyk Vasyl, Meita Olexandr, Pobigaylo Vitalii, Kalinchyk Vitalii, Filyanin Danylo
Rocznik Ochrona Środowiska
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2021
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Tom 23
484--492
EN
This research paper investigates the application of neural network models for forecasting in energy. The results of forecasting the weekly energy consumption of the enterprise according to the model of a multilayer perceptron at different values of neurons and training algorithms are given. The estimation and comparative analysis of models depending on model parameters is made.
5
Content available remote Neural network model of the mechatron complex “crusher mill”
Kalinchyk Vasyl, Meita Olexandr, Pobigaylo Vitalii, Kalinchyk Vitalii, Filyanin Danylo
Journal of New Technologies in Environmental Science
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2021
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Vol. 5, nr 2
72--81
EN
The paper discusses the use of the technology of artificial neural networks to improve technical and economic performance of crushing and milling complex. Formulated the goal and major tasks of constructing a system of automated control and monitoring to optimize the power consumption of crushing and milling complex is analyzed modes of complex mechatronic and development of multicriteria models to provide optimal technological parameters of the equipment.
6
Content available remote Neural network model for enterprise energy consumption forecasting
Kalinchyk Vasyl, Meita Olexandr, Pobigaylo Vitalii, Kalinchyk Vitalii, Filyanin Danylo
Journal of New Technologies in Environmental Science
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2021
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Vol. 5, nr 2
65--71
EN
This research paper investigates the application of neural network models for forecasting in energy. The results of forecasting the weekly energy consumption of the enterprise according to the model of a multilayer perceptron at different values of neurons and training algorithms are given. The estimation and comparative analysis of models depending on model parameters is made.
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