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Date submitted2022-10-26
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Date accepted2023-02-13
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Date published2023-07-19
Determination of the grid impedance in power consumption modes with harmonics
The paper investigates the harmonic impedance determination of the power supply system of a mining enterprise. This parameter is important when calculating modes with voltage distortions, since the determined parameters of harmonic currents and voltages significantly depend on its value, which allow the most accurate modeling of processes in the presence of distortions in voltage and current. The power supply system of subsurface mining is considered, which is characterized by a significant branching of the electrical network and the presence of powerful nonlinear loads leading to a decrease in the power quality at a production site. The modernization of the mining process, the integration of automated electrical drive systems, renewable energy sources, energy-saving technologies lead to an increase in the energy efficiency of production, but also to a decrease in the power quality, in particular, to an increase in the level of voltage harmonics. The problem of determining the grid harmonic impedance is solved in order to improve the quality of design and operation of power supply systems for mining enterprises, taking into account the peculiarities of their workload in the extraction of solid minerals by underground method. The paper considers the possibility of determining the grid impedance based on the measurement of non-characteristic harmonics generated by a special nonlinear load. A thyristor power controller based on phase regulation of the output voltage is considered as such a load. Simulation computer modeling and experimental studies on a laboratory test bench are used to confirm the proposed method. The recommendations for selecting load parameters and measuring device connection nodes have been developed.
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Date submitted2022-08-01
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Date accepted2022-11-17
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Date published2023-02-27
Use of machine learning technology to model the distribution of lithotypes in the Permo-Carboniferous oil deposit of the Usinskoye field
- Authors:
- Denis V. Potekhin
- Sergei V. Galkin
Permo-Carboniferous oil deposit of the Usinskoye field is characterized by an extremely complex type of the void space with intense cross-sectional distribution of cavernous and fractured rock. In this study, for this production site, the process of 3D geological modeling has been implemented. At the first stage, it provided for automated identification of reservoir volumes by comparing the data of core and well logging surveys; at the second stage, identification of rock lithotypes according to Dunham classification is performed on the basis of comparison of thin sections examination and well logging data. A large array of factual information enables the use of machine learning technology on the basis of Levenberg – Marquardt neural network apparatus toward achievement of our research goals. The prediction algorithms of reservoir and rock lithotype identification using well logging methods obtained on the basis of the training samples are applied to the wells without core sampling. The implemented approach enabled complementing the 3D geological model with information about rock permeability and porosity, taking into account the structural features of the identified lithotypes. For the Permo-Carboniferous oil deposit of the Usinskoye field, the volumetric zoning of the distribution of different rock lithotypes has been established. Taking into account the lithotypes identified based on machine learning algorithms, density and openness of fractures were determined, and fracture permeability in the deposit volume was calculated. In general, during the implementation, the machine learning errors remained within 3-5 %, which suggests reliability of the obtained predictive solutions. The results of the research are incorporated in the existing 3D digital geological and process model of the deposit under study.
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Date submitted2022-05-13
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Date accepted2022-09-24
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Date published2022-11-03
Rapid detection of coal ash based on machine learning and X-ray fluorescence
Real-time testing of coal ash plays a vital role in the chemical, power generation, metallurgical, and coal separation sectors. The rapid online testing of coal ash using radiation measurement as the mainstream technology has problems such as strict coal sample requirements, poor radiation safety, low accuracy, and complicated equipment replacement. In this study, an intelligent detection technique based on feed-forward neural networks and improved particle swarm optimization (IPSO-FNN) is proposed to predict coal quality ash content in a fast, accurate, safe，and convenient manner. The data set was obtained by testing the elemental content of 198 coal samples with X-ray fluorescence (XRF). The types of input elements for machine learning (Si, Al, Fe, K, Ca, Mg, Ti, Zn, Na, P) were determined by combining the X-ray photoelectron spectroscopy (XPS) data with the change in the physical phase of each element in the coal samples during combustion. The mean squared error and coefficient of determination were chosen as the performance measures for the model. The results show that the IPSO algorithm is useful in adjusting the optimal number of nodes in the hidden layer. The IPSO-FNN model has strong prediction ability and good accuracy in coal ash prediction. The effect of the input element content of the IPSO-FNN model on the ash content was investigated, and it was found that the potassium content was the most significant factor affecting the ash content. This study is essential for real-time online, accurate, and fast prediction of coal ash.
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Date submitted2021-02-20
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Date accepted2021-10-18
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Date published2021-12-16
Thermal protection implementation of the contact overheadline based on bay controllers of electric transport traction substations in the mining industry
The article presents the principle of thermal protection of the contact overheadlineand substantiates the possibility of practical implementation of this principle for rail electric transport in the mining industry. The algorithm for the implementation of modern digital protection of the contact overhead line as one of the functions of the controller is described. A mathematical model of thermal protection is proposed, which follows from the solution of the heat balance equation. The model takes into account the coefficient of the electrical networktopology, as well as the coefficient of consumption of the current-carrying core of the cable, which determines the reduction in the conducting section from contact erosion and the growth of oxide films. Corrections for air flows are introduced when receiving data from an external anemometer, via telemechanics protocol. The mathematical model was tested by writing a real thermal protection program in the C programming language for the bay controller, based on the circuitry of which is the STM32F407IGT6 microcontroller for the microcontroller unit. Verification tests were carried out on a serial bay controller in 2020. The graphs for comparing the calculated and actual values of temperatures, with different flow rates of the current-carrying conductor of the DC cable, are given. To obtain data, telemechanics protocols IEC 60870-104 and Modbus TCP, PLC Segnetics SMH4 were used.
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Date submitted2019-03-13
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Date accepted2019-05-07
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Date published2019-08-23
Modern Mathematical Forecast Methods of Maintenance and Support Conditions for Mining Tunnel
- Authors:
- S. A. Ignatyev
- A. E. Sudarikov
- A. Zh. Imashev
The research focuses on mathematical methods of mining pressure forecast to develop rational support patterns for mining tunnels and to ensure safety of mining operations. The purpose of research is to develop the methodology of applying advanced calculation methods and software solutions based on neural networks to reduce dispersion of factors influencing stability of mining tunnels, as well as to define rational parameters of mining tunnel support. The authors review the algorithm of geomechanical process examination, which is divided into several stages. First of all, it is proposed to use cluster analysis to examine location conditions of man-made outcrops, which allows to divide all the diversity of existing conditions for mining tunnel construction. Cluster analysis first allows to reduce the dispersion of factors that influence the stability of mining tunnels in various clusters, and then to determine rational parameters of tunnel support in each cluster. After the problem of cluster analysis is solved, it is proposed to use software programs that allow to study geomechanical processes in each cluster. At this stage, both standard methods (normative techniques, numerical modelling, analogies use, etc.) and the most advanced methods – neural networks – can be applied. Described algorithm of solving geomechanical problems, which utilizes advanced numerical methods and a software package based on neural networks, ensures an individual approach to estimation of mining pressure under varying conditions of man-made outcrop location in the rock mass.
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Date submitted2009-10-26
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Date accepted2009-12-27
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Date published2010-09-22
Support of geodynamic safety in mining of the Khibini deposits
The paper deals with the problems of geodynamics in mining of the Khibini deposits. Description is given to the complex of organizational-technical arrangements for provision of geodynamic safety at the Apatit Co and to principal trends of its development.