The objective of this work was to study the possibility of upgrading the control system of the drum shear mechanism by using neural network PI controllers to improve the efficiency of the sheet-metal cutting process. The developed detailed model of the mechanism, including a dual DC electric drive with three subordinate control loops for the voltage of the thyristor converter, current and speed of the motors, a 6-mass kinematic system with viscoelastic connections as well as a model of the metal cutting process, made it possible to uncover that the interaction of electric drives with the mechanical part leads to significant speed fluctuations during the cutting process, which worsens the quality of the sheet-metal edge. A modified system of current and speed controllers with built-in three-layer fitting neural networks as nonlinear components of proportional-integral channels is proposed. An algorithm for the fast learning of neural controllers using the gradient descent method in each cycle of calculating the controller signal is also proposed. The developed neuro-regulators make it possible to reduce the amplitude of speed fluctuations during the cutting process by four times, ensuring the effective damping of oscillations and reducing the duration of transient processes to 0.1 s.

This study presents the development process of a digital twin for a complex dynamic object using Artificial Neural Networks. A separately excited DC motor is considered as an example, which, despite its well-known electromechanical properties, remains a non-trivial object for neural network modeling. It is shown that describing the motor using a generalized neural network with various configurations does not yield satisfactory results. The optimal solution was based on a separation into two distinct nonlinear autoregressive with exogenous inputs (NARX) artificial neural networks with cross-connections for the two main machine variables: one for modeling the armature current with exogenous inputs of voltage and armature speed, and another for modeling the angular speed with inputs of voltage and armature current. Both neural networks are characterized by a relatively small number of neurons in the hidden layer and a time delay of no more than 3 time steps. This solution, consistent with the physical understanding of the motor as an object where electromagnetic energy is converted into thermal and mechanical energy (and vice versa), allows the model to be calibrated for the ideal no-load mode and subsequently account for the influence of torque loads of various natures and changes in the control object parameters over a wide range. The study demonstrates that even for modeling an object such as a DC electric drive with cascaded control, reducing errors at the boundaries of the known operating range requires generating test signals covering approximately 120% of the nominal speed range and 250–400% of the nominal current. Analysis of various test signals revealed that training with a sequence of step changes and linear variations across the entire operating range of armature current and speed provides higher accuracy compared to training with random or uniform signals. Furthermore, to ensure the neural network model’s functionality under varying load torque, a mechanical load observer was developed, and a model architecture incorporating an additional input for disturbance was proposed. The SEDCM_NARX_LOAD neural network model demonstrates a theoretically justified response to load application, although dynamic and static errors arise. In the experiment, the current error was 7.4%, and the speed error was 0.5%. The practical significance of the research lies in the potential use of the proposed model for simulating dynamic and static operational modes of electromechanical systems, tuning controllers, and testing control strategies without employing a physical motor.

This article presents a comprehensive modeling and feasibility study for a photovoltaic system in the city of Saran, Karaganda region. In the context of ordered research tasks, a methodology based on an iterative process is proposed. The scientific novelty of the study lies in the step-by-step division of the project into intervals, each of which is characterized by its indicators, dependent on the modeled impacts and conditions. The main technical and technological solutions offered by the solar power plant are designed to realize the expected effect of the project and indicators are compared based on the analysis of financial advantages, sensitivity, and environmental advantages to find the most optimized. The article analyzes the economic feasibility of solar photovoltaic systems, in terms of total cash flow and payback period, for a system with a capacity of 100 MW (the most optimized).

В данной статье представлена система автоматического регулирования электропривода летучих ножниц горячей прокатки, которая разработана в рамках докторской диссертации «Автоматизация процесса резки проката на летучих ножницах стана горячей прокатки». В статье рассмотрено основные конструкции системы автоматического регулирования летучих ножниц и определены их требования. Рассмотрены принцип работы, механизм регулирования, технологический процесс и схема регулирования (контур напряжения, тока, скорости и положения) летучих ножниц. Построены структурные схемы по каждым контурам и определены передаточная функция объекта регулирования. Вычисления и анализ качества системы автоматического регулирования (САР) электропривода летучих ножниц был выполнен в программном комплексе MATLAB. Для определения анализа и качества регулирования построены (переходной процесс и логарифмической амплитудно-частотной с фазо-частотной характеристикой) модели контуров в Matlab. По результатам исследования получены следующие параметры: частота среза, запас устойчивости по фазе, перерегулирование, время достижения первого согласования, время достижения первого максимума и время регулирования. В результате работы можно сделать следующий вывод: все контуры имеют необходимый запас устойчивости по фазе и по частоте в соответствии с теорией классических систем подчиненного регулирования. Ключевые слова: стан, ПК MATLAB, система автоматического регулирования, летучие ножницы, математическое моделирование, электропривод.

The presented literature review contains the results of the analysis of scientific articles for 2000-2026 years, considering the use of blast furnace slag in various areas of road construction. It has been established that there are clear differences between the application options for different types of blast furnace slag. Air-cooled blast furnace slag (ACBFS) is more often used as a coarse or fine aggregate for cement concrete and asphalt concrete, and ground granulated blast furnace slag (GGBFS) is used as an additional binder or as a mineral/reactive filler. When using blast furnace slag in the production of concrete, the highest results in a number of studies were achieved when replacing 20-40% of the aggregate with slag, although this indicator was not a universal optimal value. In some articles, it was pointed out that the use of blast furnace slag as part of the base of a highway or for soil stabilization was associated with an increase in CBR. Some authors point to a reduction in the consumption of Portland cement when using complex binders containing granular blast furnace slag. Data on the improvement of water resistance, fatigue resistance, Marshall stability, and rutting resistance of asphalt concrete with the introduction of ACBFS and GGBFS in various proportions are also presented. Pretreatment of porous ACBFS was also important: selective crushing and related separation methods reduced water absorption, crushing value, and the proportion of weak porous particles. The main practical conclusion is that BFS should not be treated as a single interchangeable material. Its use should be selected according to slag type, particle quality, the component being replaced, and the required pavement property. Further studies should examine whether improvements achieved by slag pretreatment at the aggregate level are retained in concrete, asphalt mixtures, and pavement-base materials under long-term service conditions.

The global energy system is undergoing a significant transformation driven by rapid electrification, urbanization, and the emergence of new categories of electricity consumers. In particular, the increasing load density in low-voltage distribution networks within urban areas requires a reconsideration of conventional methodologies for the placement of transformer substations. Traditional planning approaches are often based on empirical service radii or static demand factors and therefore fail to adequately reflect the complexity of modern urban power systems. This study proposes a multi-objective optimization model for the optimal placement of transformer substations in 10/0.4 kV urban distribution networks. The proposed model simultaneously considers power losses, economic costs, and system reliability. In addition, the design load model is extended through the introduction of a comfort coefficient that captures additional electricity consumers typical of modern urban infrastructure, including HVAC systems, elevators, pumping systems, and electric vehicle charging stations. In contrast to traditional empirical approaches, the transformer service radius is modeled as a physical parameter determined by voltage drop limits, cable thermal constraints, and failure intensity. The optimization problem is solved using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Each candidate solution generated by the algorithm is validated through AC load-flow simulations performed in the DIgSILENT PowerFactory environment. The proposed methodology is evaluated using real data from a 0.48 km² urban area in the city of Tashkent. The results indicate that increasing the transformer service radius reduces capital investment costs but leads to higher power losses and longer interruption durations. According to the Pareto analysis, a service radius of approximately 300 m represents the optimal compromise between technical, economic, and reliability criteria for the studied area. The proposed methodology can serve as an effective tool for the scientifically grounded planning of urban power supply systems and for improving energy efficiency in modern distribution networks.