Steel hoisting ropes play an important role in metallurgical equipment, ensuring reliability and efficiency of lifting operations. One of the key features of their operation is the high level of contamination typical of metallurgical operations. Metallurgical processes are often accompanied by dust, metal chips and other abrasive particles that can significantly degrade ropes, causing wear and corrosion. To maintain the efficient operation of equipment it is necessary to monitor the condition of hoisting ropes in real time, which makes the task of improving automatic systems for monitoring the condition of ropes urgent. The paper reviews the methods of optical control of defects in hoisting steel ropes, the advantages and limitations of different approaches are considered. The aim of the work is to justify the effectiveness of the authors' developed method of analyzing rope defect images using neural networks in relation to the method based on the discrete Fourier transform. It is revealed that one of the most promising in terms of technical and economic efficiency of inspection methods is the application of vision system with image processing based on convolutional neural network technology, which allows to effectively detect defects in complex and changing operating conditions, such as metallurgical and mining production, where the background of the image may be non-uniform, and the distance between the camera and the rope varies.

Keywords: lifting ropes, vision systems, optical control methods, fast Fourier transform, hidden Markov models, convolutional neural networks

The desliming process plays a key role in mineral processing technology, ensuring efficient particle separation based on their magnetic properties. The article examines the issues arising from the use of traditional magnetic deslimers under elevated slurry temperatures. One of the main drawbacks of existing methods is the decrease in magnetite density as temperature rises, which impairs separation quality and leads to losses of the valuable product. The article explores the physical aspects of this problem and proposes methods for its mitigation, including control of the magnetic field strength to optimize flocculation. It also considers the possibility of upgrading deslimers by replacing permanent magnets with electromagnets, enabling more precise process control. Magnetic field modeling with ANSYS Maxwell software confirms the effectiveness of the proposed solutions. The work’s primary focus is the development of a hybrid intelligent control system for the desliming process. The proposed system consists of three control loops: water supply, electromagnet excitation current, and desliming discharge. Each loop is managed by proportional–integral–derivative regulators, which are automatically adjusted based on data regarding changing slurry parameters and external conditions. Applying these methods can significantly improve the quality of the iron ore concentrate, increase the iron content in the product, reduce losses in tailings, and ensure stable equipment operation under varying environmental conditions.

Keywords: magnetic desliming, flocculator, automated control system, PID controller, iron ore processing, optimal control