Computer-integrated technology for classifying crushed ore by particle size
DOI:
https://doi.org/10.33216/1998-7927-2026-304-6-195-203Keywords:
ore, classification, model, control, system, automation, energy efficiencyAbstract
A computer-based integrated technology for classifying crushed ore by particle size has been developed, based on model-predictive control of a fine wet screening screen for ore pulp. It has been demonstrated that the use of an electromagnetic drive offers greater flexibility in adjusting vibration parameters and, consequently, improves the results of the screening process. Simulation results showed that the relationship between the vibration parameters of the screen and the ore load on it, the position of the solenoid plunger of the electromagnetic drive, and the magnitude of the electric current is nonlinear. Taking into account these characteristics of the controlled system and the need to simultaneously manipulate a set of variables subject to technological constraints on their values, model-predictive control (MPC) was selected to establish effective control of the fine wet screening process of ore. This approach has two distinctive features: the use of a dynamic model of the controlled process for online prediction and the application of an optimization procedure to find the optimal control trajectory in real time. For the first time, a model-based predictive control system for the fine wet screening process of ore slurry has been proposed, which differs from existing systems in that the mass flow rate of the ore slurry at the screen feed and its viscosity are used as controlled variables, the amplitude and frequency of the two electromagnetic drives of the screen vibrations, and the screening efficiency parameter is used as the controlled variable, which is determined using an intelligent recognition procedure based on indirect data. Analysis of the results of modeling the model-predictive control system for the fine wet screening of ore shows that the application of the proposed technology allows maintaining screening efficiency at a nominal set value under conditions of varying physical-mechanical and chemical-mineralogical characteristics of the ore and equipment condition, reducing the duration of transient processes in the system by 25–30% compared to existing local systems based on proportional-integral-derivative (PID) controllers, and thereby reducing the specific energy consumption of the process by 4–7%.
References
1. Morkun V., Morkun, N., Tron V. Distributed closed-loop control formation for technological line of iron ore raw materials beneficiation. Metallurgical and Mining Industry. 2015. Vol.7. P. 16–19. URL: http://ds.knu.edu.ua/jspui/bitstream/123456789/2264/1/Morkun%20V.%20Distributed%20closed-loop%20control%20formation%20for%20technological%20line%20of%20iron%20ore.pdf
2. Costa P. K., Vaz P. N., Calixto M. F., Torga D. S., Bergerman M. G., Delboni H., Jr. Performance Assessments of an Advanced Control System in an Iron Ore Industrial Grinding Circuit. Minerals. 2025. Vol 15, P. 1172. DOI: https://doi.org/10.3390/min15111172.
3. Meyer E.J., Olivier M.C., Matumba L., Craig I.K. Model predictive control simulation, implementation and performance assessment of a coal comminution circuit. Minerals Engineering. 2019. Vol. 144, 106024. DOI: https://doi.org/10.1016/j.mineng.2019.106024.
4. Di Cairano S., Bemporad A., Kolmanovsky I., Hrovat D. Model predictive control of nonlinear mechatronic systems: an application to a magnetically actuated mass spring damper. IFAC Proceedings Volumes. 2006. Vol. 39(5). P. 241-246. DOI: https://doi.org/10.3182/20060607-3-IT-3902.00044.
5. Knuth M., Ström C. Process Optimization: Innovative Vibration Monitoring Technology Packs a Punch, SPM Instrument.URL:
6. Моркун В.С., Моркун Н.В., Грищенко С.М., Грищенко Я.О. Моделювання комбінованого електромагнітного та електромеханічного приводу грохота для підвищення його ефективності. Вісник Східноукраїнського національного університету імені Володимира Даля. 2025. Вип. 1(287). C. 46-56. DOI: https://doi.org/10.33216/1998-7927-2025-287-1-46-56.
7. Simscape Block Libraries. URL: https://se.mathworks.com/help/simscape/ug/introducing-the-simscape-block-libraries.html.
8. Solenoid. https://se.mathworks.com/help/sps/ref/solenoid.html.
9. Моркун В.С., Моркун Н.В., Грищенко Я.О., Азарян А.А., Гриценко А.М. Моделювання автоматизованого керування об’єктами гірничого виробництва на основі хвильового підходу. Вісник Східноукраїнського національного університету імені Володимира Даля. 2025. Вип. 10 (296). С. 33-42. DOI: https://doi.org/10.33216/1998-7927-2025-296-10-33-42.
10. Ławryńczuk M. Input convex neural networks in nonlinear predictive control: A multi-model approach. Neurocomputing. 2022. Vol. 513(7). P. 273-293. DOI: https://doi.org/10.1016/j.neucom.2022.09.108.
11. Maciejowski J.M. Predictive control with constraints. Prentice Hall, Harlow. 2002. P. 331. ISBN 0201398230, 9780201398236.
12. Tatjewski P. Advanced control of industrial processes, structures and algorithms. Springer, London. Springer Science & Business Media. 2007. P. 332. ISBN 1846286352, 9781846286353.
13. Camacho E.F., Bordons C. Model Predictive Control. Springer, London. 1999. P. 300. ISBN 3-540-76241-8.
14. Model Predictive Control Toolbox. Design and simulate model predictive controllers.
URL: https://se.mathworks.com/products/model-predictive-control.html.
15. Paar A. The influence of particles on suspension rheology.URL:
https://wiki.anton-paar.com/en/the-influence-of-particles-on-suspension-rheology/.
16. Моркун В.С., Моркун Н.В., Поркуян О.В., Грищенко Я.О. Визначення впливу властивостей рудної пульпи на параметри п’єзокерамічного перетворювача. Вісник Східноукраїнського національного університету імені Володимира Даля. 2026. Вип. 2(300), C. 61-69. DOI: https://doi.org/10.33216/1998-7927-2026-300-2-61-69.
17. Chen B., Yan J., Mo W., Xu C., Zhang L., Tamma K.K. DEM simulation and experimental study on the screening process of elliptical vibration mechanical systems. Journal of vibroengineering. 2019. Vol. 21(8). DOI: https://doi.org/10.21595/jve.2019.19993
18. Exact Linearization Algorithm. URL: https://se.mathworks.com/help/slcontrol/ug/exact-linearization-algorithm.html
