MODELLING THE FLOTATION PROCESS AS A SET OF PARALLEL FIRST-ORDER APERIODIC LINKS
Abstract and keywords
Abstract:
Flotation is a key technological process in mineral beneficiation, based on separating coal and ore particles by their wettability. The efficiency of flotation process control often determines the economic viability of mining and processing plants. The interest of a number of researchers in automating the flotation process has led to the emergence of various approaches to considering the flotation process as a separate control object. This paper proposes a model describing flotation as a set of parallel first-order aperiodic links, a control of consuming flotation reagents taking into account different particle size classes of the valuable component and waste rock. The article also describes applying the Simoyu method for parametric identification of control system components and assesses the adequacy of the developed model using the coefficient of determination and the Fisher criterion.

Keywords:
flotation, automatic control system, the Simoyu method, flotation process modelling, flotation kinetics
References

1. Liu X., Aldrich C. Flotation Froth Image Recognition Using Vision Transformers. IFAC-PapersOnLine. 2023;56(2):2329-2334.

2. Massinaei M., Jahedsaravani A., Mohseni H. Recognition of Process Conditions of a Coal Column Flotation Circuit Using Computer Vision and Machine Learning. International Journal of Coal Preparation and Utilization. 2022;42(7): 2204-2218.

3. Tsvetichanin L, et al. Effect of Galena Grain Size on Flotation Kinetics. Journal of Mining Science. 2015;51(3):151-155.

4. Beloglazov I.N. Equation of Flotation Process Kinetics. Journal of Mining Institute. 2008;177: 128-131.

5. Quintanilla P., Neethling S.J., Brito-Parada P.R. Modelling for Froth Flotation Control: A Review. Minerals Engineering. 2021;162:106718.

6. Fedotov PK, et al. Study of Gold Ore Processing by Flotation Methods. Earth Sciences and Subsoil Use. 2022;45(2):162-171.

7. Sun B, et al. An Integrated Multi-Mode Model of Froth Flotation Cell Based on Fusion of Flotation Kinetics and Froth Image Features. Minerals Engineering. 2021;172:107169.

8. Bhondayi C. Flotation Froth Phase Bubble Size Measurement. Mineral Processing and Extractive Metallurgy Review. 2022;43(2):251-273.

9. Ignatkina VA, et al. A Kinetic Study of Reagent Floatation to Improve the Flotation Contrast of Sulfide Minerals. Non-Ferrous Metals Journal. 2023;10:15-22.

10. Achaye I., Wiese J., McFadzean B. Effect of Mineral Particle Size on Froth Stability. Mineral Processing and Extractive Metallurgy. 2021;130(3):253-261.

11. Jahedsaravani A., Massinaei M., Marhaban M.H. An Image Segmentation Algorithm for Measurement of Flotation Froth Bubble Size Distributions. Measurement. 2017;111:29-37.

12. Gharehchobogh BK, et al. Flotation Froth Image Segmentation Using Mask R-CNN. Minerals Engineering. 2023;192:107959.

13. Romashev AO, Kalmykova TD. Identification of Kinetic Dependencies in Order to Optimize the Flotation Enrichment Process. Herald of the Kola Science Centre of RAS. 2019;4(11):62-68.

14. Simoyu MP. Determination of Transfer Function Coefficients of Linearized Links and Automatic Control Systems. Automation and Remote Control. 1957;8(6):514-528.

15. Ran J, et al. Effects of Particle Size on Flotation Performance in the Separation of Copper, Gold and Lead. Powder Technology. 2019;344:654-664.

16. Şimşek S, et al. Application of Flotation Kinetics Models to Chalcopyrite Flotation: Determination of Optimum Flotation Times. Journal of Dispersion Science and Technology. 202445(14):1-11.

17. Virstyuk A.Yu., Mishina V.S. Application of Regression Analysis to Evaluate the Efficiency of Oil Wells Operating with the Paraffin Oil. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering. 2020;331(1):117-124.

18. Dong H, et al. Flotation Equipment Automation and Intelligent Froth Feature Extraction in Flotation Process: A Review. Reviews in Chemical Engineering. 2025;41(3):225-239.

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