System analysis of design features of air centrifugal classifiers and prospects for their improvement
A. Khrutskyi¹, https://orcid.org/0000-0002-9332-1748
M. Franuzo¹ https://orcid.org/0009-0003-4354-0875
¹Kryvyi Rih National University, Kryvyi Rih, Ukraine
Coll.res.pap.nat.min.univ. 2025, 82:183-195
Full text (PDF)
https://doi.org/10.33271/crpnmu/82.183
ABSTRACT
Purpose. To conduct a systematic analysis and generalization of the structural features of dynamic centrifugal air classifiers in order to identify typical and rare parameters, interrelations, and patterns of their geometric and functional evolution, with the aim of developing recommendations for the design, modernization, and selection of equipment for the classification of fine and ultrafine materials.
The methods. Sixty-eight designs described by ten parameters were studied. Principal Component Analysis (PCA) was applied for dimensionality reduction and visualization, and DBSCAN clustering was used to identify groups. An evolution tree of the working element – the air flow – was constructed.
Findings. A systematic analysis and generalization of the structural features of dynamic centrifugal air classifiers were carried out. It was determined that the most common designs have: a vertical axis (92.6%), a cylindrical housing (86.8%), and dynamic blades (66%). Typical combinations account for ≤16% of models, indicating significant diversity. Two principal components explain 71.04% of the variance. DBSCAN identified 4 groups and one atypical design. Correlations were found: air circulation is always accompanied by flat rotating blades; horizontal models only have centrifugal wheels or none; static designs have no additional air supply. Two promising designs were identified – an existing one (snail-shaped housing, vertical axis) and a hypothetical one (snail-shaped housing, horizontal axis). The evolutionary tree illustrates the development of the working unit of centrifugal air separators.
The originality. For the first time, a comprehensive multidimensional analysis of classifiers was performed using PCA and DBSCAN, systematizing designs and patterns of their evolution, and proposing a development tree of the working element.
Practical implementation. Optimization of designs can improve efficiency, reduce energy consumption and wear, integrate recommendations into CAD/CAM systems, and serve as a basis for CFD modeling.
Keywords: mineral beneficiation, air centrifugal classifiers, parameters of centrifugal classifiers, frequency distribution of parameters, dimensionality reduction, clustering, systematic analysis, evolution tree, air flow.
References
1. Abohelwa, M., Benker, B., Javadi, M., Wollmann, A., & Weber, A. P. (2023). Limitation in the Performance of Fine Powder Separation in a Turbo Air Classifier. Processes, 11(10), 2817. https://doi.org/10.3390/pr11102817
2. Abohelwa, M., Wollmann, A., Benker, B., Plack, A., Javadi, M., & Weber, A. P. (2024). Size Classification and Material Sorting of Fine Powders with a Deflector Wheel Air Classifier and an Electrostatic Separator. Powders, 3(4), 550–573. https://doi.org/10.3390/powders3040029
3. Adamčíka M., Svěráka T. & Peciar P. (2017). Parameters Affecting Forced Vortex Formation in Blade Passageway of a Dynamic Air Classifier. Acta Polytechnica, 57(5):304, 304–315. https://doi.org/10.14311/AP.2017.57.0304
4. Barimani M., Green S. & Rogak S. (2018). Particulate concentration distribution in centrifugal air classifiers. Minerals Engineering, 126, 44–51. https://doi.org/10.1016/j.mineng.2018.06.007
5. Betz, M., Nirschl, H., & Gleiss, M. (2022). Development of Prediction Models for Pressure Loss and Classification Efficiency in Classifiers. Processes, 10(4), 627. https://doi.org/10.3390/pr10040627
6. Chu K.W., Wang B., Xu D.L., Chen Y.X., Yu A.B. Chu K., Wang B., Xu D., Chen Y. & Yu A. (2011). CFD–DEM simulation of the gas–solid flow in a cyclone separator. Chemical Engineering Science, 66, 834–847. https://doi.org/10.1016/j.ces.2010.11.026
7. Esmaeilpoura M., Mohebbia A. & Ghalandarib V.. (2023). CFD simulation and optimization of an industrial cement gas-solid air classifier. Particuology,89(2). https://doi.org/10.1016/j.partic.2023.10.011
8. Eswaraiah C., Narayanan S.S. & Jayanti S. (2008). A reduced efficiency approach-based process model for a circulating air classifier. Chemical Engineering and Processing: Process Intensification, 47, 1887–1900. https://doi.org/10.1016/j.cep.2007.10.016
9. Guizani R., Mhiri H. & Bournot P. (2014). CFD study of the effect of rotation speed on dynamic air separator flow characteristics and pressure drop. Conference: 2014 5th International Renewable Energy Congress (IREC), March. https://doi.org/10.1109/IREC.2014.6826939
10. Gyrotor Air Classifier System. Technical specification. (n.d.). Metso Corporation https://www.metso.com/globalassets/saleshub/documents---episerver/gyrotor-air-classifier-system-technical-data-sheet-en.pdf?r=3
11. Ho, N. X., Dinh, H. T., Dau, N. T., & Nguyen, B. H. (2024). A Numerical Study on the Flow Field and Classification Performance of an Industrial-Scale Micron Air Classifier under Various Outlet Mass Airflow Rates. Processes, 12(9), 2035. https://doi.org/10.3390/pr12092035
12. Ho, N. X., Dinh, H. T., & Dau, N. T. (2025). Numerical Study to Optimize the Operating Parameters of a Real-Sized Industrial-Scale Micron Air Classifier Used for Manufacturing Fine Quartz Powder and a Comparison with the Prototype Model. Processes, 13(1), 106. https://doi.org/10.3390/pr13010106
13. Klumpar I.V., Currier F.N. & Ring T.A. (1986). Air classifiers. Chemical engineering,March 3, 77–92.
14. Salman F., & Fauziah. (2023). Comparison Analysis of K-Means and DBSCAN Algorithms for Improving Budget Absorption Efficiency in EIS. Brilliance: Research of Artificial Intelligence, 3(2), 378–383. https://doi.org/10.47709/brilliance.v3i2.3373
15. Shapiro M. & Galperin V. (2004). Air classification of solid particles. Chemical Engineering and Processing – Process Intensification 44(2),279–285. https://doi.org/10.1016/j.cep.2004.02.022
16. Shpakovsky N. (2016). Tree of Technology Evolution. CreateSpace Independent Publishing Platform.
17. Sun Z., Sun G., Liu J., & Yang X. (2017). CFD simulation and optimization of the flow field in horizontal turbo air classifiers. Advanced Powder Technology, 28(6). https://doi.org/10.1016/j.apt.2017.03.016
18. Sun Z., Liang L., Liu C., Zhu Y., Zhang L., & Yang G. (2021). CFD simulation and performance optimization of a new horizontal turbo air classifier. Advanced Powder Technology, 32, 977–986. https://doi.org/10.1016/j.apt.2021.01.041
19. Wani A.A. (2025). Comprehensive review of dimensionality reduction algorithms: challenges, limitations, and innovative solutions. PeerJ Computer Science 11:e3025 https://doi.org/10.7717/peerj-cs.3025].
20. Wang Z., Yang H., Sun Z., Yao Y., & Yang G. (2023). Structure optimization of rotor cage blades for turbo air classifier based on entropy production analysis. Advanced Powder Technology, 34(8), 104103. https://doi.org/10.1016/j.apt.2023.104103
21. Zeng, Y., Zhang, S., Zhou, Y., & Li, M. (2020). Numerical Simulation of a Flow Field in a Turbo Air Classifier and Optimization of the Process Parameters. Processes, 8(2), 237. https://doi.org/10.3390/pr8020237
22. Zeng, Y., Huang, B., Qin, D., Zhou, S., & Li, M. (2022). Numerical and Experiment Investigation on Novel Guide Vane Structures of Turbo Air Classifier. Processes, 10(5), 844. https://doi.org/10.3390/pr10050844