Journals →  CIS Iron and Steel Review →  2020 →  #1 →  Back

Raw Materials and Mineral Processing
ArticleName Evaluation of bulk material behavior control method in technological units using DEM. Part 1
DOI 10.17580/cisisr.2020.01.01
ArticleAuthor A. V. Boikov, R. V. Savelyev, V. A. Payor, O. O. Erokhina

St. Petersburg Mining University (St. Petersburg, Russia):

A. V. Boikov, Ph.D., Assistant Professor, Dept. of Automation of Technological Processes and Production, e-mail:
R. V. Savelyev, Student
V. A. Payor, Student
O. O. Erokhina, Student


Nowadays pelletizing drums are widely used in the steel industry. These units are characterized by high endurance and low cost of maintenance. However, use and control of these units in the process of coarsening have a number of issues. For most of the cases pelletizing drums are “black box” and control accuracy can not be estimated exactly. It is explained by low existing theoretical basis of this production process. Particularly it is tied up with the variability of the bulk materials (charges) parameters supplied to the unit. Overcome of this issues can be reached with development of intelligent control systems for drum pelletizing machines. Main requirement for such systems is possibility to level or consider the effect of charges properties variability in control. However, it is necessary to study the behavior of bulk materials inside the units. Visual assessment of pelletization does not allow to evaluate the ongoing physical processes. Development of mathematical and numerical models can help studying the process and take a lot of parameters into account including charges properties and even interaction with water. But the adequacy of the resulting models also has to be clarified using physical devices to record or capture bulk materials behavior inside the units. This research proposes a DEM simulation test of the concept for bulk material behavior control through the recognition of the mixture movement fragments using special capsules. This part is dedicated to the simulation model set up and extracting the particles trajectories for further processing.

keywords DEM-modeling, pelletizing drums, classification of motion modes, neural networks, bulk materials

1. Adetayo A. A., Litster J. D., Desai M. The effect of process parameters on drum granulation of fertilizers with broad size distributors. Chemical engineering science. 1993. Vol. 48. No. 23. pp. 3951–3961.
2. Yuzov O. V., Petrakova T. M., Ilyichev I. P., Yuzov S. G. Tendencies of variation of the production and economic parameters for the Russian metallurgical works. CIS Iron and Steel Review. 2016. Vol. 11. pp. 16–22.
3. Shinkin V. N. Mathematical model of technological parameters’ calculation of flanging press and the formation criterion of corrugation defect of steel sheet’s edge. CIS Iron and Steel Review. 2017. Vol. 13. pp. 44–47.
4. Korotich V. I. Theoretical Foundations of Pelletizing Iron Ore Materials. Moscow. Metallurgiya. 1966. 152 p.
5. Shapovalov A. N., Ovchinnikova E. V., Maistrenko N. A. Effect of the type of magnesia materials on the sintering process indicators at “Ural Steel” JSC. Chernye metally. 2018. No. 11. pp. 38–42.
6. Litster J. D., Waters A. G. Kinetics of iron ore sinter feed granulation. Powder Technology. 1990. Vol. 62. No. 2. pp. 125–134.
7. Terentyev D. V., Ogarkov N. N., Platov S. I., Nekit V. A. Increase of tightness of the cone charging apparatus for blast furnaces. Chernye metally. 2017. No. 6. pp. 19–24.
8. Fernández-González D. et al. Iron ore sintering: Raw materials and granulation. Mineral Processing and Extractive Metallurgy Review. 2017. Vol. 38. No. 1. pp. 36–46.
9. Legrand A. C. et al. Machine vision systems in the metallurgy industry. Journal of Electronic Imaging. 2001. Vol. 10. No. 1. pp. 274–283.
10. Sizyakov V. M., Vlasov A. A., Bazhin V. Yu. Strategic tasks of Russian metallurgical complex. Tsvetnye metally. 2016. No. 1. pp. 32–38.
11. Sha Y., Chao Y., Guo Y. Analysis of acoustic signal and BP neural network-based recognition of level of coal in ball mill. Journal — Northeastern University Natural Science. 2006. Vol. 27. No. 12. pp. 1319.
1 2. Ibrahim A. A. S. M. Development of a Standing Wave Tube Rotary Ultrasonic Piezoelectric Motor for LiDAR Systems : dissertation. University of Toronto (Canada), 2018.
13. Post J., Groen M., Klaseboer G. Physical model based digital twins in manufacturing processes. Form. Technol. Forum. 2017. Vol. 2017. pp. 6.
14. Xiang F., Zhi Z., Jiang G. Z. Digital Twins technology and its data fusion in iron and steel product life cycle. 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). IEEE. 2018. pp. 1–5.
15. Gospodarikov A. P., Vykhodtsev Y. N., Zatsepin M. A. Mathematical modeling of seismic explosion waves impact on rock mass with a working. Journal of Mining Institute. 2017. Vol. 226. pp. 405–411.
16. Lugovskoy N. Yu., Utkov V. A. Research of pelletizing process for poli-dispersed and fine-dispersed sintering charges. Tekhnika i tekgnologiya. 2013. No. 2. pp. 30–33.
17. Coetzee C. J., Els D. N. J. Calibration of granular material parameters for DEM modeling and numerical verification by blade–granular material interaction. Journal of Terramechanics. 2009. Vol. 46. No. 1. pp. 15–26.
18. Boikov A. V., Savelev R. V., Payor V. A. DEM Calibration Approach: design of experiment. Journal of Physics: Conference Series. 2018. Vol. 1015. No. 3. pp. 032017.
19. Boikov A. V., Savelev R. V., Payor V. A., Erokhina O. O. The control method concept of bulk material behaviour in the pelletizing drum for improving the results of DEM-modeling. CIS Iron and Steel Review. 2019. Vol. 17. pp. 10–13.
20. Kotsiantis S. B., Zaharakis I. D., Pintelas P. E. Machine learning: a review of classification and combining techniques. Artificial Intelligence Review. 2006. Vol. 26. No. 3. pp. 159–190.
21. Homburg H. et al. A Benchmark Dataset for Audio Classification and Clustering. ISMIR. 2005. Vol. 2005. pp. 528–531.
22. Mannini A., Sabatini A. M. Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors. 2010. Vol. 10. No. 2. pp. 1154–1175.
23. Soda R. et al. Analysis of granules behavior in continuous drum mixer by DEM. ISIJ international. 2009. Vol. 49. No. 5. pp. 645–649.

Full content Evaluation of bulk material behavior control method in technological units using DEM. Part 1