Saint-Petersburg Mining University (Saint-Petersburg, Russia):
Vasilyeva N. V., Associate Professor, Candidate of Engineering Sciences, Vasileva_NV@pers.spmi.ru
Erokhina O. O., Student, o.o.erokhina@gmail.com

Discrete element modeling (DEM) is associated with a number of difficulties, one of which is the calibration of DEM parameters for granular materials. The values of these parameters are critical for the accuracy of the resulting model. Ores, on the other hand, may have different grain-size compositions with varying particle shapes and sizes. Establishing the appropriate values of the recovery coefficient for ores is, therefore, quite a labor-intensive task. The article provides an overview of the existing methods for measuring the recovery coefficients. The study presents new original methods for establishing the coefficient of recovery for particle-to-particle (CoRPP) and particle-to-surface (CoRPB) interactions. These are based on visual imaging of the behavior of a particle system using a high-speed camera and converting the data collected into specific coefficient values. The methods proposed have been tested in simulations based on the discrete element modeling method using experimental stands for various particle shapes and sizes. A procedure has been developed for determining CoRPB values with sufficient accuracy. A test stand has been manufactured for its specific implementation. The results of the study demonstrate the need for developing an indirect CoRPP measurement methodology. Moreover, part of the methods developed for the CoRPP may be applied with due account of the restrictions imposed. These methods may be applicable both in the mining industry and in metallurgy, pharmaceuticals, and the food industry, and may be used to design a universal stand for determining all DEM modeling parameters.

1. Mei C., Peng X., Zhou P., Zhou J., Zhou N. Simulation and optimization of furnaces and kilns for nonferrous metallurgical engineering. Berlin, Heidelberg: Springer-Verlag, 2010. 450 p.
2. Data A., Mishra B. K., Rajamani R. K. Analysis of power draw in ball mills by the discrete element method. Canadian Metallurgical Quarterly. 1999. Vol. 38, Iss. 2. pp. 133–140.
3. Sizyakov V. M., Vlasov A. A., Bazhin V. Yu. Strategy tasks of the russian metallurgical complex. Tsvetnye Metally. 2016. No. 1. pp. 32–38. DOI: 10.17580/tsm.2016.01.05.
4. Gospodarikov A. P., Vykhodtsev Y. N., Zatsepin M. A. Mathematical modeling of seismic explosion waves impact on rock mass with a working. Zapiski Gornogo Instituta. 2017. Vol. 226. pp. 405–411.
5. 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. No. 1. pp. 10–13. DOI: 10.17580/cisisr.2019.01.02.
6. Baum W. Ore characterization, process mineralogy and lab automation a roadmap for future mining. Minerals Engineering. 2014. Vol. 60. pp. 69–73.
7. Kalala J. T., Breetzke M., Moys M. H. Study of the influence of liner wear on the load behaviour of an industrial dry tumbling mill using the Discrete Element Method (DEM). International Journal of Mineral Processing. 2008. Vol. 86, Iss. 1–4. pp. 33–39.
8. Arsentyev V. А., Blekhman I. I., Blekhman L. I., Vaisberg L. А., Ivanov K. S., Krivtsov А. М. Dynamics of particles and discrete element methods as a tool of studies and optimization of natural and man-made materials processing. Obogashchenie Rud. 2010. No. 1. pp. 30–35.
9. Demidov I. V., Vaisberg L. A., Blekhman I. I. Vibrational dynamics of paramagnetic particles and processes of separation of granular materials. International Journal of Engineering Science. 2019. Vol. 141. pp. 141–156.
10. Feoktistov A. Yu., Kamenetskiy A. A., Blekhman L. I., Vasilkov V. B., Skryabin I. N., Ivanov K. S. The application of discrete element method to mining and metallurgy process modeling. Zapiski Gornogo Instituta. 2011. Vol. 192. pp. 145–149.
11. Vasilyeva N., Koteleva N., Ivanov P. Quality analysis of technological process control. International Journal for Quality Research. 2018. Vol. 12, No. 1. pp. 111–128.
12. Coetzee C. J. Calibration of the discrete element method and the effect of particle shape. Powder Technology. 2016. Vol. 297. pp. 50–70.
13. Wu C. Y. DEM simulations of die filling during pharmaceutical tableting. Particuology. 2008. Vol. 6, Iss. 6. pp. 412–418.
14. Torsten G., Katterfeld A. On the numerical calibration of discrete element models for the simulation of bulk solids. Computer Aided Chemical Engineering. 2006. Vol. 21. pp. 533–538.
15. Yan Z., Wilkinson S. K., Stitt E. H., Marigo M. Discrete element modelling (DEM) input parameters: understanding their impact on model predictions using statistical analysis. Computational Particle Mechanics. 2015. Vol. 2. pp. 283–299.
16. Do H. Q., AragÓn A. M., Schott D. L. A calibration framework for discrete element model parameters using genetic algorithms. Advanced Powder Technology. 2018. Vol. 29, Iss. 6. pp. 1393–1403.
17. Rackl M., Hanley K. J. A methodical calibration procedure for discrete element models. Powder Technology. 2017. Vol. 307. pp. 73–83.
18. Ye F., Wheeler C., Chen B., Hu J., Chen K., Chen W. Calibration and verification of DEM parameters for dynamic
particle flow conditions using a backpropagation neural network. Advanced Powder Technology. 2019. Vol. 30, Iss. 2. pp. 292–301.
19. Zhao S., Zhou X., Liu W. Discrete element simulations of direct shear tests with particle angularity effect. Granular Matter. 2015. Vol. 17, Iss. 6. pp. 793–806.
20. Al-Hashemi H. M. B., Al-Amoudi O. S. B. A review on the angle of repose of granular materials. Powder Technology. 2018. Vol. 330. pp. 397–417.
21. Frankowski P., Morgeneyer M. Calibration and validation of DEM rolling and sliding friction coefficients in angle of repose and shear measurements. AIP Conference Proceedings. 2013. Vol. 1542. pp. 851–854.
22. Zhou H., Hu Z., Chen J., Lv X., Xie N. Calibration of DEM models for irregular particles based on experimental design method and bulk experiments. Powder Technology. 2018. Vol. 332. pp. 210–223.
23. Ghodki B. M., Patel M., Namdeo R., Carpenter G. Calibration of discrete element model parameters: soybeans. Computational Particle Mechanics. 2019. Vol. 6, Iss. 1. pp. 3–10.
24. Wang L., Zhou W., Ding Zh., Li X., Zhang Ch. Experimental determination of parameter effects on the coefficient of restitution of differently shaped maize in threedimensions. Powder Technology. 2015. Vol. 284. pp. 187–194.
25. Krull F., Hesse R., Breuninger P., Antonyuk S. Impact behaviour of microparticles with microstructured surfaces: Experimental study and DEM simulation. Chemical Engineering Research and Design. 2018. Vol. 135. pp. 175–184.
26. Wang L., Wu B., Wu Z., Li R., Feng X. Experimental determination of the coefficient of restitution of particleparticle collision for frozen maize grains. Powder Technology. 2018. Vol. 338. pp. 263–273.
27. Imre B., Räbsamen S., Springman S. M. A coefficient of restitution of rock materials. Computers & Geosciences. 2008. Vol. 34, Iss. 4. pp. 339–350.
28. Hlosta J., Žurovec D., Rozbroj J., Ramírez-Gómez Á., Nečas J., Zegzulka J. Experimental determination of particle–particle restitution coefficient via double pendulum method. Chemical Engineering Research and Design. 2018. Vol. 135. pp. 222–233.