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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
ArticleAuthorData

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

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

Abstract

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
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