Selecting parameters of feed control algorithm for wet autogenous mill for grinding iron ore

Authors: Osipova N. V.

The main problems connected with control of grinding machines in closed-loop operation with classifiers are listed. The known solutions in automatic control over mill feed are reviewed. The main algorithms for milling machine automation are the optimizing control and proportional control with hardness observance. The methods to control the mill fill are briefly described. The basic requirements of the mill fill stabilization improvement are formulated. The mathematical description is given for the system components, including an apron feeder motor, a conveyor, a wet autogenous mill, a classifier and a washing drum. The estimates of the model equation coefficients, their mathematical expectations and the standard deviations are presented. The optimizing control of milling is set as an objective, and the method of dynamic programming by Bellman is chosen to this effect. The integrated control criterion and the weight number calculation procedure are described. The computation algorithm is presented for the optimized command variable—feeder drive brush voltage which governs the shaft speed and, consequently, the ore flow rate to the conveyor. Control is a function of the mill feed indirectly monitored by the readings of active component wattage signal at vibration frequency of the ore load center and by the ground product flow rate determined from the classifier motor current. The system control optimization is modeled in Matlab with simulation of feed of ore having different physical and mechanical properties by means of varying the coefficients in the model of dynamic processes in the mill.

Keywords: apron feeder, mill, classifier, finished size yield, optimal control, integrated criterion, dynamic programming method by Bellman, Matlab, normal distribution law.
For citation:

Osipova N. V. Selecting parameters of feed control algorithm for wet autogenous mill for grinding iron ore. MIAB. Mining Inf. Anal. Bull. 2021;(10):146-156. [In Russ]. DOI: 10.25018/0236_1493_2021_10_0_146.

Issue number: 10
Year: 2021
Page number: 146-156
ISBN: 0236-1493
UDK: 681.513.5
DOI: 10.25018/0236_1493_2021_10_0_146
Article receipt date: 15.12.2020
Date of review receipt: 21.01.2021
Date of the editorial board′s decision on the article′s publishing: 10.09.2021
About authors:

N.V. Osipova, Cand. Sci. (Eng.), Assistant Professor, e-mail:, National University of Science and Technology «MISiS», 119049, Moscow, Russia.


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