Increasing Energy Efficiency and Productivity of the Comminution Process in Tumbling Mills by Indirect Measurements of Internal Dynamics—An Overview

Mateusz Góralczyk,Radosław Zimroz,Pavlo Krot,Szymon Ogonowski

doi:10.3390/en13246735

Abstract

Tumbling mills have been widely implemented in many industrial sectors for the grinding of bulk materials. They have been used for decades in the production of fines and in the final stages of ore comminution, where optimal levels for the enrichment particles’ sizes are obtained. Even though these ubiquitous machines of relatively simple construction have been subjected to extensive studies, the industry still struggles with very low energy efficiency of the comminution process. Moreover, obtaining an optimal size for the grinding product particles is crucial for the effectiveness of the following processes and waste production reduction. New, innovative processing methods and machines are being developed to tackle the problem; however, tumbling mills are still most commonly used in all ranges of the industry. Since heavy equipment retrofitting is the most costly approach, process optimization with dedicated models and control systems is the most preferable solution for energy consumption reduction. While the classic technological measurements in mineral processing are well adopted by the industry, nowadays research focuses on new methods of the mill’s internal dynamics analysis and control. This paper presents a retrospective overview of the existing models of internal load motion, an overview of the innovations in process control, and some recent research and industrial approaches from the energy consumption reduction point of view.

Highlights

Up to 70% of the costs of materials size-reduction operations fall on the rock particle’s size reduction from 30–50 mm to 20–50 microns [1,2]
Circuit control systems are being implemented widely in order to control the feed rate and run the mill at the optimum efficiency level, which means, depending on the specific objective function: maximizing output, minimizing energy consumption, or providing an accurate particle size distribution for the further stages of mineral processing. Examples of such systems are: Grinding Circuit Control (GCC) solutions, used in Canadian processing plants [51] (e.g., Strathcona Mill, Raglan Mill, Eland Mill), MillVis system [52] developed by AMEplus and KGHM Polska Miedź S.A. for all of the Polish milling sections, where rod-primary-mills and ball-mills for regrinding are used in Divisions of Concentrators (O/ZWR Lubin, O/ZWR Polkowice, O/ZWR Rudna), or an intelligent optimal-setting control (IOSC)
The possible sources of informative data to be acquired on a tumbling mill or in its direct neighborhood, which were used by scientists and constitute an input for the process control systems used in the raw materials industry are acoustic emission in the surrounding of the mill, vibrations measured on the shell, digital records of the output ore stream, current and other signals to be acquired from the motor control unit

Summary

Introduction

Up to 70% of the costs of materials size-reduction operations fall on the rock particle’s size reduction from 30–50 mm to 20–50 microns [1,2]. The instrumentation for signals’ measurement and processing, in combination with process parameters’ control—based on existing automation systems—is needed in such cases Such control needs in-depth knowledge of intra-mill load dynamics and its stages estimation by the different channels—electric motors current or power, vibration, and acoustic emission inside the equipment, or spatial sounds and mechanical torques of multi-motor drives. The realization of such an approach can only be based on advanced instrumentation—including wireless sensors, and dynamical models (both analytical and discrete elements) and signal processing techniques. This paper intends to represent a comprehensive retrospective overview of existing methods and recent trends in ball mills modeling and control aimed at increasing their energy efficiency and productivity by indirect measurements of internal dynamics with signals of different physical nature

State-of-the-Art in Mill Control

Methods of Measurements and Optimization of Tumbling Mills

Mathematical Modelling

Discrete Element Method

Findings

Discussion and Conclusions