Analysis of methods for determining the granulometric composition of slurry at the output of a ball mill using ultrasonic methods and enhancing efficiency by considering changes in slurry concentration

Abstract

Ore grinding at a mining beneficiation plant, using ball mills, is a crucial stage in the production of ferrous metals. This process is resource-intensive and influences the quality of further processing. Implementing modern information technologies for decision-making automation during repairs and management helps enhance equipment utilization efficiency and reduce economic costs. Currently, there exist ultrasonic methods for investigating the granulometric composition of slurry at the output of a ball mill, as described in several scientific studies. However, despite their promise, these methods have not seen widespread use due to a number of issues that need to be addressed for further research. One such method is determining the granulometric composition of slurry at the output of a ball mill. The ultrasonic method of slurry investigation is based on analyzing the reflection and scattering of ultrasonic waves of different frequencies as they pass through the suspension. This method enables real-time data acquisition on particle size distribution, providing precise and timely control over the grinding process. Among the advantages of the method are high sensitivity to changes in granulometric composition and the ability for continuous monitoring. Implementing ultrasonic systems for slurry control facilitates optimization of ball mill operation modes, reduces energy consumption, and improves product quality. However, there are several issues limiting the widespread adoption of the ultrasonic method. Key issues include the high cost of equipment and the need for regular calibration. Additionally, measurement accuracy may be compromised by the presence of air bubbles in the slurry, variations in composition and concentration of the solid phase, and potential sensor contamination. Interpreting the obtained data requires highly skilled personnel and specialized software. Further research aims to address these technical issues, reduce system costs, and integrate with other automated process control systems.