Predicting mill feed grind characteristics through acoustic measurements. (1st September 2021)

• Record Type: Journal Article
• Authors: Owusu, Kwaku Boateng; Karageorgos, John; Greet, Christopher; Zanin, Massimiliano; Skinner, William; Asamoah, Richmond K.
• Is Part Of: Minerals engineering. Volume 171(2021)
• Publication Date: 2021-09-01
• Subjects: Acoustic sensors -- Ore grindability -- Refractory and complex ores -- Mill feed variability -- Predictive ore grinding -- Magotteaux ball mill; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Mines and mineral resources; Periodicals; Electronic journals; 622
• DOI: 10.1016/j.mineng.2021.107099
• Languages: English
• ISSNs: 0892-6875

Abstract

Graphical abstract: Highlights: Acoustic measurements could reflect the grinding behaviour of different mill feeds. Quartz emits higher acoustic energy compared with iron ore. Acoustic response reduces as a function of increasing grind time. Different mill load pulp densities showed marginal differences in acoustic emission. Change in acoustic response relates not only to particle size but ore characteristics. Abstract: The present study investigates the propensity of predicting ore grindability characteristics and varying pulp densities through acoustic measurements on the Magotteaux ball mill. Specifically, the grinding behaviour of two different mill feeds (model quartz and iron ore) together with solid loadings (50, 57, and 67 wt% solids) were correlated against measured acoustic signals. The acoustic response analysis by root mean square (RMS) and power spectral density techniques indicated that model quartz sample emits higher energies than iron ore sample during grinding, relating to their different hardness properties. RMS analysis also showed that the noise intensities of both samples depreciate considerably as a function of increasing grind time, which corresponds well with their grind calibration curves. The selected pulp densities showed marginal differences in acoustic emission, which was reflected in their product size distribution. Results from this study further show the potential of using acoustic sensors as a proxy for real-time mill feed characteristics, mill operation monitoring and optimisation.