1. Predicting mill feed grind characteristics through acoustic measurements
- Author
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Massimiliano Zanin, Richmond K. Asamoah, Kwaku Boateng Owusu, John Karageorgos, Christopher Greet, William Skinner, Owusu, Kwaku Boateng, Karageorgos, John, Greet, Christopher, Zanin, Massimiliano, Skinner, William, and Asamoah, Richmond Komla
- Subjects
Materials science ,Calibration curve ,Mechanical Engineering ,mill feed variability ,predictive ore grinding ,Mineralogy ,General Chemistry ,Geotechnical Engineering and Engineering Geology ,Grinding ,Root mean square ,Noise ,Acoustic emission ,acoustic sensors ,Control and Systems Engineering ,Grind ,ore grindability ,Mill ,Ball mill - 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 Refereed/Peer-reviewed
- Published
- 2021