XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 3753
achievement of stabilised grinding. Furthermore, by track-
ing the mill acoustic emission, the study has shown the
possibility of distinguishing the change in the properties of
the feed ore (hardness and size distribution) in the grinding
mill in real-time. The implication of this will mean that the
fluctuations caused by feed ore heterogeneity in AG/SAG
mill can be detected, as a pathway for mitigating the mill
disturbances, providing consistent operating conditions,
and optimising the mill performance.
RECOMMENDATIONS
1. The experiments were mostly conducted in a
30 cm diameter laboratory-scale AG/SAG mill. To
improve the collision-energy spectrum inside the
AG/SAG mill, a similar laboratory AG/SAG mill
with an increased diameter of not less than 1 m is
recommended.
2. The study was mostly performed under batch
operating conditions, which is not typical of an
industrial mill. Further studies involving a con-
tinuous mode of operation should be carried out
to ascertain the implication of industrial AG/SAG
mill conditions.
ACKNOWLEDGMENTS
This research has been supported by the SA Government
through the PRIF RCP Industry Consortium. The authors
also acknowledge the support from Future Industries
Institute (FII), University of South Australia, Australia.
APPENDIX
A summary of the entire study is presented in Table 1, with
further details available in the thesis (Owusu, 2022)
REFERENCES
Almond, D. and Valderrama, W., 2004. Performance
enhancement tools for grinding mills, International
Platinum Conference ‘Platinum Adding Value’, The
South African Institute of Mining and Metallurgy,
pp. 103–110.
Amelunxen, P., 2002. The application of the SAG Power
Index to ore body hardness characterization for the
design and optimization of autogenous grinding cir-
cuits. Master of Engineering Thesis, McGill University.
Amelunxen, P., Berrios, P. and Rodriguez, E., 2014. The
SAG grindability index test. Minerals Engineering, 55:
42–51.
Ballantyne, G., Powell, M. and Tiang, M., 2012. Proportion
of energy attributable to comminution, Proceedings
of the 11th Australasian Institute of Mining and
Metallurgy Mill Operator’s Conference, pp. 25–30.
Das, S.P., Das, D.P., Behera, S.K. and Mishra, B.K., 2011.
Interpretation of mill vibration signal via wireless sens-
ing. Minerals Engineering, 24(3–4): 245–251.
Hodouin, D., 2011. Methods for automatic control, obser-
vation, and optimization in mineral processing plants.
Journal of Process Control, 21(2): 211–225.
Hodouin, D., Jämsä-Jounela, S.-L., Carvalho, M. and
Bergh, L., 2001. State of the art and challenges in min-
eral processing control. Control Engineering Practice,
9(9): 995–1005.
Hosseini, P., Martins, S., Martin, T., Radziszewski, P. and
Boyer, F.-R., 2011. Acoustic emissions simulation
of tumbling mills using charge dynamics. Minerals
Engineering, 24(13): 1440–1447.
Li, Y., Bao, J., Yu, A. and Yang, R., 2021. ANN predic-
tion of particle flow characteristics in a drum based
on synthetic acoustic signals from DEM simulations.
Chemical Engineering Science, 246: 117012.
Morrell, S., 2003. The influence of feed size on autogenous
and semi-autogenous grinding and the role of blast-
ing in its manipulation, XXII International Mineral
Processing Congress, Cape Town, South Africa.
Morrell, S. and Valery, W., 2001. Influence of feed size on
AG/SAG mill performance. SAG2001, Vancouver,
BC, Canada: 203–214.
Nayak, D.K., Das, D., Behera, S. and Das, S., 2018.
Wavelet-based Classification of Mineral Hardness by
Vibration Signal Processing of a Ball Mill, International
Conference on Recent Trends In Computational
Engineering and Technologies (ICTRCET),
pp. 744–749.
Nayak, D.K., Das, D.P., Behera, S.K. and Das, S.P., 2020.
Monitoring the fill level of a ball mill using vibra-
tion sensing and artificial neural network. Neural
Computing and Applications, 32(5): 1501–1511.
Owusu, K., Greet, C., Skinner, W. and Asamoah, R.,
2021a. Influence of lifter height on mill acoustics and
performance, Proceedings of the International Future
Mining Conference, Online, pp. 6–8.
Owusu, K., Karageorgos, J., Greet, C., Zanin, M.
and Skinner, W., 2020a. Non-Contact Acoustic
and Vibration Sensors in Autogenous and Semi-
Autogenous (AG/SAG) Mills: A Brief Review, XXX
International Mining Processing Congress, South
Africa, pp. 3292–3304.
achievement of stabilised grinding. Furthermore, by track-
ing the mill acoustic emission, the study has shown the
possibility of distinguishing the change in the properties of
the feed ore (hardness and size distribution) in the grinding
mill in real-time. The implication of this will mean that the
fluctuations caused by feed ore heterogeneity in AG/SAG
mill can be detected, as a pathway for mitigating the mill
disturbances, providing consistent operating conditions,
and optimising the mill performance.
RECOMMENDATIONS
1. The experiments were mostly conducted in a
30 cm diameter laboratory-scale AG/SAG mill. To
improve the collision-energy spectrum inside the
AG/SAG mill, a similar laboratory AG/SAG mill
with an increased diameter of not less than 1 m is
recommended.
2. The study was mostly performed under batch
operating conditions, which is not typical of an
industrial mill. Further studies involving a con-
tinuous mode of operation should be carried out
to ascertain the implication of industrial AG/SAG
mill conditions.
ACKNOWLEDGMENTS
This research has been supported by the SA Government
through the PRIF RCP Industry Consortium. The authors
also acknowledge the support from Future Industries
Institute (FII), University of South Australia, Australia.
APPENDIX
A summary of the entire study is presented in Table 1, with
further details available in the thesis (Owusu, 2022)
REFERENCES
Almond, D. and Valderrama, W., 2004. Performance
enhancement tools for grinding mills, International
Platinum Conference ‘Platinum Adding Value’, The
South African Institute of Mining and Metallurgy,
pp. 103–110.
Amelunxen, P., 2002. The application of the SAG Power
Index to ore body hardness characterization for the
design and optimization of autogenous grinding cir-
cuits. Master of Engineering Thesis, McGill University.
Amelunxen, P., Berrios, P. and Rodriguez, E., 2014. The
SAG grindability index test. Minerals Engineering, 55:
42–51.
Ballantyne, G., Powell, M. and Tiang, M., 2012. Proportion
of energy attributable to comminution, Proceedings
of the 11th Australasian Institute of Mining and
Metallurgy Mill Operator’s Conference, pp. 25–30.
Das, S.P., Das, D.P., Behera, S.K. and Mishra, B.K., 2011.
Interpretation of mill vibration signal via wireless sens-
ing. Minerals Engineering, 24(3–4): 245–251.
Hodouin, D., 2011. Methods for automatic control, obser-
vation, and optimization in mineral processing plants.
Journal of Process Control, 21(2): 211–225.
Hodouin, D., Jämsä-Jounela, S.-L., Carvalho, M. and
Bergh, L., 2001. State of the art and challenges in min-
eral processing control. Control Engineering Practice,
9(9): 995–1005.
Hosseini, P., Martins, S., Martin, T., Radziszewski, P. and
Boyer, F.-R., 2011. Acoustic emissions simulation
of tumbling mills using charge dynamics. Minerals
Engineering, 24(13): 1440–1447.
Li, Y., Bao, J., Yu, A. and Yang, R., 2021. ANN predic-
tion of particle flow characteristics in a drum based
on synthetic acoustic signals from DEM simulations.
Chemical Engineering Science, 246: 117012.
Morrell, S., 2003. The influence of feed size on autogenous
and semi-autogenous grinding and the role of blast-
ing in its manipulation, XXII International Mineral
Processing Congress, Cape Town, South Africa.
Morrell, S. and Valery, W., 2001. Influence of feed size on
AG/SAG mill performance. SAG2001, Vancouver,
BC, Canada: 203–214.
Nayak, D.K., Das, D., Behera, S. and Das, S., 2018.
Wavelet-based Classification of Mineral Hardness by
Vibration Signal Processing of a Ball Mill, International
Conference on Recent Trends In Computational
Engineering and Technologies (ICTRCET),
pp. 744–749.
Nayak, D.K., Das, D.P., Behera, S.K. and Das, S.P., 2020.
Monitoring the fill level of a ball mill using vibra-
tion sensing and artificial neural network. Neural
Computing and Applications, 32(5): 1501–1511.
Owusu, K., Greet, C., Skinner, W. and Asamoah, R.,
2021a. Influence of lifter height on mill acoustics and
performance, Proceedings of the International Future
Mining Conference, Online, pp. 6–8.
Owusu, K., Karageorgos, J., Greet, C., Zanin, M.
and Skinner, W., 2020a. Non-Contact Acoustic
and Vibration Sensors in Autogenous and Semi-
Autogenous (AG/SAG) Mills: A Brief Review, XXX
International Mining Processing Congress, South
Africa, pp. 3292–3304.