1126 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
enables to analyze the whole plant as Suisse watch and
obtained the key operating variables that maximize the
copper production rate for the whole plant including the
tailings ponds. Awareness is improved by having the system
provide real-time visualization, operational alerts and event
frame in real time. The ease of the real-time information,
with right data in the Digital Twin Model, simplifies the
use of the historical facts by any person anywhere. The his-
torical events and data aggregation at the right degree of the
detail is available in the CLOUD to be analyzed using tools
such as Microsoft, Power BI for overall production effec-
tiveness for improved decision support at the control room,
plant and remotely. This novel strategy improves collabo-
ration and provides real-time feedback on decisions made
at all levels of the organization. This strategy allows the
operating and support team to proactively detect shifts in
process performance. It can provide structure and a means
of determining the root cause of the shifts and, if needed,
propose corrective action.
Acknowledgments. The author acknowledges the sup-
port and the participation of many companies and people
that have contributed to this topic over the years. Parts of
the document has been extracted for the author books and
papers. The mentoring of Dr. Edward Freeh, Professor Dr.
D.W. Fuerstenau, Professor Dr. Fernando Concha Arcil,
Sergio Castro, Professor Dr. John A. Herbst, Dr. Patrick
J. Kennedy have been vital. The author especially thanks
Hugo Carvajal and Freddy Retamal and team at Lumina
Copper and OSB Digital, LLC for support in creating this
technical paper.
REFERENCES
Bascur, O.A. 1991a. Integrated grinding/flotation con-
trols and management. Presented at the Copper 91/
Cobre 91 International Symposium, Ottawa, Ontario,
Canada.
Bascur, O.A. 1991b. A unified solid liquid separation frame-
work. Fluid Particle Separation Journal 4:117–122.
Bascur, O.A. 2013. A dynamic flotation framework for
performance management. In Separation Technologies
for Minerals, Coal and Earth Resources. Edited
by, C. Young and G. Luttrel. Englewood, CO: SME.
pp. 40–45.
Bascur, O.A. 2019. Chapter 2.6: Process control and
operational intelligence. In SME Mineral Processing
and Extractive Metallurgy Handbook. Edited by R.C.
Dunne, S.K. Kawatra, and C.A. Young. Englewood,
CO: SME.
Bascur, O.A. 2020. Digital Transformation in the Process
Industries: A Road Map. Boca Raton, FL: CRC Press.
Bascur, O.A., 2020, Digital Transformation in Mining
Plants, Automining 2020 Proceedings, Gecamin,
Santiago, Chile.
Bascur, O.A., and Gorrie, T. 2021. Maximizing copper
recovery using digital twins’ plant. In Proceedings
of Procemin Geomet 2021: 17th International
Conference on Mineral Processing and Geometallurgy.
Santiago, Chile: Gecamin.
Bascur, O.A., and Herbst, J.A. 1982. Dynamic of a flo-
tation cell for automatic control. Presented at IMPC
XIV, Toronto, Canada.
Bascur, O.A., and Herbst, J.A. 1986. Improved thickener
performance through the use of an extended Kalman
filter. In Design and Installation of Concentration
and Dewatering Circuits. Edited by A.L. Mular and
M.A. Anderson. Littleton, CO: SME. pp. 835–845.
Bascur, O.A., and Junge, P.A. 2011. Dynamill, Dynaflote
and Optimill [iPad software apps]. https://www.apple
.com/app-store/.
Bascur, O.A., and Soudek, A. 2019. Grinding and flo-
tation optimization using operational intelligence.
Mining, Metallurgy and Exploration 36:139–149. doi:
10.1007/s42461-018-0036-4.
Bascur, O.A., Retamal, F., and Espinosa, S. 2022. Treating
low grades ores using a digital in mining and mineral
processing plants. In Proceedings of the Copper 2022
International Conference: IIMChile. Vol. 7: Process
Optimization through Innovation, Technology,
Automation and Design. Santiago, Chile.
Bischofberger, C. and Schubert, H. 1978, Zum Einflus der
Hydrodynamik auf die Flotierbarkeit unterschiedlicher
Korngroseb. Freiber Forsch H. A594, 77–92.
Brunton, S.L., and Kutz, J.N. 2021, Data-Driven Science
and Engineering: Machine Learning, Dynamical
Systems, and Control, Cambridge, University Press,
Cambridge, United Kingdom.
Concha F., Bascur O.A. 2024. The Engineering Science
of Mineral Processing: A Fundamental and Practical
Approach, CRC Press (Taylor and Francis), Boca
Raton, FL.
Dunne, R.C., Kawatra, S.K., and Young, C.A., eds. 2019.
SME Mineral Processing and Extractive Metallurgy
Handbook. Englewood, CO: SME.
Fuerstenau, D.W. 2001. Processing challenges facing
the mineral industry, In Proceedings of VI Southern
Hemisphere Symposium on Mineral Technology, Rio
de Janeiro, Brazil, May 2001.
Fuerstenau, D.W. 1999. The Froth Flotation Century. In
Advanced in Flotation Technology, Edited by Parekh
and Miller, J.D., Littleton, CO: SME. p. 3–21.
enables to analyze the whole plant as Suisse watch and
obtained the key operating variables that maximize the
copper production rate for the whole plant including the
tailings ponds. Awareness is improved by having the system
provide real-time visualization, operational alerts and event
frame in real time. The ease of the real-time information,
with right data in the Digital Twin Model, simplifies the
use of the historical facts by any person anywhere. The his-
torical events and data aggregation at the right degree of the
detail is available in the CLOUD to be analyzed using tools
such as Microsoft, Power BI for overall production effec-
tiveness for improved decision support at the control room,
plant and remotely. This novel strategy improves collabo-
ration and provides real-time feedback on decisions made
at all levels of the organization. This strategy allows the
operating and support team to proactively detect shifts in
process performance. It can provide structure and a means
of determining the root cause of the shifts and, if needed,
propose corrective action.
Acknowledgments. The author acknowledges the sup-
port and the participation of many companies and people
that have contributed to this topic over the years. Parts of
the document has been extracted for the author books and
papers. The mentoring of Dr. Edward Freeh, Professor Dr.
D.W. Fuerstenau, Professor Dr. Fernando Concha Arcil,
Sergio Castro, Professor Dr. John A. Herbst, Dr. Patrick
J. Kennedy have been vital. The author especially thanks
Hugo Carvajal and Freddy Retamal and team at Lumina
Copper and OSB Digital, LLC for support in creating this
technical paper.
REFERENCES
Bascur, O.A. 1991a. Integrated grinding/flotation con-
trols and management. Presented at the Copper 91/
Cobre 91 International Symposium, Ottawa, Ontario,
Canada.
Bascur, O.A. 1991b. A unified solid liquid separation frame-
work. Fluid Particle Separation Journal 4:117–122.
Bascur, O.A. 2013. A dynamic flotation framework for
performance management. In Separation Technologies
for Minerals, Coal and Earth Resources. Edited
by, C. Young and G. Luttrel. Englewood, CO: SME.
pp. 40–45.
Bascur, O.A. 2019. Chapter 2.6: Process control and
operational intelligence. In SME Mineral Processing
and Extractive Metallurgy Handbook. Edited by R.C.
Dunne, S.K. Kawatra, and C.A. Young. Englewood,
CO: SME.
Bascur, O.A. 2020. Digital Transformation in the Process
Industries: A Road Map. Boca Raton, FL: CRC Press.
Bascur, O.A., 2020, Digital Transformation in Mining
Plants, Automining 2020 Proceedings, Gecamin,
Santiago, Chile.
Bascur, O.A., and Gorrie, T. 2021. Maximizing copper
recovery using digital twins’ plant. In Proceedings
of Procemin Geomet 2021: 17th International
Conference on Mineral Processing and Geometallurgy.
Santiago, Chile: Gecamin.
Bascur, O.A., and Herbst, J.A. 1982. Dynamic of a flo-
tation cell for automatic control. Presented at IMPC
XIV, Toronto, Canada.
Bascur, O.A., and Herbst, J.A. 1986. Improved thickener
performance through the use of an extended Kalman
filter. In Design and Installation of Concentration
and Dewatering Circuits. Edited by A.L. Mular and
M.A. Anderson. Littleton, CO: SME. pp. 835–845.
Bascur, O.A., and Junge, P.A. 2011. Dynamill, Dynaflote
and Optimill [iPad software apps]. https://www.apple
.com/app-store/.
Bascur, O.A., and Soudek, A. 2019. Grinding and flo-
tation optimization using operational intelligence.
Mining, Metallurgy and Exploration 36:139–149. doi:
10.1007/s42461-018-0036-4.
Bascur, O.A., Retamal, F., and Espinosa, S. 2022. Treating
low grades ores using a digital in mining and mineral
processing plants. In Proceedings of the Copper 2022
International Conference: IIMChile. Vol. 7: Process
Optimization through Innovation, Technology,
Automation and Design. Santiago, Chile.
Bischofberger, C. and Schubert, H. 1978, Zum Einflus der
Hydrodynamik auf die Flotierbarkeit unterschiedlicher
Korngroseb. Freiber Forsch H. A594, 77–92.
Brunton, S.L., and Kutz, J.N. 2021, Data-Driven Science
and Engineering: Machine Learning, Dynamical
Systems, and Control, Cambridge, University Press,
Cambridge, United Kingdom.
Concha F., Bascur O.A. 2024. The Engineering Science
of Mineral Processing: A Fundamental and Practical
Approach, CRC Press (Taylor and Francis), Boca
Raton, FL.
Dunne, R.C., Kawatra, S.K., and Young, C.A., eds. 2019.
SME Mineral Processing and Extractive Metallurgy
Handbook. Englewood, CO: SME.
Fuerstenau, D.W. 2001. Processing challenges facing
the mineral industry, In Proceedings of VI Southern
Hemisphere Symposium on Mineral Technology, Rio
de Janeiro, Brazil, May 2001.
Fuerstenau, D.W. 1999. The Froth Flotation Century. In
Advanced in Flotation Technology, Edited by Parekh
and Miller, J.D., Littleton, CO: SME. p. 3–21.