1408 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
ACKNOWLEDGMENTS
The authors would like to thank all the professionals at
Freeport McMoRan who provided data access, assistance,
and commentary on the research included in this paper.
The authors would also like to extend a special thank you
to Dr. Angelina Anani for her invaluable assistance regard-
ing discrete event simulation methods, as well as everyone
at MOSIMTEC for providing the initial exposure to the
potential and power of DES approaches to operational
management. Lastly, the authors would like to thank
Freeport McMoRan, the Society for Mining, Metallurgy
&Exploration, the University of Arizona, and the Lowell
Institute for Mineral Resources for their financial support.
REFERENCES
David, D.M., 2019. Geometallurgy. In SME Mineral
Processing &Extractive Metallurgy Handbook. 1st ed.
Edited by S.K. Kawatra, C.A. Young, and R.C. Dunne.
Englewood, CO:SME.
Koch, P.-H., Computational method and strategies for
geometallurgy. Luleå University of Technology, Luleå,
Sweden.
Navarra, A., Wilson, R., Parra, R., Toro., N., Ross, A.,
Nave, J.-C., Mackey, P.J., 2020. Processes 8(1478):1–22.
Órdenes, J., Toro, N., Quelopana, A., Navarra, A., 2022.
Data-driven dynamic simulations of gold extraction
which incorporate head grade distribution statistics,
Metals 12(1372):1–24.
Órdenes, J., Wilson, R., Peña-Graf., F., Navarra, A., 2021.
Incorporation of geometallurgical input into gold min-
ing system simulation to control cyanide consumption,
Minerals 11(1023):1–15.
Parian, M.A., 2017, Development of a geometallurgical
framework for iron ores—A mineralogical approach
to particle-based modeling, Luleå University of
Technology, Luleå, Sweden.
Peña Graf, F., Órdenes, J., Wilson, R., and Navarra, A.,
2022. Discrete Event Simulation for Machine-Learning
Enabled Mine Production Control with Application to
Gold Processing, Metals 12(225):1–21.
Saldaña, M., Toro, N., Castillo, J., Hernández, P.,
Navarra, A., 2019. Optimization of the heap leaching
process through changes in modes of operation and
discrete event simulation, Minerals 9(421):1–13.
Tijsseling, L.T., Dehaine, Q., Rollinson, G.K., Glass, H.J.,
2020, Mineralogical prediction of flotation perfor-
mance for a sediment-hosted copper-cobalt sulphide
ore, Minerals, 10(474):1–21.
Wambuke, T., 2018. Data assimilation in the minerals
industry: real-time updating of spatial models using
online production data, Delft University of Technology
Dissertation.
Wilson, R., Toro, N., Naranjo, O., Emery, X., Navarra, A.,
2021. Integration of geostatistical modeling into dis-
crete event simulation for development of tailings
dam retreatment applications. Minerals Engineering
164:106814.
Wilson, R., Perez, K., Toro, N., Parra, R., Mackey, P.J.,
Navarra, A., 2022. Mine-to-smelter integration frame-
work for regional development of porphyry cop-
per deposits within the Chilean context, Canadian
Metallurgical Quarterly 61(1):48–62.
Wilson, R., Mercier, P.H.J., Navarra, A., 2022. Integrated
artificial neural network and discrete event simulation
framework for regional development of refractory gold
systems, Mining 2:123–154.
Wilson, R., Mercier, P.H.J., Patarachao, B., and Navarra, A.,
2021. Partial least squares regression of oil sands pro-
cessing variables within discrete event simulation digi-
tal twin, Minerals 11(689):1–30.
ACKNOWLEDGMENTS
The authors would like to thank all the professionals at
Freeport McMoRan who provided data access, assistance,
and commentary on the research included in this paper.
The authors would also like to extend a special thank you
to Dr. Angelina Anani for her invaluable assistance regard-
ing discrete event simulation methods, as well as everyone
at MOSIMTEC for providing the initial exposure to the
potential and power of DES approaches to operational
management. Lastly, the authors would like to thank
Freeport McMoRan, the Society for Mining, Metallurgy
&Exploration, the University of Arizona, and the Lowell
Institute for Mineral Resources for their financial support.
REFERENCES
David, D.M., 2019. Geometallurgy. In SME Mineral
Processing &Extractive Metallurgy Handbook. 1st ed.
Edited by S.K. Kawatra, C.A. Young, and R.C. Dunne.
Englewood, CO:SME.
Koch, P.-H., Computational method and strategies for
geometallurgy. Luleå University of Technology, Luleå,
Sweden.
Navarra, A., Wilson, R., Parra, R., Toro., N., Ross, A.,
Nave, J.-C., Mackey, P.J., 2020. Processes 8(1478):1–22.
Órdenes, J., Toro, N., Quelopana, A., Navarra, A., 2022.
Data-driven dynamic simulations of gold extraction
which incorporate head grade distribution statistics,
Metals 12(1372):1–24.
Órdenes, J., Wilson, R., Peña-Graf., F., Navarra, A., 2021.
Incorporation of geometallurgical input into gold min-
ing system simulation to control cyanide consumption,
Minerals 11(1023):1–15.
Parian, M.A., 2017, Development of a geometallurgical
framework for iron ores—A mineralogical approach
to particle-based modeling, Luleå University of
Technology, Luleå, Sweden.
Peña Graf, F., Órdenes, J., Wilson, R., and Navarra, A.,
2022. Discrete Event Simulation for Machine-Learning
Enabled Mine Production Control with Application to
Gold Processing, Metals 12(225):1–21.
Saldaña, M., Toro, N., Castillo, J., Hernández, P.,
Navarra, A., 2019. Optimization of the heap leaching
process through changes in modes of operation and
discrete event simulation, Minerals 9(421):1–13.
Tijsseling, L.T., Dehaine, Q., Rollinson, G.K., Glass, H.J.,
2020, Mineralogical prediction of flotation perfor-
mance for a sediment-hosted copper-cobalt sulphide
ore, Minerals, 10(474):1–21.
Wambuke, T., 2018. Data assimilation in the minerals
industry: real-time updating of spatial models using
online production data, Delft University of Technology
Dissertation.
Wilson, R., Toro, N., Naranjo, O., Emery, X., Navarra, A.,
2021. Integration of geostatistical modeling into dis-
crete event simulation for development of tailings
dam retreatment applications. Minerals Engineering
164:106814.
Wilson, R., Perez, K., Toro, N., Parra, R., Mackey, P.J.,
Navarra, A., 2022. Mine-to-smelter integration frame-
work for regional development of porphyry cop-
per deposits within the Chilean context, Canadian
Metallurgical Quarterly 61(1):48–62.
Wilson, R., Mercier, P.H.J., Navarra, A., 2022. Integrated
artificial neural network and discrete event simulation
framework for regional development of refractory gold
systems, Mining 2:123–154.
Wilson, R., Mercier, P.H.J., Patarachao, B., and Navarra, A.,
2021. Partial least squares regression of oil sands pro-
cessing variables within discrete event simulation digi-
tal twin, Minerals 11(689):1–30.