Pamparana, Kracht, Ortiz, Haas
Presented at COM 2019, At Vancouver, Canada
This work explores the effect of ore hardness variability on the design of a solar photovoltaic system with battery storage and grid backup, to operate a semi-autogenous grinding mill (SAG). First, a case of two synthetic ore populations was studied, without any consideration of spatial distribution. The mean and variance of the ore hardness (measured in specific energy consumption) of both populations were subjected to a sensitivity analysis. The operation was evaluated with one and two stockpiles. The latter allows classifying according to ore hardness to adapt the SAG energy consumption to the solar cycle.
The results show that using two stockpiles allows achieving a larger autonomy (i.e. less energy imports) at lower total costs, especially when the difference of mean hardness between the two populations is large. The difference in variance (between the two populations) has a smaller impact on the design recommendations. Second, a case study was performed considering the spatial distribution of geometallurgical units. Two cases were considered: (a) brownfield, when there is historical information of the actual variability of the ore hardness; and (b) greenfield, when the system design relies mainly on geometallurgical modeling. Brownfield and greenfield planning result in similar sizing recommendations of the solar power plant if precise drill cores are available. In this case, geometallurgical modeling is an effective tool for forecasting the energy demand necessary for designing the power plant.
Geometallurgy, Stochastic Simulation, Geostatistics, SAG, Solar Energy, Battery Storage
*G. Pamparana - Norman B. Keevil Institute of Mining Engineering, University of British Columbia
W. Kracht - Department of Mining Engineering, Universidad de Chile, Av. Tupper 2069, Santiago, Chile 837 0451
J. M. Ortiz - Robert M. Buchan Department of Mining, Queen’s University, Goodwin Hall, 332 - 25 Union Street, Kingston, Ontario, Canada K7L 3N6
J. Haas - Department of Stochastic Simulation and Safety Research for Hydrosystems, University of Stuttgart, Pfaffenwaldring 5a, Stuttgart, Germany D-70569
This work was supported by the Chilean National Commission for Scientific and Technological Research (CONICYT), through the Solar Energy Research Center SERC-Chile (FONDAP 15110019) and the Solar Mining project (CONICYT-BMBF 20140019), and the German Academic Exchange Service (DAAD).
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