System and method for activating deep raffinate injection based on leach analytic data
The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.
1. A method comprising:
acquiring, by a processor, mining data from sensors that monitor mining production,
wherein leaching operations are conducted for the mining operations;
determining, by the processor, deviations between forecast data for a mining production target during a time period and the mining data for the mining production after the time period;
determining, by the processor, a contribution to the deviations by each of a plurality of parameters, in response to changing each of the plurality of parameters from the forecast data to the mining data;
determining, by the processor, that one of the plurality of parameters indicates leach solutions resulting in a potential for unleached copper in an area of a stockpile;
activating, by the processor, deep raffinate injection in the area of the stockpile.
2. The method of claim 1 , wherein the potential for unleached copper in the area of the stockpile is based on the leach solutions channeling within the area of the stockpile.
3. The method of claim 1 , wherein the potential for unleached copper in the area of the stockpile is based on the leach solutions not contacting ore particles uniformly.
4. The method of claim 1 , wherein the activating the deep raffinate injection comprises sending a signal to a machine to drill holes at a depth to target the unleached copper in the area of the stockpile.
5. The method of claim 1 , wherein the activating the deep raffinate injection comprises tailoring leach solution chemistry to mineralogy of the unleached copper to use for the deep raffinate injection.
6. The method of claim 1 , further comprising training, by the processor, a predictive model with historical data to create a trained predictive model to determine the forecast data.
7. The method of claim 6 , further comprising conducting, by the processor, leaching operations to obtain the historical data.
8. The method of claim 6 , wherein the historical data is for the stockpile during a time period.
9. The method of claim 6 , further comprising validating, by the processor, the predictive model using test data with a time lag.
10. The method of claim 1 , further comprising adding, by the processor, future assumption data to a trained predictive model for determining the forecast data, wherein the future assumption data is based on mine plan data.
11. The method of claim 1 , further comprising running, by the processor, a forecast engine for the plurality of parameters to obtain the forecast data for the mining production target.