System and method for activating deep raffinate injection based on leach analytic data

Patent metadata

Field	Value
Publication number	US-12169796-B2
Application number	US-202418433776-A
Country	US
Kind code	B2
Filing date	Feb 6, 2024
Priority date	Jun 27, 2022
Publication date	Dec 17, 2024
Grant date	Dec 17, 2024

Abstract

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.

First claim

We claim:

1. A method comprising:
acquiring, by one or more processors, mining data;
determining, by the one or more processors, deviations between forecast data for a mining production target during a time period and the mining data for mining production after the time period;
determining, by the one or more processors, a contribution to the deviations by one or more of a plurality of parameters, in response to changing one or more of the plurality of parameters from the forecast data to the mining data;
determining, by the one or more processors, that one or more of the plurality of parameters indicates a potential for unleached minerals in an area of a stockpile; and
activating, by the one or more processors, deep raffinate injection in the area of the stockpile,
wherein the deep raffinate injection is conducted for the mining production, in response to the activating.

2. The method of claim 1 , wherein the acquiring the mining data includes acquiring the mining data from sensors that monitor the mining production.

3. The method of claim 1 , wherein the mining production includes leaching operations.

4. The method of claim 1 , further comprising determining, by the one or more processors, that the one or more of the plurality of parameters indicate leach solutions resulting in the potential for the unleached minerals in the area of a stockpile.

5. The method of claim 1 , further comprising simulating, by the one or more processors, adjustments to the one or more of the plurality of parameters in a model for leaching operations for the stockpile to optimize the mining production based on the one or more of the plurality of parameters.

6. The method of claim 1 , further comprising forecasting, by the one or more processors at a first time, the mining production using one or more of the plurality of parameters, future production assumptions and a predictive model, wherein the first time is before the mining production is complete.

7. The method of claim 1 , further comprising backcasting, by the one or more processors at a second time, an expected mining production with the mining production having unexpected operational changes, wherein the backcasting uses a predictive model to determine the deviations from the expected mining production, and wherein the second time is after forecasting at a first time and after the mining production is complete.

8. The method of claim 1 , further comprising refining, by the one or more processors, a predictive model based on the deviations.

9. The method of claim 1 , further comprising adjusting, by the one or more processors, the one or more of the plurality of parameters based on the deviations to obtain one or more adjusted parameters that represent the mining production.

10. The method of claim 9 , further comprising re-forecasting, by the one or more processors, the mining production using the one or more adjusted parameters.

11. The method of claim 1 , wherein the potential for the unleached minerals in the area of the stockpile is based on leach solutions channeling within the area of the stockpile.

12. The method of claim 1 , wherein the potential for the unleached minerals in the area of the stockpile is based on leach solutions not contacting ore particles uniformly.

13. 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 minerals in the area of the stockpile.

14. The method of claim 1 , wherein the activating the deep raffinate injection comprises tailoring leach solution chemistry to mineralogy of the unleached minerals to use for the deep raffinate injection.

15. The method of claim 1 , further comprising training, by the one or more processors, a predictive model with historical data to create a trained predictive model to determine the forecast data.

16. The method of claim 15 , further comprising validating, by the one or more processors, the predictive model using test data with a time lag.

17. The method of claim 1 , further comprising adding, by the one or more processors, future assumption data to a trained predictive model for determining the forecast data, wherein the future assumption data is based on mine plan data.

18. The method of claim 1 , further comprising running, by the one or more processors, a forecast engine for the plurality of parameters to obtain the forecast data for the mining production target.

19. An article of manufacture including one or more non-transitory, tangible computer readable storage mediums having instructions stored thereon that, in response to execution by one or more processors, cause the one or more processors to perform operations comprising:
acquiring, by the one or more processors, mining data;
determining, by the one or more processors, deviations between forecast data for a mining production target during a time period and the mining data for mining production after the time period;
determining, by the one or more processors, a contribution to the deviations by one or more of a plurality of parameters, in response to changing one or more of the plurality of parameters from the forecast data to the mining data;
determining, by the one or more processors, that one or more of the plurality of parameters indicates a potential for unleached minerals in an area of a stockpile; and
activating, by the one or more processors, deep raffinate injection in the area of the stockpile,
wherein the deep raffinate injection is conducted for the mining production, in response to the activating.

20. A system comprising:
one or more processors; and
one or more tangible, non-transitory memories configured to communicate with the one or more processors,
the one or more tangible, non-transitory memories having instructions stored thereon that, in response to execution by the one or more processors, cause the one or more processors to perform operations comprising:
acquiring, by the one or more processors, mining data;
determining, by the one or more processors, deviations between forecast data for a mining production target during a time period and the mining data for mining production after the time period;
determining, by the one or more processors, a contribution to the deviations by one or more of a plurality of parameters, in response to changing one or more of the plurality of parameters from the forecast data to the mining data;
determining, by the one or more processors, that one or more of the plurality of parameters indicates a potential for unleached minerals in an area of a stockpile; and
activating, by the one or more processors, deep raffinate injection in the area of the stockpile,
wherein the deep raffinate injection is conducted for the mining production, in response to the activating.

Assignees

• Freeport Minerals Corp

Inventors

• Geislinger Dana
• Gaddie Travis
• Tinsley Margaret Alden
• Alam Muneeb
• Richardson Steven Chad
• Sanyal Akaash
• Crossman Raquel
• Ni Tianfang
• Demieville Cory A
• Gerdes Luke
• Freeman Robyn
• Klesov Oleksandr
• Issoe Luciano Kiniti

Classifications

• G06Q10/0631

  Resource planning, allocation, distributing or scheduling for enterprises or organisations · CPC title

• C22B15/0067

  with acids or salts thereof · CPC title

• C22B3/06

  in inorganic acid solutions {, e.g. with acids generated in situ; in inorganic salt solutions other than ammonium salt solutions} · CPC title

• C22B15/0095

  Process control or regulation methods · CPC title

• G06Q50/02

  Agriculture; Fishing; Forestry; Mining · CPC title