System and method for adjusting leaching operations based on leach analytic data

Patent metadata

Field	Value
Publication number	US-12288169-B2
Application number	US-202418736402-A
Country	US
Kind code	B2
Filing date	Jun 6, 2024
Priority date	Jun 27, 2022
Publication date	Apr 29, 2025
Grant date	Apr 29, 2025

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:
forecasting, by one or more processors at a first time, mining production using a plurality of parameters, future production assumptions, a predictive model and a simulation model,
wherein the simulation model simulates adjustments to the plurality of parameters for leaching operations for a stockpile to optimize the mining production based on the plurality of parameters;
backcasting, by the one or more processors at a second time, an expected mining production, wherein the backcasting uses the predictive model to determine deviations from the expected mining production as compared to results of the mining production;
refining, by the one or more processors, the predictive model based on the deviations;
adjusting, by the one or more processors, one or more of the plurality of parameters based on the deviations to obtain one or more adjusted parameters;
re-forecasting, by the one or more processors, the mining production using the one or more adjusted parameters;
transmitting, by the one or more processors, an activation signal to a mining machine, wherein the activation signal is based on the re-forecasting of the mining production; and
activating, by the one or more processors, the mining machine, in response to the activation signal, to pump raffinate as part of the leaching operations to continue and to optimize the mining production, based on the one or more adjusted parameters.

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

3. The method of claim 1 , wherein the first time is before the mining production is complete, and wherein the second time is after the first time and after the mining production is complete.

4. The method of claim 1 , wherein the deviations from the expected mining production includes unexpected operational changes.

5. The method of claim 1 , wherein the one or more adjusted parameters at least partially compensate for the deviations from the expected mining production.

6. The method of claim 1 , further comprising training, by the one or more processors, the predictive model with historical data from the leaching operations to create a trained predictive model.

7. The method of claim 6 , wherein the historical data comprises at least one of ore map data, mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement, blower data, PLS (pregnant leach solution) data, stockpile data, section mapping data, economic data or accounting data.

8. The method of claim 6 , wherein the historical data is for the stockpile during a time period.

9. The method of claim 7 , wherein the mineralogy data includes at least one of a total percentage of copper in a sample (TCu), average acid-soluble copper grade of material in all sections currently under irrigation on a given day (XCu) percentage, quick leach test (QLT) percentage, ore size or tons.

10. The method of claim 7 , wherein the irrigation data includes at least one of raffinate application rate, area under irrigation or days under leach (DUL).

11. The method of claim 7 , wherein the ore map data comprises at least one of data from a column test predictive model for a column test, data from a test pad or section mapping data that includes creating a polygon map.

12. The method of claim 11 , wherein a machine learning model receives data from the column test to create the column test predictive model.

13. The method of claim 11 , wherein the column test provides output data including at least one of days under leach of a mineral, a percentage of each mineral reacted or an amount of acid consumed.

14. The method of claim 11 , wherein input data from the column test includes at least one of raffinate TFe, raffinate acid, leach additive, leach catalyst, raffinate Cu 2+ , DUL, XCu, QLT, application rate or cure acid.

15. The method of claim 1 , further comprising adding, by the one or more processors, future assumption data to the predictive model, wherein the future assumption data is based on mine plan data from sensors.

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

17. The method of claim 1 , wherein the adjusting the one or more of the plurality of parameters comprises changing ore routing in real-time.

18. The method of claim 1 , further comprising validating, by the one or more processors, the predictive model using test data with a time lag.

19. An article of manufacture including a non-transitory, tangible computer readable storage medium having instructions stored thereon that, in response to execution by one or more processors, cause the one or more processors to perform operations comprising:
forecasting, by the one or more processors at a first time, mining production using a plurality of parameters, future production assumptions, a predictive model and a simulation model,
wherein the simulation model simulates adjustments to the plurality of parameters for leaching operations for a stockpile to optimize the mining production based on the plurality of parameters;
backcasting, by the one or more processors at a second time, an expected mining production, wherein the backcasting uses the predictive model to determine deviations from the expected mining production as compared to results of the mining production;
refining, by the one or more processors, the predictive model based on the deviations;
adjusting, by the one or more processors, one or more of the plurality of parameters based on the deviations to obtain one or more adjusted parameters;
re-forecasting, by the one or more processors, the mining production using the one or more adjusted parameters;
transmitting, by the one or more processors, an activation signal to a mining machine, wherein the activation signal is based on the re-forecasting of the mining production; and
activating, by the one or more processors, the mining machine, in response to the activation signal, to pump raffinate as part of the leaching operations to continue and to optimize the mining production, based on the one or more adjusted parameters.

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:
forecasting, by the one or more processors at a first time, mining production using a plurality of parameters, future production assumptions, a predictive model and a simulation model,
wherein the simulation model simulates adjustments to the plurality of parameters for leaching operations for a stockpile to optimize the mining production based on the plurality of parameters;
backcasting, by the one or more processors at a second time, an expected mining production, wherein the backcasting uses the predictive model to determine deviations from the expected mining production as compared to results of the mining production;
refining, by the one or more processors, the predictive model based on the de

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