Granted Patent B2

US 12,475,412 · App. 19/187,744 · Granted Nov 18, 2025

Inventors: Dana Geislinger (Chandler, AZ); Travis Gaddie (Phoenix, AZ); Margaret Alden Tinsley (Titusville, FL); Muneeb Alam (Alexandria, VA); Steven Chad Richardson (Thatcher, AZ); Akaash Sanyal (Boston, MA); Raquel Crossman (Mesa, AZ); Tianfang Ni (Boston, MA); Cory A. Demieville (Scottsdale, AZ); Luke Gerdes (Framingham, MA); Robyn Freeman (Menlo Park, CA); Oleksandr Klesov (Lexington, MA); Luciano Kiniti Issoe (Sao Paulo, BR)

Assignee: FREEPORT MINERALS CORPORATION

G06Q10/04C22B3/06C22B15/0067C22B15/0095G06Q10/0631G06Q50/02

Abstract

Claims (24)

1 . A method comprising:

determining, by one or more processors, a location of an estimated remaining amount of mineral in a section of a stockpile and the estimated remaining amount of mineral in the section of the stockpile;

generating, by the one or more processors, a prediction of an amount of mineral recovery from the estimated remaining amount of mineral at the location in the section of the stockpile;

generating, by the one or more processors, a mineral recovery curve to estimate an amount of mineral recovery from leaching over a period of time, by using the prediction of the amount of mineral recovery; and

activating, by the one or more processors and based on the mineral recovery curve, deep injection of raffinate at a depth to target the estimated remaining amount of mineral at the location in the section of the stockpile to implement the leaching over the period of time to achieve the estimated amount of the mineral recovery,

wherein the activating causes a drilling machine to drill holes at the depth to target the estimated remaining amount of mineral at the location in the section of the stockpile, and

wherein the activating causes a pumping machine to pump the raffinate to implement the leaching over the period of time to achieve the estimated amount of mineral recovery.

2 . The method of claim 1 , wherein a machine learning model is used for at least one of the determining the estimated remaining amount of mineral in the section of the stockpile or the generating the prediction of the amount of mineral recovery from the estimated remaining amount of mineral in the section of the stockpile.

3 . The method of claim 2 , wherein the machine learning model incorporates at least one of a value of an agglomeration acid concentration or a value of an agglomeration additive concentration applied in a column test.

4 . The method of claim 1 , wherein the determining the estimated remaining amount of mineral in the section of the stockpile uses a column test predictive model of a column test of a column of ore from the section of the stockpile.

5 . The method of claim 4 , further comprising obtaining, by the one or more processors, days under leach, chemistry data and mineralogy data of mineralogy from the column test of the column of ore from the section of the stockpile.

6 . The method of claim 4 , further comprising simulating, by the one or more processors, the leaching in the column of ore in the section of the stockpile from days under leach, chemistry data and mineralogy data.

7 . The method of claim 4 , further comprising adjusting, by the processor, agglomeration acid concentration applied in the column test.

8 . The method of claim 4 , further comprising adjusting, by the one or more processors, process parameters applied to the column of ore.

9 . The method of claim 8 , wherein the adjusting of the process parameters impacts simulating of the leaching in the column of ore.

10 . The method of claim 1 , further comprising obtaining, by the one or more processors, adjusted chemistry data and adjusted mineralogy data based on adjusting of process parameters and simulating of the leaching in the column of ore using the adjusted process parameters.

11 . The method of claim 1 , further comprising providing, by the one or more processors, days under leach, adjusted chemistry data and adjusted mineralogy data to a machine learning model to build a column test predictive model.

12 . The method of claim 1 , wherein the activating the deep injection of the raffinate comprises sending a signal to the drilling machine to drill the holes at the depth to target the estimated remaining amount of mineral in the section of the stockpile.

13 . The method of claim 1 , wherein the activating the deep injection of the raffinate comprises adjusting chemistry of the raffinate for the actual leaching to react with mineralogy of the estimated remaining amount of mineral in the section of the stockpile, wherein the raffinate is used for the deep injection of the raffinate.

14 . The method of claim 1 , wherein the activating the deep injection of the raffinate comprises enhancing the raffinate pumped into the estimated remaining amount of mineral in the section of the stockpile with at least one of sulfuric acid, ferric ions, air bubbles, oxygen bubbles, heat, or microbes.

15 . The method of claim 1 , wherein the activating the deep injection of the raffinate comprises enhancing the raffinate pumped into the estimated remaining amount of mineral in the section of the stockpile with at least one of an acid, oxidant or leach enhancing additive.

16 . The method of claim 1 , wherein the activating the deep injection of the raffinate comprises pumping the raffinate under pressure into a raffinate well within the estimated remaining amount of mineral in the section of the stockpile.

17 . The method of claim 8 , wherein the process parameters include at least one of type of film, a period of time for implementing the leaching, total depth of the section of the stockpile, raffinate application flowrate, raffinate temperature, air application rate, air wet bulb temperature, evaporation percentage, shortwave infrared radiation absorptivity, longwave absorbed infrared radiation emissivity, longwave out radiation emissivity, exotherm heat generation, heat transfer by convection at a surface of the stockpile or average temperature at an end of the period of time for the leaching.

18 . The method of claim 6 , wherein the chemistry data and the mineralogy data include at least one of an amount of Fe in the raffinate, Fe2, raffinate acid, raffinate additive, raffinate temperature, an amount of Cu in the raffinate, acid soluble copper, an indicator of a presence of leachable minerals in the ore, application rate or cure acid.

Assignments (1)

Continuity (8)

References Cited (172)

Cited By (1)

US 12,675,750