System and method for activating deep raffinate injection based on ore placement

Patent metadata

Field	Value
Publication number	US-2024124951-A1
Application number	US-202318398613-A
Country	US
Kind code	A1
Filing date	Dec 28, 2023
Priority date	Jun 27, 2022
Publication date	Apr 18, 2024
Grant date	—

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:
receiving, by a processor and from sensors, ore placement data for a stockpile; determining, by the processor, ore placement locations for the stockpile, based on the ore placement data; conducting, by the processor, leaching operations on the stockpile to obtain historical leaching process data for the stockpile; determining, by the processor, an amount of mineral extracted from the stockpile, based on the historical leaching process data for the stockpile; determining, by the processor, recovery locations for recoverable mineral in an area of the stockpile, based on historical leaching process data for the stockpile; and activating, by the processor, deep raffinate injection in the area of the stockpile.

2 . 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 recovery locations for the recoverable mineral in the area of the stockpile.

3 . The method of claim 1 , wherein the activating the deep raffinate injection comprises tailoring leach solution chemistry to mineralogy of the recoverable mineral to use for the deep raffinate injection.

4 . The method of claim 1 , wherein the activating the deep raffinate injection comprises enhancing raffinate solutions over the recoverable mineral with at least one of sulfuric acid, ferric ions, air bubbles, oxygen bubbles, heat, or microbes.

5 . The method of claim 1 , wherein the activating the deep raffinate injection comprises enhancing raffinate solutions over the recoverable mineral with at least one of another acid, oxidant or beneficial additive.

6 . The method of claim 1 , wherein the activating the deep raffinate injection comprises pumping leaching solutions under pressure into a deep raffinate well within the recoverable mineral.

7 . The method of claim 1 , wherein the recovery locations for the recoverable mineral in the area of the stockpile for the deep raffinate injection is based on the leach solutions channeling within the area of the stockpile.

8 . The method of claim 1 , wherein the recovery locations for the recoverable mineral in the area of the stockpile for the deep raffinate injection is based on the leach solutions not contacting ore particles uniformly.

9 . The method of claim 1 , wherein the ore placement data comprises dispatch data, haul truck sensor data, polygon data, assay data and mineralogy data.

10 . The method of claim 1 , wherein the ore placement data comprises mineralogy data from a block model and included in mine material tracking data.

11 . The method of claim 1 , wherein the determining the ore placement locations for the stockpile includes determining mineralogy of recoverable minerals for the stockpile.

12 . The method of claim 1 , wherein the determining the ore placement locations for the stockpile includes determining mineralogy for the stockpile by:
aggregating mineralogy details to a section level by combining mine material tracking (MMT) truckload data at a dump level, MMT imputation data at the dump level and MMT final section mapping data at the dump level and the section level; obtaining maximum days under leach (DUL) for each section at the section level by using irrigation data over all stockpiles at the section level; and determining an intermediate ore map for a stockpile by combining the aggregating mineralogy details, the maximum DUL for each section and a primary new section polygon.

13 . The method of claim 1 , wherein the determining the amount of mineral extracted from the stockpile comprises determining a primary ore map for the stockpile.

14 . The method of claim 13 , wherein the determining the primary ore map for the stockpile comprises adding flow data, irrigation data and a remaining mineral prediction from a machine learning model to obtain information by section and by date for the stockpile.

15 . The method of claim 1 , further comprising reallocating ore in neighboring sections to side slopes of the stockpile, wherein the ore is determined by the ore placement data.

16 . The method of claim 1 , further comprising providing a visualization of the recovery locations for the recoverable mineral in the area of the stockpile.

17 . The method of claim 1 , further comprising determining at least one of x,y,z coordinates or time-series layering information for the recovery locations for the recoverable mineral in the area of the stockpile.

18 . The method of claim 1 , further comprising providing a visualization of section mineralogy populated on a map of the stockpile.

19 . The method of claim 1 , further comprising defining boundaries of the stockpile based on polygons recorded in a geographic information system (GIS).

20 . The method of claim 1 , further comprising estimating the recoverable mineral in the area of the stockpile based on a column test model.

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

• G06Q10/04 Primary

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · 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

• C22B15/0067

  with acids or salts thereof · CPC title

• C22B15/0095

  Process control or regulation methods · CPC title

• G06Q10/0631

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