A device for recovering entrained organic particles from an aqueous solution is provided. The device comprises a tank configured to receive a mixed solution through an inlet, wherein the mixed solution comprises an aqueous solution and organic particles, a plurality of curved impellers configured to impel a torque on the mixed solution, wherein in response to receiving the torque the mixed solution moves in a rotational motion within the tank, a deflector wall coupled to the floor extending vertically from the floor and surrounding the plurality of curved impellers to form an inner well configured to direct a flow of the rotating mixed solution towards a top surface of the tank, and a skim launder coupled to a boundary wall of the tank and positioned at the top surface, wherein the skim launder is configured to recover organic particles from the tank as they separate from the aqueous solution.
A method and system for real-time monitoring and optimization of a heap leaching process using permittivity measurements is provided. The method includes deriving a relationship between a measured permittivity of an ore and a measured metal concentration of the ore, placing a plurality of antennas configured to measure permittivity in a leaching heap of the ore, taking a plurality of permittivity measurements of the leaching heap during a leaching process, and using the derived relationship and the permittivity measurements of the leaching heap to estimate an overall metal recovery of the leaching process.
G01N 22/00 - Investigating or analysing materials by the use of microwaves or radio waves, i.e. electromagnetic waves with a wavelength of one millimetre or more
The system may include a secondary irrigation feature that determines a percent of overlap of each of a plurality of submodules in a second lift over each of a plurality of submodules in a first lift and adjusts at least one of leaching operations or a leaching model based on the total tonnage weighted average of metal in the second lift. The method may further comprise determining an acid gap based on a difference between total acid given and total acid consumption; and further adjusting at least one of the leaching operations or the leaching model based on the acid gap. The method may further comprise determining a percentage of compacted material based on the material that is compacted and irrigated divided by the material that is irrigated; and further adjusting at least one of the leaching operations or the leaching model based on the percentage of compacted material.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
Methods for recovering a metal value from a metal-bearing material are provided. The method comprises leaching the metal-bearing material with a leaching solution to produce a pregnant leaching solution, wherein the leaching solution comprises the raffinate, the chelating agent, an oxidant, and a low concentration of a silver agent, wherein the low concentration of the silver agent is in the range of about 10 ppm to about 80 ppm, and wherein the metal-bearing material comprises a concentration of iron, and recovering the metal value from the pregnant leaching solution to produce the raffinate.
Methods for recovering a metal value from a metal-bearing material are provided. The method comprises leaching the metal-bearing material with a leaching solution to produce a pregnant leaching solution, wherein the leaching solution comprises the raffinate, the chelating agent, an oxidant, and a low concentration of a silver agent, wherein the low concentration of the silver agent is in the range of about 10 ppm to about 80 ppm, and wherein the metal-bearing material comprises a concentration of iron, and recovering the metal value from the pregnant leaching solution to produce the raffinate.
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
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.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
C22B 3/04 - Extraction of metal compounds from ores or concentrates by wet processes by leaching
8.
SYSTEM AND METHOD FOR ACTIVATING DEEP RAFFINATE INJECTION BASED ON COLUMN TEST PREDICTIVE MODEL
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
An Unmanned Aerial Vehicle ("UAV") system includes a UAV configured for lifting, positioning, and placing irrigation lines on a heap including side slopes. The UAV can be equipped with an irrigation line support structure configured for supporting, transporting and/or releasing one or more irrigation lines. Placement of the irrigation lines via the UAV system can enable accurate distribution of raffinate solutions to enhance leaching efficiency in areas which are typically difficult to access.
The system comprises an automated data platform for reconciliation. The system may use geologic data and mine data to digitize and automate reconciliation, to determine the impact of various models, to assist in real time decision making for adjusting mine operations and/or to improve mining production. The method comprises associating shovel load locations with a forecast model block and a district model block; selecting a plurality of shovel loads that are associated with the forecast model block and the district model block, based on the shovel load locations; matching the plurality of shovel loads with the truck load; aggregating the plurality of shovel loads into the truck load; comparing forecast model block characteristics of the forecast model block and district model block characteristics of the district model block with target block characteristics of a target block; and creating a reconciliation report of the target block characteristics of the target block based on the forecast model block characteristics and the district model block characteristics.
G06Q 10/047 - Optimisation of routes or paths, e.g. travelling salesman problem
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
11.
SYSTEM AND METHOD FOR DETERMINING A DUMP LOCATION FOR A VEHICLE
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.
The system and method comprise an automated data platform for reconciliation. The method comprises associating shovel load locations with a forecast model block and a district model block; selecting a plurality of shovel loads that are associated with the forecast model block and the district model block, based on the shovel load locations; matching the plurality of shovel loads with the truck load; aggregating the plurality of shovel loads into the truck load; comparing forecast model block characteristics of the forecast model block and district model block characteristics of the district model block with target block characteristics of a target block; and creating a reconciliation report of the target block characteristics of the target block based on the forecast model block characteristics and the district model block characteristics.
E21C 41/26 - Methods of surface miningLayouts therefor
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
E21C 39/00 - Devices for testing in situ the hardness or other properties of minerals, e.g. for giving information as to the selection of suitable mining tools
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
The system may include a secondary irrigation feature that determines a percent of overlap of each of a plurality of submodules in a second lift over each of a plurality of submodules in a first lift and adjusts at least one of leaching operations or a leaching model based on the total tonnage weighted average of metal in the second lift. The method may further comprise determining an acid gap based on a difference between total acid given and total acid consumption; and further adjusting at least one of the leaching operations or the leaching model based on the acid gap. The method may further comprise determining a percentage of compacted material based on the material that is compacted and irrigated divided by the material that is irrigated; and further adjusting at least one of the leaching operations or the leaching model based on the percentage of compacted material.
C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
The system may include a secondary irrigation feature that determines a percent of overlap of each of a plurality of submodules in a second lift over each of a plurality of submodules in a first lift and adjusts at least one of leaching operations or a leaching model based on the total tonnage weighted average of metal in the second lift. The method may further comprise determining an acid gap based on a difference between total acid given and total acid consumption; and further adjusting at least one of the leaching operations or the leaching model based on the acid gap. The method may further comprise determining a percentage of compacted material based on the material that is compacted and irrigated divided by the material that is irrigated; and further adjusting at least one of the leaching operations or the leaching model based on the percentage of compacted material.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
The system may include a secondary irrigation feature that determines a percent of overlap of each of a plurality of submodules in a second lift over each of a plurality of submodules in a first lift and adjusts at least one of leaching operations or a leaching model based on the total tonnage weighted average of metal in the second lift. The method may further comprise determining an acid gap based on a difference between total acid given and total acid consumption; and further adjusting at least one of the leaching operations or the leaching model based on the acid gap. The method may further comprise determining a percentage of compacted material based on the material that is compacted and irrigated divided by the material that is irrigated; and further adjusting at least one of the leaching operations or the leaching model based on the percentage of compacted material.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
C22B 1/00 - Preliminary treatment of ores or scrap
The system may include a secondary irrigation feature that determines a percent of overlap of each of a plurality of submodules in a second lift over each of a plurality of submodules in a first lift and adjusts at least one of leaching operations or a leaching model based on the total tonnage weighted average of metal in the second lift. The method may further comprise determining an acid gap based on a difference between total acid given and total acid consumption; and further adjusting at least one of the leaching operations or the leaching model based on the acid gap. The method may further comprise determining a percentage of compacted material based on the material that is compacted and irrigated divided by the material that is irrigated; and further adjusting at least one of the leaching operations or the leaching model based on the percentage of compacted material.
C22B 3/04 - Extraction of metal compounds from ores or concentrates by wet processes by leaching
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
The system may include a secondary irrigation feature that determines a percent of overlap of each of a plurality of submodules in a second lift over each of a plurality of submodules in a first lift and adjusts at least one of leaching operations or a leaching model based on the total tonnage weighted average of metal in the second lift. The method may further comprise determining an acid gap based on a difference between total acid given and total acid consumption; and further adjusting at least one of the leaching operations or the leaching model based on the acid gap. The method may further comprise determining a percentage of compacted material based on the material that is compacted and irrigated divided by the material that is irrigated; and further adjusting at least one of the leaching operations or the leaching model based on the percentage of compacted material.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
The present disclosure provides a method comprising determining an ore map for a heap to identify a location of a recoverable metal-bearing material in the heap, wherein the metal-bearing material comprises iron and at least one other metal value, delivering a leaching solution from a leaching solution source to a leaching solution regulating system, wherein the leaching solution comprises an effective amount of citric acid and hydrogen peroxide, regulating at least one of a pressure, a mass flow rate, or a volumetric flow rate of the leaching solution to achieve a target operational condition, wherein the target operational condition is selected to optimize a set of operational parameters to maximize recovery of the at least one other metal value, delivering the leaching solution at the target operational condition from the leaching solution regulating system to the subsurface leaching distribution system, and delivering the leaching solution at the target operational condition from the subsurface leaching distribution system to the location of the recoverable metal-bearing material under a surface of the heap to leach and recover the at least one other metal value.
C22B 60/02 - Obtaining thorium, uranium or other actinides
C22B 61/00 - Obtaining metals not elsewhere provided for in this subclass
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
The present disclosure provides a method comprising determining an ore map for a heap to identify a location of a recoverable metal value in the heap, delivering a leaching solution from a leaching solution source to a leaching solution regulating system, regulating at least one of a pressure, a mass flow rate, or a volumetric flow rate of the leaching solution to achieve a first target operational condition, wherein the first target operational condition is selected to optimize a set of operational parameters to maximize recovery of the recoverable metal value, delivering the leaching solution at the first target operational condition from the leaching solution regulating system to a subsurface leaching distribution system, and delivering the leaching solution at the first target operational condition from the subsurface leaching distribution system to the location of the recoverable metal value under a surface of the heap to leach and recover at least one metal value.
C22B 3/04 - Extraction of metal compounds from ores or concentrates by wet processes by leaching
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
An Unmanned Aerial Vehicle (“UAV”) system includes a UAV configured for lifting, positioning, and placing irrigation lines on a heap including side slopes. The UAV can be equipped with an irrigation line support structure configured for supporting, transporting and/or releasing one or more irrigation lines. Placement of the irrigation lines via the UAV system can enable accurate distribution of raffinate solutions to enhance leaching efficiency in areas which are typically difficult to access.
Arizona Board of Regents on behalf of Arizona State University (USA)
FREEPORT MINERALS CORPORATION (USA)
Inventor
Delgado, Anca
Miranda, Evelyn
Hamdan, Nasser
Santisteban, Leonard
Grubb, Dennis
Abstract
Described herein is a method of purifying contaminated fluid influent, the method comprising: providing a reactor comprising a reactor inlet, a reactor outlet, and a purification composition; circulating contaminated fluid influent through the reactor to create a slag-treated fluid; providing a biochemical reactor comprising a biochemical reactor inlet, a biochemical reactor outlet, and a purification media; and circulating the slag-treated fluid through the biochemical reactor to generate a purified fluid. Also described herein is a contaminated fluid influent purification system, comprising: a reactor having a reactor inlet, a reactor outlet, and a purification composition; a biochemical reactor having a biochemical reactor inlet, a biochemical outlet, and a purification media; a settling tank; and a mixing tank; wherein the mixing tank is fluidly connected to a contaminated fluid influent source, the reactor and the settling tank; and wherein the settling tank is fluidly connected to the mixing tank and the biochemical reactor.
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.
A chain-install apparatus for facilitating installation of a chain into a conveyor system (e.g., a conveyor system for a cathode stripping machine) comprises: a support structure; a hub assembly coupled to the support structure, a brake assembly operably coupled to the hub assembly, and an attachment arrangement configured to facilitate lifting of the chain-install apparatus by a crane.
B65H 49/34 - Arrangements for effecting positive rotation of packages
B66C 1/10 - Load-engaging elements or devices attached to lifting, lowering, or hauling gear of cranes, or adapted for connection therewith for transmitting forces to articles or groups of articles by mechanical means
25.
SYSTEM AND METHOD FOR DETERMINING A LOCATION OF ORE IN A STOCKPILE
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.
G01N 15/0227 - Investigating particle size or size distribution by optical means using imagingInvestigating particle size or size distribution by optical means using holography
The system may include a secondary irrigation feature that determines a percent of overlap of each of a plurality of submodules in a first lift over each of a plurality of submodules in a second lift and adjusts at least one of leaching operations or a leaching model based on the total tonnage weighted average of metal in the second lift. The method may further comprise determining an acid gap based on a difference between total acid given and total acid consumption; and further adjusting at least one of the leaching operations or the leaching model based on the acid gap. The method may further comprise determining a percentage of compacted material based on the material that is compacted and irrigated divided by the material that is irrigated; and further adjusting at least one of the leaching operations or the leaching model based on the percentage of compacted material.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
The system may include a secondary irrigation feature that determines a percent of overlap of each of a plurality of submodules in a first lift over each of a plurality of submodules in a second lift and adjusts at least one of leaching operations or a leaching model based on the total tonnage weighted average of metal in the second lift. The method may further comprise determining an acid gap based on a difference between total acid given and total acid consumption; and further adjusting at least one of the leaching operations or the leaching model based on the acid gap. The method may further comprise determining a percentage of compacted material based on the material that is compacted and irrigated divided by the material that is irrigated; and further adjusting at least one of the leaching operations or the leaching model based on the percentage of compacted material.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
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.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
The system may include a secondary irrigation feature that determines a percent of overlap of each of a plurality of submodules in a second lift over each of a plurality of submodules in a first lift and adjusts at least one of leaching operations or a leaching model based on the total tonnage weighted average of metal in the second lift. The method may further comprise determining an acid gap based on a difference between total acid given and total acid consumption; and further adjusting at least one of the leaching operations or the leaching model based on the acid gap. The method may further comprise determining a percentage of compacted material based on the material that is compacted and irrigated divided by the material that is irrigated; and further adjusting at least one of the leaching operations or the leaching model based on the percentage of compacted material.
C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
A method of acid mist suppression in copper electrowinning is described. In various embodiments, at least one liquid licorice root extract, powdered licorice root extract, or reconstituted licorice extract is added in an amount sufficient to the acidic electrolyte solution of the copper electrowinning process to suppress acid mist from the acidic electrolyte solution during the copper electrowinning process. In various embodiments, combinations of licorice extract and surfactant show synergies in acid mist suppression during copper electrowinning.
A device and method for recovering a metal-bearing solution from a leaching system is provided. The method includes collecting, using the collector of a solution sampling device, a metal-bearing solution from a leaching system, storing, using the storage reservoir of the solution sampling device, the metal-bearing solution, pressurizing, using an air pressure line of the solution sampling device, the storage reservoir, and discharging, using a solution sampling line of the solution sampling device, the metal-bearing solution from the storage reservoir.
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
A drum assembly for a ball mill system may comprise: a drum having a first flange extending axially forward from a first radial wall, a second flange extending axially aft from a second radial wall, and a cylinder shell extending axially from a first radially outer end of the first radial wall to a second radially outer end of the second radial wall; a frame coupled to the first flange; and an inlet liner coupled to the frame, the inlet liner comprising a plurality of inlet segments disposed circumferentially adjacent to each other, the inlet liner defining an inlet radius for the drum assembly.
B02C 17/10 - Disintegrating by tumbling mills, i.e. mills having a container charged with the material to be disintegrated with or without special disintegrating members such as pebbles or balls with one or a few disintegrating members arranged in the container
B02C 17/18 - Disintegrating by tumbling mills, i.e. mills having a container charged with the material to be disintegrated with or without special disintegrating members such as pebbles or balls Details
34.
METHODS AND SYSTEMS FOR LEACHING A METAL-BEARING MATERIAL
Methods for recovering a metal value from a metal-bearing material are provided. The method comprises agglomerating the metal-bearing material with an agglomeration solution comprising a raffinate, an oxidant, and citric acid or salts thereof to form an agglomerated metal-bearing material; leaching the agglomerated metal-bearing material with a leaching solution comprising the raffinate and the citric acid or salts thereof to produce a pregnant leaching solution and a leached material; re-oxidizing the leached material with a curing solution comprising the raffinate and the oxidant; and recovering the metal value from the pregnant leach solution to produce the raffinate.
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
A method of acid mist suppression in copper electrowinning is described. In various embodiments, at least one liquid licorice root extract, powdered licorice root extract, or reconstituted licorice extract is added in an amount sufficient to the acidic electrolyte solution of the copper electrowinning process to suppress acid mist from the acidic electrolyte solution during the copper electrowinning process. In various embodiments, combinations of licorice extract and surfactant show synergies in acid mist suppression during copper electrowinning.
The system may include a secondary irrigation feature that determines a percent of overlap of each of a plurality of submodules in a first lift over each of a plurality of submodules in a second lift and adjusts at least one of leaching operations or a leaching model based on the total tonnage weighted average of metal in the second lift. The method may further comprise determining an acid gap based on a difference between total acid given and total acid consumption; and further adjusting at least one of the leaching operations or the leaching model based on the acid gap. The method may further comprise determining a percentage of compacted material based on the material that is compacted and irrigated divided by the material that is irrigated; and further adjusting at least one of the leaching operations or the leaching model based on the percentage of compacted material.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
A drum assembly for a ball mill system may comprise: a drum having a first flange extending axially forward from a first radial wall, a second flange extending axially aft from a second radial wall, and a cylinder shell extending axially from a first radially outer end of the first radial wall to a second radially outer end of the second radial wall; a frame coupled to the first flange; and an inlet liner coupled to the frame, the inlet liner comprising a plurality of inlet segments disposed circumferentially adjacent to each other, the inlet liner defining an inlet radius for the drum assembly.
B02C 17/10 - Disintegrating by tumbling mills, i.e. mills having a container charged with the material to be disintegrated with or without special disintegrating members such as pebbles or balls with one or a few disintegrating members arranged in the container
B02C 17/18 - Disintegrating by tumbling mills, i.e. mills having a container charged with the material to be disintegrated with or without special disintegrating members such as pebbles or balls Details
The present disclosure provides a method comprising determining an ore map for a heap to identify a location of a recoverable metal value in the heap, delivering a leaching solution from a leaching solution source to a leaching solution regulating system, regulating at least one of a pressure, a mass flow rate, or a volumetric flow rate of the leaching solution to achieve a first target operational condition, wherein the first target operational condition is selected to optimize a set of operational parameters to maximize recovery of the recoverable metal value, delivering the leaching solution at the first target operational condition from the leaching solution regulating system to a subsurface leaching distribution system, and delivering the leaching solution at the first target operational condition from the subsurface leaching distribution system to the location of the recoverable metal value under a surface of the heap to leach and recover at least one metal value.
The present disclosure provides a method comprising determining an ore map for a heap to identify a location of a recoverable metal-bearing material in the heap, wherein the metal-bearing material comprises iron and at least one other metal value, delivering a leaching solution from a leaching solution source to a leaching solution regulating system, wherein the leaching solution comprises an effective amount of citric acid and hydrogen peroxide, regulating at least one of a pressure, a mass flow rate, or a volumetric flow rate of the leaching solution to achieve a target operational condition, wherein the target operational condition is selected to optimize a set of operational parameters to maximize recovery of the at least one other metal value, delivering the leaching solution at the target operational condition from the leaching solution regulating system to the subsurface leaching distribution system, and delivering the leaching solution at the target operational condition from the subsurface leaching distribution system to the location of the recoverable metal-bearing material under a surface of the heap to leach and recover the at least one other metal value.
E21B 43/28 - Dissolving minerals other than hydrocarbons, e.g. by an alkaline or acid leaching agent
G01F 1/36 - Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using mechanical effects by measuring pressure or differential pressure the pressure or differential pressure being created by the use of flow constriction
G01F 15/00 - Details of, or accessories for, apparatus of groups insofar as such details or appliances are not adapted to particular types of such apparatus
G01F 1/34 - Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using mechanical effects by measuring pressure or differential pressure
G01F 1/74 - Devices for measuring flow of a fluid or flow of a fluent solid material in suspension in another fluid
43.
SYSTEM AND METHOD FOR DETERMINING CHANGES TO A HEAT PROFILE OF A STOCKPILE
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
C22B 3/04 - Extraction of metal compounds from ores or concentrates by wet processes by leaching
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.
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
C22B 3/04 - Extraction of metal compounds from ores or concentrates by wet processes by leaching
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
47.
SYSTEM AND METHOD FOR DETERMINING LIFT HEIGHTS OF A STOCKPILE
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.
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
C22B 3/04 - Extraction of metal compounds from ores or concentrates by wet processes by leaching
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
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.
G05B 19/41 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by interpolation, e.g. the computation of intermediate points between programmed end points to define the path to be followed and the rate of travel along that path
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
C22B 3/04 - Extraction of metal compounds from ores or concentrates by wet processes by leaching
55.
System and method for determining estimated remaining mineral in a stockpile
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.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
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.
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.
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
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.
G01N 15/0227 - Investigating particle size or size distribution by optical means using imagingInvestigating particle size or size distribution by optical means using holography
62.
System and method for determining lift heights of a stockpile
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.
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.
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Various embodiments provide a leaching solution monitoring module comprising a first leaching solution distribution system interface, a flow meter in fluid communication with the first leaching solution distribution system interface, the flow meter in fluid communication a 3-way pressure regulator, and a second leaching solution distribution system interface in fluid communication with the 3-way pressure regulator.
A drum assembly for a ball mill system may comprise: a drum having a first flange extending axially forward from a first radial wall, a second flange extending axially aft from a second radial wall, and a cylinder shell extending axially from a first radially outer end of the first radial wall to a second radially outer end of the second radial wall; a frame coupled to the first flange; and an inlet liner coupled to the frame, the inlet liner comprising a plurality of inlet segments disposed circumferentially adjacent to each other, the inlet liner defining an inlet radius for the drum assembly.
B02C 17/18 - Disintegrating by tumbling mills, i.e. mills having a container charged with the material to be disintegrated with or without special disintegrating members such as pebbles or balls Details
67.
System and method for adjusting leaching operations 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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
The present disclosure relates to a metal recovery process comprising a solvent extraction process. In an exemplary embodiment, the solution extraction system comprises a plant with a first and second circuit. A high-grade pregnant leach solution (“HGPLS”) is provided to the first and second circuit, and a low-grade pregnant leach solution (“LGPLS”) is provided to the second circuit. The first circuit produces a rich electrolyte, which can be forwarded to a primary metal recovery, and a low-grade raffinate, which can be forwarded to a secondary metal recovery process. The second circuit produces a rich electrolyte, which can also be forwarded to the primary metal recovery process. The first and second circuits are in fluid communication with each other.
A method of acid mist suppression in copper electrowinning is described. In various embodiments, at least one liquid licorice root extract, powdered licorice root extract, or reconstituted licorice extract is added in an amount sufficient to the acidic electrolyte solution of the copper electrowinning process to suppress acid mist from the acidic electrolyte solution during the copper electrowinning process. In various embodiments, combinations of licorice extract and surfactant show synergies in acid mist suppression during copper electrowinning.
A method of acid mist suppression in copper electrowinning is described. In various embodiments, at least one liquid licorice root extract, powdered licorice root extract, or reconstituted licorice extract is added in an amount sufficient to the acidic electrolyte solution of the copper electrowinning process to suppress acid mist from the acidic electrolyte solution during the copper electrowinning process. In various embodiments, combinations of licorice extract and surfactant show synergies in acid mist suppression during copper electrowinning.
A method of acid mist suppression in copper electrowinning is described. In various embodiments, at least one liquid licorice root extract, powdered licorice root extract, or reconstituted licorice extract is added in an amount sufficient to the acidic electrolyte solution of the copper electrowinning process to suppress acid mist from the acidic electrolyte solution during the copper electrowinning process. In various embodiments, combinations of licorice extract and surfactant show synergies in acid mist suppression during copper electrowinning.
Methods for recovering a metal value from a metal-bearing material are provided. The method comprises agglomerating the metal-bearing material with an agglomeration solution comprising a raffinate, an oxidant, and citric acid or salts thereof to form an agglomerated metal-bearing material; leaching the agglomerated metal-bearing material with a leaching solution comprising the raffinate and the citric acid or salts thereof to produce a pregnant leaching solution and a leached material; re-oxidizing the leached material with a curing solution comprising the raffinate and the oxidant; and recovering the metal value from the pregnant leach solution to produce the raffinate.
A system and method for recovering a metal value from a metal-bearing material is provided. The method includes agglomerating the metal-bearing material with an agglomeration solution, which contains a raffinate and hydrogen peroxide, to form an agglomerated metal-bearing material. The method further includes leaching the agglomerated metal-bearing material with a leaching solution, which contains the raffinate and citric acid, to produce a pregnant leaching solution. The method further includes recovering the metal value from the pregnant leaching solution to produce the raffinate.
Methods for recovering a metal value from a metal-bearing material are provided. The method comprises agglomerating the metal-bearing material with an agglomeration solution comprising a raffinate, an oxidant, and citric acid or salts thereof to form an agglomerated metal-bearing material; leaching the agglomerated metal-bearing material with a leaching solution comprising the raffinate and the citric acid or salts thereof to produce a pregnant leaching solution and a leached material; re-oxidizing the leached material with a curing solution comprising the raffinate and the oxidant; and recovering the metal value from the pregnant leach solution to produce the raffinate.
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
The present invention provides a method and system for separating a liquid from organic particles. The mixer-settler extraction cell includes a flow distributor. The flow distributor comprises a chevron-shaped series of welded plates, which separates the incoming flow stream of liquid and organic particles from one another.
Various embodiments provide a leaching solution monitoring module comprising a first leaching solution distribution system interface, a flow meter in fluid communication with the first leaching solution distribution system interface, the flow meter in fluid communication a 3-way pressure regulator, and a second leaching solution distribution system interface in fluid communication with the 3-way pressure regulator.
The present disclosure provides a method of recovering copper, molybdenum, and a precious metal value from a metal-bearing material, the method comprising bulk flotation of the metal-bearing material to form a flotation product, wherein the metal-bearing material comprises a copper compound, a molybdenum compound, at least one precious metal value, and a silicate, pressure oxidizing the flotation product to form a pressure oxidized discharge, separating the pressure oxidized discharge to form a separated liquid and separated solid, extracting molybdenum, via a molybdenum solution extraction, from the separated liquid to form a molybdenum-containing stream and a copper-containing stream, extracting copper, via a copper solution extraction, from the copper-containing stream, and extracting the precious metal value, via a cyanide leaching process, from the separated solid.
The present disclosure provides a method of recovering copper, molybdenum, and a precious metal value from a metal-bearing material, the method comprising bulk flotation of the metal-bearing material to form a flotation product, wherein the metal-bearing material comprises a copper compound, a molybdenum compound, and at least one precious metal value, pressure oxidizing the flotation product to form a pressure oxidized discharge, separating the pressure oxidized discharge to form a separated liquid and separated solid, extracting molybdenum, via a molybdenum solution extraction, from the separated liquid to form a molybdenum-containing stream and a copper-containing stream, extracting copper, via a copper solution extraction, from the copper-containing stream, and extracting the precious metal value, via a cyanide leaching process, from the separated solid.
The present disclosure relates to a metal recovery process comprising a solvent extraction process. In an exemplary embodiment, the solution extraction system comprises a plant with a first and second circuit. A high-grade pregnant leach solution (“HGPLS”) is provided to the first and second circuit, and a low-grade pregnant leach solution (“LGPLS”) is provided to the second circuit. The first circuit produces a rich electrolyte, which can be forwarded to a primary metal recovery, and a low-grade raffinate, which can be forwarded to a secondary metal recovery process. The second circuit produces a rich electrolyte, which can also be forwarded to the primary metal recovery process. The first and second circuits are in fluid communication with each other.
Various embodiments provide a leaching solution monitoring module comprising a first leaching solution distribution system interface, a flow meter in fluid communication with the first leaching solution distribution system interface, the flow meter in fluid communication a 3-way pressure regulator, and a second leaching solution distribution system interface in fluid communication with the 3-way pressure regulator.
The present invention provides a method and system for separating a liquid from organic particles. The mixer-settler extraction cell includes a flow distributor. The flow distributor comprises a chevron-shaped series of welded plates, which separates the incoming flow stream of liquid and organic particles from one another.
The present invention provides a method and system for separating a liquid from organic particles. The mixer-settler extraction cell includes a flow distributor. The flow distributor comprises a chevron-shaped series of welded plates, which separates the incoming flow stream of liquid and organic particles from one another.
The present disclosure relates to a metal recovery process comprising a solvent extraction process. In an exemplary embodiment, the solution extraction system comprises a plant with a first and second circuit. A high-grade pregnant leach solution (“HGPLS”) is provided to the first and second circuit, and a low-grade pregnant leach solution (“LGPLS”) is provided to the second circuit. The first circuit produces a rich electrolyte, which can be forwarded to a primary metal recovery, and a low-grade raffinate, which can be forwarded to a secondary metal recovery process. The second circuit produces a rich electrolyte, which can also be forwarded to the primary metal recovery process. The first and second circuits are in fluid communication with each other.
A system and method for recovering a metal value from a metal-bearing ore material are provided. A metal-bearing ore can be mixed with certain substances and to form an agglomerated ore. In an intermediate state, between agglomeration and heap formation, bacteria can be added to the metal-bearing ore material to produce an augmented ore. The augmented ore can then be formed into a heap. Bacteria from the heap may be fortified to assist in bacterial growth.
Various embodiments provide a leaching solution monitoring module comprising a first leaching solution distribution system interface, a flow meter in fluid communication with the first leaching solution distribution system interface, the flow meter in fluid communication a 3-way pressure regulator, and a second leaching solution distribution system interface in fluid communication with the 3-way pressure regulator.
The present disclosure relates to a metal recovery process comprising a solvent extraction process. In an exemplary embodiment, the solution extraction system comprises a plant with a first and second circuit. A high-grade pregnant leach solution (“HGPLS”) is provided to the first and second circuit, and a low-grade pregnant leach solution (“LGPLS”) is provided to the second circuit. The first circuit produces a rich electrolyte, which can be forwarded to a primary metal recovery, and a low-grade raffinate, which can be forwarded to a secondary metal recovery process. The second circuit produces a rich electrolyte, which can also be forwarded to the primary metal recovery process. The first and second circuits are in fluid communication with each other.
A process and system is provided for recovery of one or more metal values using solution extraction techniques and for metal value recovery. In an exemplary embodiment, the solution extraction system comprises a first solution extraction circuit and a second solution extraction circuit. A first metal-bearing solution is provided to the first and second circuit, and a second metal-bearing solution is provided to the first circuit. The first circuit produces a first rich electrolyte solution, which can be forwarded to primary metal value recovery, and a low-grade raffinate, which is forwarded to secondary metal value recovery. The second circuit produces a second rich electrolyte solution, which is also forwarded to primary metal value recovery. The first and second solution extraction circuits have independent organic phases and each circuit can operate independently of the other circuit.
C25C 1/08 - Electrolytic production, recovery or refining of metals by electrolysis of solutions of iron group metals, refractory metals or manganese of nickel or cobalt
C22B 3/26 - Treatment or purification of solutions, e.g. obtained by leaching by liquid-liquid extraction using organic compounds
C22B 61/00 - Obtaining metals not elsewhere provided for in this subclass
C25C 1/10 - Electrolytic production, recovery or refining of metals by electrolysis of solutions of iron group metals, refractory metals or manganese of chromium or manganese
89.
Systems and methods for monitoring metal recovery systems
Various embodiments provide a leaching solution monitoring module comprising a first leaching solution distribution system interface, a flow meter in fluid communication with the first leaching solution distribution system interface, the flow meter in fluid communication a 3-way pressure regulator, and a second leaching solution distribution system interface in fluid communication with the 3-way pressure regulator.
Various embodiments provide a leaching solution monitoring module comprising a first leaching solution distribution system interface, a flow meter in fluid communication with the first leaching solution distribution system interface, the flow meter in fluid communication a 3-way pressure regulator, and a second leaching solution distribution system interface in fluid communication with the 3-way pressure regulator.
The present disclosure relates to a process and system for recovery of one or more metal values using solution extraction techniques and to a system for metal value recovery. In an exemplary embodiment, the solution extraction system comprises a first solution extraction circuit and a second solution extraction circuit. A first metal-bearing solution is provided to the first and second circuit, and a second metal-bearing solution is provided to the first circuit. The first circuit produces a first rich electrolyte solution, which can be forwarded to primary metal value recovery, and a low-grade raffinate, which is forwarded to secondary metal value recovery. The second circuit produces a second rich electrolyte solution, which is also forwarded to primary metal value recovery. The first and second solution extraction circuits have independent organic phases and each circuit can operate independently of the other circuit.
C25C 1/22 - Electrolytic production, recovery or refining of metals by electrolysis of solutions of metals not provided for in groups
C25C 1/08 - Electrolytic production, recovery or refining of metals by electrolysis of solutions of iron group metals, refractory metals or manganese of nickel or cobalt
C25C 7/00 - Constructional parts, or assemblies thereof, of cellsServicing or operating of cells
C25C 1/00 - Electrolytic production, recovery or refining of metals by electrolysis of solutions
C22B 3/26 - Treatment or purification of solutions, e.g. obtained by leaching by liquid-liquid extraction using organic compounds
B01D 11/04 - Solvent extraction of solutions which are liquid
C22B 3/44 - Treatment or purification of solutions, e.g. obtained by leaching by chemical processes
C22B 3/00 - Extraction of metal compounds from ores or concentrates by wet processes
Various embodiments provide a leaching solution monitoring module comprising a first leaching solution distribution system interface, a flow meter in fluid communication with the first leaching solution distribution system interface, the flow meter in fluid communication a 3-way pressure regulator, and a second leaching solution distribution system interface in fluid communication with the 3-way pressure regulator.
The present disclosure relates to a metal recovery process comprising a solvent extraction process. In an exemplary embodiment, the solution extraction system comprises a plant with a first and second circuit. A high-grade pregnant leach solution (“HGPLS”) is provided to the first and second circuit, and a low-grade pregnant leach solution (“LGPLS”) is provided to the second circuit. The first circuit produces a rich electrolyte, which can be forwarded to a primary metal recovery, and a low-grade raffinate, which can be forwarded to a secondary metal recovery process. The second circuit produces a rich electrolyte, which can also be forwarded to the primary metal recovery process. The first and second circuits are in fluid communication with each other.
The present disclosure relates to a process and system for recovery of one or more metal values using solution extraction techniques and to a system for metal value recovery. In an exemplary embodiment, the solution extraction system comprises a first solution extraction circuit and a second solution extraction circuit. A first metal-bearing solution is provided to the first and second circuit, and a second metal-bearing solution is provided to the first circuit. The first circuit produces a first rich electrolyte solution, which can be forwarded to primary metal value recovery, and a low-grade raffinate, which is forwarded to secondary metal value recovery. The second circuit produces a second rich electrolyte solution, which is also forwarded to primary metal value recovery. The first and second solution extraction circuits have independent organic phases and each circuit can operate independently of the other circuit.
C25C 1/10 - Electrolytic production, recovery or refining of metals by electrolysis of solutions of iron group metals, refractory metals or manganese of chromium or manganese
C22B 3/44 - Treatment or purification of solutions, e.g. obtained by leaching by chemical processes
C22B 3/00 - Extraction of metal compounds from ores or concentrates by wet processes
C22B 60/02 - Obtaining thorium, uranium or other actinides
C22B 61/00 - Obtaining metals not elsewhere provided for in this subclass
C25C 1/08 - Electrolytic production, recovery or refining of metals by electrolysis of solutions of iron group metals, refractory metals or manganese of nickel or cobalt
C25C 7/00 - Constructional parts, or assemblies thereof, of cellsServicing or operating of cells
95.
Methods and systems for leaching a metal-bearing ore
A system and method for recovering a metal value from a metal-bearing ore material are provided. A metal-bearing ore can be mixed with certain substances and to form an agglomerated ore. In an intermediate state, between agglomeration and heap formation, bacteria can be added to the metal-bearing ore material to produce an augmented ore. The augmented ore can then be formed into a heap. Bacteria from the heap may be fortified to assist in bacterial growth.
A treatment method includes the steps of: Providing an initial supply of an ammonium octamolybdate precursor powder having a bi-modal particle size distribution; applying a quantity of solvent to the initial supply of ammonium octamolybdate precursor powder to form a moistened intermediate powder; and allowing the moistened intermediate powder to adsorb the applied solvent over a time period, the quantity of solvent applied and the time period being sufficient to form a treated ammonium octamolybdate powder composition having a substantially uni-modal particle size distribution.
Systems and methods for basic leaching are provided. In various embodiments, a method is provided comprising leaching a slurry comprising a copper bearing material and a leach medium comprising ammonia and ammonium carbonate, adding copper powder to the slurry, separating the slurry into a pregnant leach solution and solids, and performing a solvent extraction on the pregnant leach solution to produce an loaded aqueous stream.
Systems and methods for basic leaching are provided. In various embodiments, a method is provided comprising leaching a slurry comprising a copper bearing material and an ammonia leach medium, adding copper powder to the slurry, separating the slurry into a pregnant leach solution and solids, and performing a solvent extraction on the pregnant leach solution to produce an loaded aqueous stream.
C22B 3/14 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic alkaline solutions containing ammonia or ammonium salts
Metal value recovery processes for the recovery of multiple metal values from metal-bearing materials are provided. Processes and methods provided may include the use of multiple leach stages, as well as multiple solvent extraction stages. Raffinates from the solvent extraction stages can be combined to improve metal value recovery.
A system and method for recovering a metal value from a metal-bearing ore material are provided. A metal-bearing ore can be mixed with certain substances and to form an agglomerated ore. In an intermediate state, between agglomeration and heap formation, bacteria can be added to the metal-bearing ore material to produce an augmented ore. The augmented ore can then be formed into a heap. Bacteria from the heap may be fortified to assist in bacterial growth.
