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.

IPC Classes  ?

• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06Q 50/02 - Agriculture; Fishing; Mining
• C22B 15/00 - Obtaining copper
• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

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.

IPC Classes  ?

• G01C 5/00 - Measuring height; Measuring distances transverse to line of sight; Levelling between separated points; Surveyors' levels
• C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions

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.

IPC Classes  ?

• 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
• C22B 11/00 - Obtaining noble metals
• G06Q 50/02 - Agriculture; Fishing; Mining
• G06Q 10/063 - Operations research, analysis or management

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.

IPC Classes  ?

• G06Q 50/02 - Agriculture; Fishing; Mining
• G01N 33/24 - Earth materials
• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations

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.

IPC Classes  ?

• 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
• C22B 11/00 - Obtaining noble metals
• G06Q 50/02 - Agriculture; Fishing; Mining
• G06Q 10/063 - Operations research, analysis or management

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.

IPC Classes  ?

• G06Q 50/02 - Agriculture; Fishing; Mining
• 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 15/00 - Obtaining copper
• C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
• C22B 3/08 - Sulfuric acid

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.

IPC Classes  ?

• G06Q 50/02 - Agriculture; Fishing; Mining
• 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 15/00 - Obtaining copper
• C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
• C22B 3/08 - Sulfuric acid

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.

IPC Classes  ?

• G06Q 50/02 - Agriculture; Fishing; Mining
• 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 15/00 - Obtaining copper
• C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
• C22B 3/08 - Sulfuric acid

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.

IPC Classes  ?

• 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 management; Enterprise or organisation planning; Enterprise or organisation modelling
• G06Q 50/02 - Agriculture; Fishing; Mining

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.

IPC Classes  ?

• 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 management; Enterprise or organisation planning; Enterprise or organisation modelling
• C22B 15/00 - Obtaining copper
• C22B 3/06 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic acid solutions
• G06Q 50/02 - Agriculture; Fishing; Mining

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.

IPC Classes  ?

• 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
• C22B 11/00 - Obtaining noble metals
• G06Q 50/02 - Agriculture; Fishing; Mining
• G06Q 10/063 - Operations research, analysis or management

Abstract

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.

IPC Classes  ?

• C25C 1/12 - Electrolytic production, recovery or refining of metals by electrolysis of solutions of copper
• C25D 3/38 - Electroplating; Baths therefor from solutions of copper
• C25D 21/04 - Removal of gases or vapours

Abstract

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.

IPC Classes  ?

• C22B 3/04 - Extraction of metal compounds from ores or concentrates by wet processes by leaching
• C22B 15/00 - Obtaining copper
• C22B 3/08 - Sulfuric acid
• C22B 3/16 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in organic solutions

Abstract

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.

IPC Classes  ?

• C22B 3/00 - Extraction of metal compounds from ores or concentrates by wet processes
• C22B 11/08 - Obtaining noble metals by cyaniding
• C22B 15/00 - Obtaining copper
• C22B 19/00 - Obtaining zinc or zinc oxide
• C22B 23/00 - Obtaining nickel or cobalt
• C22B 34/34 - Obtaining molybdenum

Abstract

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.

IPC Classes  ?

• C22B 3/00 - Extraction of metal compounds from ores or concentrates by wet processes
• C22B 11/08 - Obtaining noble metals by cyaniding
• C22B 15/00 - Obtaining copper
• C22B 19/00 - Obtaining zinc or zinc oxide
• C22B 23/00 - Obtaining nickel or cobalt
• C22B 34/34 - Obtaining molybdenum

Abstract

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.

IPC Classes  ?

• C22B 3/04 - Extraction of metal compounds from ores or concentrates by wet processes by leaching

Abstract

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.

IPC Classes  ?

• C22B 15/00 - Obtaining copper
• C22B 3/02 - Apparatus therefor
• C22B 3/12 - Extraction of metal compounds from ores or concentrates by wet processes by leaching in inorganic alkaline solutions
• C22B 3/26 - Treatment or purification of solutions, e.g. obtained by leaching by liquid-liquid extraction using organic compounds

Abstract

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.

IPC Classes  ?

• 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
• C22B 3/02 - Apparatus therefor
• C22B 15/00 - Obtaining copper
• C22B 3/26 - Treatment or purification of solutions, e.g. obtained by leaching by liquid-liquid extraction using organic compounds

Abstract

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.

IPC Classes  ?

• C22B 3/04 - Extraction of metal compounds from ores or concentrates by wet processes by leaching
• C22B 3/26 - Treatment or purification of solutions, e.g. obtained by leaching by liquid-liquid extraction using organic compounds

Abstract

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.

IPC Classes  ?

• C22B 3/18 - Extraction of metal compounds from ores or concentrates by wet processes with the aid of microorganisms or enzymes, e.g. bacteria or algae
• C22B 15/00 - Obtaining copper