Abrégé

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.

Classes IPC  ?

• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• C22B 3/04 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés par lixiviation
• C22B 11/00 - Obtention des métaux nobles
• G06Q 50/02 - Agriculture; Pêche; Exploitation minière
• G06Q 10/063 - Recherche, analyse ou gestion opérationnelles

Abrégé

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.

Classes IPC  ?

• G06Q 50/02 - Agriculture; Pêche; Exploitation minière
• G01N 33/24 - Matériaux de la terre
• G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations

Abrégé

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.

Classes IPC  ?

• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• C22B 3/04 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés par lixiviation
• C22B 11/00 - Obtention des métaux nobles
• G06Q 50/02 - Agriculture; Pêche; Exploitation minière
• G06Q 10/063 - Recherche, analyse ou gestion opérationnelles

Abrégé

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.

Classes IPC  ?

• G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
• G06Q 50/02 - Agriculture; Pêche; Exploitation minière
• C22B 15/00 - Obtention du cuivre
• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"

Abrégé

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.

Classes IPC  ?

• G01C 5/00 - Mesure des hauteurs; Mesure des distances transversales par rapport à la ligne de visée; Nivellement entre des points séparés; Niveaux à lunette
• C22B 3/06 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés par lixiviation dans des solutions inorganiques acides

Abrégé

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.

Classes IPC  ?

• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G06Q 50/02 - Agriculture; Pêche; Exploitation minière

Abrégé

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.

Classes IPC  ?

• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• C22B 15/00 - Obtention du cuivre
• C22B 3/06 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés par lixiviation dans des solutions inorganiques acides
• G06Q 50/02 - Agriculture; Pêche; Exploitation minière

Abrégé

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.

Classes IPC  ?

• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• C22B 3/04 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés par lixiviation
• C22B 11/00 - Obtention des métaux nobles
• G06Q 50/02 - Agriculture; Pêche; Exploitation minière
• G06Q 10/063 - Recherche, analyse ou gestion opérationnelles

Abrégé

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.

Classes IPC  ?

• G06Q 50/02 - Agriculture; Pêche; Exploitation minière
• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
• C22B 15/00 - Obtention du cuivre
• C22B 3/06 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés par lixiviation dans des solutions inorganiques acides
• C22B 3/08 - Acide sulfurique

Abrégé

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.

Classes IPC  ?

• G06Q 50/02 - Agriculture; Pêche; Exploitation minière
• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
• C22B 15/00 - Obtention du cuivre
• C22B 3/06 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés par lixiviation dans des solutions inorganiques acides
• C22B 3/08 - Acide sulfurique

Abrégé

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.

Classes IPC  ?

• G06Q 50/02 - Agriculture; Pêche; Exploitation minière
• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
• C22B 15/00 - Obtention du cuivre
• C22B 3/06 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés par lixiviation dans des solutions inorganiques acides
• C22B 3/08 - Acide sulfurique

Abrégé

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.

Classes IPC  ?

• C25C 1/12 - Production, récupération ou affinage électrolytique des métaux par électrolyse de solutions du cuivre
• C25D 3/38 - Dépôt électrochimique; Bains utilisés à partir de solutions de cuivre
• C25D 21/04 - Enlèvement des gaz ou des vapeurs

Abrégé

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.

Classes IPC  ?

• C22B 3/04 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés par lixiviation
• C22B 15/00 - Obtention du cuivre
• C22B 3/08 - Acide sulfurique
• C22B 3/16 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés par lixiviation dans des solutions organiques

Abrégé

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.

Classes IPC  ?

• C22B 3/00 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés
• C22B 11/08 - Obtention des métaux nobles par cyanuration
• C22B 15/00 - Obtention du cuivre
• C22B 19/00 - Obtention du zinc ou de l'oxyde de zinc
• C22B 23/00 - Obtention du nickel ou du cobalt
• C22B 34/34 - Obtention du molybdène

Abrégé

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.

Classes IPC  ?

• C22B 3/00 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés
• C22B 11/08 - Obtention des métaux nobles par cyanuration
• C22B 15/00 - Obtention du cuivre
• C22B 19/00 - Obtention du zinc ou de l'oxyde de zinc
• C22B 23/00 - Obtention du nickel ou du cobalt
• C22B 34/34 - Obtention du molybdène

Abrégé

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.

Classes IPC  ?

• C22B 3/04 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés par lixiviation

Abrégé

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.

Classes IPC  ?

• C22B 15/00 - Obtention du cuivre
• C22B 3/02 - Appareillage à cet effet
• C22B 3/12 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés par lixiviation dans des solutions inorganiques alcalines
• C22B 3/26 - Traitement ou purification de solutions, p.ex. de solutions obtenues par lixiviation par extraction liquide-liquide utilisant des composés organiques

Abrégé

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.

Classes IPC  ?

• C22B 3/14 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés par lixiviation dans des solutions inorganiques alcalines contenant de l'ammoniaque ou des sels d'ammonium
• C22B 3/02 - Appareillage à cet effet
• C22B 15/00 - Obtention du cuivre
• C22B 3/26 - Traitement ou purification de solutions, p.ex. de solutions obtenues par lixiviation par extraction liquide-liquide utilisant des composés organiques

Abrégé

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.

Classes IPC  ?

• C22B 3/04 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés par lixiviation
• C22B 3/26 - Traitement ou purification de solutions, p.ex. de solutions obtenues par lixiviation par extraction liquide-liquide utilisant des composés organiques

Abrégé

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.

Classes IPC  ?

• C22B 3/18 - Extraction de composés métalliques par voie humide à partir de minerais ou de concentrés à l'aide de micro-organismes ou d'enzymes, p.ex. de bactéries ou d'algues
• C22B 15/00 - Obtention du cuivre