Abstract: 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.

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.

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.

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.

Abstract: 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.

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.

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.

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.

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.

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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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.

Abstract: 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.

Abstract: 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.

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.

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.

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.

Abstract: 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.