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.

A method for monitoring copper-arsenic sulfidation separation in copper electrolyte purification process includes by PLC, timely acquiring changes in copper and arsenic concentrations in first-stage sulfidation monitoring module, determining a critical point where arsenic concentration slightly decreases, and interlocking gas inlet valve to close and liquid outlet valve to open, achieving high-copper precipitation with minor-arsenic precipitation; timely acquiring changes in copper and arsenic concentrations in second-stage sulfidation monitoring module, determining a critical point where copper concentration decreases to near zero, and interlocking gas inlet valve to close and liquid outlet valve to open, achieving complete-copper precipitation with minimal-arsenic precipitation; and timely acquiring changes in copper and arsenic concentrations in third-stage sulfidation monitoring module, determining a critical point where arsenic concentration decreases to a limit value, and interlocking gas inlet valve to close and liquid outlet valve to open, achieving stable arsenic concentration.

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.

This invention patent application addresses a system for the detection and quantification of mineralogical species via x-ray diffraction (XRD) of the concentrate of dry copper before it is injected into a converter or melting furnace. Specifically, it addresses a device that performs a mineralogical analysis, in line and in real time, of the concentrate of copper in the bath smelting furnace via x-ray diffraction (XRD), which allows for control over the ideal mixture for the optimal process for copper sulfide (Cu2S)-white metal, iron sulfide (FeS)-Slag and pyritic sulfur (S2)-temperature.

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.

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.