Provided is a method for preparing metal powder; a metal nitrate or metal sulfate is combined with ammonia water to produce an ammonia-containing complex metal salt solution; the said solution is then quantitatively jet-mixed with the solution containing the hydroxylamine compounds, and reacted under intense stirring; a dispersant solution is added during the reaction process, and after the reaction is complete, the solution is separated by centrifugation to yield the metal power. The method of the present invention can effectively control the reaction rate during the production process, and well control the nucleation rate and dispersity, with the produced metal powder having very good crystallinity, sphericity, tap property and dispersity.

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.

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.

A method of operating a concentrator may comprise producing a final copper concentrate and periodically or continuously analyzing the produced final copper concentrate to obtain a grade value of the produced final copper concentrate. The produced final copper concentrate may be sent to a downstream smelting operation if/when the grade value of the produced final copper concentrate is at or above a minimum acceptable grade threshold and may be sent to a downstream hydrometallurgical operation capable of producing copper cathode, if/when the grade value of the produced final copper concentrate is below or falls below the minimum acceptable grade threshold.

The processes of the present disclosure can comprise feeding a furnace with a raw material chosen from a copper-containing material, a nickel-containing material, a cobalt-containing material and mixtures thereof. These materials can be quite complex and contain various levels of impurities and valuable metals (base metals, precious metals, platinum group metals, minor metals). The processes allow the volatilization of arsenic and indium contained therein, thereby obtaining a material at least partially depleted in at least one of arsenic and indium, wherein before volatilizing the material, composition of the material is optionally modified so as to obtain a ratio % S/(% (Cu/2)+% Ni+% Co) of about 0.5 to about 2. The processes can comprise feeding a melting device with the depleted material, and with a source of carbon in order to obtain a multi-layer product and an off gas, wherein before melting the depleted material, the depleted material composition is optionally modified so as to obtain a ratio % S/(% (Cu/2)+% Ni+% Co) of about 0.5 to about 2. While one of the main purposes of the processes of the present disclosure is to recover Cu, Ni and Co from complex materials, it also provides a means of recovering several other metals, including In, Ge, Pb, Bi, precious metals and platinum group metals. Cu, Ni, Co and other metals are conveniently recovered in different products from the processes (gaseous, dust, slag, matte, speiss and metal).

Provided are smelting optimization methods and devices of copper-containing concentrate. The smelting optimization method includes: obtaining a water content and physical parameter of copper-containing concentrate powder; in response to the water content meeting a first preset condition, mixing the copper-containing concentrate powder with oxygen-rich gas to obtain a mixture, spraying the mixture into a smelting furnace by a nozzle; determining a first production parameter for producing matte based on the physical parameter, an oxygen content of the oxygen-rich gas and a gas temperature of the oxygen-rich gas; determining a second production parameter for producing matte by processing the physical parameter, the oxygen content of the oxygen-rich gas and the gas temperature of the oxygen-rich gas using a prediction model, wherein the prediction model is a machine learning model; and determining a target production parameter based on the first production parameter and the second production parameter.

A method and system of controlling a melting process of copper in a copper melting furnace including measuring at least one furnace parameter, wherein the at least one furnace parameter includes one or both of a furnace temperature and a furnace exhaust oxygen concentration, calculating a first rate of change of the furnace parameter over a first time period, calculating a second rate of change of the furnace parameter over a second time period at least a portion of which occurs after the first time period, comparing the first rate of change with the second rate of change, and indicating substantial completion of a process phase in the furnace when the second rate of change deviates by a predetermined threshold percentage from the first rate of change.

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.