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 present disclosure provides methods and systems for recovering metals from lithium-ion batteries, and specifically to methods and systems for recovering cobalt and nickel jointly in metallic form via electrowinning processes. The present disclosure further provides methods and systems for preparing lithium-ion battery materials for use in metal recovery processes.

A process for recovery of one or more target metals, selected from precious metals and chalcophile metals as respectively herein defined, from materials containing precious and/or chalcophile metal/s, said process including: (i) leaching the metal containing material with an aqueous solution containing: a metal liberator comprising an amino acid; and a metal retainer comprising one or more of ammonia, ammonium salts, carboxylic acids, carboxylic acid salts, dicarboxylic acids, dicarboxylic acid salts, hydroxy-carboxylic acids, hydroxy-carboxylic acid salts, ethylene diamine tetra-acetic acid (EDTA) and EDTA salts, to produce a leachate containing the target metal/s; and (ii) extracting the metal from the leachate.

The present invention relates to use of alkysulfonic acids, with optionally an oxidant peroxide to leach copper from copper sulfide minerals or copper concentrates, a dichromate oxidant or a nitrate oxidant, or a combination thereof.

A method for processing lithium ion battery scrap includes a leaching step of leaching lithium ion battery scrap and subjecting the resulting leached solution to solid-liquid separation to obtain a first separated solution; an iron removal step of adding an oxidizing agent to the first separated solution and adjusting a pH of the first separated solution in a range of from 3.0 to 4.0, then performing solid-liquid separation and removing iron in the first separated solution to obtain a second separated solution; and an aluminum removal step of neutralizing the second separated solution to a pH range of from 4.0 to 6.0, then performing solid-liquid separation and removing aluminum in the second separated solution to obtain a third separated solution.

A method of extracting metals from polymetallic sulphide ores or concentrates comprising at least Cu, Zn, Pb and Ag, comprising a first step of atmospheric leaching in sulphate medium in the presence of recycled silver for extracting Cu and Zn and a second step of atmospheric leaching in chloride medium for extracting Pb and Ag.

A process for recovering copper from heap leach residues containing residual copper, includes identifying a production zone within the heap leach residues for secondary leaching, drilling wells into the heap at locations suitable for delivering leach solution into the production zone, injecting the leach solution including ferric ions through the wells and aerating the production zone to facilitate oxidative reactions within the production zone, and collecting effluent from the heap for copper recovery therefrom.

A heap of a material to be leached to recover a valuable metal from the material includes an electromagnetic heating system to generate heat in situ in the heap.