System and method for activating deep raffinate injection based on ore placement
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.
1 . A method comprising:
determining, by one or more processors, an amount of minerals extracted from first areas of ore placement locations of ore in a stockpile, based on historical leaching process data for the first areas of the ore placement locations in the stockpile;
determining, by the one or more processors, recovery locations for remaining recoverable minerals in second areas of the ore placement locations in the stockpile, based on the historical leaching process data for the first areas of the ore placement locations in the stockpile, wherein the first areas are different from the second areas;
reallocating the ore from the ore placement locations to side slopes of the stockpile, wherein the side slopes are in neighboring sections to the ore placement locations; and
activating, by the one or more processors, deep raffinate injection in the second areas of the ore placement locations in the stockpile that are determined to have the recovery locations for the remaining recoverable minerals.
2 . The method of claim 1 , wherein the ore placement locations relate to where the ore is located in the stockpile.
3 . The method of claim 1 , further comprising receiving, by the one or more processors and from sensors, ore placement data for the ore in the stockpile.
4 . The method of claim 3 , wherein the ore placement data comprises dispatch data, haul truck sensor data, polygon data, assay data, mineralogy data from a block model or mineralogy data.
5 . The method of claim 1 , further comprising determining, by the one or more processors, the ore placement locations for the ore in the stockpile, based on the ore placement data.
6 . The method of claim 1 , wherein the determining the amount of minerals extracted from the first areas of the ore placement locations in the stockpile is further based on a predictive model that is at least one of trained or tested using the historical leaching process data for the stockpile.
7 . The method of claim 1 , wherein the determining the amount of minerals extracted from the first areas of the ore placement locations in the stockpile comprises determining a primary ore map for the stockpile.
8 . The method of claim 7 , wherein the determining the primary ore map for the stockpile comprises adding flow data, irrigation data and a remaining mineral prediction from a machine learning model to obtain information by section and by date for the stockpile.
9 . The method of claim 1 , wherein the determining the ore placement locations for the stockpile includes determining mineralogy of the remaining recoverable minerals for the stockpile.
10 . The method of claim 1 , wherein the activating the deep raffinate injection comprises at least one of: sending a signal to a machine to drill holes at a depth to target the recovery locations for the remaining recoverable minerals in the second areas of the stockpile, or tailoring leach solution chemistry to mineralogy of the remaining recoverable minerals to use for the deep raffinate injection.
11 . The method of claim 1 , wherein the activating the deep raffinate injection comprises enhancing raffinate solutions over the remaining recoverable minerals with at least one of sulfuric acid, ferric ions, air bubbles, oxygen bubbles, heat, or microbes.
12 . The method of claim 1 , wherein the activating the deep raffinate injection comprises enhancing raffinate solutions over the remaining recoverable minerals with at least one of an acid, oxidant or additive.
13 . The method of claim 1 , wherein the recovery locations for the remaining recoverable minerals in the second areas of the stockpile for the deep raffinate injection is based on at least one of: the deep raffinate injection of leach solutions channeling within the second areas of the stockpile, or the leach solutions not contacting ore particles uniformly.
14 . The method of claim 1 , wherein the determining the ore placement locations for the stockpile includes determining mineralogy for the stockpile by:
aggregating mineralogy details to a section level by combining mine material tracking (MMT) truckload data at a dump level, MMT imputation data at the dump level and MMT final section mapping data at the dump level and the section level;
obtaining maximum days under leach (DUL) for each section at the section level by using irrigation data over all stockpiles at the section level; and
determining an intermediate ore map for the stockpile by combining the aggregating mineralogy details, the maximum DUL for each section and a primary new section polygon.
15 . The method of claim 1 , wherein leaching operations are conducted on the first areas of the ore placement locations in the stockpile to obtain the historical leaching process data for the stockpile.
16 . The method of claim 1 , further comprising at least one of:
determining x, y, z coordinates or determining time-series layering information for the recovery locations for the remaining recoverable minerals in the second areas of the stockpile.
17 . The method of claim 1 , further comprising at least one of:
providing a visualization of section mineralogy populated on a map of the stockpile;
providing a visualization of the recovery locations for the remaining recoverable minerals in the second areas of the stockpile;
defining boundaries of the stockpile based on polygons recorded in a geographic information system (GIS); or
estimating the remaining recoverable minerals in the second areas of the stockpile based on a column test model.
18 . A method comprising:
determining, by one or more processors, an amount of minerals extracted from first areas of ore placement locations of ore in a stockpile, based on historical leaching process data for the first areas of the ore placement locations in the stockpile,
wherein the ore placement locations are determined by:
aggregating mineralogy details to a section level by combining mine material tracking (MMT) truckload data at a dump level, MMT imputation data at the dump level and MMT final section mapping data at the dump level and the section level;
obtaining maximum days under leach (DUL) for each section at the section level by using irrigation data over the stockpile at the section level; and
determining an intermediate ore map for the stockpile by combining the aggregating mineralogy details, the maximum DUL for each section and a primary new section polygon;
determining, by the one or more processors, recovery locations for remaining recoverable minerals in second areas of the ore placement locations in the stockpile, based on the historical leaching process data for the first areas of the ore placement locations in the stockpile, wherein the first areas are different from the second areas; and
activating, by the one or more processors, deep raffinate injection in the second areas of the ore placement locations in the stockpile that are determined to have the recovery locations for the remaining recoverable minerals.
19 . A method comprising:
determining, by the one or more processors, an amount of minerals extracted from a stockpile by determining a primary ore map for the stockpile,
wherein the determining the primary ore map for the stockpile comprises adding flow data, irrigation data and a remaining mineral prediction from a machine learning model to obtain information by section and by date for the stockpile;
determining, by the one or more processors, recovery locations for remaining recoverable minerals in areas of the stockpile, based on historical leaching process data for the stockpile; and
activating, by the one or more processors, deep raffinate injection in the areas of the stockpile that are determined to have the recovery locations for the remaining recoverable minerals.
20 . A method comprising:
determining, by one or more processors, an amount of minerals extracted from first areas of ore placement locations of ore in a stockpile by determining a primary ore map for the stockpile, based on historical leaching process data for the first areas of the ore placement locations in the stockpile,
wherein the determining the primary ore map for the stockpile comprises adding flow data, irrigation data and a remaining mineral prediction from a machine learning model to obtain information by section and by date for the stockpile;
determining, by the one or more processors, recovery locations for remaining recoverable minerals in second areas of the ore placement locations in the stockpile, based on the historical leaching process data for the first areas of the ore placement locations in the stockpile, wherein the first areas are different from the second areas; and
activating, by the one or more processors, deep raffinate injection in the second areas of the ore placement locations in the stockpile that are determined to have the recovery locations for the remaining recoverable minerals.
21 . A method comprising:
determining ore placement locations for ore in a stockpile by determining mineralogy for the stockpile by:
aggregating, by one or more processors, mineralogy details to a section level by combining mine material tracking (MMT) truckload data at a dump level, MMT imputation data at the dump level and MMT final section mapping data at the dump level and the section level;
obtaining, by the one or more processors, maximum days under leach (DUL) for each section at the section level by using irrigation data over all stockpiles at the section level; and
determining, by the one or more processors, an intermediate ore map for a stockpile by combining the aggregating mineralogy details, the maximum DUL for each section and a primary new section polygon;
determining, by the one or more processors, an amount of minerals extracted from first areas of the ore placement locations of the ore in the stockpile, based on historical leaching process data for the first areas of the ore placement locations in the stockpile;
determining, by the one or more processors, recovery locations for remaining recoverable minerals in second areas of the ore placement locations in the stockpile, based on the historical leaching process data for the first areas of the ore placement locations in the stockpile, wherein the first areas are different from the second areas; and
activating, by the one or more processors, deep raffinate injection in the second areas of the ore placement locations in the stockpile that are determined to have the recovery locations for the remaining recoverable minerals.