XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 427
very useful, especially for metal ores, where the whole par-
ticle volume is analyzed, when compared to other sensing
methods. The combination of this sensor with either color
analysis (RGB camera) or hyperspectral SWIR analysis,
becomes very effective in identifying some minerals or min-
eral groups, as shown below.
XRT Sensors
The XRT sensors can be used for many applications with-
out combining them with other sensing techniques. Table 1
shows the results from pre-concentration of the Zn/Pb ore
in the industrial sorter CXR-1000. In this case the feed
material had about 1.8% of Zn and 0.8% of Pb and the
upgraded product fraction provided 10.96% of Zn and
5.4% of Pb.
The yield of the product fraction was only 13%, which
means that 87% of the input stream was removed as the
waste fraction. The sorting process provided recoveries of
Zn at 79.2% and Pb at 87%, which were still high. The
metal content in the waste fraction, was acceptable and far
below the cut-off grade of 0.55% of total Zn+Pb. Therefore,
this sorting process became very attractive when compared
to the DMS which was an alternative and providing similar
results (Wieniewski et al., 2015).
Another example of the similar efficient application of
the same CXR-1000 sorting unit is shown in Table 2, where
iron ore was pre-concentrated. In this case the feed material
had 58.5% of Fe, which was slightly below the market level
for iron ore (over 63% Fe). After sorting, the product was
increased to 63.3% of Fe with over 78% yield, which cor-
responded to 84.3% metal recovery. The waste fraction was
reduced to 30% and corresponded to only 4.9% of metal
losses, which was very low. The middlings fraction was at
49.8% (with 10.3% recovery) and was defined as suitable
for further traditional processing (crushing and gravity
separation). The main benefit from this sorting process was
the possibility to generate 78.3% of the product fraction
in form of the lumpy ore with the acceptable level of Fe,
without any further processing. Only 12.2% was defined as
necessary to process by traditional methods to increase the
iron content to the acceptable level.
The XRT sensors can therefore be used for metal ores,
which are easy to sort like Fe, Zn, Pb and other similar
ones. Significant advantage can be achieved when only
XRT sensor is applied due to the lack of cleaning/wash-
ing of the processed material. In addition, the XRT sen-
sor sorter is simple in operation, tuning, optimization and
maintenance, without any need of special care related to
cleaning of additional sensors or light sources.
XRT and RGB Sensors
The combination of the X-ray sensor and the optical cam-
era can provide many advantages. The simple test has been
done to see direct influence of this combination when
compared to the single X-ray sensor. The XRT sensors were
adjusted to identify higher density material areas where
the concentrated gold and copper intrusions were present
(mainly copper sulfides). The optical sensor (RGB camera)
had a task to identify the non-gold and non-copper ori-
ented materials based on color and texture. The feed mate-
rial had the size of 10–40 mm to allow the best sorting
results. The product and the waste fraction definitions were
based on the limit of about 0.5 g/t of gold as the economic
level for further processing, according to one of the gold
producers. To simplify the test work, a representative feed
material was selected at the mine site (several dozen kg).
It has been further divided into the waste fraction and the
gold/copper bearing material by XRF analysis of each indi-
vidual particle (portable XRF spectrometer). In this way, it
was possible to perform many different tests with various
Table 1. Sorting results using X-ray sensor for Zn/Pb ore
Fraction Yield [%]
Metal Content and Recovery
Zn [%]Zn rec. [%]Pb [%]Pb rec.[%]
Feed 100 1.8 100 0.8 100
Product 13.0 10.96 79.2 5.4 87.1
Waste 87.0 0.43 20.8 0.12 12.9
Table 2. Sorting results using X-ray sensor for Fe ore
Fraction Yield [%]Fe Content [%]Fe Recovery [%]
Feed 100 58.5 100
Product 78.3 63.3 84.3
Middlings 12.2 49.8 10.3
Waste 9.5 30.1 4.9
very useful, especially for metal ores, where the whole par-
ticle volume is analyzed, when compared to other sensing
methods. The combination of this sensor with either color
analysis (RGB camera) or hyperspectral SWIR analysis,
becomes very effective in identifying some minerals or min-
eral groups, as shown below.
XRT Sensors
The XRT sensors can be used for many applications with-
out combining them with other sensing techniques. Table 1
shows the results from pre-concentration of the Zn/Pb ore
in the industrial sorter CXR-1000. In this case the feed
material had about 1.8% of Zn and 0.8% of Pb and the
upgraded product fraction provided 10.96% of Zn and
5.4% of Pb.
The yield of the product fraction was only 13%, which
means that 87% of the input stream was removed as the
waste fraction. The sorting process provided recoveries of
Zn at 79.2% and Pb at 87%, which were still high. The
metal content in the waste fraction, was acceptable and far
below the cut-off grade of 0.55% of total Zn+Pb. Therefore,
this sorting process became very attractive when compared
to the DMS which was an alternative and providing similar
results (Wieniewski et al., 2015).
Another example of the similar efficient application of
the same CXR-1000 sorting unit is shown in Table 2, where
iron ore was pre-concentrated. In this case the feed material
had 58.5% of Fe, which was slightly below the market level
for iron ore (over 63% Fe). After sorting, the product was
increased to 63.3% of Fe with over 78% yield, which cor-
responded to 84.3% metal recovery. The waste fraction was
reduced to 30% and corresponded to only 4.9% of metal
losses, which was very low. The middlings fraction was at
49.8% (with 10.3% recovery) and was defined as suitable
for further traditional processing (crushing and gravity
separation). The main benefit from this sorting process was
the possibility to generate 78.3% of the product fraction
in form of the lumpy ore with the acceptable level of Fe,
without any further processing. Only 12.2% was defined as
necessary to process by traditional methods to increase the
iron content to the acceptable level.
The XRT sensors can therefore be used for metal ores,
which are easy to sort like Fe, Zn, Pb and other similar
ones. Significant advantage can be achieved when only
XRT sensor is applied due to the lack of cleaning/wash-
ing of the processed material. In addition, the XRT sen-
sor sorter is simple in operation, tuning, optimization and
maintenance, without any need of special care related to
cleaning of additional sensors or light sources.
XRT and RGB Sensors
The combination of the X-ray sensor and the optical cam-
era can provide many advantages. The simple test has been
done to see direct influence of this combination when
compared to the single X-ray sensor. The XRT sensors were
adjusted to identify higher density material areas where
the concentrated gold and copper intrusions were present
(mainly copper sulfides). The optical sensor (RGB camera)
had a task to identify the non-gold and non-copper ori-
ented materials based on color and texture. The feed mate-
rial had the size of 10–40 mm to allow the best sorting
results. The product and the waste fraction definitions were
based on the limit of about 0.5 g/t of gold as the economic
level for further processing, according to one of the gold
producers. To simplify the test work, a representative feed
material was selected at the mine site (several dozen kg).
It has been further divided into the waste fraction and the
gold/copper bearing material by XRF analysis of each indi-
vidual particle (portable XRF spectrometer). In this way, it
was possible to perform many different tests with various
Table 1. Sorting results using X-ray sensor for Zn/Pb ore
Fraction Yield [%]
Metal Content and Recovery
Zn [%]Zn rec. [%]Pb [%]Pb rec.[%]
Feed 100 1.8 100 0.8 100
Product 13.0 10.96 79.2 5.4 87.1
Waste 87.0 0.43 20.8 0.12 12.9
Table 2. Sorting results using X-ray sensor for Fe ore
Fraction Yield [%]Fe Content [%]Fe Recovery [%]
Feed 100 58.5 100
Product 78.3 63.3 84.3
Middlings 12.2 49.8 10.3
Waste 9.5 30.1 4.9