3674 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
10 μm. A significant increase in the content of minus (-)
10 μm minerals in magnetic separation and flotation can
worsen the separation effect through magnetic agglom-
eration or incomplete liberation in flotation, respectively.
Therefore, the increase in fine-grained minerals will
increase the difficulty of preparing ultrapure iron concen-
trates. Based on above, a detailed experimental study on the
preparation of high-purity iron concentrate was carried out
with 10 different kinds of ordinary iron concentrates, and
the response to the PCMF beneficiation process was finally
determined. The beneficiation processes and indicators of
different samples are shown in Table 2.
A comprehensive analysis of the process mineralogy
characteristics and beneficiation test results of ten ordinary
iron concentrates shows a strong connection between them
(Table 2). The content of magnetite wrapping, and anti-
wrapping, conjoins in samples 1, 9, and 10 decreased, while
the content of –10 μm particles in sample No. 9 was higher.
Therefore, only samples No. 1 and 10 resulted in ultra-pure
iron concentrates. The remaining samples with high wrap-
ping content or high content of –10 μm particle grains
made it challenging to prepare ultra-pure iron concentrate.
However, their high-purity iron concentrate. Summarizing
its internal relationships, the evaluation system shown in
Table 3 was obtained, which is consistent with the research
of Sun (Sun et al., 2018). This method is used to predict
whether ordinary iron concentrate can prepare high-purity/
ultra-pure iron concentrate. However, further experimental
research still needs to verify its application effect.
Process Mineralogy Analysis
Chemical analysis results (Table 4) showed that the sam-
ple was ordinary iron concentrate. Its total Fe grade was
65.46%, and the main gangue mineral was SiO2. At the
same time, this result was verified in the mineral phase
component analysis of the sample (Figure 3).
The mineral composition of the samples was analyzed
by optical microscopy using thin sections of the ore. As
shown in Table 5, the metallic minerals in the samples
are mainly magnetite, accompanied by small quantites of
hematite and pyrite. The non-metallic mineral content is
11.7%. This result was verified in the iron chemical phase
Table 1. Chemical composition analysis of samples
Number Total Fe FeO SiO
2 Al
2 O
3 TiO
2 CaO MgO S P
1 66.46 27.79 0.19 6.23 0.57 0.06 0.24 0.007 0.012
2 64.17 27.27 8.16 0.71 0.34 0.39 0.58 0.340 0.008
3 66.13 26.88 5.63 1.24 0.33 0.11 0.33 0.045 0.013
4 66.38 29.33 4.55 0.94 0.82 0.19 0.37 0.360 0.007
5 66.20 27.94 5.32 1.03 0.55 0.03 0.30 0.056 0.008
6 66.28 24.44 4.82 0.97 0.59 0.10 0.49 0.036 0.008
7 66.15 28.90 4.36 1.53 1.14 0.01 0.32 0.280 0.006
8 63.46 25.90 3.48 0.86 5.39 0.50 0.24 0.150 0.041
9 65.78 26.58 6.13 0.64 0.29 0.14 0.22 0.014 0.012
10 66.86 28.59 7.23 0.18 0.02 0.17 0.31 0.076 0.012
1 2 3 4 5 6 7 8 9 10
0
20
40
60
80
100
adjoining type wrapping type anti-wrapping type
Numbering
(a)
0 1 2 3 4 5 6 7 8 9 10
0
10
20
30
40
50
60
70
Numbering
(b)
Figure 2. (a) Connection relationship, left, and (b) particle size distribution, right, of experiment samples
Content
(%)
10 μm. A significant increase in the content of minus (-)
10 μm minerals in magnetic separation and flotation can
worsen the separation effect through magnetic agglom-
eration or incomplete liberation in flotation, respectively.
Therefore, the increase in fine-grained minerals will
increase the difficulty of preparing ultrapure iron concen-
trates. Based on above, a detailed experimental study on the
preparation of high-purity iron concentrate was carried out
with 10 different kinds of ordinary iron concentrates, and
the response to the PCMF beneficiation process was finally
determined. The beneficiation processes and indicators of
different samples are shown in Table 2.
A comprehensive analysis of the process mineralogy
characteristics and beneficiation test results of ten ordinary
iron concentrates shows a strong connection between them
(Table 2). The content of magnetite wrapping, and anti-
wrapping, conjoins in samples 1, 9, and 10 decreased, while
the content of –10 μm particles in sample No. 9 was higher.
Therefore, only samples No. 1 and 10 resulted in ultra-pure
iron concentrates. The remaining samples with high wrap-
ping content or high content of –10 μm particle grains
made it challenging to prepare ultra-pure iron concentrate.
However, their high-purity iron concentrate. Summarizing
its internal relationships, the evaluation system shown in
Table 3 was obtained, which is consistent with the research
of Sun (Sun et al., 2018). This method is used to predict
whether ordinary iron concentrate can prepare high-purity/
ultra-pure iron concentrate. However, further experimental
research still needs to verify its application effect.
Process Mineralogy Analysis
Chemical analysis results (Table 4) showed that the sam-
ple was ordinary iron concentrate. Its total Fe grade was
65.46%, and the main gangue mineral was SiO2. At the
same time, this result was verified in the mineral phase
component analysis of the sample (Figure 3).
The mineral composition of the samples was analyzed
by optical microscopy using thin sections of the ore. As
shown in Table 5, the metallic minerals in the samples
are mainly magnetite, accompanied by small quantites of
hematite and pyrite. The non-metallic mineral content is
11.7%. This result was verified in the iron chemical phase
Table 1. Chemical composition analysis of samples
Number Total Fe FeO SiO
2 Al
2 O
3 TiO
2 CaO MgO S P
1 66.46 27.79 0.19 6.23 0.57 0.06 0.24 0.007 0.012
2 64.17 27.27 8.16 0.71 0.34 0.39 0.58 0.340 0.008
3 66.13 26.88 5.63 1.24 0.33 0.11 0.33 0.045 0.013
4 66.38 29.33 4.55 0.94 0.82 0.19 0.37 0.360 0.007
5 66.20 27.94 5.32 1.03 0.55 0.03 0.30 0.056 0.008
6 66.28 24.44 4.82 0.97 0.59 0.10 0.49 0.036 0.008
7 66.15 28.90 4.36 1.53 1.14 0.01 0.32 0.280 0.006
8 63.46 25.90 3.48 0.86 5.39 0.50 0.24 0.150 0.041
9 65.78 26.58 6.13 0.64 0.29 0.14 0.22 0.014 0.012
10 66.86 28.59 7.23 0.18 0.02 0.17 0.31 0.076 0.012
1 2 3 4 5 6 7 8 9 10
0
20
40
60
80
100
adjoining type wrapping type anti-wrapping type
Numbering
(a)
0 1 2 3 4 5 6 7 8 9 10
0
10
20
30
40
50
60
70
Numbering
(b)
Figure 2. (a) Connection relationship, left, and (b) particle size distribution, right, of experiment samples
Content
(%)