General Profile

The field application of sorting Module was defined by the company’s origin. The GAMAYUN group including Promtechnologie, Ltd – a process
engineering company is located in the heart of heavy industry of Ukraine in Kryvyi Rih. Emerge of our method was a correspondent result of
our practical experience in dealing with the world and domestic mining, steel and ferroalloy companies.

Such giant of Mining and Steel Industry as full-cycle steel plant ArcelorMittal Kryvyi Rih, former Krivorizhstal and 5 other large mining and exploration factories met over 50% of demand in the former USSR in iron ore and they are still located in Kryvyi Rih. Due to the industrial facilities the innovative technology potential has been developed in the ferrous metal industry.

Developing in a such prominent industrial environment Sorting Modules meet the requirements of iron and steel industry  and the Process engineering company Promtechnologie, Ltd is a global top ranking company in a rather narrow marketing segment of iron and steel slag and ore sorting (including  Fe, Mn, Cr, Ni compounds).

Steel and ferroalloy slags

Technically, it is quite a simple task because we sort the binary medium: metal ones with perfect electrical conduction and dielectric mineral ones.
The problem of separating of metal and mineral based slag constituents is the key issue in recycling and effective processing steel and ferroalloy
slags.

Non-magnetic metals not refined by means of traditional technologies turns into large-scale financial losses for a company. At the same time, their presence in slags restricts their application as construction materials. Beneath you can find the data of metal extraction by our Sorting Modules out of various steel and ferroalloy slags.

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Table №3

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Technology tests reports:

(FeTi) KFP.pdf    (FeCr) SFP.pdf    (Al) SEAL & Co.pdf    (Al) SZSNFM.pdf  (Cr) KCP.pdf  (FeTi) Uralredmet.pdf

Sorting Module operation video

Ferrous metal ore sorting

Comparing to steel and iron slags this task is much more difficult because ore lumps contain combinations of various minerals often similar in
properties. So it is the task for more sophisticated sensor based sorting Modules with customized software to be able to process larger amount of
information.

Iron ore sorting

Case. There is an X company specializing in production of DSO (Direct Shipping Ore) iron ore. The extracted ore included three grade ranges
depending on Fe content: high (H) > 55%; medium (M) from 50 to 55% and low (L) less than 50%.
Problem. (M) quality ore is to be beneficiated to (H) quality grade. There could be some reasons:

  • discounted prices for low grade ore;
  • high specific weight of (M) in the ratio of (M+H);
  • forced effective processing of (M) in order to achieve (H), which is located deeper;
  • large residual stocks of (M) after complete recovery of (H) with availability of all the technical facilities  (crushing and screening plants, constructions, roads, communications, etc.).

Magnetic separation methods are not efficient enough for this type of ore. Gravity concentration is to be hardly implemented due to low contrast between target lumps and waste rock. There are also some limits of power consumption.

Solution. It’s possible to apply a dry method of high-speed separation e.g. sensor based sorting Module handling the ore fractions of +40 ….-100 mm. The aim is to get tail output consisted of the separated lumps with highly improved content of waste rock. Capacity of 1 sorting Module (with fractions up to 100.0mm) makes up to 130ton/hour and it consumes power round 0.2kW/ton.  Application of a mobile sorting Module allows integrating the equipment easily into a technological process.

Primary sorting figures. Pic 3,4, table 4,5.

Pic.3

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Pic.4

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Table 4. Sorting hetite hematite martite iron ore  to recover target concentrate  (Fe=56 %)  at PJSC ArcelorMittal Kryvyi Rih

# Input raw material, % Beneficiated ore,%   Tailings, %
Fe SiO2 Quantity  Fe  SiO2 Output  Fe SiO2   Output
1 54,03 15,44 100 56 11,8  91,3 28,2  53,6 8,7
2 49,21 19,24 100 56 11,9  51,3 40,2  29,1 48,7
3 45,29 24,69 100 56 12,1 37,4 41,8 26,4 62,6

Table 5. Sorting hetite martite iron ore to recover target concentrate (Fe=56 %) at EVRAZ Sukhaya Balka JSC in Kryvyi Rih, Ukraine.

# Input raw material, % Beneficiated ore,% Tailings, %
Fe SiO2 Quantity Fe SiO2 Output Fe SiO2   Output
1 53,6 20,12 100 56 18,2 76,9 40,9 36,36 22,3
2 48,43 24,37 100 56 16,36 37,9 43,6 28,2 62,3
3 42,37 37,13 100 56 15,45 12,6 40,0 40,0 87,4
Reports of the technological tests are available here:

Sorting of manganese ores Example 1

Case. There is an X company developing  manganese ore deposits with Mn 15 – 25 %;    Fe 10 – 20 %;    SiO2 30 – 40 % content.

The manganese mineralization model is typical for hydrothermal deposits and manganiferous deposits in beds of clay.  You can find examples of such deposit location in Pic. 5 (Pilbara Manganese Province).

Problem. It is required to achieve Mn target concentration of 44%.

Application of wet beneficiation methods solves the problem only partially due to tailing out of SiO2. The next step is to increase Mn content due to reducing Fe one. However it is hardly ever possible to separate these elements applying any gravity concentration method because of their almost equal specific weights.

Solution. We can apply a dry method of high-speed separation e.g. sensor based sorting Module handling the ore fractions of +20….-80mm. The aim is to discharge the tailings (separated lumps of waste rock with high content of SiO2 and Fe). The sensor based sorting Module allows to get the target manganese concentrate: Mn = 38 – 44%; Fe = 3 – 5%; SiO2 = 12 – 17%. Such result is apparently a top ranking one, that has never been achieved by any of dry separating methods. The capacity for one sorting Module (fractions of up to 80.0mm) achieves up to 90 tons/hour with power consumption of 0.3 kW per ton of input raw material.

Pic. 5 

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Results expected. The results of sorting are shown in Рics 6,7 and Tables 6,7.

Pic. 6

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Pic. 7

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Table 6. Target concentrate  recovery (Mn=44 %) out of input raw material with  Mn~15 % content

# Input  raw material, % Beneficiated ore,% Tailings, %
Mn Fe SiO2 Quantity  Mn  Fe SiO2  Output Mn Fe SiO2   Output
1 16,2 11,4 43,4 100 44,0 3,1 11,5 18,1 11,1 10,9 49,2 81,9
2 14,5 20,6 39,4 100 44,0 3,8 11,4 11,8 10,8 23,8 41,8 88,2

Table 7. Target concentrate recovery (Mn=44 %) out of input raw material with  Mn~25 % content

# Input  raw material, % Beneficiated ore,% Tailings, %
Mn Fe SiO2 Quantity Mn Fe SiO2 Output Mn Fe SiO2   Output
1 25,1 10,1 37,4 100 44,0 3,8 12,3 34,1 15,4 13,1 48,5 65,9
2 25,2 19,6 33,8 100 44,0 4,9 9,9 25,2 17,7 24,6 45,4 74,8

Sorting of manganese ores Example 2

Case. There is an “X” company that develops manganese ore deposits with the next properties: Mn = 30 %; Fe = 10 – 20 %; SiO2 = 20 – 25%.

You can find such type of ferromanganese deposits in Pic. 8

Problem. The core matter is to achieve target concentrate of Mn:Fe=6:1 with the Mn content of 35%.
Application of traditional practices (crushing, screening, magnet separation, gravity concentration etc.) is not able to meet the quality demands for the target concentration.

Solution. We are offering to apply there a dry method of high-speed separation e.g. sensor based sorting Module handling the ore fractions of +20….-80 mm. The aim is to get tail output including only separated lumps of waste rock with high content of  Fe until the target ratio of Mn:Fe=6:1 and concentration of Mn=35 % are achieved.

Capacity of 1 sorting Module operating with fractions up to 80 mm may achieve 90 tons/hour and it consumes approximately 0.3kW per ton of input raw material.   See pic.9 Sorting features fot target concentrate: Mn=30%  (Fe~10 %, Fe~20 %)

Sorting results . The results of sorting are shown in Pic. 9 and Table 8.

Table 8. Target concentrate recovery (Mn:Fe ratio makes 6:1 and Mn content ≥ 35 %)

#  Input  raw material, % Beneficiated ore,% Tailings, %
Mn Fe SiO2 Quantity Mn Fe SiO2 Output Mn Fe SiO2   Output
1 27,9 10,9 25,5 100 35,2 5,5 29,9 78,1 7,2 31,4 22,8 21,9
2 28,5 14,9 18,3 100 35,4 6,4  16,4 59,5 15,1 36,4 20,7 40,5

Pic. 8 

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Pic. 9 

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Sorting of manganese ores Example 3

Case. There is an “X” company developing post-jigging dumps with the properties: Mn = 7-9 %; Fe = 20 – 35 %; SiO2 = 25 – 40%. Mineralization corresponds to Pic.8

Problem. The core issue is the environmental impact of soil revegetation and the matter to extract Manganese at target concentration of 35% whereby the tradition practices are not applicable.

Solution. Application of a dry method of high-speed separation e.g. sensor based sorting Module (ore fractions of +20….-80 mm). The aim is to pick up particular lumps with Mn high content forming the target manganese ore concentrate with up to 35% of Mn content   Capacity  of 1 sorting Module operating with fractions up to 80  mm makes 100 tons/hour and it consumes 0.3 kW per ton of input raw material.

Results results. The results of sorting are shown in Pic.10 and Table 9.

Table 9. Target manganese concentrate recovery (Mn ≥ 35 %)  out of anthropogenic waste.

#  Input  raw material, % Beneficiated ore,% Tailings,%
Mn Fe SiO2 Quantity Mn Fe SiO2 Output Mn Fe SiO2   Output
 1 9,1 22,4 36,2 100 35 10,9 14,2 12,5 5,3 23,3 38,7 87,5
 2 7,6 33,2 24,6 100 35 14,2 11,8 11,0 3,3 36,1 28,6 89,0

Pic. 10

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Sorting of crome ores

Case. There is an “X” company extracting chromite ores in Coobina, Western Australia. As a simple method of beneficiation they use jig concentration achieving the target concentrate  of  basic elements: Cr2O3=42,6%, Cr/Fe ratio makes 1.47.

Problem. The market demands, to increase Cr/Fe  ratio up to 1.8 – 1.9 with constant Cr2O3 quality.

Solution. The idea is rather simple: just tail out the waste rock with Fe contaminant. Then we divide chrome ore into 3 groups (high grade, medium grade, low grade ones) and carry out mineralogical analysis, the results you can see in Table 10.

Table 10. Mineralogical compositions of chrome ore

# input group Mineralogical compositions
Chromites (Fe,Mg)(Cr,Al,Fe)2O4 Enclosing rocks ….Ʃ….
Pyroxene (Ca,Mg,Fe)Si2O4 Olivine (Mg,Fe)2Si2O4 Serpentine Mg6Si4O10 (OH)8 Magnetite Fe3O4 MagnesiteMg (CO3)
 1 High grade 88,3 2,8 1,5 5,8 1,1 0,5 100
 2 Medium grade 71,6 5,2 2,9 16,1 3,2 1,0 100
 3 Low grade 51,4 7,8 4,0 30,4 4,9 1,5 100
The method of sensor based lump separation enables to control independently each grade share in the total mass of ore. But implying this method of high resolution, it is not so easy to solve the above mentioned problem. Study the reasons:
  • The useful chromite composites already contain Fe in their  chemical formula;
  • 3 out of 5 minerals of enclosing rock (pyroxene, olivine, magnetite) contain Fe. But their share is not determinative. The dominating mineral is serpentine which, unfortunately, does not contain Fe. Its share in the total volume of enclosing rock for high grade ore makes 49,6% , for medium grade – 56,7%, for low grade – 62,6%.
Sorting Results. Tailing (pic. 12) makes up 30% of total volume and the other is beneficiated ore (pic.11).

Pic.11

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Pic.12

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The result of our solution implementation doesn’t exceed 16.1% of  tail output referring the total volume and maintains the target concentrate output of the correspondent quality (+1,7% Cr2O3;  +0,09% Cr/Fe). For more details, please see table 11.

 Table 11. Results of sorting of chrome ore
# Component Input raw material (balance), % Products of  separation
Tailings Concentrate
1 Cr2O3 42,6 34 44,3
2 Cr:Fe 1,47 1,06 1,56
3 SiO2 6,4 10,6 5,6
4 Al2O3 9 6,9 9,4
5 MgO 13,4 15,8 12,9

This solution is more technical rather than commercial. In this particular case we are not sure if sensor based separating module application is reasonable due to disadvantageous peculiarities of enclosing rocks (see Table 10). There are a number of other deposits where chromite ores sorting will be reasonably more effective.

Sorting of nickel ores

All of the mentioned above cases come from a simple one to more difficult one in the solving trend of Fe→Mn→Cr→Ni. So dealing with Ni we have to solve all the previous problems including extra ones. Nickel ores are special ones in the fact that Ni is always accompanied by Fe, which leads to the following difficulties:

  • Fe ratio in the ore content is much higher than the Ni one;
  • Both elements obtain ferromagnetic properties and the  ones of Fe are stronger.

To solve the problem we require very accurate and sophisticated sensors integrated in sorting Module. Primary beneficiation of Ni ore fractions +10 -50 up to achieve concentrate of Ni=3.5-5.0% is to be carried out before implementation of further beneficiation by the Module. The results depend on the input raw material properties and are the matter to show them for particular customer and for particular sample.

Summary 

  1. Sorting Module is a piece of equipment of high resolution allowing to keep accurate quality control over beneficiated ores as well as
    their separate elements: Fe , Mn, Cr, Ni.
  2. The achieved results in dry sorting methods are the most sophisticated for present. The sensor based sorting Module enables to solve difficult tasks: highest possible recovery of the target minerals and/or elements out of the raw medium, maintenance of a target ratio between two elements, etc.
  3. We can install the Sorting Module in the technological chain of the factory either as an independent unit or as an integral part
    connecting with other simple processes.
  4. The data for the mentioned above cases and problem solving by sorting Module application is achieved in experiments with ore
    samples of 50–500 kg with typical properties for the mentioned mineralisation zones. For more detailed information or how to apply for the sencor based sorting technology for certain deposits, please check for Partners section .