|
型号Model
|
矿料粒径（单位CM）Mineral particle size
(cm)
|
产量（T/H）Capacity
(T/H)
|
气源动力（Mpa）
Air pressure (Mpa)
|
选净率（%）
Sorting accuracy
|
带出比Optimized Carryover ratio
|
功率（kW）
Power(kW)
|
尺寸（mm）
Dimension(mm)
|
重量（kg）
Weight(kg)
|
|
MAI-D4
|
3<d≤6
|
30~45
|
0.55
|
96
|
10:1
|
4.5
|
4000*2650*1760
|
2100
|
|
1<d≤3
|
10~12
|
0.55
|
|
0.5≤d≤1
|
6~8
|
0.5
|
|
MAI-D5
|
3<d≤6
|
38~52
|
0.55
|
96
|
10:1
|
5.0
|
4000*3160*1760
|
2250
|
|
1<d≤3
|
12.5~15
|
0.55
|
|
0.5≤d≤1
|
8~10
|
0.5
|
|
MAI-D6
|
3<d≤6
|
45~68
|
0.55
|
96
|
10:1
|
5.5
|
4000*3670*1760
|
2350
|
|
1<d≤3
|
15~18
|
0.55
|
|
0.5≤d≤1
|
9~12
|
0.5
|
|
MAI-S4
|
3<d≤6
|
35~50
|
0.55
|
99.8
|
20:1
|
6.0
|
4850*2650*2750
|
3000
|
|
1<d≤3
|
12~14
|
0.55
|
|
0.5≤d≤1
|
7~9
|
0.5
|
|
MAI-S5
|
3<d≤6
|
44~60
|
0.55
|
99.8
|
20:1
|
6.5
|
4850*3160*2750
|
3250
|
|
1<d≤3
|
13.5~16
|
0.55
|
|
0.5≤d≤1
|
9~11
|
0.5
|
|
MAI-S6
|
3<d≤6
|
48~75
|
0.55
|
99.8
|
20:1
|
7.0
|
4850*3670*2750
|
3500
|
|
1<d≤3
|
17.5~21
|
0.55
|
|
0.5≤d≤1
|
10.5~14
|
0.5
|
The above index is based
on 15% impurity content quartz as an example,The specific index varies with the
material and impurity content.
Technical Advantages
1,Introduced artifical
intelligence methods such as deep convolutional neural networks(CNN) in the
field of visible light photoelectric sorting to analyze and process material
images.
2,AI photoelectric
sorting technology can automatically extract the multi-dimensional characteristics
of materials,like texture,shape,color,quality,luster,etc., which greatly
improves the sorting effect,expands the sorting scene and material types,to
meets the market diversification and
personalized sorting requirements,and solves the problem of limited color sorting materials in current color sorter market.
3,Photoelectric sorting
requires high real-time performance,while CNN operation is relatively slow.In
this regard, we adopt the model compression technology to accelerate CNN
operation speed and greatly improve the recognition efficiency.
4,In view of the
situation that many mineral materials cannot obtain massive data,our company
adopts transfer lerning technology and industrial image sample enhancement
technology to ensure the recognition accuracy of non-massive data training.
5, The AI sorting
machine uses a gigabit camera to transmit image data to multi-GPU computing
platform,which adopts CNN to analyze material types and accurately identify
material surface features and texture structures.
Product feature of AI sorting machine
1,it’s the first to
introduce artificial intelligence means of neural network in the field of
sorting,which solves the problem that color sorting machine can only separate
according to simple criteria.
2, The sorting mode is
established according to users’ sorting requirements to meet users’ diversified
and personalized sorting requirements.
3,We own the
self-developed software system and closed machine structure, the main internal
components are all imported,which can adapt to the harsh environment
requirements like high dust,high pollution, and high corrosion in the industry
and mining area.It has a wider application range and longer life.
4,Flexible track-type
material conveying system,with samll drop,large output,and suitable for the
sorting of more materials.
5,The vibrating feeding
part and the main part of the equipment adopt a split structure to avoid the
impact of vibration generated during the feeding process on the host and make
the equipment operation more stable.
6, the sorting effect
can be continuously improved by learning mode, and the deep learning mode can
be developed.
7,High
intelligence,remote debugging,smart monitoring,remote service and software
upgrading.
Sortable ores
Applicable sorting ore
materials
Talc, wollastonite ,calcite,
quartzite
Fluorite, Potassium
feldspar, Magnesite, Lithium pyroxene
Phosphate ore, Gold ore,
High crystal silicon, oxide copper ore
Barite, bauxite ,lead-zinc
ore and fluorite, barite lead-zinc ore
Not limited to the above
ores, eye visible distinctions are applicable to AI sorting machines!
