Ai Deep Learning Xrt Ore Sorting Machine Automatic Algorithm Optimization for Ore Variations

Product Details
Type: X-ray Intelligent Dry Sorter
Voltage: 380V
Weight: 10T
Gold Member Since 2025

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  • Ai Deep Learning Xrt Ore Sorting Machine Automatic Algorithm Optimization for Ore Variations
  • Ai Deep Learning Xrt Ore Sorting Machine Automatic Algorithm Optimization for Ore Variations
  • Ai Deep Learning Xrt Ore Sorting Machine Automatic Algorithm Optimization for Ore Variations
  • Ai Deep Learning Xrt Ore Sorting Machine Automatic Algorithm Optimization for Ore Variations
  • Ai Deep Learning Xrt Ore Sorting Machine Automatic Algorithm Optimization for Ore Variations
  • Ai Deep Learning Xrt Ore Sorting Machine Automatic Algorithm Optimization for Ore Variations
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  • Overview
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  • FAQ
Overview

Basic Info.

Model NO.
MTX20-300C
Material
Carbon Steel
Material Feature
Corrosion Resistance
Certification
CE, ISO9001:2015
Energy Saving
Energy Saving
Warranty
1 Year
Color
Blue
Customized
Customized
Condition
New
After-sales Service
on-Site Installation Guidance
Sorting Mineral Size
50-300mm
Capacity
190 T/H
Belt Width
2000mm
Transport Package
Wooden Carton
Specification
8556*2300*2800
Trademark
MINGDE
Origin
China
HS Code
8474100000
Production Capacity
2000 Units/Year

Product Description

Ai Deep Learning Xrt Ore Sorting Machine Automatic Algorithm Optimization for Ore Variations
Product Description
The X-ray intelligent sorting machine uses X-rays to transmit scan the selected ores, obtaining the atomic number data of the minerals contained inside the ores. Through means such as convolutional neural networks, the data is processed to establish a recognition model, identifying the mineral ores and gangue, and then driving the actuator to sort the ores. Visible light or infrared cameras can also be installed according to the different characteristics of the ores for compound identification, further improving the sorting accuracy.
Ai Deep Learning Xrt Ore Sorting Machine Automatic Algorithm Optimization for Ore Variations
Ai Deep Learning Xrt Ore Sorting Machine Automatic Algorithm Optimization for Ore Variations
Sorting principle
(1) The vibrating feeder lays the material in a single layer to ensure that each piece of material passes through the detection area separately to avoid overlapping interference.
(2) The X-ray detector generates a grayscale image of the material by scanning the ore through transmission. High-density areas (such as metals) appear bright, and low-density areas (such as rocks) appear dark. Some equipment is equipped with high-definition cameras to capture the differences in color, luster, texture, shape, spots, etc. of the ore to identify ores with small density differences but obvious surface features.
(3) The density distribution, shape, texture and other characteristics of the material are extracted through intelligent algorithms. An AI sorting model is established for deep learning to distinguish minerals from rocks.
(4) After identifying the target mineral, the drive system triggers the jet valve to blow the rock into the designated collection box, and the mineral falls into another collection box. The jet valve response time can reach milliseconds, and the sorting accuracy is as high as 99%.
Technical Parameters

50-300mm Large Particle(Calculated with density 1.4g/m³)
Product
MTX12-300
MTX14-300
MTX16-300
MTX18-300
MTX20-300
MTX24-300
MTX28-300
MTX32-300
MTX36-300
Width of track belt(mm)
1200
1400
1600
1800
2000
2400
2800
3200
3600
Capacity
(t/h)
110
130
150
170
190
230
260
300
340
25-100mm Middle Particle(Calculated with density 1.4g/m³)
Product
MTX12-100
MTX14-100
MTX16-100
MTX18-100
MTX20-100
MTX24-100
MTX28-100
MTX32-100
MTX36-100
Width of track belt(mm)
1200
1400
1600
1800
2000
2400
2800
3200
3600
Capacity
(t/h)
66
78
90
102
114
126
155
180
205
10-60mm Small Particle(Calculated with density 2.7g/³)
Product
MTX14-30
MTX16-30
MTX18-30
MTX20-30
MTX24-30
MTX28-30
MTX32-30
MTX36-30
MTX40-30
Width of track belt(mm)
1400
1600
1800
2000
2400
2800
3200
3600
4000
Capacity
(t/h)
42
49
56
63
77
91
105
119
133
The actual capacity should be calculated according to the mineral density
Technical features
1. The independently developed high-precision dual-energy mining and transmission system is capable of identifying minerals with both large density differences and high contents as well as those with small density differences and low contents, making it applicable for the sorting of a wide range of mineral types. 
2. Using advanced artificial intelligence techniques such as deep learning, a customized mathematical model is established for the ore at each mine site to ensure precise identification, thereby achieving efficient and accurate sorting. 
3. Through the integration of Internet of Things (IoT) technology, the system enables real-time online monitoring of machine operation status, complete with fault alerts and alarms. Additionally, the system supports self-learning and automatic updates, continuously enhancing the sorting performance. 
4. Professional software, in conjunction with top-tier global components, ensures the stable and high-efficiency operation of the entire system.
Application Scope: 
1. Construction of new mines, technological transformation of old mines, and make the ores that reach economic mining value after sorting.
2. The ore is subjected to waste disposal treatment, significantly reducing the amount of waste rock entering the "crushing - grinding - sorting" process, lowering the comprehensive cost of the beneficiation plant, and reducing the amount of tailings.
3. Sort and enrich low-grade ores to make use of resources that have no mining value, thereby expanding the available range of minerals.
4. Re-sort the waste materials generated during the metallurgical production process to extract useful minerals.
Ai Deep Learning Xrt Ore Sorting Machine Automatic Algorithm Optimization for Ore Variations
The ores we can sort
1.Non-ferrous metal ores: lead, zinc, copper, molybdenum, nickel, gold, silver, tin, aluminum, etc
2. Ferrous metal ores: manganese, iron, chromium, vanadium, titanium, tungsten, etc
3. Non-metallic ores: fluorite, brucite, magnesite, coal and coal gangue, etc
4. Others: Phosphate rock, perlite, solid waste, smelting waste residue, etc
Ai Deep Learning Xrt Ore Sorting Machine Automatic Algorithm Optimization for Ore Variations
Ai Deep Learning Xrt Ore Sorting Machine Automatic Algorithm Optimization for Ore Variations
Certifications
Ai Deep Learning Xrt Ore Sorting Machine Automatic Algorithm Optimization for Ore Variations
FAQ
1.Can we visit your factory?
2.How long about the machine guarantee period?
Yes,our factory is located in Hefei city,Anhui Province, 2.5 hour bullet train distance from Shanghai,warmly welcome your visiting.
One year. And we supply lifelong software upgrade services for our customers.
3.Can you test samples to check sorting effect?
Yes,sample sending for testing is welcomed.

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