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Design and application of coal gangue sorting system based on deep learning.
Kun Zhang1,2,3, Zhen Wang1, Zengbao Zhang3
1Shandong Provincial Key Laboratory of Robotics and Intelligent Technology, Shandong University of Science and Technology, Qianwangang Road 579, Qingdao, 266590, Shandong, China.
Scientific Reports
|July 17, 2024
Summary
This study introduces an intelligent system for coal gangue sorting using deep learning, significantly improving accuracy and speed over traditional methods. The new non-contact pneumatic system enhances raw coal purity in modern coal washing plants.
Area of Science:
- Engineering
- Computer Science
- Materials Science
Background:
- Coal washing plants are modernizing with intelligent systems for green development.
- Traditional gangue removal methods (manual, robotic arms) have limitations in accuracy, efficiency, and labor intensity.
Purpose of the Study:
- To develop a deep learning-based, non-contact system for intelligent gangue recognition and pneumatic sorting.
- To improve the efficiency and accuracy of gangue separation in coal washing processes.
Main Methods:
- A deep learning model was employed for non-contact gangue recognition.
- A dynamic database was created storing gangue features (contour, quality, center of mass).
- The system analyzed relationships between physical parameters and impact energy for pneumatic separation.
Main Results:
- The system achieved over 97% gangue identification accuracy.
- A sorting rate exceeding 91% was recorded.
- Separation time from identification to ejection was less than 3 seconds.
Conclusions:
- The proposed deep learning-based pneumatic sorting system significantly outperforms traditional robotic arm methods.
- The system effectively enhances raw coal purity by improving gangue identification and separation.
- This technology supports the advancement of intelligent, green, and high-quality coal industry operations.
