Recognition of coal and gangue based on dielectric characteristics and geometric constraints under multi factors

ABSTRACT

Water and coal slime are interference factors in actual production, which adversely affect the feature extraction, feature credibility, and recognition accuracy of coal and coal gangue. In this paper, from two ways of simulation and experiment, combined with statistical methods, it is concluded that the influence of water content and slime cover on the dielectric and geometric characteristics of coal and gangue can be ignored. The Variational Mode Decomposition (VMD) denoising method is improved. After the response signal is processed by VMD- Kalman denoising method, the signal-to-noise ratio is as high as 26.2232, and the root means the square error is as low as 0.0019587. The dielectric and geometric features of coal and coal gangue under the action of water and slime are extracted to form feature vectors, and the reliability of feature vectors is tested by using classification models such as back propagation neural network, Bayesian, proximity algorithm, and support vector machine, all of which achieve recognition accuracy of over 92.86%. The research shows that the identification method of coal and gangue based on dielectric characteristics and geometric constraints can adapt to the working conditions with multiple factors.

KEYWORDS:

• Water
• coal slime
• dielectric characteristics
• geometric features
• gangue

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant (No.51904007, 51874004), in part by the Anhui Province Science and Technology Major Special Funding Project under Grant (No.18030901049), in part by General Project of China Postdoctoral Science Foundation (No.2019M662133), in part by the Anhui Natural Science Foundation Project under Grant (No.1908085QE227), and in part by the Key Research and Development Program of Anhui Province under Grant (No.202004a07020043); the Key Research and Development Program of Anhui Province under Grant [202004a07020043]; the Anhui Province Science and Technology Major Special Funding Project under Grant [18030901049]; the National Natural Science Foundation of China under Grant [51874004,51904007]; the Anhui Natural Science Foundation Project under Grant [1908085QE227]; General Project of China Postdoctoral Science Foundation [2019M662133].

Notes on contributors

Xinquan Wang

Xinquan Wang is currently pursuing the Ph.D.degree with the Anhui University of Science and Technology. His research interests include coal gangue separation, machine learning, and photoelectric recognition.

Shuang Wang

Shuang Wang received the Ph.D.degree in mine electromechanical engineering from the Anhui University of Science and Technology, in 2018. She is currently an associate Professor with the Anhui University of Science and Technology. Her research interests include intelligent mining equipment, magnetic drive and magnetic levitation technology, and mine robot.

Yongcun Guo

Yongcun Guo received the Ph.D.degree from University of Science and Technology of China. He is currently aProfessor with the Anhui University of Science and Technology. His research interests include mine robot, intelligent mining equipment and photoelectric detection. His main research interests are coal beneficiation and clean utilization.

Kun Hu

Kun Hu is aprofessor and currently works at the School of Mechanical Engineering of Anhui University of Science and Technology. received the Ph.D.degree in mine electromechanical engineering from the Anhui University of Science and Technology, in 2012. His research interests include intelligent mining technology and equipment, magnetic levitation and magnetic transmission technology, mining robot.

Wenshan Wang

Wenshan Wang is currently pursuing the Ph.D.degree with the Anhui University of Science and Technology. His research interests include permanent magnet variable frequency motor, smart steel technology, and intelligent mining equipment.