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.
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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.