Application of density-based clustering algorithm and capsule network to performance monitoring of antimony flotation process. (30th June 2022)

• Record Type: Journal Article
• Authors: Cen, Lihui; Wu, Yuming; Hu, Jian; Xia, E; Xie, Yongfang; Tang, Zhaohui
• Is Part Of: Minerals engineering. Volume 184(2022)
• Publication Date: 2022-06-30
• Subjects: Deep learning -- Flotation monitoring -- Density-based clustering -- Samples denoising -- Capsule network; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Mines and mineral resources; Periodicals; Electronic journals; 622
• DOI: 10.1016/j.mineng.2022.107603
• Languages: English
• ISSNs: 0892-6875

Abstract

Highlights: An antimony grade prediction model based on CapsNet is proposed for performance monitoring of the flotation process. An improved DBSCAN algorithm is proposed to denoise the training data set. The use of CapsNet can solve the problem of the small amount of training data. Abstract: This paper presents an application of the capsule network to predict the antimony grade of pulp in the roughing cell of an antimony flotation plant in the Hunan Province, China. In this plant, because the chemical testing for analyzing the antimony grade only generated eight data points every day, data could be collected in small amounts and were mixed with some abnormal images. An improved density-based clustering algorithm is introduced to eliminate abnormal images from the training dataset. To use a small amount of data, a capsule network rather than a CNN is adopted to build the recognition model named Froth-CapsNet. Finally, the application of Froth-CapsNet to monitor the working conditions of the antimony flotation process indicates that this model can provide a guide for operators to precisely adjust the dosage of flotation reagents in real-time so that the antimony recovery rate can be improved.