Application of density-based clustering algorithm and capsule network to performance monitoring of antimony flotation process

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.