Multimode Froth Flotation Process Operating Performance Assessment Based on Deep Embedded Graph Clustering Network

The process operating performance assessment (POPA) of froth flotation under multimode is an important means to improve production efficiency and product quality. In the plant, froth flotation presents multimode characteristics due to the uncontrollable of the objective conditions of production. In this article, a method based on graph representation learning is studied to solve the POPA problem of a multimode froth flotation process. Based on the qualitative and quantitative information represented by the froth images and process variables of the flotation process, an appropriate convolutional neural network is selected to fuse the multisource heterogeneous information. The powerful representation capabilities of deep clustering methods are used to divide working modes. A deep embedded graph clustering network is proposed to learn the node representation, on which the clustering effect of the model is improved by reconstructing the topological structure and node content of the graph data. In addition, a dual self-monitoring mechanism is designed to guide the model parameter update. Finally, a soft recognition method is designed to fuse the performance grades under different modes by using Bayesian fusion theory, which improves the accuracy of assessment. The proposed method is applied to a gold flotation process to illustrate the feasibility of the method.