Process Operating Performance Assessment for Plant-Wide Froth Flotation via Distributed Multigraph Deep Embedding Graph Clustering Network

The process operating performance assessment (POPA) of the flotation process is of great significance for monitoring the flotation operating performance and improving the product quality. Flotation process is a typical industry process with multiple subprocesses, and the subprocesses are interconnected. In addition, due to the feed characteristics, the flotation process presents various working modes, and the standards of operating behavior under various modes are different. Therefore, the POPA problem of the plant-wide flotation needs to take into account the coupling relationship of the subprocesses under various working modes. In this article, a novel distributed multigraph deep embedding graph clustering (DMG-DEGC) network is proposed for the flotation process, which uses graph representation to fully mine the structural features related to data and process. A distributed structure is adopted based on the multiple subprocesses characteristics of the plant-wide process. Among them, the strong influence subprocess is used for the division of multiple working modes. The adjustment of several variables in the weak influence subprocess indirectly affects the performance, and the variable augmentation method is used to guide the performance assessment of the subprocess. DMG-DEGC uses spatial domain, temporal domain, and prior knowledge to construct multigraph. The information between multigraph is aggregated, and the consistency and complementarity information between multigraph is retained. Finally, the proposed method is applied to the actual flotation process to illustrate its effectiveness and superiority.