Distributed Dynamic Graph Embedding for Quality-Related Monitoring in the Flotation Process

The industrial flotation process plays a crucial role in metallurgical production. Accurate monitoring of the industrial flotation process is particularly important. However, the industrial flotation process is intricate, involving numerous variables, making it challenging to achieve precise control over quality parameters. To address these issues, distributed dynamic graph embedding (DDGE) for quality-relevant process monitoring is proposed. This method incorporates the quality index and enhances local information by integrating data timestamps and neighborhood information. First, the mutual information (MI) and distributed partition methods are used to segment into several quality-related blocks and quality-unrelated blocks. Subsequently, the distributed dynamic graph embedded model is established and integrated into a global decision process based on Bayesian inference along with statistics and control limits. The feasibility and effectiveness of the proposed method are demonstrated using the Tennessee Eastman (TE) benchmark process and the industrial flotation process.