Knowledge-Data-Based Synchronization States Analysis for Process Monitoring and Its Application to Hydrometallurgical Zinc Purification Process

Modern industrial processes generate many interassociated variables, which are more likely to implicit associations knowledge for describing irregular changes at different times to accurately describe behavior changes. Motivated by this issue, a novel knowledge-data-based synchronization states analysis method is proposed in this article for process monitoring. Its advantage mainly refers to integrating physical-chemical mechanism knowledge to handle the representation of associated relationships between numerous monitor variables. Furthermore, this method utilizes the trend distributions of variable changes to observe the differences between operation states and their parents online, which can maintain the simple, practical, and efficient advantage of data-driven process monitoring. Specifically, global process monitoring can be achieved by the synchronization status exceeding its corresponding threshold (chi(2) distribution). At the same time, the local cause of backtracking can also be identified by whether the weighting of eigenvector components of each variable exceeds their corresponding thresholds (chi(2) distribution). This novel proposed process monitoring method is applied to one practical hydrometallurgical zinc purification process consisting of copper and cobalt removal processes. The application's comparable performance shows the applicability and effectiveness of this proposed method.