Quality-related monitoring of distributed process systems using dynamic concurrent partial least squares

Process monitoring is critical to ensure process safety and maintain quality stabilization. Currently, a large-scale industrial process commonly consists of multiple operation units or manufacturing plants, and a huge number of variables that reflect operation conditions can be collected easily. The conventional monitoring methods usually utilize all process variables to model, which will submerge the correlation between the process and quality variables. In this paper, a new dynamic concurrent partial least squares (DCPLS) monitoring scheme based on variable importance in the projection (VIP) is proposed for large-scale processes. Firstly, process variables are firstly partitioned into quality-related and weakly quality-related space based on their VIP value. Then, DCPLS model is used to monitor abnormal events in these two spaces, respectively. Finally, fusing the statistics of these two spaces through support vector data description to provide an overall indication. The proposed distributed process monitoring scheme not only automatically realizes the division of variables but also characterizes the dynamic information of data. The monitoring results of the Tennessee Eastman process indicate that the proposed VIP-DCPLS method is more efficient than other monitoring methods.

Automated summary

Process monitoring is critical for ensuring process safety and maintaining quality stabilization in large-scale industrial processes. The traditional monitoring methods may overlook the correlation between process and quality variables. A new dynamic concurrent partial least squares (DCPLS) monitoring scheme based on variable importance in the projection (VIP) is proposed in this paper, which proves to be more efficient in monitoring abnormal events.