Two-dimensional Multiphase Batch Process Monitoring Based on Sparse Canonical Variate Analysis

Most industrial batch processes involve inherent dynamic characteristics in both within-batch time direction and batch-wise direction. In order to ensure process safety and improve process performance, the two-dimensional dynamics should be analyzed during batch process monitoring. In this work, Firstly, two-dimensional region of support (2D-ROS) is constructed to select and preserve the relevant samples for the current measured sample by calculating autoregressive orders with Akaike Information Criterion (AIC) in time direction and measuring the similarity with weighted Euclidean distance in batch-wise direction. Afterward, sparse canonical variate analysis (SCVA) algorithm is performed to yield sparse canonical vectors, which is especially advantageous for eliminating the irrelevant variables and facilitating the interpretation of underlying relationships of process variables. The upper control limits (UCLs) in 2D-SCVA can be estimated using kernel density estimation (KDE). The achieved results clearly verify that the proposed method performs well for detecting abnormal operation for the batch process.