Multivariate statistical monitoring of multiphase two-dimensional dynamic batch processes

To ensure product quality and operation safety, multivariate statistical process control (MSPC) techniques have been applied to batch process monitoring. Batch processes have several important features which should be taken into consideration in statistical monitoring, such as two-dimensional (2D) dynamics along both time and batch axes and multiple operation phases within a batch. In this paper, a multiphase two-dimensional dynamic PCA (2D-DPCA) method is proposed to deal with these characteristics simultaneously. An iterative procedure is designed for phase division, region of support (ROS) determination for each phase and phase 2D-DPCA modeling. The transition regions from phase to phase are also identified and modeled. Then, the phase and transition 2D-DPCA models are utilized in online monitoring. A three-water-tank process with 2D dynamics and a simulated two-phase batch process are used as case studies, to show the effectiveness of the proposed method.