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.

[1]  S. Wold.  Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[2]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[3]  J. Macgregor,et al.  Monitoring batch processes using multiway principal component analysis , 1994 .

[4]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[5]  Fuli Wang,et al.  Two‐dimensional dynamic PCA for batch process monitoring , 2005 .

[6]  J. E. Jackson.  A User's Guide to Principal Components , 1991 .

[7]  John F. MacGregor,et al.  Multivariate SPC charts for monitoring batch processes , 1995 .

[8]  Furong Gao,et al.  Batch Process Monitoring in Score Space of Two-Dimensional Dynamic Principal Component Analysis (PCA) , 2007 .

[9]  Furong Gao,et al.  Stage-Oriented Statistical Batch Processes Monitoring, Quality Prediction and Improvement , 2008 .

[10]  Paul Nomikos,et al.  Detection and diagnosis of abnormal batch operations based on multi-way principal component analysis World Batch Forum, Toronto, May 1996 , 1996 .

[11]  Fuli Wang,et al.  Sub-PCA Modeling and On-line Monitoring Strategy for Batch Processes (R&D Note) , 2004 .

[12]  John F. MacGregor,et al.  Multivariate monitoring of batch processes using batch‐to‐batch information , 2004 .

[13]  John F. MacGregor,et al.  Multi-way partial least squares in monitoring batch processes , 1995 .

[14]  Furong Gao,et al.  Two-Dimensional Dynamic Principal Component Analysis with Autodetermined Support Region , 2009 .

[15]  N. Draper,et al.  Applied Regression Analysis , 1967 .

[16]  Fuli Wang,et al.  Stage-based soft-transition multiple PCA modeling and on-line monitoring strategy for batch processes , 2007 .

[17]  Yuan Yao,et al.  Phase and transition based batch process modeling and online monitoring , 2009 .