Inner-Phase Analysis Based Statistical Modeling and Online Monitoring for Uneven Multiphase Batch Processes

The multiplicity of operation phases is inherent in the nature of many batch processes, and each phase exhibits significantly different underlying behaviors. In addition, within each phase, normal processes in general follow certain underlying operation rules, called inner-phase evolution here, which however have not been addressed before. In this paper, a new statistical modeling and online monitoring method is proposed for multiphase batch processes. A two-level phase division algorithm is proposed to capture the process trend and trace inner-phase evolutions. It reveals that the inner-phase process in general goes through three statuses sequentially, i.e., transition, steady phase, and transition. Principal component analysis (PCA) and qualitative trend analysis (QTA) are combined to distinguish different inner-phase process statuses. Their different characteristics are then modeled and monitored separately, revealing more accurate process operation information. Meanwhile, the problem of uneven-duratio...

[1]  A. J. Morris,et al.  Batch process monitoring for consistent production , 1996 .

[2]  Enrico W. Coiera,et al.  Learning Qualitative Models of Dynamic Systems , 2004, Machine Learning.

[3]  I. Jolliffe Principal Component Analysis , 2002 .

[4]  Furong Gao,et al.  Statistical analysis and online monitoring for handling multiphase batch processes with varying durations , 2011 .

[5]  Venkat Venkatasubramanian,et al.  Signed Digraph based Multiple Fault Diagnosis , 1997 .

[6]  Bhavik R. Bakshi,et al.  Representation of process trends—III. Multiscale extraction of trends from process data , 1994 .

[7]  Ira J. Haimowitz,et al.  Managing temporal worlds for medical trend diagnosis , 1996, Artif. Intell. Medicine.

[8]  William W. Melek,et al.  Comparison of trend detection algorithms in the analysis of physiological time-series data , 2005, IEEE Transactions on Biomedical Engineering.

[9]  John A. Meech,et al.  Using fuzzy logic for on-line trend analysis , 1993, Proceedings of IEEE International Conference on Control and Applications.

[10]  Venkat Venkatasubramanian,et al.  A B-spline based method for data compression, process monitoring and diagnosis , 1998 .

[11]  J.F. MacGregor,et al.  Multi-way PCA applied to an industrial batch process , 1994, Proceedings of 1994 American Control Conference - ACC '94.

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

[13]  Chunhui Zhao,et al.  Improved Knowledge Extraction and Phase-Based Quality Prediction for Batch Processes , 2008 .

[14]  Dong Dong,et al.  Multi-stage batch process monitoring , 1995, Proceedings of 1995 American Control Conference - ACC'95.

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

[16]  S. Wold,et al.  The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses , 1984 .

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

[18]  I. D. Coope,et al.  Circle fitting by linear and nonlinear least squares , 1993 .

[19]  Raghunathan Rengaswamy,et al.  Fault Diagnosis by Qualitative Trend Analysis of the Principal Components , 2005 .

[20]  Venkat Venkatasubramanian,et al.  Automatic generation of qualitative descriptions of process trends for fault detection and diagnosis , 1991 .

[21]  Fuli Wang,et al.  PCA-Based Modeling and On-line Monitoring Strategy for Uneven-Length Batch Processes , 2004 .

[22]  Fuli Wang,et al.  Quality prediction based on phase-specific average trajectory for batch processes , 2008 .

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

[24]  Raghunathan Rengaswamy,et al.  Fuzzy-logic based trend classification for fault diagnosis of chemical processes , 2003, Comput. Chem. Eng..

[25]  G. Stephanopoulos,et al.  Representation of process trends—Part I. A formal representation framework , 1990 .

[26]  Svante Wold,et al.  Modelling and diagnostics of batch processes and analogous kinetic experiments , 1998 .

[27]  Chunhui Zhao,et al.  Adaptive Monitoring Method for Batch Processes Based on Phase Dissimilarity Updating with Limited Modeling Data , 2007 .

[28]  A qualitative shape analysis formalism for monitoring control loop performance , 2001 .

[29]  David Tak-Wai Hau,et al.  Learning Qualitative Models from Physiological Signals , 1994 .

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

[31]  A. J. Morris,et al.  Performance monitoring of a multi-product semi-batch process , 2001 .

[32]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

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

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

[35]  Raghunathan Rengaswamy,et al.  A Novel Interval-Halving Framework For Automated Identification of Process Trends , 2004 .

[36]  Andrew W. Dorsey,et al.  Monitoring of batch processes through state‐space models , 2004 .

[37]  J. Edward Jackson,et al.  A User's Guide to Principal Components: Jackson/User's Guide to Principal Components , 2004 .

[38]  Ali Cinar,et al.  Statistical monitoring of multistage, multiphase batch processes , 2002 .

[39]  Ira J. Haimowitz,et al.  Clinical monitoring using regression-based trend templates , 1995, Artif. Intell. Medicine.