Multivariate Trajectory-Based Local Monitoring Method for Multiphase Batch Processes

This paper proposes a new method combining the multivariate trajectory analysis and the principal component analysis (PCA) for multiphase batch process monitoring. To handle the uneven length problem, the trajectories of process variables are calculated instead of the original samples. For online monitoring, similar trajectories are extracted by just-in-time learning (JITL) with historical trajectories and the PCA model is constructed, which can deal with the missing data problem as well. Furthermore, to acquire a more reliable monitoring performance, a new distance-based measurement is proposed to show the location of samples. For performance evaluation, case studies of a numerical example and a simulated penicillin fermentation process are provided, with detailed comparisons to traditional methods.

[1]  A. J. Morris,et al.  Comparison of Methods for Dealing with Uneven Length Batches , 1998 .

[2]  Zhiqiang Ge,et al.  Bagging support vector data description model for batch process monitoring , 2013 .

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

[4]  Yi Hu,et al.  Enhanced batch process monitoring using just-in-time-learning based kernel partial least squares , 2013 .

[5]  Padhraic Smyth,et al.  Trajectory clustering with mixtures of regression models , 1999, KDD '99.

[6]  David Wang,et al.  Robust Data-Driven Modeling Approach for Real-Time Final Product Quality Prediction in Batch Process Operation , 2011, IEEE Transactions on Industrial Informatics.

[7]  Furong Gao,et al.  Review of Recent Research on Data-Based Process Monitoring , 2013 .

[8]  Jie Yu,et al.  A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses , 2012, Comput. Chem. Eng..

[9]  Julian Morris,et al.  Dynamic model-based batch process monitoring , 2008 .

[10]  Furong Gao,et al.  A survey on multistage/multiphase statistical modeling methods for batch processes , 2009, Annu. Rev. Control..

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

[12]  Furong Gao,et al.  Stage-based process analysis and quality prediction for batch processes , 2005 .

[13]  Theodora Kourti,et al.  Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start‐ups and grade transitions , 2003 .

[14]  Jarke J. van Wijk,et al.  Interactive visualization of multivariate trajectory data with density maps , 2011, 2011 IEEE Pacific Visualization Symposium.

[15]  Zengliang Gao,et al.  Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes , 2012 .

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

[17]  Andrey Bogomolov,et al.  Multivariate process trajectories: capture, resolution and analysis , 2011 .

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

[19]  Staffan Folestad,et al.  Real-time alignment of batch process data using COW for on-line process monitoring , 2006 .

[20]  Zhi-huan Song,et al.  Online Monitoring and Quality Prediction of Multiphase Batch Processes with Uneven Length Problem , 2014 .

[21]  S. Qin,et al.  Multiway Gaussian Mixture Model Based Multiphase Batch Process Monitoring , 2009 .

[22]  Jesús Picó,et al.  Multi‐phase analysis framework for handling batch process data , 2008 .

[23]  John F. MacGregor,et al.  Multivariate analysis and optimization of process variable trajectories for batch processes , 2000 .

[24]  Zhiqiang Ge,et al.  Utilizing transition information in online quality prediction of multiphase batch processes , 2012 .

[25]  P. A. Taylor,et al.  Synchronization of batch trajectories using dynamic time warping , 1998 .

[26]  Min-Sen Chiu,et al.  Nonlinear process monitoring using JITL-PCA , 2005 .

[27]  Furong Gao,et al.  Batch process monitoring based on support vector data description method , 2011 .

[28]  Chunhui Zhao,et al.  Inner-Phase Analysis Based Statistical Modeling and Online Monitoring for Uneven Multiphase Batch Processes , 2013 .

[29]  Zhiqiang Ge,et al.  Nonlinear quality prediction for multiphase batch processes , 2012 .

[30]  Zhiqiang Ge,et al.  Incorporating setting information for maintenance-free quality modeling of batch processes , 2013 .

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