Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

A nonlinear modeling and online monitoring method for the batch process using multiple local PCA

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Lijie Zhao ; Sch. of Inf. Eng., Shenyang Inst. of Chem. Technol., China ; Tian-You Chai ; Gang Wang

Producing good quality products is the common objective in industries. However, Achieving this objective can be very difficult in batch process, especially when quality measurements are not available on-line or they have long time delays. This paper proposes a simple and straight nonlinear dynamic modeling with multi-model structure for batch process online monitoring using minimum window principal component analysis (MWPCA). MWPCA replaces single linear MPCA with multiple local linear sub-models for batch process modeling. It does not estimate any deviations of the ongoing batch from the average trajectories. Since the proposed method eliminates prediction error, the accuracy of process performance monitoring increases. MWPCA modeling procedures, principle and its application are discussed in detail. The presented methodology is successfully applied to PVC batch process.

Published in:

Machine Learning and Cybernetics, 2003 International Conference on  (Volume:2 )

Date of Conference:

2-5 Nov. 2003