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Super model-based techniques for batch performance monitoring

Lookup NU author(s): Lindsay McPherson, Emeritus Professor Julian Morris, Professor Elaine Martin


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By combining mechanistic and empirical-based models, a process performance monitoring representation of a dynamic, non-linear process can be developed with the model-plant mismatch forming the basis of the monitoring scheme. In practice, the mechanistic model will not be perfect and therefore the residuals will contain structure. A modified model-based approach, Super Model-Based PCA (SMBPCA), is proposed which incorporates an additional residual modelling stage to remove structure from the residuals. The approach is evaluated on a simulation of a batch process using a number of residual modelling techniques including Partial Least Squares (PLS), dynamic PLS, ARX and dynamic Canonical Correlation Analysis (CCA). The out-of-control average run lengths for these techniques show that the SMBPCA approach gives improved process monitoring and fault detection compared to standard multivariate techniques. © 2002 Elsevier B.V. All rights reserved.

Publication metadata

Author(s): McPherson L, Morris J, Martin E

Editor(s): Johan Grievink and Jan van Schijndel

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 12th European Symposium on Computer Aided Process Engineering (ESCAPE-12)

Year of Conference: 2002

Pages: 523-528

ISSN: 1570-7946

Publisher: Computer Aided Chemical Engineering: Elsevier BV


DOI: 10.1016/S1570-7946(02)80115-8

Library holdings: Search Newcastle University Library for this item

ISBN: 9780444511096