Integrated Multiscale Latent Variable Regression and Application to Distillation Columns
Joint Authors
Nounou, Hazem N.
Madakyaru, Muddu
Nounou, Mohamed N.
Source
Modelling and Simulation in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-04-11
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Proper control of distillation columns requires estimating some key variables that are challenging to measure online (such as compositions), which are usually estimated using inferential models.
Commonly used inferential models include latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least squares (PLS), and regularized canonical correlation analysis (RCCA).
Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction abilities of inferential models.
Therefore, noisy measurements need to be filtered to enhance the prediction accuracy of these models.
Multiscale filtering has been shown to be a powerful feature extraction tool.
In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR) modeling algorithm that integrates modeling and feature extraction.
The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion.
The performance of the developed IMSLVR algorithm is illustrated using three examples, one using synthetic data, one using simulated distillation column data, and one using experimental packed bed distillation column data.
All examples clearly demonstrate the effectiveness of the IMSLVR algorithm over the conventional methods.
American Psychological Association (APA)
Madakyaru, Muddu& Nounou, Mohamed N.& Nounou, Hazem N.. 2013. Integrated Multiscale Latent Variable Regression and Application to Distillation Columns. Modelling and Simulation in Engineering،Vol. 2013, no. 2013, pp.1-17.
https://search.emarefa.net/detail/BIM-494102
Modern Language Association (MLA)
Madakyaru, Muddu…[et al.]. Integrated Multiscale Latent Variable Regression and Application to Distillation Columns. Modelling and Simulation in Engineering No. 2013 (2013), pp.1-17.
https://search.emarefa.net/detail/BIM-494102
American Medical Association (AMA)
Madakyaru, Muddu& Nounou, Mohamed N.& Nounou, Hazem N.. Integrated Multiscale Latent Variable Regression and Application to Distillation Columns. Modelling and Simulation in Engineering. 2013. Vol. 2013, no. 2013, pp.1-17.
https://search.emarefa.net/detail/BIM-494102
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-494102