PLS-Based Robust Inferential Control for a Packed-Bed Reactor
- Creators
- Budman, H. M.
- Holcomb, T.
- Morari, M.
Abstract
This paper compares the performance of two different inferential schemes when applied to an experimental packed-bed reactor. The first scheme, proposed initially by Brosilow, is designed based on Kalman filter estimation. The second less traditional design uses an estimator computed from the Partial Least Squares regression method (PLS). The second approach was found to give superior performance when the nonlinear system under study is operated is a wide range of operating points. Due to the nonlinearity of the system it is essential to address the issue of robustness of the proposed schemes. This is formally done in this work using Structured Singular Value Theory. For the robustness analysis it is crucial to develop a realistic but not overly conservative uncertainty description. Since the PLS estimator uses a large number of measurements, a robust design based on the uncertainty associated with each one of the measurements would be very conservative. To overcome this problem a lumped uncertainty description is proposed which is identified directly from experiments.
Additional Information
© 1991 IEEE. This proceedings paper is a condensation of the the journal article submitted to Industrial and Engineering Chemistry Research [2]. Hector Budman would like to acknowledge the financial support of the Rothschild and Bentrell Foundations. Tyler Holcomb is the recipient of a National Science Foundation Graduate Fellowship.Attached Files
Published - 04791367.pdf
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Additional details
- Eprint ID
- 78351
- Resolver ID
- CaltechAUTHORS:20170619-172208220
- Rothschild Foundation
- Bentrell Foundation
- NSF Graduate Research Fellowship
- Created
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2017-06-20Created from EPrint's datestamp field
- Updated
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2019-10-03Created from EPrint's last_modified field