García, Míriam R. and Vilas, Carlos and Banga, Julio R. and Alonso, Antonio A. (2008) Exponential Observers for Distributed Tubular (Bio)Reactors. AIChE Journal, 54 (11). pp. 2943-2956. ISSN 1547-5905
Download (850kB)
|
Abstract
In this work, the dissipative nature of spatially distributed process systems is exploited to develop efficient exponential state observers based on a low dimensional dynamic representation of the original set of partial differential equations. The approach we suggest combines standard observer design techniques for reactors where the reaction rates are unknown with efficient model reduction methodologies based on projection of the original concentration and temperature fields on low dimensional subspaces capturing the slow dynamics of the process. The global exponential stability of the resulting observer is derived combining classical Lyapunov analysis with a transformation that allows us to obtain a diffusion system from a diffusionconvection system. In addition aspects related to the location of sensors and their influence on the ability to reconstruct the necessary fields to feed the observer will also be considered.
Item Type: | Article |
---|---|
Additional Information: | Preprint version of original published article. The definitive version of the article is available at http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291547-5905/ DOI: 10.1002/aic.11571 |
Keywords: | Observer; Distributed Process Systems; Tubular (Bio)Reactors; Optimal sensor location; |
Academic Unit: | Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
Item ID: | 3626 |
Depositing User: | Miriam Garcia |
Date Deposited: | 01 May 2012 15:29 |
Journal or Publication Title: | AIChE Journal |
Publisher: | Wiley-Blackwell |
Refereed: | No |
URI: | |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only(login required)
Item control page |
Downloads
Downloads per month over past year