State and Input Estimation of Nonlinear Chromatographic Processes

Alexander Hørsholt, Lasse Hjuler Christiansen, Tobias Kasper Ritschel, Kristian Meyer, Jakob Kjøbsted Huusom, John Bagterp Jørgensen*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

We investigate two algorithms for state estimation of nonlinear chromatographic processes: the Extended Kalman Filter and the Ensemble Kalman Filter. We consider the Equilibrium Dispersive Model for modeling of packed-bed chromatography. The Equilibrium Dispersive Model is governed by a convection-dominated nonlinear partial differential equation. The model is discretized by a high-order discontinuous-Galerkin finite-element method for accurate and efficient simulation of the chromatographic process. The discretization leads to a stochastic continuous-discrete model, and we use the Extended Kalman Filter and the Ensemble Kalman Filter for state estimation of the packed-bed chromatographic model. The performance and capabilities of both filters are demonstrated in simultaneous estimation of the unknown system states and uncertain inlet concentration.
Original languageEnglish
Title of host publication2019 IEEE Conference on Control Technology and Applications (CCTA)
Number of pages6
PublisherIEEE
Publication date2019
Pages1030-1035
ISBN (Print)978-1-7281-2768-2
ISBN (Electronic)978-1-7281-2767-5
DOIs
Publication statusPublished - 2019
Event2019 IEEE Conference on Control Technology and Applications (CCTA) - City University of Hong Kong, Hong Kong, China
Duration: 19 Aug 201921 Aug 2019
Conference number: 3
https://ccta2019.ieeecss.org/

Conference

Conference2019 IEEE Conference on Control Technology and Applications (CCTA)
Number3
LocationCity University of Hong Kong
CountryChina
CityHong Kong
Period19/08/201921/08/2019
Internet address

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