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State and Input Estimation of Nonlinear Chromatographic Processes | IEEE Conference Publication | IEEE Xplore

State and Input Estimation of Nonlinear Chromatographic Processes


Abstract:

We investigate two algorithms for state estimation of nonlinear chromatographic processes: the Extended Kalman Filter and the Ensemble Kalman Filter. We consider the Equi...Show More

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.
Date of Conference: 19-21 August 2019
Date Added to IEEE Xplore: 05 December 2019
ISBN Information:
Conference Location: Hong Kong, China

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