Publication Date:
2015-12-26
Description:
We address the issue of state estimation of an aggregation process through (i) using model reduction to obtain a tractable approximation of the governing dynamics, and (ii) designing a fast moving-horizon estimator for the reduced-order model. We first use the method of moments to reduce the governing integro-differential equation down to a nonlinear ordinary differential equation (ODE). This reduced-order model is then simulated for both batch and continuous processes and the results are shown to agree with constant Number Monte Carlo (cNMC) simulation results of the original model. Next, the states of the reduced order model are estimated in a Moving Horizon Estimation (MHE) approach. For this purpose we first employ Carleman linearization and represent the nonlinear system in a bilinear form. This representation lessens the computation burden of the estimation problem by allowing for analytical solution of the state variables as well as sensitivities with respect to decision variables. This article is protected by copyright. All rights reserved.
Print ISSN:
0001-1541
Electronic ISSN:
1547-5905
Topics:
Chemistry and Pharmacology
,
Process Engineering, Biotechnology, Nutrition Technology
