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Processes 2016, 4(2), 9; doi:10.3390/pr4020009

Gaussian Mixture Model-Based Ensemble Kalman Filtering for State and Parameter Estimation for a PMMA Process

Department of Chemical and Materials Engineering, University of Alberta, 12th Floor—Donadeo Innovation Centre for Engineering (ICE), 9211—116 Street, Edmonton, AB T6G 1H9, Canada
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Author to whom correspondence should be addressed.
Academic Editor: Masoud Soroush
Received: 24 November 2015 / Accepted: 21 March 2016 / Published: 30 March 2016
(This article belongs to the Special Issue Polymer Modeling, Control and Monitoring)
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Abstract

Polymer processes often contain state variables whose distributions are multimodal; in addition, the models for these processes are often complex and nonlinear with uncertain parameters. This presents a challenge for Kalman-based state estimators such as the ensemble Kalman filter. We develop an estimator based on a Gaussian mixture model (GMM) coupled with the ensemble Kalman filter (EnKF) specifically for estimation with multimodal state distributions. The expectation maximization algorithm is used for clustering in the Gaussian mixture model. The performance of the GMM-based EnKF is compared to that of the EnKF and the particle filter (PF) through simulations of a polymethyl methacrylate process, and it is seen that it clearly outperforms the other estimators both in state and parameter estimation. While the PF is also able to handle nonlinearity and multimodality, its lack of robustness to model-plant mismatch affects its performance significantly. View Full-Text
Keywords: Gaussian mixture model; ensemble Kalman filter; particle filter; expectation maximization; polymethyl methacrylate; state and parameter estimation Gaussian mixture model; ensemble Kalman filter; particle filter; expectation maximization; polymethyl methacrylate; state and parameter estimation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, R.; Prasad, V.; Huang, B. Gaussian Mixture Model-Based Ensemble Kalman Filtering for State and Parameter Estimation for a PMMA Process. Processes 2016, 4, 9.

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