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Processes 2019, 7(2), 75; https://doi.org/10.3390/pr7020075

Incremental Parameter Estimation under Rank-Deficient Measurement Conditions

1
Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
2
ETH Zürich, Institute of Environmental Engineering, 8093 Zürich, Switzerland
3
Laboratoire d’Automatique, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
*
Author to whom correspondence should be addressed.
Received: 13 December 2018 / Revised: 25 January 2019 / Accepted: 27 January 2019 / Published: 2 February 2019
(This article belongs to the Special Issue Process Modelling and Simulation)
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Abstract

The computation and modeling of extents has been proposed to handle the complexity of large-scale model identification tasks. Unfortunately, the existing extent-based framework only applies when certain conditions apply. Most typically, it is required that a unique value for each extent can be computed. This severely limits the applicability of this approach. In this work, we propose a novel procedure for parameter estimation inspired by the existing extent-based framework. A key difference with prior work is that the proposed procedure combines structural observability labeling, matrix factorization, and graph-based system partitioning to split the original model parameter estimation problem into parameter estimation problems with the least number of parameters. The value of the proposed method is demonstrated with an extensive simulation study and a study based on a historical data set collected to characterize the isomerization of α -pinene. Most importantly, the obtained results indicate that an important barrier to the application of extent-based frameworks for process modeling and monitoring tasks has been lifted. View Full-Text
Keywords: extents; graph theory; model identification; observability; optimal clustering; parameter estimation; state decoupling extents; graph theory; model identification; observability; optimal clustering; parameter estimation; state decoupling
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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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Villez, K.; Billeter, J.; Bonvin, D. Incremental Parameter Estimation under Rank-Deficient Measurement Conditions. Processes 2019, 7, 75.

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