Application of MHE to large-scale nonlinear processes with delayed lab measurements

The paper addresses nonlinear estimation problems on nonlinear processes containing several lab measurements sampled slowly and with long delay, which is the usual case in industrial polymerization applications. A moving horizon estimation algorithm is developed to compute the theoretical optimal so...

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Bibliographic Details
Published in:Computers & chemical engineering Vol. 80; pp. 63 - 72
Main Authors: Ji, Luo, Rawlings, James B.
Format: Journal Article
Language:English
Published: Elsevier Ltd 02.09.2015
Subjects:
Algorithms
Chemical engineering
Computer simulation
Delay
Mathematical models
Moving horizon estimation
Multi-rate sampling measurements
Nonlinear state estimation
Nonlinearity
Observability
Optimization
Polymer process
Polymerization
Polymer process
Nonlinear state estimation
Observability
Moving horizon estimation
Multi-rate sampling measurements
ISSN:0098-1354, 1873-4375
Online Access:Get full text
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Summary:The paper addresses nonlinear estimation problems on nonlinear processes containing several lab measurements sampled slowly and with long delay, which is the usual case in industrial polymerization applications. A moving horizon estimation algorithm is developed to compute the theoretical optimal solution given the multi-rate measurements. In this algorithm, the MHE window is recalculated as the new lab measurement becomes available. Simulation studies on a polymerization process with plant model mismatch are performed. Observability analysis and estimation results of MHE with and without lab measurements show that lab measurements help identify the disturbances and can improve the performance of both estimation and closed-loop control.
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ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2015.04.015