Real-Time Nonlinear State Estimation in Polymerization Reactors for Smart Manufacturing

Tronci, Stefania;Baratti, Roberto;
2018-01-01

Abstract

A high order Geometric Observer (GO) and a hybrid discrete-time extended Kalman filter (h-DEKF) are assessed in real-time towards data reconciliation and comprehensive polymer characterization. The nonlinear estimators are tested under a set of experiments using the data provided by the state-of-the-art smart sensor Automatic Continuous Online Monitoring of Polymerization reactions (ACOMP). To design the GO three different high-order structures are evaluated in terms of their robustness and performance. The gains of the GO are set following a systematic approach with a distinction between pure measurements and Lie derivatives. The h-DEKF design follows an auto-tuned error driven initialization of the free-parameters where different metaheuristic algorithms are employed for tuning. Both nonlinear estimators are contrasted for applications in the Smart Manufacturing of polymers. The aqueous polymerization of acrylamide using potassium persulfate as initiator demonstrates the effectiveness and flexibility of the estimator for real-time applications.
2018
Inglese
28th European Symposium on Computer Aided Process Engineering
9780444642356
Elsevier B.V.
Amsterdam
PAESI BASSI
Anton Friedl, et al.
43
1207
1212
6
https://www.sciencedirect.com/science/article/pii/B9780444642356502102
28th European Symposium on Computer Aided Process Engineering
Contributo
Esperti anonimi
10-13 June 2018
Graz, Austria
internazionale
scientifica
extended Kalman filter; Free-radical polymerization; geometric observer; nonlinear state estimation; Chemical Engineering (all); Computer Science Applications1707 Computer Vision and Pattern Recognition
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Salas, Santiago D.; Chebeir, Jorge; Tronci, Stefania; Baratti, Roberto; Romagnoli, José A.
273
5
4.1 Contributo in Atti di convegno
reserved
info:eu-repo/semantics/conferencePaper
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