Dynamic Optimization and Non-linear Model Predictive Control to Achieve Targeted Particle Morphologies

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Date
2019-03
Author
Gerlinger, Wolfgang
Asua González, José María
Chaloupka, Tomas
Faust, Johannes M.M.
Gjertsen, Fredrik
Hamzehlou, Shaghayegh
Olav Hauger, Svein
Jahns, Ekkehard
Joy, Preet J.
Kosek, Juraj
Lapkin, Alexei
Leiza Recondo, José Ramón ORCID
Mhamdi, Adel
Mitsos, Alexander
Naeem, Omar
Rajabalinia, Noushin
Singstad, Peter
Suberu, John
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  Estadisticas en RECOLECTA
(LA Referencia)

Chemie Ingenieur Technik 91(3) : 323-335 (2019)
URI
http://hdl.handle.net/10810/32865
Abstract
An event-driven approach based on dynamic optimization and nonlinear model predictive control (NMPC) is investigated together with inline Raman spectroscopy for process monitoring and control. The benefits and challenges in polymerization and morphology monitoring are presented, and an overview of the used mechanistic models and the details of the dynamic optimization and NMPC approach to achieve the relevant process objectives are provided. Finally, the implementation of the approach is discussed, and results from experiments in lab and pilot-plant reactors are presented.
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This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Except where otherwise noted, this item's license is described as This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)