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

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 t...

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Detalles Bibliográficos
Autores principales: Gerlinger, Wolfgang, Asua, José Maria, Chaloupka, Tomáš, Faust, Johannes M.M., Gjertsen, Fredrik, Hamzehlou, Shaghayegh, Hauger, Svein Olav, Jahns, Ekkehard, Joy, Preet J., Kosek, Juraj, Lapkin, Alexei, Leiza, Jose Ramon, Mhamdi, Adel, Mitsos, Alexander, Naeem, Omar, Rajabalinia, Noushin, Singstad, Peter, Suberu, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6743714/
https://www.ncbi.nlm.nih.gov/pubmed/31543521
http://dx.doi.org/10.1002/cite.201800118
Descripción
Sumario: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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