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Dynamic Optimization and Non‐linear Model Predictive Control to Achieve Targeted Particle Morphologies
Author(s) -
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
Publication year - 2019
Publication title -
chemie ingenieur technik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 36
eISSN - 1522-2640
pISSN - 0009-286X
DOI - 10.1002/cite.201800118
Subject(s) - model predictive control , process (computing) , process control , process optimization , control (management) , computer science , nonlinear system , particle (ecology) , control engineering , process engineering , control theory (sociology) , engineering , chemical engineering , physics , artificial intelligence , oceanography , quantum mechanics , geology , operating system
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.