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

Chem Ing Tech 2019 Mar 21;91(3):323-335. Epub 2018 Dec 21.

University of Cambridge Department of Chemical Engineering and Biotechnology Philippa Fawcett Drive CB3 0AS Cambridge United Kingdom.

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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http://dx.doi.org/10.1002/cite.201800118DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6743714PMC
March 2019

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