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 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. An event‐driven approach based on dynamic optimization and nonlinear model predictive control is investigated together with Raman spectroscopy for process monitoring. The benefits and challenges in morphology monitoring are presented together with the used mechanistic models. Results from experiments in lab and pilot‐plant reactors are shown. [ABSTRACT FROM AUTHOR]