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

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.

Impact Statement

LC-TEM was utilized as part of a larger pilot study on developing particle morphology controlled processes specifically to address the possibility of using the technique to study the morphology of polymer nanoparticles, and if images with suitable contrast could be obtained.  Morphological features were identifiable in polymer nanoparticles  using LC-TEM, but the contrast was considered too low to take advantage of current automated image analysis processes.  The authors conclude that with advantages in data collection and sampling methods it may be possible to utilize LC-TEM for particle analysis in the future.