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.
During emulsion polymerization, at
least two phases are present:
a continuous aqueous phase and dispersed polymer particles in which
most of the polymerization reaction occurs. A water-soluble initiator
forms a radical that enters the particle at its surface, where the
polymerization starts. Thus, the probability of finding a radical
near the surface might be high. The probability is dependent on specific
reaction kinetics, so a random walk model and simplified diffusion-reaction
model are developed in this work to determine whether a significant
radical distribution in the particle can occur. Furthermore, for the
cases in which radical distribution is relevant, a hybrid kinetic
Monte Carlo model, which was previously developed for relevant real
industrial processes, was extended to consider 3D spatial dislocation
of molecules and radicals. The results of the extended spatially distributed
model are compared to those of the original uniform particle model
for linear polymerization as well as those for polymerization with
long chain branching to determine whether previously predominantly
used models can be far away from physical reality. The presented results
are, namely, the influence of radical distribution on molecular weight
distribution and chain branching as representations of polymer properties
which consequently highly influence polymer viscosity and other macroscopic
properties.
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