Real-time
optimization-based control methodologies in emulsion
(co)polymerization allow achievement of a significant intensification
of the process and increase of the product quality. This paper describes
the development of the fast and computationally simple process model
of continuous styrene and n-butyl acrylate emulsion
copolymerization for use in nonlinear model predictive control (NMPC)
of a smart-scale tubular reactor. The model predictions agree well
with experimental data for monomer conversion, copolymer composition,
temperature profile, average particle size, and number-average molecular
weight. To account for the slower reaction rate at the beginning of
the reaction, the model incorporates a thermodynamic description of
comonomer partitioning between particle, water, and droplet phases
based on Morton equations. For the purpose of the process model, a
simple empirical function representing the solution of Morton partitioning
was implemented. The number concentration of particles was estimated
from measured monomer conversion profiles, as the predictions by first-principle
nucleation models generally provide values substantially different
from experiments. After the model incorporation into a framework for
online state estimation and control, it will be used for open- and
closed-loop control of the smart-scale tubular reactor.