  Microgels are cross-linked, colloidal polymer networks with great potential
for stimuli-response release in drug-delivery applications, as their size in
the nanometer range allows them to pass human cell boundaries. For applications
with specified requirements regarding size, producing tailored microgels in a
continuous flow reactor is advantageous because the microgel properties can be
controlled tightly. However, no fully-specified mechanistic models are
available for continuous microgel synthesis, as the physical properties of the
included components are only studied partly. To address this gap and accelerate
tailor-made microgel development, we propose a data-driven optimization in a
hardware-in-the-loop approach to efficiently synthesize microgels with defined
sizes. We optimize the synthesis regarding conflicting objectives (maximum
production efficiency, minimum energy consumption, and the desired microgel
radius) by applying Bayesian optimization via the solver ``Thompson sampling
efficient multi-objective optimization'' (TS-EMO). We validate the optimization
using the deterministic global solver ``McCormick-based Algorithm for
mixed-integer Nonlinear Global Optimization'' (MAiNGO) and verify three
computed Pareto optimal solutions via experiments. The proposed framework can
be applied to other desired microgel properties and reactor setups and has the
potential of efficient development by minimizing number of experiments and
modelling effort needed.
