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@PHDTHESIS{Kaven:988558,
author = {Kaven, Luise Friederike},
othercontributors = {Mitsos, Alexander and Wessling, Matthias},
title = {{I}n-silico and in-situ optimization for enhanced synthesis
of functional microgels},
volume = {31},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2024-06268},
series = {Aachener Verfahrenstechnik series - AVT.SVT - Process
Systems Engineering},
pages = {1 Online-Ressource : Illustrationen},
year = {2024},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2024},
abstract = {Microgels are functional polymers with the potential for
versatile applications. Each application requires tailored
properties of the functional microgels. To enable a
tailor-made production, insights during the reaction via
process monitoring are essential, as well as mathematical
modeling for predicting and optimizing microgel properties.
Thus, this thesis provides advancements in microgel
synthesis regarding monitoring, modeling, and optimization
to unlock the full potential of these versatile polymers.
First, concentration monitoring via Raman spectroscopy is
established for the continuous microgel production mode.
Transferring this monitoring technique from batch to
continuous flow setups poses challenges that are
systematically addressed. A quality criterion for Raman
spectra is derived to allow functional outlier detection
during continuous synthesis. Hence, overall, a guideline for
in-line monitoring transfer is established. Second, the
capabilities of Raman spectroscopy are enhanced by
presenting a method to determine the microgel size from
Raman measurements. Recent developments in machine learning
are leveraged to improve size determination quality. The
resulting accuracy is comparable with state-of-the-art
off-line analysis tools. Thus, combining Raman spectroscopy
and advanced machine learning methods is a promising
approach for in-line polymer size determination. Third,
Raman spectroscopy is also enabled for monitoring the
charged microgel synthesis. Applying indirect spectral hard
modeling resolves the complexities caused by the
multi-component solutions with dissociated and undissociated
states. Therefore, a detailed insight into the reaction
phenomena during the charged microgel synthesis is enabled.
Fourth, a mechanistic, dynamic model for the synthesis of
microgels is extended to account for integration of
functional epoxy groups. Unknown parameter values are
calculated within a parameter estimation. By strategically
including quantum mechanically calculated parameter values,
the fit between model prediction and experimental
measurements can be improved while reducing the
calculational effort. By applying the identified synthesis
model, the distribution of functional epoxy groups within
the microgel is predicted. Fifth, a mechanistic, dynamic
model is enhanced to capture the synthesis of charged
microgels with regard to pH changes during the process.
Again, missing parameter values are calculated within a
parameter estimation, including quantum mechanically
computed values strategically. The developed model presents
a robust framework for predicting and optimizing the
performance of charged microgels in diverse scenarios,
paving the way for designing more efficient and tailored
microgel-based systems. Sixth, a data-driven
hardware-in-the-loop approach is presented to synthesize
microgels of a desired size successfully. Data-driven
approaches, particularly Bayesian optimization, are employed
for microgel synthesis optimization for multiple objectives
regarding product and process properties simultaneously. The
proposed framework includes global deterministic
optimization and has the potential for efficient microgel
development by minimizing the number of experiments and
modeling efforts needed. The thesis brings together
advancements in the fields of process analytical technology,
mathematical modeling, and data-driven optimization while
combining experimental real-world development (in-situ) with
theoretical considerations (in-silico). Therefore, this
thesis's findings provide one step toward synthesizing
tailored microgels with specific compositions or
functionalities at increased production scale. Finally, many
findings of this thesis are relevant not only for other
polymer systems but also for method development in the field
of spectroscopy-based size determination and
hardware-in-the-loop optimization for all kinds of
(chemical) systems.},
cin = {416710},
ddc = {620},
cid = {$I:(DE-82)416710_20140620$},
pnm = {SFB 985 G02+ - In-line Monitoring von Mikrogel
Produktionsprozessen (G02+) (221489508) / SFB 985 B04 -
Synthese von Mikrogelen: Kinetik, Partikelbildung und
Reaktordesign (B04) (221473487) / DFG project 191948804 -
SFB 985: Funktionelle Mikrogele und Mikrogelsysteme
(191948804)},
pid = {G:(GEPRIS)221489508 / G:(GEPRIS)221473487 /
G:(GEPRIS)191948804},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2024-06268},
url = {https://publications.rwth-aachen.de/record/988558},
}
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