THESIS
2022
1 online resource (xxiii, 158 pages) : illustrations (chiefly color)
Abstract
Self-assembly is a promising route for the fabrication of advanced materials with novel
properties. Applications of self-assembly include colloids, lipid bilayers, and the assembly
of proteins into crystals. Despite the promising applications of self-assembly, external
controls employing electric and magnetic fields, or fluid flows are often necessary to reliably
guide self-assembly processes. Dynamic models can determine optimal input trajectories
of manipulated variables during directed self-assembly. The second chapter of this thesis
presents a dynamic model for the simulation of particle trajectories in the presence of
an electric field with time-varying properties. The model is based on first principles
and has been experimentally validated under different conditions. The model is...[
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Self-assembly is a promising route for the fabrication of advanced materials with novel
properties. Applications of self-assembly include colloids, lipid bilayers, and the assembly
of proteins into crystals. Despite the promising applications of self-assembly, external
controls employing electric and magnetic fields, or fluid flows are often necessary to reliably
guide self-assembly processes. Dynamic models can determine optimal input trajectories
of manipulated variables during directed self-assembly. The second chapter of this thesis
presents a dynamic model for the simulation of particle trajectories in the presence of
an electric field with time-varying properties. The model is based on first principles
and has been experimentally validated under different conditions. The model is used to
predict the dynamic trajectories of particles when considering electrokinetic forces and
fluid drag with the assumption of negligible Brownian motion for a system with low spatial
particle density. The importance of Brownian motion and particle-particle interactions
are then considered. The model can be used to predict the dynamic development of the
spatial particle density distribution and the probability density associated with particle
positions. The dynamic model has been utilized in a novel open-loop control scheme for
shaping local particle densities. The dynamic model has great promise to be used for
model-based control of directed self-assembly of colloidal particles.
Mixing or agitation is essential for the reliable self-assembly of proteins into crystals.
Mixing of protein solutions requires a balance between fast mixing and low shear under
laminar flow conditions to minimize supersaturation gradients. The third chapter of this thesis addresses the problem of obtaining homogeneous supersaturated solutions in protein
crystallization in the laminar flow regime with a novel Kenics mixer. The novel mixer
design features gaps between the mixing elements, which can achieve the same level of
mixing as the conventional design but with fewer mixing elements and a substantially
lower pressure drop and shear rate. The mixing effects at the entrances and exits of the
mixing elements are enhanced by the introduction of gaps between the elements. The
performances of Kenics mixers based on the right-left and right-right configurations with
different gap lengths are characterized in terms of pressure drop, coefficient of variance of
concentration, residence time distribution, and extensional efficiency with computational
fluid dynamics simulations. Furthermore, the coefficient of variance of concentration is
measured experimentally with several 3-D printed devices. The gaps reduce the mixing
length when the design is based on the right-right configurations and the gap-to-diameter
ratio is 0.5 or 1.0 compared to the corresponding conventional design. Additionally,
Taylor dispersion is suppressed with the introduction of gaps, which enables a narrower
residence time distribution. The presence of gaps between mixing elements introduces an
additional degree of freedom, which can be utilized to strike a compromise between the
required mixing length and pressure drop.
Seeding is often used in the fine chemicals and the pharmaceutical industries to reliably
control the quality of crystalline products with a narrow crystal size distribution (CSD)
by suppressing nucleation. However, the generation of small, non-agglomerated crystals
with narrow crystal size distributions can be laborious. The conventional approach to seed
generation which generally involves milling, sieving, and ripening to obtain seed crystals
is challenging for applications involving proteins due to the susceptibility of proteins
to denaturation. The challenge of generating small protein seed crystals with narrow
crystal size distributions is addressed in the fourth chapter of this thesis using a gapped
Kenics tubular crystallizer (gKTC) as a seed generation device. The gapped Kenics
tubular crystallizer is based on the R-R Kenics mixer with a gap-to-diameter ratio of 1.0.
Compared with a stirred tank crystallizer, smaller non-agglomerated protein crystals are
consistently generated in the gKTC at shorter residence times. The higher nucleation
rates in the gapped Kenics tubular crystallizer may be attributed to the promotion of
heterogeneous nucleation by the mixing elements or differences in hydrodynamics between
the gapped Kenics tubular crystallizer and the stirred tank crystallizer. Different seeding policies can be implemented by adjusting the transfer of crystals from the gKTC to a
batch crystallizer, which offers flexible control over the CSD of the batch product. This
flexibility is demonstrated through an open-loop control strategy in which an optimal
seeding policy to obtain a flat-top CSD is derived from a population balance model,
which is then implemented experimentally. The obtained CSD is close to the specified
one, which cannot be achieved well with conventional batch crystallization.
Finally, a novel data-driven methodology is presented for developing mathematical
models for crystallization processes. The data-driven approach iterates between a partial
least-squares fit and a sparsity-promoting step leading to the discovery of sparse interpretable
models. The data-driven method is robust against noise. The performance of the
data-driven methodology is characterized for the identification of crystallization kinetics
in an MSMPR as well as the crystallization of lysozyme in a batch stirred tank. Remarkable
agreement is obtained between the data-driven model and the data obtained from
seeded batch protein crystallization experiments. The presented data-driven approach can
be attractive for industrial crystallization processes where process analytical technology
(PAT) tools are available for the measurement of process variables.
The work presented in this thesis can provide the basis for modelling, design, and
control of chemical engineering processes. For instance, the first-principle model presented
in Chapter 2 can be employed in the design of closed-loop model-based controllers for
colloidal particle self-assembly. The novel mixer that has been characterized here, may also
be interesting for continuous crystallization applications in the pharmaceutical industry.
Furthermore, the CFD model presented for the simulation of the Kenics static mixers
can be utilized for the design and optimization of other static mixers. Additionally, the
seeding approach introduced in this thesis may be exploited for the generation of seed
crystals for other model compounds while the data-driven approach presented in Chapter
5 may be further investigated for crystallization processes involving complicated secondary
phenomena such as agglomeration and the breakage of crystals.
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