Chemical Engineering July 2023 - 42

Sopat
FIGURE 10. Production lines with inline particle-size monitoring can increase
process stability and product quality
takes place from the shell to the core
of the particle, the mixing time needs
to be long enough for the particles to
fully polymerize.
By measuring the particle-size distribution
over time, it is possible to
track changes in the reaction kinetics
and identify any issues that may arise
during the process, such as agglomeration
or the formation of oversized
particles (Figure 8). This information
can be used to adjust the reaction
conditions in real time to ensure the
desired product quality and yield.
Image analysis
Due to the high optimization potential
and significant possible savings,
the SIBUR plant in Perm was
upgraded with two automated inline
analyzers for their two main production
vessels of EPS. The inline microscopes,
coupled with an automated
image-analysis system, was installed
to gain information about particle
shape and size. Based on the inline
results, the quality of the EPS is
monitored, and agitation speed and
chemical component feeding are
optimized (Figure 9).
The drop and particle size and their
distributions are monitored with an
inline imaging system. The system
uses a combination of high-speed
imaging and digital image analysis
to capture and analyze images of
particles in the process. The images
are analyzed using proprietary image
analysis software that can determine
the size, shape and other properties
of each particle. The software can
also differentiate between different
particle types, which eliminates the
misreading of bubbles as monomer
droplets or polymer particles.
42
The imageanalysis
system
that was used
includes digital
image processing.
The images
captured
by the camera
are processed
digitally to remove
any background
noise
or distortion,
and to enhance
the features of
the particles. A
software system
uses pattern-recognition algorithms
to identify particles and differentiates
between different types
of particles, based on their size,
shape and other properties [11].
The system can use machine learning
algorithms to learn and adapt to
new types of particles, allowing it to
accurately identify and analyze particles
even in complex or changing
environments [12]. It can also perform
statistical analysis on the size
and shape data to provide detailed
information about the particle population,
such as the mean particle size
and particle-size distribution.
By using these advanced image
analysis techniques, the installed
system provides highly accurate
and detailed information about
the monomer droplets, as well as
the polymer particles in real-time
throughout a full polymerization process
(Figure 10). The real-time monitoring
led to a significant increase in
process stability and product quality,
reducing waste through thoughtful
process optimization based on the
real-time data by 28% throughout
the fourth quarter in the production
year 2021.
n
Edited by Scott Jenkins
References
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Author
Sebastian Maass is a founding
member of SOPAT GmbH
(Bergholzstrasse 8, 12099, Berlin,
Germany; Phone: +49 30 398 20
20 00; Email: info@sopat.de;
Website: www.sopat.de; LinkedIn:
@SOPAT GmbH), where he is the
chief strategy officer. After graduating,
he earned a Ph.D. at the department
of energy and process
engineering, receiving his Ph.D. with distinction (summa
cum laude) from the Technische Universität Berlin in
2011. During his Ph.D., he received several national and
international grants for his scientific projects, he authored
and co-authored more than 40 publications and gave
more than 100 oral and poster presentations at German
and international conferences. SOPAT was founded in
2012 and is based in Berlin. It develops and distributes
photo-optical and image-based analytical measurement
technology that quantitatively characterizes multiphase
particulate systems. In combination with hardware, the
company also offers software for enabling real-time
analysis of particle size distributions, particle shape factors
and other characteristics. SOPAT offer customized
systems that allow early detection of trends to achieve
efficient process optimization. This leads to significant
cost reductions while increasing product quality.
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