Chemical Engineering June 2021 - 18

rials will be subject to, as well as the
kinetics that have to be played out.
So, the challenge was how to take all
the kinetics, rheology and formulation
data and create a system that could
move us forward, " explains Dave Parrillo,
vice president R&D, Dow Industrial
Intermediates & Infrastructure.
To build such a complex platform,
Dow relied on theory-based neural
networks that incorporated critical
correlations for kinetics and rheology.
" In a typical neural network, you feed
it lots of data, which it learns from,
and behind the scenes, it's tuning its
knobs and weighing different influences.
We can now influence those
knobs with theoretical correlations
so that the system not only learns,
but gets smarter over time, and also
starts to explore spaces where we
might not have as much data. It folds
theoretical, empirical, semi-empirical,
and experimental information into a
single tool, " says Parrillo. One of the
first major applications that Dow is trialing
for the platform is polyurethane
mattresses, with multiple applications
to follow in 2022.
Once a product formulation is
developed and manufacturing has
begun, inspection and validation are
key. Stevanato Group (Padua, Italy;
www.stevanatogroup.com) recently
launched an AI platform focused on
visual inspection of biopharmaceutical
products, looking at both particle
and cosmetic defects (Figure
5). " AI can improve overall inspection
performance in terms of detection
rate and false rejection rate. AI
can help to reduce false rejects and
costly interventions to parameterize
the machine during production, "
explains Raffaele Pace, engineering
vice president of operations at
Stevanato Group. Recently, trials of
the automatic inspection platform
have produced promising results,
including the ability to reduce falsely
rejected products tenfold, with up to
99.9% accuracy, using deep learning
(DL) techniques. " Unlike traditional
rule-based systems, DL models can
generalize their predictions and be
more flexible regarding variations, "
adds Pace. He also mentions that
such advanced inspection performance
can help to reduce the number
of " gray " items, which are flagged
on the production line but not rejected
outright. Typically, such items require
manual re-inspection, which adds
time to the process. " This helps the
entire process become more lean
and have less waste, while maintaining
and improving quality, " he
continues. The company is currently
working to enhance detection accuracy
for both liquid and lyophilized
products, and also developing an
initiative to create pre-trained neural
networks that could then adapt to
specific defects and drugs. " Producing
such models will entail training the
system with thousands of images, "
notes Pace.
n
Mary Page Bailey
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Chemical Engineering June 2021

Table of Contents for the Digital Edition of Chemical Engineering June 2021
