Chemical Engineering June 2021 - 15

IBM
FIGURE 4. Open-source AI platforms enable experiments
to be run remotely, bringing a new level of
autonomous operations into chemistry laboratories
do a cleanout, versus just scheduling
it. So what companies are doing is
taking the relevant furnace operating
data and being able to predict fouling
to prevent unplanned downtime.
Users can be sure they are cleaning
out the furnace before a real operational
issue occurs, " notes Morse.
On the optimization side, she
highlights a case where AspenTech
helped a polyethylene producer to
streamline transitions between product
grades to maximize production
value. As catalysts are changed out
to accommodate different production
slates, there is a transition period
where the resulting product is
an off-grade material. " The customer
was able to apply an AI hybrid-model
concept to look at how reactors are
actually performing, and was able to
decrease the amount of transition,
both in terms of volume throughput,
so they weren't wasting feedstock
making a product they didn't want,
but also by narrowing that transition
time, they were also spending
more reactor time making the preferred
product instead of transitiongrade
material. "
Rockwell Automation, Inc. (Milwaukee,
Wis.; www.rockwell.com) has
also done extensive work using AI to
optimize catalyst yield and product
selectivity in traditional polymerization
processes, as well. " We started
using pure neural networks to try to
learn polymer reaction coefficients.
We lean more and more into the actual
reaction kinetics and the material
balance around the reactors, trying to
control the polymer chain length in
the reactor. This is how you can get
a specific property, such as melt flow
or a melt index, on a polymer, " says
Pal Roach, oil and gas industry consultant
at Rockwell Automation. In a
particular example involving Chevron
Phillips, an AI-driven advanced control
model was applied to cut transition
times between polymer grades
by four hours. This change also led
to a 50% reduction in product variability.
In another case involving a
distillation unit for long-chain alcohols,
an AI-driven scheme applied
to a nonlinear controller helped to
cut energy consumption by around
35% and significantly reduce product-quality
variability, as well as associated
waste. " There are going to
be more and more of these types
of AI applications coming as the industry
refocuses and transitions into
greener energy and more environmental
safety and governance consciousness, "
predicts Roach.
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Chemical Engineering June 2021

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