I
developed
in
my
MASc
studies
a
new
efficient
approach
for
the
optimal
design
under
uncertainty.
The
key
idea
is
to
approximate
the
process
constraint
functions
and
outputs
using
Power
Series
Expansions
(PSE)-based
functions.
A
ranking-based
approach
was
adopted
where
the
user
can
assign
priorities
or
probabilities
of
satisfaction
for
the
different
process
constraints
and
process
outputs
considered
in
the
analysis.
The
methodology
was
tested
on
a
reactor-heat
exchanger
system,
the
Tennessee
Eastman
plant,
which
is
an
industrial
benchmark
process,
and
a
post-combustion
CO2
capture
plant,
which
is
a
large-scale
chemical
plant
that
has
recently
gained
attention
and
significance
due
to
its
potential
to
mitigate
CO2
emissions
from
fossil-fired
power
plants.
Furthermore,
a
stochastic-based
simultaneous
design
and
control
methodology
for
the
optimal
design
of
chemical
processes
under
uncertainty
that
incorporates
a
Model
Predictive
Control
(MPC)
scheme
was
also
developed
during
my
MASc
studies.
The
key
idea
is
to
determine
the
time-dependent
variability
of
the
system
that
will
be
accounted
for
in
the
process
design
using
a
stochastic-based
worst-case
variability
index.
The
MPC-based
simultaneous
design
and
control
approach
provided
more
economical
designs
when
compared
to
a
decentralized
multi-loop
PI
control
strategy,
thus
showing
that
this
method
is
a
practical
approach
to
address
the
integration
of
design
and
control
while
using
advanced
model-based
control
strategies
such
as
MPC. The
respective
publications
of
this
project
is
available
in here.
Thesis: Efficient
Ranking-Based
Methodologies
in
the
Optimal
Design
of
Large-Scale
Chemical
Processes
underUncertainty
