Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results
ACS Omega,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 13, 2025
A
major
challenge
in
bioprocess
simulation
is
the
lack
of
physical
and
chemical
property
databases
for
biochemicals.
Python-based
algorithm
was
developed
estimating
nonrandom
two-liquid
(NRTL)
model
parameters
aqueous
binary
systems
a
straightforward
manner
from
simplified
molecular-input
line-entry
specification
(SMILES)
strings
substances
system.
This
conducts
series
procedures:
(1)
fragmentation
molecules
into
functional
groups
SMILES,
(2)
calculation
activity
coefficients
under
predetermined
temperature
mole
fraction
conditions
by
employing
universal
quasi-chemical
group
coefficient
(UNIFAC)
model,
(3)
regression
NRTL
UNIFAC
results
differential
evolution
(DEA)
Nelder-Mead
method
(NMM).
The
applied
to
aqueous,
mixture
composed
37
common
biochemical
such
as
amino
acids,
organic
sugars.
obtained
were
compared
with
those
Aspen
Plus,
commercial
software,
which
has
an
equivalent
function
parameters.
percentage
mean
absolute
residuals
using
DEA,
NMM,
parameter
estimation
tool
Plus
ranges
0.05-16.69,
0.09-326.77%,
respectively.
in-house
will
be
helpful
obtaining
more
accurate
timely
facilitate
processes
process
optimization,
energy
consumption
estimation,
life
cycle
assessment.
Язык: Английский
Python-Based Algorithm for Calculating Physical Properties of Aqueous Mixtures Composed of Substances Not Available in Databases
ACS Omega,
Год журнала:
2025,
Номер
10(16), С. 16683 - 16694
Опубликована: Апрель 15, 2025
In
this
study,
we
developed
a
Python-based
open-source
algorithm
compatible
with
the
aqueous
physical
property
models
provided
in
electrolyte
templates
of
AspenTech
software.
To
validate
accuracy
model,
results
obtained
from
proposed
were
compared
to
experimental
data
for
37
binary
mixture
systems
covering
properties
such
as
density,
heat
capacity,
viscosity,
and
thermal
conductivity.
The
input
variables
included
our
previous
research
on
pure
component
prediction
nonrandom
two-liquid
(NRTL)
model
parameters
based
UNIFAC
simulations.
This
is
mean
absolute
percentage
errors
(MAPE)
conductivity
2.88,
0.355,
12.1,
10.1%,
respectively.
case
density
actual
trends
could
not
be
accurately
reflected
under
high-concentration
conditions
certain
substances.
addition,
it
was
confirmed
that
inaccurate
predictions
viscosity
commercial-scale
falling-film
evaporator
simulation
l-valine
production
led
overall
transfer
coefficient.
Therefore,
caution
required
when
predicting
missing
using
approach
significant
may
occur.
Nevertheless,
can
provide
an
initial
parameter
value
are
existing
databases
without
any
commercial
package.
Язык: Английский