Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 6, 2024
Abstract
The
water
transport
mechanisms
in
potato
microstructure
consist
of
symplastic,
apoplastic,
and
transcellular
transport.
Knowledge
the
microscale
behavior
is
important
to
increasing
productivity
food
processing
obtaining
high-grade
processed
food.
In
this
research,
a
CFD
simulation
was
performed
COMSOL
Multiphysics
for
three
different
simplified
designs
cell
units
representing
portion
microstructure,
using
equations
mass
concentration
parts,
velocity
simulated
Brinkman
equation
periodic
boundary
conditions
during
low
thermal
process.
variation
profile
similar.
average
all
same
0.72%
fraction
has
difference
highest
0.78%
with
3.22×10−
9
m
s−
1.
From
can
conclude
that
diffusivity
depend
on
both
design,
concentration,
permeability
intercellular
designs,
not
designs.
Drying Technology,
Год журнала:
2024,
Номер
42(8), С. 1240 - 1269
Опубликована: Май 24, 2024
Artificial
intelligence
(AI)
and
its
data-driven
counterpart,
machine
learning
(ML),
are
rapidly
evolving
disciplines
with
increasing
applications
in
modeling,
simulation,
control,
optimization
within
the
drying
industry.
This
paper
presents
a
comprehensive
overview
of
progress
made
ML
from
shallow
to
deep
implications
for
food
drying.
Theoretical
foundations,
advantages,
limitations
various
approaches
employed
this
domain
explored.
Additionally,
advancements
models,
particularly
those
enhanced
by
algorithms,
reviewed.
The
review
underscores
role
intelligent
configuration
which
affects
their
accuracy
ability
solve
problems
high
energy
consumption,
nutrient
degradation,
uneven
Drawing
upon
research
achievements,
integrating
AI
models
real-time
measuring
methods
is
discussed,
enabling
dynamic
determination
optimal
conditions
parameter
adjustments.
integration
facilitates
automated
decision-making,
reducing
human
errors
enhancing
operational
efficiency
Moreover,
demonstrate
proficiency
predicting
times
analyzing
usage
patterns,
thereby
minimize
resource
consumption
while
preserving
product
quality.
Finally,
identifies
current
obstacles
technology
development
proposes
novel
avenues
sustainable
technologies.
Computer Methods in Applied Mechanics and Engineering,
Год журнала:
2023,
Номер
417, С. 116401 - 116401
Опубликована: Сен. 9, 2023
Physics-Informed
Neural
Networks
(PINNs)
have
recently
gained
increasing
attention
in
the
field
of
topology
optimization.
The
fusion
deep
learning
and
optimization
has
emerged
as
a
prominent
area
insightful
research,
where
minimization
loss
function
neural
networks
can
be
comparable
to
objective
Inspired
by
concepts
PINNs,
this
paper
proposes
novel
framework,
'Complete
Network-based
Topology
Optimization
(CPINNTO)',
address
various
challenges
optimization,
particularly
related
structural
key
innovation
proposed
framework
lies
introducing
first
complete
machine-learning-based
through
integration
two
distinct
PINNs.
Herein,
Deep
Energy
Method
(DEM)
PINN
is
implemented
determine
deformation
state
corresponding
structures
numerically.
In
addition,
derivation
with
respect
design
variables
replaced
automatic
differentiation
sensitivity-analysis
(S-PINN).
feasibility
potential
CPINNTO
been
assessed
several
case
studies
while
highlighting
strengths
limitations
utilizing
PINNs
Subsequent
findings
indicate
that
achieve
optimal
topologies
without
labeled
data
nor
FEA.
numerical
examples
demonstrate
capable
stably
obtaining
for
applications,
including
compliance
problems,
multi-constrained
three-dimensional
problems.
Resulting
designs
exhibit
favorable
values
obtained
via
density-based
summary,
opens
up
interesting
possibilities
Industrial & Engineering Chemistry Research,
Год журнала:
2023,
Номер
62(44), С. 18178 - 18204
Опубликована: Окт. 26, 2023
Physics-Informed
Machine
Learning
(PIML)
is
an
emerging
computing
paradigm
that
offers
a
new
approach
to
tackle
multiphysics
modeling
problems
prevalent
in
the
field
of
chemical
engineering.
These
often
involve
complex
transport
processes,
nonlinear
reaction
kinetics,
and
coupling.
This
Review
provides
detailed
account
main
contributions
PIML
with
specific
emphasis
on
momentum
transfer,
heat
mass
reactions.
The
progress
method
development
(e.g.,
algorithm
architecture),
software
libraries,
applications
coupling
surrogate
modeling)
are
detailed.
On
this
basis,
future
challenges
highlight
importance
developing
more
practical
solutions
strategies
for
PIML,
including
turbulence
models,
domain
decomposition,
training
acceleration,
modeling,
hybrid
geometry
module
creation.
International Journal of Mechanical Sciences,
Год журнала:
2024,
Номер
275, С. 109267 - 109267
Опубликована: Апрель 7, 2024
This
paper
introduces
a
novel
Physics-Informed
Neural
Network-based
(PINN-based)
multi-domain
computational
framework
to
analyse
nonlinear
and
heterogeneous
morphological
variations
of
plant
cells
during
drying.
Here,
two
distinct
models
are
involved:
PINN-MT
simulate
mass
transfer;
PINN-NS
shrinkage.
The
coupled
examine
cellular
changes
resulting
from
moisture
loss
Firstly,
the
framework,
in
tandem
with
homogeneous
conditions,
operates
parallel,
allowing
mutual
parameters
update
between
models.
approach
demonstrates
ability
approximate
shrinkage
within
tissue,
factoring
influence
surrounding
cells.
Secondly,
non-uniform
cell
wall
properties
boundary
conditions
incorporated
into
this
through
domain
decomposition.
Inherent
capabilities
neural
networks
allow
for
seamless
integration
multiple
domains,
additional
terms
introduced
at
interfaces.
shows
capacity
account
drastic
even
under
extreme
drying
which
is
key
novelty
has
been
challenging
task
existing
traditional
methods.
Hence,
proposed
offers
an
innovative
avenue
understanding
not
only
cells,
but
also
soft
matter
general.
Measurement Food,
Год журнала:
2024,
Номер
15, С. 100187 - 100187
Опубликована: Июль 27, 2024
This
study
assessed
and
compared
the
impact
of
hot
air
oven
drying
(HAD)
at
50,
60
70
°C
microwave
(MWD)
195,
307
521
W
on
kinetics
thermophysical
properties
quality
dried
cabbage.
The
were
computed
using
established
model
equations.
proximate
composition,
bioactive
compounds,
antioxidant
capacity
(AOC),
color
functional
groups
analyzed
Association
official
analytical
chemists
(AOAC)
test
protocol,
High
performance
liquid
chromatography
(HPLC),
Ultraviolet-visible
(UV–Vis)
spectrometry
Attenuated
total
reflectance
coupled
to
Fourier
transformed
infrared
spectroscopy
(ATR-FTIR),
respectively.
moisture
ratio
was
modeled
probabilistic
computational
intelligence
modeling
approaches.
results
revealed
that
only
density,
thermal
diffusivity
effusivity
appeared
be
kinetically
controlled
by
content.
Stepwise
linear
regression
(SLR)
Gaussian
process
(GPR)
more
accurate
in
simulating
ratio.
Increasing
HAD
temperature
preserved
all
compositions
except
carbohydrates.
Similarly,
increasing
MWD
power
crude
fiber.
from
50
60–70
decreased
flavonoid
content
(TFC)
phenolic
(TPC)
21.31
%
13.60
%,
respectively
but
increased
AOC
27.2
39.6
%.
levels
195
307–521
TFC
13.2
0.7
TPC
9.72
13.14
thus
35.2
45.7
presented
higher
relative
HAD.
Drying
gave
highest
whitening
index
MWD.
ATR-FTIR
analysis
formation
new
compounds
effect.
SLR
GPR
kinetics.
affected
parameters
cabbage
differently,
choice
dryer
type
will
depend
interest.
