ARO-The Scientific Journal of Koya University,
Journal Year:
2024,
Volume and Issue:
12(2), P. 167 - 178
Published: Nov. 9, 2024
Metering
fluids
is
critical
in
various
industries,
and
researchers
have
extensively
explored
factors
affecting
measurement
accuracy.
As
a
result,
numerous
sensors
methods
are
developed
to
precisely
measure
volume
fractions
multi-phase
fluids.
A
significant
challenge
fluid
pipelines
the
formation
of
scale
within
pipes.
This
issue
particularly
problematic
petroleum
industry,
leading
narrowed
internal
diameters,
corrosion,
increased
energy
consumption,
reduced
equipment
lifespan,
and,
most
crucially,
compromised
flow
paper
proposes
non-destructive
metering
system
incorporating
an
artificial
neural
network
with
capacitive
photon
attenuation
address
this
challenge.
The
simulates
thicknesses
from
0
mm
10
using
COMSOL
multiphysics
software
calculates
counted
rays
through
Beer
Lambert
equations.
simulation
considers
10%
interval
variation
each
phase,
generating
726
data
points.
proposed
network,
two
inputs—measured
capacity
rays-and
three
outputs—volume
gas,
water,
oil—achieves
mean
absolute
errors
0.318,
1.531,
1.614,
respectively.
These
results
demonstrate
system’s
ability
accurately
gauge
proportions
three-phase
gas-water-oil
fluid,
regardless
pipeline
thickness.
Energies,
Journal Year:
2024,
Volume and Issue:
17(14), P. 3519 - 3519
Published: July 18, 2024
Multiphase
fluids
are
common
in
many
industries,
such
as
oil
and
petrochemical,
volume
fraction
measurement
of
their
phases
is
a
vital
subject.
Hence,
there
lots
scientists
researchers
who
have
introduced
methods
equipment
this
regard,
for
example,
photon
attenuation
sensors,
capacitance-based
so
on.
These
approaches
non-invasive
reason,
very
popular
widely
used.
In
addition,
nowadays,
artificial
neural
networks
(ANN)
attractive
lot
fields
because
accuracy.
Therefore,
paper,
to
estimate
proportion
three-phase
homogeneous
fluid,
new
system
proposed
that
contains
an
MLP
ANN,
standing
multilayer
perceptron
network,
sensor,
sensor.
Through
computational
methods,
capacities
mass
coefficients
obtained,
which
act
inputs
the
network.
All
these
were
divided
randomly
two
main
groups
train
test
presented
model.
To
opt
suitable
network
with
lowest
rate
mean
absolute
error
(MAE),
number
architectures
different
factors
tested
MATLAB
software
R2023b.
After
receiving
MAEs
equal
0.29,
1.60,
1.67
water,
gas,
phases,
respectively,
was
chosen
be
paper.
based
on
outcomes,
approach’s
novelty
being
able
predict
all
flow
low
error.
IEEE Access,
Journal Year:
2023,
Volume and Issue:
11, P. 143745 - 143756
Published: Jan. 1, 2023
Assessing
the
void
fraction
in
diverse
multiphase
flows
across
industries,
including
petrochemical,
oil,
and
chemical
sectors,
is
crucial.
There
are
multiple
techniques
available
for
this
objective.
The
capacitive
sensor
has
gained
significant
popularity
among
these
methods
been
extensively
utilized.
Fluid
properties
have
a
substantial
impact
on
performance
of
capacitance
sensors.
Factors
such
as
density,
pressure,
temperature
can
introduce
errors
measurements.
One
approach
to
address
issue
meticulous
laborious
routine
calibration
process.
In
current
study,
an
artificial
neural
network
(ANN)
was
developed
accurately
Assess
proportion
gas
biphasic
fluid
motion,
irrespective
variations
phase
form
or
variations,
eliminating
need
frequent
recalibration.
To
achieve
objective,
novel
combined
capacitance-based
sensors
were
specifically
designed.
simulated
by
employing
COMSOL
Multiphysics
application.
simulation
encompassed
five
distinct
liquids:
diesel
fuel,
gasoline,
crude
water.
input
training
multilayer
perceptron
(MLP)
came
from
data
gathered
through
Multiphysics,
simulations
estimating
Percentage
content
annular
two-phase
with
specific
liquid
form.
MATLAB
software
utilized
construct
model
proposed
network.
utilization
precise
apparatus
measuring
intended
MLP
demonstrated
ability
prognosticate
volume
percentage
mean
absolute
error
(MAE)
0.004.
ARO-The Scientific Journal of Koya University,
Journal Year:
2024,
Volume and Issue:
12(2), P. 167 - 178
Published: Nov. 9, 2024
Metering
fluids
is
critical
in
various
industries,
and
researchers
have
extensively
explored
factors
affecting
measurement
accuracy.
As
a
result,
numerous
sensors
methods
are
developed
to
precisely
measure
volume
fractions
multi-phase
fluids.
A
significant
challenge
fluid
pipelines
the
formation
of
scale
within
pipes.
This
issue
particularly
problematic
petroleum
industry,
leading
narrowed
internal
diameters,
corrosion,
increased
energy
consumption,
reduced
equipment
lifespan,
and,
most
crucially,
compromised
flow
paper
proposes
non-destructive
metering
system
incorporating
an
artificial
neural
network
with
capacitive
photon
attenuation
address
this
challenge.
The
simulates
thicknesses
from
0
mm
10
using
COMSOL
multiphysics
software
calculates
counted
rays
through
Beer
Lambert
equations.
simulation
considers
10%
interval
variation
each
phase,
generating
726
data
points.
proposed
network,
two
inputs—measured
capacity
rays-and
three
outputs—volume
gas,
water,
oil—achieves
mean
absolute
errors
0.318,
1.531,
1.614,
respectively.
These
results
demonstrate
system’s
ability
accurately
gauge
proportions
three-phase
gas-water-oil
fluid,
regardless
pipeline
thickness.
