International Journal of Renewable Energy Development,
Год журнала:
2024,
Номер
13(6), С. 1175 - 1190
Опубликована: Окт. 27, 2024
In
the
ongoing
search
for
an
alternative
fuel
diesel
engines,
biogas
is
attractive
option.
Biogas
can
be
used
in
dual-fuel
mode
with
as
pilot
fuel.
This
work
investigates
modeling
of
injecting
strategies
a
waste-derived
biogas-powered
engine.
Engine
performance
and
emissions
were
projected
using
supervised
machine
learning
methods
including
random
forest,
lasso
regression,
support
vector
machines
(SVM).
Mean
Squared
Error
(MSE),
R-squared
(R²),
Absolute
Percentage
(MAPE)
among
criteria
evaluations
models.
Random
Forest
has
shown
better
Brake
Thermal
Efficiency
(BTE)
test
R²
0.9938
low
MAPE
3.0741%.
once
more
exceeded
other
models
0.9715
4.2242%
estimating
Specific
Energy
Consumption
(BSEC).
With
0.9821
2.5801%
emerged
most
accurate
model
according
to
carbon
dioxide
(CO₂)
emission
modeling.
Analogous
results
monoxide
(CO)
prediction
based
on
obtained
0.8339
3.6099%.
outperformed
Linear
Regression
0.9756%
7.2056%
case
nitrogen
oxide
(NOx)
emissions.
showed
constant
overall
criteria.
paper
emphasizes
how
well
especially
prognosticate
engines.
ABSTRACT
This
study
investigates
the
thermal
properties
of
lauric
acid
(LA)
as
a
phase
change
material
(PCM)
using
K
‐Means
clustering
method
to
analyze
melting
characteristics.
focuses
on
optimization
PCMs
hybrid
methodology
analytic
hierarchy
process
(AHP)
and
clustering.
LA,
enhanced
with
zinc
oxide
(ZnO)
nanoparticles,
was
evaluated
for
its
performance.
LA's
suitability
PCM
is
based
initial
temperature,
heating
rate,
final
time
melt.
AHP
employed
determine
weightage
three
critical
outcomes:
latent
heat,
point,
conductivity.
The
weightages
assigned
were
59%,
31%,
11%,
respectively,
reflecting
relative
importance
each
outcome
in
assessing
efficiency
LA
PCM.
Furthermore,
then
applied
categorize
data
these
weighted
outcomes.
utilized
input
parameters,
assigning
27%
15%
22%
underscoring
their
significance
analysis.
optimal
conditions
identified
an
temperature
24.8°C,
ieating
rate
5.6°C/min,
81.4°C,
melt
10.6
min.
These
resulted
outcomes
208
J/g
point
80.9°C,
conductivity
0.21
W/m·K.
approach
provides
robust
framework
optimizing
performance,
facilitating
energy
storage
release
practical
applications.
International Journal of Renewable Energy Development,
Год журнала:
2024,
Номер
13(6), С. 1175 - 1190
Опубликована: Окт. 27, 2024
In
the
ongoing
search
for
an
alternative
fuel
diesel
engines,
biogas
is
attractive
option.
Biogas
can
be
used
in
dual-fuel
mode
with
as
pilot
fuel.
This
work
investigates
modeling
of
injecting
strategies
a
waste-derived
biogas-powered
engine.
Engine
performance
and
emissions
were
projected
using
supervised
machine
learning
methods
including
random
forest,
lasso
regression,
support
vector
machines
(SVM).
Mean
Squared
Error
(MSE),
R-squared
(R²),
Absolute
Percentage
(MAPE)
among
criteria
evaluations
models.
Random
Forest
has
shown
better
Brake
Thermal
Efficiency
(BTE)
test
R²
0.9938
low
MAPE
3.0741%.
once
more
exceeded
other
models
0.9715
4.2242%
estimating
Specific
Energy
Consumption
(BSEC).
With
0.9821
2.5801%
emerged
most
accurate
model
according
to
carbon
dioxide
(CO₂)
emission
modeling.
Analogous
results
monoxide
(CO)
prediction
based
on
obtained
0.8339
3.6099%.
outperformed
Linear
Regression
0.9756%
7.2056%
case
nitrogen
oxide
(NOx)
emissions.
showed
constant
overall
criteria.
paper
emphasizes
how
well
especially
prognosticate
engines.
