A review on green hydrogen production pathways and optimization techniques
Process Safety and Environmental Protection,
Год журнала:
2025,
Номер
unknown, С. 107070 - 107070
Опубликована: Март 1, 2025
Язык: Английский
Parametric Analysis Towards the Design of Micro-Scale Wind Turbines: A Machine Learning Approach
R.R. Mansour,
Syed Osama,
Hazem Ahmed
и другие.
Applied System Innovation,
Год журнала:
2024,
Номер
7(6), С. 129 - 129
Опубликована: Дек. 19, 2024
Wind
turbine
design
is
an
iterative
process.
Many
aspects
are
considered
when
designing
a
wind
turbine,
including
aerodynamic
and
power
performance,
structural
loads
behavior,
control
techniques.
In
the
preliminary
stages,
governing
equations
of
each
aspect
used
to
calculate
different
performance
outputs
while
optimizing
between
them.
This
usually
made
using
simulation
software.
work
presents
data-based
machine
learning
(ML)
approach
towards
micro-scale
turbine.
Extensive
simulations
on
45
cm
diameter
rotor
performing
parametric
analysis
QBlade
tool.
Different
parameters
conditions
were
changed
one
at
time,
data
collected
be
further
analyzed
train
ML
models.
The
measurable
models
coefficient
(CP),
normal
tangential
blade
midspan
(FN
FT),
torque
(T)
rotor.
Linear
regression
was
found
unsuitable
for
predicting
CP
due
its
high
nonlinearity;
however,
it
gave
satisfactory
results
loads.
Ensemble
give
highest
accuracy
all
desired
outputs.
model
measured
in
terms
determination
(R2),
where
could
predict
Cp,
FN,
FT,
T
with
R2
values
0.999,
0.984,
0.986
respectively.
Язык: Английский
Analyzing and Forecasting Laboratory Energy Consumption Patterns Using Autoregressive Integrated Moving Average Models
Laboratories,
Год журнала:
2024,
Номер
2(1), С. 2 - 2
Опубликована: Дек. 30, 2024
This
study
applied
ARIMA
modeling
to
analyze
the
energy
consumption
patterns
of
laboratory
equipment
over
one
month,
focusing
on
enhancing
management
in
laboratory.
By
explicitly
examining
AC
and
DC
equipment,
this
obtained
detailed
daily
operating
cycles
periods
inactivity.
Advanced
differencing
diagnostic
checks
were
used
verify
model
accuracy
white
noise
characteristics
through
enhanced
Dickey–Fuller
testing
residual
analysis.
The
results
demonstrate
model’s
predicting
consumption,
providing
valuable
insights
into
use
model.
highlights
adaptability
validity
environments,
contributing
more
competent
practices.
Язык: Английский
Integrating Deep Learning and Energy Management Standards for Enhanced Solar–Hydrogen Systems: A Study Using MobileNetV2, InceptionV3, and ISO 50001:2018
Hydrogen,
Год журнала:
2024,
Номер
5(4), С. 819 - 850
Опубликована: Ноя. 10, 2024
This
study
addresses
the
growing
need
for
effective
energy
management
solutions
in
university
settings,
with
particular
emphasis
on
solar–hydrogen
systems.
The
study’s
purpose
is
to
explore
integration
of
deep
learning
models,
specifically
MobileNetV2
and
InceptionV3,
enhancing
fault
detection
capabilities
AIoT-based
environments,
while
also
customizing
ISO
50001:2018
standards
align
unique
needs
academic
institutions.
Our
research
employs
comparative
analysis
two
models
terms
their
performance
detecting
solar
panel
defects
assessing
accuracy,
loss
values,
computational
efficiency.
findings
reveal
that
achieves
80%
making
it
suitable
resource-constrained
InceptionV3
demonstrates
superior
accuracy
90%
but
requires
more
resources.
concludes
both
offer
distinct
advantages
based
application
scenarios,
emphasizing
importance
balancing
efficiency
when
selecting
appropriate
system
management.
highlights
critical
role
continuous
improvement
leadership
commitment
successful
implementation
universities.
Язык: Английский