Computational Methods, Artificial Intelligence, Modeling, and Simulation Applications in Green Hydrogen Production Through Water Electrolysis: A Review
Hydrogen,
Год журнала:
2025,
Номер
6(2), С. 21 - 21
Опубликована: Март 25, 2025
Green
hydrogen
production
is
emerging
as
a
crucial
component
in
global
decarbonization
efforts.
This
review
focuses
on
the
role
of
computational
approaches
and
artificial
intelligence
(AI)
optimizing
green
technologies.
Key
to
improving
electrolyzer
efficiency
scalability
include
fluid
dynamics
(CFD),
thermodynamic
modeling,
machine
learning
(ML).
As
an
instance,
CFD
has
achieved
over
95%
accuracy
estimating
flow
distribution
polarization
curves,
but
AI-driven
optimization
can
lower
operational
expenses
by
up
24%.
Proton
exchange
membrane
electrolyzers
achieve
efficiencies
65–82%
at
70–90
°C,
solid
oxide
reach
90%
temperatures
ranging
from
650
1000
°C.
According
studies,
combining
renewable
energy
with
reduces
emissions
improves
grid
reliability,
curtailment
rates
less
than
1%
for
biomass-driven
systems.
integration
ensures
long-term
transition
while
also
addressing
security
environmental
concerns.
Язык: Английский
Edge Artificial Intelligence for Electrical Anomaly Detection Based on Process-In-Memory Chip
Electronics,
Год журнала:
2024,
Номер
13(21), С. 4255 - 4255
Опубликована: Окт. 30, 2024
Neural-networks
(NNs)
for
the
current
feature
analysis
bring
novel
electrical
safety
functions
in
smart
circuit
breakers
(CBs),
especially
preventing
fire
hazard
from
electric
vehicle/bike
battery
charging.
In
this
work,
edge
artificial
intelligence
(AI)
solutions
anomaly
detection
were
designed
and
demonstrated
based
on
process-in-memory
(PIM)
AI
chip.
The
ultra-low
power
high-performance
character
of
PIM
chips
enable
solution
to
embed
limited
space
inside
breaker
detect
improper
charging
at
millisecond
latency.
Язык: Английский
Improving Electrical Fault Detection Using Multiple Classifier Systems
Energies,
Год журнала:
2024,
Номер
17(22), С. 5787 - 5787
Опубликована: Ноя. 20, 2024
Machine
Learning-based
fault
detection
approaches
in
energy
systems
have
gained
prominence
for
their
superior
performance.
These
automated
can
assist
operators
by
highlighting
anomalies
and
faults,
providing
a
robust
framework
improving
Situation
Awareness.
However,
existing
predominantly
rely
on
monolithic
models,
which
struggle
with
adapting
to
changing
data,
handling
imbalanced
datasets,
capturing
patterns
noisy
environments.
To
overcome
these
challenges,
this
study
explores
the
potential
of
Multiple
Classifier
System
(MCS)
approaches.
The
results
demonstrate
that
ensemble
methods
generally
outperform
single
dynamic
like
META-DES
showing
remarkable
resilience
noise.
findings
highlight
importance
model
diversity
strategies
classification
accuracy
under
real-world,
conditions.
This
research
emphasizes
MCS
techniques
as
solution
enhancing
reliability
systems.
Язык: Английский