Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(21), P. 9555 - 9555
Published: Nov. 2, 2024
For
decades,
fossil
fuels
have
been
the
backbone
of
reliable
energy
systems,
offering
unmatched
density
and
flexibility.
However,
as
world
shifts
toward
renewable
energy,
overcoming
limitations
intermittent
power
sources
requires
a
bold
reimagining
storage
integration.
Power-to-X
(PtX)
technologies,
which
convert
excess
electricity
into
storable
carriers,
offer
promising
solution
for
long-term
sector
coupling.
Recent
advancements
in
machine
learning
(ML)
revolutionized
PtX
systems
by
enhancing
efficiency,
scalability,
sustainability.
This
review
provides
detailed
analysis
how
ML
techniques,
such
deep
reinforcement
learning,
data-driven
optimization,
predictive
diagnostics,
are
driving
innovation
Power-to-Gas
(PtG),
Power-to-Liquid
(PtL),
Power-to-Heat
(PtH)
systems.
example,
has
improved
real-time
decision-making
PtG
reducing
operational
costs
improving
grid
stability.
Additionally,
diagnostics
powered
increased
system
reliability
identifying
early
failures
critical
components
proton
exchange
membrane
fuel
cells
(PEMFCs).
Despite
these
advancements,
challenges
data
quality,
processing,
scalability
remain,
presenting
future
research
opportunities.
These
to
decarbonizing
hard-to-electrify
sectors,
heavy
industry,
transportation,
aviation,
aligning
with
global
sustainability
goals.
Language: Английский
Rational construction of N-containing carbon sheets atomically doped NiP-CoP nanohybrid electrocatalysts for enhanced green hydrogen and oxygen production
Electrochimica Acta,
Journal Year:
2024,
Volume and Issue:
unknown, P. 145236 - 145236
Published: Oct. 1, 2024
Language: Английский