Sustainability,
Год журнала:
2024,
Номер
16(21), С. 9555 - 9555
Опубликована: Ноя. 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.
Chemical Engineering & Technology,
Год журнала:
2024,
Номер
47(12)
Опубликована: Окт. 24, 2024
Abstract
The
iron
and
steelmaking
industries
play
a
significant
role
in
the
manufacturing
sector
but
result
greenhouse
gas
emissions.
Biochar
has
recently
gained
attention
as
potential
substitute
for
coal
metallurgical
processes
due
to
its
carbon
capture
potential.
This
review
explores
of
biochar
sustainable
industries.
Notable
research
works
have
shown
that
substituting
amounts
ranging
from
low
5
%
high
50
can
be
feasible
beneficial
such
coke
making,
sintering,
blast
furnaces,
electric
furnaces.
information
presented
this
applied
create
competitive
alternatives
fossil
fuels
help
decarbonize
Sustainability,
Год журнала:
2024,
Номер
16(21), С. 9555 - 9555
Опубликована: Ноя. 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.
