Energies,
Год журнала:
2024,
Номер
17(17), С. 4501 - 4501
Опубликована: Сен. 8, 2024
This
review
paper
thoroughly
explores
the
impact
of
artificial
intelligence
on
planning
and
operation
distributed
energy
systems
in
smart
grids.
With
rapid
advancement
techniques
such
as
machine
learning,
optimization,
cognitive
computing,
new
opportunities
are
emerging
to
enhance
efficiency
reliability
electrical
From
demand
generation
prediction
flow
optimization
load
management,
is
playing
a
pivotal
role
transformation
infrastructure.
delves
deeply
into
latest
advancements
specific
applications
within
context
systems,
including
coordination
resources,
integration
intermittent
renewable
energies,
enhancement
response.
Furthermore,
it
discusses
technical,
economic,
regulatory
challenges
associated
with
implementation
intelligence-based
solutions,
well
ethical
considerations
related
automation
autonomous
decision-making
sector.
comprehensive
analysis
provides
detailed
insight
how
reshaping
grids
highlights
future
research
development
areas
that
crucial
for
achieving
more
efficient,
sustainable,
resilient
system.
The Science of The Total Environment,
Год журнала:
2024,
Номер
944, С. 173999 - 173999
Опубликована: Июнь 13, 2024
Membrane
technologies
have
become
proficient
alternatives
for
advanced
wastewater
treatment,
ensuring
high
contaminant
removal
and
sustainable
resource
recovery.
Despite
significant
progress,
ongoing
research
efforts
aim
to
further
optimize
treatment
performance.
Among
the
challenges
faced,
membrane
fouling
persists
as
a
relevant
obstacle
in
technologies,
necessitating
development
of
more
effective
mitigation
strategies.
Mathematical
models,
widely
employed
predicting
performance,
generally
exhibit
low
accuracy
suffer
from
uncertainties
due
complex
variable
nature
wastewater.
To
overcome
these
limitations,
numerous
studies
proposed
artificial
intelligence
(AI)
modeling
accurately
predict
technologies'
performance
mechanisms.
This
approach
aims
provide
simulations
predictions,
thereby
enhancing
process
control,
optimization,
intensification.
literature
review
explores
recent
advancements
membrane-based
processes
through
AI
models.
The
analysis
highlights
enormous
potential
this
field
efficiency
technologies.
role
defining
optimal
operating
conditions,
developing
strategies
mitigation,
novel
improving
fabrication
techniques
is
discussed.
These
enhanced
optimization
control
driven
by
ensure
improved
effluent
quality,
optimized
consumption,
minimized
costs.
contribution
cutting-edge
paradigm
shift
toward
examined.
Finally,
outlines
future
perspectives,
emphasizing
that
require
attention
current
limitations
hindering
integration
plants.
Sensors,
Год журнала:
2025,
Номер
25(6), С. 1652 - 1652
Опубликована: Март 7, 2025
The
study
addresses
the
critical
issue
of
accurately
predicting
ammonia
nitrogen
(NH3-N)
concentration
in
a
sequencing
batch
reactor
(SBR)
system,
achieving
reduced
consumption
through
automatic
control
technology.
NH3-N
serves
as
key
indicator
treatment
efficiency
and
environmental
impact;
however,
its
complex
dynamics
scarcity
measurements
pose
significant
challenges
for
accurate
prediction.
To
tackle
this
problem,
an
innovative
Transformer-long
short-term
memory
(Transformer-LSTM)
network
model
was
proposed,
which
effectively
integrates
strengths
both
Transformer
LSTM
architectures.
component
excels
at
capturing
long-range
dependencies,
while
is
adept
modeling
sequential
patterns.
innovation
proposed
methodology
resides
incorporation
dissolved
oxygen
(DO),
electrical
conductivity
(EC),
oxidation-reduction
potential
(ORP)
input
variables,
along
with
their
respective
rate
change
cumulative
value.
This
strategic
selection
features
enhances
traditional
utilization
water
quality
indicators
offers
more
comprehensive
dataset
prediction,
ultimately
improving
accuracy
reliability.
Experimental
validation
on
datasets
from
SBR
system
reveals
that
significantly
outperforms
existing
advanced
methods
terms
root
mean
squared
error
(RMSE),
absolute
(MAE),
coefficient
determination
(R2).
Furthermore,
by
integrating
real-time
sensor
data
Transformer-LSTM
control,
substantial
improvements
processes
were
achieved,
resulting
26.9%
reduction
energy
or
time
compared
fixed
processing
cycles.
provides
reliable
tool
concentrations,
contributing
to
sustainability
ensuring
compliance
emission
standards.
