Water Science & Technology,
Journal Year:
2024,
Volume and Issue:
90(10), P. 2747 - 2763
Published: Nov. 15, 2024
ABSTRACT
Wastewater
treatment
plants
(WWTPs)
comprise
energy-intensive
processes,
serving
as
primary
contributors
to
overall
WWTP
costs.
This
research
study
proposes
a
novel
approach
that
integrates
support
vector
regression
(SVR)
with
the
firefly
algorithm
(FFA)
for
prediction
of
energy
consumption
in
Chlef
City,
Algeria.
The
database
comprises
comprehensive
set
1,653
samples,
capturing
diverse
information
categories.
It
includes
chemical
and
physical
characteristics,
encompassing
oxygen
demand,
5-day
biochemical
potential
hydrogen,
water
temperature,
total
suspended
sediment
basin,
influent
N-NH3
concentration,
number
aerators,
operating
time.
Additionally,
hydraulic
energy-related
parameters
are
represented
by
flow
entered
at
station
consumed
respectively.
Finally,
meteorological
data,
comprising
rainfall,
relative
humidity,
aridity
index,
part
dataset
required
analysis.
In
this
regard,
15
different
models
correspond
combinations
input
assessed
study.
results
show
SVR–FFA-15
can
render
an
improvement
accuracy
WWTPs.
provides
useful
tool
managing
wastewater
makes
insightful
recommendations
future
savings.
Journal of Molecular Liquids,
Journal Year:
2024,
Volume and Issue:
410, P. 125592 - 125592
Published: July 20, 2024
Heavy
metals
pose
a
significant
threat
to
ecosystems
and
human
health
because
of
their
toxic
properties
ability
bioaccumulate
in
living
organisms.
Traditional
removal
methods
often
fall
short
terms
cost,
energy
efficiency,
minimizing
secondary
pollutant
generation,
especially
complex
environmental
settings.
In
contrast,
molecular
simulation
offer
promising
solution
by
providing
in-depth
insights
into
atomic
interactions
between
heavy
potential
adsorbents.
This
review
highlights
the
for
removing
types
pollutants
science,
specifically
metals.
These
powerful
tool
predicting
designing
materials
processes
remediation.
We
focus
on
specific
like
lead,
Cadmium,
mercury,
utilizing
cutting-edge
techniques
such
as
Molecular
Dynamics
(MD),
Monte
Carlo
(MC)
simulations,
Quantum
Chemical
Calculations
(QCC),
Artificial
Intelligence
(AI).
By
leveraging
these
methods,
we
aim
develop
highly
efficient
selective
unravelling
underlying
mechanisms,
pave
way
developing
more
technologies.
comprehensive
addresses
critical
gap
scientific
literature,
valuable
researchers
protection
health.
modelling
hold
promise
revolutionizing
prediction
metals,
ultimately
contributing
sustainable
solutions
cleaner
healthier
future.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(4), P. 1516 - 1516
Published: Feb. 10, 2024
Supercapacitors
(SCs)
are
gaining
attention
for
Internet
of
Things
(IoT)
devices
because
their
impressive
characteristics,
including
high
power
and
energy
density,
extended
lifespan,
significant
cycling
stability,
quick
charge–discharge
cycles.
Hence,
it
is
essential
to
make
precise
predictions
about
the
capacitance
lifespan
supercapacitors
choose
appropriate
materials
develop
plans
replacement.
Carbon-based
supercapacitor
electrodes
crucial
advancement
contemporary
technology,
serving
as
a
key
component
among
numerous
types
electrode
materials.
Moreover,
accurately
forecasting
storage
may
greatly
improve
efficient
handling
system
malfunctions.
Researchers
worldwide
have
increasingly
shown
interest
in
using
machine
learning
(ML)
approaches
predicting
performance
The
driven
by
its
noteworthy
benefits,
such
improved
accuracy
predictions,
time
efficiency,
cost-effectiveness.
This
paper
reviews
different
charge
processes,
categorizes
SCs,
investigates
frequently
employed
carbon
components.
supercapacitors,
which
applications,
affected
number
capacity,
cycle
longevity.
Additionally,
we
provide
an
in-depth
review
several
recently
developed
ML-driven
models
used
substance
properties
optimizing
effectiveness.
purpose
these
proposed
ML
algorithms
validate
anticipated
accuracies,
aid
selection
models,
highlight
future
research
topics
field
scientific
computing.
Overall,
this
highlights
possibility
techniques
advancements
energy-storing
device
development.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(15), P. 6598 - 6598
Published: Aug. 1, 2024
In
recent
years,
wastewater
reuse
has
become
crucial
for
addressing
global
freshwater
scarcity
and
promoting
sustainable
water
resource
development.
Accurate
inflow
volume
predictions
are
essential
enhancing
operational
efficiency
in
treatment
facilities
effective
utilization.
Traditional
decomposition
integration
models
often
struggle
with
non-stationary
time
series,
particularly
peak
anomaly
sensitivity.
To
address
this
challenge,
a
differential
model
based
on
real-time
rolling
forecasts
been
developed.
This
uses
an
initial
prediction
machine
learning
(ML)
model,
followed
by
using
Complete
Ensemble
Empirical
Mode
Decomposition
Adaptive
Noise
(CEEMDAN).
A
Time-Aware
Outlier-Sensitive
Transformer
(TS-Transformer)
is
then
applied
integrated
predictions.
The
ML-CEEMDAN-TSTF
demonstrated
superior
accuracy
compared
to
basic
ML
models,
other
Transformer-based
models.
hybrid
explicitly
incorporates
time-scale
differentiated
information
as
feature,
improving
the
model’s
adaptability
complex
environmental
data
predictive
performance.
TS-Transformer
was
designed
make
more
sensitive
anomalies
peaks
issues
such
anomalous
data,
uncertainty
suboptimal
forecasting
accuracy.
results
indicated
that:
(1)
introduction
of
significantly
enhanced
accuracy;
(2)
higher
ML-CEEMDAN-Transformer;
(3)
TS-Transformer-based
consistently
outperformed
those
LSTM
eXtreme
Gradient
Boosting
(XGBoost).
Consequently,
research
provides
precise
robust
method
predicting
reclaimed
volumes,
which
holds
significant
implications
clean
environment
management.
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(22), P. 10689 - 10689
Published: Nov. 19, 2024
This
study
examines
an
algorithm
for
collecting
and
analyzing
data
from
wastewater
treatment
facilities,
aimed
at
addressing
regression
tasks
predicting
the
quality
of
treated
classification
preventing
emergency
situations,
specifically
filamentous
bulking
activated
sludge.
The
feasibility
using
obtained
under
laboratory
conditions
simulating
technological
process
as
a
training
dataset
is
explored.
A
small
collected
actual
plants
considered
test
dataset.
For
both
tasks,
best
results
were
achieved
gradient-boosting
models
CatBoost
family,
yielding
metrics
SMAPE
=
9.1
ROC-AUC
1.0.
set
most
important
predictors
modeling
was
selected
each
target
features.