Water Environment Research,
Journal Year:
2024,
Volume and Issue:
96(8)
Published: Aug. 1, 2024
Abstract
In
this
study,
we
employed
the
response
surface
method
(RSM)
and
long
short‐term
memory
(LSTM)
model
to
optimize
operational
parameters
predict
chemical
oxygen
demand
(COD)
removal
in
electrocoagulation‐catalytic
ozonation
process
(ECOP)
for
pharmaceutical
wastewater
treatment.
Through
RSM
simulation,
quantified
effects
of
reaction
time,
ozone
dose,
current
density,
catalyst
packed
rate
on
COD
removal.
Then,
optimal
conditions
achieving
a
efficiency
exceeding
50%
were
identified.
After
evaluating
ECOP
performance
under
optimized
conditions,
LSTM
predicted
(56.4%),
close
real
results
(54.6%)
with
0.2%
error.
outperformed
predictive
capacity
initial
concentration
effluent
discharge
standards,
intelligent
adjustment
operating
becomes
feasible,
facilitating
precise
control
based
model.
This
strategy
holds
promise
enhancing
treatment
scenarios.
Practitioner
Points
study
utilized
optimization.
(56.4%)
closely
matched
experimental
(54.6%),
minimal
error
0.2%.
demonstrated
superior
capacity,
enabling
parameter
adjustments
enhanced
control.
Intelligent
improving
Water,
Journal Year:
2024,
Volume and Issue:
16(24), P. 3710 - 3710
Published: Dec. 22, 2024
Real-time
control
(RTC)
can
be
applied
to
optimize
the
operation
of
anaerobic–anoxic–oxic
(A2O)
process
in
wastewater
treatment
for
energy
saving.
In
recent
years,
many
studies
have
utilized
deep
reinforcement
learning
(DRL)
construct
a
novel
AI-based
RTC
system
optimizing
A2O
process.
However,
existing
DRL
methods
require
use
mechanistic
models
training.
Therefore
they
specified
data
construction
models,
which
is
often
difficult
achieve
plants
(WWTPs)
where
collection
facilities
are
inadequate.
Also,
training
time-consuming
because
it
needs
multiple
simulations
model.
To
address
these
issues,
this
study
designs
data-driven
method.
The
method
first
creates
simulation
model
using
LSTM
and
an
attention
module
(LSTM-ATT).
This
established
based
on
flexible
from
LSTM-ATT
simplified
version
large
language
(LLM),
has
much
more
powerful
ability
analyzing
time-sequence
than
usual
but
with
small
architecture
that
avoids
overfitting
dynamic
data.
Based
this,
new
framework
constructed,
leveraging
rapid
computational
capabilities
accelerate
proposed
WWTP
Western
China.
An
built
used
train
reduction
aeration
qualified
effluent.
For
simulation,
its
mean
squared
error
remains
between
0.0039
0.0243,
while
R-squared
values
larger
0.996.
strategy
provided
by
DQN
effectively
reduces
average
DO
setpoint
3.956
mg/L
3.884
mg/L,
acceptable
provides
pure
WWTPs
DRL,
effective
saving
consumption
reduction.
It
also
demonstrates
purely
process,
providing
decision-support
management.
Energy Harvesting and Systems,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: Jan. 1, 2024
Abstract
To
control
water
quality
and
seawater
desalination
dosage,
modeling
the
coagulation
process
of
saltwater
is
crucial.
With
a
focus
on
features
with
long
lag,
machine-learning
sequence-based
approach
suggested.
The
link
between
influent
effluent
turbidities,
flow
rates,
flocculant
coagulant
dosages,
other
parameters
modeled
using
structured
units
such
as
gate
recurrent
unit
encoder
linear
network
decoder.
model’s
validity
confirmed
by
numerical
experiments
based
real
operating
data,
which
also
offer
solid
foundation
for
managing
assistance
reduction.
Water Environment Research,
Journal Year:
2024,
Volume and Issue:
96(8)
Published: Aug. 1, 2024
Abstract
In
this
study,
we
employed
the
response
surface
method
(RSM)
and
long
short‐term
memory
(LSTM)
model
to
optimize
operational
parameters
predict
chemical
oxygen
demand
(COD)
removal
in
electrocoagulation‐catalytic
ozonation
process
(ECOP)
for
pharmaceutical
wastewater
treatment.
Through
RSM
simulation,
quantified
effects
of
reaction
time,
ozone
dose,
current
density,
catalyst
packed
rate
on
COD
removal.
Then,
optimal
conditions
achieving
a
efficiency
exceeding
50%
were
identified.
After
evaluating
ECOP
performance
under
optimized
conditions,
LSTM
predicted
(56.4%),
close
real
results
(54.6%)
with
0.2%
error.
outperformed
predictive
capacity
initial
concentration
effluent
discharge
standards,
intelligent
adjustment
operating
becomes
feasible,
facilitating
precise
control
based
model.
This
strategy
holds
promise
enhancing
treatment
scenarios.
Practitioner
Points
study
utilized
optimization.
(56.4%)
closely
matched
experimental
(54.6%),
minimal
error
0.2%.
demonstrated
superior
capacity,
enabling
parameter
adjustments
enhanced
control.
Intelligent
improving
