Journal of Circuits Systems and Computers,
Journal Year:
2024,
Volume and Issue:
33(16)
Published: May 30, 2024
The
coagulation
cooling
system
is
a
common
key
component
in
many
industrial
processes,
and
reasonable
temperature
control
crucial.
However,
due
to
the
complexity
of
system,
traditional
methods
often
cannot
achieve
optimal
performance.
To
solve
this
problem,
we
design
an
intelligent
decision
model
by
combination
genetic
algorithm
fuzzy
neural
network.
study
firstly
utilizes
optimize
objective
function
constraint
conditions
PID
system.
At
same
time,
network
fused
with
establish
dedicated
T-S/2
structure,
completing
complete
study.
Finally,
efficiency,
task
completion
rate
stability
analysis
are
evaluated
on
real-world
datasets.
validate
proposed
model,
example
was
constructed
laboratory
compared
methods.
experimental
results
show
that
proposal
can
significantly
improve
performance
reduce
energy
consumption
under
different
conditions.
In
addition,
has
characteristics
adaptability
optimization
performance,
effectively
uncertain
complex
environments.
The Science of The Total Environment,
Journal Year:
2024,
Volume and Issue:
944, P. 173999 - 173999
Published: June 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.
RSC Advances,
Journal Year:
2024,
Volume and Issue:
14(43), P. 31259 - 31273
Published: Jan. 1, 2024
Addressing
global
freshwater
scarcity
requires
innovative
technological
solutions,
among
which
desalination
through
thin-film
composite
polyamide
membranes
stands
out.
The
performance
of
these
plays
a
vital
role
in
desalination,
necessitating
advanced
predictive
modeling
for
optimization.
This
study
harnesses
machine
learning
(ML)
algorithms,
including
support
vector
(SVM),
neural
networks
(NN),
linear
regression
(LR),
and
multivariate
(MLR),
alongside
their
ensemble
techniques
to
predict
enhance
average
water
flux
(AWF)
salt
rejection
(ASR)
essential
metrics
efficiency.
To
ensure
model
interpretability
feature
importance
analysis,
SHapley
Additive
exPlanations
(SHAP)
were
employed,
providing
both
local
insights
into
contributions.
Initially,
the
individual
models
validated,
with
NN
demonstrating
superior
AWF
ASR,
achieving
lowest
mean
absolute
error
(MAE
=
0.001)
root
squared
(RMSE
0.0111)
an
MAE
0.0107
RMSE
0.0982
ASR.
accuracy
predictions
improved
significantly
models,
as
evidenced
by
near-perfect
Nash-Sutcliffe
efficiency
(NSE)
values.
Specifically,
(NN-E)
Linear
Regression
(LR-E)
reached
0.001
0.0111,
respectively,
AWF.
For
NN-E
reduced
0.0013
0.0089,
while
LR-E
maintained
competitive
0.0133
0.0936.
SHAP
analysis
revealed
that
features
such
MDP
TMC
critical
drivers
performance,
showing
most
significant
positive
impact
on
These
findings
demonstrate
dominance
methods
over
algorithms
predicting
key
parameters.
enhanced
precision
estimating
ASR
offered
neuro-intelligent
ensembles,
combined
provided
can
lead
environmental
operational
improvements
membrane
optimizing
resource
usage
minimizing
ecological
impacts.
paves
way
integrating
intelligent
ML
ensembles
SHAP-based
practical
field
technology,
marking
step
forward
toward
sustainable
efficient
processes.
Climate
change
always
had
a
massive
effect
on
worldwide
cities.
which
can
only
be
decreased
through
considering
renewable
energy
sources
(wind
energy,
solar
energy).
However,
the
need
to
focus
wind
prediction
will
best
solution
world
electricity
petition.
Wind
power
(WP)
estimating
techniques
have
been
used
for
diverse
literature
studies
many
decades.
The
hardest
way
improve
WP
is
its
nature
of
differences
that
make
it
tough
undertaking
forecast.
In
line
with
outdated
ways
predicting
speed
(WS),
employing
machine
learning
methods
(ML)
has
become
an
essential
tool
studying
such
problem.
methodology
this
study
focuses
sanitizing
efficient
models
precisely
predict
regimens.
Two
ML
were
employed
“Gaussian
Process
Regression
(GPR),
and
Feed
Forward
Neural
Network
(FFNN)”
WS
estimation.
experimental
prediction.
prophecy
trained
using
24-hour’
time-series
data
driven
from
Kano
state
Region,
one
biggest
cities
in
Nigeria.
Thus,
investigating
forecast
performance
was
done
terms
coefficient
determination
(R²),
linear
correlation
(R),
Mean
Square
Error
(MSE),
Root
square
error
(RMSE).
Were.
predicted
result
shows
FFNN
produces
superior
outcomes
compared
GPR.
With
R²=
1,
R
=
MSE
6.62E-20,
RMSE
2.57E-10
