Water Practice & Technology,
Journal Year:
2024,
Volume and Issue:
19(8), P. 3330 - 3349
Published: Aug. 1, 2024
ABSTRACT
The
majority
of
the
environmental
outputs
from
gas
refineries
are
oily
wastewater.
This
research
reveals
a
novel
combination
response
surface
methodology
and
artificial
neural
network
to
optimize
model
oil
content
concentration
in
Response
based
on
central
composite
design
shows
highly
significant
linear
with
P
value
<0.0001
determination
coefficient
R2
equal
0.747,
R
adjusted
was
0.706,
predicted
0.643.
In
addition
analysis
variance
flow
effective
parameters
other
optimization
results
verification
revealed
minimum
8.5
±
0.7
ppm
when
initial
991
ppm,
temperature
46.4
°C,
pressure
21
Mpa,
flowrate
27,000
m3/day
which
is
nearly
closed
suggested
ppm.
An
(ANN)
technique
employed
this
study
estimate
treatment
process.
remarkably
accurate
at
simulating
process
under
investigation.
A
low
mean
squared
error
(MSE)
relative
(RE)
1.55
×
10−7
2.5,
respectively,
were
obtained
during
training
phase,
whilst
testing
demonstrated
high
(R2)
0.99.
ACS Applied Materials & Interfaces,
Journal Year:
2024,
Volume and Issue:
16(13), P. 16271 - 16289
Published: March 21, 2024
Significant
progress
has
been
made
in
designing
advanced
membranes;
however,
persistent
challenges
remain
due
to
their
reduced
permeation
rates
and
a
propensity
for
substantial
fouling.
These
factors
continue
pose
significant
barriers
the
effective
utilization
of
membranes
separation
oil-in-water
emulsions.
Metal–organic
frameworks
(MOFs)
are
considered
promising
materials
such
applications;
they
encounter
three
key
when
applied
oil
from
water:
(a)
lack
water
stability;
(b)
difficulty
producing
defect-free
(c)
unresolved
issue
stabilizing
MOF
separating
layer
on
ceramic
membrane
(CM)
support.
In
this
study,
hydrolytically
stable
zirconium-based
was
formed
through
two-step
method:
first,
by
situ
growth
UiO-66-NH2
into
voids
polydopamine
(PDA)-functionalized
CM
during
solvothermal
process,
then
facilitating
self-assembly
with
PDA
using
pressurized
dead-end
assembly.
A
attained
enriching
support
amines
hydroxyl
groups
PDA,
which
assisted
assembly
stabilization
UiO-66-NH2.
The
PDA-s-UiO-66-NH2–CM
displayed
air
superhydrophilicity
underwater
superoleophobicity,
demonstrating
its
resistance
high
antifouling
behavior.
shown
exceptionally
permeability
capacity
challenging
This
is
attributed
numerous
nanochannels
adhesion.
showed
excellent
stability
over
15
continuous
test
cycles,
indicates
that
developed
MOFs
layers
have
low
tendency
be
clogged
droplets
separation.
Machine
learning-based
Gaussian
process
regression
(GPR)
models
as
nonparametric
kernel-based
probabilistic
were
employed
predict
performance
efficiency
outcomes
compared
vector
machine
(SVM)
decision
tree
(DT)
algorithm.
includes
various
metrics
related
accuracy,
feature
engineering
identify
utilize
most
affecting
membrane's
performance.
results
proved
reliability
GPR
optimization
highest
prediction
accuracy
validation
phase.
average
percentage
increase
model
SVM
DT
6.11
42.94%,
respectively.
Environmental Technology & Innovation,
Journal Year:
2023,
Volume and Issue:
33, P. 103456 - 103456
Published: Dec. 4, 2023
This
study,
for
the
first
time,
reported
utilization
of
ZIF-60
adsorptive
removal
organic
pollutant
from
contaminated
water.
Characterization
techniques
were
used
to
confirm
synthesis
adsorbent
which
was
then
study
uptake
crystal
violet
(CV)
aqueous
system.
A
remarkably
high
experimental
(in
excess
7000
mg/g)
CV
onto
noted
due
careful
selection
process
parameters
while
conducting
batch
adsorption
studies.
Response
surface
methodology
technique
utilized
as
tool
response
optimization
and
studying
effect
varying
parameters.
Temperature
had
most
significant
influence
on
followed
by
concentration,
dose
pH.
Adsorption
kinetics
isotherm
investigations
conducted
based
optimized
conditions,
obtained
good
data
fitting
with
more
than
one
model
suggests
an
intricate
process.
On
other
hand,
thermodynamic
studies
revealed
a
highly
endothermic
spontaneous
The
post
analysis
π-π
stacking
interaction
dominant
force
driving
ZIF-60.
Additionally,
several
machine
learning
models
that
distinct
algorithms
prediction
An
ensemble
combination
created
using
voting
technique;
this
proved
be
accurate
in
predicting
suggested
its
superior
value
evaluation
metrics.
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.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Aug. 28, 2024
This
study
presents
an
innovative
approach
for
predicting
water
and
groundwater
quality
indices
(WQI
GWQI)
in
the
Eastern
Province
of
Saudi
Arabia,
addressing
critical
challenges
scarcity
pollution
arid
regions.
Recent
literature
highlights
increasing
attention
towards
WQI
based
on
index
(WPI)
GWQI
as
essential
tools
simplifying
complex
hydrogeological
data,
thereby
facilitating
effective
management
protection.
Unlike
previous
works,
present
research
introduces
a
novel
hybrid
method
that
integrates
non-parametric
kernel
Gaussian
learning
(GPR),
adaptive
neuro-fuzzy
inference
system
(ANFIS),
decision
tree
(DT)
algorithms.
marks
first
application
prediction
offering
significant
advancement
field.
Through
laboratory
analysis
combination
various
machine
(ML)
techniques,
this
enhances
capabilities,
particularly
unmonitored
sites
semi-arid
The
study's
objectives
include
feature
engineering
dependency
sensitivity
to
identify
most
influential
variables
affecting
GWQI,
development
predictive
models
using
ANFIS,
GPR,
DT
both
indices.
Furthermore,
it
aims
assess
impact
different
data
portions
predictions,
exploring
divisions
such
(70%
/
30%),
(60%
40%),
(80%
20%)
training
testing
phase,
respectively.
By
filling
gap
resource
management,
offers
implications
regions
facing
similar
environmental
challenges.
its
methodology
comprehensive
analysis,
contributes
broader
effort
managing
protecting
resources
areas.
result
proved
GPR-M1
exhibited
exceptional
phase
accuracy
with
RMSE
=
0.0169
GWQI.
Similarly,
WPI,
ANFIS-M1
achieved
high
skills
0.0401.
results
emphasize
role
quantity
enhancing
model
robustness
precision
assessment.
