Water,
Journal Year:
2024,
Volume and Issue:
16(19), P. 2863 - 2863
Published: Oct. 9, 2024
The
scope
of
the
present
study
is
estimation
key
operational
parameters
a
drinking
water
treatment
plant
(DWTP),
particularly
dosages
chemicals,
using
artificial
neural
networks
(ANNs)
based
on
measurable
in
situ
data.
case
consists
Aposelemis
DWTP,
where
operator
had
an
ANN
output
for
required
chemicals
observed
quality
and
other
at
time.
estimated
DWTP
main
included
residual
ozone
(O3)
used:
anionic
polyelectrolyte
(ANPE),
poly-aluminum
chloride
hydroxide
sulfate
(PACl),
chlorine
gas
(Cl2(g)).
Daily
results
sample
analysis
recordings
from
Supervisory
Control
Data
Acquisition
System
(SCADA),
covering
period
38
months,
were
used
as
input
network
(1188
values
each
14
parameters).
These
included:
raw
supply
(Q),
turbidity
(T1),
treated
(T2),
free
(Cl2),
concentration
aluminum
(Al),
filtration
bed
inlet
(T3),
daily
difference
height
reservoir
(∆H),
pH
(pH1),
(pH2),
consumption
electricity
(El).
Output/target
were:
O3
after
ozonation
(O3),
A
total
304
different
models
tested,
best
test
performance
(tperf)
indicator.
one
with
optimum
indicator
was
selected.
scenario
finally
chosen
100
networks,
nodes,
42
hidden
10
inputs,
4
outputs.
This
model
achieved
excellent
simulation
testing
indicator,
which
suggests
that
ANNs
are
potentially
useful
tools
prediction
DWTP’s
parameters.
Further
research
could
explore
by
smaller
number
to
ensure
greater
flexibility,
without
prohibitively
reducing
reliability
model.
prove
cases
much
higher
size,
given
data-demanding
nature
ANNs.
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.
Water Science & Technology,
Journal Year:
2024,
Volume and Issue:
90(3), P. 731 - 757
Published: July 26, 2024
Artificial
intelligence
(AI)
is
increasingly
being
applied
to
wastewater
treatment
enhance
efficiency,
improve
processes,
and
optimize
resource
utilization.
This
review
focuses
on
objectives,
advantages,
outputs,
major
findings
of
various
AI
models
in
the
three
key
aspects:
prediction
removal
efficiency
for
both
organic
inorganic
pollutants,
real-time
monitoring
essential
water
quality
parameters
(such
as
pH,
COD,
BOD,
turbidity,
TDS,
conductivity),
fault
detection
processes
equipment
integral
treatment.
The
accuracy
(
Agronomy,
Journal Year:
2025,
Volume and Issue:
15(3), P. 703 - 703
Published: March 14, 2025
Estimating
the
quality
of
treated
wastewater
is
a
complex,
nonlinear
challenge
that
traditional
statistical
methods
struggle
to
address.
This
study
introduces
hybrid
machine
learning
approach
predict
key
effluent
parameters
from
an
advanced
biological
treatment
plant
and
assesses
reuse
potential
for
irrigation.
Three
artificial
intelligence
(AI)
models,
Artificial
Neural
Networks
(ANNs),
Adaptive
Neuro-Fuzzy
Inference
System
(ANFIS),
Fuzzy
Logic-Mamdani
(FLM),
were
applied
three
years
daily
inlet
outlet
water
data.
Logic
was
employed
usability
wastewater,
with
ANFIS
categorizing
ANN-based
high-performance
models
(low
MSE,
74–99%
R2)
in
fuzzy
inference
system.
The
qualitative
agricultural
irrigation
ranged
69%
72%
based
on
best-performing
model.
It
estimated
could
irrigate
approximately
35%
20,000-hectare
area.
By
integrating
this
research
enhances
accuracy
interpretability
predictions,
providing
reliable
framework
sustainable
resource
management.
findings
support
optimization
processes
highlight
AI’s
role
advancing
strategies
agriculture,
ultimately
contributing
improved
efficiency
environmental
conservation.
Polymers,
Journal Year:
2024,
Volume and Issue:
16(7), P. 920 - 920
Published: March 27, 2024
This
study
presents
two
modified
polymers
for
Cu2+
ion
removal
from
aqueous
media.
Shredded
maize
stalk
(MC)
and
a
strong-base
anionic
resin
(SAX)
were
with
indigo
carmine
(IC)
in
order
to
obtain
different
complexing
polymers,
i.e.,
IC-MC
SAX-IC.
Initially,
the
complex
reaction
between
IC
solution
was
studied.
Additionally,
formation
Cu2+-IC
liquid
solutions
evaluated
at
pH
ranges
of
1.5,
4.0,
6.0,
8.0,
10.0,
respectively.
For
ions,
adsorption
onto
IC-SAX
batch
experiments
conducted.
The
contact
time
evaluating
optimum
ions
on
materials
established
1
h.
Efficient
SAX-IC
=
10
achieved.
depends
quantity
retained
MC
SAX.
At
2.63
mg
IC/g
MC(S4)
22
SAX(SR2),
high
amount
reported.
highest
capacity
(Qe)
obtained
0.73
mg/g,
IC-SAX,
it
attained
10.8
mg/g.
Reusability
performed
using
HCl
(0.5
M)
solution.
High
regeneration
reusability
studies
confirmed,
suggesting
that
they
can
be
used
many
times
remove
matrices.
Therefore,
development
could
suitable
wastewater.
Sustainability,
Journal Year:
2025,
Volume and Issue:
17(4), P. 1592 - 1592
Published: Feb. 14, 2025
Nitrous
oxide
(N2O)
is
a
potent
greenhouse
gas
and
contributor
to
ozone
depletion,
with
wastewater
treatment
plants
(WWTPs)
serving
as
significant
sources
of
emissions
due
biological
processes
involving
bacteria.
This
study
evaluates
research
on
the
role
bacteria
in
N2O
from
WWTPs
between
2000
2023
based
an
analysis
Web
Science
Core
Collection
Database
using
keywords
“bacteria”,
“nitrous
oxide”,
“emission”,
“wastewater
plant”.
The
findings
reveal
substantial
growth
past
decade,
leading
publications
appearing
Water
Research,
Bioresource
Technology,
Environmental
&
Technology.
China,
United
States,
Australia
have
been
most
active
contributors
this
field.
Key
topics
include
denitrification,
treatment,
emissions.
microbial
community
composition
significantly
influences
WWTPs,
bacterial
consortia
playing
pivotal
role.
However,
further
needed
explore
strain-specific
genes,
enzyme
expressions,
differentiation
contributing
production
emission.
System
design
operation
must
also
consider
dissolved
oxygen
nitrite
concentration
factors.
Advances
genomics
artificial
intelligence
are
expected
enhance
strategies
for
reducing
WWTPs.
IGI Global eBooks,
Journal Year:
2025,
Volume and Issue:
unknown, P. 285 - 306
Published: Feb. 18, 2025
The
increase
in
the
urban
population
and
climate
change
have
driven
development
of
Smart
Water
systems,
which
integrate
artificial
intelligence
to
improve
drinking
water
management.
AI
optimizes
distribution,
monitors
quality
real-time,
detects
leaks,
manages
demand
efficiently,
thus
addressing
current
challenges
resource
aim
this
research
is
analyse
applications
management
within
systems.
method
used
includes
a
literature
review,
case
studies
an
analysis
data
obtained.
results
show
that
improves
through
continuous
monitoring
its
quality,
accurate
leak
detection,
optimization
distribution
efficient
use
resources,
prediction
management,
predictive
maintenance.
In
addition,
it
reduces
energy
consumption
treatment
distribution.
However,
there
are
technical,
economic,
regulatory
need
be
addressed
order
achieve
effective
sustainable
implementation.
