Chemosphere,
Journal Year:
2024,
Volume and Issue:
368, P. 143801 - 143801
Published: Nov. 1, 2024
Due
to
environmental
concerns
and
economic
value,
the
adsorption
process
using
agricultural
wastes
is
one
of
promising
methods
remove
lead
(Pb)
from
contaminated
water.
The
relationships
between
waste
properties,
conditions,
maximum
Pb
capacity
selected
adsorbents
have
not
been
adequately
explored.
A
thorough
understanding
these
interactions
crucial
for
optimizing
processes
enhancing
efficiency
as
sustainable
adsorbents.
To
assess
by
identify
key
influencing
factors,
three
artificial
intelligence
techniques,
namely
Extreme
Learning
Machine
(ELM),
Adaptive
Nuro-Fuzzy
Inference
Systems
(ANFIS),
Group
Method
Data
Handling
(GMDH)
employed
in
this
study.
Seven
input
variables,
time,
ratio,
initial
ion
concentration,
type
wastes,
pH,
temperature,
agitation
speed,
771
data
points
were
used
inputs
model
development,
while
quantity
adsorbed
was
chosen
target
parameter.
best
combinations
with
seven
127
models
defined
analyzed
ELM
integrated
cross-validation
technique.
results
highlighted
that
concentration
most
critical
factor
heavy
metal
adsorption,
temperature
least
important
factor.
top
models,
utilizing
variable(s),
then
modeled
ANFIS
GMDH.
Subsequently,
all
compared.
GMDH
four
variables
(initial
adsorbent,
speed)
demonstrated
highest
performance
terms
accuracy
simplicity.
International Journal of Molecular Sciences,
Journal Year:
2024,
Volume and Issue:
25(9), P. 4771 - 4771
Published: April 27, 2024
This
work
aimed
to
describe
the
adsorption
behavior
of
Congo
red
(CR)
onto
activated
biochar
material
prepared
from
Haematoxylum
campechianum
waste
(ABHC).
The
carbon
precursor
was
soaked
with
phosphoric
acid,
followed
by
pyrolysis
convert
into
biochar.
surface
morphology
adsorbent
(before
and
after
dye
adsorption)
characterized
scanning
electron
microscopy
(SEM/EDS),
BET
method,
X-ray
powder
diffraction
(XRD),
Fourier-transform
infrared
spectroscopy
(FTIR)
and,
lastly,
pHpzc
also
determined.
Batch
studies
were
carried
out
in
following
intervals
pH
=
4–10,
temperature
300.15–330.15
K,
dose
1–10
g/L,
isotherms
evaluated
process
determine
maximum
capacity
(Qmax,
mg/g).
Kinetic
performed
starting
two
different
initial
concentrations
(25
50
mg/L)
at
a
contact
time
48
h.
reusability
potential
adsorption–desorption
cycles.
obtained
Langmuir
isotherm
model
114.8
mg/g
300.15
5.4,
1.0
g/L.
study
highlights
application
advanced
machine
learning
techniques
optimize
chemical
removal
process.
Leveraging
comprehensive
dataset,
Gradient
Boosting
regression
developed
fine-tuned
using
Bayesian
optimization
within
Python
programming
environment.
algorithm
efficiently
navigated
input
space
maximize
percentage,
resulting
predicted
efficiency
approximately
90.47%
under
optimal
conditions.
These
findings
offer
promising
insights
for
enhancing
similar
processes,
showcasing
environmental
remediation.
Journal of Environmental Science and Health Part A,
Journal Year:
2025,
Volume and Issue:
unknown, P. 1 - 16
Published: Feb. 2, 2025
There
are
several
uses
for
biomass-derived
materials
(BDMs)
in
the
irrigation
and
farming
industries.
To
solve
problems
with
material,
process,
supply
chain
design,
BDM
systems
have
started
to
use
machine
learning
(ML),
a
new
technique
approach.
This
study
examined
articles
published
since
2015
understand
better
current
status,
future
possibilities,
capabilities
of
ML
supporting
environmentally
friendly
development
applications.
Previous
applications
were
classified
into
three
categories
according
their
objectives:
material
process
performance
prediction
sustainability
evaluation.
helps
optimize
BDMs
systems,
predict
properties
performance,
reverse
engineering,
data
difficulties
evaluations.
Ensemble
models
cutting-edge
Neural
Networks
operate
satisfactorily
on
these
datasets
easily
generalized.
neural
network
poor
interpretability,
there
not
been
any
studies
assessment
that
consider
geo-temporal
dynamics;
thus,
building
methods
is
currently
practical.
Future
research
should
follow
workflow.
Investigating
potential
system
optimization,
evaluation
sustainable
requires
further
investigation.
