Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Апрель 18, 2025
The
contamination
of
water
and
soils
with
heavy
metals
poses
a
significant
environmental
threat,
making
the
development
effective
removal
strategies
global
priority.
Hence,
determination
can
play
an
essential
role
in
monitoring
assessment.
In
current
research,
ensemble
machine
learning
(ML)
models
(i.e.,
Random
Forest
Regressor
(RFR),
Adaptive
Boosting
(Adaboost),
Gradient
(GB),
HistGradientBoosting,
Extreme
(XGBoost),
Light
Gradient-Boosting
Machine
(LightGBM))
were
applied
attempt
to
predict
adsorption
efficiency
several
Pb,
Cd,
Ni,
Cu,
Zn)
according
different
factors
including
temperature,
pH,
biochar
characteristics.
Data
collected
from
open-source
literature
review
353
samples.
At
first
stage,
data
processing
was
performed
outliers'
scaling
for
better
modeling
applicability;
whereas,
second
stage
predictive
conducted.
results
showed
that
XGBoost
model
attained
superior
accuracy
comparison
other
by
achieving
highest
coefficient
(R2
=
0.92).
research
extended
investigate
feature
importance
analysis
which
indicated
initial
concentration
ratio
pH
most
influential
toward
followed
Pyrolysis
while
features
like
physical
properties
as
surface
area
pore
structure
had
minimal
effect
on
efficiency.
These
findings
highlighted
using
ML
guiding
solutions
it
provides
efficient
prediction
ease
selection
application.
Resources,
Год журнала:
2024,
Номер
13(1), С. 8 - 8
Опубликована: Янв. 5, 2024
The
global
impact
of
water
and
soil
contamination
has
become
a
serious
issue
that
affects
the
world
all
living
beings.
In
this
sense,
multiple
treatment
alternatives
have
been
developed
at
different
scales
to
improve
quality.
Among
them,
biochar
suitable
alternative
for
environmental
remediation
due
its
high
efficiency
low
cost,
raw
material
used
production
comes
from
residual
biomass.
A
is
carbonaceous
with
interesting
physicochemical
properties
(e.g.,
surface
area,
porosity,
functional
groups),
which
can
be
prepared
by
synthesis
methods
using
agricultural
wastes
(branches
banana
rachis,
cocoa
shells,
cane
bagasse,
among
others)
as
feedstock.
This
state-of-the-art
review
based
on
general
description
remediation.
Biochar’s
production,
synthesis,
uses
also
analyzed.
addition,
work
shows
some
thus
several
applications,
like
removing
heavy
metals,
oil,
dyes,
other
toxic
pollutants.
Physical
chemical
modifications,
precursors,
dopants,
promoting
agents
Fe
N
species)
discussed.
Finally,
primary
corresponding
mechanism
quality
(via
adsorption,
heterogeneous
photocatalysis,
advanced
oxidation
processes)
described,
both
laboratory
medium
large
scales.
Considering
advantages,
methods,
promising
potential
mitigate
problems
improving
quality,
reducing
greenhouse
gas
emissions,
circular
economy
through
biomass,
generating
value-added
products
uses.
Biofuels Bioproducts and Biorefining,
Год журнала:
2024,
Номер
18(2), С. 567 - 593
Опубликована: Фев. 5, 2024
Abstract
Biochar
is
emerging
as
a
potential
solution
for
biomass
conversion
to
meet
the
ever
increasing
demand
sustainable
energy.
Efficient
management
systems
are
needed
in
order
exploit
fully
of
biochar.
Modern
machine
learning
(ML)
techniques,
and
particular
ensemble
approaches
explainable
AI
methods,
valuable
forecasting
properties
efficiency
biochar
properly.
Machine‐learning‐based
forecasts,
optimization,
feature
selection
critical
improving
techniques.
In
this
research,
we
explore
influences
these
techniques
on
accurate
yield
range
sources.
We
emphasize
importance
interpretability
model,
improves
human
comprehension
trust
ML
predictions.
Sensitivity
analysis
shown
be
an
effective
technique
finding
crucial
characteristics
that
influence
synthesis
Precision
prognostics
have
far‐reaching
ramifications,
influencing
industries
such
logistics,
technologies,
successful
use
renewable
These
advances
can
make
substantial
contribution
greener
future
encourage
development
circular
biobased
economy.
This
work
emphasizes
using
sophisticated
data‐driven
methodologies
synthesis,
usher
ecologically
friendly
energy
solutions.
breakthroughs
hold
key
more
environmentally
future.
Environmental Chemistry Letters,
Год журнала:
2023,
Номер
21(6), С. 3159 - 3244
Опубликована: Авг. 17, 2023
Abstract
Traditional
fertilizers
are
highly
inefficient,
with
a
major
loss
of
nutrients
and
associated
pollution.
Alternatively,
biochar
loaded
phosphorous
is
sustainable
fertilizer
that
improves
soil
structure,
stores
carbon
in
soils,
provides
plant
the
long
run,
yet
most
biochars
not
optimal
because
mechanisms
ruling
properties
poorly
known.
This
issue
can
be
solved
by
recent
developments
machine
learning
computational
chemistry.
Here
we
review
phosphorus-loaded
emphasis
on
chemistry,
learning,
organic
acids,
drawbacks
classical
fertilizers,
production,
phosphorus
loading,
release.
Modeling
techniques
allow
for
deciphering
influence
individual
variables
biochar,
employing
various
supervised
models
tailored
to
different
types.
Computational
chemistry
knowledge
factors
control
binding,
e.g.,
type
compound,
constituents,
mineral
surfaces,
binding
motifs,
water,
solution
pH,
redox
potential.
Phosphorus
release
from
controlled
coexisting
anions,
adsorbent
dosage,
initial
concentration,
temperature.
Pyrolysis
temperatures
below
600
°C
enhance
functional
group
retention,
while
450
increase
plant-available
phosphorus.
Lower
pH
values
promote
release,
higher
hinder
it.
Physical
modifications,
such
as
increasing
surface
area
pore
volume,
maximize
adsorption
capacity
biochar.
Furthermore,
acid
affects
low
molecular
weight
acids
being
advantageous
utilization.
Lastly,
biochar-based
2–4
times
slower
than
conventional
fertilizers.
