Journal of the Air & Waste Management Association,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 11, 2024
This
study
demonstrates
the
potential
for
reusing
commercial
activated
carbon
(CAC)
to
capture
high
molecular
weight
and
high-boiling
point
volatile
organic
compounds
(HBPVOCs).
Through
comprehensive
characterization
performance
evaluation,
we
found
that
CAC
effectively
adsorbs
Butyl
Cellosolve
(BCS),
a
common
industrial
solvent,
with
adsorption
capacity
increasing
pressure.
The
process
follows
pseudo-first-order
kinetics,
indicating
single-layer
physical
adsorption.
Additionally,
highlights
recyclability
of
CAC,
as
desorption
subsequent
analysis
revealed
minimal
changes
in
pore
structure,
maintaining
significant
portion
its
original
BET
value.
These
findings
suggest
is
not
only
effective
BCS
but
also
sustainable
repeated
use,
offering
an
efficient
eco-friendly
solution
managing
HBPVOCs.
Chemistry & Chemical Technology,
Journal Year:
2024,
Volume and Issue:
18(2), P. 211 - 231
Published: June 14, 2024
From
the
perspective
of
converting
waste
into
valuable
products
and
reducing
environmental
pollution,
up-recycling
biomass
carbon-rich
materials
is
attracting
widespread
attention.
This
literature
review
presents
possibilities
using
solid
product
one-stage
carbonization
(char)
plant-origin
biomass.
Several
applications
are
discussed,
including
production
sorbents,
energy
storage
materials,
catalyst
carriers,
agricultural
applications.
Although
composting
has
many
advantages
in
the
treatment
of
organic
waste,
there
are
still
problems
and
challenges
associated
with
emissions,
like
NH3,
VOCs,
H2S,
as
well
greenhouse
gases
such
CO2,
CH4,
N2O.
One
promising
approach
to
enhancing
conditions
is
used
novel
analytical
methods
bad
on
artificial
intelligence.
To
predict
optimize
emissions
(CO,
NH3)
during
process
kinetics
thought
mathematical
models
(MM)
machine
learning
(ML)
were
utilized.
Data
about
everyday
from
laboratory
compost’s
biochar
different
incubation
(50,
60,
70
°C)
doses
(0,
3,
6,
9,
12,
15%
d.m.)
for
MM
ML
selections
training.
not
been
very
effective
predicting
(R2
0.1
-
0.9),
while
acritical
neural
network
(ANN,
Bayesian
Regularized
Neural
Network;
R2
accuracy
CO:0,71,
CO2:0,81,
NH3:0,95,
H2S:0,72))
decision
tree
(DT,
RPART;
CO:0,693,
CO2:0,80,
NH3:0,93,
H2S:0,65)
have
demonstrated
satisfactory
results.
For
first
time
CO
H2S
demonstrated.
Further
research
a
semi-scale
field
study
needed
improve
developments
models.
