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.
Environmental Chemistry Letters,
Год журнала:
2023,
Номер
21(4), С. 1959 - 1989
Опубликована: Май 9, 2023
Abstract
The
rising
amount
of
waste
generated
worldwide
is
inducing
issues
pollution,
management,
and
recycling,
calling
for
new
strategies
to
improve
the
ecosystem,
such
as
use
artificial
intelligence.
Here,
we
review
application
intelligence
in
waste-to-energy,
smart
bins,
waste-sorting
robots,
generation
models,
monitoring
tracking,
plastic
pyrolysis,
distinguishing
fossil
modern
materials,
logistics,
disposal,
illegal
dumping,
resource
recovery,
cities,
process
efficiency,
cost
savings,
improving
public
health.
Using
logistics
can
reduce
transportation
distance
by
up
36.8%,
savings
13.35%,
time
28.22%.
Artificial
allows
identifying
sorting
with
an
accuracy
ranging
from
72.8
99.95%.
combined
chemical
analysis
improves
carbon
emission
estimation,
energy
conversion.
We
also
explain
how
efficiency
be
increased
costs
reduced
management
systems
cities.
npj Materials Sustainability,
Год журнала:
2024,
Номер
2(1)
Опубликована: Апрель 8, 2024
Abstract
Data-driven
modeling
is
being
increasingly
applied
in
designing
and
optimizing
organic
waste
management
toward
greater
resource
circularity.
This
study
investigates
a
spectrum
of
data-driven
techniques
for
treatment,
encompassing
neural
networks,
support
vector
machines,
decision
trees,
random
forests,
Gaussian
process
regression,
k
-nearest
neighbors.
The
application
these
explored
terms
their
capacity
complex
processes.
Additionally,
the
delves
into
physics-informed
highlighting
significance
integrating
domain
knowledge
improved
model
consistency.
Comparative
analyses
are
carried
out
to
provide
insights
strengths
weaknesses
each
technique,
aiding
practitioners
selecting
appropriate
models
diverse
applications.
Transfer
learning
specialized
network
variants
also
discussed,
offering
avenues
enhancing
predictive
capabilities.
work
contributes
valuable
field
modeling,
emphasizing
importance
understanding
nuances
technique
informed
decision-making
various
treatment
scenarios.
Circular Economy,
Год журнала:
2024,
Номер
3(2), С. 100088 - 100088
Опубликована: Май 31, 2024
Biological
treatment
technologies
(such
as
anaerobic
digestion,
composting,
and
insect
farming)
have
been
extensively
employed
to
handle
various
degradable
organic
wastes.
However,
the
inherent
complexity
instability
of
biological
processes
adversely
affect
production
renewable
energy
nutrient-rich
products.
To
ensure
stable
consistent
product
quality,
researchers
invested
heavily
in
control
strategies
for
treatment,
with
machine
learning
(ML)
recently
proving
effective
optimizing
predicting
parameters,
detecting
disturbances,
enabling
real-time
monitoring.
This
review
critically
assesses
application
ML
providing
an
in-depth
evaluation
key
algorithms.
study
reveals
that
artificial
neural
networks,
tree-based
models,
support
vector
machines,
genetic
algorithms
are
leading
treatment.
A
thorough
investigation
applications
farming
underscores
its
remarkable
capacity
predict
products,
optimize
processes,
perform
monitoring,
mitigate
pollution
emissions.
Furthermore,
this
outlines
challenges
prospects
encountered
applying
highlighting
crucial
directions
future
research
area.
