Desalination and Water Treatment,
Journal Year:
2024,
Volume and Issue:
317, P. 100257 - 100257
Published: Jan. 1, 2024
Anaerobic
digestion
is
a
complex
biological
process
widely
used
for
organic
waste
treatment
and
biogas
production.
Understanding
the
intermediate
stages
biochemicals
essential
effective
management.
This
study
uses
ANN
modeling
as
well
genetic
algorithm
optimization
to
explore
predict
how
these
intermediates
behave.
By
scrutinizing
interactions
between
VFAs
CH4
production,
within
context
of
our
VFA
Complex
Feed
characterized
by
unique
concentrations,
this
model
underscores
paramount
significance
three
VFAs:
acetate,
propionate,
butyrate.
Notably,
in
distinctive
study,
contrary
prior
research,
acetate
manifests
deleterious
influence
on
production
(CI
=
-1.92),
whereas
propionate
+1.22)
butyrate
+1.14)
exhibit
favorable
impact.
exerts
most
substantial
absolute
(AAS
+4.7)
when
juxtaposed
with
other
VFAs.
These
results
support
supporting
its
validity.
combining
machine
learning
theoretical
knowledge,
advances
comprehension
anaerobic
offers
valuable
insights
optimizing
process.
Carbon Neutrality,
Journal Year:
2024,
Volume and Issue:
3(1)
Published: Jan. 8, 2024
Abstract
The
utilization
of
biochar
derived
from
biomass
residue
to
enhance
anaerobic
digestion
(AD)
for
bioenergy
recovery
offers
a
sustainable
approach
advance
energy
and
mitigate
climate
change.
However,
conducting
comprehensive
research
on
the
optimal
conditions
AD
experiments
with
addition
poses
challenge
due
diverse
experimental
objectives.
Machine
learning
(ML)
has
demonstrated
its
effectiveness
in
addressing
this
issue.
Therefore,
it
is
essential
provide
an
overview
current
ML-optimized
processes
biochar-enhanced
order
facilitate
more
systematic
ML
tools.
This
review
comprehensively
examines
material
flow
preparation
impact
comprehension
reviewed
optimize
production
process
perspective.
Specifically,
summarizes
application
techniques,
based
artificial
intelligence,
predicting
yield
properties
residues,
as
well
their
AD.
Overall,
analysis
address
challenges
recovery.
In
future
research,
crucial
tackle
that
hinder
implementation
pilot-scale
reactors.
It
recommended
further
investigate
correlation
between
physicochemical
process.
Additionally,
enhancing
role
throughout
entire
holds
promise
achieving
economically
environmentally
optimized
efficiency.
Graphical
npj Materials Sustainability,
Journal Year:
2024,
Volume and Issue:
2(1)
Published: April 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.
Molecules,
Journal Year:
2025,
Volume and Issue:
30(5), P. 1092 - 1092
Published: Feb. 27, 2025
This
study
explores
the
application
of
various
machine
learning
(ML)
models
for
real-time
prediction
FOS/TAC
ratio
in
microbial
electrolysis
cell
anaerobic
digestion
(MEC-AD)
systems
using
data
collected
during
a
160-day
trial
treating
brewery
wastewater.
investigated
including
decision
trees,
XGBoost,
support
vector
regression,
variant
(SVM),
and
artificial
neural
networks
(ANNs)
their
effectiveness
soft
sensing
system
stability.
The
ANNs
demonstrated
superior
performance,
achieving
an
explained
variance
0.77,
were
further
evaluated
through
out-of-fold
ensemble
approach
to
assess
selected
model's
performance
across
complete
dataset.
work
underscores
critical
role
ML
enhancing
operational
efficiency
stability
bio-electrochemical
(BES),
contributing
significantly
cost-effective
environmental
management.
findings
suggest
that
not
only
aids
maintaining
health
communities,
which
is
essential
biogas
production,
but
also
helps
reduce
risks
associated
with
instability.
Circular Economy,
Journal Year:
2024,
Volume and Issue:
3(2), P. 100088 - 100088
Published: May 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.
