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.
Renewable and Sustainable Energy Reviews,
Journal Year:
2023,
Volume and Issue:
180, P. 113305 - 113305
Published: April 25, 2023
High-solid
anaerobic
digestion
(HSAD)
is
an
attractive
organic
waste
disposal
method
for
bioenergy
recovery
and
climate
change
mitigation.
The
development
of
HSAD
facing
several
challenges
such
as
low
biogas
methane
yields,
reaction
rates,
ease
process
inhibition
due
to
mass
diffusion
mixing
limitations
the
process.
Therefore,
recent
progress
in
critically
reviewed
with
a
focus
on
transport
phenomena
modelling.
Specifically,
work
discusses
hydrodynamic
phenomena,
biokinetic
mechanisms,
HSAD-specific
reactor
simulations,
state-of-the-art
multi-stage
designs,
industrial
ramifications,
key
parameters
that
enable
sustained
operation
processes.
Further
research
novel
materials
bio-additives,
adsorbents,
surfactants
can
augment
efficiency,
while
ensuring
stability.
Additionally,
generic
simulation
tool
urgent
need
better
coupling
between
hydrodynamics,
heat
transfer
would
warrant
scale-up.
Environmental Science & Technology,
Journal Year:
2024,
Volume and Issue:
58(29), P. 12989 - 12999
Published: July 10, 2024
The
denitrifying
sulfur
(S)
conversion-associated
enhanced
biological
phosphorus
removal
(DS-EBPR)
process
for
treating
saline
wastewater
is
characterized
by
its
unique
microbial
ecology
that
integrates
carbon
(C),
nitrogen
(N),
(P),
and
S
biotransformation.
However,
operational
instability
arises
due
to
the
numerous
parameters
intricates
bacterial
interactions.
This
study
introduces
a
two-stage
interpretable
machine
learning
approach
predict
conversion-driven
P
efficiency
optimize
DS-EBPR
process.
Stage
one
utilized
XGBoost
regression
model,
achieving
an
