Water and Environment Journal,
Journal Year:
2024,
Volume and Issue:
38(4), P. 554 - 572
Published: July 8, 2024
Abstract
Wastewater
treatment
plants
(WWTPs)
are
high‐energy
consumers
and
major
Greenhouse
Gas
(GHG)
emitters.
This
review
offers
a
comprehensive
global
overview
of
the
current
utilization
machine
learning
(ML)
to
optimize
energy
usage
reduce
emissions
in
WWTPs.
It
compiles
analyses
findings
from
over
hundred
studies
primarily
conducted
within
last
decade.
These
organized
into
five
primary
areas:
consumption
(EC),
aeration
(AE),
pumping
(PE),
sludge
(STE)
greenhouse
gas
(GHG).
Additionally,
they
further
categorized
based
on
type,
scale
application,
geographic
location,
year,
performance
metrics,
software,
etc.
ANNs
emerged
as
most
prevalent,
closely
trailed
by
FL
RF.
While
GA
PSO
predominant
metaheuristic
approaches.
Despite
increasing
complexity,
researchers
inclined
towards
employing
hybrid
models
enhance
performance.
Reported
reductions
or
GHG
spanned
various
ranges,
falling
0–10%,
10–20%
>20%
brackets.
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.
Ultrasonics Sonochemistry,
Journal Year:
2023,
Volume and Issue:
95, P. 106408 - 106408
Published: April 18, 2023
Atractylodis
Macrocephalae
Rhizoma
(AMR)
is
the
dried
rhizome
of
Atractylodes
macrocephala
Koidz,
which
widely
used
in
development
health
products.
AMR
contains
a
large
number
polysaccharides,
but
at
present
there
are
fewer
applications
for
these
polysaccharides.
In
this
study,
effects
different
extraction
methods
on
polysaccharide
(AMRP)
yield
were
investigated,
and
conditions
ultrasound-assisted
optimized
by
response
surface
methodology
(RSM)
three
neural
network
models
(BP
network,
GA-BP
ACO-GA-BP
network).
The
best
liquid-to-solid
ratio
17
mL/g,
ultrasonic
power
400
W,
temperature
72
°C,
time
40
min,
yielded
31.31%
AMRP.
kinetic
equation
AMRP
was
determined
compared
with
results
predicted
models.
It
finally
that
conditions,
processes
GA-ACO-BP
optimal.
addition,
characterized
using
SEM,
FTIR,
HPLC,
UV,
XRD,
NMR,
structural
study
revealed
has
rough
exterior
porous
interior;
moreover,
it
high
levels
glucose
(5.07%),
arabinose
(0.80%),
galactose
(0.74%).
crystal
structures,
consisting
two
β-type
monosaccharides
one
α-type
monosaccharide.
Additionally,
effectiveness
as
an
antioxidant
demonstrated
vitro
experiment.
