ACS Omega,
Journal Year:
2024,
Volume and Issue:
9(50), P. 49692 - 49706
Published: Dec. 7, 2024
Efforts
addressing
sludge
management,
food
security,
and
resource
recovery
have
led
to
novel
approaches
in
these
areas.
Electrically
assisted
conversion
of
stands
out
as
a
promising
technology
for
sewage
valorization,
producing
nitrogen
phosphorus-based
fertilizers.
The
adoption
this
technology,
which
could
lead
fertilizer
circular
economy,
holds
the
potential
catalyze
transformative
change
wastewater
treatment
facilities
toward
process
intensification,
innovation,
sustainability.
This
paper
provides
insights
into
economic
aspects
policy
considerations,
challenges
involved
realizing
electrified
processes
valorization.
To
demonstrate
impact
case
study
its
implementation
United
States
assuming
municipal
plants
market
is
discussed.
It
was
found
that
electrically
enable
phosphorus
from
waste,
representing
up
9%
32%
consumption
U.S.
use.
also
enables
full
electrification
modularization
process,
thereby
presenting
significant
environmental
opportunities.
Energies,
Journal Year:
2024,
Volume and Issue:
17(19), P. 4983 - 4983
Published: Oct. 5, 2024
The
increasing
focus
on
sustainability
and
the
circular
economy
has
brought
waste-to-energy
technologies
to
forefront
of
renewable
energy
research.
However,
environmental
impacts
management
contaminants
associated
with
these
remain
critical
issues.
This
article
comprehensively
reviews
converting
sewage
sludge
into
fertilizers,
focusing
managing
potential
assessing
implications
ecological
risks.
It
also
highlights
latest
trends
in
technologies,
waste-to-soil
amendment,
their
integration
frameworks.
discussion
encompasses
challenges
opportunities
optimizing
processes
wastewater
treatment
plants
minimize
pollutants
enhance
sustainability.
Addressing
is
essential
for
ensuring
long-term
viability
acceptance
solutions,
making
this
topic
highly
relevant
timely.
Water Environment Research,
Journal Year:
2024,
Volume and Issue:
96(10)
Published: Sept. 25, 2024
Abstract
This
study
investigates
the
use
of
machine
learning
(ML)
models
for
wastewater
treatment
plant
(WWTP)
sludge
predictions
and
explainable
artificial
intelligence
(XAI)
techniques
understanding
impact
variables
behind
prediction.
Three
ML
models,
random
forest
(RF),
gradient
boosting
(GBM),
tree
(GBT),
were
evaluated
their
performance
using
statistical
indicators.
Input
variable
combinations
selected
through
different
feature
selection
(FS)
methods.
XAI
employed
to
enhance
interpretability
transparency
models.
The
results
suggest
that
prediction
accuracy
depends
on
choice
model
number
variables.
found
be
effective
in
interpreting
decisions
made
by
each
model.
provides
an
example
production
applying
understand
factors
influencing
it.
Understandable
interpretation
can
facilitate
targeted
interventions
process
optimization
improve
efficiency
sustainability
processes.
Practitioner
Points
Explainable
play
a
crucial
role
promoting
trust
between
real‐world
applications.
Widely
practiced
used
predict
United
States
plant.
Feature
methods
reduce
required
input
without
compromising
accuracy.
explain
driving
Environmental Technology,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 14
Published: May 15, 2024
Sludge
is
an
inevitable
by-product
of
the
sewage
treatment
process
and
its
high
moisture
content
poses
significant
challenges
for
disposal.
This
study
focuses
on
technology
sludge
deep
dewatering
using
liquefied
dimethyl
ether
(DME)
explores
relationship
between
operating
parameters
(DME/sludge
ratio,
extraction
time
stirring
speed)
water
after
dewatering.
After
dewatering,
sludge's
lower
heating
value
(LHV)
was
significantly
increased.
The
dehydrated
filtrate
highly
biodegradable
could
be
treated
together
with
sewage.
Based
response
surface
method
central
composite
design,
a
second-order
regression
model
above
three
variables
as
established.
Finally,
conditions
diagram
drawn
by
target
(36.96
wt.%)
which
meets
requirement
self-sustained
incineration
equation.
provides
valuable
perspective
drying
fuelisation.
Advances in Environmental and Engineering Research,
Journal Year:
2024,
Volume and Issue:
05(04), P. 1 - 23
Published: Oct. 17, 2024
Increasing
urban
wastewater
and
rigorous
discharge
regulations
pose
significant
challenges
for
treatment
plants
(WWTP)
to
meet
regulatory
compliance
while
minimizing
operational
costs.
This
study
explores
the
application
of
several
machine
learning
(ML)
models
specifically,
Artificial
Neural
Networks
(ANN),
Gradient
Boosting
Machines
(GBM),
Random
Forests
(RF),
eXtreme
(XGBoost),
hybrid
RF-GBM
in
predicting
important
WWTP
variables
such
as
Biochemical
Oxygen
Demand
(BOD),
Total
Suspended
Solids
(TSS),
Ammonia
(NH₃),
Phosphorus
(P).
Several
feature
selection
(FS)
methods
were
employed
identify
most
influential
variables.
To
enhance
ML
models’
interpretability
understand
impact
on
prediction,
two
widely
used
explainable
artificial
intelligence
(XAI)
methods-Local
Interpretable
Model-Agnostic
Explanations
(LIME)
SHapley
Additive
exPlanations
(SHAP)
investigated
study.
Results
derived
from
FS
XAI
compared
explore
their
reliability.
The
model
performance
results
revealed
that
ANN,
GBM,
XGBoost,
have
great
potential
variable
prediction
with
low
error
rates
strong
correlation
coefficients
R<sup>2</sup>
value
1
training
set
0.98
test
set.
also
common
each
model’s
prediction.
is
a
novel
attempt
get
an
overview
both
LIME
SHAP
explanations
