Water and Environment Journal,
Год журнала:
2024,
Номер
38(4), С. 554 - 572
Опубликована: Июль 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.
Sustainability,
Год журнала:
2023,
Номер
15(6), С. 4749 - 4749
Опубликована: Март 7, 2023
Mining
leads
to
serious
degradation
of
the
ecological
values
landscape.
After
mining
is
completed,
degraded
areas
should
be
reclamated
in
order
mitigate
destructive
effects
activities.
Effective
reclamation
aims
initiate
soil-forming
processes.
The
paper
evaluates
land
post-mining
12
14
years
after
process.
assessment
was
based
on
a
determination
activity
selected
enzymes.
Municipal
sewage
sludge
compost
(SSC)
and
with
composition
70%
municipal
+
30%
fly
ash
(SSFAC)
were
used
as
an
external
source
organic
matter
reclamation.
dehydrogenases,
phosphatases,
urease
determined.
fertilization
reclaimed
soil
caused
significant
increase
assessed
Significantly
higher
dehydrogenase
found
treated
SSC.
SSFAC
characterized
by
phosphatase
urease.
one-time
application
composts
from
ash,
introduction
mixture
grasses,
allow
for
permanent
effect.
An
additional
advantage
this
model
waste
management,
which
part
circular
economy
strategy.
Journal of Ecological Engineering,
Год журнала:
2024,
Номер
25(7), С. 70 - 81
Опубликована: Май 20, 2024
The
aim
of
the
research
was
to
assess
quality
organic
matter
contained
in
sewage
sludge
composting
products
and
their
co-composting
with
fly
ash
mineral
wool.The
object
were
composts
produced
using
stabilized
from
municipal
treatment
plant
(SS_1C)
addition
20%
(SSF_2C)
30%
(SSF_3C)
5%
(SSW_4C)
10%
(SSW_5C)
wool.Selected
physicochemical
properties,
fractional
composition
humic
compounds,
degree
rate
humification
determined
compost
samples
taken
after
180
days
composting.The
reaction
evaluated
close
optimal
for
mature
composts.Co-composting
wool
increased
sorption
capacity
compared
SS_1C.Due
content
available
P
Mg,
discussed
formed
SS_1C>SSF_2C
SSF_3C>SSW_4C
SSW_5C
series.However,
terms
K
content:
SSF_2C
SSW_5C>SS_1C.In
SS_1C
carbon
(TOC)
slightly
higher,
but
no
statistically
significant
effect
on
TOC
confirmed.The
significantly
total
nitrogen
content.Due
index,
series:
SSW_4C
>
SSF_3C.The
values
C-KH/C-KF
ratio
typical
good
soils,
while
remaining
lower.The
assessed
characterized
by
poorly
humified
materials,
highest
this
indicator
found
indicators
indicate
that
100%
quality.
Water,
Год журнала:
2025,
Номер
17(6), С. 793 - 793
Опубликована: Март 10, 2025
Wastewater
treatment
plants
consist
of
many
biological
reactors
and
a
settler,
representing
an
example
large-scale,
nonlinear
systems.
The
wastewater
plant
in
this
study
operates
using
activated
sludge
system,
which
relies
on
processes
to
treat
effectively.
It
is
for
reason
that
iterative
process
modeling
was
used
through
the
implementation
Extended
Kalman
Filter
(EKF)
predict
height
layer
secondary
clarifiers,
where
accumulation
occurs
during
sedimentation
process.
This
technique
consists
maximum
likelihood
estimation
works
more
consistently
various
noise
scenarios.
As
result
evaluation
model
estimated
by
(EKF),
suitability
tends
be
concluded
on.
In
sense,
prediction
sewage
systems
represents
complicated
heteroscedastic
process,
can
understood
as
phenomenon
influenced
variety
factors.
Therefore,
does
not
identify
problems
estimates
thorough
examination
residuals.
state-space
increases
adaptability
adjustability
achieve
structural
optimization
plant.
approach
viable
effective
solution
efficient
management
polluting
levels
minimizing
possible
environmental
impact
out-of-control
situations
plants.
Royal Society of Chemistry eBooks,
Год журнала:
2025,
Номер
unknown, С. 117 - 134
Опубликована: Апрель 25, 2025
This
chapter
on
composting
process
modelling
offers
a
comprehensive
overview
of
the
current
state
modelling,
highlighting
two
primary
approaches:
mechanistic
and
probabilistic.
Mechanistic
models
focus
biological,
mass,
heat
transfer
processes
within
composting,
considering
factors
like
organic
matter
biodegradability,
microbial
biomass,
environmental
conditions
such
as
temperature
humidity.
These
typically
employ
deterministic
methods
to
simulate
process,
though
some
stochastic
approaches
also
exist
account
for
variability.
explores
role
artificial
intelligence
(AI)
machine
learning
(ML)
in
modelling.
techniques,
particularly
neural
networks
genetic
algorithms,
are
increasingly
used
predict
outcomes
optimize
processes,
complementing
by
providing
insights
into
complex,
non-linear
relationships.
However,
limitations
AI
ML,
data
dependency
interpretability
challenges,
discussed.
emphasizes
need
further
research
areas
maturation
phase,
passive
aeration
nutrient
cycling,
well
integration
with
enhance
accuracy
applicability
simulations.
