International Journal of Coal Preparation and Utilization,
Год журнала:
2024,
Номер
unknown, С. 1 - 26
Опубликована: Дек. 10, 2024
To
increase
the
accuracy
of
clean
coal
ash
content
prediction
during
dense
medium
separation
process
and
address
time
lag
issue
encountered
when
measuring
content,
a
model
based
on
WaOA-VMD-SGMD-WaOA-LSTM
was
proposed.
The
adopts
dual
decomposition
techniques
optimized
Variational
Mode
Decomposition
(VMD)
Symplectic
Geometric
(SGMD),
which
can
completely
decompose
original
data,
uses
Walrus
optimization
algorithm
(WaOA)
to
optimize
hyperparameters
Long
Short-Term
Memory
(LSTM)
model.
In
construction,
characteristic
data
ore
(𝑍2),
raw
(𝑍3),
heavy
mesoporous
cyclone
pressure
(𝑍4),
suspension
density
(𝑍5),
magnetic
(𝑍6)
were
combined
with
decomposed
cleaned
grouping
S-IMF0~S-IMFn,
CO-IMF1,
CO-IMF2
as
input
variables
construct
multiple
LSTM
models.
Finally,
value
is
superimposed
realize
content.
Based
industrial
preparation
plant
in
Shanxi,
China,
results
show
that
coefficient
determination
(R2)
0.9974.
After
adding
secondary
technology,
average
absolute
error
reduced
by
60.99%
compared
single
strategy.
Abstract
Volatile
organic
compounds
(VOCs)
are
ubiquitous
in
vehicle
cabin
environments,
which
can
significantly
impact
the
health
of
drivers
and
passengers,
whereas
quick
intelligent
prediction
methods
lacking.
In
this
study,
we
firstly
analyzed
variations
environmental
parameters,
VOC
levels
potential
sources
inside
a
new
car
during
7
summer
workdays,
indicating
that
formaldehyde
had
highest
concentration
about
one
third
measurements
exceeded
standard
limit
for
in-cabin
air
quality.
Feature
importance
analysis
reveals
most
important
factor
affecting
emission
behaviors
is
material
surface
temperature
rather
than
temperature.
By
introducing
attention
mechanism
ensemble
strategy,
present
an
LSTM-A-E
deep
learning
model
to
predict
concentrations
12
observed
typical
VOCs,
together
with
other
five
models
comparison.
comparing
prediction–observation
discrepancies
evaluation
metrics,
demonstrates
better
performance,
more
consistent
field
measurements.
Extension
developed
predicting
10-day
realistic
residence
further
illustrates
its
excellent
adaptation.
This
study
probes
not-well-explored
dynamics
via
observation
approaches,
facilitating
rapid
exposure
assessment
VOCs
micro-environment.
