ACS Omega,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 27, 2025
In
response
to
the
issues
of
abnormal
bed
temperature
fluctuations
and
inefficient
combustion
that
occur
during
flexible
operation
circulating
fluidized
(CFB)
boiler
system,
which
is
characterized
by
strong
coupling,
nonlinearity,
large
inertia,
this
paper
presents
a
novel
monitoring
optimization
approach
integrates
deep
learning
model
with
mechanism
model.
Specifically,
Informer
algorithm
utilized
construct
range
prediction
model,
thus
enabling
real-time
intricate
change
trends
in-furnace
temperature.
Considering
poor
reliability
target
values
derived
from
data
predictions
constraints
on
upper
limit,
article
further
combines
comprehensively
determine
design
perspective.
The
results
indicated
exhibited
root-mean-square
error
(RMSE)
3.385
°C,
mean
absolute
(MAE)
2.45
percentage
(MAPE)
0.268%
rolling
test
set,
demonstrating
high
overall
accuracy.
steady-state
operational
generating
unit
at
300
200
MW
were
selected
validate
particular
emphasis
comparing
distribution
pressure
along
height
furnace.
outcomes
revealed
good
consistency,
indicating
model's
accuracy
in
performance
simulation.
By
integrating
for
optimizing
under
conditions
determined.
comparison
using
alone,
240
280
operating
conditions,
average
thermal
efficiency
increased
0.19
0.13%,
respectively.
Concurrently,
coal
consumption
rate
power
generation
decreased
0.6707
0.4453
g/(kW·h),
respectively,
carbon
emissions
reduced
per
kilowatt-hour
electricity
generated
1.6738
1.1113
g,
ChemistryOpen,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 29, 2025
Abstract
The
integration
of
digital
products
and
sensors
significantly
enhances
motion
monitoring
accuracy,
addressing
the
limitations
hawk‐eye
technology.
Triboelectric
nanogenerators
(TENGs)
provide
innovative,
low‐cost
solutions
for
developing
intelligent
systems.
In
this
study,
we
designed
a
multi‐mode
triboelectric
nanogenerator
(M‐TENG)
that
incorporates
multiple
working
modes,
enabling
switching
between
internal
electrodes
to
adjust
output
mode.
This
dual
mode
adaptability
device
football
applications.
versatile
design
allows
M‐TENG
perform
both
energy
harvesting
trigger‐based
sensing
monitoring,
contributing
advancement
intelligence
movement
technologies.
After
optimization,
achieved
transferred
charge
(Qsc)
88.38
nC,
short‐circuit
current
(Isc)
8.58
μA,
open‐circuit
voltage
(Voc)
85.91
V,
showcasing
excellent
electrical
performance.
charged
1
μF
capacitor
5
V
in
36
seconds,
delivering
peak
power
178
μW,
maintained
stable
with
only
14
%
decrease
over
60
days.
Additionally,
effectively
detects
harvests
from
impacts,
generating
consistent
signals
each
interaction,
making
it
promising
candidate
real‐time
sports
equipment
without
need
an
external
source.
