Natural and Engineering Sciences,
Journal Year:
2024,
Volume and Issue:
9(2), P. 164 - 183
Published: Oct. 17, 2024
With
the
popularization
of
new
energy
vehicles,
lithium
battery
systems,
as
main
components
have
characteristics
short
life
cycles
and
harmful
substances
inside.
The
green
treatment
systems
has
become
a
research
hotspot.
Disassembly
recycling
are
essential
means
reusing
waste
in
systems.
Due
to
wide
variety
lack
unified
design
standards,
high
flexibility
requirements
for
disassembly,
manual
disassembly
is
currently
primary
method
used.
However,
this
can
cause
health
hazards
oneself
when
dismantling
some
components.
optimization
process
route
batteries
crucial
step
before
dismantling,
which
directly
determines
economic
benefits
dismantling.
unlike
general
electromechanical
products,
prominent
safety
issues
during
process,
so
their
relatively
high.
Given
substantial
absence
parametric
evaluation
modification
prior
research,
work
investigates
influence
most
significant
factors
on
power
density
biosensors.
A
conduction-based
framework
was
employed
ascertain
these
variables,
calculations
were
performed
utilizing
neural
network.
network
developed
with
Particle
Swarm
Optimization
(PSO).
Based
this,
article
considers
studying
maximize
comprehensively.
lithium-ion
an
analysis
conducted
allocation
difficulty
level
human-machine
cooperation
tasks
impact
indicators
task
allocation.
Then,
product
hybrid
diagram
established,
basis,
multiple
sets
sequences
generated.
Finally,
multi-objective
model
cost,
difficulty,
time
established.
taking
Tesla
Model
1sPBS
example,
prediction
solved
verify
effectiveness
above
method.
Energy Storage,
Journal Year:
2025,
Volume and Issue:
7(1)
Published: Jan. 6, 2025
ABSTRACT
Hydrogen
is
one
of
the
most
promising
alternatives
to
fossil
fuels
for
energy
as
it
abundant,
clean
and
efficient.
Storage
transportation
hydrogen
are
two
key
challenges
faced
in
utilizing
a
fuel.
Storing
H
2
form
metal
hydrides
safe
cost
effective
when
compared
its
compression
liquefaction.
Metal
leverage
ability
metals
absorb
stored
can
be
released
from
hydride
by
applying
heat
needed.
A
multi‐step
methodology
proposed
identify
intermetallic
compounds
that
thermodynamically
stable
have
high
storage
capacity
(HSC).
It
combines
compound
generation,
thermodynamic
stability
analysis,
prediction
properties
ranking
discovered
materials
based
on
predicted
properties.
The
US
Department
Energy
(DoE)
Materials
Database
Open
Quantum
(OQMD)
utilized
building
testing
machine
learning
(ML)
models
enthalpy
formation
compounds,
formation,
equilibrium
pressure
HSC
hydrides.
here
require
only
attributes
elements
involved
compositional
information
inputs
do
no
need
any
experimental
data.
Random
forest
algorithm
was
found
accurate
amongst
ML
algorithms
explored
predicting
all
interest.
total
349
772
hypothetical
were
generated
initially,
out
which,
8568
stable.
highest
these
3.6.
Magnesium,
Lithium
Germanium
constitute
majority
compounds.
predictions
using
present
not
DoE
database
reasonably
close
data
published
recently
but
there
scope
improvement
accuracy
with
HSC.
findings
this
study
will
useful
reducing
time
required
development
discovery
new
used
check
practical
applicability
Geothermal Energy,
Journal Year:
2025,
Volume and Issue:
13(1)
Published: Jan. 12, 2025
Abstract
Geothermal
energy
is
a
sustainable
resource
for
power
generation,
particularly
in
Yemen.
Efficient
utilization
necessitates
accurate
forecasting
of
subsurface
temperatures,
which
challenging
with
conventional
methods.
This
research
leverages
machine
learning
(ML)
to
optimize
geothermal
temperature
Yemen’s
western
region.
The
data
set,
collected
from
108
wells,
was
divided
into
two
sets:
set
1
1402
points
and
2
995
points.
Feature
engineering
prepared
the
model
training.
We
evaluated
suite
regression
models,
simple
linear
(SLR)
multi-layer
perceptron
(MLP).
Hyperparameter
tuning
using
Bayesian
optimization
(BO)
selected
as
process
boost
accuracy
performance.
MLP
outperformed
others,
achieving
high
$$\text
{R}^{2}$$
R2
values
low
error
across
all
metrics
after
BO.
Specifically,
achieved
0.999,
MAE
0.218,
RMSE
0.285,
RAE
4.071%,
RRSE
4.011%.
BO
significantly
upgraded
Gaussian
model,
an
0.996,
minimum
0.283,
0.575,
5.453%,
8.717%.
models
demonstrated
robust
generalization
capabilities
(MAE
RMSE)
sets.
study
highlights
potential
enhanced
ML
techniques
novel
optimizing
exploitation,
contributing
renewable
development.
