Prediction of Corrosion Initiation Time in Sustainable Concrete Blended with Microsilica and Portland Slag Cement Under Extreme Chloride Conditions
Arabian Journal for Science and Engineering,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 27, 2025
Language: Английский
Optimizing Solar Water-Pumping Systems Using PID-Jellyfish Controller with ANN Integration
Aimen M. Alshireedah,
No information about this author
Zıyodulla Yusupov,
No information about this author
Javad Rahebi
No information about this author
et al.
Electronics,
Journal Year:
2025,
Volume and Issue:
14(6), P. 1172 - 1172
Published: March 17, 2025
This
study
presents
a
novel
approach
to
improving
the
efficiency
and
reliability
of
solar
water
pumping
systems
by
integrating
proportional–integral–derivative
(PID)
controller
with
Jellyfish
Algorithm
(PID-JC)
artificial
neural
networks
(ANN).
Solar
water-pumping
are
gaining
attention
due
their
sustainable
eco-friendly
nature;
however,
performance
is
often
limited
fluctuating
irradiance
varying
demand.
To
address
these
challenges,
Monte
Carlo
simulations
were
employed
account
for
system
uncertainties.
Traditional
PID
controllers,
although
widely
used,
struggle
adapt
effectively
dynamic
environmental
conditions.
The
proposed
utilizes
an
ANN
predict
demand
patterns
based
on
historical
data,
enabling
real-time
adjustments
pump
operations
through
PID-JC.
inspired
adaptive
behavior
jellyfish
in
environments.
PID-JC
adjusts
parameters
dynamically
predictions,
optimizing
performance.
Simulation
experimental
results
conducted
Mrada
City,
Northeastern
Libya,
demonstrated
significant
improvements
delivery,
energy
consumption,
compared
conventional
controllers.
PID-JC’s
ability
diverse
conditions
ensures
robust
across
various
geographical
locations
seasonal
changes.
Additionally,
comparisons
other
optimization
algorithms,
such
as
Firefly
Golden
Eagle
Optimization,
show
that
outperforms
them
6.30%
improvement
cost
function
28.13%
reduction
processing
time
Firefly,
26.81%
20.69%
Optimization.
Language: Английский
Insightful database-driven prediction and sensitivity analysis of electrical resistivity of nano-graphite-modified cementitious composites: a supervised machine learning regression
Materials Today Communications,
Journal Year:
2024,
Volume and Issue:
unknown, P. 110511 - 110511
Published: Sept. 1, 2024
Language: Английский