Integrating thermal phase-change material energy storage with solar collectors: A comprehensive review of techniques and applications
International Communications in Heat and Mass Transfer,
Journal Year:
2025,
Volume and Issue:
162, P. 108606 - 108606
Published: Jan. 22, 2025
Language: Английский
Optimizing Humidification–Dehumidification Desalination Systems: Impact of Nozzle Position and Geometric Configuration on Performance and Efficiency
Mohammad Alrbai,
No information about this author
Ahmad Masri,
No information about this author
Dareen Makawii
No information about this author
et al.
International Journal of Thermofluids,
Journal Year:
2025,
Volume and Issue:
26, P. 101117 - 101117
Published: Jan. 31, 2025
Language: Английский
Unveiling the potential of solar cooling technologies for sustainable energy and environmental solutions
Energy Conversion and Management,
Journal Year:
2024,
Volume and Issue:
321, P. 119034 - 119034
Published: Sept. 12, 2024
Language: Английский
A review of axial and radial ejectors: Geometric design, computational analysis, performance, and machine learning approaches
Ghassan Al-Doori,
No information about this author
Khalid Saleh,
No information about this author
Ahmed Al-Manaa
No information about this author
et al.
Applied Thermal Engineering,
Journal Year:
2025,
Volume and Issue:
unknown, P. 125694 - 125694
Published: Jan. 1, 2025
Language: Английский
Innovative Valorization of Waste Tire by Integrating Pyrolysis with Steam Rankine Cycle, Multi-generation, and Desalination: Novel Process Design, Simulation and Comprehensive Analysis
Yusha Hu,
No information about this author
Jianzhao Zhou,
No information about this author
Qiming Qian
No information about this author
et al.
Energy,
Journal Year:
2025,
Volume and Issue:
unknown, P. 135812 - 135812
Published: March 1, 2025
Language: Английский
Optimization of Wastewater Treatment Through Machine Learning-Enhanced Supervisory Control and Data Acquisition: A Case Study of Granular Sludge Process Stability and Predictive Control
Automation,
Journal Year:
2024,
Volume and Issue:
6(1), P. 2 - 2
Published: Dec. 27, 2024
This
study
presents
an
automated
control
system
for
wastewater
treatment,
developed
using
machine
learning
(ML)
models
integrated
into
a
Supervisory
Control
and
Data
Acquisition
(SCADA)
framework.
The
experimental
setup
focused
on
laboratory-scale
Aerobic
Granular
Sludge
(AGS)
reactor,
which
utilized
synthetic
to
model
real-world
conditions.
models,
specifically
N-BEATS
Temporal
Fusion
Transformers
(TFTs),
were
trained
predict
Biological
Oxygen
Demand
(BOD5)
values
historical
data
real-time
influent
contaminant
concentrations
obtained
from
online
sensors.
predictive
approach
proved
essential
due
the
absence
of
direct
BOD5
measurements
inconsistent
relationship
between
Chemical
(COD),
with
correlation
approximately
0.4.
Evaluation
results
showed
that
demonstrated
highest
accuracy,
achieving
Mean
Absolute
Error
(MAE)
0.988
R2
0.901.
integration
SCADA
enabled
precise,
adjustments
reactor
parameters,
including
sludge
dose
aeration
intensity,
leading
significant
improvements
in
granulation
stability.
effectively
reduced
standard
deviation
organic
load
fluctuations
by
2.6
times,
0.024
0.006,
thereby
stabilizing
process
within
AGS
reactor.
Residual
analysis
suggested
minor
bias,
likely
limited
number
features
model,
indicating
potential
through
additional
inputs.
research
demonstrates
value
learning-driven
offering
resilient
solution
dynamic
environments.
By
facilitating
proactive
management,
this
supports
scalability
treatment
technologies
while
enhancing
efficiency
operational
sustainability.
Language: Английский