Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Aug. 31, 2024
Elektrik
üretiminde
yenilenebilir
enerji
kaynaklarının
uygun
sistemlerle
entegre
edilerek
kullanım
alanlarının
genişletilmesi
önemli
bir
husustur.
Bu
doğrultuda,
ORÇ
kullanımı
düşük
ve
orta
sıcaklıkta
kaynaklardan
elektrik
ön
plana
çıkmaktadır.
çalışma,
jeotermal
tabanlı
geleneksel
Organik
Rankine
çevriminin
(ORÇ)
enerji,
ekserji
eksergo-ekonomik
analizlerini
(3E)
içermektedir.
Eksergo-ekonomik
analiz
yöntemi
olarak
Modifiye
Edilmiş
Üretim
Yapısı
Analizi
(MOPSA)
kullanılmıştır.
MOPSA
yöntemi,
sistem
bileşenlerinin
oranlarının
maliyetlendirilmesine
olanak
tanıyan
yöntemdir
bu
yönüyle
diğer
ekserji-ekonomik
yöntemlerden
ayrılmaktadır.
Analizler
sonucunda,
önerilen
sistemin
toplam
verimliliği
(η_ex)
%50.23
bulunurken,
en
yüksek
yıkımına
sahip
bileşeni
43.97
kW
değeri
ile
evaporatör
olmuştur.
Sistemin
yıkım
70.67
bulunmuş
yıkımının
birim
maliyeti
(c_s)
1.872
$/GJ
hesaplanmıştır.
Önerilen
ürün
(〖c_(p,total)〗^MOPSA)
3.662
$/GJ'dür.
Energies,
Journal Year:
2025,
Volume and Issue:
18(3), P. 543 - 543
Published: Jan. 24, 2025
The
yield
of
photovoltaic
hydrogen
production
systems
is
influenced
by
a
number
factors,
including
weather
conditions,
the
cleanliness
modules,
and
operational
efficiency.
Temporal
variations
in
conditions
have
been
shown
to
significantly
impact
output
systems,
thereby
influencing
production.
To
address
inaccuracies
capacity
predictions
due
weather-related
temporal
different
regions,
this
study
develops
method
for
predicting
using
long
short-term
memory
(LSTM)
neural
network
model.
proposed
integrates
meteorological
parameters,
temperature,
wind
speed,
precipitation,
humidity
into
model
estimate
daily
solar
radiation
intensity.
This
approach
then
integrated
with
prediction
region’s
capacity.
validate
accuracy
feasibility
method,
data
from
Lanzhou,
China,
2013
2022
were
used
train
test
its
performance.
results
show
that
predicted
agrees
well
actual
values,
low
mean
absolute
percentage
error
(MAPE)
high
coefficient
determination
(R2).
winter
has
MAPE
0.55%
an
R2
0.985,
while
summer
slightly
higher
0.61%
lower
0.968,
irradiance
levels
fluctuations.
present
captures
long-term
dependencies
time
series
data,
improving
compared
conventional
methods.
offers
cost-effective
practical
solution
production,
demonstrating
significant
potential
optimization
operation
diverse
environments.
Polymers,
Journal Year:
2024,
Volume and Issue:
16(23), P. 3368 - 3368
Published: Nov. 29, 2024
The
integration
of
machine
learning
(ML)
into
material
manufacturing
has
driven
advancements
in
optimizing
biopolymer
production
processes.
ML
techniques,
applied
across
various
stages
production,
enable
the
analysis
complex
data
generated
throughout
identifying
patterns
and
insights
not
easily
observed
through
traditional
methods.
As
sustainable
alternatives
to
petrochemical-based
plastics,
biopolymers
present
unique
challenges
due
their
reliance
on
variable
bio-based
feedstocks
processing
conditions.
This
review
systematically
summarizes
current
applications
techniques
aiming
provide
a
comprehensive
reference
for
future
research
while
highlighting
potential
enhance
efficiency,
reduce
costs,
improve
product
quality.
also
shows
role
algorithms,
including
supervised,
unsupervised,
deep
