Drying Time, Energy and Exergy Efficiency Prediction of Corn (Zea mays L.) at a Convective-Infrared-Rotary Dryer: Approach by an Artificial Neural Network
Energies,
Journal Year:
2025,
Volume and Issue:
18(3), P. 696 - 696
Published: Feb. 3, 2025
Energy
consumption
in
the
drying
industry
has
made
an
energy-intensive
operation.
In
this
study,
time,
quality
properties
(color,
shrinkage,
water
activity
and
rehydration
ratio),
specific
energy
(S.E.C),
thermal,
exergy
efficiency
of
corn
using
a
hybrid
dryer
convective-infrared-rotary
(CV-IR-D)
were
analyzed.
addition,
parameters
predicted
artificial
neural
network
(ANN)
technique.
The
experiments
conducted
at
three
rotary
rotation
speeds
4,
8
12
rpm,
temperatures
45,
55
65
°C,
infrared
power
0.25,
0.5
0.75
kW.
By
increasing
temperature,
speed,
S.E.C
decreased
while
Deff,
energy,
thermal
increased.
highest
values
ratio
redness
(a*)
lowest
brightness
(L*),
yellowness
(b*)
color
changes
(ΔE)
obtained
kW,
air
temperature
°C
speed
rpm.
range
S.E.C,
during
process
was
5.05–28.15
MJ/kg,
3.26–29.29%,
5.5–32.33%
21.22–55.35%.
prediction
results
ANNs
showed
that
R
for
data
0.9938,
0.9906,
0.9965,
0.9874
0.9893,
respectively,
indicating
successful
prediction.
Language: Английский
Optimizing Drying and Storage for Edible Mushrooms: Study on Gamma Irradiation Levels, Drying Temperatures, and Packaging Materials with SVM-Based Predictions
Case Studies in Thermal Engineering,
Journal Year:
2025,
Volume and Issue:
unknown, P. 106288 - 106288
Published: May 1, 2025
Language: Английский