Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer
Case Studies in Thermal Engineering,
Год журнала:
2024,
Номер
60, С. 104743 - 104743
Опубликована: Июнь 24, 2024
In
this
study,
eXtreme
Gradient
Boosting
(XGBoost)
and
Light
(LightGBM)
algorithms
were
used
to
model-predict
the
drying
characteristics
of
banana
slices
with
an
indirect
solar
drier.
The
relationships
between
independent
variables
(temperature,
moisture,
product
type,
water
flow
rate,
mass
product)
dependent
(energy
consumption
size
reduction)
established.
For
energy
consumption,
XGBoost
demonstrates
superior
performance
R2
0.9957
during
training
0.9971
testing,
alongside
minimal
MSE
0.0034
0.0008
testing
phase
indicating
high
predictive
accuracy
low
error
rates.
Conversely,
LGBM
shows
lower
values
(0.9061
training,
0.8809
testing)
higher
0.0747
0.0337
reflecting
poorer
performance.
Similarly,
for
shrinkage
prediction,
outperforms
LGBM,
evidenced
by
(0.9887
0.9975
(0.2527
0.4878
testing).
comparative
statistics
showed
that
regularly
outperformed
LightGBM.
game
theory-based
Shapley
functions
revealed
temperature
types
most
influential
features
model.
These
findings
illustrate
practical
applicability
LightGBM
models
in
food
operations
towards
optimizing
conditions,
improving
quality,
reducing
consumption.
Язык: Английский
Leveraging Artificial Intelligence to Enhance Port Operation Efficiency
Polish Maritime Research,
Год журнала:
2024,
Номер
31(2), С. 140 - 155
Опубликована: Июнь 1, 2024
Abstract
Maritime
transport
forms
the
backbone
of
international
logistics,
as
it
allows
for
transfer
bulk
and
long-haul
products.
The
sophisticated
planning
required
this
form
transportation
frequently
involves
challenges
such
unpredictable
weather,
diverse
types
cargo
kinds,
changes
in
port
conditions,
all
which
can
raise
operational
expenses.
As
a
result,
accurate
projection
ship’s
total
time
spent
port,
anticipation
potential
delays,
have
become
critical
effective
activity
management.
In
work,
we
aim
to
develop
management
system
based
on
enhanced
prediction
classification
algorithms
that
are
capable
precisely
forecasting
lengths
ship
stays
delays.
On
both
training
testing
datasets,
XGBoost
model
was
found
consistently
outperform
alternative
approaches
terms
RMSE,
MAE,
R2
values
turnaround
waiting
period
models.
When
used
model,
had
lowest
RMSE
1.29
during
0.5019
testing,
also
achieved
MAE
0.802
0.391
testing.
It
highest
0.9788
0.9933
Similarly,
outperformed
random
forest
decision
tree
models,
with
greatest
phases.
Язык: Английский
Artificial Intelligence in Maritime Transportation: A Comprehensive Review of Safety and Risk Management Applications
Applied Sciences,
Год журнала:
2024,
Номер
14(18), С. 8420 - 8420
Опубликована: Сен. 19, 2024
Maritime
transportation
is
crucial
for
global
trade
but
faces
significant
risks
and
operational
challenges.
Ensuring
safety
essential
protecting
lives,
the
environment,
economic
stability.
This
review
explores
role
of
artificial
intelligence
(AI)
in
enhancing
maritime
risk
management.
Key
AI
applications
include
analysis,
crew
resource
management,
hazardous
material
handling,
predictive
maintenance,
navigation
systems.
systems
identify
potential
hazards,
provide
real-time
decision
support,
monitor
materials,
predict
equipment
failures,
optimize
shipping
routes.
Case
studies,
such
as
Wärtsilä’s
Fleet
Operations
Solution
ABB
Ability™
Marine
Pilot
Vision,
illustrate
benefits
improving
efficiency.
Despite
these
advancements,
integrating
poses
challenges
related
to
infrastructure
compatibility,
data
quality,
regulatory
issues.
Addressing
successful
implementation.
highlights
AI’s
transform
safety,
emphasizing
need
innovation,
standardized
practices,
robust
frameworks
achieve
safer
more
efficient
operations.
Язык: Английский
АНАЛІЗ ВИКЛИКІВ ТА МОЖЛИВОСТЕЙ ЗАСТОСУВАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В УПРАВЛІННІ МОРСЬКИМИ ВАНТАЖНИМИ ПОТОКАМИ
Наука і техніка сьогодні,
Год журнала:
2024,
Номер
7(35)
Опубликована: Авг. 2, 2024
АНАЛІЗ
ВИКЛИКІВ
ТА
МОЖЛИВОСТЕЙ
ЗАСТОСУВАННЯ
ШТУЧНОГО
ІНТЕЛЕКТУ
В
УПРАВЛІННІ
МОРСЬКИМИ
ВАНТАЖНИМИ
ПОТОКАМИАнотація.Управління
морськими
вантажними
потоками
є
надзвичайно
складним
завданням,
що
вимагає
високої
точності,
координації
та
швидкої
реакції
на
зміни.Сучасні
виклики,
з
якими
зіштовхується
ця
сфера,
потребують
новітніх
технологічних
рішень
для
забезпечення
максимальної
ефективності,
безпеки
стійкості
операцій.Впровадження
штучного
інтелекту
у
сферу
управління
відкриває
нові
можливості
підвищення
зниження
витрат
покращення
логістичних
операцій.Водночас,
використання
ШІ
морській
логістиці
супроводжується
численними
викликами
обмеженнями,
які
ретельного
аналізу
вирішення
Exhaust Gas Heat Recovery from a Marine Engine Using a Thermal Oil System
Polish Maritime Research,
Год журнала:
2024,
Номер
31(4), С. 89 - 99
Опубликована: Дек. 1, 2024
Abstract
The
recovery
of
exhaust
gas
from
marine
engines
is
gaining
attention
in
regard
to
saving
fuel
and
improving
system
efficiency.
Waste
heat
particularly
beneficial
for
providing
thermal
electric
power,
offers
efficient
solutions
both
economic
environmental
challenges.
use
waste
technology
the
opportunity
lower
consumption
improve
systems,
this
approach
also
falls
line
with
stringent
emissions
guidelines
International
Maritime
Organization.
This
paper
describes
a
unique
which
oil
used
feed
cargo,
order
exploitation
costs
while
addressing
issues.
CFD
simulations
unit
plain
finned
helix
coils
provide
important
insights
into
their
performance
pressure
characteristics.
results
indicate
that
incorporation
fins
could
markedly
enhance
transfer
performance.
Finned
configurations
are
found
have
higher
outlet
temperatures,
reaching
up
145.4°C
case
rectangular
configuration.
In
general,
study
contributes
advancement
technologies
applications.
Язык: Английский