Computational Intelligence-Driven Design and Optimization of Polyurethane Belt-Type Oil Skimmer for Sustainable Manufacturing Using Solidworks 3D CAD
Advances in computational intelligence and robotics book series,
Год журнала:
2025,
Номер
unknown, С. 445 - 464
Опубликована: Фев. 21, 2025
This
study
focuses
on
developing
a
belt-type
oil
skimmer
to
effectively
remove
from
water
surfaces,
promoting
green
industry
and
reducing
global
pollution.
The
uses
belt
mechanism
that
density
differences
oil,
achieving
an
efficiency
of
62%
92%
depending
the
type.
SOLIDWORKS
create
detailed
3D
model,
adhering
best
practices.
research
extends
beyond
environmental
protection
aquatic
ecosystems,
aligning
with
eco-friendly
industrial
practices
showcasing
impact
technical
advancements
challenges.
demonstrates
potential
improving
oil-water
separation-dependent
operations
Язык: Английский
AI and Machine Learning Applications in Sustainable Smart Cities
Advances in electronic government, digital divide, and regional development book series,
Год журнала:
2024,
Номер
unknown, С. 1 - 32
Опубликована: Ноя. 15, 2024
Harnessing
the
power
of
artificial
intelligence
(AI)
and
machine
learning
(ML),
this
chapter
delves
into
pivotal
role
these
technologies
play
in
crafting
sustainable
smart
cities.
As
urbanization
surges,
AI
offers
solutions
for
optimizing
energy
consumption,
enhancing
transportation
systems,
revolutionizing
waste
management.
By
analyzing
data
from
sensors
devices,
empowers
city
planners
residents
to
make
informed
decisions,
leading
significant
reductions
usage
greenhouse
gas
emissions.
Additionally,
AI-driven
optimization
improves
traffic
flow,
reduces
congestion,
promotes
efficient
collection,
thus
fostering
more
environmentally
friendly
urban
environments.
Ultimately,
ML
hold
potential
transform
landscape
development,
paving
way
efficient,
resilient,
eco-conscious
cities
future
generations.
Язык: Английский
Integrating Taguchi Optimization for Multi-Criteria Decision Making in Engineering Applications
Advances in computational intelligence and robotics book series,
Год журнала:
2024,
Номер
unknown, С. 125 - 150
Опубликована: Окт. 25, 2024
This
chapter
discusses
the
use
of
Taguchi
optimization,
a
statistical
method
for
process
optimization
in
engineering,
to
solve
multi-criteria
decision
making
(MCDM)
problems.
It
focuses
on
achieving
robust
designs
by
minimizing
variations
and
defects
identifying
optimal
control
factors.
The
uses
orthogonal
arrays
efficient
experimentation
signal-to-noise
ratios
performance
measurement.
incorporates
utility
concepts,
weighted
principal
component
analysis,
multi-objective
optimization.
has
real-world
applications
automotive,
electronics,
chemical
engineering.
Taguchi's
efficiency
cost-effectiveness
are
compared
response
surface
methodology
genetic
algorithm
reduces
experimental
runs,
improves
product
quality,
effectively
handles
MCDM
Future
advancements
could
involve
machine
learning
integration
broader
application
emerging
fields.
Язык: Английский
AI-Enabled Sustainable Urban Planning and Management
Advances in computational intelligence and robotics book series,
Год журнала:
2024,
Номер
unknown, С. 233 - 260
Опубликована: Ноя. 22, 2024
This
chapter
explores
the
transformative
potential
of
Artificial
Intelligence
(AI)
in
sustainable
urban
planning.
As
cities
grapple
with
rapid
urbanization
and
climate
change,
AI
offers
innovative
solutions
through
data
analytics,
predictive
modeling,
optimization.
Successful
AI-enabled
planning
projects
demonstrate
importance
data-driven
decision-making,
community
engagement,
interdisciplinary
collaboration,
scalability.
The
examines
applications
traffic
management,
energy
efficiency,
waste
design,
engagement.
Ethical
considerations,
including
privacy,
algorithmic
bias,
digital
divide,
are
discussed,
emphasizing
need
for
responsible
development
inclusive
provides
insights
into
how
AI-enhanced
can
promote
sustainability,
resilience,
social
equity.
Key
considerations
adoption,
such
as
decision-making
highlighted.
Язык: Английский
Multi-Objective Optimization in Industry 5.0: Human-Centric AI Integration for Sustainable and Intelligent Manufacturing
Processes,
Год журнала:
2024,
Номер
12(12), С. 2723 - 2723
Опубликована: Дек. 2, 2024
The
shift
from
Industry
4.0
to
5.0
represents
a
significant
evolution
toward
sustainable,
human-centric
manufacturing.
This
paper
explores
how
advanced
multi-objective
optimization
techniques
can
integrate
Artificial
Intelligence
(AI)
with
human
insights
enhance
both
sustainability
and
customization
in
We
investigate
specific
methods,
including
genetic
algorithms
(GAs),
Particle
Swarm
Optimization
(PSO),
reinforcement
learning
(RL),
which
are
tailored
balance
efficiency,
waste
reduction,
carbon
footprint.
Our
proposed
framework
enables
creativity
interact
AI-driven
processes,
embedding
input
into
computational
structure
that
adapts
dynamically
operational
goals.
By
linking
directly
environmental
impacts,
such
as
reducing
waste,
energy
consumption,
emissions,
this
study
establishes
pathway
environmentally
sustainable
production.
research
fills
existing
gaps
by
offering
detailed,
practical
model
harmonizes
theoretical
applications
personalized
manufacturing
environments.
In
regard,
it
contributes
the
ongoing
development
of
5.0,
emphasizing
AI
collaboration
foster
intelligent,
adaptable,
systems.
Язык: Английский
Digital Transformation Across Generations
Advances in human and social aspects of technology book series,
Год журнала:
2024,
Номер
unknown, С. 23 - 40
Опубликована: Ноя. 29, 2024
The
integration
of
robotics
and
artificial
intelligence
(AI)
is
transforming
industries,
enhancing
efficiency,
safety,
productivity.
This
chapter
explores
the
impact
autonomous
systems
across
various
sectors,
including
manufacturing,
healthcare,
transportation,
agriculture.
convergence
AI
enables
adaptive
to
perform
complex
tasks
independently,
driving
innovation
reshaping
business
operations.
Machine
learning,
computer
vision,
sensor
fusion
empower
robots
learn
from
data,
recognize
patterns,
interact
with
humans.
Successful
applications
include
self-driving
cars,
robotic-assisted
surgery,
precision
farming,
smart
home
devices.
However,
challenges
persist,
such
as
reliability,
ethics,
data
privacy,
complexity
implementation.
As
continue
evolve,
they
will
drive
sustainable
practices,
optimize
resource
use,
encourage
interdisciplinary
collaboration.
Future
research
should
focus
on
developing
robust
algorithms,
safety
protocols,
establishing
ethical
guidelines.
Язык: Английский