Advances in computational intelligence and robotics book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 329 - 366
Published: Sept. 27, 2024
The
chapter
discusses
the
need
for
efficient
energy
consumption
in
high-performance
computing
systems
and
proposes
integration
of
artificial
intelligence
machine
learning
techniques
to
optimize
efficiency.
It
explores
AI-driven
like
reinforcement
learning,
neural
networks,
predictive
analytics
energy-aware
scheduling,
workload
allocation,
adaptive
power
management.
effectiveness
optimization
strategies
real-world
HPC
infrastructures,
highlighting
potential
savings
while
maintaining
computational
performance.
also
future
directions
challenges
AI-enabled
smart
management,
including
algorithm
refinement,
with
emerging
technologies,
scalability
considerations.
holistic
approach
highlights
transformative
impact
AI
ML
creating
sustainable,
energy-efficient
paradigms
within
ecosystems.
Advances in chemical and materials engineering book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 317 - 350
Published: June 28, 2024
This
chapter
emphasizes
the
significance
of
sustainable
manufacturing
processes
in
mitigating
environmental
impacts.
It
highlights
strategies
to
reduce
carbon
footprints,
improve
operational
efficiency,
and
enhance
competitiveness.
Important
areas
include
renewable
energy
integration,
efficiency
measures,
material
waste
reduction,
supply
chain
optimization,
technological
innovations,
best
practices.
Renewable
sources
like
solar,
wind,
hydroelectric
power
can
reliance
on
fossil
fuels.
Energy
such
as
equipment
upgrades
energy-saving
technologies,
lower
costs.
Material
reduction
initiatives
minimize
resource
consumption
generation.
A
circular
economy
be
promoted
through
recycling,
reuse,
waste-to-energy
programs.
Sustainable
is
innovations
3D
printing,
IoT-enabled
systems,
AI-driven
optimization.
Real-world
case
studies
provide
valuable
insights.
Advances in civil and industrial engineering book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 113 - 146
Published: July 5, 2024
This
chapter
explored
how
artificial
intelligence
(AI)
can
be
applied
to
internet
of
things
(IoT)
communication
networks
increase
urban
sustainability
and
connectivity.
AI
algorithms
network
performance
has
been
enhanced
by
facilitating
automated
decision-making,
predictive
analytics,
real-time
data
processing.
The
infrastructure
become
flexible
with
the
environment.
AI-powered
traffic
management
systems
that
minimize
pollution
congestion
have
analyzing
patterns
optimizing
signal
timing.
used
for
anomaly
detection
encryption
improve
IoT
security
privacy.
It
encourages
trash
management,
environmental
monitoring,
development
smart
grids,
all
which
livability
sustainability.
Advances in environmental engineering and green technologies book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 285 - 308
Published: June 28, 2024
Drone
and
GPS
technologies
are
advancing
productive,
efficient,
sustainable
agricultural
systems
through
data
driven
methods,
transforming
crop
recording
of
real-time
for
soil
analysis,
pest
control,
logistics
use,
specific
tasks
to
monitor
health.
Providing
drones
with
precisely
localized
terrain,
enables
automated
mechanical
guidance
precise
use
operations,
mapping
fields
helps
provide
more
accurate
farming
strategies
This
accuracy
in
decision
making
operational
efficiency
reduces
waste
environmental
impact.
chapter
describes
how
the
integration
drone
agriculture
demonstrates
advantages
infrastructure
management,
yield
improvement,
as
well
potential
enhance
practices
a
development
preservation
environment.
Advances in environmental engineering and green technologies book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 169 - 208
Published: Feb. 7, 2025
This
chapter
explores
advanced
IoT
solutions
designed
to
boost
agricultural
productivity
by
integrating
cutting-edge
technologies.
It
examines
the
deployment
of
sensors
and
devices
for
real-time
monitoring
soil
conditions,
crop
health,
environmental
factors.
Key
innovations
discussed
include
precision
irrigation
systems,
automated
climate
control,
predictive
analytics
that
leverage
big
data
machine
learning
optimize
yields.
The
also
highlights
case
studies
demonstrating
successful
applications
in
various
settings,
emphasizing
impact
on
resource
efficiency
yield
improvement.
Challenges
such
as
security,
integration
with
existing
cost
implications
are
addressed,
along
strategies
overcoming
these
obstacles.
By
providing
a
comprehensive
overview
current
technologies
their
practical
applications,
this
offers
valuable
insights
researchers,
practitioners,
policymakers
aiming
enhance
through
technological
advancement.
Advances in environmental engineering and green technologies book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 209 - 248
Published: Feb. 7, 2025
The
chapter
focuses
on
artificial
intelligence
in
its
integration
into
modern
systems
of
aquaponics
and
modernizing
potential
contemporary
agriculture.
AI-driven
solutions
enhance
the
efficiency,
sustainability,
productivity
through
optimization
water
quality,
nutrient
cycling,
fish-plant
interactions.
This
will
look
applications
AI
machine
learning
algorithms
for
predictive
analytics,
automated
monitoring
real-time
data
collection,
decision
support
dynamic
resource
management.
It
also
involves
scaling
operations
role
can
play
increasing
crop
yield
while
reducing
impact
environment.
Case
studies
have
been
done
showing
successful
implementations
AI-enhanced
aquaponics,
system
resilience
efficiency
use.
Finally,
concludes
with
indication
future
directions
as
a
pathway
toward
more
sustainable
intelligent
agricultural
practices
environmental
sustainability
goals.
Advances in environmental engineering and green technologies book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 81 - 118
Published: Feb. 7, 2025
This
chapter
will
delve
into
the
integration
of
aeroponics
artificial
intelligence
and
deep
learning
techniques
for
agricultural
productivity
its
sustainability.
is
a
no-soil
farming
method
where
plants
grow
in
nutrient-rich
mist.
Therein
lies
couple
major
advantages:
water
efficiency
an
accelerated
pace
plant
growth.
The
reasoning
behind
inclusion
AI
computer
vision
predictive
analytics
ability—how
best
it
can
help
determine
if
this
has
potential
to
optimally
run
aeroponic
systems.
Monitoring
health
environmental
state
real
time,
using
AI-driven
sensors
analytics,
capable
identifying
data
patterns
predicting
growth
optimizing
practices
delivery
nutrients.
Some
specific
successful
cases
novel
innovations
are
exemplified
next,
showing
how
new
breakthroughs
solve
existing
challenges
agriculture,
improve
yield
quality,
ultimately
reduce
resource
consumption.
Advances in computational intelligence and robotics book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 27 - 52
Published: Feb. 28, 2025
This
chapter
delves
into
how
ML
and
QC
combine
in
the
development
of
theory
quantum
systems.
With
an
increase
system
complexity,
traditional
approaches
to
analysis
suffer
from
extremely
vast
computational
limitations.
Incorporation
algorithms
along
with
frameworks
computation
allows
for
novel
solutions
classification,
optimization,
noise
mitigation.
We
present
key
techniques;
both
supervised
unsupervised
learning,
their
synthesis
algorithms,
such
as
Quantum
Approximate
Optimization
Algorithm
(QAOA),
Variational
Eigensolver,
among
others.
The
latter
will
also
focus
on
application
real-world
activities
like
chemistry,
cryptography,
material
science,
synergy
increases
efficiency
better
accuracy.
work
gives
a
comprehensive
roadmap
harnessing
revolutionize
systems
solve
previously
intractable
problems
by
addressing
current
challenges
outlining
future
directions.
Advances in civil and industrial engineering book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 67 - 92
Published: Feb. 14, 2025
AI
integration
in
smart
grids
enhances
efficiency,
reliability,
and
sustainability
through
machine
learning
deep
techniques.
Smart
utilize
these
technologies
for
precise
demand
forecasting,
real-time
grid
optimization,
fault
detection.
advancements
enhance
energy
distribution
minimize
transmission
losses,
facilitate
renewable
predictive
analytics
adaptive
control
systems.
Advanced
AI-powered
models
enable
management
of
DER
dynamic
pricing
demand-response
management,
improving
the
robustness
grids.
Proactive
maintenance
cybersecurity
are
also
advanced
high-scale
data
anomalous
malicious
patterns.
This
chapter
discusses
AI/ML
applications
grids,
challenges
practice,
future
perspectives
like
edge
computing
decentralized
intelligence.
The
synergy
hence,
offers
transformative
opportunities
that
could
meet
surging
rising
demands
with
economic
viability.
Advances in civil and industrial engineering book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 159 - 182
Published: Feb. 14, 2025
The
integration
of
energy
optimization
and
AI-powered
adsorption
technologies
holds
significant
potential
for
sustainable
water
treatment.
This
chapter
discusses
advanced
methods
adsorption,
amplified
by
artificial
intelligence,
in
a
bid
to
solve
the
challenge
quality
with
minimized
consumption.
Some
focus
areas
include
predictive
models
AI-driven
efficiency,
real-time
monitoring
contaminants,
adsorbent
materials.
is
an
area
where
machine
learning
deep
techniques
enhance
treatment
systems
greater
operational
less
footprint,
more
scalable.
Other
novel
adsorbents
nanomaterials
which
have
been
discussed
this
chapter,
discussion
on
possibility
realizing
high
capacity
selectivity.
explores
AI
analytics
enhancing
global
sustainability
goals
presenting
case
studies
practical
frameworks
demonstrate
feasibility
real-world
applications.
Advances in civil and industrial engineering book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 113 - 138
Published: Feb. 14, 2025
Artificial
intelligence
integration
into
power
systems
has
been
the
revolution
that
transformed
how
energy
is
generated,
distributed,
and
consumed.
In
this
regard,
chapter
discusses
AI-driven
methodologies
for
system
design,
optimization,
operation
with
regards
to
their
potential
reduce
carbon
emissions.
Some
of
key
applications
in
regard
include
predictive
maintenance,
smart
grid
management,
demand
forecasting,
all
which
work
towards
improving
reliability
minimizing
waste
energy.
Advanced
AI
models,
including
machine
learning
deep
learning,
allow
real-time
decision-making,
optimization
renewable
integration,
dynamic
load
balancing.
They
support
installation
distributed
resources,
solar
wind,
promotes
shift
cleaner
systems.
The
advances
can
spur
transformative
reductions
greenhouse
gas
emissions
while
paving
way
resilient,
intelligent,
sustainable
by
addressing
challenges
such
as
stability
scalability.
