Fucoxanthin
is
a
carotenoid
that
possesses
various
beneficial
medicinal
properties
for
human
well-being.
However,
the
current
extraction
technologies
and
quantification
techniques
are
still
lacking
in
terms
of
cost
validation,
high
energy
consumption,
long
time,
low
yield
production.
To
date,
artificial
intelligence
(AI)
models
can
assist
improvise
bottleneck
fucoxanthin
process
by
establishing
new
processes
which
involve
big
data,
digitalization,
automation
efficiency
This
review
highlights
application
AI
such
as
neural
network
(ANN)
adaptive
neuro
fuzzy
inference
system
(ANFIS),
capable
learning
patterns
relationships
from
large
datasets,
capturing
non-linearity,
predicting
optimal
conditions
significantly
impact
yield.
On
top
that,
combining
metaheuristic
algorithm
genetic
(GA)
further
improve
parameter
space
discovery
ANN
ANFIS
models,
results
R2
accuracy
ranging
98.28%
to
99.60%
after
optimization.
Besides,
support
vector
machine
(SVM),
convolutional
networks
(CNNs),
have
been
leveraged
fucoxanthin,
either
computer
vision
based
on
color
images
or
regression
analysis
statistical
data.
The
findings
reliable
when
modeling
concentration
pigments
with
66.0%
−
99.2%.
paper
has
reviewed
feasibility
potential
purposes,
reduce
cost,
accelerate
yields,
development
fucoxanthin-based
products.
Water,
Год журнала:
2025,
Номер
17(2), С. 170 - 170
Опубликована: Янв. 10, 2025
Artificial
intelligence
(AI)
uses
highly
powerful
computers
to
mimic
human
intelligent
behavior;
it
is
a
major
research
hotspot
in
science
and
technology,
with
an
increasing
number
of
applications
wider
range
fields,
including
complex
process
supervision
control.
Wastewater
treatment
example
involving
many
uncertainties
external
factors
achieve
final
product
specific
requisites
(effluents
prescribed
quality).
Reducing
energy
consumption,
greenhouse
gas
emissions,
resources
recovery
are
additional
requirements
these
facilities’
operation.
AI
could
extend
the
purpose
expected
results
previously
adopted
tools
present
operational
approaches
by
leveraging
superior
simulation,
prediction,
control,
adaptation
capabilities.
This
paper
reviews
current
wastewater
field
discusses
achievements
potentials.
So
far,
almost
all
sector
involve
predictive
studies,
often
at
small
scale
or
limited
data
use.
Frontline
aimed
creation
AI-supported
digital
twins
real
systems
being
conducted,
few
encouraging
but
still
applications.
aims
identifying
discussing
key
barriers
adoption
field,
which
include
laborious
instrumentation
maintenance,
lack
expertise
design
software,
instability
control
loops,
insufficient
incentives
for
resource
efficiency
achievement.
ChemBioEng Reviews,
Год журнала:
2023,
Номер
11(1), С. 39 - 59
Опубликована: Ноя. 6, 2023
Abstract
The
growing
potential
of
sustainable
materials
such
as
polyhydroxyalkanoates
(PHAs),
polylactic
acid
(PLA),
alginate,
carrageenan,
and
ulvan
for
bioplastics
production
presents
an
opportunity
to
promote
a
circular
economy.
This
review
investigates
their
properties,
applications,
challenges.
Bioplastics
derived
from
algae
offer
environmentally
friendly
alternative
petroleum‐based
plastics,
shift
paramount
importance
society
due
the
escalating
environmental
concerns
associated
with
traditional
plastics.
role
internet‐of‐things
(IoT)
machine
learning
in
refining
these
bioplastics'
development
processes
is
emphasized.
IoT
monitors
cultivation
conditions,
data
collection,
process
control
more
production.
Machine
can
enhance
cultivation,
increasing
supply
raw
algal
improving
efficiency
output.
study
results
indicate
promise
algae‐based
bioplastics,
IoT,
fostering
future.
By
harnessing
advanced
technologies,
optimization
bioplastic
possible,
potentially
revolutionizing
industry
addressing
existing
challenges
toward
achieving
Industrial Marketing Management,
Год журнала:
2024,
Номер
119, С. 75 - 89
Опубликована: Апрель 17, 2024
Sustainability
is
at
the
top
of
agenda
most
tech
companies.
Specifically,
companies
increasingly
utilize
artificial
intelligence
(AI)
to
meet
their
sustainability
goals.
However,
little
known
about
how
can
leverage
AI
accelerate
by
formulating
and
implementing
appropriate
strategies.
To
better
understand
intertwined
nature
from
a
strategy
perspective,
this
research
conceptually
develops
novel
x
framework
drawing
nested
model
integrating
insights
different
literature
streams.
It
then
applies
six
leading
Big
Tech
(i.e.,
Amazon,
Google,
IBM,
Meta,
Microsoft,
SAP)
conducting
comprehensive
document
analysis
69
documents
describing
244
individual
initiatives
reveal
whether
these
appear
follow
specific
Lastly,
an
exploratory
survey
with
potential
companies'
clients
(N
=
192)
sheds
light
on
perceive
communicated
strategic
positioning
based
framework.
The
provides
new
theoretical
insights,
serves
as
blueprint
for
other
companies,
including
implications
positioning,
offers
variety
future
directions.
Environmental Chemistry Letters,
Год журнала:
2024,
Номер
22(5), С. 2293 - 2318
Опубликована: Май 21, 2024
Abstract
The
access
to
clean
and
drinkable
water
is
becoming
one
of
the
major
health
issues
because
most
natural
waters
are
now
polluted
in
context
rapid
industrialization
urbanization.
Moreover,
pollutants
such
as
antibiotics
escape
conventional
wastewater
treatments
thus
discharged
ecosystems,
requiring
advanced
techniques
for
treatment.
Here
we
review
use
artificial
intelligence
machine
learning
optimize
pharmaceutical
treatment
systems,
with
focus
on
quality,
disinfection,
renewable
energy,
biological
treatment,
blockchain
technology,
algorithms,
big
data,
cyber-physical
automated
smart
grid
power
distribution
networks.
Artificial
allows
monitoring
contaminants,
facilitating
data
analysis,
diagnosing
easing
autonomous
decision-making,
predicting
process
parameters.
We
discuss
advances
technical
reliability,
energy
resources
management,
cyber-resilience,
security
functionalities,
robust
multidimensional
performance
platform
distributed
consortium,
stabilization
abnormal
fluctuations
quality
