Processes,
Год журнала:
2024,
Номер
12(4), С. 682 - 682
Опубликована: Март 28, 2024
This
paper
studies
the
use
of
varying
threshold
in
statistical
process
control
(SPC)
batch
processes.
The
motivation
is
driven
by
how
when
multiple
phases
are
implicated
each
repetition,
distributions
features
behind
vary
with
or
even
time;
thus,
it
inconsistent
to
uniformly
bound
them
an
invariant
threshold.
In
this
paper,
we
paved
a
new
path
for
learning
and
monitoring
processes
based
on
efficient
framework
integrating
model
termed
conditional
dynamic
variational
auto-encoder
(CDVAE).
Phase
indicators
first
used
split
data
then
separated,
serving
as
extra
input
order
alleviate
complexity.
Dissimilar
routine
using
across
all
timescales,
only
relevant
local
timestamps
aggregated
calculation,
producing
that
more
specific
variations
occurring
among
timeline.
Leveraged
upon
idea,
fault
detection
panel
devised,
deep
reconstruction-based
contribution
diagram
illustrated
locating
faulty
variables.
Finally,
comparative
results
from
two
case
highlight
superiority
both
accuracy
diagnostic
performance.
The Science of The Total Environment,
Год журнала:
2024,
Номер
946, С. 174236 - 174236
Опубликована: Июнь 26, 2024
Since
the
discovery
of
antibiotics,
penicillin
has
remained
top
choice
in
clinical
medicine.
With
continuous
advancements
biotechnology,
production
become
cost-effective
and
efficient.
Genetic
engineering
techniques
have
been
employed
to
enhance
biosynthetic
pathways,
leading
new
derivatives
with
improved
properties
increased
efficacy
against
antibiotic-resistant
pathogens.
Advances
bioreactor
design,
media
formulation,
process
optimization
contributed
higher
yields,
reduced
costs,
accessibility.
While
biotechnological
advances
clearly
benefited
global
this
life-saving
drug,
they
also
created
challenges
terms
waste
management.
Production
fermentation
broths
from
industries
contain
residual
by-products,
other
contaminants
that
pose
direct
environmental
threats,
while
consumption
intensifies
risk
antimicrobial
resistance
both
environment
living
organisms.
The
current
geographical
spatial
distribution
antibiotic
dramatically
reveals
a
worldwide
threat.
These
are
being
addressed
through
development
novel
management
techniques.
Efforts
aimed
at
upstream
downstream
processing
minimize
costs
improve
yield
efficiency
lowering
overall
impact.
Yield
using
artificial
intelligence
(AI),
along
biological
chemical
treatment
waste,
is
explored
reduce
adverse
impacts.
implementation
strict
regulatory
frameworks
guidelines
essential
ensure
proper
disposal
waste.
This
review
because
it
explores
key
remaining
development,
scope
machine
learning
tools
such
as
Quantitative
Structure-Activity
Relationship
(QSAR)
modern
biotechnology-driven
production,
for
discovering
alternative
path
reducing
use
agriculture
meat
addressing
practices,
offering
effective
recommendations.
IEEE Access,
Год журнала:
2023,
Номер
11, С. 104373 - 104391
Опубликована: Янв. 1, 2023
Industrial
processes
are
nonlinear
and
complicated,
requiring
accurate
fault
identification
to
minimize
performance
deterioration
respond
quickly
emergencies.
This
work
investigates
industrial
process
defect
isolation,
which
is
analytically
difficult
owing
their
complexity.
paper
carefully
analyzes
four
design
methods
for
flaw
isolation
based
on
Principal
Component
Analysis
(PCA),
Fisher
Discriminant
(FDA),
Kernel
(KFDA),
Sequential
quadratic
programming
(SQP).
Our
study
includes
the
Tennessee
Eastman
Process
(TEP)
Penicillin
Fermentation
(PFP),
among
other
comparable
methods.
We
assess
proposed
detection
through
detailed
analysis
comparison.
The
simulation
findings
from
our
extensive
investigation
provide
remarkable
insights.
Simulation
show
that
FDA
KFDA
well
in
but
PCA
has
certain
limits.
also
considered
SQP
as
a
TEP
improvement
tool.
noted
its
success
restricted
optimization
problems,
making
it
ideal
complicated
processes.
Data-driven
approaches
increase
problem
with
greater
reliability
efficiency
than
PCA-based
shows
advanced
data-driven
techniques
can
improve
diagnosis,
improving
operational
safety
system
by
leveraging
FDA,
KFDA,
SQP.
Computers,
Год журнала:
2024,
Номер
13(3), С. 64 - 64
Опубликована: Фев. 29, 2024
Over
the
past
decade,
Unmanned
Aerial
Vehicles
(UAVs)
have
begun
to
be
increasingly
used
due
their
untapped
potential.
Li-ion
batteries
are
most
power
electrically
operated
UAVs
for
advantages,
such
as
high
energy
density
and
number
of
operating
cycles.
Therefore,
it
is
necessary
estimate
Remaining
Useful
Life
(RUL)
prediction
batteries’
capacity
prevent
UAVs’
loss
autonomy,
which
can
cause
accidents
or
material
losses.
In
this
paper,
authors
propose
a
method
RUL
using
data-driven
approach.
To
maximize
performance
process,
three
machine
learning
models,
Support
Vector
Machine
Regression
(SVMR),
Multiple
Linear
(MLR),
Random
Forest
(RF),
were
compared
batteries.
The
implemented
within
Predictive
Maintenance
(PdM)
systems.
