Application of machine learning for environmentally friendly advancement: exploring biomass-derived materials in wastewater treatment and agricultural sector − a review
Journal of Environmental Science and Health Part A,
Год журнала:
2025,
Номер
unknown, С. 1 - 16
Опубликована: Фев. 2, 2025
There
are
several
uses
for
biomass-derived
materials
(BDMs)
in
the
irrigation
and
farming
industries.
To
solve
problems
with
material,
process,
supply
chain
design,
BDM
systems
have
started
to
use
machine
learning
(ML),
a
new
technique
approach.
This
study
examined
articles
published
since
2015
understand
better
current
status,
future
possibilities,
capabilities
of
ML
supporting
environmentally
friendly
development
applications.
Previous
applications
were
classified
into
three
categories
according
their
objectives:
material
process
performance
prediction
sustainability
evaluation.
helps
optimize
BDMs
systems,
predict
properties
performance,
reverse
engineering,
data
difficulties
evaluations.
Ensemble
models
cutting-edge
Neural
Networks
operate
satisfactorily
on
these
datasets
easily
generalized.
neural
network
poor
interpretability,
there
not
been
any
studies
assessment
that
consider
geo-temporal
dynamics;
thus,
building
methods
is
currently
practical.
Future
research
should
follow
workflow.
Investigating
potential
system
optimization,
evaluation
sustainable
requires
further
investigation.
Язык: Английский
Analysis of trace CF4 in SF6 by plasma emission gas chromatography
Journal of Physics Conference Series,
Год журнала:
2025,
Номер
2996(1), С. 012024 - 012024
Опубликована: Апрель 1, 2025
Abstract
Sulfur
hexafluoride
(SF
6
)
is
widely
used
as
an
insulating
gas
in
electrical
equipment.
The
detection
of
its
decomposition
products
great
significance
for
assessing
the
operating
condition
Carbon
tetrafluoride
(CF
4
a
major
product
SF
,
and
accurate
determination
content
crucial
early
equipment
failures
assessment
quality.
This
study
explores
application
plasma
emission
chromatography
analysis
trace
CF
6.
By
optimizing
chromatographic
conditions,
efficient
sensitive
analytical
method
has
been
established.
experimental
results
show
that
when
argon
carrier
gas,
limit
this
low
0.5
ppm,
with
relative
standard
deviation
(RSD)
less
than
3%,
demonstrating
excellent
repeatability.
Moreover,
good
linear
range
(R
2
=
0.998).
accuracy
reliability
have
further
verified
through
actual
samples.
Plasma
provides
efficient,
sensitive,
stable
new
approach
it
expected
to
be
Язык: Английский