Sustainable Enzymatic Desizing of Cotton with Bio-surfactant Extracted from Soapnut
Textile & Leather Review,
Год журнала:
2024,
Номер
7, С. 327 - 339
Опубликована: Фев. 27, 2024
Surfactant
is
one
of
the
major
consuming
auxiliaries
in
textile
processing.
The
rising
demand
for
petroleum-based
surfactants
focus
and
it
tremendously
utilized
to
fulfil
need
industries.
These
are
pollutants
wastewater.
Many
attempts
have
been
made
replace
this
with
low
toxicity
make
process
sustainable.
present
investigation
works
on
same
objective
surfactant
from
desizing
by
using
soapnut
extract
as
a
wetting
agent.
was
optimised
modern
statistical
technique
Response
Surface
Methodology
[RSM].
initial
designing
conducted
10
g/l
2%
enzyme
30
min
at
75
°C
found
satisfactory
results.
Additional
experiments
were
performed
optimize
RSM
weight
loss
primary
outcome.
An
recipe
provided
DOE
numerical
optimisation,
viz.,
concentration
40
min,
validate.
findings
demonstrate
that
optimum
(6.58%)
desirable
levels
absorbency
(14
s),
whiteness
(73.52),
yellowness
(22.84
indices,
bending
length
(2.1
cm),
Flexural
rigidity
(98.13
mg.cm),
while
minimally
affecting
tensile
strength
(10.77).
Enzymatic
synthetic
or
soapnut-extracted
agents
yields
identical
results
satisfies
performance
standards
industrial
use.
Sustainable
way
enzymatic
cotton
bio-surfactant
extracted
may
be
green
alternative
surfactant-based
desizing.
Язык: Английский
Predicting the Bending Rigidity and Formability of Plasma-Treated Spunbond Nonwoven Fabrics Using Artificial Intelligence
Textile & Leather Review,
Год журнала:
2024,
Номер
7, С. 1021 - 1038
Опубликована: Июнь 10, 2024
Nonwoven
fabrics
are
used
in
many
industries,
and
surface
treatment
by
plasma
can
significantly
change
their
physical
mechanical
properties
changing
the
chemistry
morphology.
In
this
paper,
oxygen/argon
has
been
applied
to
spunbond
nonwoven
predict
obtained
properties.
Therefore,
using
central
composite
design
considering
4
independent
factors
including
fabric
weight,
direction,
duration
oxygen
ratio,
51
various
samples
were
prepared
bending
rigidity
formability
measured.
SEM
images
showed
that
roughness
increases
due
treatment.
Statistical
analysis
revealed
all
mentioned
have
a
significant
effect
on
measured
parameters
directly
or
reciprocally.
Also,
use
of
neural
network
model
with
two
hidden
layers
optimized
method
according
genetic
algorithm
based
errors
less
than
7%
9%,
respectively.
The
resulting
from
response
for
same
order
about
19%
17%.
introduced
be
as
tool
help
researchers
field
Язык: Английский