Bulletin of the Tomsk Polytechnic University Geo Assets Engineering,
Journal Year:
2024,
Volume and Issue:
335(7), P. 71 - 80
Published: July 23, 2024
Relevance.
A
promising
fuel
from
an
environmental
point
of
view
is
coal-water
suspensions.
Multiple
studies
have
shown
that
when
they
are
burned,
emissions
anthropogenic
gases
into
the
Earth's
atmosphere
significantly
reduced
compared
to
coal
burning.
But
large-scale
introduction
suspensions
overall
balance
energy
production
difficult
due
a
significant
delay
in
their
ignition,
which
can
reach
several
tens
seconds
at
relatively
low
oxidizer
temperatures
(Tg≤1073
K).
One
possible
ways
solve
this
problem
use
new
technologies
for
preparation
combustion,
example,
additional
microwave
heating
and
special
additives
composition
suspensions,
accelerate
ignition.
The
such
lead
change
rheological
characteristics.
Aim.
To
determine
degree
impact
concentration
type
wood
additive
suspension
on
characteristics
droplets
fuel,
as
well
possibility
reducing
duration
thermal
latter
exposed
heating.
Object.
Water-coal
based
long-flame
with
addition
sawdust
pine
needles
(Bio-water-coal
suspension).
Methods.
Experimental
time
water-coal
ignition
were
carried
out
using
high-speed
video
camera
FASTCAM.
Fuel
combustion
was
flow-through
chamber
equipped
magnetrons
fuel.
Dynamic
viscosity
determined
Brookfield
RVDV-II
+
Pro
viscometer.
Results.
It
has
been
established
(up
15%)
reduce
Bio-water-coal
(673
biomass
affects
ambient
up
753
K;
further
increase
temperature
oxidizer,
does
not
effect.
studying
suspension,
dynamic
go
beyond
characteristic
value
1200
Pa
s
shear
rate
100
rpm
no
more
than
2%
6%
introduced
suspension.
Energy Sources Part A Recovery Utilization and Environmental Effects,
Journal Year:
2024,
Volume and Issue:
46(1), P. 5434 - 5450
Published: April 11, 2024
Designing
efficient
biomass
energy
systems
requires
a
thorough
understanding
of
the
physicochemical,
thermodynamic,
and
physical
properties
biomass.
One
crucial
parameter
in
assessing
potential
is
higher
heating
value
(HHV),
which
quantifies
its
content.
Conventionally,
HHV
determined
through
bomb
calorimetry,
but
this
method
limited
by
factors
such
as
time,
accessibility,
cost.
To
overcome
these
limitations,
researchers
have
proposed
diverse
range
empirical
correlations
machine-learning
approaches
to
predict
based
on
proximate
ultimate
analysis
results.
The
novelty
research
explore
universal
applicability
developed
for
predicting
Higher
Heating
Value
(HHV)
identify
best
correlations,
nearly
400
different
feedstocks
were
comprehensively
tested
with
45
use
(21
correlations),
(16
correlations)
combined
ultimate-proximate
(8
data
feedstocks.
A
quantitative
statistical
was
conducted
assess
performance
their
types.
results
demonstrated
that
utilizing
provided
more
accurate
predictions
compared
those
or
data.
Two
specific
including
coefficients
each
element
(C,
H,
N)
interactions
(C*H)
demonstrate
prediction
lowest
MAE
(~0.49),
RMSE
(~0.64),
MAPE
(~2.70%).
Furthermore,
some
other
carbon
content
being
major
determinant
also
provide
good
from
point
view;
(~0.5–0.8),
(~0.6–0.9),
(~2.8–3.8%).
