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Fuel 82 (2003) 919–927
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Specific heat and thermal conductivity of softwood bark
and softwood char particlesq
Murlidhar Guptaa,b, Jin Yanga, Christian Roya,*
a
Department of Chemical Engineering, Université Laval, Pavillon Pouliot, Sainte-Foy, Que., Canada G1K 7P4
b
CANMET Energy Technology Center, 1 Haanel Drive, Ottawa, Ont., Canada
Received 5 August 2002; revised 28 November 2002; accepted 10 December 2002; available online 6 January 2003
Abstract
Very few data exist regarding the thermal properties of softwood bark and therein derived softwood chars. This work describes the
measurement of specific heat and particle thermal conductivity of softwood (SW), softwood bark (SB) and therein derived softwood char
(SC). Differential scanning calorimetery (DSC) was used to measure the specific heat. At 313 K, the measured specific heat was found to be
1172, 1364 and 768 J kg21 K21 for SW, SB and SC, respectively. The specific heat of SW and SB increased linearly from 1172 to 1726 and
1364 to 1777 J kg21 K21, respectively, with an increase in temperature from 313 to 413 K. With an increase in temperature from 313 to
713 K, the specific heat of SC doubled from 768 to 1506 J kg21 K21 and followed a polynomial relationship with temperature. A modified
Fitch apparatus was constructed, calibrated and used for measurement of particle conductivity of SW, SB and SC. The particle thermal
conductivity of SB was found to be twice that of SC, i.e. 0.2050 and 0.0946 W m21 K21, respectively, at 310 K. The particle thermal
conductivity of SW, SB and SC followed a linear increase with temperature.
q 2003 Elsevier Science Ltd. All rights reserved.
Keywords: Bark; Particle; Softwood; Char; Specific heat; Thermal conductivity; Heat transfer; Pyrolysis; Measurement
1. Introduction
Significant volumes of lignocellulosic residues are
generated annually by the forest industry. A large portion
of these residues is made of softwood bark. The North
American forest industry alone produces more than 35 Mt of
softwood bark every year [1,2]. Approximately 50% of
these barks is currently burned for energy production while
the rest is disposed in landfills raising serious concerns to
the ground-water pollution. Alternatively bark can be
converted to energy and value added chemicals such as
Biophene through the vacuum pyrolysis technology for
example [3]. Knowledge of thermal properties such as
specific heat and thermal conductivity is necessary,
however, to design a vacuum pyrolysis system and to
model complex heat transfer phenomena [4].
Thermal properties of wood are also widely needed in
building construction, thermal conversion of biomass, wood
* Corresponding author. Tel.: þ 1-418-656-7406; fax: þ1-418-656-2091.
E-mail addresses: [email protected] (C. Roy), [email protected]
(M. Gupta).
q
Published first on the web via Fuelfirst.com—http://www.fuelfirst.com
drying, etc. A substantial amount of information can be
traced in the literature for specific heat and thermal
conductivity of wood [5 – 9].Thermal properties of woody
materials are often influenced by various factors, such as the
wood species, density, moisture, and fiber orientation.
Moreover, it is very difficult to find data for softwood
bark (SB) or softwood char (SC) derived from bark. The
properties of wood charcoal are highly dependent on the
source and the process conditions during the pyrolysis
process.
For determination of the specific heat of lignocellulosic
materials, the method of mixtures is the most common
method reported in the literature [10]. Although this
technique is direct, it is not accurate [11]. Chakrabarty
and Johnson [12] described the methodology for the
determination of specific heat of tobacco by differential
scanning calorimetry (DSC). Recently the DSC method has
been frequently used to measure the specific heat of food
and agricultural materials, i.e. peanut kernels, cumin seeds,
etc. [11,13]. The advantage of DSC method over the
conventional method is the accuracy and speed with which
data can be obtained and the small sample size required [14,
0016-2361/03/$ - see front matter q 2003 Elsevier Science Ltd. All rights reserved.
PII: S 0 0 1 6 - 2 3 6 1 ( 0 2 ) 0 0 3 9 8 - 8
920
M. Gupta et al. / Fuel 82 (2003) 919–927
Nomenclature
A
a
cp
cp;c
cp;r
cp;s
mc
heat transfer area of copper plug (m2)
coefficient of linear thermal expansion for copper
(1.65 £ 1025 K21)
specific heat (J kg21 K21)
specific heat of perfect conductor, copper
(J kg21 K21)
specific heat of reference (J kg21 K21)
specific heat of sample (J kg21 K21)
mass of copper plug (kg)
15]. Moreover, the dynamic nature of DSC allows the
determination of specific heat as a function of temperature.
Most of the published thermal conductivity measurements on wood samples are conducted with standard hotplate apparatus (steady state method), in which the samples
were placed and left under constant conditions for a
sufficient length of time to insure an uniform temperature
gradient throughout the sample. The temperature of the test
surfaces are then recorded, and the rate of heat flow
calculated from the electric input to the heating element
[16]. Recently Suleiman et al. [9] have developed and used a
transient plane source (TPS) technique to measure the
thermal conductivity of cylindrical shaped Swedish hardwood (birch) samples having a diameter of 30 mm and a
thickness of 10 mm. Hot wire probe apparatus for
measuring the thermal conductivity of small agricultural
samples have been used by many workers [13,17]. To
reduce the error, however, the ratio between the length and
diameter of the probe has to be over 20 [18]. More than 99%
of the particles in SB and SC samples used in this work were
smaller than 7 mm [19]. Thus the probe method is still
difficult to use in materials like SB and SC particles because
of size constraints. The Fitch method [20] has been the most
common transient methods used to measure the conductivity of poor conductors, such as food particles [10,21].
Zuritz et al. [18] developed a modified Fitch apparatus to
measure the thermal conductivity of small food particles
that can be formed into small slabs.
In the present work, the specific heat and the thermal
conductivity of bark and char derived were experimentally
measured as a function of temperature. While DSC method
has been used to measure the specific heat, the modified Fitch
apparatus [18] has been used to measure the particle thermal
conductivity of softwood (SW), SB and SC from bark.
2. Materials and methodology
mr
ms
lp
T
T0
T1
Yr
Ys
weight of the reference (mg)
weight of the sample (mg)
particle thermal conductivity (W m21 K21)
temperature (K)
initial temperature of copper plug (K)
temperature of copper rod (K)
difference between the DSC curves of the empty
container and the reference
difference between the DSC curves of empty
container and the sample
consists of two components: a residual SW fraction and pure
bark (SB). Besides color and other physical properties, there
is considerable difference in shape and size for these two
components. The SW fraction consists of yellowish wood
particles. Most of these wood particles are compact bundles
of fibrous needles. The SB fraction consists of dark-brown
and multi-layered flaky particles of highly irregular shapes.
They are quite brittle in comparison to the SW part.
In the present study, the softwood bark feedstock, which
is basically a mixture of pure softwood particle and bark
particles, was procured from Pyrovac Institute, Inc., in SteFoy, Province of Quebec. The feedstock was composed of
balsam fir Abies balsamea (31 vol%), white spruce Picea
glauca (55 vol%) and black spruce Picea mariana
(14 vol%). The pure SW and pure SB samples were
prepared by separation of the bulk softwood bark feedstock
from Pyrovac. The feedstock contained around 81% (w/w)
of pure bark and 19% (w/w) of softwood. The density and
proximate analysis of the SW and pure SB fraction is given
in Table 1. The carbon content in SW and SB vary due to the
varying lignin and extractive contents. The mineral content
of SW is less than 1%, but it can be over 6 times in SB as
shown in Table 1. The composition of mineral matter can
vary between and within each tree. This variation
contributes to significant variation in thermo-physical as
well as thermo-chemical properties of SB [7].
2.2. Softwood char
SC is the solid residue obtained after pyrolysis reactions.
During the pyrolysis process, complex chemical reactions
and mechanical wear and tear produce highly porous and
Table 1
Density and proximate analysis of SW, SB and SC particles
Sample
Density (kg m23) [19]
Proximate analysis (wt%, anhydrous)
Volatile matter
Ash
Fixed carbon
78.8
70.2
13.0
0.6
3.7
6.4
20.6
26.1
80.6
2.1. Softwood bark
SB is the outer shell of the softwood stem. It is produced
in large quantities when softwood logs are debarked. It
SW
SB
SC
360
482
299
M. Gupta et al. / Fuel 82 (2003) 919–927
921
brittle char particles. Like mother bark particles, the char
particles are also irregular in shape and size. In the present
investigation, the SC sample (CH000413/12:50 –0.2)) was
produced by Pyrovac Institute, Inc., Jonquière, Province of
Québec, in a 3000 kg h21 Pyrocyclinge demonstration
plant which has been described elsewhere [3]. The total
pressure in the reactor was kept below 20 kPa while the char
outlet temperature was maintained at approximately 475 8C
(< 750 K). SC is rich in carbon. The density and proximate
analysis of char used in this study are given in Table 1.
2.3. Measurement of specific heat by DSC
The specific heat in this study was measured by using the
DSC technique by means of a SII DSC6100 (Seiko
Instruments, Inc., Japan). Three types of measurement
were carried out: (i) baseline measurement; (ii) reference
measurement and (iii) sample measurement. In the baseline
measurement, empty crucibles were placed in both the
sample and the reference holders. For a reference measurement, an empty crucible was put on the reference side
holder, while another crucible with a reference material (i.e.
sapphire) in the sample side holder. In the sample run, an
empty crucible and a crucible with sample were loaded at
the reference and the sample side holders, respectively.
Aluminum crucibles were used. The empty crucible was
always covered by a lid which was in contact with the
bottom of the crucible; thus no air was enclosed in the
crucible. When the reference or the sample material were
enclosed, the crucible was covered with a lid which touched
the top surface of the sample material.
A three step heating program was applied for the
measurements: (i) an isothermal heating at 308 K for
10 min; (ii) a dynamic heating from 308 K to the final
temperature Tf ; at a heating rate of 5 K min21 and (iii)
an isothermal heating at Tf for 10 min. The value of Tf
was set to be 413 K for SW and SB samples, and 700 K
for the char sample. To ensure good reproducibility, each
test was repeated at least twice until satisfactory
reproducibility was observed in the DSC curves. Since
the SW and SW bark samples contained about 5%
moisture, a preheating run from 308 to 413 K was
always carried out for each sample to remove any
residual moisture. Similarly the cp measurement of the
SC samples was performed after preheating from 308 to
873 K to remove the residual volatile matter condensed
during char cooling process at the reactor outlet.
Before DSC tests, the SW, SB and SC samples were
crushed into fine particles, to ensure homogeneity and get
rid of air inside the samples in the crucible.
To calculate specific heat from DSC measurement, the
SEIKO EXSTAR 6000 is equipped with softwares such as
DSC SUB, DSC ANALYSIS and DSC CP (Fig. 1). The
Fig. 1. The flowchart used by SEIKO apparatus to calculate the cp :
principle of cp calculation is expressed as
cps ¼
Ys mr
c
Yr ms pr
ð1Þ
where cps and cpr are the specific heat of sample and
reference material, respectively. Ys and Yr represent the
difference in heat flow between the DSC curves of the
baseline and sample runs. ms and mr are the mass of sample
and the reference material, respectively.
As shown in Fig. 1, the value of Y is determined by using
the DSC SUB program. Then by using the DSC ANALYSIS
and DSC CP programs which are developed based on Eq.
(1), the final values of specific heat are calculated over the
programmed temperature range.
2.4. Measurement of particle thermal conductivity
The thermal conductivity of SW, SB and SC particles
was determined using the modified Fitch apparatus,
designed and constructed according to guidelines described
elsewhere [18]. The device consists of a constant temperature vacuum flask (thermos bottle) with a specially designed
cork stopper. A solid cylindrical copper rod is pierced
through the cork stopper to seal the vacuum flask. The
copper rod has two co-axial solid cylinders in series with
diameters of 19 and 10 mm, respectively, as shown in Fig. 2.
The lower diameter section is emerged outside the cork
stopper, while the larger diameter is pierced though and
beyond the stop cork into the flask water. A polystyrene disc
is glued on top of the cork stopper. Thermocouple wires are
installed in the copper rod about 2 mm from the exposed end
for measurement of source temperature T1 : The exposed
end of the copper is machined to 6.35 mm diameter and
922
M. Gupta et al. / Fuel 82 (2003) 919–927
In the second boundary condition, m and cp;c represent
the mass/unit heat transfer area and the specific heat,
respectively, of the perfect conductor (copper here).
The analytical solution for the above equation is given by
Carslaw and Jaeger [22]. However, assumption of quasisteady conduction heat transfer through the sample, i.e. the
rate of heat gain by the heat sink (copper plug) equals the
rate of heat transfer through the specimen, yields the
following simplified equation [10,20]
Alp ðT 2 T1 Þ
dT
¼ mc cp;c
L
dt
ð6Þ
subject to the following initial condition:
T ¼ T0 at t ¼ 0
The solution to Eq. (6) is:
Alp t
T0 2 T1
ln
¼
Lmc cp;c
T 2 T1
Fig. 2. The Fitch apparatus: (1) copper rod, (2) vacuum flask, (3) copper
plug, (4) polystyrene disc, (5) thermocouple for copper rod temperature T1,
(6) thermocouple for copper plug temperature T, (7) the sample slab, (8) the
stopper cork. (all dimensions in mm).
thermocouple was installed at the axis, 2 mm above the
exposed surface. The copper plug is also insulated with
polystyrene. Two screws in the copper plug insulation pass
through two matching holes in the polystyrene discs to
maintain the alignment and avoid the movement during the
experiments.
In the modified Fitch apparatus, the sample is sandwiched between the copper rod and the copper plug. The
copper rod acts as the heat source while the copper plug is
the heat sink. The mathematical formulation of the sample
Fitch apparatus system is that of the case of a slab with one
face being in contact with a layer of perfect conductor
(copper plug) while the opposite face is kept at a constant
temperature (T1). The governing differential equations for
the temperature field within the sample are expressed as
›T
›2 T
¼a 2
›t
›z
ð3Þ
And boundary conditions
T ¼ T0 at t . 0 for z ¼ 0
2lp
›T
›2 T
¼ mcp;c 2 at t . 0 for z ¼ L
›t
›z
where thermal diffusivity a ¼ lp =rcp;c :
The values of T, T0 and T1 are obtained by measurements. A
plot of temperature ratio ðT0 2 T1 Þ=ðT 2 T1 Þ vs time on a
semi-log scale gives a straight line. The thermal conductivity of the specimen sample is calculated from the slope
ðAlp =Lmc cp;c Þ of the temperature history.
2.4.1. Sample preparation
To prepare the slabs for the test, few large particles
(oversize US mesh #4) were chosen. The large particles
were then carefully sanded into thin slabs required for the
tests. The samples were made such that their surfaces were
smooth, parallel and large enough to accommodate a circle
with a diameter of at least 6.5 mm. Samples were then oven
dried and kept in air tight bottles for overnight to come in
equilibrium with the room temperature. The average
thickness of the samples was measured. For this study,
two slabs of SW with the mean thickness of 1.42
(SD ¼ 0.02) and 1.05 mm (SD ¼ 0.02) were used. For
SB, the sample thickness used were 1.49 (SD ¼ 0.03) and
0.96 mm (SD ¼ 0.07), respectively. For SC samples, the
thickness used were 0.71 (SD ¼ 0.01) and 1.17 mm
(SD ¼ 0.06), respectively. To avoid the moisture absorption, the samples were kept inside the air tight bottles except
for the experimental duration.
ð2Þ
with the initial condition
T ¼ Ti at t ¼ 0 for 0 , z , L
ð7Þ
ð4Þ
ð5Þ
2.4.2. Procedure
The Fitch apparatus used in this study, was calibrated
with a standard reference material (SRM 1453, Expanded
Polystyrene Board) supplied by Building and Research
Laboratory, NIST, Maryland, USA. SRM is a low thermal
conductivity material (lp ¼ 0.03511 W m21 K21 at 313 K).
For calibration, two samples of SRM were prepared with
mean thickness, of 1.73 (SD ¼ 0.01) and 2.25 mm
(SD ¼ 0.01), respectively. Four tests on each sample were
conducted and the mean thermal conductivity at 309 K was
compared with the standard value. The mean values
M. Gupta et al. / Fuel 82 (2003) 919–927
obtained were found to be low and hence a calibration factor
of 0.9233 was introduced [10].
To measure the thermal conductivity of SW, SB and SC,
tests were performed on each specimen at an average
temperature of 310 K. Four replications were used for each
specimen. To study the influence of temperature, the water
temperature T1 was changed and the same procedure was
adapted for the thicker SW, SB and SC samples. Two
replicates for each set were used.
The temperature histories of each replicate were
recorded for 300 s using a 10 s interval and values of
thermal conductivity were calculated on EXCEL spreadsheets using Eq. (7).
3. Results and discussion
3.1. Specific heat
The output of the DSC measurement is a thermogram, in
which the ordinate depicts the heat flow rate as a function of
temperature. In line with the procedure described in Section
2.3, a typical DSC thermogram output for cp determination
is given in Fig. 3. The curve A shows the baseline, while
curve B and curve C represent the DSC curves of reference
and sample, respectively. The curve D shows the heating
program during the experiment. The difference between the
baseline and the sample DSC curves represents the sensible
heat of sample consumed during the heating. Fig. 3 shows
that as the temperature increases the base-reference and
base-sample difference increase. The specific heat of SW
923
increases with temperature. Similar thermograms were
obtained for SB and SC. The samples of SW and SB
could be tested up to a temperature of about 413 K only, as
at higher temperatures, the devolatization of the samples
starts. However, char samples could be tested to temperatures up to those seen during pyrolysis (up to 700 K).
The reproducibility of DSC measurements was found to be
within 2, 4 and 5% for SW, SB and SC, respectively.
The specific heat cp of the samples was calculated using
the SEIKO software version 5.5 and using the flow chart
shown in Fig. 1 and Eq. (1). The calculated results of cp of
SW, SB and SC are illustrated in Fig. 4. The values of SB
are found to be higher than SC over the same temperature
range. In both cases, however, the value of specific heat
increases with the increase in the temperature. The specific
heat of wood is reported to vary between 670 and
2500 J kg21 K21 [23]. Fig. 4 shows that with an increase
of 100 K, the specific heat of SW increases by 47% and
basically follows a linear pattern in line with the reported
values of wood by other workers [6,7,24 –26]. The linear
regression equation for SW (313 , T , 413) is
cp ¼ 5:46T 2 524:77
ð8Þ
with a correlation coefficient of 0:995
On an average, the specific heat of SB is observed to
increase from 1364 J kg 21 K 21 at 313 K to
1777 J kg21 K21 at 413 K, accounting for a 30% increase
compared to 47% for SW. However, the values obtained for
SB are greater than SW for the corresponding temperatures.
A peak is observed between 313 and 341 K for SB.
Fig. 3. Typical thermogram for specific heat determination of softwood sample. (A) Base run, (B) reference run, (C) sample run, (D) temperature.
924
M. Gupta et al. / Fuel 82 (2003) 919–927
Fig. 4. Specific heat cp vs temperature, (A) SW, (B) SB, (C) SC.
Repeated experiments gave the same pattern. To avoid the
influence of residual moisture, the samples were preheated
till 423 K before DSC analysis. Thus the effect of latent heat
of water evaporation was ruled out. To the authors’
knowledge, this phenomenon has not been reported before.
However, the mechanism of this unusual peak was not
further investigated as it was falling well beyond the
objectives of this study.
The linear regression equation fitted to the overall data
(313 , T , 413) is
cp ¼ 3:69T þ 231:06
with a correlation coefficient of 0:924
ð9Þ
The values obtained for SC are shown in Fig. 4. Compared
to SW and SB, the specific heat of char is lower for the
corresponding temperatures and does not follow a linear
pattern. The values for char rather follow a second order
polynomial, in line with the work of Gronli and Melaaen
[25]. Following regression equation (313 , T , 686) is
obtained
cp ¼ 20:0038T 2 þ 5:98T 2 795:28
ð10Þ
and the correlation coefficient is 0.991.
With the increase in temperature, the specific heat of the
SC almost doubles from 768 J kg21 K21 at 313 K to
1506 J kg21 K21 at 413 K. This shows that the variation
Fig. 5. A typical temperature history data for samples. K SW ðL ¼ 1:42 mmÞ; V SB ðL ¼ 1:49 mmÞ; X SC ðL ¼ 1:17 mmÞ:
M. Gupta et al. / Fuel 82 (2003) 919–927
Table 2
Design parameters used in the modified Fitch apparatus
Parameter
Value
Heat transfer area of copper plug, A
Mass of copper plug, mc
Specific heat of copper, cp;c
3.16 £ 1025 m2
5.24 £ 1023 kg
376 J kg21 K21
of the specific heat with temperature should not be
neglected, as it is often done in modeling studies [7,27,28].
3.2. Particle thermal conductivity
A typical plot of temperature history of SW, SB and SC
samples is shown as Fig. 5. The semi-log temperature
history of the samples almost follows a linear profile. The
thermal conductivity of the samples was determined by
calculating the slope of the temperature histories of each run
in EXCEL spreadsheets and using Eq. (7). Four replicates of
each sample were used for calculation of the mean thermal
conductivity. The values of design parameters used in
calculations are given in Table 2. The calculated mean
thermal conductivity of SW, SB and SC is given in Table 3.
The mean thermal conductivity of SW was calculated to be
0.0986 W m21 K21 at 310 K. The mean thermal conductivity of SB and SC were determined to be
0.2050 W m21 K21 at 310 K and 0.0946 W m21 K21 at
308 K, respectively.
The values measured here for SW were basically the
thermal conductivity values measured along the transverse
directions (perpendicular to direction of fibers). Because of
the size limitations of the softwood feedstock, it was not
possible to prepare a sample for the measurement along the
longitudinal direction.
Fig. 6 gives the variation of thermal conductivity lp of
SW, SB and SW particles as a function of temperature. With
an increase in temperature from 310 to 341 K, the values of
lp for SW, SB and SC increased by 13, 13 and 22%,
respectively. The values almost follow a linear pattern. Due
to practical limitations of the Fitch apparatus, the thermal
925
conductivity was measured only up to the mean temperature
of 348 K; within this range, bark values are always higher
than wood and char. After 325 K, however, the lp for char
increased at a higher rate compared to wood and bark. The
thermal emissivity of bark and char is reported to be 0.80
and 0.95, respectively [7,28]. Also the absolute porosity of
bark and char is calculated to be 0.68 and 0.80, respectively
[19]. The effective scattering factor in porous materials
depends upon the material emissivity and the porosity [29].
Thus, the effective scattering factor of char is almost twice
that of bark at 2.0 and 1.1, respectively. However,
considering the relative standard deviation of thermal
conductivity measurements in the range of 6% (Table 3),
no conclusion can be drawn.
One might be concerned that a possible thermal
expansion of the copper plug and copper rod results in a
compression of the sample density. Copper has a very low
coefficient of linear expansion ða ¼ 1:65 £ 1025 K21 Þ [30].
In the modified Fitch apparatus (Fig. 2), when the sample is
sandwiched between the copper rod and copper plug, the
maximum temperature variation is around 32 K. The copper
plug thickness being less than 21 mm (Fig. 2), the maximum
possible linear expansion of the copper plug is 0.0107 mm.
This is just 1.5% of the thinnest sample tested (0.71 mm).
However, it must be noted that the top end of the cover is not
bolted. Rather it is free end and lies on the sample.
Therefore, despite any vertical expansion in copper plug and
rod, there will be no additional pressure or compression in
the sample. Consequently, we do not believe that the
reported measurements are affected by the temperature
variation in the copper.
Table 4 gives a comparative review of the thermal
conductivity of wood, bark and char. While significant
literature has been reported on thermal conductivity of SW,
little has been reported on SB and SC. The values for SW
are in good agreement with the reported values for Norway
spruce and Douglas fir [6,26]. Gronli et al. [25] have
reported thermal conductivity of birch/pine/spruce at
0.35 W m21 K21 which is about three times higher than
the values reported in the literature and in the present work
(Table 4). In general, the thermal conductivity of softwood
Table 3
Particle thermal conductivity of SW, SW and SC
Sample
Mean temperature, Tmean (K)
Thickness
L (mm)
Particle thermal conductivity
SD (%)
lp (W m
21
21
K )
Mean particle thermal conductivity
SD (%)
lp (W m21 K21)
SW
309
310
1.42
1.05
1.4
1.9
0.0975
0.0997
6.2
6.0
0.0986
SB
310
310
1.49
0.96
2.0
7.3
0.1993
0.2107
1.2
2.5
0.2050
SC
309
307
0.71
1.17
11.3
5.1
0.0934
0.0958
5.9
3.6
0.0946
SD here is basically relative standard deviation in %.
926
M. Gupta et al. / Fuel 82 (2003) 919–927
Fig. 6. Particle thermal conductivity lp as a function of temperature: K SW; V SB; X SC.
is reported to increase linearly with the temperature.
Temperature dependency of thermal conductivity of SW
in this work, is in close agreement with values reported by
Ragland et al. [7]. For an increase in temperature from 420
to 575 K, Suuberg et al. [23] have reported thermal
conductivity values for virgin cellulose pellets to increase
from 0.05 to 0.07 W m21 K21. For the corresponding
temperature range, our values (through extrapolation) are
observed to be higher (Table 4).
The thermal conductivity of SB is almost twice the
thermal conductivity of SW. Reported literature on softwood bark thermal conductivity is rare [5,27] The values
range from 0.055 to 0.48 W m21 K21 (Table 4). In absence
of precise sample specifications and measurement conditions, it is difficult to compare the results from the present
work with reported values.
The thermal conductivity values obtained for the char
and for the softwood are close to each other in this study.
Table 4
A comparative review of thermal conductivity values of wood, bark and char
Material
Sample form
Density (kg m23)
Temperature (K)
Thermal conductivity (W m21 K21)
Reference
Wood
Fir
Spruce
Norwegian birch/pine/spruce
Hardwood birch
Norway spruce
Douglas fir
Wood1
Virgin cellulose
Present work
Particle
Particle
Particle
Particle
Particle
Particle
–
Pellets
Particle
540
410
–
680
350
512
350
458
360
–
–
–
294– 373
–
–
T
420– 575
310– 341
0.138
0.109
0.350
0.214–0.250
0.100
0.110
0.035 þ 1.73 £ 1024T
0.05–0.07
0.0986–0.1114
[26]
[26]
[25]
[9]
[6]
[6]
[7]
[23]
Bark
Paper bark
Bark1
Present work
Bed
–
Particle
118
400 –600
482
–
–
310– 348
0.48
0.055–0.074
0.2050–0.2313
[31]
[5]
Particle
bed
Particle
pellets
Particle
–
120
999
199
425
299
–
819
320
–
420– 520
310– 341
0.1
0.620
0.370
0.062
0.05–0.06
0.0946–0.1156
[25]
[26]
[32]
[5]
[23]
Char
Norwegian (birch/pine/spruce) char
Graphite char
Coal char
Char1
Cellulose char
Present work
1
Source not specified.
M. Gupta et al. / Fuel 82 (2003) 919–927
The thermal properties of char highly depend upon the
source of raw material, the pyrolysis conditions and the
composition of the char. Although many workers have
reported values of char particles (Table 4), a direct
comparison is not possible as char precise specifications
are lacking in these studies. Nevertheless, the values
obtained in this work are close to values reported by Gronli
et al. [25] for Norwegian (birch/pine/spruce) char. The
thermal conductivity values of char in the present work are
much lower than the values reported for graphite char [26]
or char obtained from coal [32], in which cases the source
and processing conditions are very different in comparison
with the present work.
For low temperature conduction in the lignocellulosic
materials, the density plays an important role [9]. As shown
in Table 1, the density of the char is the lowest and the
density of bark is the highest among the three species.
Perhaps this is the reason why SB which has the highest
density among SW, SB and SC, exhibited the highest
conductivity while SC which has the lowest density,
exhibited the lowest conductivity.
4. Conclusion
The specific heat of SW, SB and SC particles was
measured by DSC apparatus. A modified Fitch apparatus
was constructed, calibrated and used to measure the particle
thermal conductivity of SW, SB and SC.
1. The specific heat of SW and SB increased linearly from
1172 to 1726 and 1364 to 1777 J kg21 K21 with an
increase in temperature from 313 to 413 K. With an
increase in temperature from 313 to 713 K, the specific
heat of SC doubled from 768 to 1506 J kg21 K21 and
followed a polynomial relationship with temperature.
2. The thermal conductivity of SW, SB and SC was
determined
to
be
0.0986,
0.2050
and
0.0946 W m21 K21, respectively, at 310 K (for bark,
the thermal conductivity has been measured in the
transverse direction only). The thermal conductivity of
SW, SB and SC linearly increased with temperature.
The measured thermal properties of SW, in this work, are
in good agreement with the reported values in the literature
for similar wood species. The measurements in this work
provide new contribution for the thermal characterization of
softwood bark and softwood char in terms of thermal
conductivity and specific heat.
Acknowledgements
We are grateful to the Fonds pour la Formation de
Chercheurs et l’Aide à la Recherche (FCAR, Gouvernement
927
du Québec), for partial financial support of the first author of
this article.
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