Determining Thermal Transport Properties for Softwoods Under

Dec 12, 2016 - No distinction was made as to whether heart or sap wood were used to yield each 5 cm blocks (samples were made from each type) while ba...
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Research Article pubs.acs.org/journal/ascecg

Determining Thermal Transport Properties for Softwoods Under Pyrolysis Conditions C. Luke Williams,* Tyler L. Westover, Lucia M. Petkovic, Austin C. Matthews, Daniel M. Stevens, and Kelli R. Nelson Idaho National Laboratory, 2525 Freemont Avenue, Idaho Falls, Idaho 83415, United States S Supporting Information *

ABSTRACT: The thermal properties of biomass over a range of pyrolysis temperatures have been measured using a Transient Plane Source (TPS) instrument and a differential thermal analyzer (DTA). In this study, thermal property measurements were made on six softwoods: subalpine fir, Douglas fir, Engelmann spruce, loblolly pine, lodgepole pine, and ponderosa pine. Results from this method show that the average thermal conductivity for these softwoods decreases by almost a factor of 3, from 0.198 to 0.091 W/(m K), as the wood goes from ambient conditions to a pyrolysis temperature of 453 °C. Over the same temperature range the average thermal diffusivity increases from 0.313 to 0.427 mm2/s, and the specific heat decreases from 1.58 to 0.93 kJ/(kg K). Investigation of the anisotropic nature of heat transport through lignocellulosic biomass found that heat transport is generally three to four times faster along the grain of the wood than across the wood pores, and studies on the rate at which thermal conductivity and diffusivity change with temperature revealed only a slight increase over 50−300 °C. It has also been shown that the thermal conductivity of a material correlates strongly with the density throughout the pyrolysis regime. This correlation with density has been shown before for the moisture content of green wood but not through the range of material changes associated with pyrolysis. The direct measurement of these anisotropic thermal properties has the ability to enhance modeling of lignocellulosic biomass pyrolysis and provide new insight into heat transfer through a naturally occurring lignocellulosic material. KEYWORDS: Biomass, Conductivity, Diffusivity, Pyrolysis, Anisotropic

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60% and can be as high as 80% by weight.7,12,13 Oil quality can also vary greatly, and all pyrolysis oils require upgrading to reduce acidity, stabilize oil viscosity, and increase energy content.13−15 Many researchers have attempted to clarify the complex nature of biomass pyrolysis by developing computational models. These models have often been based on detailed experiments16−18 or a combination of experiments followed by the formulation of computational models.19−24 In the development of these models, it has been found that thermal conductivity is one of the most sensitive parameters.19,21 However, despite the importance of well characterized thermal transport properties, such as conductivity, there is very little data available on the thermal properties of biomass, and the published data spans a wide range of values. Additionally the published data rarely includes uncertainty analysis for the measurements or quantification of the anisotropic nature of the heat transport in lignocellulosic biomass.

INTRODUCTION Increased national interest in domestic and renewable energy, fuels, and materials has led to a surge in the use of agricultural waste and other biomass sources to fulfill these needs. By 2020, the United States aims to produce 5% of its power, 10% of its fuels, and 18% of its chemicals from renewable sources.1 However, one of the major challenges associated with the production of renewable fuel and chemicals is the highly varied nature of the biomass feedstocks.2,3 This difficulty with feedstock variability is exacerbated by the myriad of available conversion technologies.4,5 Because of the variety in available feedstocks and conversion technologies, future production of biobased products could be produced through the hybrid bio/ thermochemical process.6 These processes could lead to the production of either fuels7 or chemicals (like the polyethylene terephthalate used in consumer plastics).8 One prominent thermochemical pathway being investigated for the production of renewable fuels and chemicals is pyrolysis.9−11 Pyrolysis involves rapidly heating dry, ground biomass at temperatures of 300−500 °C in an inert atmosphere. The amount of oil extracted in these processes depends greatly on the biomass feedstock, reaction conditions, and the presence of a catalyst, but typical oil yields are around © 2016 American Chemical Society

Received: September 26, 2016 Revised: December 6, 2016 Published: December 12, 2016 1019

DOI: 10.1021/acssuschemeng.6b02326 ACS Sustainable Chem. Eng. 2017, 5, 1019−1025

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each 160 s measurement period. The underlying theory to perform the measurements and determine the properties for isotropic materials is well developed and presented in refs 30, 31, and 34. For anisotropic materials, the development is similar and is described below. The average temperature increase of the sensor is given by35:

Thermal conductivity is the most widely measured thermal property for lignocellulosic biomass with reported values varying between 0.09 W/(m K)25 and 0.323 W/(m K),26,27 with values near 0.15 W/(m K)28 being the most common. These measurements have typically been performed at room temperature with moisture contents around 10%; very few studies report the thermal properties of pyrolysis products, such as char.29 One experimental method capable of studying these thermal properties over a wide range of temperatures is the transient plane source (TPS) technique developed previously by Gustaffson.30,31 This method has been shown to work for the study of anisotropic materials, such as wood and concrete.32 The TPS method utilizes a resistive nickel element that acts as both a heat source and a thermometer. A short electrical pulse is passed through the nickel element to cause its temperature, and the temperature of the sample, to rise (usually 2−5 °C). From measurements of the change in sensor resistance during a constant power input a temperature difference can be measured between the actual sensor temperature and what the sensor temperature would be if there was no dissipation to the surrounding sample. It is this temperature difference measurement that allows the thermal properties of the sample to be calculated. Importantly, the time duration of the electrical pulse must be sufficiently short that temperature gradients do not reach the sample boundaries during the time period of the test so that the material behaves like an infinite medium. To date there have been no experimental studies on how the thermal properties of wood change as the structure and chemistry changes with pyrolysis. Thunman and Leckner have noted the importance of understanding how thermal properties change with combustion, but the models they developed primarily relied on unreacted wood and char due to a lack of data for wood undergoing pyrolysis.33 The studies herein investigate how the thermal properties change for six softwoods undergoing pyrolysis over the temperature range up to 500 °C.

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ΔT =

P D(τ ) π 3/2Rko

(1)

where P is the electric power provided to the sensor, R is the sensor radius, ko is the geometric thermal conductivity, and D(τ) is a dimensionless function that is related to time through

τ=

1 tko φ (ρCp)−1 R

(2) −1

where ρ is mass density with units (kg m ), Cp is specific heat with units J K g−1, and φ is the ratio of the thermal conductivity in the radial (kr) and axial directions (kz), respectively (φ = kr/kz). Note that τ is dependent upon kr because ko = (krkz)1/2, not kz as previously asserted.35 The function D(τ) has been previously defined.30 As is evident in eq 1, ΔT forms a straight line when plotted as a function of D(τ). The geometric thermal conductivity is determined from the relationship between ΔT and D(τ) as given by eq 1. Software provided with the Thermtest 1500 TPS system performs this calculation iteratively. For isotropic materials (φ = 1), the algorithm assumes initial values for ko and ρCp and then adjusts the values until the best straight-line fit is achieved. For anisotropic materials, there are too many unknowns in eq 2 and either φ or ρCp must be measured independently. For the experiments described here, ρCp was measured separately as described below using differential thermal analysis (DTA). Measurements using the TPS system were made along the grain of the wood with the sensor placed on the radial−tangent plane of the wood grain (this method has been verified elsewhere for anisotropic materials32). To ensure an accurate infinite medium approximation, these experiments utilized two wood blocks that were 2.5 cm thick by 5 cm on each side with a Mica 5082 sensor (6.631 mm radius) placed between them. Importantly, sample temperatures in the furnace are actually lower than the set temperature of the furnace because the heater controller of the furnace uses a thermocouple placed near the heating coils to measure the furnace temperature. The relationship between the furnace temperature (TF) and the sample temperature (TS) was determined by comparing the furnace temperature to the temperature reported by a second thermocouple placed in a 16 mm deep, 2.8 mm diameter hole in wood and stainless samples during steady state conditions over the furnace temperature range of 25−510 °C. The relationship between TF and TS was found to be highly linear with a proportionality constant of 0.906. During measurements of thermal property, the sample temperature was not measured directly but was determined using the known correlation between TF and TS. Samples were initially dried and analyzed at room temperature (RT) before being pyrolyzed at a furnace temperature (TF) of either 300 or 500 °C (corresponding to sample temperatures of 272 and 453 °C, respectively) for 8 h using a 1 °C/min heating ramp. After pyrolysis at 272 °C (i.e., torrefaction), samples were analyzed at room temperature followed by the determination of thermal properties using a heating ramp up to a furnace temperature of 250 °C (TS = 226 °C) in 50 °C increments starting at 50 °C. Once samples were analyzed at 226 °C, they were treated further at a furnace temperature of 500 °C (TS = 453 °C), and the same tests were repeated, except the heating ramp experiments started at 75 °C and went to a furnace temperature of 450 °C (TS = 408 °C) in 75 °C increments. For material treated at 300 °C (TS = 272 °C), the temperature during measurements of properties was kept at 250 °C (below the treatment level) to prevent further sample pyrolysis from interfering with sample measurements. Samples treated at 500 °C (TS = 453 °C) were tested at a maximum furnace temperature of 450 °C for the same reason. When processing the data for the high temperature ramp studies, it was noted that there were some anomalies in the data between 300 and 500 °C due to interference from the Curie transition of the nickel sensor (∼354 °C).

EXPERIMENTAL SECTION

Samples. The woods samples tested were subalpine fir (abies lasiocarpa), Douglas fir (Pseudotsuga menziesii), Engelmann spruce (picea engelmannii), lodgepole pine (Pinus contorta), ponderosa pine (pinus ponderosa), and loblolly pine (pinus taeda). Loblolly pine samples were collected as 2 m raw log sections from Butler County, Alabama. The remaining wood samples were obtained from Yellowstone Log Homes LLC, in Jefferson County, Idaho. Each sample block was prepared from 0.3 to 0.6 m sections of at least 0.2 m diameter raw logs. Each log was further cut using a standard table saw into 5 cm wafer sections. Each wafer section was then cut into 5 cm cubes, followed by slicing each cube in half across the wood grain to yield two blocks measuring 2.5 cm × 5 cm × 5 cm each. Supporting Information Section 1 contains additional information on the wood blocks and how they were placed in the experimental setup. No distinction was made as to whether heart or sap wood were used to yield each 5 cm blocks (samples were made from each type) while bark, cambium, and pith sections were avoided in this study. Density measurements were made before and after pyrolysis by weighing the blocks on a standard four point balance and measuring the block dimensions with a digital micrometer. Thermal Conductivity and Diffusivity by Transient Plane Source (TPS) Method. The TPS instrument was used to measure anisotropic thermal conductivity and diffusivity of six softwood samples. The experimental setup consisted of a Thermtest Inc. model 1500 TPS used in combination with a Vecstar muffle furnace (model number LF2 SP). The analysis method was set to ensure sample temperature remained stable for 40 s before a 50 mW electrical current was pulsed through the nickel heating element/thermistor for 1020

DOI: 10.1021/acssuschemeng.6b02326 ACS Sustainable Chem. Eng. 2017, 5, 1019−1025

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Figure 1. Thermal property measurements for subalpine fir, Douglas fir, Engelmann spruce, loblolly pine, lodgepole pine, and ponderosa pine made at room temperature with the sample being dry (solid bar), pyrolyzed at 272 °C (hollow bar), or pyrolyzed at 453 °C (hatched bar). (A) Thermal conductivity (W/(m K)), (B) thermal diffusivity (mm2/s), (C) specific heat (kJ/(kg K)), (D) density (kg/m3). This phenomenon is recognized by the equipment manufacturer, and it is suggested not to operate around the Curie transition temperature. Therefore, our data has been truncated to the points below 300 °C. Measurement of Specific Heat by Differential Thermal Analysis (DTA). The specific heat of the wood samples was measured using differential thermal analysis (DTA) under temperature ramping conditions in flowing 50 sccm UHP helium (Norco, Idaho) on a PerkinElmer Pyris/Diamond TG/DTA. This instrument houses two alumina pans, one was kept empty, and the other was loaded with either the sample to be analyzed or α-Al2O3 (PerkinElmer) as a reference material. Both sample and reference material were used in powder form. The specific heat of each wood sample was determined by correlating the temperature difference between the reference material and the wood sample using the following formula: Cpsample = Cpref ×

(T sample − T empty) M ref × sample M (T ref − T empty )

This pyrolysis was performed in a Vecstar muffle furnace (model number LF2 SP) under N2 purge using a heating ramp of 1 °C/min. The block were then ground to 60 mesh (250 μm) and mixed before loading into the DTA cell. Additional details of the specific heat measurements can be found in the Supporting Information Section 2.

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RESULTS Experimental measurements of thermal conductivity and diffusivity have been made for subalpine fir (SAF), Douglas fir (DF), Engelmann spruce (EGS), loblolly pine (LLP), lodgepole pine (LPP), and ponderosa pine (PP). Thermal property measurements were made on materials at ambient conditions for virgin wood and wood that had been pyrolyzed at either 226 or 453 °C for 8 h. The TPS allowed for the measurement of anisotropic thermal properties with the sensor placed in the radial-tangent plane of the wood grain to make measurements primarily along the axis of the grain Measurements of geometric thermal conductivity ko, geometric thermal diffusivity (αo = koρ−1Cp−1), specific heat (Cp), and density (ρ) are shown in Figure 1. Figure 1A shows a clear trend with ko at room temperature decreasing with increasing thermal treatment from ambient to 453 °C. However, αo shown in Figure 1B does not have an easily distinguishable trend with increasing thermal treatment but seems, in general, to stay constant up to a temperature of around 272 °C but increase once the material has been treated at 453 °C. Figure 1C illustrates how increasing thermal treatment causes a decrease in specific heat as well as a decrease in density, shown in Figure 1D. The final density of

(3)

where Cp, M, and T are specific heat, mass, and temperature, respectively. The measurement protocol consisted of drying each sample in the TG/DTA Instruments at 100 °C for 60 min, letting it cool to room temperature, and then initiating a 10 °C/min temperature ramp to 700 °C. Although sample weight, program temperature, and sample temperature were recorded continuously during the whole analysis, only the temperature range during the temperature ramp where sample weight changes were negligible was used to calculate specific heat. To obtain the specific heat of partially pyrolyzed wood samples a 1.5 in. cube of material free of knots, cracks, bark, or other defects, from each type of softwood was pyrolyzed for 8 h at either 272 or 453 °C. 1021

DOI: 10.1021/acssuschemeng.6b02326 ACS Sustainable Chem. Eng. 2017, 5, 1019−1025

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Table 1. Anisotropic Properties for Axial and Radial Thermal Conductivity and Diffusivity As Well As the Ratios of the Axial to Radial Components, For Six Different Softwoods That Have Undergone Pyrolytic Treatment typea

axial conductivity (W/(m K))

axial diffusivity (m2/s)

radial conductivity (W/(m K))

radial diffusivity (m2/s)

kz/kr

αz/αr

dry

SAF EGS PP DF LLP LPP

0.305 0.422 0.450 0.387 0.246 0.358

0.429 0.786 0.778 0.581 0.417 0.470

0.112 0.087 0.105 0.118 0.111 0.131

0.157 0.163 0.182 0.176 0.188 0.172

2.72 4.85 4.29 3.28 2.22 2.73

2.73 4.82 4.27 3.30 2.22 2.73

272 °C

SAF EGS PP DF LLP LPP

0.338 0.325 0.344 0.265 0.198 0.180

0.709 0.771 0.848 0.511 0.453 0.377

0.079 0.068 0.068 0.082 0.079 0.086

0.166 0.160 0.167 0.158 0.180 0.180

4.28 4.78 5.06 3.23 2.51 2.09

4.27 4.82 5.08 3.23 2.52 2.09

453 °C

SAF EGS PP DF LLP LPP

0.255 0.223 0.186 0.247 0.188 0.216

1.314 1.203 0.675 0.969 0.848 0.785

0.034 0.032 0.052 0.049 0.046 0.072

0.176 0.173 0.189 0.194 0.209 0.262

7.50 6.97 3.58 5.04 4.09 3.00

7.47 6.95 3.57 4.99 4.06 3.00

dry 272 °C 453 °C

all all all

0.361 0.275 0.219

0.577 0.612 0.966

0.111 0.077 0.048

0.173 0.169 0.200

3.25 3.57 4.56

3.34 3.62 4.83

treatment

a

SAF = subalpine fir; EGS = Engelmann spruce; PP = ponderosa pine; DF = Douglas fir; LLP = loblolly pine; LPP = lodgepole pine.

Figure 2. Change in thermal conductivity (A) and thermal diffusivity (B) with temperature for the geometric average data for subalpine fir, Douglas fir, Engelmann spruce, loblolly pine, lodgepole pine, and ponderosa pine after having been pyrolyzed at 272 °C (red) or 453 °C (black) for 8 h. For comparison the geometric average of thermal conductivity for dry softwoods is 0.199 W m−1 K−1 and the geometric average for the thermal diffusivity is 0.312 m2/s.

mm2/s, and specific heats between 0.8 and 1.1 kJ/(kg K). All the values given above are the average value for all tested softwoods within a 90% confidence interval. While Figure 1 shows the geometric averages for the axial and radial heat transport properties, Table 1 contains the directional components for thermal conductivity and thermal diffusivity as well as the geometric averages shown earlier. It can be seen in Table 1 that axial heat transfer through woody biomass averages at least three times greater than that of the radial direction. This trend shifts from axial heat transfer being three times faster in material that has not undergone thermal treatment to an average of four and a half times faster for material that has been treated at 453 °C (up to a factor of 7

the material appears to be independent of material type at high levels of thermal treatment. If the properties illustrated in Figure 1 are averaged across all of the softwoods, it can be seen that the average thermal conductivity is between 0.18 and 0.22 W/(m K), the thermal diffusivity is between 0.27 and 0.36 mm2/s, and the specific heat is between 1.4 and 1.8 kJ/(kg K) for dried softwood biomass. As a softwood is pyrolyzed at 272 °C, the average properties yield thermal conductivities between 0.13 and 0.16 W/(m K), thermal diffusivities between 0.26 and 0.35 mm2/s, and specific heats between 1.2 and 1.4 kJ/(kg K). Further pyrolysis at 453 °C yields thermal conductivities between 0.09 and 0.11 W/(m K), thermal diffusivities between 0.39 and 0.47 1022

DOI: 10.1021/acssuschemeng.6b02326 ACS Sustainable Chem. Eng. 2017, 5, 1019−1025

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ACS Sustainable Chemistry & Engineering times for some species of fir). The observed difference in axial versus radial transport is in line with the literature, which reports values that vary from one and a half to five times greater in the axial direction where the lower differences in axial vs radial heat transfer occur when the wood is moist.32,36,37 Additionally, the geometric average of the thermal conductivity across all softwoods tested matches well with the literature for both virgin wood27,38 and wood char. Literature values for char range from 0.0839 to 0.09540 and should closely approximate wood that has been pyrolyzed at 453 °C. In addition to knowing thermal properties at room temperature, it is desirable to know how these properties change with temperature, especially since pyrolysis reactors can be operated over a wide temperature range. To easily visualize the change in thermal conductivity and diffusivity with changing temperature, the data for individual softwoods (subalpine fir, Douglas fir, Engelmann spruce, loblolly pine, lodgepole pine, and ponderosa pine) was averaged to create a single data set called “softwood”. The data for the individual woods can be found in Supporting Information Section 4. Materials that were previously thermally treated (pyrolyzed) at either 272 or 453 °C showed consistent changes in their thermal properties up to 272 °C. It can be seen in Figure 2 that both thermal conductivity and thermal diffusivity increases slightly over the temperature range from 25 to 300 °C. Figure 2A shows the change in thermal conductivity while the change in thermal diffusivity can be seen in Figure 2B. For materials treated at 272 and 453 °C, the thermal conductivity increases slowly, at a rate of 7.26 × 10−5(T) and 8.29 × 10−5(T), respectively, where as the thermal diffusivity increases at a rate of 1.67 × 10−4(T) and 3.63 × 10−4(T) (where T is in °C). As has been stated previously in the literature for woody biomass at ambient conditions, there is a strong correlation between thermal conductivity and density that is often associated with the moisture content of green wood.25,26,28 In this work, we show that this correlation extends through the pyrolysis regime to include char, as shown in Figure 3. It can be

conductivity and in Figure 1C for specific heat agree well with the range found in the literature for both virgin wood and char. Additionally, the experiments herein show the same strong correlation between thermal conductivity and density as seen in previous work. This strong agreement with typically measured thermal properties and trends allows more confidence in the determination of the thermal transport properties for partially pyrolyzed material as well as measurements that are unique to the TPS system, i.e., thermal diffusivity and anisotropic heat transport. This general decrease in thermal conductivity and specific heat is expected as the density decreases due to chemical changes that produce volatiles and restructuring of the lignocellulosic matrix, resulting in a greater number of internal defects and void space as the individual cell walls expand and fracture.41 This increase in defects upon heating can be seen visually by the growth of cracks and increased surface roughness of material that has undergone thermal treatment and is a cause of the decrease in thermal conductivity with increasing thermal treatment. The change in thermal diffusivity with increasing pyrolysis shown in Figure 1B generally illustrates that the thermal diffusivity of a lignocellulosic material increases after pyrolytic treatment at 453 °C. However, ponderosa pine does not show the same pattern as the other softwoods, including the other species of pine. This could be due in part to the fact that the ponderosa pine shows a much greater decrease than average in the thermal conductivity and a much smaller change in specific heat than the other softwoods tested. This greater decrease in thermal conductivity and lesser reduction in specific heat reduces the change in thermal diffusivity compared to the other woods. Studies on the anisotropic nature of the heat transport in wood, shown in Table 1, clearly illustrate greater thermal transport along the axis of the wood grain for both thermal conductivity and diffusivity. The peculiar component to note here is that while the thermal conductivity decreases in both the axial and radial components with increasing pyrolysis, the thermal diffusivity increases along the axial component but remains relatively constant in the radial direction for the average softwood. This anisotropic heat transport in biomass has been investigated in the past and shown to be on the order of one and a half to five times greater in the axial direction depending on the moisture content and density of the wood.32,36,37 However, to the authors knowledge this work is the first to study the properties of pyrolyzed wood. It can be seen here that for wood that has undergone pyrolytic treatment the difference between thermal conductivity along the axis and across the grain continues to increase as pyrolysis continues. For dry wood, heat conduction happens roughly three times faster along the axis, but as the wood shrinks during pyrolysis, heat conduction along the axis can become up to seven times faster than across the grain. This fact indicates that understanding the anisotropic nature of heat transport in wood becomes even more important during pyrolysis processes, especially for larger particle sizes, lower aspect ratios, and for wood types that have not been studied, due to the high variability across many wood types. It should be noted that these thermal transport properties measured here are comprised of a lumped measurement of conduction, convection, and radiation. However, the temperature difference across the sample is only a couple of °C, which will reduce the influence of radiative heat transport, and the

Figure 3. Thermal conductivity decreases as density decreases for softwoods undergoing pyrolysis.

seen that as density decreases from approximately 450 kg/m3 to 225 kg/m3 during pyrolysis, the bulk thermal conductivity decreases from approximately 0.20 W/(m K) to 0.09 W/(m K).

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DISCUSSION The values presented here in Figure 1A for the geometric average of the axial and radial component of thermal 1023

DOI: 10.1021/acssuschemeng.6b02326 ACS Sustainable Chem. Eng. 2017, 5, 1019−1025

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ACS Sustainable Chemistry & Engineering gases within the pores will be fairly stagnant due to isolation from external currents. Incidentally, heat transport through conduction is likely to dominate the measurements made in this work. If radiative heat transport played a dominating role, the increase in thermal conductivity with temperature, over the range of ∼300 °C shown in Figure 2, would have a more exponential growth curve rather than the observed linear trend. It should also be noted that the TPS measures heat transfer in a system that is free of many of the complicating effects that would exist during actual pyrolysis. Under pyrolysis conditions heat transport will be influenced by the formation and transport of bio-oils and vapors through the pores structure, in addition to larger temperature gradients across the material leading to a greater contribution from radiative heat transfer. Simulations of biomass pyrolysis will have to take into account these additional complicating effects.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript. The authors declare no competing financial interest.

CONCLUSION

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A combination of DTA and TPS were used to measure specific heat, thermal conductivity, and thermal diffusivity for several softwoods including subalpine fir, Douglas fir, Engelmann spruce, loblolly pine, lodgepole pine, and ponderosa pine. While DTA provided information on the specific heats of these woods, the TPS provided insight into the anisotropic nature of thermal conductivity and diffusivity. Importantly, new data for thermal properties was collected for softwoods undergoing pyrolysis and information was obtained on how the thermal properties of these pyrolyzed softwoods change with temperature up to a furnace temperature of 500 °C. The geometric average of the axial and radial components yielded thermal properties within the range of literature values for both thermal conductivity and specific heat and provided novel values for thermal diffusivity. For dry biomass, thermal conductivity ranged between 0.18 and 0.22 W/(m K), thermal diffusivity varied from 0.27 to 0.36 mm2/s, and the specific heat was found to be between 1.4 and 1.8 kJ/(kg K). After the softwoods were pyrolyzed at 272 °C, the average thermal conductivities were found to be between 0.12 and 0.14 W/(m K), the thermal diffusivities were between 0.26 and 0.35 mm2/s, and the specific heats were between 1.2 and 1.4 kJ/(kg K). Further pyrolysis at 453 °C yielded thermal conductivities between 0.08 and 0.11 W/(m K), thermal diffusivities between 0.39 and 0.47 mm2/s, and specific heats between 0.8 and 1.1 kJ/(kg K). Investigation into the anisotropic nature of heat transport in woody biomass has revealed that the conductivity along the wood grain averages three times faster for dried wood that has not been pyrolyzed but up to seven times faster along the grain for wood that has been pyrolyzed at 453 °C. It has also been shown that the thermal conductivity of pyrolyzed biomass increases with temperature at rates of 7.26 × 10−5(T), and 8.29 × 10−5(T), for wood pyrolyzed at 272 and 453 °C, respectively, where T is measured in degrees Celsius. In conclusion, the anisotropic nature of heat transport through biomass has been reported for several softwoods undergoing pyrolysis. This new data will aid modeling efforts in the field of pyrolysis for the production of biorenewable fuels and chemicals.

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Pictures of the TPS setup and samples, figures and equations related to the specific heat measurements and differential thermal analysis, TPS measurements on standards, and data for individual samples in graphical format to complement the averages shown in the manuscript (PDF)

ACKNOWLEDGMENTS This research was supported by the U.S. Department of Energy under Department of Energy Idaho Operations Office Contract No. DE-AC07-05ID14517. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssuschemeng.6b02326. 1024

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DOI: 10.1021/acssuschemeng.6b02326 ACS Sustainable Chem. Eng. 2017, 5, 1019−1025