Weight Loss and Tar Evolution during Coal Devolatilization at Various

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Weight Loss and Tar Evolution during Coal Devolatilization at Various Heating Rates Su Pan, Shien Hui, Ling Liang, Changchun Liu, Congxin Li, and Xuxu Zhang Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/ef502031v • Publication Date (Web): 06 Jan 2015 Downloaded from http://pubs.acs.org on January 27, 2015

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Weight Loss and Tar Evolution during Coal Devolatilization at Various Heating Rates Su Pan,† Shien Hui,*† Ling Liang,‡ Changchun Liu,† Congxin Li§, Xuxu Zhang,† † ‡ §

School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China Guodian New Energy Technology Research Institute, Beijing, 102209, China Nuclear and Radiation Safety Center, Ministry of Environmental Protection of the People’s Republic of China, Beijing, 100082,

China

ABSTRACT：：Temperature-resolved weight loss and tar yield during atmospheric devolatilization of pulverized coal have been obtained on a wire mesh reactor (WMR), which applies prescribed thermal histories covering wide range of heating rates on coals from lignite to anthracite. The accuracy of measurements has been improved by diminishing nonisothermality in the sample, ensuring independence of yields on loading density, and the development of a convenient tar collection method that inhibits secondary pyrolysis but also secures capture completeness. We reconfirm the continuous rank effects in terms of reaction dynamics and partitioning between tar and noncondensables, but at disparate heating rates of 5 K/s and 1000 K/s. In addition, we depict the constant variation in noncondensables evolution histories among various coals before the cessation of tar release, whereas since the wake of tar evolution, variations in gas formation kinetics for different coals gradually shrink for increasing temperature. Larger fraction of total gases is found expelled after tar evolution by coals of higher rank. The sensitivity of tar yield to heating rate maintains the same over the range 5 K/s to 1000 K/s, but varies with rank, being greatest for lignites and low-volatile bituminous coals but exhibits a minimum for high-volatile bituminous coals.

1. INTRODUCTION Many parameters in coal devolatilization models cannot be derived directly from analytical methods, because coal is highly heterogeneous in constitution and devolatilization is governed not by simple kinetic rate laws but by competing physiochemical phenomena. Instead, they must be assigned by fitting conversion data. Potentially useful data either directly reflects reaction environment relevant to applications, or covers wide range of operating conditions to function as the basis for extrapolation. In addition, errors in time-temperature histories and product collection must be within acceptable tolerances, since they affect the interpretation of the data and translate into parameters of the models in an attempt to fit the data. Considering these prerequisites, wire mesh reactor (WMR) is arguably the most suitable laboratory workhorse to build up database for modelers,1 primarily by virtue of its coverage of wide range of heating rates and generally effective suppression of secondary pyrolysis. Nevertheless, it is not perfectly immune from legitimate concerns. The transient lag in readings from thermocouple bead during rapid heat-up kept uncertain (possibly varied lab-to-lab due to diverse configurations of thermocouples on the mesh) until the introduction of melting salt test.2 Nonisothermality that keeps the system from batch mode was not routinely monitored in earlier studies, and this issue probably remains a trouble even since extreme temperatures across the sample were regularly averaged by researchers at Imperial College,3 since their temperature-resolved weight loss4,5 still exhibit substantial scatter (about ± 4 wt %) in the regions where yields are sensitive to peak temperature. Tar collection also raises concerns. The first problem is extraparticle tar cracking. Recirculation of evolved tar onto 1

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the heated mesh was encountered by many earlier studies before getting mitigated by the usage of right-to-mesh sweep gas.6 What is more subtle is the impact of loading density (sample loading per unit mesh surface area), because too dense loading reduces WMR to behave like a fixed bed, especially for softening coals when they strongly swell and melt upon rapid heating and then block the mesh openings. The result is lowered tar yields by artifact. This issue was exclusively studied by Freihaut and Proscia,2 but they themselves included, no previous study with WMR had explicitly ensured the independence of loading density. The next concern is the completeness of tar collection. Except for a few cases where good closure of mass balance was reported,7-9 technical details were insufficient to help evaluate the performance. For example, in ref. 6, although additional tar was found to be collected by a longer trap, no definitive verification was made. The above concerns should be paid attention when trying to enrich the existing WMR database. It is surprising that WMR database in the literature includes only two early attempts to resolve reaction dynamics over the whole rank spectrum, by Ko10 first and later by Freihaut and Proscia,11 respectively. The results represent rapid atmospheric devolatilization and qualitatively depict variations in reaction dynamics and product composition in terms of rank effect. However, these data may be inadequate for quantitative modeling: tar yields obtained by Ko for low rank coals are substantially lower than other rapid-heating measurements with noncondensibles distributions12,13 by 8-15 wt %, perhaps as a result of tar cracking; in Freihaut and Procia’s data, for more than once tar yields obtained with or without isothermal period don’t converge even at highest temperatures; in addition, both datasets are very scattered. Meanwhile, even though work by Xu and Tomita on a Curie-point reactor (CPR)14 already provides accurate information on devolatilization kinetics for various coals, continued effort with WMR can still be informative. While drastically varied heating rates alter competing physiochemical phenomena that underlie devolatilization, giving rise to variations in char morphology,15 reaction dynamics and finally the composition of products,4,5,16,17 the invariable heating rate (several thousand K/s) of CPR inhibits it from providing data covering wide range of heating rate for extrapolation, whereas variable heating rates are typical of WMR. Ultimate yields as a function of heating rate reflect the overall effect of heating rate. On this issue, previous studies heavily focused on the high-volatile bituminous rank,5,15,17 whereas low rank and low-volatile bituminous coals are barely represented, with ref. 18 and ref. 4,19 as respective exceptions for the two ranks. More data is needed to reach definitive conclusions. The main purpose of this paper is to provide high-fidelity devolatilization data with WMR for model evaluations. The program includes temperature-resolved reaction dynamics at disparate heating rates and ultimate yields over wide range of heating rate, in terms of weight loss and tar yield. Both datasets cover the whole rank spectrum.

2. EXPERIMENTAL SECTION 2.1. Reactor Configuration and Tar Collection Method. A schematic of the apparatus appears in Figure 1. Coal sample of 0.3–1.5 mg is dispersed in the center of a 250 mesh stainless steel woven mesh (1) folded only once without forming a flap. The mesh also functions as the resistant heater and is stretched between a fixed (2) and a moveable, spring-loaded (3) brass electrode. The pair of springs (4) are driven by a couple of screwed nuts (5) mounted on a stainless steel frame (6), to regulate the tension on the mesh at will and prevent major distortion during heating. A 5-mm-thick quartz plate (7) with a 30-mm-diam opening in the center rests on the step of the electrodes to support the sample holder, with another identical plate located above the mesh. With the aid of metal clips, the plates loosely fasten the two layers of mesh together and seal the open side. Like in 2


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ref. 6, upon entering the cell from the bottom, the stream of helium is first diffused by three successive glass-sinter disks (total thickness 12 mm) (8) enclosed in a brass frame (9). The evenly distributed stream then flows through a ceramic honeycomb (square opening of 2-mm i.d.) (10) to avoid transition to turbulence, before flowing through the mesh and directing tar vapor into the quartz trap (11) immediately above the mesh.

Figure 1. Configuration of the wire mesh reactor (numbered parts described in the Experimental Section).

Weight loss is determined as weight change of the sample holder plus the sample before and after the run with a six-figure balance. Tar yield is also measured gravimetrically. During a run, the trap is cooled by alcohol-dry ice mixture in the reservoir (12) to 213 K, nucleating tar vapor in the helium flow into aerosols. Tar condensation occurs on aluminum foil liners (13) that cover the internal surface of the trap and on an aluminum foil roll (14) (40 mm in length, serves to enhance heat transfer) packed inside the liners. Attached to the end of the inner tube by O-ring and screw cap, a disposable microfilter (15) (about 150 mg in weight) scavenges the stream. It is cooled by alcohol-dry ice mixture in the reservoir of the screw cap (16) at about 203 K. This microfilter is assembled by firstly enveloping a single piece of glass fiber paper filter (opening size 0.7-1.0 µm, not subject to appreciable moisture absorption but routinely heated by air blow at 343 K before assembling) 3


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between folded wire mesh to prevent material loss by friction, and then folding the whole assembly with aluminum foil (circular opening formed on both surfaces to permit gas flow). Typically 70% to 90% of the tar is captured by the microfilter. After a run, the aluminum roll, liners and the microfilter are left at ambient condition to remove the moisture either evolved from the sample or condensed from the air before weighting. Most of light oils, if captured, may have evaporated away in this stage, since subsequent treatment of collection surfaces in air blow at 323 K leads to virtually no change of weight uptake. Combined weight uptakes of the three components determine tar yield directly. Capture efficiency is verified by using two microfilters together in a run at 5 K/s, where very rarefied tar vapor could be expected, but no discernible capture appears on the second filter. Hence, considering the nominal accuracy of the six-figure balance used for weighting, the filter efficiency is no worse than 99.5%. Our tar collection method is indeed an integration of practices used in MIT7,8,15 and Imperial College,6,20 adopting respective merits while avoiding individual shortcomings. In one way, the right-to-mesh sweep gas eliminates recirculation of evolved tar onto the hot zone, mitigating tar cracking at very high peak temperatures. In another way, direct capture of tar by aluminum liners and filter avoids the use of solvent washing and subsequent evaporation, thereby circumvents concerns on the solubility of tar in the solvent and random uncertainty during the complicated procedure. More important, the completeness of capture can be easily verified and robustly maintained, compared to using trial and error6 to optimize character of the trap every time the velocity of sweep gas is varied. Total gas yield is reached by difference. Since remnant moisture in coal evaporates into the gas phase with chemically formed moisture, we first reduce moisture content of loaded sample from measured weight loss, as explained later, so the difference between corrected weight loss and measured tar yield represents yields of all noncondensable gases including chemically formed water. Finally all the yields are converted to dry, ash-free (daf) basis using moisture content of the sample and ash levels in Table 1. 2.2. Temperature Measurement-Control System. For each run, temperatures are measured by one 50-µm-diam K-type thermocouple (17) located at the center of the coal-loaded area (18). The thermocouple is made by first twisting individual bare wire to form a bead and then twisting two beads together before threading the two wires through the mesh within 1 mm apart. The wires are eventually connected to terminals (19) with slight tension to secure the final bead (150 to 200 µm in diameter) from detaching from the mesh. Heating power is fed from 24-volt storage battery via an insulated-gate bipolar transistor (IGBT) module, to which a data acquisition card delivers a digital pulse train featured by fixed frequency (100 Hz) but varying PID-calculated turn-on period. Thus, the average power in each 10-ms cycle is regulated by the ratio of the turn-on period (only during which the circuit is on, 8 ms maximum conduction time) to the period of the individual signal. Both digital pulse train and thermocouple logging are nominally triggered by another signal provided by a counter at a frequency of 100 Hz. However, to eliminate interference of heating current on thermocouple signal, a 1.5-ms delay is placed before every single digital pulse is actually generated. Therefore, during the 0.5-ms period for thermocouple reading there is no current on the mesh. We choose DC heating instead of AC to circumvent difficulties arose from phase-angle shift between the transformers in the latter system.3 To impose well-defined temperature-time history during heating on the particles, the combined feed-forward and feed-back control scheme is developed following established principles.21 The entire measurement-control hardware system runs under the command of LabVIEW code. 2.3. Coal Properties. 7 coals ranging from lignite to low-volatile bituminous are selected from either Argonne Premium Coal Sample Bank22 or Penn State Coal Sample Bank.23 Properties from the distributors are 4


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listed in Table 1. Also included are 4 Chinese non-coking coals. Each sample was briefly dry-sieved by hand in air to obtain 125 to 150 µm size fraction. To remove very fine particles that cannot be rejected by dry sieving, the sample was then wet-sieved, before being dried at 313 K for 7 hours in a vacuum oven that had been evacuated and refilled with nitrogen for several times. The low drying temperature was chosen to avoid cross-linking for low rank coals.24 The final sample is stored in an evacuated (0.2 bar) desiccator. Temperature-resolved weight loss and tar yield of 4 samples from lignite to low-volatile bituminous coal were obtained by heating at 5 K/s and 1000 K/s, respectively, to various target temperatures before immediate cooling. The effect of heating rate on their ultimate yields are obtained at heating rates from 5 to 1000 K/s, and final temperature of 973 K with 2 or 20 s hold. All the experiments were conducted under 1.01 to 1.04 atm. Helium flow rates of 5 l/min and 10 l/min are chosen for 5-20 K/s and 60-1000 K/s runs, respectively, giving a superficial speed of about 0.225 m/s and 0.45 m/s through the mesh. The varied flow rates are adequate to entrain tar vapor away from the hot zone at respective heating rate. Table 1. Coal Analyses c

VMdaf

Ad

Cdaf

Hdaf

Ndaf

Sorgdaf

Odaf

FSI

lig

49.78

9.72

72.94

4.83

1.15

0.70

20.38

n.d.

subbit

49.03

8.77

75.01

5.35

1.12

0.47

18.05

0

Illinois #6 (DECS-24)

hvCb

47.14

13.39

76.26

5.30

1.32

3.05

14.07

3.0

Ohio #4A (DECS-33)

hvBb

48.94

12.02

79.01

5.81

1.35

2.64

11.19

4.0

hvAb

43.53

9.44

81.95

5.63

1.49

1.80

9.13

7.5

hvAb

37.64

19.84

82.58

5.25

1.56

0.65

9.96

3.5

lvb

19.19

4.60

89.87

4.90

1.14

0.55

3.54

6.0

b

Beulah-Zap (APCS-8)

a

Wyodak-Anderson (APCS-2)

a

Pittsburgh (DECS-23) Lewiston-Stockton (APCS-7) Pocahontas #3 (DECS-19)

a

b

Rank

Coal type

a

a

c

rank

VMdaf

Ad

Cdaf

Hdaf

Ndaf

Sdaf

Odaf

FSI

Shenmu

hvBb

35.80

6.00

80.80

5.20

0.90

0.60

12.50

1.0

Datong

hvAb

32.60

8.50

82.20

5.00

1.20

0.60

11.00

1.0

Yangquan

an

9.50

7.20

92.50

3.40

1.30

0.90

1.90

n.d.

Fengfeng

an

3.30

10.40

95.90

1.80

0.80

0.30

1.20

n.d.

b

c

subject to both slow and rapid heating. by difference. FSI = free-swelling index (FOR REVIEWERS ONLY: the ultimate analyses of

Shenmu and Datong coals have been re-measured and updated.)

2.4. Experimental Uncertainty and Error

Figure 2. (left) Thermal history from the melting salt test. (right) Thermal histories from the center and periphery of the 5


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coal-loaded area (solid and dashed curves) heated at 1000 K/s, and transient temperature difference between the two readings (○).

The accuracy of transient thermometry is verified by melting salt test,2,3 in which the mesh loaded with NaCl particles is subject to electrical power capable of ensuring heating rate around 1000 K/s and peak temperature in excess of NaCl’s melting point. The arrested temperature plateaus during heat-up and cool-down are within 8 K of the actual melting point (1074 K) as shown in Figure 2 (left), indicating cumulative error of thermocouple reading at the highest heating rate in our experiment. To mitigate nonisothermality, we confine the coal-loaded area into an 8-mm-diam circle. More important, compared to a water-cooled support,3 the quartz plates provide only moderate cooling to the mesh by slow conduction. Hence, the reduced heat flux from the mesh center to its periphery results in diminished temperature gradients. As shown in Figure 2 (right), temperature difference across the sample during 1000 K/s heat-up keeps within 4.0 K for peak temperatures below 973 K, almost negligible compared to 30-40 K observed on earlier apparatus.4,5 Our rapid heating data (shown later) demonstrate that variation of ± 10 K in final temperature leads to change in weight loss from ± 2.0 wt % to ± 3.0 wt %, for lignite and high-volatile bituminous coal, respectively. Thereby, nonisothermality-related uncertainty is as small as ± 0.5 wt % for any coal tested here, substantiating the soundness of using only one central thermocouple for each actual run. Indeed, scatter in our 1000 K/s data primarily results from ambiguities in cooling rates. With cooling rate much slower than several thousand K/s,25 decomposition during cooling period can be substantial. For example, with Lewiston-Stockton coal, fluctuations in cooling rate around peak temperatures 827 K and 865 K caused scatter as large as ± 2.0 wt % in yields. For the sake of future modelers, actual thermal history at 1000 K/s for the 4 coals could be described as with holding time (herein defined as residence time spent within 5 K of the peak temperature) of 0.02-0.08 s and initial cooling rates that increase linearly with the logarithm of peak temperature, as illustrated in Figure 3.

Figure 3. Variation of initial cooling rates from 1000 K/s runs against peak temperature, Beulah-Zap (▼), Wyodak-Anderson (▽), Lewiston-Stockton (○), Pocahontas #3 (●).

To avoid artifact by loading density, we carried out preliminary tests for various coals. As shown in Figure 4, typical results at 1000 K/s to 973 K indicate that loading density affects yields until it’s reduced to a certain level. The loading-density-independent regimes identified are usually considered for 1000 K/s runs: less than 1.5 mg/cm2 for lignite, subbituminous and softening high-volatile bituminous coals, less than 2.0 mg/cm2 for Pocahontas #3, and less than 3.0 mg/cm2 for the non-softening Chinese coals. For slow heating and rapid heating to lower temperatures, the limit could be relaxed to improve resolution of the yields. Example of such 6


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trade-off consideration appears in Figure 5, where loading density for each run is plotted versus peak temperature for Lewiston-Stockton and Beulah-Zap.

Figure 4. Effect of loading factor on weight loss (closed symbol) and tar yields (open symbol), tested at 1000 K/s to 973 K with no holding time. Beulah-Zap (●○), Lewiston-Stockton (▼▽), Pocahontas #3 (▲△).

Figure 5. Actual loading factor values versus final temperatures, from 1000 K/s runs of Lewiston-Stockton(○) and Beulah-Zap (▼).

Given such effort, discernible decomposition of tar does occur for some coals (Beulah-Zap, Wyodak-Anderson and Lewiston-Stockton). After reaching the maximum value, their tar yields keep constant for a 100-120 K wide plateau before slightly decreasing by 1.0-1.5 wt % (2.6-6.6 % of the maximum values) if peak temperature is further increased. The decrease in tar yield was observed to coincide with the appearance of minor dichloromethane-insoluble deposition on the sample holder. The plateau value could represent the actual yield of pristine tar, whereas the drop in tar yield reflects consequence of tar decomposition in the heated zone. Since tar transforms into light oils, soot and noncondensible gases during secondary pyrolysis,26 the minor drop roughly equals to the amount of decomposed tar, because most light oils are too evaporative to be collected by our system and soot only deposits on the sample holder and char surface. However, we cannot correct weight loss for deposited soot since its simultaneous deposition on mesh and char makes its yield immeasurable, but the associated error must be within 1.5 wt %. Due to the lower drying temperature, some moisture remains in samples of low rank coals. Plotting raw data of weight loss versus tar on the basis of “as loaded” in Figure 6 shows that yields during tar evolution form a straight line. The intercept of weight loss is possibly a precise measure of remnant moisture because it 7


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evaporates upon heating and enters the observed weight loss as a constant offset. The correction and conversion to daf basis for weight loss and tar for the two low rank coals should be

 WL − Mal  WLdaf =  al  (100 − Ad )  100 − Mal 

(1)

 Taral  Tardaf =   (100 − Ad ) 100 − M al  

(2)

in which “al” and “d” denote “as loaded” and “dry”, “M” and “A” refer to moisture and ash, respectively. Values of Mal for the two coals are assigned as intercepts in Figure 6, 4.5 wt % and 4.0 wt % for Beulah-Zap and Wyodak-Anderson, respectively.

Figure 6. Data on the as-loaded basis from low rank coals as an indicator of sample moisture content, 1000 K/s runs (▼) and 5 K/ s runs (▽), with no holding time.

3. RESULTS

8


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Figure 7. Temperature-resolved weight loss (▼) and tar yields (▽) from 4 premium coals heated at 5 K/s and 1000 K/s with no holding time. The 4 coal types are arranged in order of increasing rank from parts a through d.

10


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Figure 8. Temperature-resolved weight loss (▼)and tar yields (▽) from 4 Chinese coals heated at 1000 K/s with no holding time. The 4 coal types are arranged in order of increasing rank from parts a through d.

Temperature-resolved weight loss and tar yields at 5 K/s and 1000 K/s for 4 premium coals appear in Figure 7, arranged in order of increasing rank. Data from Chinese non-coking coals at 1000 K/s appears in Figure 8 and supplements rapid heating behavior. Evidently, the scattering level of data at 1000 K/s is much reduced compared to previous studies, primarily resulting from the diminished nonisothermality in the sample. Several rank-dependent phenomena are depicted. Before the cessation of tar release, gases account for up to half of the weight loss for low rank coals, but only take a minor part for bituminous coals. For coals of increasing rank, the onset of weight loss and appearance of tar shift to higher temperatures, and tar evolution does not cease until a higher temperature is reached. These trends are consistent with earlier studies under rapid heating conditions,14,27 but Figure 7 illustrates that drastically varied heating rate doesn’t perturb any of them. Instead, faster heating pushes devolatilization to higher temperatures, enhancing tar release but suppressing gas formation. As a result, temperature-resolved weight loss doesn’t exhibit enhancement until sufficiently high temperature is achieved, consistent with earlier observations on weight loss dynamics from high-volatile bituminous coals.4,5,16

11


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Figure 9. Tar yield versus weight loss from temperature-resolved data of 4 premium coals, no holding time. (a) Beulah-Zap [1000 K/s (▽) and 5 K/s (▼)] and Wyodak-Anderson [1000 K/s (○) and 5 K/s (●)], (b) Lewiston-Stockton [1000 K/s (▽) and 5 K/s (▼)], (c) Pocahontas #3 [1000 K/s (▽) and 5 K/s (▼)].

In Figure 9, tar yield is plotted versus weight loss. For any coal at a given heating rate, correlation of the data points before the end of tar evolution indicates a quite straight line, in agreement with several earlier studies with or without isothermal reaction period.8,9,13,28 Note that for any coal, the 1000 K/s data invariantly fall to the right hand side of the 5 K/s data, i.e., higher fraction of tar in any level of weight loss by faster heating. It indicates a shift to generate tar rather than noncondensable gases at every conversion level before the end of tar evolution by faster heating.

Figure 10. Fractional gas yields at (a) 5 K/s and (b) 1000 K/s with no holding time from 4 premium coals. All values are 12


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normalized by the yields at 1073 K (5K/s) or yields at 1160 K (1000 K/s), respectively. Yields before and after cessation of tar release are respectively denoted by closed and open symbols. Beulah-Zap (●○), Wyodak-Anderson (▲△), Lewiston-Stockton (▼▽), Pocahontas # 3 (◆◇).

Gas evolution is not inhibited by evaporation conditions that affect tar evolution rates, so evolution history of the former provides more relevant information on chemical kinetics in the condensed phase.29 In this perspective, total gas yields are normalized by ultimate values (values at 1073 K for the 5 K/s runs and values at 1160 K for the 1000 K/s runs) in Figure 10, with yields obtained during tar evolution shown as filled symbols. Our data at both heating rates reconfirm shifted gas formation to higher temperatures and progressively shrunk temperature range of gas evolution for coals of higher rank, in agreement with Xu and Tomita.14 Extra rank-dependent features are also depicted. First, ignoring values near the onset of product appearance due to significant percentage error, the difference in temperature to arrive at a certain conversion level for the whole rank spectrum keeps almost constant at about 120 K before the end of the tar release, and then diminishes gradually as final temperature continues to rise. Second, maximum gas conversion level before the cessation of tar evolution gradually decreases for higher rank coals, from 0.54 for lignite to 0.32 for low-volatile bituminous coal at 5 K/s, and from 0.48 to 0.42 at 1000 K/s, indicating that larger part of total gas is expelled after tar evolution is over for coals of increasing rank. However, it is worthwhile to note that escaped tar molecules keep shuttling gas precursors into the gas phase, so the concentration of gas precursors in the condensed phase doesn’t depend solely on its own conversion pathways.

Figure 11. Effect of heating rate on weight loss (▼)and tar yield (▽) from 4 premium coals. Operating conditions are heating rates between 5 and 1000 K/s with 2 s holding time. (also including weight loss (●) with 20 s holding time for Beulah-Zap and Wyodak-Anderson) The 4 coal types are arranged in order of increasing rank from parts a through d.

Heating rate dependence of ultimate yields appears in Figure 11. Extending holding time from 2 s to 20 s 13


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yields up to 2.5 wt % extra weight loss for low rank coals, but hardly enhances weight loss of the bituminous coals beyond experimental uncertainty. So, isothermal periods of 20 s and 2 s are regarded as adequate to obtain “ultimate yields” for the two groups of coals at 973 K, respectively. Generally, both tar and weight loss increase almost linearly with the logarithm of rising heating rate, but the increase in tar keeps outweighing enhancement in weight loss, indicating a suppression of gas formation by faster heating. Note that in most of previous studies on softening high-volatile bituminous coal, tar yield becomes less sensitive to heating rate higher than roughly 100 K/s,4,15,17 but according to our results for Lewiston-Stockton, the sensitivity maintains throughout the domain tested, thanks to our scrutiny of loading density at high heating rates. Our results depict the rank-dependent sensitivity of tar yield to heating rate: the relative enhancement is largest for the Beulah-Zap lignite (by 70%), then reduces for the Wyodak-Anderson subbituminous coal (by 57%), reaching a minimum for the Lewiston-Stockton high-volatile bituminous coal (34%) before rising again for the Pocahontas #3 low-volatile bituminous coal (by 65%). Compared to literature data, quantitative discrepancy appears for lignite and low-volatile bituminous coal. The enhancement in tar yield reported by Sathe et al.18 is substantially greater, from 4 wt % to 23 wt % by rising heating rate from 1 K/s to 2000 K/s. Our tar yields are comparable to their measurements but remarkably higher for heating rates lower than 100 K/s. Note their ultimate gas yield (by difference) at 1 K/s is in excess of 55 wt %, exceptionally higher than obtained in our experiment (31 wt %) at 5 K/s and Solomon et al.’s results (around 35 wt %) at 0.5 K/s for lignites.30 In contrast, we observe stronger sensitivity of low-volatile bituminous coal’s tar yield to heating rate than reported by ref. 19 (by 38 %). Note also in ref. 19 and 4, the enhancements in weight loss (by 10-15 %) are also smaller than what we measured (by 26 %). Perhaps the confined loading density used by us inhibits secondary pyrolysis to a greater extent under rapid heating conditions. We would like to mention the surprisingly high tar yield and its fraction in weight loss from high-volatile coals provided by the two premium coal sample banks. Table 2 lists conversion data prepared by heating sample at 5 K s-1 to 873 K (793 K for Illinois #6) with no holding time. These tar yields are comparable to earlier results 17,31 obtained by dissimilar tar quantification methods for coals from the same distributors, but are equal or even higher than observed with all the other coal samples by faster heating. The most possible reason lies in the proved superior process and storage techniques of the two sample banks that stringently prevent samples from low temperature oxidation, which gives rise to lower tar yield, decreased swelling behavior and substantial loss of thermoplasticity. For example, Griffin et al.15 observed char particles of Pittsburgh No.8 (PSOC-1451D) reach 1.5 times the raw coal’s mean diameter at 1000 K/s, while we observed more than 6.0 times augment in diameter with Pittsburgh No.8 from DECS series under the same heating conditions. Table 2. Yields of high-volatile bituminous coals from premium coal sample banks

Tar yield

Weight loss

wt % daf

wt % daf

Illinois #6 (DECS-24)

29.5

39.2

Ohio #4A (DECS-33)

39.3

51.0

Pittsburgh (DECS-23)

38.7

44.7

Lewiston-Stockton (APCS-7)

28.8

36.3

Coal Type

Conclusion (1) Primarily by diminishing nonisothermality in the sample, uncertainty in the temperature-resolved data 14


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Energy & Fuels

from WMR under rapid heating conditions can be substantially reduced. (2) The impact of loading density in WMR experiments depends on operating conditions and coal type. For rapid heating experiment with low rank coals or softening high-volatile bituminous coals, it is essential to ensure the independence of yields on loading density. (3) We develop a convenient tar collection method for WMR. It mitigates secondary pyrolysis by right-to-mesh sweep gas, and captures tar directly by aluminum liners and filter with easily-secured completeness. (4) The obtained temperature-resolved data over the whole rank spectrum reconfirms the continuous rank effects in terms of reaction dynamics and partitioning between tar and noncondensables, but at disparate heating rates of 5 K/s and 1000 K/s. (5) Constant variation of about 120 K exists in noncondensables evolution histories among various coals before the cessation of tar release. Since the wake of tar evolution, variations in gas formation kinetics for different coals gradually shrink for increasing temperatures. Larger fraction of total noncondensible gases is expelled after tar evolution for coals of higher rank. (6) The sensitivity of tar yield to heating rate maintains the same over the range 5 K/s to 1000 K/s, but varies with rank, being greatest for lignites and low-volatile bituminous coals but exhibits a minimum for high-volatile bituminous coals.

AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected] (S. Hui). Tel: +86 29 82668784; fax: +86 29 82668703. Notes

The authors declare no competing financial interest.

ACKNOWLEDGEMENTS We acknowledge Drs. Lei Chen and Cai Zeng for advice on apparatus design, and Ms. Wei Song for providing the Datong coal sample. Financial support from the National Energy Application Technology Study and Demonstration Project of China (Contract NY2013040303) and the National Key Technology R&D Program of China (Contract 2012BAA09B01) are gratefully acknowledged.

REFERENCES (1) Niksa, S. J.; Liu, G. S.; Hurt, R. H. Coal Conversion Submodels for Design Applications at Elevated Pressures. Part I. Devolatilization and Char Oxidation. Prog. Energy Combust. Sci. 2003, 29, 425-477. (2) Freihaut, J. D.; Proscia, W. M. Tar Evolution in Heated-Grid Apparatus. Energy Fuels 1989, 3, 625-635. (3) Gibbins, J. R.; King, R. A. V.; Wood, R. J.; Kandiyoti, R. Variable-Heating-Rate Wire-Mesh Pyrolysis Apparatus. Rev. Sci. Instrum. 1989, 60, 1129-1139. (4) Gibbins-Matham, J.; Kandiyoti, R. Coal Pyrolysis Yields from Fast and Slow Heating in a Wire-Mesh Apparatus with a Gas Sweep. Energy Fuels 1988, 2, 505-511. (5) Gibbins, J. R.; Kandiyoti, R. The Effect of Variations in Time-Temperatur History on Product Distribution from Coal Pyrolysis. Fuel 1989, 68, 895-903. (6) Güell, A. J.; Kandiyoti, R. Development of a Gas-Sweep Facility for the Direct Capture of Pyrolysis Tars in a Variable Heating Rate High-Pressure Wire-Mesh Reactor. Energy Fuels 1993, 7, 943-952. (7) Suuberg, E. M.; Peters, W. A.; Howard, J. B. Product Composition and Kinetics of Lignite Pyrolysis. 15


Energy & Fuels

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from Ultimate Analyses Alone. Energy Fuels 1994, 8, 659-670. (30) Solomon, P. R.; Serio, M. A.; Deshpande, G. V.; Kroo, E. Cross-Linking Reactions during Coal Conversion. Energy Fuels 1990, 4, 42-54. (31) Solomon, P. R.; Serio, M. A.; Carangelo, R. M.; Bassilakis, R.; Gravel, D.; Baillargeon, M.;Baudais, F.; Vail, G. Analysis of the Argonne Premium Coal Samples by Thermogravimetric Fourier Transform Infrared Spectroscopy. Energy Fuels 1990, 4, 319-333.

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Weight Loss and Tar Evolution during Coal Devolatilization at Various

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