Char combustion reactivities for a suite of diverse solid fuels and char-forming organic model compounds

Char combustion reactivities for a suite of diverse solid fuels and char-forming organic model compounds

Proceedings of the Combustion Institute, Volume 29, 2002/pp. 423–431 CHAR COMBUSTION REACTIVITIES FOR A SUITE OF DIVERSE SOLID FUELS AND CHAR-FORMING...

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Proceedings of the Combustion Institute, Volume 29, 2002/pp. 423–431

CHAR COMBUSTION REACTIVITIES FOR A SUITE OF DIVERSE SOLID FUELS AND CHAR-FORMING ORGANIC MODEL COMPOUNDS TODD LANG and ROBERT H. HURT Division of Engineering Brown University Providence, Rhode Island 02912, USA

A bewildering variety of organic materials are now serious candidate fuels for commercial power generation, where they are burned alone or in cofiring configurations with coal. While char reactivity trends are well-established for coals, there have been no analogous comparative studies on large sets of diverse alternate fuels. This article presents reactivity measurements of chars formed at 700 C and 1000 C for 31 materials, including 20 solid fuels (international coals, petroleum cokes, tires, wood, agricultural residues, and refuse-derived fuel) and 11 organic model materials chosen for their low levels of potentially catalytic inorganic matter (graphites, petroleum pitches, resins, commercial organic chemical reagents, and microcrystalline cellulose). Standard reactivities at 500 C in air varied by over 4 orders of magnitude and correlate poorly with organic elemental composition or char surface area. The essentially non-catalytic model materials exhibit uniformly low reactivities that fall in a narrow band as a function of weight percent of carbon (daf) and correlate reasonably well with char surface area. A model is developed that combines statistical analysis of the data with chemical insights from the literature on the form and dispersion of catalytic inorganic matter to explain most of the observed reactivity variation. The model indicates that catalytic effects are significant for almost all of the practical solid fuels, due most particularly to the presence of nano-dispersed potassium, calcium, and magnesium.

properties. To gain a deeper understanding of the origin of char reactivity in this data set, the sample suite was augmented by a series of char-forming chemical reagents and model materials that are nearly free of the inorganic contamination that is ubiquitous in practical solid fuels.

Introduction Concern over the potential effects of global warming is driving a diversification in solid fuel selection, a shift from almost sole reliance on coal to a broader fuel mix encompassing organic matter of diverse origin and composition. Comparative studies are needed in which large sets of these diverse alternate fuels are burned under standard conditions chosen to reveal intrinsic fuel-to-fuel differences in each of the fundamental combustion subprocesses: pyrolysis, char oxidation, mineral transformations, and pollutant formation. For coals, the char combustion subprocess has been extensively studied and reviewed [1–4], and the major reactivity trends have been established through comparative studies using a range of coals, including studies below 1000 K [5– 8] and at flame temperatures [9–10]. There are no comparable studies on large sets of alternate fuels, although the recent literature does contain valuable information on individual fuels [11–14] or small sets of fuels of interest in a particular region [15–18]. The goal of the present work was to measure char combustion reactivities for a large set of traditional and alternative solid fuels under standardized lowtemperature conditions, free from the influence of mass- and heat-transfer processes (zone I). A secondary goal was to identify possible quantitative relationships between char reactivity and parent fuel

Materials and Experimental Procedures A suite of 31 materials was assembled for this study (see Table 1), including 20 solid fuels and 11 organic model materials chosen for their low levels of potentially catalytic inorganic matter. Each of the 31 raw materials was pyrolyzed in a benchtop tube furnace at 700 C for 1 h using a thin bed of sample spread in an oblong alumina boat, purged with highpurity helium (600 mL/min). Seventeen of the raw materials were also pyrolyzed at 1000 C for 1 h to investigate the effect of heat treatment temperature. The subsequent chars were crushed and sieved to obtain a 75–106 lm size fraction for study. Because of the enormous reactivity range within this sample suite (over 4 orders of magnitude), it is not practical to measure all reactivities at a common temperature. Non-isothermal thermogravimetric analysis (TGA) was therefore carried out using a Cahn TG-2141 apparatus fed with dry air (21% oxygen) at 40 mL/min and atmospheric pressure. Char samples of 3–10 mg

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1. Critical temperature values, Tcr defined as the temperature at which the combustion rate first equals 0.05 g/min-g-initial-char. Other researchers have reported similarly defined Tcr values [7] as a convenient reactivity index that does not require kinetic assumptions. (Note that Charpenay et al. [7] use a combustion rate of 0.065 minⳮ1 to define Tcr, but some samples in the present study never achieved rates this high at the chosen heating rate and gas environment.) 2. Reactivity values, R, as burning rates in air at a standard reference temperature (Tref) of 500 C, defined as: 1 dm exp[ⳮ(E/R)(1/Trefⳮ1/T)] m dt

Rⳮ

Fig. 1. Correlation between the two reactivity indices reported in this paper: critical temperatures, Tcr [K], and standard reactivities at 500 C in air, R [sⳮ1]. For interconversion between the two indices one can use the following empirical expression: 2 log10 R ⳱ 12.6 ⳮ 2.77 • 10ⳮ2Tcr Ⳮ 9.97 • 10ⳮ6T cr

were spread as a thin bed on a platinum pan to avoid mass- and heat-transfer effects. Time, sample temperature, and mass were recorded as the sample temperature was raised first to 105 C to drive off any moisture, then at 7 C/min to 950 C, at which point complete burnout was achieved for all samples. All reactivity measurements were made in triplicate and the mean values reported. A 14-sample subset of the 700 C chars was chosen for surface area measurements. Some fresh (unreacted) chars exhibit strong molecular sieving behavior in which the nitrogen and carbon dioxide surface areas are grossly different, an effect that is typically eliminated by only slight oxidative conversion [19,20]. To avoid this problem, chars slated for vapor adsorption measurements were first partially reacted by slow air oxidation in a tube furnace to conversions from 15% to 35%—nominally 20%, at which the reactivity indices were derived (see below). Each of these chars was outgassed for 20 h at 300 C, followed by measurement of 80-point vapor adsorption isotherms in nitrogen at 77 K and in carbon dioxide at 195 K, from which the BrunauerEmmet-Teller (BET) surface areas were computed. Definition of Standard Reactivity Indices The non-isothermal TGA profiles were used to extract standard char reactivities reported here in two different but essentially equivalent forms:

(1)

where m, dm/dt, and T are determined at 20% conversion (daf). The exponential term brings the raw rates to a common temperature for convenient fuel-to-fuel comparison. This temperature normalization was made using an activation energy of 35 kcal/mol (146 kJ/mol), a typical value for zone I char oxidation [2,8,21,22]. The use of a single activation energy is not a significant disadvantage of this particular reactivity index because activation energies vary over only a modest range for the low-temperature, disordered chars of this study, and because Tref is chosen near the center of the range of actual temperatures, making the temperature correction term inherently small. Indeed, there is an excellent correlation between Tcr and R as shown in Fig. 1, so either index may serve as a valid expression of the relative reactivity of different chars in the sample suite. Results Standard reactivities as Tcr and/or R values are presented in Table 1 and Figs. 2 and 3. Reactivities of the 1000 C chars are presented in Fig. 3. Surface areas are presented in Table 1 and are cross-plotted against reactivity in Fig. 4. Trends in reactivity with fuel type are most easily identified using Fig. 2, which shows that reactivities of the 700 C chars vary by almost 5 orders of magnitude under identical conditions. Considering only practical solid fuels, the reactivities still span over 3 orders of magnitude, from high-rank coal chars (lowest reactivity) to cornstalk chars (highest reactivity). The 1000 C chars in Fig. 3 show a similar data pattern, except the total variation in reactivity spans just over 3 orders of magnitude. A strong correlation is seen between reactivity and carbon (daf) content of the vitrinite-rich coals, as observed previously [5,6,10]. This trend has no relevance for biomass fuels, however, which cluster closely in composition (40%–50% daf carbon),

0.1 19.8 – 0.1 3.2 9.4 16.2 0.6 3.8 0.2 0.5 0.6 – – – – – – 12.6 – 0.1 – 0.1 – – 22.4 – – – – –

90.7 71.3 – 76.4 74.8 68.8 63.6 80.2 78.4 79.4 76.1 80.5 – – – – – – 26.5 – 6.2 – 11.0 – – 5.6 – – – – –

41.9 41.9 42.1 42.4 44.0 44.2 44.6 45.8 45.9 46.1 46.1 46.4 63.7 73.1 75.5 77.8 78.1 78.2 78.7 79.7 87.4 87.6 87.8 89.9 90.3 90.5 94.5 95 95 96.3 100.0

6.4 6.5 6.5 6.5 6.3 6.4 6.4 6.2 6.3 6.3 6.3 6.1 9.2 4.5 5.7 6.0 5.4 4.2 4.8 4.2 2.4 7.6 4.0 4.9 4.0 2.0 5.1 5 5 0.4 0.0

51.7 51.5 51.4 48.0 48.9 48.6 48.7 47.8 47.6 47.6 46.0 47.3 26.9 20.6 18.8 16.2 9.7 15.2 13.8 16.0 5.1 2.5 1.4 3.3 4.3 5.9 0.2 0.0 0.0 0.10 0.0

0.0 0.0 0.01 3.1 0.7 0.6 0.3 0.2 0.2 0.0 1.5 0.1 0.01 1.0 0.01 0.1 1.3 1.1 2.0 0.0 2.6 0.4 1.4 1.1 0.8 0.7 0.0 0.0 0.0 0.9 0.0

0.0 0.0 0.01 0.0 0.1 0.2 0.0 0.0 0.1 0.0 0.1 0.1 0.3 0.8 0.0 0.01 5.4 1.3 0.7 0.1 2.5 1.9 5.4 0.8 0.6 0.9 0.2 0.0 0.0 2.4 0.0

10 23,600 100 67 106 70 52 166 3,100 170 220 10 100 6,000 100 100 16,000 13,000 22,600 100 37 2,200 17 6,000 18,000 27,800 – – – 100 1

5 120 100 983 1,900 1,720 650 1,470 648 408 700 2,450 100 18,000 100 100 5,900 13,000 6,330 100 102 2,700 43 600 400 6,250 – – – 100 2

10 74 100 289 130 52 170 270 3,700 240 250 295 100 6,100 100 100 23,000 8,500 2,630 300 307 1,500 213 7,000 3,300 6,900 – – – 500 1

40 40 100 480 15,300 15,500 7,880 1,450 1,300 738 997 2,360 100 500 100 100 2,500 500 689 100 10 500 10 400 4,600 5,700 – – – 100 0

10 44 100 218 1,600 1,140 400 380 440 150 217 300 100 5,600 100 100 800 2,600 1,940 100 20 600 11 800 600 1,440 – – – 100 0 59 253 100 452 41 72 72 71 206 77 1,590 68 100 6,600 100 100 800 1,200 278 100 136 400 52 500 200 2,730 – – – 100 1

1 1 100 1 1 1 7 1 15 1 1 1 100 0 100 100 14 1 8 100 476 200 198 7 17 19 – – – 100 0 12 62,400 100 256 2,380 7,880 71,800 954 8,770 910 745 644 100 5,000 100 100 37,000 23,000 25,400 500 193 5,000 67 7,000 29,000 46,700 – – – 200 7

1 1 50 1 1 1 1 1 8 1 1 1 50 14 50 50 3 2 16 50 477 50 501 9 21 41 – – – 280 0 550 340 590 – 210 260 – – – – – 430 390 160 – – – – – – – – – 110 320 170 – 140 130 – 5.1

b

Elemental analyses performed by Huffman Laboratories (Golden, CO) unless otherwise noted. Multipoint BET surface areas of chars (He, 1 h, 700 C) at 20% conversion (daf). c Critical temperatures for the chars prepared at 700 C, defined as the temperature at which the combustion rate first equals 0.05 g/min-g-initial-char. d Standard reactivity at 500 C in air of chars prepared at 700 C or 1000 C, as labeled. e Model materials selected for low amounts of potentially catalytic impurities. f L-Ascorbic acid 6-palmitate. g Elemental analysis reported by Department of Energy Coal Bank at Penn State data sheets. h 2,2⬘-dimethyl-1,1⬘-bianthraquinone. i Synthetic mesophase pitches from naphthalene polymerization, high (IIP) and medium (MP) grades; Elemental analysis reported by manufacturer, Mitsubishi Gas Chemical. j Elemental composition reported by vendor (Aldrich).

a

Microcrystallinee Cellulose Synthetic RDF Sucrose, C12H22O7e Dried sludge Corn stalk Wheat straw Rice hulls Hardwood Bagasse sachurum Pinus radiata Softwood Populus deltoides C22H38O7e,f Beulah (ligA)g Polycarbon, resine Phenol-for, resine Illinois no. 6 (hvCb)g Rosebud (subB)g Koonfontain (mvb) C30H18O4e,h Fluid coke Tire bits Delayed coke Pocahontas (lvb)g Lykens Valley (an)g Lei Yang (an) Petroleum pitche AR-IIP pitche,i AR-MP pitche,i Graphite powdere Graphite rodse,j

Sample

650 900 ⳮ4.35 350 905 ⳮ4.41 640 900 ⳮ4.35 – 838 ⳮ3.48 330 701 ⳮ1.70 320 716 ⳮ2.12 – 751 ⳮ2.69 – 781 ⳮ3.07 – 873 ⳮ3.98 – 845 ⳮ3.80 – 781 ⳮ3.08 460 762 ⳮ2.82 500 912 ⳮ4.51 320 730 ⳮ2.44 – 848 ⳮ3.85 – 886 ⳮ4.20 – 823 ⳮ3.59 – 765 ⳮ2.90 – 904 ⳮ4.08 – 847 ⳮ3.80 – 951 ⳮ4.66 – 931 ⳮ4.53 – 911 ⳮ4.39 140 974 ⳮ4.94 380 961 ⳮ4.88 200 975 ⳮ5.01 – 974 ⳮ4.97 180 966 ⳮ4.90 160 964 ⳮ4.90 – 1,052 ⳮ5.54 6.1 1,201 ⳮ6.39

ⳮ4.91 ⳮ4.84 ⳮ4.58 – ⳮ2.67 ⳮ3.34 – ⳮ3.18 – – – – ⳮ5.05 ⳮ3.83 – ⳮ4.81 ⳮ4.46 – – – ⳮ5.07 ⳮ4.89 – – – ⳮ5.15 – ⳮ5.76 ⳮ5.97 ⳮ5.55 ⳮ6.15

N2 CO2 log10 Rd log10 Rd Areab Areab Tcrc 700 C 1000 C Ash VM C H O N S Al Ca Fe K Mg Na Ni Si V (sⳮ1) (sⳮ1) (dry %) (dry %) (daf %) (daf %) (daf %) (daf %) (daf %) (ppmw) (ppmw) (ppmw) (ppmw) (ppmw) (ppmw) (ppmw) (ppmw) (ppmw) (m2/g) (m2/g) (K)

TABLE 1 Sample suite of solid fuels and organic model substances: Propertiesa and char reactivities

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ENERGY PRODUCTION—Biomass, Coal, and Char Combustion

Fig. 3. Master plot of measured reactivities for chars prepared at 1000 C from a 17-sample subset of the 31sample suite. The absolute reactivities are generally lower, but the fuel-to-fuel differences fall in a pattern very similar to the 700 C chars in Fig. 2. Literature data from Jenkins et al. [5] on coal chars prepared under similar conditions is shown for comparison.

Fig. 2. Master plot of measured reactivities for chars prepared at 700 C from the 31 precursors, plotted versus the percentage carbon content of the parent material (daf). The thick dashed line shows a strong correlation between reactivity and rank among vitrinite-rich coals, similar to the rank trends reported in other studies of low-temperature kinetics [5,6], and qualitatively similar to rank trends reported at high temperature [10] in the form of zone II reactivities that are influenced both by chemical surface reactivity and pore diffusion resistance. The dotted lines define a relatively narrow region that encompasses all of the model materials that are nearly free of potentially catalytic inorganic matter. The lower dotted line is taken as a non-catalytic baseline in the model discussed in the section on reactivity/property relations. A spliced empirical relation for the non-catalytic baseline is: log10 Rcarb ⳱ ⳮ4.1 ⳮ 6.25 • 10ⳮ3 (wt % C) for wt % C  80 log10 Rcarb ⳱ ⳮ4.52 ⳮ 2.11 • 10ⳮ7 exp[0.16(wt % C)] for wt % C  80

while their reactivities span almost 3 orders of magnitude. Carbon content is a very poor indicator of biomass reactivity. By analyzing the data in Table 1, it can be further stated that no organic composition variable (C, H, O, N, S) provides enough information for making even a crude estimate of biomass char reactivity. It is notable in Fig. 2 that the model materials all have reactivities that lie in a relatively narrow band in the low-reactivity region. ‘‘Model’’ materials are those with less than 100 ppmw of each of the potentially active catalysts (Na, K, Ca, Mg, V, Ni); one sample (a graphite) has 500 ppmw iron, but iron is a relatively poor oxidation catalyst. These non-catalytic char reactivities show almost no composition dependence between 40% and 80% carbon (daf), but do fall away above 90% carbon (daf). Overall, the patterns revealed in Fig. 2 strongly suggest that organic composition is secondary to other factors determining reactivity in the char of precursors below 80% carbon (the majority of practical solid fuels). Reactivity/Property Relations The goal of this section is to investigate quantitative relationships between reactivity and properties of the parent material, focusing on the larger data set of 700 C chars. Statistical software (SPSS Inc.) used for multivariate linear regression revealed positive correlations with statistical significance between

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Fig. 4. Cross-plot of reactivity vs. total char surface area (N2 BET) for pure and non-catalytic materials (left-hand side) and for all samples (right-hand side). Note the different reactivity scales in the two graphs. Circles (pure materials) are the nearly inorganic-free model substances from Table 1: triangles (non-catalytic materials) are samples containing significant amounts of inorganic matter, but predicted by dispersion arguments to react in a primarily non-catalytic mode (see section on reactivity/property relations).

reactivity and three properties: weight percent K, Ca, and Mg. A simple linear correlation based on these variables, however, does not provide an adequate description of the data set. Such blind statistical analyses are easy to perform, but make no use of insights gained from decades of research on combustion and fuel chemistry. We therefore pursued a hybrid approach that combines statistical analysis with chemical insights derived from the literature data on different fuel classes. We begin by expressing the reactivity as a sum of two contributions: R ⳱ Rcarb Ⳮ Rcat

(2)

where Rcarb represents reaction on non-catalytic (carbon) active sites [23] and Rcat represents reaction on catalytic active sites [22], assumed to be independent, parallel processes. The existence of the narrow model substance band on Fig. 2 suggests that variations in Rcarb are only a small part of the total reactivity variation for fuels up to 80% carbon (daf) content. We therefore, as an approximation, define a single, non-catalytic baseline reactivity (shown in Fig. 2) that establishes the model parameter Rcarb as a function of the parent material carbon content. The primary challenge is to investigate relations between the catalytic residual, Rcat, and fuel properties. Studies involving demineralization and/or direct catalyst addition clearly reveal which elements are intrinsically catalytic for char oxidation: group I metals, group II metals, and many transition metals including V, Ni, and Fe, which have a particular importance in some solid fuels [5,22,24–27]. Another key consideration is catalyst dispersion, or particle size, which is expected to vary greatly across the spectrum of solid fuels. Although dispersion is a

continuum variable, one can consider two important limiting cases: (1) granular dispersion, particulate matter of supramicron dimension arising from extraneous matter or bulk additives in the fuels, and (2) nanophase dispersion, finely dispersed particulate matter of submicron dimensions originating from nanophase or atomically dispersed metals in the parent materials that have partially sintered during carbonization [22]. This highly active material may originate as dissolved salts in plant water, as cations exchanged on carboxylic sites, or as organometallic compounds with essential function (e.g., chlorophyll, porphyrins). Granular material has low catalytic activity by virtue of its low surface area [28], which suggests the relation: Rcat ⳱ ␣(Knano) Ⳮ b(Canano Ⳮ Mgnano) Ⳮ c(Vnano)

(3)

Here Mnano indicates the weight fraction (ppmw) of the nano-dispersed or atomically dispersed form of catalytic metal, M, present in the parent fuel, and ␣, b, and c are specific activity coefficients to be determined from statistical analysis of the data. Although Na, Ni, and Fe are known catalysts for char oxidation [26,29], they are not observed to make statistically significant contributions to reactivity in this particular sample set and thus cannot be included in equation 3. The alkaline earth metals are grouped because our statistical analysis revealed a strong cross-correlation, making any attempt to distinguish the separate effects of Ca and Mg statistically meaningless. Investigations of coal chars [5,6] and of graphite [26] have revealed similar specific activities for Ca and Mg, so their linear combination here with equal weight is a useful approximation.

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Fig. 5. Compilation of literature data on the abundance of carboxylic groups in coals of various rank. Sources: circles are data from Ihnatowicz in Ref. [38], diamonds are data from Morgan, Jenkins, and Walker [31], squares are data from Blom in Ref. [38], and triangles are data from Otake and Walker [39]. The fitted curve for use in the hybrid chemical/statistical model is: (wt % O as COOH) ⳱ 3.051 • 109 ⳯ exp[ⳮ0.2706 wt % C (daf)] for wt % C  90 Above 90 wt % C, COOH site density is taken to be 0 in the model.

Before ␣, b, and c can be determined, specific rules are needed for estimating the degree of catalyst dispersion in the various fuel classes. In coals, mineral matter is a combination of granular material and atomically dispersed material [30,31], with the major source of atomically dispersed matter in the form of cations exchanged on carboxylic sites [22,31]. The compilation of literature data in Fig. 5 shows that the carboxylic site density is strongly dependent on coal rank. Here, we use the correlation shown in Fig. 5 to estimate carboxylic site density from weight percent carbon (daf) and carry out a stoichiometric calculation to determine what fraction of the group I and II metals in the sample is needed to saturate those COOH sites. The calculation assumes one COOH site per group I cation or two COOH sites per group II cation. There is insufficient literature data to assign cation-specific binding probabilities, so the calculation assumes equal probabilities. Applying this algorithm reveals that COOH sites, not metals content, limits the number of exchanged cations possible for many coals. In low-rank coals, a large fraction of the group I and II metals can be

accommodated on ion-exchange sites, while in highrank coals only a small fraction of the metal atoms find COOH sites available and a large portion of the metal must exist in other forms, presumably as granular mineral phases. This approach is an approximation intended only to capture the major trends with coal rank. More quantitative data on cation loadings would certainly improve the predictive ability, but would require chemical fractionation, a nonstandard laboratory procedure that would subvert the goal of estimating reactivity from standard fuel analyses. This simple two-step correlative technique gives a reasonable estimate of Knano, Canano, and Mgnano for use in equation 3. For the highly variable alternate fuels, we estimate dispersion trends on the basis of direct measurements of the dominant chemical forms of the inorganic matter in various fuel types reported in the literature. Dispersion of metals in biomass is also a mixture of granular and atomically dispersed forms [32–35]. In most plants, however, potassium salts are essential nutrients, and a high fraction of the potassium (80%–90%) is either water soluble or ion exchangeable [32,33,35,36]. Most calcium also tends to be water soluble or ion exchangeable, which is indicative of high dispersion. An exception is sugar cane bagasse, which is washed during processing and has lost soluble salts [33,35]. For purposes of the model, we take the group I and II metals in biomass to be finely dispersed, with the exception of bagasse, in which the remaining matter is taken to be primarily granular. For the non-biomass alternate fuels, we take the inorganic components in tire fuel to be granular, while the vanadium in cokes is taken to be nano-dispersed, originating as atomically dispersed material within porphyrin structures in petroleum [37]. The above set of dispersion rules, along with equations 2 and 3, the baseline function in Fig. 2, and the carboxylic correlation in Fig. 5, define the hybrid chemical/statistical model. Application of the model to our data set leads to the final correlation and the optimal least squares values of ␣, b, and c shown in Fig. 6. The now complete hybrid model provides a link between parent fuel elemental composition and char reactivity at 500 C in air for chars prepared at 700 C from arbitrary and diverse organic precursors. This analysis makes clear that catalysis plays a dominant role in the low-temperature reactivity of chars prepared at 700 C. Through examination of the data patterns in Fig. 3, this same conclusion appears to hold for chars prepared at 1000 C. Using the final values of ␣, b, and c as sensitivity coefficients, it can be shown that as little as 44 ppmw of nano-dispersed potassium or 270 ppmw of nano-dispersed Ca or Mg is sufficient for the catalytic component of reactivity to exceed the non-catalytic component for materials below 80 wt % carbon (daf).

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vary over 4 orders of magnitude when chars are prepared at 700 C and over 3 orders of magnitude when prepared at 1000 C. Reactivities correlate poorly with organic elemental composition or with char surface area. However, a subset of specially acquired model materials with minimal inorganic matter exhibit low reactivities that fall in a narrow band as a function of weight percent of carbon, and their reactivities correlate reasonably well with total char surface area. We show that some degree of modelbased reactivity estimation is possible using only the elemental analyses of the parent fuels, but only if the model makes use of the existing body of knowledge on the fuel-specific origin, form, and dispersion of potentially catalytic inorganic matter, which has a dominating influence of the reactivity of most fuels under these conditions. Acknowledgments Fig. 6. Comparison of measured reactivities with reactivities given by the hybrid chemical/statistical model. The plot contains every solid fuel and model material listed in Table 1 except graphites (which cannot be meaningfully described as 700 C chars) and the Permian southern hemisphere coal, Koonfontain (for which Fig. 6 does not contain relevant data on carboxylic sites). The model reactivity is equal to:

Financial support from the DOE’s National Energy Technology Center, DE-FG26-OONT40815, is gratefully acknowledged. The authors wish to acknowledge Kevin Davis of Reaction Engineering International for supplying wood and coke samples, Indrek Aarna and Eric Suuberg for tire samples, and Andrew Lambe and Beth Wasserman of Brown University for technical contributions.

R ⳱ Rcarb Ⳮ ␣(Knano) Ⳮ b(Canano Ⳮ Mgnano) Ⳮ c(Vnano) where Rcarb is the baseline function defined in Fig. 2, ␣ ⳱ 7.9 • 10ⳮ7, b ⳱ 1.3 • 10ⳮ7, and c ⳱ 3.1 • 10ⳮ8, where the nano- or atomically dispersed metals are measured in ppmw of the parent fuel.

With these sensitivities it is difficult to exclude catalytic effects in all but the purest materials. Indeed, the elemental analysis sensitivity limits for some of the nearly pure model materials are 100 ppmw, so residual catalysis may even be important for some of these compounds, such as C30H18O4 and polycarbonate resin, which lie in the upper region of the model substance band in Fig. 2. Among the practical solid fuels, only the highest rank coals (with no COOH sites) are adequately approximated as noncatalytic. Previous studies employing acid demineralization have also suggested that catalytic effects are significant for most coals [5], although questions have often been raised about collateral effects of strong acids on the organic matrix. The present work reaches the same conclusion by statistical inference from reactivity data without the need for aggressive mineral solvents. Conclusions Char combustion reactivities at 500 C in air from a diverse set of fuels and organic model compounds

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ENERGY PRODUCTION—Biomass, Coal, and Char Combustion

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29. Zhang, D.-K., and Poeze, A., Proc. Combust. Inst. 28:2337 (2000). 30. Baxter, L. L., Mitchell, R. E., and Fletcher, T. H., Combust. Flame 108:494 (1997). 31. Morgan, M. E., Jenkins, R. G., and Walker Jr., P. L., Fuel 60:189 (1981). 32. Epstein, E., Mineral Nutrition of Plants: Principles and Perspectives, Wiley and Sons, New York, 1972. 33. Jenkins, B. M., Baxter, L. L., Miles Jr., T. R., and Miles, T. R., Fuel Process. Technol. 54:17 (1998). 34. Dayton, D. C., Jenkins, B. M., Turn, S. Q., Bakker, R. R., Williams, R. B., Belle-Oudry, D., and Hill, L. M., Energy Fuels 13:860 (1999). 35. Baxter, L. L., Miles, T. R., Miles Jr., T. R., Jenkins, B. M., Milne, T., Dayton, D., Bryers, R. W., and Oden L., Fuel Process. Technol. 54:47 (1998). 36. Jensen, P. A., Frandsen, F. J., Dam-Johansen, K., and Sander, B., Energy Fuels 14:1280 (2000). 37. Ma´rquez, N., Ysambertt, F., and De La Cruz, C., Anal. Chim. Acta 395:343 (1999). 38. Krevelen, D. W., and Schuyer J., Coal Science: Aspects of Coal Combustion, Elsevier, Princeton, NJ, 1957. 39. Otake, Y., and Walker Jr., P. L., Fuel 72:139 (1993).

COMMENTS A. N. Hayhurst, University of Cambridge, UK. Did you dope any of the solids with metallic additives? Author’s Reply. None of the samples used in these experiments were doped with metal additives. There is a large literature on catalytic oxidation/gasification, in which doping is used extensively. The other technique commonly employed is acid demineralization for materials with naturally occurring inorganic matter. Our intention was to conduct a different type of study to complement these two traditional approaches; we examine practical solid fuels in their natural state containing complex catalytic mixtures and infer catalytic effects from statistical trends. The use of inorganic-free model substances was very useful in this regard. We believe our approach is a useful addition to the literature, since neither doping nor demineralization are perfect techniques. Doped catalysts do not necessarily have the same form or dispersion as natural catalysts, and demineralization with strong acids can arguably alter the organic matrix. ● Alejandro Molina, University of Utah, USA. Did you try to normalize solid reactivaties by the surface area of your samples? Author’s Reply. Table 1 in our paper gives all the data necessary to examine the reactivities on a mass basis or an area-normalized basis. For the data set as a whole, including catalytic materials, char reactivity variation was found

to be much greater than could be accounted for by surface area effects, as seen in Fig. 4 of our paper. For the noncatalytic materials, the data can be unified largely using total surface area (Fig. 4). We didn’t use area in our final correlation because the non-catalytic reactivity also correlated well with carbon content, which is a standard, readily available fuel property. Indeed it is equally or more difficult to measure char surface area than to measure the reactivity itself which we wish to predict. ● Akira Tomita, Tohoku University, Japan. You said that the information on the form and amount of nano-size catalytic impurities could be obtained from the literature. However, I suspect there would be very few articles that provide high-quality information on this subject. Perhaps you had better determine a unified evaluation method. Author’s Reply. We agree that existing literature information is not sufficient to assign quantitative proportions of nano-dispersed and granularly dispersed mineral matter, although the basic trends are clear enough. Our intention was to determine how well we could explain the reactivity trends based on readily available (standard) fuel properties alone, which do not include chemical fractionation data. Indeed, we believe that the largest source of residual error in our correlation arises from uncertainty in estimating dispersion. ●

CHAR REACTIVITIES OF DIVERSE SOLID FUELS Brian Haynes, University of Sydney, Australia. You do not find any statistically significant catalysis by sodium. Many studies have shown sodium and potassium to be very effective oxidation catalysts. Could you comment on your results, whether your sample set could have been expected to show effects of sodium, if sodium and potassium were about equally effective? In addition, why sodium would not be like potassium in its effectiveness as a catalyst? Author’s Reply. Sodium is a known catalyst for carbon oxidation (Ref. [26] in paper) [1–4], and our results should not be interpreted as casting doubt on its catalytic effectiveness. While some studies indicate that sodium is likely to be dispersed to a lesser degree than potassium in many coals and biomass samples (Refs. [34,35] in paper) [5], the most likely explanation for our results is lower concentrations of sodium relative to K, Ca, and Mg in almost all of our samples. As such, its effect does not achieve statistical

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significance in this particular sample set, which is large by the standards of most studies, but not large enough to reveal all relevant factors.

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