Environ. Sci. Technol. 2008, 42, 2004–2008
Identification of Significant Factors in Reburning with Coal Volatiles RYAN ZARNITZ AND SARMA PISUPATI* Energy and Mineral Engineering Department, The Pennsylvania State University, 110 Hosler Building, University Park, Pennsylvania 16802
Received August 27, 2007. Revised manuscript received October 25, 2007. Accepted December 27, 2007.
In laboratory-scale flow reactor studies, fuel staging using coal volatiles as a reburning fuel has given superior NOx reduction performance compared to reburning using natural gas. This superior performance of coal volatiles may be caused by an increased yield of hydrocarbon radicals and free nitrogen species in the reburning zone. In this study, kinetic models were used to predict the composition of coal volatiles used in reburning. The reburning process was then examined using a set of designed experiments to examine the components of the coal volatiles and their effects on NOx reduction. Within the concentration ranges studied, reburning temperature and air concentration were shown to control NOx reduction, and reburn gas component concentrations had only minor effects.
Introduction Since the enactment of the Clean Air Act in 1970, power plant pollutant emissions, particularly SO2, have decreased significantly. NOx emissions, however, have continued to climb, and future limits on emissions are likely to become more stringent. One method that may be used to reduce NOx is reburning. In the reburning process, a high-temperature fuel-rich zone is created downstream of the primary combustion zone followed by rapid burnout and quenching with the addition of air. The objective of reburning is to reduce NOx by reaction with hydrocarbon radicals in the fuel-rich zone followed by a quenching step designed to rapidly reduce the air temperature below that which is required to produce NOx. The use of natural gas, rather than coal, for reburning will generally give higher NOx reduction efficiencies (1). It is also less complicated to install, and will have less difficulty with respect to solids being unable to burn out due to decreased boiler residence times (1). Although natural gas reburning systems are associated with a lower capital cost, they also have a higher operating cost in the case of coal combustion power plants because they require the purchase of natural gas. Also, coal volatiles include nitrogen-containing gases such as NH3 and HCN in addition to light hydrocarbons. These nitrogen-containing gases may enhance the NOx reduction capability in a manner similar to that of selective noncatalyltic reduction (SNCR), where ammonia or urea alone are used to reduce NOx emissions, and advanced reburning, where natural gas reburning is enhanced by injecting ammonia with the reburn fuel stream. In this study, the use of coal pyrolysis gas generated in a separate reactor as a reburn fuel is examined. By using coal volatiles for reburning in coal-fired boilers, operating costs * Corresponding author e-mail:
[email protected]. 2004
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compared to natural gas reburning are reduced due to the fact that supplies of coal are readily available, although the capital costs are increased due to additional equipment required for the devolatilization process. The commercial-scale system ultimately under consideration will utilize flue gas as a devolatilization medium, and hot boiler flue gas will provide heat for devolatilization. After pyrolysis, the gaseous products and solid product (coal char) will be separated in a cyclone. The pyrolysis gas will then be injected as a reburning fuel, and the char will be combusted in the primary combustion zone. Limited work has been performed in the area of pyrolysis under partial oxidizing conditions. With regard to the effect of partial oxidizing conditions on fuel nitrogen distribution, little effect was reported by Rüdiger et al. up to an air ratio of 0.132 (2). Partially oxidizing conditions may be beneficial to the production of efficient reburn gas because light tar nitrogen species yield increased due to the preferential oxidation of carbon over nitrogen (3). However, this benefit may be counteracted because the most easily oxidized hydrocarbons have been shown to have the greatest reburn effectiveness (4). In any case, there is reason to believe that reburn gas produced under partially oxidizing conditions will be quite serviceable; reburn gas produced from biomass and sewage under partially oxidizing conditions has been shown to work effectively for NOx reduction (2). Reburn Conditions. The apparent superiority of the coal pyrolysis gas is shown in Figure 1. The pyrolysis gas allows for significant NOx reductions at relatively high air ratios. Operation at a higher air ratio permits higher combustion efficiencies and burnout, reducing particulate emissions and increasing overall fuel efficiency. Also, Figure 1 shows that pyrolysis gas reburning may decrease NOx concentrations by up to 60% relative to methane reburning levels. In the study associated with Figure 1, the residence time for the coal pyrolysis gas reburn was varied between 0.2 and 2 s, and the reburn reactions occurred under isothermal conditions (5). Little benefit was derived from increasing residence time beyond 1 s. For natural gas reburning, increased temperatures in a fuel-rich zone leads to increased NOx reduction (4), and that trend was shown to be true for coal pyrolysis gas as well. Increasing temperature also has the advantage of increasing the optimal air ratio of the reburn zone while maintaining NOx emission levels.
Pyrolysis Gas Production Introduction. Before the computational model of the coal pyrolysis gas reburning system is constructed, a determi-
FIGURE 1. The relative performance of coal pyrolysis gas as a reburn fuel (5) 10.1021/es7021329 CCC: $40.75
2008 American Chemical Society
Published on Web 02/09/2008
TABLE 1. Rank Independent Kinetic Expressions and Functional Group Composition for Pittsburgh No. 8 Bituminous Coal (11) gas
functional group
CO2 extra loose CO2 loose CO2 tight H2O loose H2O tight CO ether loose CO ether tight HCN loose HCN tight NH3 CHx aliphatic CH4 extra loose CH4 loose CH4 tight H• aromatic Tar a
carboxyl carboxyl hydroxyl hydroxyl ether O
H(al) methoxy methyl methyl H (aromatic)
rate expression k k k k k k k k k k k k k k k k
) ) ) ) ) ) ) ) ) ) ) ) ) ) ) )
0.56 0.65 0.11 0.22 0.17 0.14 0.15 0.17 0.69 0.12 0.84 0.84 0.75 0.34 0.20 0.86
× × × × × × × × × × × × × × × ×
1018 1017 1016 1019 1014 1019 1016 1019 1013 1013 1015 1015 1014 1012 1014 1015
exp(-(30000 exp(-(33850 exp(-(38315 exp(-(30000 exp(-(32700 exp(-(40000 exp(-(40500 exp(-(30000 exp(-(42500 exp(-(27300 exp(-(30000 exp(-(30000 exp(-(30000 exp(-(30000 exp(-(45500 exp(-(27700
functional group composition ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (
2000)/T) 1500)/T) 2000)/T) 1500)/T) 1500)/T) 6000)/T) 1500)/T) 1500)/T) 4750)/T) 3000)/T) 1500)/T) 1500)/T) 2000)/T) 2000)/T) 1500)/T) 1500)/T)
0.000 0.006 0.005 0.011 0.011 0.050 0.022 0.009 0.022 0.000 0.190 0.020 0.015 0.015 0.012 0.11a
Results obtained from thermogravametric analysis (TGA) at a heating rate of 30 K/min to 900 °C.
nation must be made as to the composition of the volatile material. Three types of coal pyrolysis models—the kinetic model (6), the macromolecular network model (7), and correlations based on coal ultimate analysis—were examined (8). The kinetic model is useful in the prediction of light gas evolution (6), and the macromolecular network model is useful in predicting the evolution of classes of high molecular weight molecules, fluidity (Metaplast) formation, and solvent swelling behavior (7). Kinetic models and correlations based on ultimate analysis are roughly equal in terms of accuracy, but the kinetic models were more freely available in literature. In this study, kinetic models were used to determine the light gas and tar evolution during pyrolysis. Each kinetic model was also validated using data from pilot-scale experiments because there is significant variation between their predictions. The pilot-scale results were obtained from data reported on the ORC (Occidental Research Corporation) flash pyrolysis process (9). The Kinetic Models. The kinetic treatment of light gas evolution from the rapid pyrolysis of small particles originated with Suuberg et al. in 1979 (6). This model described the pyrolysis gas evolution (Vi) in terms of parallel reactions (eqs 1 and 2), dVi ) dt
∑ k (V ij
/ ij - Vij)
model was improved by Serio et al., where FTIR emission/ transmission spectroscopy was used to accurately determine particle temperatures (11). This model is based on the following equations (12): Wi ) Wi(1 - e-kt)
(1)
j
(3)
for gas evolution, and
and kij ) kijo exp(-Eij ⁄ RT)
FIGURE 2. The ORC flash pyrolysis process (9).
(2)
for the jth reaction and the ith component molecule, where Vij/ is the ultimate yield of the component, kij is the reaction rate, Eij is the reaction activation energy, T is the temperature, R is the universal gas constant, and t is time. This kinetic modeling concept was then extended to create a rank-independent model of coal pyrolysis (10). In this model, the kinetic parameters that govern pyrolysis gas evolution are held constant for all coals, and a functional group analysis using Fourier-transform infrared spectroscopy (FTIR) is used to determine the ultimate yield of a particular component (10). Tar and soot yield data was gathered experimentally using an entrained flow reactor, which was used to confirm the light gas yield predictions (10). By sacrificing the rank-independent concept in this model it is possible to make slightly more accurate predictions, but the additional accuracy is not sufficient to offset the convenience of the rank-independent concept (11). The rank-independent model was first introduced by Solomon et al. in 1982. The
X ) Xe-ktart
(4)
for tar evolution, where Wi and X are the yield of a particular component and tar, respectively, Wi° and X° are the maximum yield of a particular component and tar, respectively, k is the reaction rate constant, and t is the reaction time. The parameters for this model are given in Table 1, and functional group compositions are given for Pittsburgh No. 8 bituminous coal. The Occidental Research Corporation (ORC) Flash Pyrolysis Process. To validate the models proposed by Suuberg, Solomon, and Serio, each model was compared against pilot-scale data obtained by the ORC flash pyrolysis process. The ORC process had a coal feed rate of approximately 200 lbs/hr, and a flow map is shown in Figure 2. In this process, coal is fed into the pyrolysis reactor where hot char is mixed with the coal. Having thus been heated to high temperature, the coal is pyrolyzed and is moved to a set of cyclones, where the tar (still in the gas phase) and light gases are separated from the char. The tar and light gas mixtures are then cooled and separated as the primary VOL. 42, NO. 6, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Selected ORC Process Runs and Conditions (9) run number
residence time, s
devolatilization temperature, K
180 173 172 174 175 176 177 178 170
1.3 2.1 4.2 3.8 1.4 4.1 1.5 1.3 4.2
916 922 922 933 933 883 883 933 916
TABLE 3. Actual and Predicted Gas Yields for Run 174 gas yield (by mass)/unit coal
CO2 CO CH4 C2H4 C2H6 C3H8
ORC report
Suuberg
Solomon
Serio
1.50 2.66 4.08 0.93 0.53 0.59
0.42 0.44 0.74 0.21 0.50 1.01
0.61 0.48 1.95
0.73 4.36 4.17
TABLE 4. Actual and Predicted Gas Yields for Run 175 gas yield (by mass)/unit coal
CO2 CO CH4 C2H4 C2H6 C3H8
ORC report
Suuberg
Solomon
Serio
1.12 2.92 3.78 1.03 0.60 0.44
0.46 0.40 0.68 0.20 0.47 1.13
1.03 0.45 1.58
1.01 3.16 5.09
TABLE 5. Pyrolysis Reactor Output
FIGURE 3. Tar Yields of Various Kinetic Models products. Most of the char is then sent to the combustor and some of the char is combusted to produce heat for the process. After the combustion step, the combustion gases are separated from the remaining char. The remaining char is then used to heat the incoming coal. Typical char recycle ratios were between 10:1 and 15:1 (9). At first, this process resulted in lower than expected tar yields due to unexpected tar cracking (9). This cracking occurred on char active sites in the pyrolysis reactor, which was especially important given the large char recycle ratios (9). Once the problem was identified, it was alleviated by using reactive gases such as steam or CO2 rather than N2 in the pyrolysis reactor environment. The CO2 and steam adsorbed on the char active sites and prevented cracking (9). N2 was not active enough to undergo adsorption onto the char active sites (9). For this reason, only data gathered in the reactive gas (CO2 and H2O) runs were considered in the comparison. The ORC report included a kinetic model for the prediction of coal weight loss and tar formation, but no model included the prediction of the composition of the pyrolysis gas. Unfortunately, there was also not enough data available from only reactive gas runs to construct such a model. The reaction conditions under which the pilot-scale data considered here was generated is summarized in Table 2. Comparing the Kinetic Models with the ORC Data. To ascertain the appropriateness of each of the proposed kinetic models, they were compared against the pilot-scale data described above. A summary of the results of the comparison is shown in Figure 3. In this comparison, the model proposed by Suuberg underpredicts the tar yield, whereas the model proposed by Solomon et al. generally overpredicts the tar yield (with the exception of Run 177). The model proposed by Serio et al. generally makes the best predictions of tar yield. For this comparison, the tar and aliphatics yield from the Solomon and Serio models were added together because the ORC data did not distinguish between tar and aliphatic compound yields. Light gas yields were available for runs 2006
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N2 O2 CO2 H2O CO HCN NH3 alphatics CH4 Tar
yield (lb/lb coal)
gas composition (mass %)
0.90 0.06 0.17 0.10 0.04 0.0024 0 0.28 0.03 0.17
0.56 0.037 0.11 0.06 0.02 0.0015 0 0.18 0.02 0.10
174 and 175 only and are compared to the light gas yields predicted by the models in Tables 3 and 4. Shown in Tables 3 and 4, the Serio model has an average error of 0.41 mass units of gas per mass unit coal, whereas the Suuberg and Solomon models have average errors of 1.13 and 1.66, respectively. Because the Serio model was the most accurate of the models, it was selected for use in this study in determining the pyrolysis gas composition for the reburning experiment. The Pyrolysis Gas Composition. The temperature and residence time conditions selected for the coal pyrolysis reaction were 1073 K and 2 s. The residence time was selected by maximizing the gas yield based on the devolatilization model. The temperature was selected because austenitic stainless steels should provide resistance to corrosion and oxidation at 1073 K (13). Reforming and gasification reactions by CO2 and H2O were not considered in the pyrolysis gas composition calculations, because these reactions have been shown to be inconsequential at temperatures below 1400 K, when uncatalyzed (14). Catalytic reactions may occur in the pyrolysis reactor if the pyrolysis gas is exposed to metal surfaces, but this possibility was not considered. The calculations were also made under the assumption that flue gas consisting of CO2 (14%), N2 (75%), H2O (7%), and O2 (5%) was used to transport coal into the pyrolysis reactor. The throughput of transport gas was determined by multiplying the coal mass input by 1.2, a standard value for effective pulverized coal transport (13). The pyrolysis gas composition to be considered in the models is shown in Table 5. Gibbs free energy minimization was used to predict the light
TABLE 6. Factors Assumed to Affect NOx Reduction Levels and Their Highest and Lowest Possible Levels
TABLE 7. High and Low Levels for the Component Factors factor
factor
ID
temperature (K) air (mole fraction) CH4 (mole fraction) H2 (mole fraction) C2H4 (mole fraction) C2H2 (mole fraction) HCN (mole fraction) CO (mole fraction) C2H6 (mole fraction)
A B C D E F G H J
high 2500 0.967 0.534 0.143 0.0882 0.0882 0.003 16 0.127 0.000 792
low 1000 0.353 0.0240 0.000 164 0.002 17 0.002 17 0.000 021 4 0.002 18 0.000 021 4
air
high level 37.5 (mol/time) low level 5 (mol/time)
CH4
H2
C2H4 C2H2
HCN
CO
C2H6
6
1.05
0.6
0.6
0.02
0.9 0.005
1
0.0075
0.1
0.1
0.001 0.1 0.001
hydrocarbon species that are formed by the thermal decomposition of tar and aliphatics.
Reburning Factor Analysis Introduction. To develop effective reburning experiments and to understand the results, a brief study was undertaken to find the most important factors affecting NOx reduction for the pyrolysis gas reburn. A series of designed experiments was used to rank factors affecting NOx reduction by order of importance. The type of designed experiment used in this case is called a two-level factor analysis (15). The first step in beginning the factor analysis is to create a list of possible factors affecting the response, which is the output NOx concentration. Each factor is then evaluated using a high and low level. The high level is generally somewhat higher than levels typically seen in the situation under investigation, and the low level somewhat lower than may be typical. The list of factors and their respective highest and lowest possible levels for this design are shown in Table 6. The factors selected are temperature, air mole fraction, and concentration of each of the components of the reburn fuel except for CO2, which was assumed to be inert. The high levels are about 50% higher than they are for typical reburning cases, and the low levels are about 50% lower than is typical in a reburning case. The temperatures, air-to-fuel ratios, and residence time were selected to provide consistency with past studies (16, 17). The factor analysis used in this case not only shows the effect of the factors by themselves, but also the effects of combinations of factors. For example, the response, NOx output, may be affected not only by the amount of CH4 by itself, but also by the combination of temperature and CH4, which is known as a two-component interaction. The design of the factor analysis used in this study is set so as to find interactions of up to four components. The Experiments. Each experiment in the design was run computationally, using the advanced reburning CHEMKIN mechanism from Han et al. (18) The reactions are run in a simulated plug-flow reactor with a residence time of 0.3 s. An inlet NO concentration of 400 ppm is used in all cases. The temperature for each experiment was held constant at either 1000 or 2500 K, as described in Table 6. The experimental set for this design consists of 128 experiments. The design was evaluated using the commercial statistical analysis software program Stat-Ease (19). The high and low levels for the components in terms of molar flow rate are given in Table 7. Results of the Factor Analysis. The first step in examining the results in a factor analysis is to determine the significant factors by examining a half-normal plot, which in this case was generated automatically by Stat-Ease (19) and is shown in Figure 4. In the half-normal plot, all factors that have effects that are due to randomness, and therefore conform to a Gaussian distribution, appear along the line (15). All factors whose effects are not random, and are therefore significant,
FIGURE 4. Half-normal plot of experimental design.
TABLE 8. List of Factors in Order of Their Effect in Terms of F-Value factor
F-value
air temperature*air temperature temperature*CH4 CH4 temperature*air*CH4*C2H4 temperature*CH4*C2H4 temperature*air*C2H4 C2H2
521.54 454.20 103.56 76.39 39.68 20.35 13.57 10.15 7.22
appear off the line. The greater the effect of the factor, the farther away from the line that factor appears. In this case, the most significant factor is B (air mole fraction), followed by the interaction of A and B (temperature and air mole fraction), and so on through F (C2H2 mole fraction). The letter identification of each factor is given in Table 6. Once the significant factors have been identified, a model was used to fit the data. In this case, the model is the following equation:5 ln(NO) ) -11.18 + 3.36B + 3.13AB - 1.5A - 1.28AC 0.93C - 0.39F + . . . . . . 0.47ABE - 0.66ABCE + 0.54ACE (5) NO is given in mol fraction, and each letter term is a value between the high and low levels, with -1 indicating the low level and 1 indicating the high level. The factors for the model are listed in Table 8 according to each F-value, an index of the significance of that term in the model (15). In the factor column, the effects separated by an asterisk indicate interaction effects. Note that the factors listed in Table 8 are dominant only for situations in the ranges shown in Table 6 and not necessarily for all reburning situations. If a factor that is shown not to be significant in this analysis is used outside the range in Table 6, it may become significant. The F-value for the model as a whole is 138.52, indicating VOL. 42, NO. 6, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 5. Predicted vs Actual plot for eq 1. Axis values are given by ln(NO) that there is less than a 0.01% chance that the model is occurring due to experimental error. The predicted versus actual plot for the model is shown in Figure 5. Within the limits of the model accuracy (the average for the experiments is 11%), the temperature and the mole fractions of air and hydrocarbons at the inlet control the outlet concentration of NO. In the range typical of the reburning experiments for NO, about 400-500 ppm; this implies that other factors may account for about 40-50 ppm of the total NO concentration. Differences in NO concentration greater than 40-50 ppm may therefore be attributed to factors listed in Table 8.
Literature Cited (1) Liu, H.; Hampartsoumian, E.; Gibbs, B. Evaluation of the optimal fuel characteristics for efficient NO reduction by coal reburning. Fuel 1997, 76 (11), 985. (2) Rüdiger, H.; Greul, U.; Spleithoff, H.; Hein, K. Distribution of fuel nitrogen in pyrolysis products used for reburning. Fuel 1997, 76 (3), 201.
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(3) Baumann, H.; Möller, P. Erdöl und Kohle — Erdgass Petrochemie 1991, 44 (29), (4) Bilbao, R.; Millera, A.; Alzueta, M.; and Prada, L. Evaluation of the use of different hydrocarbon fuels for gas reburning. Fuel 1997, 76 (14), 1401. (5) Spleithoff, H.; Gruel, U.; Rüdiger, H.; Hein, K. Basic effects on NOx emissions in air staging and reburning at a bench scale test facility. Fuel 1996, 75 (5), 560. (6) Suuberg, E. M.; Peters, W. A.; Howard, J. B. Product Compositions and Formation Kinetics in Rapid Pyrolysis of Pulverized Coal — Implications for Combustion. 17th International Symposium on Combustion, The Combustion Institute:1979, p 117 (7) Solomon, P. R.; Fletcher, T. H.; Pugmire, R. J. Progress in Coal Pyrolysis. Fuel 1993, 72 (5), 587. (8) Niksa, S. Predicting the devolatilization behavior of any coal from its ultimate analysis. Combust. Flame 1995, 100 (3), 384– 394. (9) Che, S. C.; Duraiswamy, K.; Knell, E. W.; Lee, C. K. Report No. FE-2244–26, Flash Pyrolysis Coal Liquefaction Process Development: United States Department of Energy: Washington DC, 1979. (10) Solomon, P. R.; Hamblen, D. G.; Carangelo, R. M.; Krause, J. L. Coal Thermal Decomposition in an Entrained Flow Reactor: Experiments and Theory. 19th International Symposium on Combustion. The Combustion Institute 1982, p 1139. (11) Serio, M. A.; Hamblen, D. G.; Markham, J. R.; Solomon, P. R. Kinetics of Volatile Product Evolution in Coal Pyrolysis: Experiment and Theory. Energy and Fuels 1987, 1, 138. (12) Solomon, P. R.; Colket, M. B. Coal Devolatilization. 17th International Symposium on Combustion. The Combustion Institute 1979, p. 131. (13) Perry, R. H.; Green, D. W. Perry’s Chemical Engineer’s Handbook, 7th ed.; McGraw-Hill: New York, 1997. (14) Karim, G. A.; Metwally, M. M. A Kinetic Investigation of the Reforming of Natural Gas for the Production of Hydrogen. Int. J. Hydrogen Energy 1980, 5, 293. (15) Montgomery, D. C. Design and Analysis of Experiments, 5th ed.; John Wiley and Sons: Singapore, 2000. (16) Zarnescu, V.; Pisupati, S. V. An integrative approach for combustor design using CFD methods. Energy Fuels 2002, 16 (3), 622–633. (17) Zarnitz, R.; Pisupati, S. V. Evaluation of the use of coal volatiles as reburning fuel for NOx reduction. Fuel 2007, 86 (4), 554–559. (18) Han, D.; Mungal, M. G.; Zamansky, V. M.; Tyson, T. J. Prediction of NOx control by basic and advanced gas reburning using the two-stage Lagrangian model. Combust. Flame 1999, 119, 483–493. (19) Stat-Ease—Statistical Analysis Software. Available at http:// www.statease.com.
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