K2CO3-Catalyzed CO2 Gasification of Ash-Free Coal: Kinetic Study

K2CO3-Catalyzed CO2 Gasification of Ash-Free Coal: Kinetic Study. Jan Kopyscinski† ..... Renewable and Sustainable Energy Reviews 2015 41, 615-632 ...
0 downloads 0 Views 1MB Size
Article pubs.acs.org/EF

K2CO3‑Catalyzed CO2 Gasification of Ash-Free Coal: Kinetic Study Jan Kopyscinski,† Rozita Habibi,† Charles A. Mims,‡ and Josephine M. Hill*,† †

Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Drive Northwest, Calgary, Alberta T2N 1N4, Canada ‡ Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, Ontario M5S 3E5, Canada S Supporting Information *

ABSTRACT: The kinetics of K2CO3-catalyzed CO2 gasification of ash-free coal was investigated with a thermogravimetric analyzer and compared to raw coal and uncatalyzed ash-free coal. At 750 °C, the gasification of ash-free coal dry mixed with 20 wt % K2CO3 was approximately 3 and 60 times faster than the raw coal and ash-free coal without catalyst, respectively. Increasing the amount of catalyst from 20 to 45 wt % increased the gasification rate 3-fold. The gasification rate of ash-free coal containing potassium catalyst strongly depended upon the pretreatment (i.e., heating gas atmosphere and heating time) because it directly affected the degree of catalyst reduction. The catalytic gasification behavior could only be predicted with the extended random pore model, whereas the random pore model and integrated model were essentially equal for fitting the gasification rate for raw and ash-free coal. The activation energy for the catalyzed ash-free coal gasification was approximately 100 kJ mol−1 larger than for raw coal and the uncatalyzed ash-free coal. This increase might be due to the energy required for the potassium (i.e., catalyst) transfer to a new carbon site or caused by the pyrolysis process, because the formed char might have different properties. oxidized.9,19,20 The catalyst, potassium in the present case, takes oxygen from the reaction gas (in this case, CO2) (eq 1) and transfers it to the surface where oxygen reacts with carbon to form carbon monoxide (eq 2).

1. INTRODUCTION Since early 2000, research on catalytic gasification has again become prominent especially for coal,1−3 petroleum coke,4−7 biomass, and their mixtures8 for the production of hydrogen, methane, and/or synthesis gas. With the oil crisis in the 1970s and 1980s, much work has been done on catalytic coal gasification.9 A few pilot plants were constructed during this time, but no commercial catalytic gasifier was ever build.10 The main reasons might be of political−economic nature as the oil crisis ended, but technical issues, such as catalyst deactivation, might also have played a role. Alkali (e.g., potassium and sodium) and alkali earth (e.g., magnesium and calcium) metals, nickel, iron, and other metals have been used as catalysts to promote the gasification reaction of coal and other carbon sources.9 Today, special attention has been given to co-feeding biomass species, such as switchgrass, which are rich in alkali and alkaline earth metals. Here, potassium naturally present in the switchgrass ash catalyzes the gasification of coal and/or petcoke.11 However, these catalysts, especially potassium and calcium, deactivate during the process as these components react with alumina- and silica-containing mineral matter from the coal ash to form stable potassium or calcium aluminosilicates.11−13 Thus, when the ash content (80%) can be avoided, which is common if a constant Δt interval is used. 2.3. Model Discrimination. The model discrimination was based on the Akaike information criterion (AIC),25 as shown in eq 10, with the assumption of normally distributed errors AIC = m

2 1 + ln RSS n n

{ }

Figure 1. Weight decrease profile of GEN-AF + 20 wt % K2CO3 during heating with 15 °C min−1 to 725 °C in N2 (A), kept for 150 min at 700 °C in N2 (B), and gasifying with CO2 (C).

+ 20 wt % K2CO3. In stage A, the sample was heated at 15 °C min−1 to the desired temperature (e.g., 725 °C) in N2. The weight decrease in this section was attributed to the devolatilization (i.e., pyrolysis) process. In the next stage (B), the sample was kept at this temperature for 150 min in a N2 atmosphere. Here, the mass loss was attributed to the release of CO because of the reduction of the catalyst to form an active surface intermediate as discussed in our previous study.14 For GEN-AF samples without catalyst, the mass was constant in stage B (not shown). The last stage (C) was the gasification of the char with CO2. Figure 2 depicts the char conversion for (a) GEN-raw, (b) GEN-AF, and (c) GEN-AF + 20 wt % K2CO3 as a function of the gasification time for different temperatures. The symbols represent the experimental results, and the lines represent the best fit model, as discussed in section 3.2. As expected, the higher the gasification temperature, the faster the char conversion for all samples. However, the gasification behavior of the three samples differed significantly. For example, GENraw gasified at 850 °C required ∼8 h for complete conversion, while GEN-AF was only 60% converted after the same time (panels a and b of Figure 2). A 60% conversion was reached after approximately 2 h for the GEN-raw gasified at 850 °C. Ash-free coal exhibited very low gasification reactivity between 750 and 900 °C. Adding potassium to the ash-free coal sample improved and accelerated the gasification significantly (Figure 2c). GEN-AF + 20 wt % K2CO3 gasified at 750 °C completely after 8 h, which was the same time needed for the GEN-raw sample at 850 °C. Thus, a temperature decrease of 100 °C can be achieved by removing the ash and adding a potassium catalyst. The reactivity index, i.e., inverse time to reach 5% (1/t5) and 50% (1/t50) char conversion, has been calculated to compare the gasification rates at 750 °C for the three samples (Table 2).

(10)

where m is the number of estimated parameters and n is the number of observations. The number of observations was 100 per temperature. The model with the lowest AIC number is the preferred model. The different models can be compared by calculating the Akaike probability share (πAIC), which is given by

πAIC =

Lk k

∑i = 1 Lk

(11)

where Lk is the relative likelihood of model k that is defined as Lk = exp

{

AICmin − AICk 2

}

(12) 2

In the present paper, the RSS and R values were also calculated. 4877

dx.doi.org/10.1021/ef400552q | Energy Fuels 2013, 27, 4875−4883

Energy & Fuels

Article

3.1.2. Influence of the Potassium Concentration. To investigate the influence of the potassium concentration on the char conversion, all experiments were carried out at 700 °C with a holding time of 150 min prior to CO2 gasification to ensure that the potassium catalyst was sufficiently reduced (i.e., activated).14 The results showed that increasing the amount of K2CO3 from 20 to 45 wt % enhanced the CO2 gasification (Figure 3; symbols represent observed data). The main

Figure 3. Influence of K2CO3 loading on the CO2 gasification behavior of GEN-AF at 700 °C. Symbols represent observed data, and lines represent the best fit model (eRPM).

difference can be observed in the first 2 h with char conversions of 34, 51, and 74% for catalyst loadings of 20, 33, and 45 wt %, respectively. After 2 h, the slope of catalyzed char conversion decreased. The slower gasification rates might be explained by collapsing the pore structure and/or the slow transfer of potassium to new carbon sites (eq 3), which required a certain degree of mobility. On the basis of the amount of K2CO3 mixed with the ash-free coal and the carbon content in the char, the initial K/C ratios were 0.12, 0.25, and 0.43 for the GEN-AF samples loaded with 20, 33, and 45 wt % K2CO3, respectively. 3.1.3. Influence of the Heating Protocol. The heating protocol influenced the char conversion for ash-free coal mixed with K2CO3, as shown for GEN-AF + 45 wt % K2CO3 in Figure 4 (symbols represent observed data). Here, sample (a) was heated with 15 °C min−1 in N2 to 700 °C, and after a further

Figure 2. Influence of the CO2 gasification temperature on the char conversion of (a) GEN-raw, (b) GEN-AF, and (c) GEN-AF + 20 wt % K2CO3. Symbols represent observed data, and lines represent the best fit model (eRPM for GEN-raw and GEN-AF + 20 wt % K2CO3 and RPM for GEN-AF).

Table 2. Reactivity Index Based on the Time to Reach 5% (1/t5) and 50% (1/t50) Char Conversion at 750 °C for GENraw, GEN-AF, and GEN-AF + 20 wt % K2CO3 sample GEN-AF + 20 wt % K2CO3 GEN-raw GEN-AF a

1/t5 (min−1) −1

2.7 × 10 7.7 × 10−2 3.8 × 10−3

r Na 70 20 1

1/t50 (min−1) −2

2.3 × 10 5.9 × 10−3 3.6 × 10−4

r Na 64 17 1

Normalized reactivity to that of GEN-AF.

Normalized to the GEN-AF sample, GEN-raw and GEN-AF + 20 wt % K2CO3 had a 20 and 70 times faster initial gasification rate (i.e., 1/t5), respectively. To reach 50% char conversion, the normalized reactivity index (1/t50) for GEN-raw and GEN-AF + 20 wt % K2CO3 decreased slightly but were still 16 and 64 times higher compared to GEN-AF. The reason for the slow gasification of the GEN-AF sample was the negligible amount of ash minerals (e.g., Ca, Fe, and Na) that could accelerate the reaction. The ash of the GEN-raw sample, on the other hand, contained the following catalytically active components: 4.2 wt % Ca, 2.0 wt % Fe, 1.9 wt % Na, 0.8 wt % Mg, and 0.6 wt % K.14 On the basis of the experimental results, the gasification of GEN-raw can be considered a catalyzed process.

Figure 4. Influence of the heating protocol of the CO2 gasification behavior of GEN-AF + 45 wt % K2CO3 at 700 °C: (a) 150 min holding time, (b) 10 min holding time, (c) 0 min holding time in N2 at 700 °C before switched to CO2, and (d) CO2 heating. Symbols represent observed data, and lines represent the best fit model (eRPM). 4878

dx.doi.org/10.1021/ef400552q | Energy Fuels 2013, 27, 4875−4883

Energy & Fuels

Article

Table 3. Estimated Kinetic Parameters for All Considered Models for GEN-raw, GEN-AF, and GEN-AF + 20 wt % K2CO3a parameter

GEN-raw

GEN-AF

GEN-AF + 20 wt % K2CO3

VM

model

k1,Tref

3.6 × 10−3 ± 2 × 10−4

6.5 × 10−4 ± 2 × 10−5

3.3 × 10−3 ± 3 × 10−4

SM

Ea k1,Tref

131 ± 2 1.8 × 10−4 ± 3 × 10−5

124 ± 3 1.4 × 10−5 ± 6 × 10−7

265 ± 16 1.5 × 10−4 ± 3 × 10−5

IM

Ea k1,Tref

195 ± 9 7.3 × 10−3 ± 6 × 10−4

185 ± 5 1.3 × 10−4 ± 3 × 10−5

396 ± 33 2 × 10−2 ± 4 × 10−3

RPM

Ea n k1,Tref

116 ± 2 1.14 ± 0.02 3.6 × 10−3 ± 2 × 10−4

149 ± 6 0.83 ± 0.03 6.1 × 10−4 ± 2 × 10−5

185 ± 13 1.4 ± 0.1 3.3 × 10−3 ± 3 × 10−4

131 ± 2 0 (lower bound) 7.5 × 10−4 ± 5 × 10−5

124 ± 3 0.45 ± 0.1

eRPM

Ea ψ k1,Tref

265 ± 16 0 (lower bound) 1.1 × 10−4 ± 1 × 10−5

Ea ψ c p

131 ± 1 4.3 ± 0.6 3.5 ± 0.3 1.52 ± 0.14

264 ± 4 64 ± 11 16 ± 2 2.7 ± 0.1

a

VM, volumetric model; SM, shrinking model; IM, integrated model; RPM, random pore model; and eRPM, extended random pore model (only for GEN-raw and GEN-AF + 20 wt % K2CO3). Tref = 1023 K (GEN-raw), 1073 K (GEN-AF), and 973 K (GEN-AF + 20 wt % K2CO3).

of the parameters and the model. The elements in the covariance matrix are limited to the interval (−1, 1), where a value of 1 indicates a strong correlation between two parameters and a value of −1 indicates a strong anti-correlation. VM, SM, IM, and RPM showed a very high anti-correlation between the pre-exponential factor (kTref) and the activation energy (Ea), whereas eRPM did not (see Tables S1−S3 of the Supporting Information). High anti-correlation values can be explained by the fact that VM and SM are models with only two parameters (i.e., Ea and kTref), which are connected via an exponential function. The structural parameter ψ for RPM could not be estimated, because its value always approached zero (lower boundary) during the parameter estimation. Thus, the second term in the RPM equation (eq 7) was always 1, collapsing this model to the VM. That is, the parameters for the RPM were the same as for the VM (Table 3). All estimated parameters had a tight fit because the confidence interval was very small (within ±10%) compared to the best value. The activation energies estimated with the VM, RPM, and eRPM were essentially the same (131 kJ mol−1), whereas the SM estimated a higher activation energy (195 kJ mol−1) and the IM estimated a lower activation energy (116 kJ mol−1). Table 4 summarizes the model discrimination

holding time of 150 min, the gas was switched to CO2. Samples (b) and (c) were heated with the same heating rate to 700 °C, but the holding time in N2 (time prior to CO2) was reduced to 10 and 0 min, respectively. A 10 min holding time was chosen because the devolatilization of the ash-free coal at 700 °C was finished and the mass of the uncatalyzed sample did not change significantly after this time.14 Sample (d) was heated with CO2 to 700 °C (15 °C min−1). The gasification time of zero was defined when the gas was switched to CO2 (cases a−c) or when the temperature of 700 °C was reached (case d). The sample with the longest holding time (curve a in Figure 4) showed the fastest char conversion, while the sample heated in CO2 (curve d) showed the slowest char conversion. The initial slopes of the char conversion for samples (c) and (d) were comparable to sample (a). This result can be explained by an overlapping of the devolatilization step with the gasification reaction in the first 10 min. Thereafter, the slope decreased, and the char conversion after 2 h differed significantly; sample (a) had a conversion of ∼75%, whereas samples (b), (c), and (d) had char conversions of only ∼53, ∼39, and ∼22%, respectively. After 5 h, the char conversion for samples (a−c) were >80%. The results indicate that a longer holding time in a N2 atmosphere at a operating temperature prior to the gasification lead to a higher degree of catalyst reduction and, thus, to a faster char gasification, as we have seen previously.14 Moreover, CO2 inhibited the catalyst reduction. At lower K2CO3 loadings, the effect of the heating protocol was less pronounced but the phenomenon was still observed (not shown). 3.2. Modeling Results. 3.2.1. Influence of the Gasification Temperature. For the present kinetic study, it was assumed that the parameters n, ψ, c, and p from eqs 6−8 were independent of the temperature. In addition, all data at all temperatures were fit simultaneously, and the pre-exponential factor follows the Arrhenius equation, as described above. The kinetic parameters with the 95% confidence interval for the GEN-raw, GEN-AF, and GEN-AF + 20 wt % K2CO3 experiments are reported in Table 3. In addition to the best fit values, the normalized parameter covariance matrix for each model considered is shown in Tables S1−S3 of the Supporting Information. The covariance matrix in kinetic studies is seldom published, but it is an important criterion to evaluate the quality

Table 4. Model Discrimination Results for GEN-raw (700− 950 °C), with Tref = 1023 K model eRPM IM VM RPM SM

rank 1 2 3 3 5

AIC −14.79 −14.29 −13.73 −13.73 −11.79

Lk 1.000 0.779 0.589 0.589 0.223

πAIC 0.31 0.24 0.19 0.19 0.07

R2

RSS 1.46 2.43 4.30 4.30 2.99

× × × × ×

−4

10 10−4 10−4 10−4 10−3

0.9977 0.9962 0.9933 0.9933 0.9531

criteria and ranks all five considered models. In most kinetic studies, only R2 values are published. These values were all very high (>0.95) for most of the investigated models. The eRPM followed by the IM were the best model fits according to their lowest AIC values and highest relative likelihood and probability shares, πAIC. That is, the eRPM had a likelihood of 100%, whereas the IM had a likelihood of ∼78% and the VM 4879

dx.doi.org/10.1021/ef400552q | Energy Fuels 2013, 27, 4875−4883

Energy & Fuels

Article

panels a and b of Figure 2. In detail, at 750 and 800 °C, the model predicted a higher char conversion. Figure S2 of the Supporting Information depicts the observed and modeled gasification rates as a function of the char conversion. Especially at 850 and 900 °C, the rates were in good agreement over the whole conversion range. Because the gasification rate of GENAF monotonically decreased with char conversion without a clear maximum, all four models were very close to each other. In contrast, the ash-free coal mixed with potassium catalyst (GEN-AF + 20 wt % K2CO3) exhibited a clear maximum in the rate at ∼15−20% conversion (Figure 5), which is common for catalytic gasification. The VM, SM, and IM did not capture this gasification behavior, as shown by the model discrimination criteria summarized in Table 6. The eRPM has the lowest AIC

and RPM had likelihoods of only ∼60%. Thus, the probability of the IM is approximately 3/4 compared to the eRPM. Figure 2a compares the observed and calculated char conversion for GEN-raw samples gasified from 700 to 950 °C. The agreement between the observed values and the eRPM calculated data is very good. The IM and VM estimated the gasification rates almost as well as the eRPM, but the SM failed to predict the gasification, as shown in Figure S1 of the Supporting Information. As mentioned above, the gasification rate of ash-free coal (GEN-AF) was very slow. Thus, only the VM, SM, IM, and RPM were applied for the parameter estimation. The parameter results, covariance matrix, and model discrimination criteria are summarized in Table 3, Table S2 of the Supporting Information, and Table 5, respectively. The latter showed

Table 6. Model Discrimination Results for GEN-AF + 20 wt % K2CO3 (650−750 °C), with Tref = 973 K

Table 5. Model Discrimination Results for GEN-AF (750− 900 °C), with Tref = 1073 K model

rank

AIC

Lk

πAIC

RPM IM VM SM

1 2 3 4

−18.81 −18.81 −18.49 −18.45

1.000 0.995 0.852 0.835

0.272 0.270 0.231 0.227

RSS 2.69 2.71 3.71 3.85

× × × ×

10−6 10−6 10−6 10−6

R

model

2

eRPM IM VM RPM SM

0.978 0.978 0.970 0.969

rank 1 2 3 3 5

AIC −15.84 −13.69 −13.32 −13.32 −12.70

Lk 1.000 0.341 0.284 0.284 0.208

πAIC 0.47 0.16 0.13 0.13 0.10

R2

RSS 5.12 4.43 6.45 6.45 1.20

× × × × ×

−5

10 10−4 10−4 10−4 10−3

0.9931 0.9404 0.9131 0.9131 0.8385

values and the highest probability share πAIC. For the GEN-raw and GEN-AF, the probability share of the eRPM and IM were much closer to each other (see Tables 4 and 5). For the GENAF + 20 wt % K2CO3, the difference between these two models increased significantly, consistent with the eRPM fitting the data better than the IM. More precisely, the eRPM had a 3 times higher probability share than the other models (see Table

that all four models were statistically equal; the Akaike information criteria marginally favored the RPM. Activation energies of 124 and 131 kJ mol−1 were estimated by the RPM and VM, respectively, while the SM and IM estimated higher values of 185 and 149 kJ mol−1, respectively. In comparison to the GEN-raw, the R2 values for the GEN-AF model fits were slightly lower (see Tables 4 and 5), which is also illustrated in

Figure 5. Observed (symbols) and calculated (lines) gasification rate as a function of char conversion for GEN-AF + 20 wt % K2CO3 gasified at (a) 650 °C, (b) 700 °C, (c) 725 °C, and (d) 750 °C. VM, volumetric model; SM, shrinking model; IM, integrated model; and eRPM, extended random pore model. Note the different scale for the gasification rates. 4880

dx.doi.org/10.1021/ef400552q | Energy Fuels 2013, 27, 4875−4883

Energy & Fuels

Article

our value of 131 kJ mol−1 (RPM in Table 3). When K2CO3 was added to the coal, Schumacher et al. determined a slightly higher activation energy of 164 kJ mol−1; however, no explanation for the increase was given. The reported activation energies of the catalyzed and uncatalyzed reactions varied only slightly in these studies, which is in agreement with the statement that the catalyst does not change the kinetic network fundamentally.22 The main difference with our study is that we did not impregnate K2CO3; we physically mixed K2CO3 with dry ash-free coal. Thus, the dispersion and potassium−carbon contact might not be as good as those for impregnated samples. Freund25 also dry-mixed K2CO3 with a model carbon [i.e., Spherocarb with low ash and volatile matter contents but high Brunauer−Emmett−Teller (BET) surface area of 950 m2 g−1] and obtained an activation energy of 242 kJ mol−1 for CO2 gasification carried out between 600 and 800 °C in a TGA. However, coal char, activated carbon, and Spherocarb are different carbon materials compared to our ash-free coal (i.e., volatile matter of ∼70 wt % and a CO2 surface area of ∼10 m2 g−1).14 The pyrolysis process of the ash-free coal with and without catalyst differed as shown by Kopyscinski et al.14 Thus, the produced chars have different properties and gasification behavior. An increasing activation energy in the presence of a catalyst does not seem logical. However, we have to keep in mind that catalytic gasification with a solid−solid contact is different compared to a heterogeneous catalytic process (i.e., gas−solid contact). In the latter, the catalyst is not mobile, often bound to a support, and directly promotes the reaction between the adsorbed gas species, leading to a decrease in the activation energy. In the gasification process, however, the solid carbon must react with oxygen and/or hydrogen from the gas phase, which then results in the destruction of the solid matrix. Thus, the catalyst must (a) be able to promote the oxygen/hydrogen transfer from the gas phase onto the solid and (b) be mobile and move to a new carbon site. 3.2.2. Influence of the Potassium Concentration. The influence of the potassium concentration was investigated at a gasification temperature of 700 °C, as shown in section 3.1.2. For the kinetic parameter estimation, only the eRPM with the assumption of a constant parameter ψ (i.e., independent from the catalyst loading) was applied and the parameters c and p were determined. The value of the structure parameter was ψ = 63.8, estimated in the previous section. The physical interpretation of this parameter indicates that, during the gasification, a maximum surface area exists. The larger the ψ value, the higher the ratio between initial and maximum surface areas.27 The increasing surface area might be promoted by the accelerated potassium-catalyzed gasification. Figure 3 and Figure S4 of the Supporting Information illustrate the good agreement between the observed and modeled gasification behavior for GEN-AF with 20, 33, and 45 wt % K2CO3. When the amount of K2CO3 was increased in the ash-free coal, the maximum gasification rate increased by a factor of 3 and shifted from 12 to 20% conversion as well (see Figure S4 of the Supporting Information). This behavior was reflected in the parameters c and p. Parameter p predominately influences the shape of the gasification curve and the position of the maximum rate (i.e., the higher the p value, the more the maximum rate shifts to a lower conversion value). Parameter c, on the other hand, is correlated with a value of the maximum gasification rate. Thus, a larger c value means a higher rate. The parameter c increased linearly, while p decreased with a log function as the

6). The distinct maximum in the gasification rate could only be modeled with the eRPM. If the char conversion is plotted as a function of the gasification time (Figure 2c and Figure S3 of the Supporting Information), the distinction between the different models especially up to 50% is difficult. Only above 50% conversion does the eRPM appear to fit the data the best. Thus, Figure 5, in which the rate as a function of char conversion is shown, better distinguishes the gasification behavior of the catalyzed sample. For the catalyzed sample, an activation energy of 264 kJ mol−1 was determined for the eRPM (Table 3 and Table S3 of the Supporting Information). The estimated activation energies for GEN-raw and GEN-AF were much lower (131 and 124 kJ mol−1, respectively). A reason for the difference could be potassium mobility, which is most likely higher at 750 °C than at 650 °C. Thus, the observed activation energy might be the sum of the intrinsic activation energy plus the energy for potassium transfer (Eobs = Ea + Etrans,K). The theory that the observed activation energy is influenced by the mobility of the potassium surface complex needs further study and is currently under investigation. The activation energies have also been calculated by means of the reactivity index (1/t50) data. The resulting Arrhenius plot (Figure 6) shows that the rates for the three samples increased

Figure 6. Arrhenius plot based on the observed t50 value (time to reach 50% char conversion) for the CO2 gasification of GEN-raw, GEN-AF, and GEN-AF + 20 wt % K2CO3.

linearly with the inverse temperature, indicating no change in the reaction regime. Thus, even at high temperatures, the gasification was reaction-controlled and not limited by mass transfer. The corresponding activation energies were 135, 155, and 241 kJ mol−1 for GEN-raw, GEN-AF, and GEN-AF + 20 wt % K2CO3, respectively, which were similar to the activation energies estimated with the best fit model. To our knowledge, no activation energies for catalyzed and uncatalyzed CO2 gasification of ash-free coals have been reported. For K2CO3-catalyzed gasification of activated carbon with a low ash content (5 wt %), a similar high activation energy of 244 kJ mol−1 was reported.30 However, the corresponding activation energy for the uncatalyzed sample was slightly higher (255 kJ mol−1). Huhn et al.31 reported activation energies of 160 and 140 kJ mol−1 for coal char and K2CO3-impregnated coal char gasified with CO2, respectively. Schumacher et al.32 observed an activation energy of 145 kJ mol−1 for the CO2 gasification of coal char, which is similar to 4881

dx.doi.org/10.1021/ef400552q | Energy Fuels 2013, 27, 4875−4883

Energy & Fuels

Article

potassium loading increased (Figure 7). Zhang et al.33 found a similar behavior of the two parameters for the catalytic CO2 gasification of activated carbon.

AF samples, respectively. (2) Increasing the amount of K2CO3 in the ash-free coal (from 20 to 45 wt %) increased the maximum gasification rate by a factor of 3. (3) The eRPM fit the gasification behaviors of GEN-raw and GEN-AF + 20 wt % K2CO3 best. The RPM and IM were equally good to predict the gasification of GEN-AF. (4) The calculated activation energy for GEN-raw and GEN-AF had similar values (i.e., 133 and 121 kJ mol−1). However, adding K2CO3 to ash-free coal doubled the calculated activation energy (264 kJ mol−1). The high activation energy might due to the energy required for the potassium transfer or caused by the pyrolysis process, which created a char with different properties. (5) The heating protocol, i.e., gas atmosphere and holding time prior to the gasification, influenced the gasification behavior and rate. None of the applied models could sufficiently fit the catalytic gasification behavior of the sample that was heated with CO2 and/or had a short holding time in a N2 atmosphere as the pyrolysis and main catalyst reduction (i.e., K2CO3 to active potassium−carbon surface intermediate) overlap with the gasification process. Catalyst reduction is inhibited under a gasification atmosphere. In addition, complete reduction might not be possible in CO2, whereas it could be possible under N2.

Figure 7. Influence of K2CO3 loading on the parameters c and p from the eRPM. The parameters were estimated at 700 °C with a constant ψ parameter (ψ = 63.87).



3.2.3. Influence of the Heating Protocol. As mentioned above the heating protocol significantly influenced the catalytic gasification behavior. Figure 4 shows the observed and eRPMcalculated char conversions, which seem to be in good agreement for all four different heating protocols. However, after plotting the gasification rate as a function of char conversion for both the experimental data and the eRPMmodeled data (see Figure S5 of the Supporting Information), the results, especially in the low conversion range, are very different. Only protocol (a), holding time of 150 min in N2 before introducing CO2, is predicted well with the eRPM. The behavior of the experiment with the other protocols (b−d, shorter holding times or heating in CO2) could not be fit sufficiently by the eRPM or the other models (not shown). The high initial rate and subsequent decline of the rate for protocols (c) and (d) can be explained by the influence of the devolatilization, which was not completed when the gasification started. The devolatilization was completed after approximately 10 min at 700 °C, as mentioned earlier. At around 10−15% conversion, the gasification rates of samples (b−d) increased as the catalyst is reduced and undergoes the redox cycle. Nevertheless, the rates for sample (a) were still higher up to 60% conversion, possibly because of the degree of catalyst reduction. Under a N2 atmosphere and sufficient holding time, the catalyst was likely fully reduced. Decreasing the holding time reduced the degree of catalyst reduction and, hence, the gasification rate. Catalyst reduction was most hindered by heating in a CO2 environment.14

ASSOCIATED CONTENT

S Supporting Information *

Estimated kinetic parameters, normalized parameter covariance matrix, and degrees of freedom for each model and sample, as described in the text (Tables S1−S3) and modeled and observed gasification rates as a function of char conversion for all models and samples, as described in the text (Figures S1− S5). This material is available free of charge via the Internet at http://pubs.acs.org.



AUTHOR INFORMATION

Corresponding Author

*Telephone: +1-403-210-9488. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

■ ■

ACKNOWLEDGMENTS The authors acknowledge the financial support from Carbon Management Canada (CMC).

4. CONCLUSION In this work, the kinetic data for K2CO3-catalyzed CO2 gasification of ash-free coal were collected at ambient pressure in a TGA. These data were compared to uncatalyzed gasification of ash-free coal (GEN-AF) and parent coal (GEN-raw). On the basis of the experimental data, the kinetic parameters for each model were estimated with the nonlinear least-squares method. In addition, the best model was determined by applying the AIC. The main conclusions from this work are as follows: (1) At 750 °C, the CO2 gasification of GEN-AF + 20 wt % K2CO3 was around 3 and 60 times faster compared to GEN-raw and GEN4882

NOMENCLATURE AIC = Akaike information criterion (see eq 10) c = empirical parameter of the eRPM (see eq 8) dof = degree of freedom (experimental data − number of parameters) Ea = observed activation energy (kJ mol−1) f i = modeled value (i.e., rate) kj = rate constant (difference) kTref = pre-exponential factor for rate constant kj (difference) LAIC = relative likelihood of model k (see eq 12) m = mass (kg) m = number of estimated parameters n = number of observations (data points) p = empirical parameter of the eRPM (see eq 8) R = universal gas constant (8.314 472 J mol−1 K−1) rN = normalized reactivity index (see Table 2) t = time (s or min) t5 and t50 = time to reach 5 and 50% char conversion, respectively (min) dx.doi.org/10.1021/ef400552q | Energy Fuels 2013, 27, 4875−4883

Energy & Fuels

Article

T = temperature (K or °C) X = char conversion yi = experimental observations

(30) Kapteijn, F.; Peer, O.; Moulijn, J. A. Fuel 1986, 65 (10), 1371− 1376. (31) Huhn, F.; Klein, J.; Jüntgen, H. Fuel 1983, 62 (2), 196−199. (32) Schumacher, W.; Mühlen, H.-J.; van Heek, K.; Jüntgen, H. Fuel 1986, 65 (10), 1360−1363. (33) Zhang, Y.; Hara, S.; Kajitani, S.; Ashizawa, M. Fuel 2010, 89 (1), 152−157.

Greek Symbols

πAIC = Akaike probability share (see eq 11) θk = parameter for the reaction constant θEa = dimensionless activation energy

Abbreviations

eRPM = extended random pore model FC = fixed carbon (see Table 1) IM = integrated model RPM = random pore model RSS = sum of squares of residuals SM = shrinking particle model VM = volumetric model VM = volatile matter (see Table 1)



REFERENCES

(1) Wang, J.; Jiang, M.; Yao, Y.; Zhang, Y.; Cao, J. Fuel 2009, 88 (9), 1572−1579. (2) Sheth, A. C.; Yeboah, Y. D.; Godavarty, A.; Xu, Y.; Agrawal, P. K. Fuel 2003, 82 (3), 305−317. (3) Sharma, A.; Saito, I.; Takanohashi, T. Energy Fuels 2008, 22 (6), 3561−3565. (4) Malekshahian, M.; Hill, J. M. Fuel Process. Technol. 2013, 113, 34−40. (5) Wu, Y.; Wang, J.; Wu, S.; Huang, S.; Gao, J. Fuel Process. Technol. 2011, 92 (3), 523−530. (6) Zhou, Z. J.; Hu, Q. J.; Liu, X.; Yu, G. S.; Wang, F. C. Energy Fuels 2012, 26 (3), 1489−1495. (7) Karimi, A.; Semagina, N.; Gray, M. R. Fuel 2011, 90, 1285−1291. (8) Brown, R. C.; Liu, Q.; Norton, G. Biomass Bioenergy 2000, 18 (6), 499−506. (9) Wood, B. J.; Sancier, K. M. Catal. Rev.: Sci. Eng 1984, 26 (2), 233−279. (10) Kopyscinski, J.; Schildhauer, T. J.; Biollaz, S. M. Fuel 2010, 89, 1763−1783. (11) Habibi, R.; Kopyscinski, J.; Masnadi, M. S.; Lam, J.; Grace, J. R.; Mims, C. A.; Hill, J. M. Energy Fuels 2013, 27 (1), 494−500. (12) Formella, K.; Leonhardt, P.; Sulimma, A.; van Heek, K.; Jüntgen, H. Fuel 1986, 65 (10), 1470−1472. (13) Bruno, G.; Buroni, M.; Carvani, L.; Piero, G.; Passoni, G. Fuel 1988, 67 (1), 67−72. (14) Kopyscinski, J.; Rahman, M.; Gupta, R.; Mims, C. A.; Hill, J. M. Fuel 2013, manuscript accepted. (15) Okuyama, N.; Komatsu, N.; Shigehisa, T.; Kaneko, T.; Tsuruya, S. Fuel Process. Technol. 2004, 85, 947−967. (16) Steel, K. M.; Patrick, J. W. Fuel 2001, 80, 2019−2023. (17) Campell, F. R.; Legg, J. F. Clean Coal: A Compendium of Canada’s Participation; Natural Resources Canada: Ottawa, Ontario, Canada, May 2007; www.nrcan.gc.ca/node/38 (accessed 2013). (18) Rahman, M.; Samanta, A.; Gupta, R. Fuel Process. Technol. 2013, 115, 88−98. (19) Long, F.; Sykes, K. J. Chim. Phys. 1950, 47 (3−4), 361−378. (20) Moulijn, J. A.; Cerfontain, M.; Kapteijn, F. Fuel 1984, 63 (8), 1043−1047. (21) Mims, C. A.; Pabst, J. K. Fuel 1983, 62 (2), 176−179. (22) Moulijn, J. A.; Kapteijn, F. Carbon 1995, 33 (8), 1155−1165. (23) Freund, H. Fuel 1985, 64, 657−600. (24) Freund, H. Fuel 1986, 65, 63−66. (25) Akaike, H. IEEE Trans. Autom. Control 1974, 19 (6), 716−723. (26) Cleveland, W. S. J. Am. Stat. Assoc. 1979, 74 (368), 829−836. (27) Bhatia, S. K.; Perlmutter, D. D. AIChE J. 1980, 26 (3), 379−386. (28) Zhang, Y.; Ashizawa, M.; Kajitani, S.; Miura, K. Fuel 2008, 87 (4−5), 475−481. (29) Stewart, W. E.; Caracotsios, M. Athena Visual Studio; www. athenavisual.com (accessed 2013). 4883

dx.doi.org/10.1021/ef400552q | Energy Fuels 2013, 27, 4875−4883