Study on the Kinetics of Plant Oil Asphalt Pyrolysis Using

Feb 24, 2014 - Theoretical Analysis of Double Logistic Distributed Activation Energy Model for Thermal Decomposition Kinetics of Solid Fuels...
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Study on the Kinetics of Plant Oil Asphalt Pyrolysis Using Thermogravimetry and the Distributed Activation Energy Model Qiang Tang, Yanyan Zheng, Tiefeng Wang,* and Jinfu Wang* Beijing Key Laboratory of Green Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University, Beijing 100084, People’s Republic of China ABSTRACT: Plant oil asphalt (POA) is a new concerned lipid-based residue biomass generated from the oleochemical industry. In this work, the pyrolysis kinetics of POA was studied by thermogravimetric analysis at heating rates of 7, 10, 20, and 30 K min−1 under a nitrogen atmosphere using the distributed activation energy model (DAEM). The kinetic parameters, including the activation energy E, distribution function of activation energy f(E), and pre-exponential factor k0, were obtained. The activation energy E ranged from 75 to 300 kJ mol−1, and the f(E) curve showed a broad peak around 155−200 kJ mol−1. The linear relationship between ln k0 and activation energy E indicated that there existed a kinetic compensation effect in pyrolysis of POA. A double-Gaussian DAEM was employed for simulation of POA pyrolysis by assuming POA as a mixture of two pseudocomponents. In comparison to the single-Gaussian DAEM, the double-Gaussian DAEM was better to predict the pyrolysis behavior of POA. This work validated the applicability of the double-Gaussian DAEM to the lipid-based material POA.

1. INTRODUCTION Biofuels derived from biomass are receiving increased attention because of the fossil resource crisis.1−3 The pyrolysis reactions play a crucial role in most biomass conversion processes, such as carbonization, gasification, liquefaction, and combustion.4−6 During these processes, the pyrolysis kinetics is of great importance for the pyrolysis behavior prediction and reactor design.7−9 Thermogravimetric analysis (TGA) is a highprecision method for investigation of pyrolysis kinetics under well-defined conditions in the kinetic-controlled regime.7,8 Biomass can be classified into two major categories: lignocellulosic biomass and lipid-based biomass. TGA has been widely used to study the pyrolysis kinetics of lignocellulosic biomass.7,8,10,11 However, TGA studies on the pyrolysis kinetics of lipid-based biomasses are still very limited. Plant oil asphalt (POA) is a lipid-based residue biomass generated in biodiesel [(fatty acid methyl ethers (FAMEs)] and fatty acid (FA) industries.12,13 POA is named after its characteristics, as a dense and viscous black liquid similar to asphalt. As residue biomass, POA is of low value when being used as a boiler oil or mold lubricant. The high yield of POA, which can exceed 20 wt % on a lipid feedstock basis, seriously affects the atom economy of the processes.12,13 Therefore, recycling or reuse of the residual POA is of great importance for the oleochemical industry. Proximate analysis showed that POA contains more than 98 wt % volatile matter on an air-dried basis.13 This property makes POA an excellent material for production of biofuels by pyrolysis. In 1985, Avni et al.14 first applied the distributed activation energy model (DAEM) to the devolatilization of lignin to account for the complex composition of biomass. Since then, the DAEM has been widely used in the studies on pyrolysis kinetics of biomass.15−18 In most DAEM applications, the activation energy distribution curve is assumed to be a singleGaussian function. Recently, Várhegyi et al.7,8 assumed that two parallel DAEM reactions occurred in pyrolysis for modeling the complexity of the biomass samples. The samples were regarded © 2014 American Chemical Society

as mixture of two pseudo-components. In comparison to the single-Gaussian DAEM, this approach gave better prediction of the experimental results and allowed for predictions beyond the experimental conditions. However, previous works focused on lignocellulosic biomass, and very limited works have been reported on the applicability of DAEM to lipid-based biomass. The present work aimed to investigate the pyrolysis kinetics of POA using thermogravimetry and the DAEM. The activation energy, distribution function of activation energy f(E), and prefrequency factor were determined from the experimental data. The applicability of the single- and double-Gaussian DAEMs to simulate the thermogravimetric (TG) and differential thermogravimetric (DTG) curves of POA were discussed.

2. EXPERIMENTAL SECTION 2.1. Samples. POA samples were provided by Shandong BioEnergy Products and Technology Co., Ltd. The POA was a byproduct of the biodiesel process using waste cooking oil and acidic oil as raw materials. In this process, the yield of POA was 15−25 wt % on a feedstock basis. 2.2. Thermogravimetric Experiments. The experiments were conducted in a thermogravimetric analyzer (Perkin-Elmer Pyris 1) with a nitrogen purge flow rate of 50 mL min−1. The POA sample (30 mg) was loaded into a platinum pan and placed in the heating zone of the thermogravimetric analyzer and was heated from 303.15 to 873.15 K under a pre-determined temperature−time program. In the thermogravimetric experiments, the sample temperature cannot be well-controlled by the pre-determined temperature−time program when the heating rate is too high.7,8 The kinetic parameters obtained from the slow pyrolysis experiments can also be used to simulate fast pyrolysis.7,8 Therefore, relatively low heating rates (7, 10, 20, and 30 K min−1) were used in this work to guarantee the measurement accuracy. At these experimental heating rates, a good Received: December 31, 2013 Revised: February 20, 2014 Published: February 24, 2014 2035

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synchronism on the sample temperature between the measured and set values was observed.

3. KINETIC MODEL 3.1. Estimation of E, k0, and f(E) in the DAEM. Using the DAEM, it is assumed that a number of independent parallel reactions with different activation energies E occur simultaneously during the pyrolysis of POA, and the distribution of the activation energy is represented by a function f(E). All of the parallel reactions are considered to be irreversible first-order with respect to the remaining volatile content. The remaining volatile content V at time t is given by19,20 1 − V /V * =

∫0

∞

exp( −k 0

∫0

t

e−E /RT dt )f (E) dE

Figure 1. DTG curves of POA under a nitrogen atmosphere at different heating rates.

(1)

where V* is the total volatile content of the POA sample, k0 is the pre-exponential factor corresponding to activation energy E, and T is the reaction temperature as a function of time t. The activation energy distribution function f(E) satisfies the following normalization condition:

∫0

dxj(t , E)/dt = k 0, j(E)e−E /RT (1 − xj(t , E))

where xj (j = 1 and 2) is the reacted fraction of a pseudocomponent. The function f j(E) was described by Gaussian distribution23

∞

f (E ) d E = 1

(2)

f j (E ) =

With the heating rate of Φ, the reaction temperature T at time t is given by T = T0 + Φt

xj(t ) =

(7)

∫0

∞

f j (E)xj(t , E) dE

(8)

The volatile content V(t) is the linear combination of that of the pseudo-components as

(4)

Using eq 4, both E and k0 could be estimated from the Arrhenius plot of ln(Φ/T2) versus 1/T at specified values of V/ V*. Following Raman et al.,20 it was assumed that the preexponential factor was the same for all parallel reactions and the number of reactions involved was large enough to permit the activation energy E to be expressed as a continuous distribution function f(E). Then, f(E) dE represents the fraction of the total potential volatile loss dV that has an activation energy between E and E + dE. Thus, dV = V*f(E) dE, and f(E) can be calculated by 1 dV f (E ) = V * dE

⎡ (E − E )2 ⎤ 1 0, j ⎢− ⎥ exp ⎢⎣ 2σE , j 2 ⎥⎦ σE , j(2π )0.5

where E0,j and σE,j are the mean activation energy and width parameter of the Gaussian distribution for pseudo-component j, respectively. The overall reacted fraction of the pseudocomponent j is obtained by integration

(3)

Miura21,22 proposed a simplified method to determine the parameters k0, E, and f(E). The derivation of this method was described in detail elsewhere.21,22 In this simplified method, the Arrhenius equation was described by ⎛k R ⎞ ⎛Φ⎞ E1 ln⎜ 2 ⎟ = ln⎜ 0 ⎟ + 0.6075 − ⎝T ⎠ ⎝ E ⎠ RT

(6)

V (t ) = V * − c1x1(t ) − c 2x 2(t )

(9)

3.3. Least-Square Estimation of Model Parameters. The model parameters were estimated by minimizing the difference between the experimental and simulated data using least-squares regression.7,8 The deviation defined by eq 10 was used to measure the agreement between the experimental and simulated data N

S=

∑ i=1

[X exp(Ti ) − X sim(Ti )]2 N

(10)

where Ti is the reaction temperature, N is the number of data points in a given experiment, and Xexp(Ti) and Xsim(Ti) are the experimental and calculated data corresponding to Ti, respectively.

(5)

3.2. Double-Gaussian DAEM for POA Pyrolysis. The DTG curves of the POA at different heating rates showed two peaks, as shown in Figure 1. This agreed well with the previous finding that POA could be considered as a mixture of FAMEs and oligomers of FAs/FAMEs.12,13 A “shoulder” was observed between the two DTG peaks. Primary tests using the singleGaussian DAEM failed to give acceptable agreement between the experimental and simulated curves in the “shoulder” area. In this work, we used a double-Gaussian DAEM7,8 for POA pyrolysis, in which POA was regarded as a mixture of two pseudo-components. A pseudo-component was the lumped component of volatile matter that could be described by the same set of reaction kinetic parameters. A first-order rate law for pseudo-component j is

4. RESULTS AND DISCUSSION 4.1. Estimation of E, k0, and f(E). Figure 2 shows the TG curves of POA under a nitrogen atmosphere at Φ = 7, 10, 20, and 30 K min−1. The mass loss started at 420−470 K and terminated at 770 K. In this case, the temperature range of 350−870 K was used for kinetics data collection. The final remaining weight slightly decreased from 2.05 to 1.63 wt % as the heating rate increased from 7 to 30 K min−1. The TGA curve shifted toward higher temperatures as the heating rate increased. This phenomenon was caused by differences in heat transfer and kinetic rates.24,25 As the heating rate increased, the 2036

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Figure 2. TG curves of POA under a nitrogen atmosphere at different heating rates.

Figure 4. V/V* versus E relationship estimated from the Arrhenius plot for POA.

heat transfer resistance caused a larger temperature difference between the heater and POA sample, thus delaying the sample decomposition. Figure 3 demonstrates the Arrhenius plot of ln(Φ/T2) versus 1/T, showing a good linear relationship (R2 > 0.99) between

Figure 5. Activation energy distribution function f(E) curves for POA.

Figure 3. Arrhenius plot of ln[Φ/T2] versus 1/T at selected V/V* values for POA.

ln(Φ/T2) and 1/T. The activation energy E and preexponential factor k0 were estimated from the slope and the intercept in each Arrhenius plot. The relationship between V/ V* and E is shown in Figure 4. The activation distribution function f(E) was obtained by differentiating V/V* with respect to E, as shown in Figure 5. The results show that the activation energy E ranged from 75 to 300 kJ mol−1 and the f(E) curve had a broad peak in the range of 155−200 kJ mol−1. It is clear that the f(E) distribution could not be approximated by a single-Gaussian distribution. This explained the large deviations of the calculated results from the experimental data when using the single-Gaussian DAME. Figure 6 demonstrates the relationship between k0 and E for POA. The k0 value ranged from the order of magnitude from 106 to 1022 min−1. The linear relationship between ln k0 and activation energy E can be written as

k 0 = 2.33e 0.17E

Figure 6. ln k0 versus E relationship estimated for POA.

effect is caused by the change in reactant properties. During pyrolysis of this multi-component sample, the decomposition reaction becomes more difficult to occur as the conversion increases, presenting a higher activation energy. In addition, the pre-exponential factor is not a constant because of the change in the reacted component. 4.2. Simulation of the TG Curves. To simulate the TG curves at different heating rates, both the single- and doubleGaussian DAEMs were used. In these models, the preexponential factor k0 was expressed as function of the activation energy E, as shown in eq 11. The model parameters were determined by minimizing the deviation between the experimental and simulated data using least-squares regression. As shown in Figure 7, the double-Gaussian DAEM showed

(11)

This indicated that there existed the kinetic compensation effect,21,22 which meant a compensatory increase of k0 with an increasing activation energy E. The kinetics compensation 2037

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Figure 7. Simulation of TG curves at different heating rates Φ for POA (blue line, simulated data using the single-Gaussian DAEM; red line, simulated data using the double-Gaussian DAEM; square data points, experimental data).

0.54, and 0.82 wt % at heating rates of 7, 10, 20, and 30 K min−1, respectively, as shown in Table 2. This showed that the double-Gaussian DAEM had much better prediction ability than the single-Gaussian DAEM for POA. 4.3. Simulation of the DTG Curves. The single- and double-Gaussian DAEMs were also used to simulate the DTG curves. The DTG data were calculated by analytical differentiation of the smoothed TG curves. The smoothing procedure adopted adjacent averaging of 150 points. As shown in Figure 8, the double-Gaussian DAEM gave much better predictions than the single-Gaussian DAEM. The insets showed the DTG curves of the two pseudo-components in the double-Gaussian DAEM. For DTG curves, the deviations between the experimental and simulated data were calculated by substituting Xexp and Xsim in eq 10 with the corresponding (dV/dT)exp and (dV/dT)sim, respectively. With the single-Gaussian DAEM, the deviation S was 0.05, 0.05, 0.04, and 0.05 × 10−2 K−1 at heating rates of 7, 10, 20, and 30 K min−1, respectively, as shown in Table 1. The double-Gaussian DAEM gave much better predictions, with the minimum deviation S being 0.03, 0.02, 0.02, and 0.02 × 10−2 K−1 at heating rates of 7, 10, 20, and 30 K min−1, respectively, as shown in Table 2.

much better agreement with the experimental data than the single-Gaussian DAEM. The insets showed the TG curves of the two pseudo-components in the double-Gaussian DAEM. For TG curves, the deviations between the experimental and simulated data were calculated by substituting Xexp and Xsim in eq 10 with the corresponding volatile content Vexp and Vsim, respectively. The parameters of single-Gaussian DAEM are listed in Table 1. With the single-Gaussian DAEM, the Table 1. Single-Gaussian DAEM Parameters Giving Minimum Deviation (S) between Experimental and Simulated Data S Φ (K min−1)

E0 (kJ mol−1)

σE (kJ mol−1)

TG (wt %)

DTG (×10−2, K−1)

7 10 20 30 average

200 201 201 202 201

5 5 5 5 5

5.17 4.35 4.27 3.97 4.44

0.05 0.05 0.04 0.05 0.05

minimum deviation S was 5.17, 4.35, 4.27, and 3.97 wt % at heating rates of 7, 10, 20, and 30 K min−1, respectively. When double-Gaussian DAEM was used, the deviation was 0.91, 0.50,

Table 2. Double-Gaussian DAEM Parameters Giving Minimum Deviation (S) between Experimental and Simulated Data S −1

−1

−1

Φ (K min )

c1 (wt %)

c2 (wt %)

E0,1 (kJ mol )

E0,2 (kJ mol )

σE,1 (kJ mol )

σE,2 (kJ mol )

TG (wt %)

DTG (×10−2, K−1)

7 10 20 30 average

0.103 0.098 0.097 0.097 0.099

0.897 0.902 0.903 0.903 0.901

155 156 158 158 156.8

193 194 194 193 193.5

3 3 3 3 3

5 5 5 5 5

0.91 0.50 0.54 0.82 0.69

0.03 0.02 0.02 0.02 0.02

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−1

−1

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Figure 8. Simulation of DTG curves at different heating rates Φ for POA (blue line, simulated data using the single-Gaussian DAEM; red line, simulated data using the double-Gaussian DAEM; circle data points, experimental data).

4.4. Relationship between Model Parameters and Kinetics Data. The obtained parameters were similar at different heating rates for either the single- or double-Gaussian DAEM. This indicated that there were no effects of self-cooling or self-heating because of the enthalpy change of reaction.7 Otherwise, the kinetic parameters would vary with the heating rate. This property made the kinetic model applicable to other heating rates when heat transfer is not the rate-limiting step. At a very high heating rate, the effect of heat transfer on the apparent reaction rate must be considered using a more complex model that includes both the intrinsic reaction kinetics and heat transfer. For the single-Gaussian DAEM, the mean activation energy E0 was 201 kJ mol−1 and the width parameter σE was 5 kJ mol−1. While for the double-Gaussian DAEM, the contents of the pseudo-components were c1 = 9.8 wt % and c2 = 90.2 wt %. For pseudo-component 1, the mean activation energy E1,0 was 157 kJ mol−1 and the width parameter σE,1 was 3 kJ mol−1. For pseudo-component 2, these parameters were E2,0 = 194 kJ mol−1 and σE,2 = 5 kJ mol−1. The double-Gaussian function was more proper than the single-Gaussian function to represent f(E) for both mathematical and physical considerations. The parameters of the double-Gaussian DAEM showed inner relationships with the kinetics data described in section 4.1. The contents of the two pseudo-components (c1 and c2) coincided well with the two weight loss peaks of the DTG curve, and the mean activation energy for the two pseudo-components (E0,1 and E0,2) were in the range of the broad peak values of f(E) at 155−200 kJ mol−1. The pre-exponential factor k0 was expressed as a function of the activation energy E in eq 11. Therefore, the double-Gaussian DAEM was more applicable for POA materials than the single-Gaussian DAEM. The parameters obtained in this work provide a good estimation for the study of other POA samples with different compositions.

5. CONCLUSION POA is a new concerned lipid-based residue biomass generated from the oleochemical industry. The pyrolysis kinetics of POA has been investigated using TGA under a nitrogen atmosphere at heating rates of 7, 10, 20, and 30 K min−1. The kinetic parameters in the DAEM, including activation energy E, pre-exponential factor k0, and activation energy distribution function f(E), were obtained. The activation energy E ranged from 75 to 300 kJ mol−1, and the f(E) curve showed a broad peak around 155−200 kJ mol−1. The linear relationship between ln k0 and activation energy E showed that there existed a kinetic compensation effect in pyrolysis of POA. The double-Gaussian DAEM was used to describe the pyrolysis kinetics of POA by assuming that POA was a mixture of two pseudo-components. In comparison to the singleGaussian DAEM calculation, the double-Gaussian DAEM was better to predict the pyrolysis behavior of POA. This work validated the applicability of the double-Gaussian DAEM to the lipid-based material POA.

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AUTHOR INFORMATION

Corresponding Authors

*Telephone: +86-10-62794132. E-mail: [email protected]. cn. *Telephone: +86-10-62773250. E-mail: [email protected]. cn. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors acknowledge Shandong Bio-Energy Products and Technology Co., Ltd. for providing the POA material. 2039

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NOMENCLATURE t = time (min) T = temperature (K) E = activation energy (kJ mol−1) k0 = pre-exponential factor (min−1) R = gas constant (8.314 × 10−3 kJ mol−1 K−1) V = remaining volatiles by time t (wt %) V* = total volatiles of the POA sample (wt %) f(E) = distribution function of the activation energy E (kJ mol−1) Φ = heating rate (K min−1) xj = reacted fraction of a pseudo-component Aj = pre-exponential factor of a pseudo-components (min−1) cj = volatiles formed from a pseudo-component (wt %) E0,j = mean activation energy in the DAEM (kJ mol−1) σE,j = width parameter of the Gaussian distribution S = least-squares deviation Xexp(t) = V or dV/dt experimental data Xsim(t) = V or dV/dt simulated data from the DAEM

Subscripts

i = digitized point on an experimental curve j = pseudo-component

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