Kinetic Study of the Pyrolysis of Canary Pine: The Relationship

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Kinetics, Catalysis, and Reaction Engineering

KINETIC STUDY OF THE PYROLYSIS OF CANARY PINE: THE RELATIONSHIP BETWEEN THE ELEMENTAL COMPOSITION AND THE KINETIC PARAMETERS Eduardo Gonzalez-Diaz, Enrique González, Marcos Frias-Garcia, and Isaac Alonso-Asensio Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b01559 • Publication Date (Web): 20 Jun 2018 Downloaded from http://pubs.acs.org on June 24, 2018

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Industrial & Engineering Chemistry Research

KINETIC STUDY OF THE PYROLYSIS OF CANARY PINE: THE RELATIONSHIP BETWEEN THE ELEMENTAL COMPOSITION AND THE KINETIC PARAMETERS Eduardo González Díaz a,*; Enrique González b; Marcos Frías García c; Isaac Alonso Asensio a a)

Department of Techniques and Projects in Engineering and Architecture. Universidad de La Laguna, 38.200. Canary Islands, Tenerife. Spain

b)

Department of Chemical Engineering. Universidad de La Laguna, 38.200. Canary Islands, Tenerife. Spain c)

General Service Research Support. Universidad de La Laguna, 38.200. Canary Islands, Tenerife. Spain

*Corresponding Author. E-mail: [email protected]

KEYWORDS Kinetic models; Pinus canariensis; Biomass; TGA; Silvicultural treatment

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ABSTRACT

The realization of several silvicultural treatment in Canary Islands entails the felling of the Canarian pine that can be an energy source as lignocellulosic biomass. The kinetic parameters of the pyrolysis of the Canary pine, which were yet unavailable in the literature, were determined. The isoconversional methods of Kissinger and Flynn-Wall-Ozawa and a model-based method were comparatively evaluated. The latter model mentioned considers that it is possible to describe the pyrolysis through a parallel reaction scheme of three pseudo components that represent hemicellulose, cellulose and lignin. The model fit was improved setting a Gaussian function centred on the peak as the inverse of the error. Using the mean proportions obtained for each pseudo-component, the elemental chemical composition of the Canarian pine was calculated. The results show a good agreement between the experimental and calculated chemical composition. The difference between both compositions was -3.6%, 0.3% and 3.7% for C, H and O, respectively.

1. INTRODUCTION The Canarian pine is an evergreen endemic conifer whose scientific name is Pinus canariensis Christen Smith ex De Candolle in Buch. This pine is the main forest species of the western Canary Islands, which usually grows to 30 or 45 m in high with a trunk of 1.5 m in diameter, but also monumental trees up to 60 m high, 2.5 m in diameter1 and 800 years old

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have been

detected. The Canarian pine forest was heavily disturbed during the last 5 centuries after the Spanish conquest of the Canary Islands 3. However, in the last 60 years, large areas of Tenerife have been reforested, but without subsequent management or monitoring. Currently, there are many areas


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of the forest with an artificially high density of trees per hectare. Nevertheless, restoration requires an appropriate pine density that resembles the original structure of a given forest, in order to promote its natural regeneration 4. Larger opening in the canopy would more closely resemble its natural condition 1. Previous research in Tenerife 5 revealed that heavy thinning of the Canarian pine plantations in Tenerife improved pine seed production, natural pine regeneration, structural complexity and understorey plant diversity. A felling intensity of 60% of basal area was found to be sufficient to improve pine growth. Currently, the public forest managers are carrying out actions aimed at the restoration of the natural pine forest. This process entails felling of trees, which can be a useful energy source as lignocellulose biomass. This biomass is a renewable source, which can be used directly as feedstock for thermochemical conversion through processes of pyrolysis, gasification and combustion. Pyrolysis is defined as a thermal decomposition of the biomass in the absence of oxygen in a relatively low range of temperature6. The pyrolysis reaction would allow solid, liquid or gaseous fractions to be obtained for use in thermoelectric generation. Some of these products have properties similar to gasoline and diesel. Pyrolytic processes thus transform dry solid biomass to biofuels7. Furthermore, this production of biofuels is disconnected from sources and products based on raw materials that compete with the food market and inflate its prices for vulnerable populations. Due to huge potential of lignocellulosic biomass for providing energy and valuable chemicals, it is essential to deepen our knowledge for further development of the biomass pyrolysis process. This study aims at investigating pyrolysis kinetics of Canarian pine using thermogravimetric analysis (TGA). The TGA data were analyzed to calculate the kinetic parameters of the pyrolysis of the Canarian pine including the activation energy and the pre-exponential factor. This allows


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better understanding of the thermal decomposition process and provides information for designing a pyrolysis process using biomass 8. There are so far no references to be found regarding the study of the pyrolysis of the Canarian pine. 2. KINETIC MODELLING The reaction rate equation of solid-state pyrolysis can be described by the following expression 6,9

:

= A ∙ exp −

∙

(1)

where Ea (J·mol-1) is the apparent activation energy; A (s-1) is the apparent pre-exponential factor; R= 8.3145 J·mol-1·K-1 is the ideal gas constant; T(K) is the absolute temperature; and f(α) is a function that depends on the reaction mechanism. The conversion, α, is defined as:

=

(2)

where m0, mt and mf, expressed in grams, are the initial mass, instant mass and final mass of the sample, respectively. If the heating rate is defined as β=dT/dt (K/s) for a thermogravimetric analysis under non-isothermal conditions, eq 1 can be written as:

= ∙ exp −

∙

(3)

2.1. Kissinger method


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The Kissinger method

10

is a model-free non-isothermal method, which allows the calculation

of the activation energy by plotting, on a logarithmic scale, the temperature data of the peak (Tm) of the thermogravimetric derivative (DTG) curve at each heating rate (β). Kissinger’s equation for any reaction model is:

= ln # − ∙

$

%−

&

∙

(4)

Taking into account the first order kinetics, where: = 1 −

(5)

the above equation takes the form of the Kissinger equation:

= −

∙

&

(6)

The Kissinger method is not exactly isoconversional because assumes a constant activation energy, i.e. it does not calculate Ea values at progressive α values 9. This method is used normally, because it is easy to apply. However, this method has two main limitations, which need to be taken into account. One limitation is associated with the fact that the accurate determination of the activation energy requires a first order kinetic model, as shown in eq 5. Another limitation of this method is that only provides a single activation energy value, regardless of the different reactions that take place in process. Consequently, the determined activation energy only represents a single-step kinetics11. In summary, the use of eq 6 is based on


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certain assumptions: A is constant, Ea is constant, reaction follows 1st order kinetics and (=(), iterating that the DTG contains single peak for a specific β12.

2.2. Flynn-Wall-Ozawa method (FWO) The FWO13,14 is an isoconversional method. In this method, the integration of the conversion function, g(α), is solved by introducing the approximation of Doyle15 to the temperature integral, p(x), where x= *+ /-(, as shown in eq 7:

. = /6

1

= /6 5 0 23 4( = $

∙ ∙

: 7 8

/8

8

49 =

∙ ∙

;9

(7)

The temperature integral can be approximated through an empirical interpolation formula proposed by Doyle15: log ;9 ≅ −5.311 − 1.052 9,

for 20 ≤ 9 ≤ 60

(8)

When the approximation of Doyle is introduced, the FWO is obtained:

ln F =

5 ∙ ,5 ∙H

− 5.331 − 1.052

,5

∙

&

I,5

(9)

where Tβ,α refers to the value of the temperature for a given conversion value and a heating rate. The expression of g(α) is constant at a given degree of conversion for a chosen reaction model.


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Several algebraic expressions that represent theoretical mechanisms have been proposed in relevant literature6,16. For a first order reaction, the expression of g(α) takes the form of eq 10: . = −1 −

(10)

Thus, for different heating rates and a given α, a linear relationship is observed by plotting ln β vs. 1/T, and the Ea,α is obtained from the slope and Aα from the intercept of the vertical axis.

2.3. Biomass pyrolysis kinetics in the parallel reaction scheme Previous works have shown the limits of the Kissinger and the iso-conversional methods for describing lignocellulosic biomass pyrolysis, which mainly relies in the assumption that the biomass can be considered as whole17,18. In fact, although a first order reaction model has demonstrated as a reasonable approximation for the hemicellulose cellulose, and lignin, other kinetic models can be applied. The multicomponent-models are an alternative approach and the parallel reaction scheme is the most representative of them for the biomass pyrolysis reaction19. This process is assumed to be the sum of the inputs of the main

17

. Eq 1 is applicable for

pyrolysis of any component, known as pseudo-component in this model. The overall reaction rate is related to the reaction rate of each pseudo-component (ci) in the parallel reaction scheme according to the following equation17

= ∑MLN& KL

L

= OL ∙ L ∙ 09; −

L

(11)

P

(12)


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The conversion rate 4/4QL for each pseudo-component (subscript i) depends on a preexponential factor (Ai), activation energy (Ei) and a final term, fi(α), representing the reaction model. For the nth order reaction model, the algebraic expression for fi(α) can be written as: L = 1 − L RP

(13)

which depends on conversion (α) and the order of reaction (n) for each pseudo-component (i). Considering the model of reaction defined in eq 13, the reaction rate of each pseudo-component in the parallel reaction scheme can be written as eq. 14, in accordance with previous studies17,18.

= OL ∙ 1 − L RP ∙ 09; −

L

P

(14)

The theoretical conversion of each pseudo-component (αi) was calculated using the RungeKutta fourth order method, which is a standard algorithm to solve ordinary differential equations. Being known the derivative of a certain variable, in this case, dα/dt, it can compute α(t) given an initial condition, i.e. α(t=0). The experimental data were adjusted using a Trust Region Reflection Algorithm20 enclosed in the SciPy module21. This algorithm is similar to the standard Levenberg-Marquardt algorithm22, whose objective is to solve the least squares problem to fit a model to certain data points. It has the advantage that bounds can be defined to avoid unphysical results. Specifically, three constraints have been imposed: one for the order of reaction (ni), one for the activation energy (Ei), and a last one for the coefficients ci. For the first one, it has been imposed that ni>0, and for the second one, Ei should be higher than 1 kJ·mol-1. These conditions


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guarantee that the three components are taken into account during the fitting process. Finally, the coefficients ci should satisfy: ∑MLN& KL = 1

(15)

In order to improve the fit of the peaks, a weight function was created to give more importance to the points with higher values. This was found to be insufficient for the successful fitting of the lignin decomposition, which occurs mainly after the peak (around 650 K). Therefore, the weight function was multiplied by a Gaussian function (G) centred on the peak, which encompasses the lignin decomposition. Hence, the final weight function (W) can be written as:

S=

T5 T T5

T UV W

XY(Z7+[ , \]

(16)

The standard deviation (σ) was set to 100K in all cases. This value represents a good estimation of two times the FWHM (Full Width at Half Maximum) of dα/dt, which encloses all the reaction process and yielded satisfactory results for the parameters. As this is an iterative method, a proper initial condition should be set. Herein, it was extracted from17. The goodness of the fit was calculated with the following equation:

^Q% =

a

`

4 b 4Q 09;,;0cd

∙ 100

(17)

where sd is in turn calculated as:


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e4 =

T5

T5

f∑Pkla T VgU T h ib j

m

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(18)

where N is the number of experimental data.

3. MATERIALS AND EXPERIMENTAL METHOD 3.1. Materials The wood samples included in this study come from silvicultural treatments in forests of Canary pine. These felled pines were of smaller diameter and most did not yet have resinous heartwood23. Sawdust with particle sizes less than 1 mm obtained by sieving from these different wood samples was used for thermogravimetric assays. The weight of the samples was similar in all cases (10.0±0.9 mg) in order to reduce differences in the heat transfer during the tests. The chemical composition was obtained by CHNS elemental analysis using FlashEA 1112 Organic Elemental Analyzer. The average values and standard deviations of the studied samples of Canary pine were by mass: C=44.8±0.1%; H=6.1±0.5%; N 99,999 %) was used as a carrier gas at 100 mL/min to obtain an inert atmosphere, which allow reducing any interaction with formed vapours and removing gaseous and condensation products. The results obtained were used to calculate the kinetics parameters of the pyrolysis process. Three different kinetic models were applied to the TGA data of the Canarian pine: the Kissinger method; the Flynn-Wall-Ozawa method (FWO) and the least squares fitting in a parallel reaction scheme with three pseudo-components: hemicellulose cellulose, and lignin.

4. RESULTS AND DISCUSSION 4.1. Thermal behavior All wood samples included in this study show a dehydration lower than 373 K during the pyrolysis process. On the other hand, the main mass loss occurs between 425 K and 725 K. These aspects coincide with the behavior of other conifers such as Pinus sylvestris

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and with

previous studies regarding the Canarian pine23. Figure 1 (left) shows the thermal decomposition


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of the Canary pine without resinous heartwood, at four heating rates under a nitrogen atmosphere.

Figure 1. The DTG (left) and conversion curves (right) for Canary pine without resinous heartwood, at different heating rates under an inert atmosphere.

The heating rate affects the position of the maximum peaks. The maximum points of DTG curves are shifted towards higher temperatures due to the heat transfer limitations. During the analysis, the system has a short reaction time at high heating rate, and therefore the temperature needed for the sample to decompose is also higher9. This behavior is consistent with that observed by other authors25,26,8. The heating rate also affects both the conversion curves and the maximum decomposition rate. Figure 1 (right) shows the extent of conversion (α) with the temperature (T) for each heating rate. The observed behavior is also similar to that found by other researchers9.


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The DTG curves for different wood species show three states: the dehydration, along with both active and passive pyrolysis9. The loss of moisture occurs during the first stage. The moisture content in the samples was determined as the difference between the mass of the sample at initial temperature (300 K) and at 373 K, and was 9.9 ± 0.7%. The second stage (active pyrolysis) corresponds to the decomposition of the cellulose and hemicellulose. It was reported that the devolatilization of the hemicellulose occurred at 493-588 K, while the cellulose decomposed in the temperature range of 587-673 K. Because of these intervals, the decomposition partially overlaps each other, showing a more or less pronounced shoulder followed by a well-defined peak in the DGT curve. Indeed, previous studies have demonstrated the importance of the hemicellulose shoulder in samples of Canary Pine that do not contain resinous heartwood23. This can be clearly observed in Figure 1 (left), where decomposition began at approximately 475 K and predominant hemicellulose decomposition took place from 570 K to 600 K, depending on the heating rate. According to the conversion curve (Figure 1, right), this range corresponded to a conversion of around 0.3, reflecting that the degradation process was mainly dominated by hemicellulose at low conversion values. Grønli et al.27 studied the thermal decomposition of softwoods from pine species and reported a similar temperature range, which was higher than that found for hardwoods. The authors related the lower hemicellulose reactivity of the softwood samples with the differences in the chemical composition of the hemicellulose. Similarly, other researchers6 report a high thermal stability of Pinus elliottii when the conversion is between 0.1 and 0.2, which was attributed to the low content con extractives, the high lignin content and the slower degradation of the hemicellulose. Regarding the cellulose decomposition, the maximum rate appeared as a defined peak between 600 and 640 K, increasing its temperature value with the heating rate (Figure 1, left). This range of temperatures is consistent with those reported for


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Pinus sylvestris and Pinus pinea (623-624 K, for a heating rate of 5 K/min)27. The cellulose decomposition proceeded until 625-670 K, achieving a conversion of about 0.90-0.95 and then slowly increased to the final temperature. This final step may be attributable to the lignin decomposition, which occurred in both stages, active and passive pyrolysis, and in a wide temperature range above 475 K. The wide range of lignin decomposition temperatures does not leave a characteristic peak attributable to this component in the DTG curves of the wood28.

Figure 2. The weight loss curves for the studied samples of Canary pine wood

Depending on the heating rate, the main loss of weight begins at approximately 500 K and continues rapidly with increasing of the temperature until approximately 600 K. Above this temperature, the loss of weight decreases very slowly. The solid residue at 725 K was 25 ± 3% (Figure 2), which is consistently with those values reported at similar final temperature (773 K) and heating rate (5 K/min) for different softwoods and hardwoods27. Nevertheless, this residue


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can be further pyrolyzed by increasing the temperature. The fixed carbon for a pine is about 10 % with a minor ash content30.

4.2. Kinetic analysis The results obtained from the thermogravimetric analysis were used to determine the kinetic parameters. For the Kissinger method, these kinetic parameters were calculated by plotting the eq 6

(Figure 3). The activation energy (Ea) obtained from the slope of the line was 173.4

kJ·mol-1, and the pre-exponential factor (A) calculated from the independent term of eq 6 was 1.88E+12 s-1.

Figure 3. Kissinger (left) and FWO (right) plot used to determinate kinetic parameters of Canary pine wood pyrolysis


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Since the maximum reaction rate in the pyrolysis process is attributed to the cellulose, it is expected that the Kissinger method typically delivers a value similar to that obtained for pure cellulose based on first order reaction (191-253 kJ·mol-1)31. In contrast, the obtained Ea in this work is slightly lower, probably due to decomposition of the other components (hemicellulose and lignin). In fact, the limitation of the Kissinger method can be seen in the wide variation values reported for other wood species, 178.0-203.9 kJ·mol-1 for beech wood17 and 153.9 kJ·mol-1 for poplar wood9, or even for another pine specie, 145.6 kJ·mol-1 for Pinus sylvestris25. The kinetic values obtained by the FWO method were calculated according to eq 9 for a given

α .The chosen conversion values were the same for all curves and ranged from 0.05 to 0.7 with increments of 0.05 until α equal 0.4, and thereafter with increases of 0.1 until α equals 0.7 (Figure 3). Higher conversions resulted in poor correlation coefficients, according to previous studies17,18. This may be due to very low reaction rates and therefore, the high uncertainty associated with the experimental data. In addition, the definition of mf was arbitrary and based on the final temperature for all the heating rates. Nevertheless, different mf values were observed for every heating rate (Figure 2). The range of apparent activation energy obtained by the FWO method has different values, from 106.2 kJ·mol-1 to 230.5 kJ·mol-1, depending on the extent of conversion (Table 1). This points to the existence of a complex multi-step mechanism that occurs in the solid state, which is not the same in the whole decomposition process9, possibly due to the presence of different components with different activation energies. Low activation energy values (106-135 kJ mol-1) were obtained at low conversions (

Kinetic Study of the Pyrolysis of Canary Pine: The Relationship

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