Pyrolysis Kinetics of Pre-Torrefied Woody Biomass Based on

Article pubs.acs.org/IECR

Pyrolysis Kinetics of Pre-Torrefied Woody Biomass Based on Torrefaction SeverityExperiments and Model Verification Amin Sarvaramini and Faïçal Larachi* Department of Chemical Engineering, Laval University, Québec, QC G1 V 0A6, Canada S Supporting Information *

ABSTRACT: A kinetic model was developed for the pyrolysis of pre-torrefied lignocellulosic biomass requiring solely knowledge of the pyrolysis kinetics of raw biomass. To predict yield and differential thermogravimetry (DTG) profiles for the pyrolysis of pre-torrefied biomass specimens, the classical three-parallel first-order pyrolysis kinetic model was modified to incorporate severity factors accounting for the impact of torrefaction time and temperature on the devolatilization of hemicellulose, cellulose, and lignin biomass components. The model also included features to account for the effect of pyrolysis heating rate on pyrolysis activation energies of the biomass components both for raw and pre-torrefied substrates. Specifically, severity factor correlations between pre-torrefaction conditions and biomass component relative weights prior to pyrolysis were developed so that the kinetic model, validated at the outset for the pyrolysis of raw biomass specimens, could also be applicable for the pre-torrefied samples. Thermal decompositions of raw and pre-torrefied birch, aspen, and sawdust specimens were tested through thermogravimetric analyses under various pyrolysis heating rates and isothermal torrefaction exposure times and temperatures. Model confrontation against pyrolysis yields and DTG rates measured at varying severities for both raw and pre-torrefied woody biomass confirmed that predicted pyrolysis yields and rates were in a good agreement with experiments.

1. INTRODUCTION Repercussions of global climate change and resource scarcity are shaping worldwide willingness to use alternative energy sources to fossil fuels.1,2 Lignocellulosic biomass ranks atop of these alternatives as an abundant, renewable, and CO2-neutral resource for energy and chemicals production.1,2 Lignocellulosic biomass could be converted to energy mainly through enzymatic or thermochemical conversions.3 Bio-oil from the pyrolysis of lignocellulosic biomass has been widely advocated as a suitable replacement for conventional liquid fossil fuels.1 However, as a fuel, bio-oil contains ca. 35−40 wt % of oxygen which represents a barrier for energy or chemical production applications.4 Due to the high oxygen content of raw biomass, bio-oil produced from the direct pyrolysis of raw lignocellulosic matrices contains considerable amounts of oxygenated organics whose strong acidity entrains low chemical and thermal stability of bio-oil and its further repolymerization and degradation during storage and transportation.1,4−6 In order to upgrade biomass quality for biooil production, thermal pretreatment of raw biomass in the form of torrefaction has been proposed.6−8 Besides breaking the weak amorphous cellulose regions in the cellulose fibers as a way to reduce grinding energy, one of the other main functions of torrefaction is to result into the partial removal of oxygen in the form of low-caloric value torrefaction gases, e.g., CO2, water, and some organic acids, by exposing biomass to temperatures as high as 200−300 °C.9−12 Torrefaction pretreatment has been shown © XXXX American Chemical Society

to considerably improve the quality of bio-oil by decreasing water and oxy-compound contents in bio-oil such as organic acids and furans and by increasing its phenolic and aromatic hydrocarbon content all resulting in bio-oils with higher heating value and better stability.5−8,13−15 The pre-torrefaction of biomass prior to pyrolysis could considerably influence the kinetics of its thermal degradation. The important changes which are induced in the biomass structure during torrefaction due to the dehydration and depolymerization of hemicellulose, the change in crystalline structure of cellulose, and the breakage of the aryl-ether linkages and propyl-side branches in lignin could considerably influence the devolatilization kinetics of torrefied biomass.15 The degree of change in the pyrolysis behavior of the pre-torrefied biomass is highly dependent to the torrefaction severity as reflected by torrefaction temperature and time. The extent of removal of thermally unstable biomass moieties such as side branches and ether linkages is amplified by increasing the torrefaction severity resulting in the formation of torrefied biomass products showcasing improved thermal stability.15 Pre-torrefaction is Special Issue: Tapio Salmi Festschrift Received: Revised: Accepted: Published: A

March 17, 2017 May 22, 2017 June 12, 2017 June 12, 2017 DOI: 10.1021/acs.iecr.7b01123 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Industrial & Engineering Chemistry Research

Table 1. Summary of Torrefaction Pre-Treatment Time, Temperature, and Pyrolysis Heating Rates Used in TGA Analysis of Aspen, Birch, and Sawdust purpose model development

biomass aspen

birch

model validation

aspen birch

sawdust

pretreatment

pyrolysis heating rate (°C/min)

raw (no pretreatment) torrefied (235 °C, 10 min; 30 min; 80 min; 135 min) torrefied (255 °C, 6 min; 15 min; 30 min; 50 min) torrefied (270 °C, 6 min; 15 min; 25 min) torrefied (285 °C, 5 min; 10 min; 15 min) raw (no pretreatment) torrefied (235 °C, 10 min; 30 min; 80 min; 135 min) torrefied (255 °C, 6 min; 15 min; 30 min; 50 min) torrefied (270 °C, 6 min; 15 min; 25 min) torrefied (285 °C, 5 min; 10 min; 15 min)

5; 30; 80 5; 30; 80 5; 30; 80 5; 30; 80 5; 30; 80 5; 30; 80 5; 30; 80 5; 30; 80 5; 30; 80 5; 30; 80

torrefied (245 °C, 45 min) torrefied (275 °C, 15 min) torrefied (245 °C, 45 min) torrefied (255 °C, 30 min) torrefied (275 °C, 15 min) raw torrefied (235 °C, 10 min; 30 min) torrefied (245 °C, 45 min) torrefied (255 °C, 15 min)

15; 50 10; 120 15; 50 10; 120 10; 120 5; 30; 80 5; 30; 80 15; 50 5; 30; 80

torrefaction time and temperature, to capture the pyrolysis kinetics of pre-torrefied biomass specimens. An evolved version of the three-parallel first-order torrefaction-severity kinetic model of pyrolysis was used in our work to describe the kinetics of raw and pre-torrefied biomass pyrolysis. The model views pyrolysis of woody biomass as a result of noninterfering thermal decomposition steps for hemicellulose, cellulose, and lignin as the main woody biomass components.22,23 The parameters of the three-parallel first-order torrefaction-severity kinetic model were fitted against yield and differential thermogravimetry (DTG) rate profiles measured using using thermogravimetric (TG) analysis. Over 100 TG experiments were carried out in this study to develop and validate the pyrolysis kinetic model of raw and pre-torrefied woody biomass samples.

shown to shift the decomposition of biomass toward higher temperatures.16,17 Consequently, the maximum volatile release peak during pyrolysis is displaced toward higher temperatures, while the corresponding devolatilization curves are reshaped mainly as a result of degradation and crystalline changes brought about on the hemicellulose and lignin components during torrefaction.15,16 A variety of kinetic models such as global single and activation energy distribution models or models with secondary tar cracking and three-independent parallel reactions model have been proposed for describing pyrolysis of raw biomass at low, medium, and high heating rates.18−23 However, applicability of models for raw biomass pyrolysis to predict the yield of pretorrefied biomass pyrolysis has been the subject of very few studies in the literature. Brostrom et al.24 studied the effect of torrefaction on the kinetics of oxidative pyrolysis of woody biomass. Their study revealed that reactivity of hemicellulose, cellulose, and lignin, as assessed from these lumps’ preexponential factors and activation energies, was not affected by the torrefaction step. It was shown that torrefaction pretreatment only altered the relative percentage of the pseudocomponents based upon which their pyrolysis model was built using raw biomass. Similar conclusions have been reached by Bach et al.17 who studied the influence of torrefaction on the pyrolysis kinetic of Norway spruce. The activation energies and pre-exponential factors corresponding to pyrolysis of hemicellulose, cellulose, and lignin of spruce pre-torrefied at mild conditions were reported to be similar to those of the raw spruce samples. However, the same authors stated that these kinetic parameters could be prone to changes under more severe torrefaction pretreatments. Despite the above studies’ attempts to rationalize the incidence of torrefaction on the pyrolysis kinetics of pretorrefied biomass, no comprehensive work has hitherto been proposed with mathematical formulations seamlessly incorporating the torrefaction history in the thermal decomposition kinetics of pre-torrefied biomass. The present study’s aim is therefore to develop a new pyrolysis model embedding the torrefaction severity, expressed in terms of

2. EXPERIMENTAL SECTION Nonisothermal pyrolysis kinetic tests were performed on both as-received and pre-torrefied woody biomass samples using thermogravimetric (TG) analysis (PerkinElmer Lab System Diamond TG-DTA) to monitor the temperature−mass loss relationship during thermal decomposition. The samples consisted of trembling aspen and birch, two abundant woody biomass sources from Québec forests (Canada), as well as woody sawdust from forestry industrial activities in Québec. Elemental and ultimate analyses of these substrates have been reported in our earlier studies.9,25−28 Thermogravimetric experiments were performed on samples obtained after grinding and sieving to isolate the size fraction finer than 500 μm for which two different sets of experiments were designed. In the first, the pyrolysis kinetics concerned the raw, i.e., nontorrefied, biomass samples. For this purpose, 5−8 mg of sieved biomass samples, placed in the thermobalance pan, were first dried at 120 °C for 15 min before initiation of pyrolysis under varying heating rates (5 to 120 °C/min) up to 500 °C. Before each pyrolysis test, O2 was removed from the reaction chamber by preventively sweeping a 200 N mL/min N2 stream to prevent biomass oxidation. The second test sets concerned torrefaction subsequently followed by pyrolysis of biomass which is referred to as pre-torrefied biomass. B

DOI: 10.1021/acs.iecr.7b01123 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

Article


Figure 1. Experimental versus model predictions of pyrolysis yield and corresponding DTG profile of (a,b) raw aspen and (c,d) raw birch. Markers represent experimental profiles, and solid lines are eq 5 model response.

This latter was obtained by drying (120 °C for 15 min) and then quickly heating 5−8 mg of the sieved biomass samples at 100 °C/ min to reach the target torrefaction temperature, between 235 and 285 °C. Torrefaction was then continued isothermally for prescribed time intervals varying between 5 and 135 min. The thus-obtained torrefied samples were then cooled to 120 °C before resumption of the pyrolysis step tests at varying heating rates up to the same target temperature as for the pyrolysis of raw biomass. A summary of the experimental conditions in terms of torrefaction temperature, time, and heating rate of aspen, birch, and sawdust is given in Table 1. The entire body of experimental pyrolysis kinetic data acquired for the temperature−mass loss relationship during thermal decomposition was split in a learning subgroup for the parameter identification and a test/validation subgroup to assess the predictive robustness of the presently derived pyrolysis kinetic model.

where airepresents the relative weight of each biomass component to the global pyrolysis; the ratio V/V* in eqs 1 and 2 is expressed in terms biomass pyrolysis yield as W0 − W (t ) 1 − Y (t ) V = = * W0 − W (∞) 1 − Y (∞ ) V

In eq 3, W0 is the initial weight of biomass on dry basis; W(t) and Y(t) are, respectively, the weight of pyrolyzed biomass remaining at time t, with its corresponding yield; W(∞) and Y(∞) are, respectively, the ultimate total remaining weight of biomass when t → ∞, and its corresponding limiting yield. Combining eqs 1−3 enables expressing, after integration over the pyrolysis time, the yield of biomass during pyrolysis Y (t ) = 1 − (1 − Y (∞)) ⎛ ⎛ ∑ ai⎜⎜1 − exp⎜⎜− ⎝ ⎝ i

3. KINETIC MODEL A three-parallel first-order kinetic model was used to represent the time evolution of pyrolysis yield of raw and pre-torrefied woody biomass. This simple model views the global release of volatiles during biomass pyrolysis as an outcome from a weighted linear combination of the volatiles released from each of its component, i.e., hemicellulose, cellulose, and lignin, whose thermal decomposition is described as follows:23 ⎛ d(Vi /V i*) V ⎞ = ki exp( −Ei /RT )⎜1 − i ⎟ dt V i* ⎠ ⎝

⎛V ⎞ ⎟ V * ⎠i

∑ ai⎜⎝ i

⎛

t

⎞ ⎞⎞ −Ei ⎟dt ⎟⎟⎟⎟ 0 + β t ) ⎠ ⎠⎠

∫t =0 ki exp⎜⎝ R(T

(4)

eq 4 can be rearranged as follows: Y (t ) = 1 −

⎛

⎛

⎝

⎝

⎛ ⎞ ⎞⎞ − Ei ki exp⎜ ⎟dt ⎟⎟⎟⎟ t=0 ⎝ R(T0 + βt ) ⎠ ⎠⎠

∑ Ci⎜⎜1 − exp⎜⎜−∫ i

t

(5)

where β (°C/min) is the pyrolysis heating rate, Ci represents the modified relative weight of each biomass components on the total yield during pyrolysis, and T0 is the initial temperature. The unknown parameters in eq 5 were estimated through minimization of a quadratic norm between measured Yexp and computed Y cal yields using the “fmincon” trust-region minimization algorithm in MATLAB.

(1)

where Vi is the volatile content of each biomass component at time t, V*i is the maximum volatile content to be released by each component, while ki and Ei are, respectively, the frequency factor and activation energy for each component the pyrolysis. Consequently, the total volatile release from pyrolysis biomass can be expressed as ⎛V ⎞ ⎜ ⎟ = ⎝ V * ⎠Total

(3)

4. RESULTS AND DISCUSSION 4.1. Pyrolysis of Raw Biomass. In order to develop a kinetic model for the pyrolysis of raw woody samples, first, thermal decomposition of aspen and birch at different heating rates was

(2) C


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Table 2. Optimized Activation Energies (kJ/mol), Pre-Exponential Factors (s−1), and Relative Weights Corresponding to Cellulose, Lignin, and Hemicellulose Pyrolysis for Aspen and Birch heating rate (°C/min)

Ec

El

Eh

kc (×1018)

kl (×103)

kh (×1012)

Cc

Cl

Ch

R2

aspen

5 30 80

229.6 238.8 244.5

58.3 50.0 45.5

142.5 146.4 149.4

7.78

1.0

4.5

0.515

0.14

0.23

0.999

birch

5 30 80

226.3 235.4 241.0

61.0 52.4 47.7

141.5 145.3 148.3

7.54

1.9

5.9

0.488

0.15

0.226

0.999

Figure 2. Normalized activation energies of (a) cellulose, (b) lignin, and (c) hemicellulose during aspen and birch pyrolysis versus heating rates.

30 °C/min/332 °C; 80 °C/min/379 °C). The same observation can be also made about cellulose decomposition where the temperatures corresponding to the highest cellulose decomposition rate for aspen and birch at 80 °C/min (434 and 428 °C, respectively) are considerably higher than those at 30 °C/min (397 and 390 °C, respectively) and 5 °C/min (344 and 336 °C, respectively). These observations confirm the importance of taking into account the effect of heating rate in the development of the kinetic model for pyrolysis of biomass. 4.2. Model Development for Kinetics of Raw Biomass Pyrolysis. The mass loss kinetics registered during pyrolysis of aspen and birch for each heating rate were fitted separately against the model shown by eq 5 to identify the nine unknown kinetic parameters, i.e., pre-exponential or frequency factor (ki), activation energy (Ei), and relative weight (Ci) for the cellulose, lignin, and hemicellulose lumps embodied in the pyrolysis model. In this regard, the dependency of the nine unknown ki, Ei, and Ci parameters for each biomass type, i.e., aspen and birch, visà-vis pyrolysis heating rate was first studied. Preliminary optimization evaluations revealed that the activation energies for cellulose, lignin, and hemicellulose were heating-rate sensitive. This was unlike the pre-exponential constants and relative weight of hemicellulose, cellulose, and lignin which unveiled a negligible heating-rate dependence. Consequently, a choice was made to assume that the six ki and Ci parameters corresponding to each biomass sample, i.e., aspen and birch, are heating-rate independent, thus leaving only the activation energies to change as a function of heating rate. For each

experimentally studied using thermogravimetric analysis. Figure 1a−d show the yield and differential thermogravimetry (DTG) profiles of aspen and birch pyrolysis versus temperature for three pyrolysis heating rates (5, 30, and 80 °C/min). The DTG profiles of aspen and birch (Figure 1b,d) clearly show the presence of two distinctive peaks regardless of the tested pyrolysis heating rate. The first DTG peak for aspen and birch is usually assigned to hemicellulose which is known to be of least resistance to thermal decomposition as compared to lignin and cellulose.9 The second DTG peak is attributed to cellulose whose devolatilization occurs at higher temperature than hemicellulose.9 Conversely, lignin decomposes at a slower rate than hemicellulose and cellulose. Its pyrolysis over broad temperature ranges makes it difficult to assign distinguishable peaks characteristic of lignin thermal decomposition.9,23 From Figure 1b and d, one can also observe that the temperatures corresponding to the onset of decomposition and to the maximum decomposition rate of hemicellulose and cellulose are heating-rate dependent. The temperature marking the onset of thermal decomposition for hemicellulose increases as the heating rate is increased as exemplified for aspen (5 °C/min/222 °C; 30 °C/min/259 °C; 80 °C/min/ 275 °C) and birch (5 °C/min/215 °C; 30 °C/min/244 °C; 80 °C/min/263 °C). The heating rate equally influences the pyrolysis temperature corresponding to the highest thermal decomposition rate of hemicellulose (Figure 1b,d). These ratepeak temperatures are shifted to the right as heating rates are increased as exemplified for aspen (5 °C/min/288 °C; 30 °C/ min/338 °C; 80 °C/min/383 °C) and birch (5 °C/min/276 °C; D


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energies of cellulose, lignin, and hemicellulose in aspen and birch were tested for model robustness to describe the pyrolysis of sawdust, a specimen that was not exposed whatsoever during the model parameter identification. This comparison will tell whether or not the activation energy−heating rate correlations for cellulose, lignin, and hemicellulose are general enough and thus reliable to predict the pyrolytic kinetics for other types of woody biomass specimens hidden to the training of the pyrolysis model. The optimized values of E0, ki, and Ci for sawdust pyrolysis yield and DTG (Table 4) are used in the fitted model which is plotted against experimental sawdust pyrolysis yields at 5, 30, and 80 °C/min (Figure 3). Deviations between model and experiments for all three heating rates are very low, and the model fits the experimental data with R2 > 0.99. In order to help better understanding the steps performed for development of the pyrolysis model, a block diagram is shown in Figure 4. Finally, it is worthy of mention that the interactions among cellulose, lignin, and hemicellulose during the pyrolysis of actual biomass specimens render any modeling approach of their global kinetics a wasted effort should it be based on the kinetic parameters inherent to, and precalibrated for, each individually isolated cellulose, lignin, and hemicellulose lumps.29−32 According to our modeling approach, such interactions in each one of the tested biomass specimens are indeed recognized to influence the values of activation energy corresponding to the pyrolysis of cellulose, hemicellulose, and lignin on account of their specific environment in each biomass specimen. This influence can be ascertained from Table 2 which demonstrates that according to our model activation energy for cellulose, lignin, and hemicellulose components take different values depending on which specimen matrix they belong to (aspen, birch, or sawdust). In brief, interactions between the woody biomass constitutive lumps are accounted for implicitly by our model as reflected in the different values for activation energies of cellulose, lignin, and hemicellulose within each biomass specimen. 4.3. Pyrolysis of Pre-Torrefied Biomass. The extent of isothermal torrefaction of aspen, birch, and sawdust depends on the process severity manifesting through temperature or exposure of woody biomass during torrefaction. An increased severity translates into higher biomass devolatilization tantamount to reduced yield of torrefied products (Figure 5). A mild torrefaction at 235 °C requires longer torrefaction time to achieve an equal yield of torrefied product as if torrefaction were carried out at temperatures >250 °C (Figure 5). Likewise, biomass devolatilization due to torrefaction certainly influences both yield and DTG rate profiles during the pyrolysis of the torrefied biomass products. Figure 6a−d compare the dynamic evolution of pyrolysis yield and DTG rate of raw aspen and birch samples to that of their pre-torrefied analogs for different torrefaction severities. Pyrolysis yields of raw aspen and birch are expectably higher than those of their pre-torrefied analogs (Figure 6a,b). Similar to Figure 1b and d assignments, the first peak in the pyrolysis DTG curve of pre-torrefied aspen and birch corresponds to hemicellulose pyrolysis (Figure 6c,d). Indeed, hemicellulose is the most thermally vulnerable biomass component and is the one to be first affected by the torrefaction step. The hemicellulose pyrolysis peak faded progressively as a result of increasing severity of the torrefaction step preceding pyrolysis (Figure 6c,d). Consequently, the relative contribution of hemicellulose to the pyrolysis yield of pre-torrefied biomass declined with increasing the torrefaction severity. However, over the torrefaction temperature range (235−285 °C) covered in this work, the cellulose and lignin components were much less

biomass type, three sets of data yields obtained at 5, 30, and 80 °C/min were used to fit the experimental yields against the eq 5 model to obtain the 15 unknown parameters (Table 2): three pre-exponential constants, three relative weight, and nine activation energies, i.e., three for each heating rates. The obtained activation energy values for cellulose, lignin, and hemicellulose lumps in aspen and birch have particular trends. While activation energies for cellulose and hemicellulose decompositions increase with heating rate, the trend for that of lignin is opposite (Figure 2). Note that similar ascending or descending trends characterize the activation energies of cellulose, lignin, and hemicellulose in aspen and birch greatly simplifies the mathematical formulation of the dependency of heating rate versus activation energy of cellulose, lignin, and hemicelluloses decomposition. The activation energies for cellulose, lignin, and hemicellulose of birch and aspen samples were normalized using their corresponding values stemming from the fits at 5 °C/min. Then, the normalized activation energies for each lump, i.e., cellulose, lignin, and hemicellulose, were correlated by means of nonlinear logarithmic functions yielding the following relations for cellulose, lignin, and hemicellulose, respectively (Figure 2): ec = 0.014ln(β)1.263 + 0.97

(6)

el = −0.079ln(β) + 1.13

(7)

eh = 0.0044ln(β)1.749 + 0.99

(8)

In the above equations, ec, el, and eh are normalized activation energies for cellulose, hemicellulose, and lignin, respectively, and β is the heating rate (°C/min). These ad hoc lump-associated correlations, by linking activation energy to pyrolysis heating rate, can be embedded in the kinetic model to help reducing the number of unknown parameters (in our case study from 15 to 9)

Ec = E0, c ·ec

(9)

El = E0, l ·el

(10)

Eh = E0, h ·eh

(11)

Here, Ec, El, and Eh are the β-dependent activation energies for cellulose, lignin, and hemicellulose, while E0,c, E0,l, and E0,h could now be obtained through reoptimization of a lesser set of parameters. Hence, the optimized values for E0,c, E0,h, and E0,l of aspen and birch, while using the same pre-exponential factors ki and relative weights Ci (Table 2), were obtained as shown in Table 3. The three-independent parallel-reaction pyrolysis model built for aspen and birch using the obtained nine optimized parameters captures very well (R2 > 0.99) both overall yield and DTG of aspen and birch pyrolyzed at 5, 30, and 80 °C/ min (Figure 1). Equations 6−8 built from fitting activation Table 3. Optimized Activation Energies (kJ/mol) for Cellulose, Lignin, and Hemicellulose

birch

aspen

lump

activation energy (E0,i)

cellulose lignin hemicellulose cellulose lignin hemicellulose

226.3 60.9 141.5 229.6 58.2 142.5 E


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Table 4. Optimized Activation Energies (kJ/mol), Pre-Exponential Factors (s−1), and Relative Weights Corresponding to Cellulose, Lignin, and Hemicellulose Pyrolysis in Sawdust sawdust

E0,c

E0,l

E0,h

kc (×1018)

kl (×103)

kh (×1012)

Cc

Cl

Ch

R2

227.6

55

143.7

6.91

0.67

6.51

0.44

0.13

0.25

0.998

fact that the pre-exponential factors and activation energies are component-inherent and must bear the same values for hemicellulose, cellulose, and lignin lumps regardless of the thermal history to be undergone by their woody biomass substrate. However, the relative weights, Ci, of cellulose, hemicellulose, and lignin are those to be impacted by changes in the severity conditions of the pre-torrefied biomass and thus need to be estimated afresh for the pyrolysis step. The best-fit Ci sets to represent the relative weights of cellulose, hemicellulose, and lignin for pre-torrefied aspen and birch pyrolysis were obtained through fitting the model against the experimental pyrolysis yield data for the various pre-torrefied conditions summarized in Table 1. Comprehensive comparisons between the three-parallel first-order kinetic model predictions for the pyrolysis of pre-torrefied samples in terms of pyrolysis yield and DTG rates at 5, 30, and 80 °C/min of heating rate are provide in Figures S1−S16 (Supporting Information). The obtained Ci values corresponding to the relative pyrolysis weight of hemicellulose, cellulose, and lignin of pre-torrefied aspen and birch samples at varying torrefaction temperatures and times are illustrated in Figures 7−9, respectively. Note that these relative weights were normalized to the Ci values of the nontorrefied substrates (Table 2) to yield fractional contributions, wi = Ci(torrefied biomass)/Ci(raw biomass), equal to one with extrapolation of torrefaction time to zero. The model captures satisfactorily the decrease in normalized relative weight, wh, of hemicellulose in aspen and birch with increasing torrefaction severity, i.e., temperature and time (Figure 7). Mild torrefaction of birch and aspen (235 and 255 °C) was unable to deplete entirely their hemicellulose content as revealed from the nonzero fractional contribution, wh, of hemicellulose fitted from the subsequent pyrolysis DTG rates. This was unlike the pretorrefied substrates exposed to high-severity torrefaction treatments (ca. 30 min at 270 or 285 °C) which divulged that hemicellulose was nonexistent in the succeeding pyrolysis step (Figure 7). Notwithstanding, wh could reach zero at any tested torrefaction temperature provided an allowance for sufficient torrefaction time. The evolution of residual hemicellulose is a function of torrefaction time and temperature. As a result of normalization, this also reflects in evolutions of cellulose (wc) and lignin (wl)

Figure 3. Experimental versus model predictions of pyrolysis yield (a) and corresponding DTG profile (b) of raw sawdust. Markers represent experimental profiles, and solid lines are eq 5 model response.

influenced by torrefaction than hemicellulose. As a result, cellulose and lignin relative contributions to the pyrolysis yield of pre-torrefied biomass tended, on the contrary, to increase with torrefaction severity as clearly shown from the cellulose second pyrolysis peak (Figure 6c,d). Attributed to devolatilization of hemicellulose during the torrefaction step, this translated into increased relative weights of cellulose and lignin in the pretorrefied samples prior to their pyrolysis. 4.4. Model Development for Kinetics of Pre-Torrefied Biomass Pyrolysis. The three-parallel first-order kinetic model introduced earlier is also used to model the pyrolysis of pretorrefied biomass samples. Hence, for each biomass type, i.e., aspen, birch, and sawdust, the set of optimized pre-exponential factors (ki) and activation energies (E0,i) retained the same values as the one identified for the pyrolysis of the non-torrefied woody materials. This assumption seems reasonable on account of the

Figure 4. Block diagram representing the pyrolysis kinetic model development steps. F


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Figure 5. Transient evolution of aspen, birch, and sawdust yields during their isothermal torrefaction between 235 and 285 °C.

Figure 6. Pyrolysis yield of raw and pre-torrefied woody biomass at different severities for (a) aspen and (b) birch and corresponding DTG curves for (c) aspen and (d) birch.

parameter accounting for the dispersion of activation energies of the ensemble of elementary reactions governing the torrefaction of each of the three biomass lumps. The integral in eq 12 is known as the severity factor which is used to merge torrefaction temperature and time

normalized relative weights to be functions of torrefaction time and temperature (Figures 8 and 9). For embedding a phenomenological description of pre-torrefaction history into the above three-parallel first-order kinetic model of pyrolysis, a simple approach advocated by Montane et al.33 for kinetic modeling of complex systems with time-dependent rate constants is extended to correlate the normalized relative weight of lump i (wi) as a function of torrefaction temperature and time. In the model proposed by Montane et al.,33 the reaction extent for a complex system uses the concept of severity parameter which has the ability to gather reaction temperature, time, and catalyst concentration. For noncatalytic systems as in our case, severity-based kinetic formulation reduces to

∫0

f

df = k Tref f−1

∫0

t

⎛ T − Tref ⎞ γ− 1 exp⎜ ⎟t dt ⎝ ω0T ⎠

R=

∫0

t

⎛ 1 ⎛ T ⎞⎞ exp⎜ ⎜1 − ref ⎟⎟t γ− 1dt T ⎠⎠ ⎝ ω0 ⎝

(13)

Using this model, the link of normalized relative weight of biomass component, wi, with torrefaction time and temperature under isothermal torrefaction can be restated as follows: ⎛ ⎛ TTorr − Tref ⎞ γ ⎞ wi = exp⎜⎜k Tref exp⎜ ⎟tTorr ⎟⎟ ⎝ ω0TTorr ⎠ ⎝ ⎠

(12)

(14)

In this equation, kTref, Tref, ω0, and γ are the unknown parameters to be identified from minimization of a least-squares problem for each biomass component subjected to known TTorr and tTorr torrefaction conditions

In eq 12, f stands for conversion, ω0 is a characteristic substratereaction parameter accounting for the torrefaction energetics, kTref is a rate constant at reference temperature, Tref, and γ is a G


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Figure 7. Normalized relative weight of hemicellulose pyrolysis of pre-torrefied aspen and birch for different severities.

Figure 8. Normalized relative weight of cellulose pyrolysis of pre-torrefied aspen and birch for different severities.

Figure 9. Normalized relative weight of lignin pyrolysis of pre-torrefied aspen and birch for different severities. 0.15

0.72

wh(TTorr , tTorr ) = e−0.01exp(TTorr − 467.9/0.0394TTorr)tTorr

wc(TTorr , tTorr ) = e 0.063exp(TTorr − 467.8/0.138TTorr)tTorr

(15) H

(16)


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Figure 10. Experimental versus model results of (a) pyrolysis yield of pre-torrefied aspen at different severities. (b) DTG curve of pre-torrefied aspen at different severities. Solid lines represent the models, and the markers represent experimental profiles.

Figure 11. Experimental versus model results of (a) pyrolysis yield of pre-torrefied birch at different severities. (b) DTG curve of pre-torrefied birch at different severities. Solid lines represent the models, and the markers represent experimental profiles. 0.65

wl(TTorr , tTorr ) = e 0.005exp(TTorr − 400.0/0.198TTorr)tTorr

a result of reduction of the hemicellulose contribution, thus the sign parametrization of kTref in eq 14. FTIR analyses performed by Zheng et al.34 on the thermal decomposition of cellulose, lignin, and hemicellulose during torrefaction at temperatures varying between 210 and 300 °C have clearly shown that hemicellulose may undergo complete degradation and crystalline structure changes during torrefaction, while cellulose and lignin mostly preserve their crystalline structure over this torrefaction temperature range. However, cellulose and lignin could also undergo depolymerization, ring breakage, fragmentation, and polycondensation reactions should they be torrefied at temperatures >300 °C with the resulting profound structural changes in these compounds.30 Consequently, the evolutions of cellulose (wc) and lignin (wl) normalized relative weights due to the torrefaction of biomass at temperatures higher than 300 °C could

(17)

The severity approach correlated very well the torrefaction time and temperature for the pyrolysis of pre-torrefied aspen and birch in terms of normalized relative weight of hemicellulose (R2 ∼ 0.99, Figure 7), cellulose (R2 > 0.97, Figure 8), and lignin (R2 > 0.97, Figure 9) left for the subsequent pyrolysis of the woody biomass substrate. Contrary to the normalized relative weight of hemicellulose which reduces with torrefaction severity, those for cellulose and lignin increase as torrefaction time and temperature are increased. As discussed earlier, increasing the torrefaction severity affected considerably the hemicellulose devolatilization which resulted in reduction of its pyrolysis relative weight. However, as cellulose and lignin are much less affected by torrefaction at temperatures lower than 300 °C than hemicellulose, their pyrolysis relative weight in the model increases as I


Article


Figure 12. Experimental versus model results of (a) pyrolysis yield of pre-torrefied sawdust at 235 °C for 10 min and (b) pyrolysis yield of pre-torrefied sawdust at 235 °C for 30 min. (c) DTG curve of pre-torrefied sawdust at 235 °C for 10 min and (d) DTG curve of pre-torrefied sawdust at 235 °C for 30 min. Solid lines represent the models, and the markers represent experimental profiles.

Figure 13. Experimental versus model results of (a) pyrolysis yield of pre-torrefied sawdust at 255 °C for 15 min and (b) pyrolysis yield of pre-torrefied sawdust at 245 °C for 45 min. (c) DTG curve of pre-torrefied sawdust at 255 °C for 15 min and (d) DTG curve of pre-torrefied sawdust at 245 °C for 45 min. Solid lines represent the models, and the markers represent experimental profiles.

be different from those obtained in this work as torrefaction temperatures were on purpose never exceeding 285 °C. Using eqs 15−17 to account for pre-torrefaction, supplemented with pre-exponential factors, kh, kc, and kl (Tables 2 and 4), activation energies, Eh, Ec, and El (Tables 2 and 4), and relative weights, Ch, Cc, and Cl (Tables 2 and 4), corresponding to the pyrolysis of the raw biomass specimens, one can predict the pyrolysis yield for the pre-torrefied biomass samples. This evolved version of the three-parallel first-order torrefactionseverity kinetic model of pyrolysis was tested for the aspen and birch samples pre-torrefied at various severities (Table 1) for

which the predicted yield and DTG of pyrolyzed samples are compared to the measured ones in Figures 10 and 11, respectively. In general, the model can predict well the experimental pyrolysis yield and DTG of pre-torrefied samples for the various torrefaction severities and pyrolysis heating rates. The difference between experimental and simulated pyrolysis yields for the pre-torrefied samples did not exceed 5%, and the pyrolysis yield trend can be well represented by the model (Figures 10a and 11a). DTG rate curves (Figures 10b and 11b) also demonstrate that the model satisfactorily captures both the maximum pyrolysis rates and corresponding temperatures for J



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ACKNOWLEDGMENTS The authors gratefully acknowledge the financial support from FL Canada Research Chair “Green processes for cleaner and sustainable energy” and the Discovery Grant to F. Larachi from the Natural Sciences and Engineering Research Council (NSERC). Mrs. J. Ouellet is gratefully acknowledged for her help in conducting the Matlab optimizations.

hemicellulose and cellulose during pyrolysis of the pre-torrefied aspen and birch at varying severities. The small deviations noted on the maxima loci could be due to the fact that pre-torrefaction could induce some changes in the biomass structure. This would result in changes in the mechanism of pyrolysis of hemicellulose, cellulose, and lignin with, consequently, changes in temperatures of maximum pyrolysis rate. No clear correlation could be established between the pre-torrefaction severity and the preexponential factors and activation energies of hemicellulose, cellulose, and lignin pyrolysis. Instead, as explained earlier, it was concluded that the best approach would be to modify the relative weights of hemicellulose, cellulose, and lignin while keeping the same (kh, kc, and kl) and (Eh, Ec, and El) sets obtained as for the pyrolysis of raw biomass specimens. Finally, the model was also tested for the pyrolysis of pre-torrefied sawdust sample which had not been used in the development of eqs 15−17. Sawdust samples were pre-torrefied at different severities and then pyrolyzed at varying heating rates (Table 1). The experimental pyrolysis yield and DTG rates of pre-torrefied sawdust samples at different severities could be explained with accuracy by the proposed kinetic model (Figures 12 and 13). Consequently, the model could be used to predict the pyrolysis yield and DTG of other pre-torrefied woody samples with known contents of hemicellulose, cellulose and lignin.

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5. CONCLUSION The pyrolysis kinetics of raw and pre-torrefied aspen, birch, and sawdust were obtained experimentally to help develop a new kinetic model based on three-parallel first-order torrefactionseverity kinetics to describe the pyrolysis yield and DTG rates of the biomass samples. The experimental results showed clearly the importance of the pyrolysis heating rate on the decomposition trend of the biomass samples. Consequently, the simple three-independent parallel reaction model was modified to account for the correlations between pyrolysis activation energy of biomass components, i.e., cellulose, lignin, and hemicellulose, and pyrolysis heating rate. A model based on the severity concept was also developed to correlate the relative weight of cellulose, lignin, and hemicellulose pyrolysis of pretorrefied samples to their pre-torrefaction time and temperature. The model developed in this study for the pyrolysis of raw and pre-torrefied biomass was in good agreement with the experimental pyrolysis yield and DTG obtained through thermogravimetric analyses.

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NOTATION Ci = relative weight for each biomass components (−) e = normalized activation energy for each biomass component (−) E = activation energy (kJ/mol) f = conversion (−) i = component in biomass (i.e., cellulose, lignin, hemicellulose) k = frequency factor, eq 1 (s−1) R = ideal gas law constant, 8.314 (J/K/mol) or severity factor (−) t = time (s) T = temperature (K) Tref = reference temperature (K) V = volatile content released during pyrolysis V* = total available volatile content to be released w = normalized relative weight of each biomass component W0 = initial weight of biomass, dry basis (g) W(t) = residual weight of pyrolyzed biomass at time t (g) W(∞) = ultimate total remaining weight of biomass as t → ∞ (g) Y = total pyrolysis yield

Greek letters

β = pyrolysis heating rate (°C/min) ω0 = characteristic substrate-reaction parameter (K) γ = homogeneity parameter of activation energy distribution (−)

Subscripts

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c = cellulose h = hemicellulose l = lignin

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.iecr.7b01123. Figures 1s to 16s. (PDF)

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Article

AUTHOR INFORMATION

Corresponding Author

*Tel.: (418) 656-2131, ext. 3566. Fax: (418) 656-5993. Email:[email protected]. ORCID

Faïçal Larachi: 0000-0002-0127-4738 Notes

The authors declare no competing financial interest. K


Article

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Pyrolysis Kinetics of Pre-Torrefied Woody Biomass Based on

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