Thermal decomposition kinetics modeling of energy cane Saccharum robustum

Thermochimica Acta 657 (2017) 56–65

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Thermal decomposition kinetics modeling of energy cane Saccharum robustum Vinícius Souza de Carvalho, Katia Tannous

MARK

⁎

School of Chemical Engineering, University of Campinas − 500, Albert Einstein Avenue, Campinas, SP, 13083-852, Brazil

A R T I C L E I N F O

A B S T R A C T

Keywords: Sugarcane Pyrolysis Thermogravimetry Kinetic triplet Model-free methods Independent parallel reaction scheme

This work aims to study the thermal decomposition kinetics of energy cane Saccharum robustum. The experiments were carried out in a thermogravimetric analyzer at heating rates of 5, 10 and 20 °C/min under nitrogen atmosphere and mean particle diameter of 253.5 μm. Three thermal decomposition stages were identified: dehydration, pyrolysis and carbonization. The activation energies were determined through three model-free methods (Friedman, Flynn-Wall-Ozawa, and Vyazovkin) varying between 107.5 and 204 kJ/mol. The master plots showed different representations of conversion functions (reaction orders from 2 to 7). The linear model fitting method validated the activation energy (177.1 kJ/mol) obtained by single-step reaction. Moreover, a multi-step method was proposed considering four independent parallel reactions (extractives, hemicellulose, cellulose, and lignin) obtaining activation energies from 60 to 181 kJ/mol, pre-exponential factor from 1.1 ∙ 102 to 5.8 ∙ 1012 1/s, 1st and 3rd reaction orders, and compositions from 0.12 to 0.43 with high quality of fit.

1. Introduction Pyrolysis is one of the most important pathways for biomass conversion into various high-value added products (biochar, bio-oil, synthesis gas, and chemicals). Bio-oil refers to the liquid fraction of pyrolytic products at room temperature, composed by a mixture of highly oxygenated organic compounds, which present low greenhouse gases emissions [1,2]. The biomass sources recommended for bio-oil production are agricultural solid residues [3,4] or fiber crops. Among them, the sugarcane is highlighted due to its fiber production potential [5]. In Brazil, the Agroecological Zoning of Sugarcane Law No. 6.961 (September 2009) established technical subsidies of public policies for the sugarcane sustainable production, regulating social issues and economic factors of land use. This law allowed the increasing (19%) of the sugarcane production from 558.72 million metric ton (crop year 2008) to 665.6 million metric ton (crop year 2015/16). From this sugarcane production, sixty percent was destined for various chemical products highlighting the production of ethanol and derivatives. Some of sugarcane species present higher fiber contents, being more suitable for the thermochemical conversion applications, so-called energy cane. Regarding native species stands out different types of sugarcane and their respective fiber contents, such as Saccharum sinense (10–15%), Saccharum barberi (10–15%), Saccharum officinarum

⁎

(5–15%), Saccharum spontaneum (24–40%), and Saccharum robustum (20–35%) [6]. The last two species produce the highest relations of growth/time and fiber yield/cultivated area [5,7]. However, Saccharum robustum has stem diameter (1.1–1.7 cm) larger than Saccharum spontaneum (0.5-0.9 cm) and can also reach twice height (10 m high) [6,8]. Besides that, the sucrose concentration of Saccharum robustum is much lower (3–7 wt.%) than Saccharum officinarum (18–25 wt.%), and consequently, it is not useful for sugar production in the traditional industries [5]. The study of devolatilization is a fundamental step to characterize the pyrolytic reactions of new materials. Some researchers have discussed about devolatilization behavior of sugarcane bagasse [9–12] and straw [13,14] in order to obtain the energetic reaction properties, such as mechanism and activation energy by thermogravimetric analysis, according to ICTAC committee recommendations [15,16]. However, in natura energy cane properties differ of these residues due to the juice content and the lignocellulosic polymer repartition (hemicellulose, cellulose, and lignin). Hence, this work aims to study the thermal decomposition through the kinetic parameters of energy cane of species Saccharum robustum through thermogravimetric analysis in an inert atmosphere, applying model-free, linear fitting model and independent parallel reaction methods. Such approaches allow for obtaining more reliable parameters for energy cane pyrolysis improving the understanding of design and

Corresponding author. E-mail addresses: [email protected] (V.S.d. Carvalho), [email protected] (K. Tannous).

http://dx.doi.org/10.1016/j.tca.2017.09.016 Received 19 May 2017; Received in revised form 19 August 2017; Accepted 15 September 2017 Available online 18 September 2017 0040-6031/ © 2017 Elsevier B.V. All rights reserved.

Thermochimica Acta 657 (2017) 56–65

V.S.d. Carvalho, K. Tannous

furnace for 4 h at a temperature of 575 ± 15° C. The fixed carbon content was calculated by mass difference considering both previous contents, on dry basis. The higher heating value (HHV) was determined using an oxygen bomb calorimeter (IKA, C200, Germany) and sample mass of 0.3110 ± 0.06 g, which was sealed and pressurized (3.106 Pa) with oxygen (99, 99% of purity, White Martins, Brazil). The lower heating value (LHV) was calculated using Eq. (1) [22], in which ΔHv is the enthalpy of vaporization of water at 25 °C and 1 atm, H is the hydrogen fraction obtained from the proximate analysis on dry basis, and U is the biomass moisture content. All samples were dried again in a drying oven (103 °C, 24 h) in order to guarantee equilibrium moisture (U).

simulation of reactors as well as process optimization. 2. Material and methods 2.1. Biomass used In this work, energy cane of Saccharum robustum Linnaeus species was used as raw material. This species was planted in the Department of Plant Biology of the Institute of Biology at University of Campinas, Campinas/Brazil. The harvest was performed manually using a pair of pliers, removing the tips, leaves, and straw to avoid high content of phenols. The stalks were cut into in cylinder geometries (5 cm of length) to facilitate the grinding. The biomass samples were cleaned with water and the moisture was removed in a drying oven at 103 ± 5 °C for 24 h (Quimis, model Q314M242, Brazil). Afterwards, they were ground in a knife mill type Willye (SOLAB, model SL 31, Brazil) and separated by sieving (Granutest, Brazil) considering mesh −48 + 65 of Tyler series. The mean particle diameter of 253.5 μm was chosen based on White et al. [17] work, in order to ensure that the experiment is entirely in a kinetic regime. Thus, the effects of internal heat and mass transfers of each particle can be neglected.

LHV (MJ/kg) = [(1−U) (HHV−9HΔHv25°C) − UΔHv25°C]

(1)

2.3. Experimental setup The biomass thermal decomposition was carried out using a thermogravimetric analyzer (TGA-50, Shimadzu Corporation, Japan) with precision of 0.001 mg. The experiments were performed from room temperature (25 ± 3 °C) up to 900 °C using three different heating rates of 5 °C/min, 10 °C/min, and 20 °C/min. These heating rates were based on the Vyazovkin et al. [15,16] recommendations. An acquisition rate of 12 measures per min was applied. The initial samples of 10 ± 0.14 mg were placed in an open alumina sample pan (8 μL). Nitrogen atmosphere (N2 = 99.996%, 4.6 FID, White Martins, Brazil) and flow rate of 50 L/min were used. Baseline runs were subtracted of biomass experimental data for each heating rate in order to avoid any buoyancy effects [15].

2.2. Biomass characterizations The compositional analysis was determined considering two stages. In the first stage, the extractive contents were removed by washing the biomass (2.0 g) considering 150 mL of distillated water (80 °C) for each gram of material, and then dried at 75 °C for 14 h in an oven (model 315SE, Fanem, Brazil). The extractive content was determined as the mass difference before and after this procedure. In the second stage, the fibers analysis was carried out applying the modified method of Van Soest from the ANKOM protocols [18,19]. The techniques were based applying F57 filters for determining of hemicellulose, cellulose and lignin contents. The hemicellulose was extracted using a vertical autoclave (Phoenix, AV18, Brazil) with a solution of 20 g of cetyltrimethylammonium bromide detergent (Neon, Brazil, 99% purity) in 1.0 L of sulfuric acid (1.0 N) for 1 h at 121 °C and 1 atm. After this time, the material was washed in water until to reach pH value 7 and dipped in acetone for 14 h at room temperature. Moreover, it was taken to the oven for drying for 4 h at 100° C and weighted in an analytical balance (model AY220, Shimadzu, Japan, 1 10−4). The cellulose content was obtained by immersing the extractive- and hemicellulose-free material in sulfuric acid solution (72% w/w) for 3 h. The same procedure from hemicellulose determination was applied to neutralize the residual fiber. It was placed on the muffle furnace for 4 h at 575 ± 15 °C for the determination of the ash content. And finally, the lignin content was estimated by difference between hemicellulose, cellulose and ash contents. The proximate analysis (CHN) was performed using an elemental analyzer (Perkin Elmer, Series II 2400, USA, ± 0.3%) and dry sample mass around 1 mg was measured in an analytical balance (PerkinElmer, AD6, USA, 5 10−7 g). The sample was submitted to 925 °C under ultrahigh purity oxygen (99.995%, White Martins, Brazil). All carbon reacts to CO2 and hydrogen to H2O. The nitrogen generates several oxides (NxOx), which flow through a column containing metallic copper and then reduced to N2. The resulting gases (CO2, N2, and H2O) are dragged by the pure helium gas (99.9999% purity, Air Products, Brazil) homogenized and, subsequently, separated by silica packed columns. The CHN were obtained by thermal conductivity detector (TCD, Perkin Elmer, USA) and oxygen content, by difference on dry and ash-free basis. The ultimate analysis was determined through volatile content (ASTM E872–82, 2013) [20]. Dry mass sample of 1.019 ± 0.008 g were heated to 950 ± 6 °C in a muffle furnace (Model 2231, Forlabo ltda, Brazil) for 7 min. The ash content (ASTM E1755–01, 2015) [21] was obtained from the volatile free samples, keeping them in the muffle

2.4. Experimental approach The thermogravimetric data were converted to normalized mass (w), Eq. (2), and its derivatives (dw/dt), Eq. (3), in which m and mi are the mass at a time t and the initial mass, respectively. The dm/dt is the experimental DTG expressed in mg/s. This procedure was essential to perform a reliable comparison of the data at different heating rates.

w = m / mi

(2)

(dw / dt ) = 1/ mi (dm / dt )

(3)

Concerning the thermogravimetric study on pyrolysis kinetics, the experimental data were transformed into conversion and conversion rate. Conversion, α (t , T )exp, as a function of time and temperature, characterizes the reaction progress obtained by Eq. (4), in which wo, wt, and wf are the normalized mass at the beginning, at a time t, and at the end of the main decomposition range (volatile release), respectively. Conversion rate, (dα/dt)exp, is defined as decomposition reaction velocity represented by Eq. (5).

α (t , T )exp = (w0 − wt )/(w0 − wf )

(4)

(dα / dt )exp = −1/(w0 − wf )(dw / dt )

(5)

2.5. Theoretical approach 2.5.1. Model-free methods The activation energies were determined by one differential [23] and two integral [24–26] model-free methods considering a single-step reaction. These methods are based on the hypothesis that the conversion rate, dα/dt, is dependent of reaction rate constant, k(T), and conversion functions, f(α). k(T) is quite well represented by the Arrhenius equation to solid-state reaction kinetics even though developed for gaseous reactions [17], resulting in Eq. (6), in which A represents the pre-exponential factor, β the heating rate, Eα the activation energy for each conversion (α), R the universal gas constant (8314 J/mol K), and T 57

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V.S.d. Carvalho, K. Tannous

Table 1 Integral and differential forms of reaction models for solid-state kinetics [4,28]. Symbol

Model

Differential form, f(α)

Integral form, g(α)

Nucleation models P2

Power Law

(2/3) α1/2

α 3/2

P3

(2) α1/2

α1/2

P4

(3) α2/3

α1/3

P5

(4) α 3/4

α1/4

Sigmoidal rate equations A1

Avarami-Erofeev

(3/2)(1 − α )[−ln (1 − α )]1/3

[−ln (1 − α )]2/3

A2

2(1 − α )[−ln (1 − α )]1/2

[−ln (1 − α )]1/2

A3

3(1 − α )[−ln (1 −

α )]2/3

[−ln (1 − α )]1/3

A4

4(1 − α )[−ln (1 − α )]3/4 α (1 − α )

[−ln (1 − α )]1/4 ln [α /(1 − α )]

2(1 − α )1/2

1 − (1 − α )1/2

α )2/3

R1 R2

Prout-Tompkins Contracting area

R3

Contracting volume

3(1 −

R4

Random nucleation (1)

(1 − α )2

1 − (1 − α )1/3 1/(1 − α )

R5

Random nucleation (2)

(1/2)(1 − α )3

1/(1 − α )2

Diffusion models D1

One-dimensional

1/2α

D2

2D Valensi-Carter

[−ln(1 − α )]−1

α2 (1 − α ) ln (1 − α ) + α

D3

3D Jander

(3/2)(1 − α )2/3/2[1 − (1 − α )1/3]

[1 − (1 − α )1/3]

D4

3D Ginstling–Brounshtein

ZH

Zhuravlev

Reaction order models F1 F2

1 α )− 3

2

(1 − 2α /3) − (1 − α )2/3

− 1⎤ (3/2)/ ⎡ (1 − ⎦ ⎣ 5/3 (2/3)(1 − α ) /[1 − (1 − α )1/3]

[1 − (1 − α )−1/3]

1st order 2nd order

(1 − α )

− ln (1 − α )

(1 − α )2

(1 − α )−1 − 1

F3

3rd order

(1 − α )3

1/2[(1 − α )−2 − 1]

F4

4th order

(1 − α ) 4

1/3[(1 − α )−3 − 1]

F7

7th order

1/6[(1 − α )−6 − 1]

Fn

nth order

(1 − α )7 (1 − α )n

(6)

p (x ) = [exp (−x ) ⁄x ][(x 3 + 18x 2 + 86x + 96) ⁄ (x 4 + 20x 3 + 120x 2 + 240x

The Friedman method, FD (differential), provides a more straightforward approach applying conversion rate equation (Eq. (6)). The activation energy, Eα, for each conversion was obtained by linearization plots of ln(βdα/dT) as a function of the temperature inverse (1/T), as shown in Eq. (7).

ln [β (dα / dT )α ] = ln [Af (α )] − Eα /(RT )

+ 120)]

(10)

The conversion range of the three model-free methods was calculated between 0 and 1 with step size of 0.01. This range was chosen in order to cover the main devolatilization temperatures and to adjust conversion rate for a single reaction. FD [23] and FWO [24,25] methods were implemented using Microsoft Excel (version 15.0.4875.1000), and for the minimization of VZ method [26], the MS Excel optimization tool (SOLVER with Generalized Reduced Gradient method) was applied. Also, in order to evaluate the quality of fit by linearization for each method, the Pearson correlation coefficient (r2) using RQUAD function in MS Excel (version 12.0.6683.5002) was applied.

(7)

Integral methods are obtained integrating Eq. (6), however it has not an analytical solution. The Flynn-Wall-Ozawa method, FWO [24,25], applies Doylés approach for the temperature integral. The activation energy (Eα) was obtained by linearization plots of heating rate logarithm (lnβ) as a function of the temperature inverse (1/T), according to Eq. (8), in which g(α) expresses the integral of dα/f(α).

ln [β ] = ln {AEα /[Rg (α )]} − 5,331 − 1,052Eα /(RT )

[(1 − α )1 − n − 1]/(n − 1)

Yang approximation, Eq. (10), to calculate the p(x) function, was chosen. This equation was validated by Pérez-Maqueda and Criado [27] obtaining errors less than 1.7 ∙ 10−6 for x ≥ 20.

the absolute temperature. The f(α) defines different reaction mechanisms (Table 1) based on decomposition phenomena.

(dα / dT )α = (A/ β ) exp [−Eα /(RT )] f (α )

2

(8)

2.5.2. Master plots method The mechanism of solid-state reaction was identified using master plots method considering the integral conversion function, g(α), as shown in Table 1. The more appropriate conversion function should exhibit similar behavior to the experimental data [15]. The left and right sides of Eq. (11) represent the theoretical model, g(α), and experimental data, p(x), respectively, in which the latter was calculated applying the 4th degree of Senum-Yang equation (Eq. (10)).

A advanced method for obtaining the activation energy was proposed by Vyazovkin, VZ, [26], which is based on direct numerical integration considering the minimization of the objective function, φ, described by Eq. (9).

φ = min {I (Eα , Tα,5) β10/[I (Eα , Tα,10 ) β5] + I (Eα , Tα,5) β20/[I (Eα , Tα,20 ) β5] + I (Eα , Tα,10) β5/[I (Eα , Tα,5) β10] + I (Eα , Tα,10 ) β20/[I (Eα , Tα,20 ) β10] + I (Eα , Tα,20) β5/[I (Eα , Tα,5) β20] + I (Eα , Tα,20 ) β10/[I (Eα , Tα,10 ) β20]}

g (α )/ g (0.5) = [AEp (x )/(βR)]α /[AEp (x )/(βR)]0.5 = [p (x )]α /[p (x )]0.5

(9)

(11)

In Eq. (9), I(Eα,Tα,β) represents the temperature integral expression, (Eα/R)[p(x)], in which x is defined by −Eα/RT. The 4th degree Senum-

The normalization of Eq. (11) eliminates the pre-exponential factor, 58

Thermochimica Acta 657 (2017) 56–65

V.S.d. Carvalho, K. Tannous

implemented in the ode113 tool of Matlab® (version 2013b), as detailed in the study from Shampine and Reichelt [31] work.

resulting in an expression that depends only on the p(x). For this normalization, the intermediate value (α=0.5) as reference, was used. The parameter x=E/RT was calculated by the average of the activation energy distribution obtained for each one of the three model-free methods (FD, FWO, and VZ), showed in section 2.5.2. The master plots were implemented in an Excel spreadsheet (MS Office Excel 2007 version 12.0.6683.5 002) using the same procedures adopted by the model-free methods in the Section 2.5.1.

(dαi/ dt ) = Ai exp [−Ei/(RT )](1 − αi )ni

The curve fitting was performed with activation energy, pre-exponential factor, reaction order and composition variations for each component, obtaining only one value of each kinetic parameter for all heating rates. Restrictions were added in order to maintain the sum of the devolatilization compositions totalizing one (1) and individual positive values. The least squares function (LSF) was calculated by Eq. (18) considering the difference between the experimental (Eq. (5)) and theoretical (Eq. (16)) conversion rates, in which the summation in “j” refers to number of experimental data (N) adopted and “β” refers to the heating rates applied from 1 to 3.

2.5.3. Linear fitting model The kinetic parameters obtained for the single-step model can also be estimated from the linear fitting method [15]. This procedure is advantageous because the conversion function is not limited to the kinetic models summarized in Table 1. The generic model, represented by Eq. (12), uses adjustment parameters (c, k, and m) for the determination of the conversion function, f(α).This function is introduced into Eq. (6) and linearized as a function of inverse temperature, resulting in Eq. (13).

f (α ) = cα m (1 − α )k

(12)

ln {(dα / dt )/[α m (1 − α )k ]} = ln(cA) − E /RT

(13)

3

LSF =

Fit (%) = 100 SS /(max dα /dt exp )

[(dα / dt )exp − (dα / dt )theo]2

j=1

⎫ ⎬ ⎭

(18)

3.1. Characterization of biomass properties Table 2 presents the chemical and thermal properties of energy cane Saccharum robustum as well as those from sugarcane bagasse [11,12] and sugarcane straw [3,13,32]. In this work, the comparison of the properties was restricted to sugarcane residues, because the literature presents a lack of energy cane studies. Extractives contents of the energy cane (23.22 wt.%) showed differences between those from bagasse [11] (9.7 wt.%) and straw [13] (5.28 wt.%) in an increasing of 13.52 wt.% and 18.94 wt.%, respectively. They were related to the energy cane juice, differing of the sucrose content (3–7 wt%) [6] due to the contribution of colloids, pigments, and reducing sugar [33]. Hemicellulose and cellulose contents obtained in this work (29.18 wt.% and 41.61 wt.%, respectively) were comparable to those of sugarcane residues (23–33 wt.%; 40–44 wt.%). The lignin content in the energy cane was lower than bagasse [11] and straw [13] residues from 10.82 wt.% to 15.59 wt.%, respectively. The ultimate analysis showed C, H, N and O contents (%) were similar to the sugarcane residues, with DV lower than 6 wt.% with exception of Zanatta et al. [12] for bagasse. In the proximate analysis, the volatile content (83.4 wt.%) was comparable to the average value of 83.7 ± 2.0 wt.% of bagasse [11,12] and straw [13]. Nevertheless, Pattyia et al. [3] and Mesa-Pérez et al. [32] showed different values (74–79 wt.%) due probably to the high inorganic mineral content in their samples. Regarding to the fixed carbon content, our results (14.0 wt.%) was 2.3 wt.% higher than the average of literature results (Table 2) of 11.7 ± 2.1 wt.%, whilst Aboyade et al. [11] for straw presented lower values (7.5–8.6 wt.%). The ash content (2.6%) was lower than those of the residues (3.9–10.3 wt.%). Low ash content was correlated to the reduction of inorganic component contents, reduction the impact of fouling, slagging, and bed agglomeration [34], favoring its application in pyrolytic fluidized bed reactors. Comparing the higher heating values (HHV) between the residues and energy cane, the variations was of 1.48 MJ/kg in relation of average value (18.33 ± 0.25 MJ/kg) from literature with exception of

[(dα / dt )exp − (dα / dt )theo]2 / N

j=1

N

∑

3. Results and discussion

N

∑

⎧ ∑ ⎨ β=1 ⎩

The number of experimental data considered was 101 points with conversion step size of 0.01, modeled using the entire devolatilization range of the lignocellulosic material. The parameter setting, LSF, was performed in Matlab® (version 2013b) with the pattern search method implemented in the patternsearch tool. The quality of fit, Eq. (14) and (15), were calculated using the same procedure in Section 2.5.3. For comparison between results from literature and our experimental data were applied the deviation (DV, %) defined by the difference between the parameter from this work (p1) and literature (p2), divided by p1 and multiplied by 100.

The activation energy was estimated from the slope of the line and the pre-exponential factor was found by y-intercept multiplied by the universal gas constant (R = 8.314 J/mol K). The best fit was obtained maximizing the r2 value of the linearity of Eq. (13) varying k and m parameters. For the model implementation, an Excel spreadsheet was used following the same procedures applied in section 2.5.1. This procedure is valid if the errors are lower than 10% over the activation energy found in model-free methods [13,29]. After obtaining the kinetic parameters, a comparison between the experimental (Eq. (5)) and theoretical (Eq. (6)) conversion rates, which was solved by Euler method (DOE), was carried out. The quality of fit, Fit (%), was calculated from Eq. (14) and Eq. (15), in which max dα / dt exp is the maximum experimental conversion rate and N is the number of experimental data. The (dα / dt )theo was determined using Eq. (6) and Eq. (12).

SS =

(17)

(14) (15)

2.5.4. Independent parallel reaction scheme The independent parallel reaction scheme (IPRS) was represented by Eq. (16), in which (dα/dt)theo is the theoretical conversion rate, described by individual reactions for each component of energy cane: extractives (E), hemicellulose (HC), cellulose (C), and lignin (L). Each component is decomposed independently generating gaseous (volatile and gases) and solid (char) products. The reaction of each component was accounted for specific conversion rate, dαi/dt, relating to its compositions (c). These compositions are the mass fractions of the volatiles evolved of each fraction.

(dα / dt )theo = cE (dαE / dt ) + cHC (dαHC / dt ) + cC (dαC / dt ) + cL (dαL/ dt ) (16) The dαi/dt for each component was described according to the model-free methods through Eq. (6). However, it was previously necessary to assume a conversion function that would better represent the thermal decomposition. In this work, according to Rueda-Ordóñez et al. [30], the nth order reaction function, Fn, was assumed. This function (Table 1) represents the conversion rate as a direct function of quantity of reagents remaining. Replacing the function Fn in Eq. (6), the theoretical conversion rate for each component of the reaction can be described by Eq. (17), in which n represents the reaction order. The latter equation was solved using the Adams-Bashforth-Multon method 59

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Table 2 Proximate and ultimate analyses, and heating values of energy cane, sugarcane bagasse, and sugarcane straw. This work S. robustum

Chemical Composition Extractives Hemicellulose Cellulose Lignin

[11] Sugarcane bagasse

[12]

[3] Sugarcane straw Leaves

Tops

[32]

[13]

a

0.01 0.02 0.03 0.01

9.7 ± 0.5 23.8 ± 0.4 44.2 ± 0.6 22.4 ± 0.3

– 27.6 42.8 26.4

– – – –

– – – –

– – – –

5.28 33.28 39.81 21.63

Ultimate Analysisa C H N S O Ash

42.89 ± 0.65 5.96 ± 0.25 0.17 ± 0.04 – 48.44 ± 0.3 2.54

43.5 ± 0.5 5.7 ± 0.1 0.3 ± 0.1 0.3 ± 0.1 40.3 ± 0.4 9.9

35.73b 7.05b 0.41b – 56.81b –

48.9 b 6.5 b 0.2 b – 44.4 b –

49.0 b 6.6 b 0.6 b – 43.8 b –

43.2 6.7 0.3 0.2 33.2 16.4

42.9 ± 0.25 6.26 ± 0.16 0.31 ± 0.05 – 46.65 ± 0.18 3.88

Proximate analysis Moisture Volatilesa Fixed Carbona,d Asha

– 83.40 ± 0.004 14.00 ± 0.002 2.60 ± 0.002

6.7 ± 0.1 91.7 ± 0.3b 8.3 ± 0.1b 10.3 ± 0.1

7.23 82.50 9.34 8.30

6.7 79.0 8.6c 5.7

6.6 74.9 12.5c 5.7

10.4 74.0 13.0 16.4

8.42 ± 0.30 86.64 ± 0.53 9.51 ± 0.53 3.85 ± 0.21

Heating value (MJ/kg)a Higher Lower

16.85 ± 0.46 15.54 ± 0.46

16.6 ± 0.2 –

18.4 17.0

18.3 17.0

18.0 17.0

18.61 15.20

a b c d

23.22 29.18 41.64 10.81

± ± ± ±

wt.% on dry basis. wt.% on ash-free basis. wt.% on wet basis. Calculated by difference.

Aboyade et al. [11] for bagasse (16.6 MJ/kg). According to Demirbas [35], the HHV could be directly affected by extractive content as it can be seen in this work. Finally, the chemical and thermal properties obtained in this work differs mainly in the extractive, lignin, ash and fixed carbon contents comparative to those reported in the literature (Table 2) for sugarcane residues. 3.2. Thermal decomposition analysis Fig. 1(a) and (b) present the normalized mass (w) and the normalized DTG (dw/dt) as a function of temperature, according to Eq. (2) and Eq. (3), respectively. The energy cane thermal decomposition was divided into three stages, such as dehydration (I), pyrolysis (II, III, and IV), and carbonization (above IV). 3.3. Dehydration stage The first stage in a thermal conversion process corresponded to the sample dehydration, which takes place from room temperature up to 150 °C. The moisture content in the energy cane showed in Fig. 1a were 7.6%, 7.5%, and 6.7% for the heating rates of 5 °C/min, 10 °C/min, and 20 °C/min, respectively. In the DTG curve (Fig. 1b), one peak (I) was presented between the temperature range of 25 °C and 100 °C for all the heating rates. The drying step will not be considered for the kinetic modeling in this paper. 3.3.1. Pyrolysis stage The biomass main decomposition (second stage) occurred in the temperature ranges from 150 °C to 400 °C, 150 °C to 415 °C, and 150 °C to 434 °C, devolatilizing 61.8, 62.0 and 63.4% of the initial mass for the heating rates of 5, 10 and 20 °C/min, respectively (Fig. 1a). In the normalized DTG curve (Fig. 1b), the pyrolysis stage was characterized by three peaks related to extractives (II), hemicellulose (III), and cellulose (IV) defined at: 219.14 °C, 228.48 °C, and 246.35 °C; 296.39 °C, 301.85 °C, and 322.59 °C; 352.63 °C, 365.85 °C, and 379.85 °C for the

Fig. 1. (a) Normalized mass, (b) e Normalized DTG as a function of the temperature.

60

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V.S.d. Carvalho, K. Tannous

same heating rates, respectively. According to White et al. [17], the lignin generally does not present an unique peak in the DTG curve due to its slow decomposition over a wide temperature range. In addition, considering sugarcane bagasse, Aboyade et al. [10] suggested that the lignin decomposition occurs from 180 to 900° C. The peak II represents the extractives decomposition, from 150 °C to 245.2 °C, 150 °C to 252.7 °C, and 150 °C to 268.6 °C for the same heating rates, respectively, which was not observed in the sugarcane bagasse [10,11,36] and straw [13]. This component contributed to the beginning of the biomass devolatilization at lower temperatures than found for sugarcane bagasse in Aboyade et al. [10] and Manyá et al. [9] works, which started at 230 °C and 200 °C, respectively. Belonging to Varhegyi and Antal Jr [37], the extractive decomposition in sugarcane bagasse was negligible or consist of broad and very low DTG signals, which do not influence the kinetic evaluation. The peak III corresponds to the hemicellulose decomposition in the temperature ranges from 245.2 °C to 305 °C, 252.7 °C to 322.8 °C, and 268.6 °C to 345.3 °C, for the same heating rates, respectively. It was observed that the initial temperatures are in agreement with the literature (200–300 °C)[13], however the final temperatures are slightly above ( < 13%) probably due to the contribution of minerals present in the biomass. The last peak (IV) depicts the cellulose decomposition between 305 °C and 389.8 °C, 322.8 °C and 406.4 °C and 345.3 °C and 433.6 °C, for the same heating rates, respectively, and corresponding to the higher mass loss rate (dw/dt) among all main components. According to the literature, the cellulose decomposition range is between 240 °C and 390 °C [10], in which the upper limit is slightly displaced ( < 10%) to the higher temperature as well as hemicellulose temperature range. Cellulose is considered the most stable biomass component with high crystallinity degree [38]. Several representations of its decomposition mechanisms could be found in the literature [1] 3.3.2. Carbonization stage The final stage (Fig. 1a) concerning to the carbonization occurs at temperatures of 400 °C, 415 °C and 434 °C (β = 5, 10 and 20 °C, respectively) up to 900 °C, representing a slow and steady devolatilization rate, attributed mainly to the lignin residual decomposition and char formation by secondary reactions. The normalized mass was of 14.5%, 11.3%, and 10.1% as well as the residual solid mass (900 °C) of 17.3%, 19.2%, and 19.8% for the same heating rates, respectively. In Fig. 1b, the DTG curves (Fig. 1b) show the main lignin decomposition characterized by a long tail in the wide temperature range (above 450 °C). The final residue, formed by mineral content and char fraction, does not decompose at the pyrolysis temperature. The ash content of char depends mainly on the initial inorganic biomass composition [1]. Char can be used as fuel, as briquettes, or as precursor for activated carbon production [39]. However, it is known that lignin decomposition begins at lower temperatures, amongst hemicellulose and cellulose decomposition. It was also observed that the lower the heating rate, slightly lower the final remaining mass (variation of 2.5%). It means that, decreasing the heating rate, the reaction time is increased, allowing the occurrence of more reactions with the break of polymer linkages of biomass compounds, leading to the increase of volatile production, and consequently, the largest biomass decomposition.

Fig. 2. (a) Correlation coefficient distributions resulting of model-free methods as a function of conversion (b) Activation energy and conversion rate as a function of conversion − β = 5 °C/min.

were obtained the activation energy distributions for each conversion and the correlation coefficient (r2) were obtained, as shown in Fig. 2a and b, respectively. The r2 (Fig. 2a) above of 0.994 (0.18 ≤ α ≤ 0.95) indicated accuracy in the activation energy, valid for three heating rates as recommended by ICTAC Committee [16], and corresponding to 150.6–204.3 kJ/mol, 142.7-183.0 kJ/mol, and 147.0–183.0 kJ/mol for the three models, FD, FWO and VZ, respectively. The activation energy obtained from Vyazovkin method resulted in a lower variation than FD and FWO methods. The highest standard deviation was obtained by FD (Fig. 2b), 169.1 ± 13.08 kJ/mol, concerning the oscillatory behavior of the derivative method, that tends to magnify experimental noise [15], and then, it was not considered for the activation energy analysis. However, the activation energy deviations of VZ model (167.6 ± 8.00 kJ/mol) and FWO (167.1 ± 8.26 kJ/mol) were lower than 3.5%. The FWO model was validated presenting less complexity, since it does not require a numerical solution. Fig. 2b also presents an example for the conversion rate (dα/dt) of 5 °C/min as a function of conversion (α) showing extractives and cellulose peaks and the hemicellulose shoulder of the decomposition reactions. The activation energies, correspondent to the extractives devolatilization, were between 107.5 and 142.7 kJ/mol for conversions from 0.05 up to 0.18. In the intermediate level, the conversion range (from 0.18 to 0.45) is related to the hemicellulose with activation energy from 142.7 to 180.2 kJ/mol. According to Anca-Couce [1], few information about decomposition reaction schemes of this component

3.4. Determination of activation energy by model-free methods The model-free methods of Friedman (FD), Ozawa-Flynn-Wall (FWO), and Vyazovkin (VZ) were carried out to determine the activation energy as a function of conversion considering three heating rates (5, 10 and 20 °C). The normalized mass and temperature ranges applied were between 0.33 (400 °C) and 0.93 (150 °C) for all the heating rates corresponding to the greatest mass loss, according to Fig. 1a. After the linearized plots 61

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Table 3 Kinetic studies considering TGA conditions, modeling and parameters for sugarcane residues in an inert atmosphere. References

Biomass

Diameter (μm)

Method

TG conditions, Sample mass (mg), Heating rate (°C/min), Temperature range (°C)

Conversion range, α

Kinetic Parameters

Ea (kJ/mol) [9]

Sugarcane bagasse

1200–2500

IPRS

[10]

125–350

FD

[36]

200

FWO VZ

[11]

[13]

[14]

Sugarcane straw

< 212

IPRS

510

FD

510

IPRS

10 5;10;20 100–900 20–50 10;20;30;40;50 105–700 20 2;10;20;50 27–673

0.05–0.5 0.5–0.8 0.1–0.4 0.5–0.8 0.1–0.4 0.5–0.8

5–25 5;10;20;30;40;50 105–900 3 1.25;2.5;5;10 25–900 3 1.5;2.5;5;10

0.2–0.5 0.5–0.8

25–900

n

A (1/s)

c 15

HC: 194.0 C: 243.3 L: 53.6 200–225 225–150

HC: 1 C: 1 L: 3 –

HC: 3.16 10 C: 7.94 1017 L: 100 –

HC: 0.13 C: 0.41 L: 0.22 –

163–173 227–235 176–184 236–244 C1: 187.6 C2: 212.4 C3: 94.3 153.9–174.0 177.5–160.0

–

–

–

–

–

– 16

C1: 1.4 C2: 1 C3: 4.2 –

C1: 8.5 10 C2: 3.6 1017 C3: 3.3 108 –

C1: 0.23 C2: 0.45 C3: 0.32 –

HC: 142 C: 195–212

HC: 1 C: 1

HC: 0.27–0.28 C: 0.51–0.54

L: 40

L: 1

HC: 1.35 1010 C: 3.16 1013–8.32 1014 L: 0.34

L: 0.20–0.21

FD: Friedman method. FWO: Flynn–Wall–Ozawa method. KAS: Kissinger–Akahira–Sunose method. IPRS: Independent and parallel reaction scheme. VZ: Vyazovkin method. C1, C2, C3: Components 1, 2, and 3. RT: Room temperature.

could be found in the literature. The last peak evaluated (dα/dt) was attributed to the cellulose decomposition corresponding to the activation energy range from 164.2 to 183.0 kJ/mol (FWO and VZ) and conversions between 0.45 and 0.95. However, in the narrow conversion range between 0.45 and 0.60, a rapid decreasing of the activation energy from 183.0 to 170.6 kJ/mol could be associated to the formation of activated cellulose [40]. For the conversions higher than 0.6, the activation energy became more stable (168.6 ± 4.4 kJ/mol) and could be related to the depolymerization and fragmentation of the activated state [40]. These activation energies are in agreement with the literature varying between 109 and 251 kJ/ mol [17]. The activation energy of lignin is usually not determined in the model-free methods, since its decomposition does not stand out in a specific peak as the other components analyzed in Section 3.2. Comparing the activation energy of Saccharum robustum with the sugarcane bagasse [11] and straw [13] (Table 3) using FD method as reference, it was found deviations, DV, of 0.6–18.4% and 2.7–6.4%, respectively. Regarding the FWO and VZ methods, was observed similarity of activation energy for the sugarcane straw with difference up to 16 kJ/mol [13], however a higher DV was found for sugarcane bagasse of 8.7–23.7% and 17.3–28.4% [36], respectively. The activation energy obtained for energy cane was lower than bagasse due the high extractive contents and low hemicellulose and cellulose contents. This low activation energy could be associated to the migration of the hot gases formed through the sample causing a reaction acceleration [41].

(interface) and the availability of reagents on this surface, which may be controlled by its mobility from crystalline structure of the solid [42]. Thus, the reaction rate can be represented by diffusion, solid geometry and reagent concentrations models [43]. According to Vyazovkin et al. [15], is not recommended the assumption of a reaction model without a previous verification through master plots, since the dominant reaction mechanisms are unknown. In Fig. 3a and b are presented the theoretical (lines and dash lines) and experimental (symbols) master plots as a function of the conversion for lower than 0.5 and from 0.5 to 1, respectively. In both figures, the theoretical curves are represented by the integral reaction models, g(α), D3, ZH, F2, F3, F4, and F7 (Table 1), compared to the experimental behavior at the three heating rates evaluated. It could observed that the most consistent was the reaction model F7 (7th order reaction function) for lower 0.5 of conversion. Since the behavior changes at the conversion of 0.5, it is not possible to determine any unique similar function to the experimental data. For higher conversion, the experimental data showed reaction models according to: F4 (0.5–0.67), F3 (0.67–0.75), and F2 (> 0.9). Reaction order models establish that the principal mechanism depend on the reactant concentrations remained. Other factors, such as particle geometry and surface, and mass transfer (diffusion) were negligible in the thermal decomposition process [42]. Then, the applicability of the master plots method analysis showed that behavior for the main devolatilization stage (pyrolysis) of the energy cane are not unique, but follow different reaction order.

3.5. Determination of reaction mechanism by master plots

3.6. Determination of the kinetic parameters by linear fitting model

Master plots method is useful to determine the theoretical function that better describes and improves the understanding of the experimental decomposition reaction model. It was commonly assumed a first or nth order reaction model for biomass studies applying model-free methods. The solid-state reaction kinetics depends mainly on the surface area

The linear fitting model, Eq. (19), was obtained, in which the slope and y-intercept were −21.295 and 26.774.

ln {(dα /dt )/[α −2.90 (1 − α )0.39]} = 26.774 − 21.295/T 2

(19)

From the experimental data in Fig. 4 (R = 0.9757), the parameters of conversion function, k and m, were 0.39 and −2.90, respectively. 62

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Fig. 3. Theoretical and experimental master plots functions from (a) 0–0.5 and (b) 0.5–1 of conversion.

widely discussed in the literature because of its practical use in simulators.

3.7. Determination of kinetic parameters by independent parallel reactions scheme Fig. 6(a), (b) and (c) show the conversion rate curves as a function of temperature at the heating rates of 5 °C/min, 10 °C/min, and 20 °C/ min, respectively, considering four independent parallel reactions scheme. The modeled data were represented in a solid line and the experimental data in symbol (o). The temperature range of 150 °C to 900 °C was used to consider the overall devolatilization behavior of the energy cane. Temperatures up to 150 °C were disregarded (drying removal) from the analysis, and high temperatures up to 900 °C were important to describe the lignin decomposition behavior. The decomposition temperatures for each component were chosen as: 153–287 °C (extractives), 228–365 °C (hemicellulose), 269–420 °C (cellulose), and 211–900 °C (lignin). After evaluate the sixteen variables applying the minimization program (Section 2.5.3), the kinetics parameters (Table 3) of extractives, hemicellulose, cellulose and lignin components were obtained the activation energies of 104.7, 153.2, 181.1 and 60.0 kJ/mol, reaction orders of 1.05, 1.31, 1.02, and 3.00, compositions of 0.13, 0.19, 0.43, and 0.25, and preexponential factors of 5.13 ∙ 108 1/s, 7.60 ∙ 1011 1/s, 5.81 ∙ 1012 1/s, and 1.11 ∙ 102 1/s, respectively. The compositions represent the mass fractions of volatiles produced for each component. The quality of fit was 3.97%, 3.88%, and 5.65% for 5, 10 and 20 °C/min, respectively, showing good adjustment with heating rate variation (DV < 2%). The activation energy of the extractives (104.7 kJ/mol) belongs to the range of 90–110 kJ/mol obtained from the model-free methods and conversions below 0.15. Comparison with literature data was not possible, since the studies found did not consider such component for modeling due to its low content in the respective biomasses (Table 3). In Table 3 are also presented results of kinetic parameters from literature obtained through IPRS. It is observed that the DV of activation energy of hemicellulose of energy cane were of 26.7% and 22.5% lower than sugarcane bagasse [9,11] and 7.3% higher than straw [14]. For the cellulose, DV were of 17.1–34.2% for the bagasse [9,11] and 7.7% for straw. Regarding to the lignin, the activation energy was lower than bagasse [9,11] with 10.6% and 28.3%, respectively, and 33.3% higher than the straw [14]. The activation energies found for hemicellulose and cellulose are in agreement with the range between 147 and 183 kJ/mol obtained by the model-free methods (section 3.3), providing more reliability of the parameters representative of the decomposition process.

Fig. 4. Linearization of the conversion rate equation as a function of the inverse of absolute temperature.

The slight scatter effect observed at 1/T between 1.9 (α < 0.18) and 2.24 (α = 0.01) is due to the presence of extractive component related to a low activation energy (107–142.7 kJ/mol), shown in Fig. 2. The activation energy (177.1 kJ/mol) was obtained through multiplication of slope by universal gas constant (8.314 J/mol K). This energy corroborates with the averages obtained by the FWO, VZ and FD models, with 5.4%, 5.7% and 4.1% of deviations, respectively. The y-intercept represents ln cA, corresponding to the factor “cA” = 4.24 1011 1/s. According to Vyazovkin et al., the parameter “c”, did not affect significantly the pre-exponential factor (A) varying from 0.973 to 4.431 for different conversion functions (e.g., contracting area, contracting volume, first order reaction, Avrami-Erofeev, and diffusion) maintaining the magnitude order of A. The conversion function, Eq. (12), has an empirical basis (SestakBerggren kinetic equation) without physical meaning [44]. According to Vyazovkin et al. [15], this function would be suitable for describing the effect of the extent of conversion on the reaction rate. Despite the agreement concerning the activation energy obtained by model-free and linear fitting model, the simulations of the single-step model were represented by only one peak, Fig 5. Therefore, it shows an unrepresentative adjustment of the biomass multiple reactions, with quality of fit of 14.2%, 13.8%, and 13.2% for the heating rates of 5, 10 and 20° C/min, respectively. Thus, the biomass decomposition is governed by multiple complex reactions, which will be discussed in the section 3.6. However, singlestep model provides the basis for parameter estimation for more accurate models, such as the independent parallel reactions scheme. The approximation of the thermal decomposition for single-step reaction is 63

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Fig. 6. Conversion rate for each lignocellulosic component and overall (IRPS scheme) as a function of temperature (a) 5 °C/min, (b) 10 °C/min and (c) 20 °C/min.

application of the linear fitting method to the global model was quite satisfactory (r2 = 0.9757), in which the activation energy (177.1 kJ/ mol) corroborates with those obtained in the model-free methods (deviation < 5.7%). However, the simulation of the single-step model proved to be poorly representative of the main components of decomposition (quality fit of 13.2–14.2%), thus indicating multi-step reactions of biomass. The multi-step method application considering independent parallel reaction scheme and four mainly components successfully presented kinetic parameters with quality of fit lower than 5.7% for all the heating rates. Therefore, this energy cane shows a promising species for bio-oil generation and our modeling were validated for designing as well as simulation applications.

Fig. 5. Comparative of conversion rates as a function of temperature for global model (—) and experimental (–) − β=5 °C/min (a), 10 °C/min (b), and 20 °C/min (c).

4. Conclusion The thermal decomposition of energy cane species Saccharum robustum from thermogravimetric analysis (TG and DTG) showed the pyrolysis stage in the temperature range of 150–400 °C, indicating the presence of three mainly components: extractive, hemicellulose and cellulose. The decomposition reaction was modeled to a single-step according to model-free and master plot methods aiding in the estimative of the activation energies of 107.5–142.7 kJ/mol, 142.7–180.2 kJ/mol, and 164.2–183.0 kJ/mol, respectively. The 64

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Conflict of interest [20]

The authors declare that they have no conflict of interest. [21]

Acknowledgements

[22]

The authors gratefully acknowledge the financial support received by the Coordination for the Improvement of Higher Education Personnel (CAPES) and Research, Teaching and Extension Support Fund (FAEPEX), UNICAMP, Brazil.

[23]

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