A generalized activation energy equation for torrefaction of hardwood biomasses based on isoconversional methods

Renewable Energy 99 (2016) 1318e1326

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Review

A generalized activation energy equation for torrefaction of hardwood biomasses based on isoconversional methods M. Grigiante*, M. Brighenti, D. Antolini University of Trento, Department of Civil, Environmental and Mechanical Engineering, Via Mesiano 77, 38123, Trento, Italy

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 January 2016 Received in revised form 11 May 2016 Accepted 21 July 2016 Available online 1 August 2016

In this work an extended kinetic analysis involving both experimental measurements and modelling procedures to describe the thermal degradation process of biomass is proposed. Three biomasses belonging to the hardwood family are investigated: ash-wood, beech-wood and hornbeam. The experiments are performed with a thermogravimetric balance working at four constant heating rate: 3, 5, 10 and 20
C/min. This study is specifically dedicated to investigate the thermal behaviour of the selected biomasses when they undergo to torrefaction temperature conditions. The modelling analysis focuses on investigating the capability of consolidated models, usually applied to solids, in determining the activation energy (Ea) of the indicated biomasses within the torrefaction range. The adopted methods belong to the so-called isoconversional “model free” methods and, in this contest, both the differential ones as those of Friedman and Flynn and the integral ones as those of Kissinger-Akahira-Sunose, Doyle and Starink have been applied. The performances reached by adopting the integral methods widely satisfy the accepted accuracy level conventionally set at values lower than 10%. At the same time it is verified even that, when the methods are applied to biomasses belonging to the same family, the resulting Ea vs. a trends are very close for all the biomasses. This condition is exploited to propose a generalized predictive approach for the Ea calculation based on the knowledge of only the conversion fraction a. The presentation of the results includes also the investigation of the limits of the proposed methods in view of indicating their reliable application range when utilized for torrefaction design procedures. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Isoconversional methods Kinetics analysis Activation energy Torrefaction

Contents 1. 2.

3.

4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1319 Experimental . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1320 2.1. Non-isothermal thermogravimetric analysis (TGA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1320 2.2. Material characterization and preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1320 2.3. Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1320 2.4. Experimental procedure and quantities calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1320 Kinetics investigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1320 3.1. Isoconversional methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1320 3.2. Differential isoconversional methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1321 3.3. Integral isoconversional methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1321 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1322 4.1. Experimental TGA results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1322 4.2. Results of the kinetic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1323 4.2.1. Models results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1323 4.2.2. Range of validity and limits and of the proposed approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1324

* Corresponding author. E-mail address: [email protected] (M. Grigiante). http://dx.doi.org/10.1016/j.renene.2016.07.054 0960-1481/© 2016 Elsevier Ltd. All rights reserved.

M. Grigiante et al. / Renewable Energy 99 (2016) 1318e1326

5. 6.

Generalization of the results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1325 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1325 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1325

Nomenclature A Ea k n R T t m

a b y F R2

1319

Pre-exponential factor (min1) Activation energy (kJ mol1) Rate constant (min1) Order of reaction () Universal gas constant (8.314 J mol1 K1) Temperature (K) Heating time (min) Mass of tested sample (g) Conversion of sample () Heating rate (K min1) Ea/(RT) () parameter () correlation coefficient ()

Subscript o Reference state sample weight (~150
C) ∞ Final state sample weight (900
C) Superscript e parameter ()

1. Introduction Thermochemical processes as co-combustion, gasification and pyrolysis draw more and more the attention of the scientific community as they represent both an interesting option for current biomasses utilization and a promising alternative for converting, in amid-term energy scenario, biomasses into upgraded fuels. In these last years torrefaction, a thermolysis low thermal treatment carried out in the range of 200e300
C, is expected to play an important role in exploiting raw biomasses in many major end energetic uses [1e4]. Torrefaction can improve, in particular, the properties of both agro-forestry, woody industrial residues and agricultural crops enhancing, therefore, the global biomass chain value [5e8]. Actually, two relevant factors limit the diffusion of this technology [9,10]: the lack of information on the performances of the existing plants and, even on, the impact of this pre-treatment on the delivering final costs of the torrefied materials. Recent studies [11,12] have identified the role of mass and energy yield ratio as guide indicators to control the quality of the final product and to define the best working conditions of the torrefaction plants [13]. Within this scenario and considering the complex mechanisms involving the thermal treatments of lignocellulosic materials, improvements on the kinetics knowledge are highly regarded. Till now torrefaction kinetics have been investigated mainly by adapting methodologies applied on biomass pyrolysis [14]. Therefore, a relevant amount of kinetics approaches have been proposed: from the first order reactions to the more complex ones improved by the introduction of pseudo-components [15e19]. Some of these investigate in particular the decomposition of the solid matrix and appear, therefore, particularly suitable to be imported in this field. These modelling techniques describe the

progress degradation rate through a single reaction expressed as product of two functions: one depending solely on temperature, the k(T) term, the other, f(a), on the conversion fraction a:

da ¼ kðTÞ$f ðaÞ dt

(1)

The parameter a represents the fraction of the total mass loss at a certain step of the process:

a¼

mo  mt;T mo  m∞

(2)

where mo is the initial mass of the sample and m∞ the final one. For isothermal conversion mt,T becomes mt and represents the weight of the sample at time t. For non isothermal mode, as the case of this study, mt,T becomes mT and represents the weight of the sample at the temperature T indicated by the thermogravimetric instrument. Conventionally, the temperature dependent function k(T) is assumed to follow the Arrhenius form:


Ea kðTÞ ¼ A$exp RT

(3)

where A and Ea are the pre-exponential factor and the activation energy respectively, R is the universal gas constant and f(a) the adopted model function. To avoid misunderstandings, it is crucial to underline that the assumption of a single reaction does not mean the process really follows a single step mechanism. On the contrary, the resulting kinetics parameters have to be identified as “global” or “apparent” parameters, to stress the fact that they could deviate from those pertaining to a multiple reactions scheme. Making reference to Eqs. (1) and (3), a kinetic model is completely defined when the kinetic triplet: A, Ea and f(a) is determined. This paper is specifically dedicated to determine the Ea quantity by applying the isoconversional approach both in its differential and integral structure. A future study, actually in progress, will extend this procedure to the complete definition of the kinetic triplet. Also when applied to biomasses, these methods are not extensively utilized and their results are usually not supported by comprehensive validations. On the Authors knowledge, a detailed comparison of the performances between differential and integral methods has never been proposed. This analysis assumes a particular interest considering that these methods, amongst the more reliable ones for treatment of solid compounds [20,21], are applied to non-homogeneous materials as biomasses. Some of the main reasons that recommend their use are: - the apparent activation energy (Ea) is determined as function of only the extent of conversion; - the Ea is evaluated without any assumption regarding the reaction mechanism, for this the name “model-free” given to these models. The novelty of this study consists in performing:

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M. Grigiante et al. / Renewable Energy 99 (2016) 1318e1326

- the experimental and models analysis of the kinetic behaviour of three types of biomasses pertaining to the hardwood family: ash-wood, beech-wood and hornbeam; - the reliability of several isoconversional methods in determining the Ea of biomasses within the torrefaction range. The main results can be summarized as follows: - the proposed isoconversional methods provide an excellent agreement of the obtained Ea. These results, verified in particular for integral methods, validate the basic hypothesis of the proposed approach and the reliability of the performed calculations; - when applied to a similar family of biomasses, a generalized equation for Ea calculation can be defined as function of the conversion fraction a and performing a high predictive accuracy level.

2. Experimental 2.1. Non-isothermal thermogravimetric analysis (TGA) One of the most extensively technique used to study the kinetics of biomass has proved to be the non-isothermal thermogravimetric analysis (TGA). It is known that the isothermal procedures are difficult to be rigorously achieved due to the presence of nonisothermal heat-up time between the heating carrier and the sample thermal profile. This problem represents one of the major debated questions associated with standard isothermal procedures. In particular, at higher temperatures, significant conversions can be observed before reaching the isothermal regime profile. In addition, at slow heating rate, the weight loss during heat-up time can heavily affect the resulting kinetics elaboration [22,23]. Thus, in isothermal procedures, the non zero extent of conversion is particularly critic and has to be carefully considered. In constant heating rate procedures, these problems are avoided by starting at temperatures well below the beginning of the decomposition [24]. For these reasons the non-isothermal approach has been chosen for this study. 2.2. Material characterization and preparation Three types of biomasses, all belonging to hardwood family, have been considered in this study: ash-wood, beech-wood and hornbeam. To make the results as much as possible representative of the selected biomasses type extensively widespread on the territory, raw materials have been collected from different sawmills. About 600 g. of each species have been brought to the laboratory and oven-dried for 2e3 h at 105
C. These dried samples have been carefully selected in terms of defect, bark and knots free, sieved to pass through a 600 mm trapezoidal mesh and then kept to a desiccator. The powdery samples have been stored in an artificial plastic container for replicates and further applications. Before beginning each tests, the moisture content of the samples was determined in triplicate according to AOAC standard method 930.15 [25]. Samples have been preliminary characterized in terms of ultimate analysis (UA), higher heating value (HHV) and fibres composition comprehensive of ash content. The Carbon, Hydrogen, Nitrogen and Sulphur (CHNS) content was determined by using the Elemental Analyzer Mod. Vario Macro Cube-Elementar (Elementar Analysen Systeme GmbH, Hanau, D) while the oxygen content was calculated by subtracting the ash and the CHNS amount from the total. The ash content was determined according to the standard method DD CEN/TS 14775:2004 [26]. The HHV was measured by

using the Oxigen Bomb Calorimeter Mod. IKA C5000 (Isoperibolic Calorimeter). These quantities are reported on the following Table 1. 2.3. Equipment The thermogravimetric dynamic analysis were performed by a Labsys Setaram analyzer. For sake of brevity, reference is made to [27] for a detailed description of this instrument. Nitrogen is used as inert carrier gas at a flow rate of 100 ml min1. It also sweeps the evolving gases from the reaction zone and limits the extent of secondary reactions such as thermal cracking and recondensations. For each run, approximately 20 mg of specimen were carefully spread uniformly on the aluminium oxide crucible of the furnace micro-balance. This small mass sample strongly insures, as much as possible, the uniformity of the tests and reduces the limitations due to heat transfer processes. To test the measurements reproducibility, 5 replicates have been performed for each of the specified runs. The good quality of the experimental procedures is confirmed by the sample temperature detection that has never indicated systematic deviations from the linear temperature selected as heating program. 2.4. Experimental procedure and quantities calculation Before proceeding with the TGA campaign, some samples of each species have been treated up to 900
C to determine the m∞ quantity required to calculate the conversion fraction a (Eq. (2)). Regarding the initial mass weight mo, it is important to remark that this quantity has been referred to the complete removal of water and light volatiles that occurs at temperature up to 150
C. The mass loss achieved till this limit has been therefore disregarded to refer the mo amount to a reproducible state for all the replicates. This assumption affects the trend of the weight loss vs. temperature as evidenced on Figs. 2, 4 and 8 where the curves start at values higher than 100%. Defining the heating rate parameter b ¼ dT/dt, four constant values of b have been selected: b ¼ 3, 5, 10 and 20
C/min. All the TGA runs have been carried out from ambient temperature to the upper limit of 400
C. The choice of this last temperature limit is explained on section 4.1. 3. Kinetics investigation 3.1. Isoconversional methods The basic assumption of the isoconversional methods states that, for a given extent of the conversion, the reaction rate depends only from temperature while the reaction mechanism is independent from the heating rate. By computing the logarithmic derivative of the reaction rate, Eq. (1), it comes:

      vlnðda=dtÞ vln KðTÞ vln f ðaÞ ¼ þ vT vT vT a a a

(4)

Since each term is assumed at a defined (constant) a value, f(a) is also constant. Considering the Arrhenius k(T) function, Eq. (3), Eq. (4) reduces to:

  vlnðda=dtÞ Ea ¼ vT R a

(5)

so that the temperature dependence of the reaction rate da/dt can be exploited to determine the Ea quantity without any assumption for the reaction model. At a selected a value, the Ea quantities are

M. Grigiante et al. / Renewable Energy 99 (2016) 1318e1326

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Table 1 e Ultimate, chemical analysis and Higher Heating Value (HHV) of the raw biomasses. Biomass

Beech-wood Ash-wood Hornbeam

Ultimate analysis [wt.%db]

Chemical components analysis [wt.%db]

Moistc

HHVdb

C

H

N

S

Oa

Hemicel.

Cellul.

Lignin

Extr.d

Ash

[wt.%]

[MJ/kg]

49.13 48.58 48.40

6.21 5.81 6.36

0.11 0.40 0.08

0.10 0.07 0.01

43.70 44.53 44.53

24.85 20.45 21.28

54.12 59.08 55.48

15.87 15.02 15.73

4.41 4.84 6.89

0.75 0.61 0.62

11.34 10.83 8.64

19.178 18.808 18.550

db

: dry basis. a: oxygen content calculated by difference. c: as received. d:extractives.

calculated step by step by including all the TGA curves available at different heating rates, thus the name of isoconversional or multicurve attributed to these methods [28,29]. Considering the non-isothermal constant heating rate procedure adopted in this study, Eq. (1) can be rearrange to give the differential form of the non-isothermal rate law:


da A Ea ¼ $exp  $f ðaÞ dT b RT

(6)

This equation lays the general form of the isoconversional constant heating methods that can be solved by means of a differential or integral approach. 3.2. Differential isoconversional methods

Eq. (6), can be rearranged to give:


da A Ea ¼ $exp  $dT f ðaÞ b RT

(9)

By adopting the variable: y ¼ Ea/RT, Eq. (9) can be re-written in its integral form:

Za 0

da A ¼ $ f ðaÞ b

ZT 0


Zy Ea A$Ea expðyÞ $dT ¼ exp  dy RT b$R y2

(10)

∞

where T is the temperature at an equivalent (fixed) state of transZy expðyÞ dy, traditionally called formation. The final integral y2 ∞

By applying the logarithmic derivative of Eq. (6), the following relationship proposed by Friedman [30,31] is derived:

“temperature integral” or “Arrhenius integral”, can be expressed in a generalized g(y) form as follows:


da Ea ¼ lnA þ ln f ðaÞ  ln b$ dT RT

gðyÞ ¼

(7)

An alternative to this approach is represented by the method introduced by Flynn [32] simply rearranging Eq. (7):

expðyÞ $hðyÞ yðu þ yÞ

(11)

Looking at these equations form, the Ea/R term can be deter
 mined by plotting, against 1/T, the terms: ln b$ddTa or lnðbÞ for the

This integral does not have an analytical solution so that a variety of approximations have been proposed by introducing suitable values for the constant parameter u and the h(y) function. For details, reference is made to [35e38]. Amongst the most consolidate forms, this study considers those derived by the elaborations proposed by Murray and White [38] that assumes h(y) ¼ 1 and u ¼ 0. This leads to the so called “direct isoconversional methods” represented by the following generalized equation:

two methods respectively. This analysis does not involve any kind of approximations and appears, potentially, particularly attractive. Nevertheless, as evidenced on the results section, the inaccuracies


 b Ea ln e ¼ F$ þ const: T RT

  f ðaÞ Ea  lnðbÞ ¼ ln A$ da=dT RT

(8)

due to the numerical computation of the ddTa term make these methods less accurate. Besides, recently investigations have evidenced inconsistencies due to the influence of the baseline inaccuracy [33]. Other problems involving specific applications of these methods are debate questions [34]. For these main reasons, methods based on integral solutions schemes have also been considered. 3.3. Integral isoconversional methods The general form of the isoconversional methods introduced by

(12)

Through this equation, the Ea/R term can be determined by
 plotting the quantity ln Tbe vs. T1, once the two parameters e and F are assigned. Reference is made to [39e41] for a deep analysis of the procedures carried out to determine these two terms. For the four methods considered in this study: Kissinger-Akahira-Sunose(KAS) [42]; Doyle [43,44]; Starink-1 and Starink-2 [39] the values are tabulated on Table 2. A graphical representation of the adopted isoconversional procedure is depicted on the following Fig. 1 for the case test of KAS method applied to ash-wood. The method has been computed by selecting a regular a increment covering the range

Table 2 e Parameters and equations form of the adopted integral isoconversional methods. Integral method

e

F

KAS (Kissinger-Akahira-Sunose)

2

1

FWO (Flynn-Wall-Ozawa, mod.Doyle)

0

1,0518

Starink-1

1,95

1

Starink-2

1,92

1,0008

Equation form
 Ea ln Tb2 ¼ RT þ cost Ea þ cos t ln b ¼ 1:0518$RT
 b Ea ln T 1:95 þ cos t ¼ RT
 b Ea ln T 1:92 þ cos t ¼ 1:0008 RT

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M. Grigiante et al. / Renewable Energy 99 (2016) 1318e1326 22 20

Deriv. Weight(%wt/min)

18

Ash-wood (DTG)

16 14 12 10 8 6 4 2 0 50

100

150

200

250

300

350

400

Fig. 3. Ash-wood DTG curves at the selected heating rates: b ¼ 3, 5, 10, 20
C/min. Fig. 1. Arrheniuselike plot for the KAS method applied to ash-wood at selected a values.

110 100 90

Weight(%wt)

80

Ash-wood (TGA)

70 60 50 40 30 20 10 0 50

100

150

200

250

300

350

400

Fig. 2. Ash-wood TGA curves at the selected heating rates: b ¼ 3, 5, 10, 20
C/min.

0.15< a < 0.85. The same procedure has been likewise applied to all the introduced methods and biomasses. 4. Results and discussion The majority of the Tables and Plots reported on this section highlight the results carried out for ash-wood selected as feedstock representative of the hardwood family. This is motivated by the fact that the three biomasses, when submitted to thermal treatment, present very similar trends as evidenced by the overlapping of the TGA curves plotted on Fig. 8. Including similar plots for the other biomasses would be therefore redundant and without additional information. However, global results for the three biomasses are included on Figs. 6 and 8 and Tables 1, 4 and 5.

the range 240
C < T < 370
C [47]. Therefore, the extension of the TGA limit up to 400
C allows to pursue, from an experimental point of view, a more defined state including almost the complete degradation of cellulose. Regarding the lignin decomposition, this fibre is more stable and its degradation slowly occurs on a wide temperature range from 100 up to 900
C being enhanced at high temperature [48]. Plots reporting experimental results vs. temperature (Figs. 2e4, 8) extend therefore up to 400
C and are referred to the average value of the five replicates. The following Figs. 2 and 3 report the TGA and DTG curves for ash-wood carried out at the selected heating rates. As well known from similar studies [49e52], the behaviour trend is confirmed in this case too: on Fig. 2, at a fixed temperature, the higher is the heating rate the lower is the weight loss; on Fig. 3 an increase of the heating rate entails a shift toward right of the curves and a corresponding shift toward higher temperatures of the DTG peaks. Thermal degradation appears to be almost complete at the end of this limit (close to 400
C) beyond which a further long tail zone occurs for a wide temperature range (not evidenced because of the adopted limit of 400
C). As extensively investigated [49,51e55], this corresponds to the slow weight loss of lignin without making any evidence of significant peaks. The thermogravimetric diagram of Fig. 4, including simultaneously the TGA and DTG curves referred to b ¼ 10
C/min, identifies three distinct zones: the first, up to 120e150
C, presents a very limited weight loss due to complete water and light volatile compounds removal; the second, up to 210
C, evidences a negligible weight loss followed by two further

4.1. Experimental TGA results In this study the experimental TGA investigations have been extended up to 400
C, hence beyond the conventional torrefaction limit usually close to 330
C. Considering that hemicellulose degradation is substantially complete at temperature below 350
C (it decomposes around 220
C < T < 280
C [45,46]), torrefaction leads to a nearly complete degradation of this fibre. A partial degradation of the cellulose is however unavoidable as it occurs in

Fig. 4. TGA and DTG curves for ash-wood for b ¼ 10
C/min.

M. Grigiante et al. / Renewable Energy 99 (2016) 1318e1326 200 190 180

KAS model hornbeam beech-wood ash-wood

170

Ea[kJ/mol]

decomposition steps (third zone): within the first step, covering the range of 210e300
C, a complex region is exhibited due to the overlapping of the hemicellulose and cellulose decomposition. A distinct separation of the hemicellulose peak is not evident also because of the relative low heating value rate (10
C/min.). As evidenced by Biagini et al. [51], the separation of the two peaks becomes more evident with the increasing of the heating rate. The degradation step evidenced within 300e375
C certainly corresponds to cellulose decomposition even though a partial contribution of the lignin degradation has to be considered.

1323

160 150 140 130

4.2. Results of the kinetic analysis 4.2.1. Models results The analysis of the models results is intentionally limited to torrefaction process so that the upper temperature limit has been set at 330
C. As evidenced on previous Fig. 2, for a selected temperature, the lower is b, the higher is the mass loss. Considering the limit of 330
C, the maximum value of a that can be achieved at the lower heating rate (b ¼ 3
C/min) results nearby a ¼ 0.7. Therefore, in this section, the reported plots (Figs. 5 and 6) cover the range 0 < a < 0.7. Fig. 5 provides a global view of the results for all the methods, while Fig. 6 reports a representation of the performances of the KAS method applied to the three biomasses. The efficacy of the applied procedures is expressed by the straight lines correlation coefficient R2 reported, on Table 3, for all the methods. These results can be generalized for all the investigated biomasses: the Ea increases with a ranging from 0.1 up to 0.4 while a plateau is observed till the assumed torrefaction a ¼ 0.7 limit. Even if the Ea trend can be rigorously identified for each biomass, the trends agree with those of recent studies obtained for different biomasses as: rice husks [51], Nigerian lignocellulosic resources [56], olive pomace [49]. As highlighted by Peterson [57] and Valor [58], the increase of Ea in the range 0.1< a < 0.4 can be re-conduced to the random scission of linear chain that leads to an increasing in activation energy. This behaviour can be led back to the cross linking effects associated with the polymeric structure of hemicellulose and others constituents yet to be degraded [59]. From a general point of view and despite the discrepancies among the models, the obtained Ea results, ranging from 130 to 220/230 kJ/mol, are in good agreement with those referred to different biomasses even if not strictly pertaining to torrefaction constrains. Lopez-Velazquez et al. [60] report Ea values for orange waste in the temperature range 200 190 180

Ea [kJ/mol]

170 160 150

ash-wood Friedman Doyle Starink-1

140 130 120 0,1

0,2

0,3

0,4

0,5

Flynn KAS Starink-2 0,6

0,7

Fig. 5. Ea [kJ/mol] vs. a for ash-wood as result of the application of all the proposed models.

120 0,1

0,2

0,3

0,4

0,5

0,6

0,7

Fig. 6. Ea [kJ/mol] vs. a as result of the KAS method applied to the three biomasses.

50
C < T < 420
C that increases from 117 kJ/mol up to 260 kJ/mol. For sugar cane bagasse and eucalyptus sawdust investigated by Wang et al. [61], the range sets from 161.9 < Ea < 202.3 kJ/mol. As further interesting result of Wang's study, the reported TGA and DTG trends are strictly comparable to those observed on Figs. 2 and 3 of this work. This is highlighted by the fact that the heating rates are exactly the same (5, 10, 15 and 20
C/min) even if the carrier gases are syngas and hydrogen. Brachi [49] applies isoconversional Vyazovkin and OzawaeFlynneWall methods to describe olive pomace decomposition within torrefaction range. Despite the different values probably due to the presence of residual oil trapped within the matrix, the reported Ea trend, in the range 0.4  a  0.70, is markedly constant (210e220 kJ/mol) as happens for the present study (Ea close to 170e180 kJ/mol). Ren et al. [62] investigate douglas fir sawdust at isothermal conditions for both raw and torrefied biomass by adopting the Friedman method. The results, ranging from 203.94 to 195.13 kJ/mol, are considered by the same Authors to be slightly higher than those expected. Di Blasi [63] proposes a comprehensive review of biomass pyrolysis making evidence the variability of the Ea trends. In particular, for low temperatures close to torrefaction, Ea sets in the range 125e174 kJ/ €m mol. This is in line with the results of a recent work of Brostro et al. [64] pertaining to the devolatilization step of a multi-stage reactions scheme. The obtained Ea sets in the range 101e213 kJ/ mol confirming, despite the multi-reactions scheme, the trend emerged in this study. The analysis of the dependency of Ea vs. a can be exploited to provide interpretations for multi-reactions stages as the two steps mechanism proposed by Prins [65] for torrefaction. Further, the analysis of the Ea (a) trend can be also exploited to complete the kinetic triplet by using model fitting techniques as proposed, for example, by Alzina et al. [66]. To analyse in deep these topics, the cited papers [65,66] can also be integrated with the works of Vyazovkin et al. [67e69]. The study of the reactions mechanisms is a complex issue that requires dedicated investigations the Authors, considering the purpose of this paper, have intentionally not included in this presentation. Considering the performances of the differential methods, the Friedman model confirms an unstable trend as evidenced also by Vyazovkin [67] and Golikeri and Luss [70]. This is mainly due to the term ddTa in Eqs. (7) and (8) that has to be numerically calculated. Despite the introduction of the crude temperature integral approximations, integral methods show a more regular trend and are claimed to give better results with respect to the differential ones. This study evidences that this approach reaches a quite similar accuracy level for all the three biomasses. Moreover, the

1324

M. Grigiante et al. / Renewable Energy 99 (2016) 1318e1326

Table 3 Activation energy and correlation coefficients for ash-wood at selected a values as result of the investigated isoconversional methods. Ash-wood

a par.

Friedman E a R2

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85

134.5 138.98 147.09 153.16 155.57 160.62 162.33 158.15 159.82 163.28 159.78 163.85 166.30 168.27 180.74 214.76 389.83

Flynn E a R2 0.9966 0.9993 0.9996 0.9991 0.9970 0.9984 0.9990 0.9995 0.9999 0.9998 0.9993 0.9992 0.9991 0.9984 0.9989 0.9967 0.9730

141.89 146.48 148.86 153.33 157.84 162.26 165.87 167.18 167.87 170.88 168.73 169.97 171.71 171.70 173.70 180.44 227.67

Doyle E a R2 0.9935 0.9982 0.9988 0.9988 0.9988 0.9989 0.9997 0.9992 0.9999 0.9999 0.9996 0.9999 0.9999 0.9997 0.9997 0.9997 0.9856

134.88 139.24 141.50 145.75 150.04 154.24 157.67 158.91 159.57 162.44 160.39 161.57 163.22 163.22 165.12 171.52 216.42

KAS Ea R2 0.9936 0.9982 0.9988 0.9988 0.9988 0.9989 0.9997 0.9992 0.9993 0.9997 0.9994 0.9995 0.9998 0.9997 0.9997 0.9987 0.9856

performances among the integral models are in very good agreement since their differences are significantly lower than 10%, the accuracy level conventionally accepted for this quantity [22]. It is to note that for Starink-1 and Starink-2 methods differences are not appreciable, contrary to what happens when they are referred to homogeneous solids. Making reference to the indications proposed by Starink [39], the Starink-1 method is claimed to perform better than the Doyle one. As highlighted in this study, this is not confirmed when they are applied to biomasses. Approaches involving numerical integrations as those of Vyazovkin [67,71e73] and Senum and Yang [37], are claimed to achieve higher accuracies. However, they have not been included in this study since they require a very heavy computational load. This appears not justified in view of the reduced offsets of the results compared to those of this work. This statement has been also confirmed by the results of similar models applied to different biomasses [49]. As further general comment, the use of these methods can be further encouraged for their simple computational implementation. This analysis and comments have however to be limited to torrefaction conditions.

4.2.2. Range of validity and limits and of the proposed approach The adopted integral isoconversional methods involve the use of a logarithm function, Eq. (12), whose value depends on the specific approximations assumed for each method. In literature [41e43] the approximation accuracy of the temperature integral is expressed in terms of the tolerance of the parameter y ¼ Ea/RT previously introduced. The most cited and accurate approximations assign the variability of this parameter in the range 15 < y < 60. This means that only those cases presenting y values within these limits are expected to provide reliable results. For the investigated biomasses and models, the resulting values of the y parameter are particularly satisfactory as they place on the central part of the indicated range,

133.05 137.41 139.63 143.96 148.34 152.62 156.10 157.30 157.89 160.81 158.59 159.76 161.44 161.37 163.32 169.99 217.07

Starink-1 Ea R2 0.9983 0.9927 0.9980 0.9987 0.9987 0.9988 0.9987 0.9991 0.9992 0.9999 0.9999 0.9996 0.9993 0.9997 0.9996 0.9996 0.9841

133.30 137.66 139.89 144.22 148.60 152.89 156.37 157.56 158.87 161.09 158.87 160.04 161.73 161.66 163.60 170.27 217.32

Starink-2 E a R2 0.9928 0.9980 0.9987 0.9987 0.9987 0.9988 0.9987 0.9982 0.9999 0.9999 0.9999 0.9999 0.9997 0.9996 0.9996 0.9996 0.9842

132.15 133.27 137.64 139.86 144.20 148.57 152.86 156.34 157.54 158.14 161.07 158.85 160.02 161.70 163.58 170.25 217.33

0.9928 0.9980 0.9987 0.9987 0.9987 0.9987 0.9997 0.9991 0.9999 0.9996 0.9999 0.9999 0.9999 0.9997 0.9997 0.9996 0.9842

as evidenced on the following Table 4. Considering that this parameter depends on b, for sake of brevity, on the indicated Table 4 only the mean, minimum and maximum values of y are reported. For an extended analysis, reference is made to Fig. 1 of the Starink paper [39]. The reliability of the proposed isoconversional procedures has been also checked for very low and high a values. The following Fig. 7 reports the results of this test obtained by applying the KAS method. They are expressed as trend of the straight lines correlation coefficient R2 vs. a. It clearly emerges that when a approaches very low/high limits, the resulting Ea values

Fig. 7. Correlation coefficient (R2) vs. a as result of the KAS method applied to ashwood.

Table 4 Mean, maximum and minimum values of the parameter y ¼ Ea/RT resulting from the integral methods applied to the selected biomasses. y par.

ymin ymax ymean

Ash-wood

Beech-wood

Hornbeam

Doyle

KAS

Star.-1

Star.-2

Doyle

KAS

Star.-1

Star.-2

Doyle

KAS

Star.-1

Star.-2

29.34 33.54 31.67

28.92 33.23 31.31

28.98 33.28 31.37

28.97 33.28 31.36

29.35 35.23 32.74

28.94 35.01 32.44

28.99 35.07 32.49

28.98 35.06 32.49

28.09 34.48 31.93

27.61 34.22 31.58

27.67 34.27 31.64

27.66 34.27 31.63

M. Grigiante et al. / Renewable Energy 99 (2016) 1318e1326

become unacceptable due to the rapid decrease of the accuracy (collapse of the R2 value) of the adopted linear correlation. On the contrary, within the range 0.1
Ea ¼ 242:94$a2 þ 227:45$a þ 112:8

(13)

- for a in the range 0.35e0.4 < a < 0.7

Ea ¼ 80:447$a2 þ 100:86$a þ 139:08

(14)

The performances of the indicated equations have been tested for all the three biomasses and the results reported on the following Table 5 in terms of AAD (Absolute Average Deviation) and MDE (Maximum Deviation Error) with respect to the corresponding Ea value of the Doyle method assumed, in this case, as reference model. Similar results, here omitted for sake of brevity, are confirmed by assuming the other integral methods as reference. The proposed equations perform very well considering that, as previously indicated, the accuracy level conventionally accepted for this quantity sets at a maximum deviation error around 10% [22]. From a general point of view, these results confirm that the proposal of applying these methods to similar families of biomasses seems promising to pursue a predictive calculation of the activation energy. These results have however to be confirmed, extended and

110 100 90

TGA (%wt.)

80

ash-wood hornbeam beech-wood

70 60 50 40 30 50

100

150

200

250

300

350

Fig. 8. TGA curves for ash-wood, hornbeam and beech-wood at 10
C/min.

400

1325

Table 5 AAD and MDE % of the Ea values calculated from the proposed Eqs. (13) and (14). by assuming the Doyle method as reference. Biomass

Beech-wood Hornbeam Ash-wood

Range 0.1
Range 0.35e0.4
AAD%

MDE%

AAD%

MDE %

1.07 0.93 2.77

2.47 2.77 4.83

0.63 1.03 4.21

1.58 2.62 6.46

tested to further biomasses and, with suitable equations, to different families. 6. Conclusions Thermogravimetric analysis (TGA) of three biomasses, ash-wood, beech-wood and hornbeam, all belonging to the hardwood family, has been conducted in nitrogen atmosphere at four constant heating rates of 3, 5, 10 and 20
C/min within the torrefaction temperature range. This paper, first part of an extended project, focuses on the determination of the activation energy (Ea) by adopting several isoconversional methods widely utilized to study the thermal degradation of solids. Both differential and integral models have been considered. Several tests and demonstrations have been carried out to verify the consistency of the proposed approach and to validate the reliability in particular of the integral methods. As first result the constitutive differences of the proposed models (parameters and equations form) have not significant impact when applied to biomasses. This is enhanced by the affinity of the structure of the investigated biomasses that is reflected on the overlapping of the profiles of both the TGA measurements and the Ea models results. This has encouraged the definition of a generalized procedure to calculate the Ea quantity by only the knowledge of the conversion fraction a. The obtained results are particularly satisfactory and, if confirmed for other biomasses and feedstock families, look promising to be proposed for incoming torrefaction applications. References [1] M.J. Prins, K.J. Ptasinski, F.J.J.G. Janssen, More efficient biomass gasification via torrefaction, Energy 31 (15) (2006a) 3458e3470. [2] W.H. Chen, M.Y. Huang, J.S. Chang, C.Y. Chen, Torrefaction operation and optimization of microalga residue for energy densification and utilization, Appl. Energy 154 (2015) 622e630. [3] C. Couhert, S. Salvador, J.M. Commandre, Impact of torrefaction on syngas production from wood, Fuel 88 (11) (2009) 2286e2290. [4] L. Chai, C.M. Saffron, Comparison Pelletization and Torrefaction depots: optimization of depot capaity of biomass moisture to determine the minimum production cost, Appl. Energy 94 (2016) 387e395. [5] A. Chauhan, R.P. Saini, Techno-economic optimization based approach for energy management of a stand alone integrate renewable energy system for remote areas of India, Energy 94 (1) (2016) 138e156. [6] A. Rentizelas, A. Tolis, I.P. Tatsiopoulos, Logistics issues of biomass: the storage problem and the multi-biomass supply chain, Renew. Sust. Energy Rev. 13 (4) (2009) 887e894. [7] A. Nordin, The Dawn of Torrefaction BE-sustainableethe Magazine of Bioenergy and the Bioeconomy, 2012, pp. 21e23. [8] A. Uslu, A.P.C. Faaij, P.C.A. Bergman, Pre-treatment technologies, and their effect on international bioenergy supply chain logistics. Techno-economic evaluation of torrefaction, fast pyrolysis and pelletisation, Energy 33 (8) (2008) 1206e1223. [9] P.W.R. Adams, J.E.J. Shirley, M.C. McManus, Comparative cradle-to-gate life cycle assessment of wood pellet production with torrefaction, Appl. Energy 138 (2015) 367e380. [10] M.K. Delivand, M. Barz, S.H. Gheewala, Logistics cost analysis of rice straw for biomass power generation in Thailand, Energy 36 (3) (2011) 1435e1441. [11] M. Grigiante, D. Antolini, Mass yield as guide parameter of the torrefaction process. An experimental study of the solid fuel properties referred to two types of biomasses, Fuel 153 (2015) 499e509. [12] G. Almeida, J.O. Brito, P. Perre, Alterations in energy properties of eucalyptus

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