Kinetics of Poplar Short Rotation Coppice Obtained from

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Kinetics of poplar short rotation coppice obtained from thermogravimetric and drop tube furnace experiments Sandrina Pereira, Paula Corte Real Martins, and Mario Costa Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b01313 • Publication Date (Web): 14 Jul 2016 Downloaded from http://pubs.acs.org on July 17, 2016

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Kinetics of poplar short rotation coppice obtained from thermogravimetric and drop tube furnace experiments Sandrina Pereira, Paula C. R. Martins and Mário Costa IDMEC, Mechanical Engineering Department, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

Submitted to Energy & Fuels July, 2016

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ABSTRACT This manuscript concentrates on the kinetics of poplar short rotation coppice obtained from thermogravimetric and drop tube furnace (DTF) experiments. Three poplar samples were studied; specifically, clone AF8 from two different plantations, one located in Italy (AF8-I) and another located in Portugal (AF8-P), and clone AF2 from a plantation located in Portugal. Initially, the combustion behaviour of the three clones was evaluated by thermogravimetry. For the devolatilization zone, the thermogravimetric experiments yielded apparent activation energies of 109.7 kJ mol-1, 117.7 kJ mol-1 and 118.6 kJ mol-1 for clones AF8-I, AF8-P and AF2, respectively. For the char oxidation stage, the apparent activation energies were 267.4 kJ mol-1, 252.0 kJ mol-1 and 264.3 kJ mol-1 for clones AF8-I, AF8-P and AF2, respectively. Subsequently, the combustion behaviour of the clone AF8-I was examined in a DTF. The data reported includes gas temperature and particle burnout measured along the DTF for five wall temperatures (900, 950, 1000, 1050 and 1100 ºC). The kinetic parameters based on the DTF data were calculated using two models – a model-fitting approach and a model proposed by Ballester and Jiménez. In the devolatilization zone, the apparent activation energies vary from 34.1 kJ mol-1 for the former model to 12.8 kJ mol-1 for the latter model, whereas in the char oxidation zone both models originate similar apparent activation energies (73.2 and 69.0 kJ mol-1). While the combustion process in the thermogravimetric experiments is controlled by kinetics, in the DTF experiments the diffusion effects also limit the process, according with the DTF temperature.

KEYWORDS Kinetics, poplar short rotation coppice, thermogravimetric, drop tube furnace.


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1. INTRODUCTION Biomass derives from plants and animals, namely wood from forest, waste from agricultural and forestry practices and human, animal and industrial wastes. Generally, biomass is divided in three main groups – wastes, forests and energy crops, where short rotation coppice (SRC) is included.1 Biomass differs from other alternative energy sources since its origin is diverse and it can be converted to energy through many conversion processes.2 Depending on the thermochemical process used, biomass can be converted into energy (electricity and/or heat), liquid biofuels, chemicals and charcoal.3 Direct combustion is widely used to convert biomass into heat and/or electricity. Biomass can be burned directly either alone or with a primary fuel, like coal.2 The co-combustion of biomass with coal in power plants will reduce their emissions since biomass is considered as CO2 neutral. Besides the reduction in the CO2 emissions, the co-combustion of biomass with coal also reduces the emissions of SOx and NOx comparatively to pure coal firing1. Additionally, Karampinis et al.4 pointed out that the investment costs needed to have co-firing in a power plant are lower than those needed for a hydropower and an onshore wind power. The co-firing of biomass with coal, namely in pulverised fuel (PF) power plants, presents some difficulties, like the nature of the biomass ashes, namely when fast-growing tree species are burned. This biomass type has high levels of alkalis and chlorine when compared with coal, which increase the slagging and fouling effects.4 Moreover, in comparison to coal, biomass presents low heating values due to its high moisture and high oxygen contents.5 Fuel combustion, namely biomass combustion, is a complex process that involves heat and mass transfer with chemical reactions and fluid flow and there is still a need to improve the current knowledge on biomass properties, namely how these properties influence the biomass


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decomposition during thermochemical conversion, namely combustion. This knowledge improvement can be used in the design and control of more efficient conversion technologies.3,6 If chemical analysis, namely proximate analysis, are suitable to assess the biomass quality as a fuel, they are not enough to predict the combustion process behaviour. For this it is necessary to have information on the rates of devolatilization and oxidation, represented classically by the Arrhenius law. The kinetic parameters can be obtained experimentally using different methods.7 The most used method to determine kinetic parameters is the thermogravimetric analysis (TGA) that measures weight loss as a function of the furnace temperature of a small quantity of sample exposed to a linear programed low heating rate with high precision and time resolution.8,9 There are a large number of studies in the literature that used the TGA method to estimate kinetic parameters, mainly for pyrolysis. Studies focused on the kinetics of poplar combustion using TGA are, however, scarce. Kok et al.10 examined the combustion behaviour of several agricultural residues and poplar; Shen et al.3 studied the decomposition of different wood species under oxidative atmospheres at different heating rates; Sait et al.11 evaluated the kinetics parameters for the pyrolysis and combustion of palm biomass waste; Fand et al.12 investigated the behaviour of different woods under pyrolysis and combustion conditions; Lopez-Gonzalez et al.13 characterised the combustion of fire wood, eucalyptus wood and pine bark; TranVan et al.14 studied the thermal decomposition in air of two samples of balsa – dry and aged hygroscopically until water saturation; Jeguirim et al.15 explored the thermal decomposition behaviour of two herbaceous crops in air; and Gua et al.16 compared the pyrolysis kinetic parameters of poplar wood sawdust and steam explosion poplar wood sawdust. Slopiecka et al.17 focused on the kinetic parameters of the pyrolysis of poplar SRC – this appear to be the only kinetic study on poplar SRC. This study performed a kinetic analysis of a slow pyrolysis process of poplar wood


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in nitrogen atmosphere using a TGA operating under different heating rates (2, 5, 10, 15 K min1

). The authors obtained activation energies between 153.9 kJ mol-1 and 158.6 kJ mol-1,

depending on the estimation method used. Combustion in a DTF simulates better the combustion behaviour in industrial equipments than combustion in a TGA.18 High heating rates, between 104-105 K/s, can be rapidly reached in the beginning of the DTF and, in very short residence times, devolatilization and oxidation of the fuel are accomplished. Furthermore, the particles are in a dynamic dilute phase allowing individual and cloud particle combustion.19 However, DTFs are much more expensive and timeconsuming than TGA. In particular, the evaluation of the kinetic parameters requires several tests at different DTF temperatures to have enough data to extrapolate meaningfully these parameters.8 DTFs have been mainly used to study ignition, flame stability and burnout of solid fuels. Studies focused on the study of kinetic parameters of the combustion of solid fuels using DTF are rather scarce. Costa et al.20 used a model-fitting approach to estimate the combustion kinetic parameters of two different raw and torrified biomass fuels using a DTF, and concluded that the torrefaction reduces the apparent activation energy. Ballester and Jiménez 7 and Jiménez et al.21 developed a numerical model, based on calculating the particle’s full combustion history, to estimate the kinetic parameters from pyrolysis and combustion experiments in an entrained flow reactor (EFR). Ballester and Jiménez 7 studied the combustion of an anthracite in an EFR and obtained an activation energy for its char oxidation of 99 kJ mol-1, and Jiménez et al.21 evaluated the devolatilization and combustion of a pulverised biomass in the same EFR and obtained an activation energy of about 11 kJ mol-1 for its devolatilization and about 63 kJ mol-1 for its char oxidation. Farrow et al.22 studied the pyrolysis of sawdust and pinewood in a DTF and estimated apparent energy activations for sawdust of 37.7 kJ mol-1 in a N2 atmosphere and


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24.2 kJ mol-1 in a CO2 atmosphere. Zellagui et al.23 investigated the pyrolysis of pulverised coal and woody biomass in a DTF and in a TGA and concluded that the release of volatiles is higher in the DTF than in the TGA and that the reactivity is higher for the chars produced in the DTF. The literature review reveals that there are no studies on the combustion kinetics of poplar SRC undertaken simultaneously in TGAs and DTFs, as it is done in the present work. The TGA experimental technique was applied to three poplar clones (AF8-I, AF8-P and AF2) in order to assess their combustion behaviour and, in light of the similarities of the TGA results for the three clones, DTF experiments were undertaken only for the clone AF8-I. The kinetic parameters from the TGA results were assessed using a model-fitting approach and the kinetic parameters from the DTF experiments were estimated using two approaches – a model-fitting approach and the Ballester and Jiménez model7,21 referred to above.

2. MATERIAL AND METHODS 2.1 Biomass fuels. Three different poplar samples were analysed – one clone AF8 cultivated in Savigliano, Italy (AF8-I), and two clones AF8 (AF8-P) and AF2 cultivated in Santarém, Portugal. All cultivations were irrigated. The Portuguese plantations were fertilised with N:14:14 at 200 kg/ha. The Italian plantations received a pre-emerge herbicide (pendimetalin or oxifluorfen) before planting, and a post-emerge herbicide (glufosinate ammonium) in March of the second year. All clones were crushed and sieved below 1 mm before being analysed in the TGA and in the DTF. Table 1 shows the characteristics of the three biomass fuels used in this study.


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2.2 Thermogravimetric analysis. The combustion thermogravimetric tests were performed in a TA INSTRUMENTS SDT 2960 simultaneous DSC-TGA, which measures temperature, heat flow and weight throughout time. The heating rate used was 10 ºC min-1 for all biomass fuels. At least three TGA tests in air atmospheres were performed for each biomass fuel.

2.3 Drop tube furnace. Figure 1 shows the schematic of the DTF and auxiliary equipment.24 The atmospheric pressure DTF is electrically heated and operates at a maximum temperature of 1100 ºC. The combustion chamber is a cylindrical ceramic tube with an inner diameter of 38 mm and a length of 1.3 m. The DTF wall temperatures are continuously monitored by eight type-K thermocouples uniformly distributed along the combustion chamber. A water-cooled injector, placed at the top end of the DTF, was used to feed the biomass and the air to the combustion chamber. A twin-screw volumetric feeder transfers the biomass to an ejector system from which the particles are gas-transported to the water-cooled injector. Local mean temperature measurements along the combustion chamber axis were obtained using 76 µm diameter fine wire platinum/platinum: 13% rhodium (type-R) thermocouples. The hot junction was installed and supported on 350 µm wires of the same material located in a twinbore alumina sheath with an external diameter of 5 mm. The uncertainty due to radiation heat transfer was estimated to be less than 5% by considering the heat transfer by convection and radiation between the thermocouple bead and the surroundings.25 Particle sampling along the combustion chamber axis was performed with the aid of a 1.5 m long, water-cooled, nitrogen-quenched stainless steel probe. The quenching of the chemical reactions was achieved by direct injection of nitrogen jets through small holes very near to the probe tip. On leaving the probe, the solid samples were collected in a Tecora total filter holder


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equipped with a quartz microfiber filter. Subsequently, the collected solid samples were placed in an oven at approximately 105 °C to dehydrate. Complete dehydration was ascertained by repeated drying and weighing of the sample until the measured mass became constant. The solid samples were subsequently analysed (ultimate analysis). Particle burnout data were obtained as follows: = (1) 1 − 1−

where is the particle burnout, is the dry ash weight fraction in the input biomass and is

the dry ash weight fraction in the collected sample.26

Uncertainties in particle burnout calculations based on the use of ash as a tracer are connected to ash volatility at high heating rates and temperatures and ash solubility in water. In this work these uncertainties were negligible.24 The tests were made for five DTF wall temperatures (900, 950, 1000, 1050, 1100 ºC) and, for each temperature, the samples were collected in six different axial distances from the injector; specifically, at 200, 300, 500, 700, 900 and 1100 mm from the injector. The biomass flow rate was 23 g h-1 and the total air flow rate was 4 L min-1. Depending on the gas temperature the

biomass residence time varied from ~0.7 s to ~4.8 s.


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3. KINETIC MODELLING The thermal decomposition of biomass under an oxidative atmosphere can be represented as a two-stage reaction scheme. The first step is composed by almost all devolatilization of the biomass components and the production of char, and the second step is mainly related to the char oxidation.3 The kinetic parameters of the biomass decomposition can be described by the first-order Arrhenius equation:

k = Ae (2) where k is the chemical reaction rate constant, Ea is the apparent activation energy (kJ mol-1), T is the absolute temperature (K), A is the pre-exponential factor (s-1) and R is the universal gas constant.17

3.1 Thermogravimetric analysis. The conversion factor, or the fraction of biomass that has reacted, is given by:

=

− (3) −

where mo, mt and mf are the mass at t = 0, t = t and the final mass of the sample, respectively. In TGA analysis the reaction rate is generally described by the following equation:27


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= ()() (4) This equation describes the linear dependence of the conversion rate with the reaction rate constant k and (), which is an hypothetical reaction mechanism given by:17

() = (1 − ) (5) where n is the reaction order. Substituting Eqs. 2 and 5 into Eq. 4 results: $% = "# &' (1 − ) (6)

Assuming a non-isothermal reaction with a constant heating rate ) = / and introducing it into Eq. 6 gives: dα A = e dT (7) (1 − α)β The integration of Eq. 7 originates:

1() = 2

6

dα " ' $3 = 2 # &' (8) (1 − α)) '5


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where 1() is the conversion integral function.13

Finally, Eq. 8 can be integrated by using the Coats-Redfern method:13 1() = 2= >3 78 9 : ; = 78 3 where 1() is given by: −78(1 − α) , 1() = G1 − (1 − )I , (1 − 8)

8=1

8≠1

(11)

In the present study, the first reaction order mechanism is used since it is the most common

one used to describe the biomass thermal decomposition. The plot of 78K1()/ : L versus 1/

originates a straight line with a high correlation factor. The apparent activation energy (Ea) can be determined from the slope of this straight line and pre-exponential factor A by the y-intercept term.13,28


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3.2 Drop tube furnace analysis. The particle weight loss along the DTF depends on the burnout () and initial weight ( ) so that it can be calculated as:29 = (1 − ) (12) The reaction rate for the devolatilised matter, V, is represented by a single reaction and usually models adopt a first order reaction:30 M = − N (1 − ) (13) where N is the chemical reaction rate constant for the devolatilization defined as: N = "N #OP

$ Q R T &'S (14)

where TP is the particle temperature, Av is the pre-exponential factor and Ev is the apparent activation energy for the devolatilization. The char oxidation is modelled based on the outer surface area of the particles:7 = −"U VWX,Y (15) where A is the particle surface area, U is the chemical reaction rate constant for the oxidation and VWX,Y is the oxygen partial pressure at the particle surface.


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The diameter () and density (Z) evolution of the particles along the combustion process can be expressed as:29 = (1 − )3 (16)

Z = Z (1 − )[ (17) where 3\ + ] = 1 for spherical particles. When particles burn in regime I, the diameter is

constant (\ = 0) and the density varies proportionally with the mass loss. For particles burning

in regime III, the density remains approximately constant (] = 0) and \ reaches its maximum value of 0.33, which represents the highest diameter variation. In regime II, both the density and the diameter vary.29

3.2.1 Model-fitting approach. In this approach the kinetic analysis of the combustion process in the DTF is performed by sections. It is assumed that devolatilization occurs only in the upper part of the DTF (between 200 and 700 mm), where three different devolatilization sections are considered (200-300 mm, 300-500 mm and 500-700 mm), and that char oxidation occurs only in the lower part of the DTF (between 700 and 1100 mm). It should be pointed out that this assumption is a simplification that may have an impact in the results obtained since the finer biomass fractions can start heterogeneous oxidation in the DTF upper section and the coarser biomass fractions can devolatilize along the DTF lower part. In this approach, based in the Arrhenius plot, more simplifications are made in order to assess their impact in the final results. Firstly, it is assumed a single representative diameter (surface mean diameter) for all particles of 0.12 mm. Secondly, the oxygen partial pressure at the particle surface (VWX,^ ) is presumed to be equal to the oxygen partial pressure of the gas stream, constant ACS Paragon Plus Environment

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along the DTF and equal to 21278 Pa. Finally, the temperature of the particles (_ ) is assumed to be equal to the gas temperature. For this model-fitting approach, Eq. 13 can be re-written as:20 M >3N ln = − + ln "N (18) −1 =_ and Eq. 15 can be re-written as: 20 >3U + ln "U (19) ln = − −"VWX,^ =b >3U , >3N , "U and "N can be obtained from Arrenhius plots. 3.2.2 Ballester and Jiménez model. Ballester and Jimenez.7, for coal, and Jiménez et al.21 for biomass, proposed a more complex model to estimate kinetic parameters; this model considers a particle size distribution to handle the non-homogeneity of the particles diameter. The model takes into account the oxygen consumption along the combustion process, the difference between the oxygen partial pressure at the particle surface and the oxygen partial pressure of the gas stream and between the particles and gas stream temperatures. The devolatilization process is based on a one-step devolatilization law and can be represented by Eq. 12. The heterogeneous char oxidation is modelled based on the outer surface area of the particles (Eq. 15), which is adapted in order to consider the distribution size of the particles and to consider that the process


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can be controlled by kinetic and diffusion effects. The char oxidation model is based in a single film carbon combustion model. More detailed information on this model can be found in Ballester and Jimenez.7 and Jiménez et al.21 In this approach, the kinetic parameters are inputs for the model and, at the same time,

unknowns to be determined. A pair of "U and Eac (or Av and Eav for the devolatilization stage) is

established in order to predict the unburnt fraction for each DTF wall temperature and each DTF axial location. Subsequently, the predicted unburnt fraction is compared with the experimental unburnt fraction. The deviations between the predicted and measured values is calculated

through cd,e = Kf(7)gb − f(7)hij Ld,e , where j and k represent the DTF axial location and the

DTF wall temperature, respectively. The error for each pair "U and Eac (or Av and Eav) is estimated as the root mean square of the deviations as follows.

1 : c=k m m cd,e (20) lh3 h3 e

d

The pair with lower error is selected as the best representing the kinetic parameters of the fuel under investigation for both the devolatilization and char oxidation.7,21

4. RESULTS AND DISCUSSION 4.1 Thermogravimetric analysis. Figures 2 and 3 show the termogravimetric (TG) and derivative (DTG) curves and Table 2 presents the combustion characteristics, namely the initial decomposition, peaks and burnout temperatures and maximum weight loss rates, all for the three biomass fuels studied. The weight loss rate (DTG) is calculated as follows:


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no = −

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d − dI × 100 (21) d − dI

It is seen that the evolution of the TG and DTG curves for clones AF8-P and AF8-I is rather similar. Each DTG curve presents two maximum weight loss rate peaks, the first one at about 320 ºC for the three biomass fuels, and the second one at about 404 ºC for AF2 and between 423 ºC and 429 ºC for both clones AF8. Note that clone AF2 reaches the second peak at lower temperature and with a lower weight loss rate. In the TG curves it is possible to observe that clone AF2 has a percentage of ash higher than the other samples, which is consistent with the results shown in Table 1. The burnout temperatures and times are similar for the three clones analysed, whereas the burnout time of clone AF2 is 1.7 minutes longer. Based on the evolution of the TG and DTG curves it is possible to define three main stages in the combustion process: moisture removal, devolatilization and char oxidation, as discussed in Ferreira et al. 28 The first stage (moisture removal) represents the loss of the water and some light volatiles from the sample.31 This stage begins around temperatures between 31 ºC and 39 ºC. At this stage, the total weight losses from both AF8 clones are similar (6.2% and 6.9%, respectively), while clone AF2 presents a weight loss value of 9.3%. These values are consistent with those shown in Table 1; specifically, 10.1%, 9.1% and 8.9% for AF2, AF8-P and AF8-I, respectively, with the differences attributable to storage environment factors. 31,32 The second stage represents the release of the volatile matter. In this stage, most of the volatile matter is released and burned. The initial decomposition temperature (Tin) represents the beginning of the second stage and starts at around temperatures between 230 ºC and 238 ºC (Table 2). These temperatures are defined as the point where the weight loss rate reaches 1%


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min-1, for the first time, after the moisture phase 33. During the second phase, at around 320 ºC it is observed a peak in the DTG curves of the three samples, which indicates a maximum weight loss rate at this stage. Clone AF8-P presents a weight loss rate of 13.9 %/min, AF2 10.8 %/min and AF8-I 12.6%/min (Table 2). The total percentage of the weight loss for both clones AF8 in the second stage is similar (about 69%), like in the first stage, but higher than that for clone AF2 (63.5%). Again, these values are consistent with the values of the proximate analysis shown in Table 1 (69.2%, 73.3% and 73.8% for AF2, AF8-P and AF8-I, respectively). The third and final stage considered in the present study represents the char oxidation. At this point most of the volatiles have been released and burned, remaining in the sample mainly fixed carbon and inorganic compounds. The temperature of the beginning of this stage and end of the second stage is defined as the point where the minimum weight loss rate is reached (cf. Figure 2), after the maximum peak of the second stage.32 These temperatures range from 362 ºC to 373 ºC depending on the clone. In the third stage a maximum peak of the weight loss rate is observed at temperatures ranging from 404 ºC to 429 ºC (Table 2). Clone AF8-P has a weight loss rate of 22.8 %/min, AF8-I of 21.3 %/min and AF2 of 17.4 %/min. The burnout temperature is defined as the temperature where the fuel has been completely burned, representing also the end of stage three. The burnout temperatures are between 433 ºC and 438 ºC. Clone AF8-P has the highest burnout temperature and AF8-I the lowest. The final solid residue (ash) at the end of the third stage is equal for both clones AF8 (1.7%), while clone AF2 presents a final residue of about 5.8% of the initial weight. The corresponding ash contents shown in Table 1 for the three biomass fuels are similar, but slightly lower than those obtained in the TGA.


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Figures 4 to 6 show the 78K1()L versus 1/T plots for the three biomass fuels studied in the TGA. It is seen that both clones AF8 have similar kinetic profiles in both stages analysed, while clone AF2 exhibits a different kinetic profile in the oxidation phase, consistently with its TG and DTG curves in the third stage. Table 3 summarizes the combustion kinetic parameters obtained from the TGA experiments for the three biomass fuels. The global apparent activation energy was calculated as follows: 34

>3 =

Oq Or >3N + > (22) Oq + Or Oq + Or 3U

where Oq is the volatile matter content, Or is the fixed carbon content, Eav is the apparent activation energy of the volatiles release stage (second stage) and Eac is the apparent activation energy of the fixed carbon oxidation stage (third stage). The global pre-exponential factor A was estimated through the Arrhenius equation, with k calculated as follows:34

=

Oq Or q + (23) Oq + Or Oq + Or r

where q and r were calculated by Eq. 2, where T is the difference temperature between stages. In the second stage, the apparent activation energies of clones AF8-P and AF2 are very similar, with values of 117.4 kJ mol-1 and 118.6 kJ mol-1, while clone AF8-I presents a value of 109.7 kJ mol-1. In the third stage, the apparent activation energies vary between 252.0 kJ mol-1 and 267.4 kJ mol-1. The global apparent activation energies range from 148.0 kJ mol-1 (for clone


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AF8-I) to 155.4 kJ mol-1 (for clone AF2). These values are consistent with the values reported in the literature, namely in Kok et al.10 that obtained activation energies from 129.2 kJ mol-1 to 219.2 kJ mol-1 for the combustion of poplar. Jeguirim et al.15 studied the combustion behaviour of two energy crops (Arundo donax and Miscanthus giganthus) in a TGA operating at 5 ºC min-1. Authors reported activation energies values of 107.2 kJ mol-1 for Arundo and 96.4 kJ mol-1 for Miscanthus for the devolatilization phase, and of 253.6 kJ mol-1 for Arundo and 279.9 kJ mol-1 for Miscanthus for the char oxidation phase, being the latter values similar to those obtained for both clones AF8.

4.2 Drop tube furnace. Figure 7 shows the temperature (without and with biomass) and burnout profiles along the axis of the DTF. The temperature axial profiles reveal similar tendencies and, as expected, in the near burner region, the temperatures are higher when biomass is fed to the DTF. As the axial distance from the DTF top (x) increases the differences between the measured temperatures diminish. The lowest burnout value measured was ~48% and was attained in the experiment performed with a DTF wall temperature of Tw = 900 ºC and at an axial distance from the DTF top of x = 200 mm. The highest burnout measured was ~96% obtained for Tw = 1050 and 1100 ºC and x = 1100 mm. The burnout axial profiles have a similar evolution regardless of the DTF wall temperatures. As the DTF wall temperature increases the differences between the corresponding burnout values decrease, being very close for the two highest temperatures (Tw = 1050 and 1100 ºC). At x = 1100 mm, the burnout values are all similar, around 95%/96%, regardless of the DTF wall temperature.


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4.2.1 Model-fitting approach. Figure 8 shows the linear regressions to evaluate the combustion kinetic parameters along the DTF, and Table 4 presents the calculated kinetic parameters obtained from the DTF experiments with the model-fitting for the clone AF8-I. It was assumed that in section 200 < x < 700 mm occurred only devolatilization and in section 700 < x < 1100 mm only char oxidation. The volatilization zone was divided in three sections, namely 200 < x < 300 mm, 300 < x < 500 mm and 500 < x < 700 mm. In the devolatilization zone, the apparent activation energy increases from 27.5 kJ mol-1 (in the first section) to 39.0 kJ mol-1 (in the third section). In the oxidation zone the calculated apparent activation energy was 73.2 kJ mol-1. The values obtained for the apparent activation energies are similar to those reported in Costa et al.20 for the combustion of pine shells in the same DTF; specifically, the authors reported values for Ea between 21.6 and 49.8 kJ mol-1 for the devolatilization zone and 130 kJ mol-1 for the char oxidation zone. Farrow et al.22 reported apparent activation energies between 24.2 kJ mol-1 and 34.7 kJ mol-1 for the pyrolysis of pinewood sawdust in a DTF, which are close to those obtained in the present study for the devolatilization phase. It is well known that the experimental conditions, the fuel nature and the setup configuration can influence the results 8, which poses difficulties in establishing comparisons between different studies. Additionally, the mathematical modelling also differs among studies since the heterogeneous nature of the biomass and the complexity of its combustion process results in the use of different approaches to assess the problem.

4.2.2 Ballester and Jiménez model. The apparent activation energy for the devolatilization (Eav) was estimated between x = 200 and 700 mm of the DTF, where the majority of the


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devolatilization process occurs. For this estimation it was considered that no oxidation occurs in this zone of the DTF. Figure 9 shows the global error δ obtained for 30 × 30 iterations of Eav/Av pairs. The unburnout for each pair is calculated and compared with the experimental ones. The lowest error corresponds to the pair Eav = 12.8 kJ mol-1 and Av = 90 s-1, which is highlighted in Figure 9, where it is possible to observe the different gradient of the errors and a black circle that represents the boundaries of the minimum errors, showing some uncertainty in the estimation of the optimal pair Eav/Av. It is estimated that the Av error is about ± 2% and the Eav error around ± 9%. The black area corresponds to a δ of 0.0162. Based on the devolatilization kinetics parameters estimated above, the optimal pair Eac/Ac for the char oxidation was also estimated. Figure 10 shows the global error δ for 10 × 10 iterations of Eac/Ac pairs. As in Figure 9, the black area in Figure 10 represents the lower error where the optimal pair is contained. This error δ is about 0.0065, and Eac = 69 kJ mol-1 ± 1.5% and Ac = 3.8 × 10-4 s-1 ± 8%. Figure 11 shows the measured and predicted burnout profiles along the axis of the DTF. The predictions were made using the optimal pairs Eav/Av and Eac/Ac referred to above. The predicted burnout values have a uniform behaviour, and are similar and in good agreement with the experimental values. The apparent activation energies obtained in the present study are similar to those obtained by Jiménez et al.21 that performed devolatilization and combustion experiments in an EFR with pulverised biomass (Cynara cardunculus). Jiménez et al.21 reported a devolatilization apparent activation energy of 11 kJ mol-1 and a char oxidation activation energy of 63 kJ mol-1.

In order to assess further the performance of the model, it is important to carry out a parameter sensitivity analysis. In this sense, the total burnout and the Eac and Ac values were estimated for


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the average values of Eav and Av calculated in the model-fitting approach – Eav = 34.1 kJ mol-1 and Av = 5.7 x 10-7 s-1 (first scenario). Additionally, an analysis was also performed considering that Eav = 6.5 kJ mol-1 and Av = 2.78 s-1, which represent the values obtained when the modelfitting approach is applied considering a unique devolatilization phase in the DTF (second scenario). To obtain these kinetic parameters, the linear regression was performed considering only a global devolatilization section (200 < x < 700 mm) instead of dividing this section in three devolatilization sections. This linear regression has a lower correlation factor, showing that the model fitting approach needs to be applied section by section in order to obtain more accurate results. Figures 12 and 13 show the global error δ for 20 × 20 iterations and 10 × 10 iterations, respectively, of Eac/Ac pairs for the two scenarios assessed. The black area represents the lower error where the optimal pair is contained. For the first scenario, this error δ is about 0.0144 (more than twice the minimum error reached previously) for the pair Eac = 82.5 kJ mol-1 and Ac= 3.7 x 106 s-1. In the second scenario, the error is about 0.0131 for the pair Eac = 64 kJ mol-1 and Ac = 5500 s-1. Hence, even with a higher error, the resulting Eac are not very different. Figure 14 shows the measured and predicted burnout profiles along the axis of the DTF. The predictions were made considering the first scenario. The predicted burnouts have a uniform evolution for the DTF wall temperatures between 1000 and 1100 ºC, but for temperatures between 900 and 950 ºC the evolution is somewhat inconsistent. The predicted burnout values are highest for the higher temperatures and lowest for the lower temperatures, in contrast with the results obtained in the reference scenario. Figure 15 shows the measured and predicted burnout profiles along the axis of the DTF. The predictions were made considering the second scenario. At the end of the DTF, where the


22

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experimental burnouts are similar for all DTF wall temperatures, the predicted curves separate, showing divergent burnout values.

4.2.3 Comparison between the two models. Since in the model-fitting approach the kinetic parameters for the devolatilization were calculated for different sections and in the Ballester and Jiménez model these parameters were calculated considering only one section (between 200 and 700 mm), to compare the two methods there is the need to standardize the estimation. In this sense, the global devolatilization apparent kinetic parameters for the model-fitting approach were estimated using Eqs. 22 and 23. The global devolatilization apparent activation energy was calculated as follows:34

>q =

OqI Oq: Oqt >qI + >q: + >qt (24) Oq' Oq' Oq'

where xVT is the total volatile matter content, xVi is the volatile matter content in section i, EV is the global apparent activation energy of the volatiles and EVi is the apparent activation energy of the volatiles in each section i. The pre-exponential factor A was estimated from k, which is given by: 34

q =

OqI Oq: Oqt qI + q: + qt (25) Oq' Oq' Oq'

Table 5 includes the apparent kinetic parameters for the devolatilization and char oxidation phases estimated by the two methods for the same sections of the DTF. The devolatilization and


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char oxidation apparent activation energies calculated by the model-fitting approach are higher than those calculated by the Ballester and Jiménez model. In the case of the char oxidation, the values are very similar, 73.2 kJ mol-1 (model-fitting) and 69.0 kJ mol-1 (Ballester and Jiménez model), but in the case of the devolatilization, the values are quite different, 34.1 kJ mol-1 (model-fitting) against 12.8 kJ mol-1 (Ballester and Jiménez model). The difference between the results obtained is related to the different assumptions considered in the two models. Biomass combustion is a very complex process and the assumptions and simplifications considered in the model-fitting approach (e.g., single representative particle diameter, VWX,^ constant along the DTF and equal to VWX,u , and _ equal to v ) do not describe the

combustion process accurately, simplifying it. Nevertheless, the apparent activation energy estimated are comparable to the ones estimated by the Ballester and Jiménez model. Numerical models such as the Ballester and Jiménez model are time-consuming, but the assumptions considered are far more realistic, representing more closely the combustion process, eliminating many of the simplifications considered in the model-fitting approach. The results show that the model-fitting approach is useful to obtain an initial estimation, but definitive results have to be derived from more complex models such as the Ballester and Jiménez model.

4.3 TGA versus DTF. Particle burnout and kinetic parameters depend on solid fuel volatile content, char structure, reactant partial pressure, furnace temperature and residence time in the furnace, among others.19 In the TGA, samples are exposed to a heat source whose temperature increases with time. The heating rates are very low and the residence times are very high compared with industrial equipment. In the DTF, samples, initially at room temperature, are rapidly injected into a constant high temperature medium, being exposed to high heating rates


24

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and short residence times, comparable to the situation in industrial equipment. Against this background, very distinct combustion behaviour is expected in the two apparatus, which will be reflected in the calculated kinetic parameters. Returning to Table 5, it is seen that the kinetic parameters obtained from the two apparatus are, as expected, quite different. In the TGA, the calculated apparent activation energy is about 190.7 kJ mol-1 for the devolatilization and 267.4 kJ mol-1 for the char oxidation, while in the DTF the corresponding values are, depending on the model used, 12.8 and 34.1 kJ mol-1 for the devolatilization and 73.2 and 69.0 kJ mol-1 for the char oxidation. The activation energies estimated from the DTF data are lower than those estimated from the TGA data. The different values obtained result from the different combustion conditions in both apparatus. Due to the low temperatures and low heating rates in the TGA along with the high oxygen concentration, the combustion process in the TGA occurs under regime I. In the DTF, the high temperatures and high heating rates along with the high oxygen concentration create conditions for the combustion process to take place under regime II so that the diffusion effects need to be taken into account. Smith35 reported values of activation energy for combustion processes occurring in regime I that are double than those obtained under regime II conditions. The diffusion effects in the DTF contributes to the lower activation energies obtained when compared with those obtained from the TGA results, where the combustion process is controlled simply by kinetics.


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5. CONCLUSIONS The TGA experimental technique was applied to three poplar clones (AF8-I, AF8-P and AF2) in order to assess their combustion behaviour and estimate their kinetic parameters. Clones AF8 have similar thermal behaviour and kinetic profiles, but clone AF2 reveals differences in the oxidation stage, with an apparent activation energy higher than that estimated for the other two clones. The apparent activation energies estimated for the devolatilization stage are 117.7 kJ mol1

and 118.6 kJ mol-1 for clones AF8-P and AF2, respectively, and 109.7 kJ mol-1 for clone AF8-I.

For the oxidation stage the apparent activation energies are 264.3 kJ mol-1 and 267.4 kJ mol-1 for clones AF2 and AF8-I, respectively, and 252.0 kJ mol-1 for clone AF8-P. The global apparent activation energies vary between 148 kJ mol-1 for clone AF8-I and 155.4 kJ mol-1 for clone AF2. Owing to the small differences in the behaviour of the clones encountered in the TGA experiments, only clone AF8-I was studied in the DTF for five wall temperatures. The kinetic parameters were calculated using two models – a model-fitting approach and a model proposed by a Ballester and Jiménez. In the char oxidation zone, the apparent activation energy obtained by the two models is similar (73.2 kJ mol-1 for the model-fitting and 69.0 kJ mol-1 for the Ballester and Jiménez model), but in the devolatilization zone the results obtained by the two methods are quite different; specifically, 34.1 kJ mol-1 for the model-fitting and 12.8 kJ mol-1 for the Ballester and Jiménez model. The DTF results indicate that the char oxidation process is controlled by both kinetics and diffusion effects, with the kinetics assuming the most important role. The impact of the diffusion effects on the DTF kinetic results explains the differences in relation to the TGA kinetic results, where the combustion process is controlled simply by kinetics.


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AUTHOR INFORMATION Corresponding Author *Email: [email protected]

Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

ACKNOWLEDGEMENTS This work was supported by Fundação para a Ciência e a Tecnologia (FCT), through IDMEC, under LAETA Pest-OE/EME/LA0022.


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6. REFERENCES (1)

Pereira S.; Costa M.; Carvalho M.G.; Rodrigues A. Energy Convers. Manage. 2016,

Article in Press. (2)

Dermibas A. Energy Convers. Manage. 2001, 42, 1357-1378.

(3)

Shen D.K.; Gu S.; Luo K.H.; Bridgwater A.V.; Fang M.X. Fuel. 2009, 88, 1024-1030.

(4)

Karampinis E.; Grammelis P.; Agraniotis M.; Violidakis I.; Kakaras E. WIREs Energy

Environ. 2014, 3, 384–399. (5)

Demirbas A. Prog. Energy Combust. Sci. 2004, 30(2), 219-230.

(6)

Jenkins B.M.; Baxter L.L.; Miles Jr. T.R.; Miles T.R. Fuel Process. Technol. 1998, 54,

17-46. (7)

Ballester J.; Jiménez S. Combust. Flame. 2005, 142, 210–222.

(8)

Carpenter A.; Skorupska N.M. IEA Coal Research, November 1993.

(9)

Authier O.; Thunin E.; Plion P.; Porcheron L. Energy Fuels. 2015, 29, 1461−1468.

(10) Kok M.V.; Özgür E. Fuel Process. Technol. 2013, 106, 739–743. (11) Sait H.H.; Hussain A.; Salema A.A.; Ani F.N. Bioresour. Technol. 2012, 118, 382–389. (12) Fang M.X.; Shen D.K.; Li Y.X.; Yu C.J.; Luo Z.Y.; Cen K.F. J. Anal. Appl. Pyrolysis. 2006, 77, 22–27. (13) López-González D.; Fernandez-Lopez M.; Valverde J.L.; Sanchez-Silva L. Bioresour. Technol. 2013, 143, 562–574.


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(14) TranVan L.; Legrand V.; Jacquemin F. Polym. Degrad. Stab. 2014, 110, 208-215. (15) Jeguirim M.; Dorge S.; Trouvé G. Bioresour. Technol. 2010, 101, 788–793. (16) Gua X.; Liua C.; Jianga X.; Maa X.; Lia L.; Chenga K.; Li Z. J. Anal. Appl. Pyrolysis 2014, 106, 177–186. (17) Slopiecka K.; Bartocci P.; Fantozzi F. Appl. Energy. 2012, 97, 491–497. (18) Katherine M. K.; Snape C.E.; McRobbie I.; Barker J. Energy Fuels. 2011, 25, 981–989. (19) Su S.; Pohl J.H.; Holcombe D.; Hart J.A. Prog. Energy Combust. Sci. 2001, 27, 75–98. (20) Costa F.F.; Wang G.; Costa M. Proc. Combust. Inst. 2015, 35, 3591–3599. (21) Jiménez S.; Remacha P.; Ballesteros J.C.; Giménez A.; Ballester J. Combust. Flame. 2008, 152, 588–603. (22) Farrow T.S.; Sun C.; Snape C.E. J. Anal. Appl. Pyrolysis. 2015, 113, 323–331. (23) Zellagui S.; Schönnenbeck C.; Zouaoui-Mahzoul N.; Leyssens G.; Authier O.; Thunin E.; Porcheron L.; Brilhac J.F. Fuel Process. Technol. 2016, 148, 99–109. (24) Wang G.; Zander R.; Costa M. Fuel. 2014, 115, 452–460. (25) Wang G.; Silva R.B.; Azevedo J.L.T.; Martins-Dias S.; Costa M. Fuel. 2014, 117, 809– 824. (26) Casaca C.; Costa M., Combust. Sci. Technol. 2003, 175 (11), 1953-1977. (27) Ebrahimi-Kahrizsangi R.; Abbasi M.H. Trans. Nonferrous Met. Soc. China. 2008, 18 (1), 217–221.


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(28) Ferreira A.F.; Soares Dias A.P.; Silva C.M.; Costa M. Energy. 2015, 92 (3), 365–372. (29) Smith I.W. Symp. (Int.) Combust. 1982, 1045-1065. (30) Williams A.; Jones J.M.; Ma L.; Pourkashanian M. Prog. Energy Combust. Sci. 2012, 38, 113-137. (31) Korkut A. J. Therm. Anal. Calorim. 2012, 109, 227–235. (32) Ferreira P. Universidade do Minho, Escola de Engenharia, 2015. [dissertation]. (33) Yuzbasi N.S.; Selçuk N. Fuel Process. Technol. 2011, 92, 1101–1108. (34) Fang X.; Jia L.; Yin L. Biomass Bioenergy. 2013, 48, 43-50. (35) Smith I.W. Combust. Flame. 1971, 17, 303-314.


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Tables Table 1. Characteristics of the three biomass fuels. ..................................................................... 32 Table 2. Combustion characteristics of the three biomass fuels. ................................................. 33 Table 3. Combustion kinetic parameters obtained from the TGA experiments for the three biomass fuels. ......................................................................................................................... 34 Table 4. Combustion kinetic parameters obtained from the DTF experiments with the modelfitting for the clone AF8-I. ..................................................................................................... 34 Table 5. Combustion kinetic parameters obtained from the TGA and DTF experiments for the clone AF8-I. ........................................................................................................................... 35


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Table 1. Characteristics of the three biomass fuels.

Proximate analysis (wt.%, as received) Lower heating value (MJ/kg) Ultimate analysis (wt.%, dry ash free)

Ash analysis (wt.%, dry basis)

Parameter Moisture Volatile matter Ash Fixed Carbon (by dif.)

AF8-I 8.9 73.8 0.8 16.5

AF8-P 9.1 73.3 1.0 16.6

AF2 10.1 69.2 3.4 17.3

LHV

18.2

19.2

17.5

C H N S O (by dif.) Al2O3 CaO Cl Fe2O3 K2O MgO MnO Na2O P2O5 SiO2 SO3 Others

45.8 6.5 0.3 0.03 47.4 2.26 33.5 0.035 0.892 10.6 12.2 0.14 2.94 23.9 4.89 8.17 0.47

46.7 6.1 0.1 0.03 47.0 2.67 36.4 0.051 0.578 11.8 16.8 0.18 2.3 18.7 3.46 6.18 0.88

49.1 6.5 0.1 0.03 44.3 2.46 38.1 0.023 0.82 10.5 22.5 0.78 1.1 13.0 6.27 3.46 0.99

% < 50 % < 100 % < 300 % < 500 % < 700 % < 1000 % < 1500

9.4 14.4 28.7 43 64.6 83.5 100

5.3 8.3 21.3 35.1 56.7 78.1 100

8.8 14.3 31 45.4 62.7 80.2 100

Particle size (µm)


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Table 2. Combustion characteristics of the three biomass fuels. AF8-I

AF8-P

AF2

Initial decomposition temperature (Tin) (ºC)

230

238

233

T1 max (ºC)

321

322

320

dm/dt max (%/min)

12.6

13.9

10.8

T2 max (ºC)

423

429

404

dm/dt max (%/min)

21.3

22.8

17.4

Burnout temperature (ºC)

433

438

437

Burnout time (min)

40.7

40.7

41.7


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Table 3. Combustion kinetic parameters obtained from the TGA experiments for the three biomass fuels.

Global

nd

2 stage

rd

3 stage

AF2

AF8-P

AF8-I

E (kJ mol-1)

155.4

150.1

148.0

A (s-1)

2.0 x 1021

4.5 x 1019

9.1 x 1020

T (ºC)

233-363

238-375

230-374

E (kJ mol-1)

118.6

117.4

109.7

A (s-1)

7.8x 1011

4.6x 1011

1.0 x 1011

R2

0.9363

0.9333

0.9394

T (ºC)

363-437

375-438

374-433

E (kJ mol-1)

264.3

252.0

267.4

A (s-1)

1.1 x 1022

2.8 x 1020

5.9 x 1021

R2

0.9385

0.9048

0.9042

Table 4. Combustion kinetic parameters obtained from the DTF experiments with the modelfitting for the clone AF8-I. EV/C (kJ mol-1)

AV/C (s-1)

R2

200

Kinetics of Poplar Short Rotation Coppice Obtained from

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