Thermal conversion of defective coffee beans for energy purposes: Characterization and kinetic modeling

Renewable Energy 147 (2020) 1275e1291

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Renewable Energy journal homepage: www.elsevier.com/locate/renene

Thermal conversion of defective coffee beans for energy purposes: Characterization and kinetic modeling
m Patrícia Alves Rocha b, Carolina Monteiro Santos a, Leandro Soares de Oliveira a, Ele Adriana Silva Franca a, * a b

^nio Carlos, 6627, Pampulha, Belo Horizonte, Brazil Mechanical Engineering Department, Federal University of Minas Gerais (UFMG), Av. Pres. Anto Federal University of Jequitinhonha and Mucuri Valleys (UFVJM), Janaúba, Brazil

a r t i c l e i n f o

a b s t r a c t

Article history: Received 29 March 2019 Received in revised form 2 August 2019 Accepted 14 September 2019 Available online 20 September 2019

This work aimed to characterize the fuel properties and to evaluate the kinetics of thermal decomposition of defective coffee beans (DCB). Three thermogravimetric-based methods were evaluated: OzawaFlynn-Wall (OFW), Kissinger-Akahira-Sunose (KAS) and Friedman. The results showed that DCB presented low activation energy and that the evaluated mathematical models, although satisfactory for describing thermal decomposition in inert atmospheres, did not provide a satisfactory description of the oxidizing process. The enthalpy values indicated that the energy differences between the reagents and the activated complex are related directly to the activation energies. Pre-exponential factors indicated first-order reactions. The immediate analysis and the lignocellulosic contents indicated a biomass with low levels of humidity and ashes, high carbon and volatile concentrations, besides thermal stability. The obtained calorific value was 19.39 MJ/kg. The overall results obtained in the present study indicate that this biomass has the potential to be used as a solid biofuel. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Biomass Model-free kinetics Kinetic parameters Thermal analysis

1. Introduction Energy demands have increased constantly through time in association to two major factors: population growth and technological/industrial development. About 80% of world's energy supply still comes from fossil fuels, and over 40% of the electricity comes from charcoal burning [1,2]. Thus, the search for technologies able to replace charcoal by clean solid alternative fuels has gained attention lately, with biomass being considered an interesting source of renewable energy due to its capability to be converted into solids, liquids and gases, all with potential use as energy resources [1]. Considered as a natural carbon fuel, biomass contains a minimal ammount of problematic components as sulfur and nitrogen, being an environmentally friendly alternative. This resource can be used directly, with or without previous treatment, or jointly with mineral coal, minimizing the impacts of this fossil fuel [1e4]. In Brazil, electric energy is predominantly supplied by hydroelectric power.

* Corresponding author. Mechanical Engineering Department, Universidade Federal de Minas Gerais, Brazil. E-mail address: [email protected] (A.S. Franca). https://doi.org/10.1016/j.renene.2019.09.052 0960-1481/© 2019 Elsevier Ltd. All rights reserved.

In drought periods, however, the use of thermoelectric plants is necessary, but still quite expensive. Thus, the use of low cost raw materials and optimization of the process are crucial for reduction of operating costs. The country's economy is largely based on agroindustry, and huge amounts of residual biomass are produced by this activity. The main Brazilian agricultural product is coffee, with the country being the world's largest producer and exporter. In order to achieve and maintain such high production levels, strippicking harvesting practices that result in coffees with high amounts of defects or low quality coffees (coming from immature, unripe, overripe beans as well as fermented fruits in contact with the ground) are employed, and these defects comprise about 20% of the total coffee production [5]. Studies aiming at providing alternative uses for defective coffee beans have focused on the production of adsorbent materials and biodiesel [6,7] and this biomass has not yet been evaluated for production of solid biofuel. Each type of biomass presents a unique physico-chemical structure, even though the main constituents, cellulose, hemicellulose and lignin, are common to all of them. The relative ammounts of such compounds vary significantly, and thus pyrolisis conditions should be modified accordingly in order to obtain the desired product. Therefore, the knowledge of the biomass composition, its combustion parameters and kinetic behavior provides

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essential information for predicting reactions and optimizing processes, necessary for the planning and installation of thermochemical plants. The chemical characterization of the biomass will allow for the prediction of its reaction behavior as well as its energetic performance. Morphological analyzes allow for a better understanding of particle fluidization, heat and mass transfer models and structure modifications. The quantity and quality of the energy contained in biomass is not only dependent on its chemical composition, but also on the reaction conditions during thermal processing. Kinetics evaluation is essential for a better understanding of biomass conversion, since simple models can provide useful information for optimization of process parameters and design of new reactors that can be employed for pyrolysis and gasification. Kinetic parameters are usually determined based on thermogravimetric (TGA) analysis, a simple and direct technique, using isothermal and non-isothermal methods, with the latter being faster and more reliable [1,3]. In isothermal methods a small weight loss is observed before the desired temperature is attained, and this can lead to errors in estimating the kinetics parameters. Non-isothermal methods are widely employed because they present various possibilities of data fitting. Among these non-isothermal methods, the model-free (isoconversional) ones allow for simple visualization of the physical and chemical processes that occur during the degradation of solids, using the data of thermogravimetric curves obtained at different heating rates [8]. They also allow for direct calculation of thermodynamics parameters. Ozawa-Flynn-Wall (OFW), KissingerAkahira-Sunose (KAS) and Friedman are the most commonly used isoconversional models, due to low errors (below 1%). Utilization of biomass as an energy resource is still a challenge due to the difficulties of modeling the decomposition reactions. However, the exorbitant volume of available material is one of the main factors in its favor. Although the number of studies and papers concerned with characterizing the kinetics of different biomass has increased in recent years, there is no work done with defective coffee beans, which is a rather distinctive carbonaceous material. Thus, this study aimed to characterize this biomass both chemically and physically, and to study its thermal degradation and decomposition. Ozawa-Flynn-Wall (OFW), Kissinger-Akahira-Sunose (KAS) and Friedman models were employed to evaluate decomposition kinetics in order to generate data for the discussion of the potential applicability of this material as an alternative energy resource. 2. Materials and methods

FTIR measurements were performed using a Shimadzu IRAffinity-1 FTIR Spectrophotometer (Shimadzu, Japan) with a DLATGS (Deuterated Triglycine Sulfate Doped with L-Alanine) detecto. Diffuse reflectance measurements were performed in diffuse reflection mode with a Shimadzu sampling accessory (DRS8000A). Each sample was mixed with KBr (1:10) and pure KBr was employed as reference material (background spectrum). Spectra were recorded in the range of 4000e400 cm
1 (4 cm
1 resolution) and submitted to background subtraction. 2.2. Thermal analysis Thermal analyses of defective coffee beans were carried out using six different heating rates: 5, 10, 15, 20, 25 and 30 K min
1, employing a TGA-51 thermal analyzer (Shimadzu). About 20 mg of sample were placed in aluminum crucibles, using air (99.9% purity) or nitrogen (99.9% purity) as carrier gases, at a flow rate of 100 mL min
1, from 303.15 K to 873.15 K. The equipment software provided the thermogravimetric (TG) curves and their derivatives (DTG). 2.3. Ignition and burnout temperatures Evaluation of the ignition (Ti) and burnout (Tb) temperatures was based on the intersection method [18], according to the data from the TG curves obtained under oxidative atmosphere and summarized as follows. Two points are marked on the TG curve: point (A) at which the vertical line drawn from the maximum DTG peak intersects, and point (B) where the primary devolatilization begins. A tangent line is drawn between points A and B, the intersection being Ti. For evaluation of the burnout temperature, two other points of the TG curve were identified: point (C) at which the vertical line intersects from the second maximum DTG peak, and point (D) where the TG curve becomes constant. A tangent line is drawn between points C and D, the intersection being Ti. 2.4. Kinetic analysis Pyrolysis of lignocellulosic biomass is a very complex phenomenon, with several reactions occurring simultaneously [19,20]. Therefore, predicting exact reaction mechanisms is not possible. However, a generic model can be used to describe the process. The single-step global model for thermal degradation of biomass can be represented as:

2.1. Biomass preparation and characterization

Biomass

!Volatiles þ Charcoal

Arabica green coffee samples were acquired from a roasting company located in Minas Gerais State, Brazil. Samples consisted of coffee beans harvested by strip-picking that were rejected by color sorting machines that were prepared and characterized according to ASTM E1757 protocol [9]. Density was determined according to E873-82 and D2854-09 protocols [10,11]; 2014). The chemical composition was evaluated according to the following protocols: T 264 CM-7, T 207 CM-08, T 222 OM-15, T9 M-54, T 203 CM-99, and D1762-84 [10e16]. Other analyses were elemental (CHNS/O Analyzer 2400 - PerkinElmer), calorific value (calorimetric-pump IKA C-200) and mineral contents (EDXA analysis - Hitachi TM3000). Elemental analysis was based on D2867-09 and D1762-84 protocols [11,17]. Total extractives were evaluated based on the sum of solubles in water and petroleum ether, obtained by sequential extraction according to TAPPI protocols T 264 cm-7 and T 207 cm08 [15]. Cellulose, hemicellulose and lignin (soluble and insoluble) contents were determined according to TAPPI protocols [13,16].

with k representing the reaction rate constant described by Arrehnius equation:

k

 k ¼ A:exp

(1)




Ea= R:T

(2)

where A is the pre-exponential factor (s
1), Ea is the activation energy (kJ/mol), R is the universal constant of gases (8.314 J/ K.mol
1), and T is the absolute temperature (K). The conversion degree da =dt for thermogravimetric experiments performed under a constant temperature rate b ¼ dT=dt, can be expressed by:

da da ¼ b ¼ kðTÞf ðaÞ dt dT

(3)

where k(T) and f(a) are temperature and conversion functions,

C.M. Santos et al. / Renewable Energy 147 (2020) 1275e1291

respectively, and a is the process conversion degree. a represents the mass loss that occurs during the thermal decomposition, and can be defined as:

a¼

mi
ma mi
mf

(4)

where mi is the initial sample mass; ma is the sample mass at a given time, and mf is the sample mass at the final of the process. A combination of equations (1) and (2) leads to:

 da ¼ A : f ðaÞ: e dt


Ea= R:T

(5)

Several different functions f (a) for the reaction model of solidstate kinetics can be found in literature and are shown in Table 2. The reaction-order kinetic models are frequently used for biomass degradation, due to their simplicity. However, as described by Vyazovkin et al. [21], the use of master plots is the correct way to determine the reaction models shown in Table 2. In this work, the Criado method was applied to determine function f(a) [22]. In this method z(a) master plots are derived by combining the differential and integral forms of the reaction models as follows:

zðaÞ ¼ f ðaÞgðaÞ ¼



   da pðxÞ T 2a dt a bTa

(6)

Where b ¼ dT=dt is the heating rate (K/s), x ¼ E=RT, g(a) is an integral function of the conversion, dependent on the reaction mechanism as equation (7), and pðxÞ [23] is an approximation for temperature integral in equation (7).

gðaÞ ¼

ða 0

da ¼ f ðaÞ

  Tða A

b


Ea

e RT dT

(7)

0

The resulting experimental values of zðaÞ can be plotted as a function of conversion˛ and compared against the theoretical zðaÞ master plots. To obtain the activation energy (Ea) and the pre-exponential factor (A), the isoconversional models herein employed were as follows: 2.4.1. Ozawa-Flynn-Wall method (OFW) To investigate the decomposition processes of the constituents of the material, the OFW method replaces the temperature integral (equation (7)) with the approximating function by Doyle [24]. The method is represented by the equation (8):

lnðbÞ ¼ ln

 A:Ea Ea
2:315
0:457 R:gðaÞ R:T



(8)

A and Ea are obtained from the intercept and slope of the plot between ln (b) versus 1/T at different heating rates. 2.4.2. Kissinger-Akahira-Sunose method (KAS) The Kissinger-Akahira-Sunose model is described by:

 ln

b

T2



 A: Ea Ea
R:gðaÞ R:T

 ¼ ln

(9)

 obtained from the intercept and slope In this model, A and  Ea are of the plot between ln Tb2 versus 1/T at different heating rates. 2.4.3. Friedman method The Friedman method is described by the following equation:

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  da Ea ¼
þ lnðA:f ðaÞÞ ln dt R:T

(10)

In this method the decomposition of the biomass is independent of the process temperature and depends only on the rate of mass loss. Six different heating rates (ß) were used for the same values of a, obtaining different absolute temperatures (T) of the thermogravimetric curves. Model plots were based on data obtained at a intervals of 0.005. In this way it was possible to characterize the thermal conversion behavior of the biomass in both air and nitrogen atmospheres, and Ea was estimated for each degree of conversion a. 2.5. Thermodynamic parameters Thermodynamic parameters were calculated according to the following equations [25,26]:

DH ¼ Ea
R:T

(11)

where DH corresponds to enthalpy variation, Ea is the activation energy, R is the universal gas constant (8.314 J/K.mol
1), and T is the absolute temperature (K). Gibbs free energy was calculated as follows:



DG ¼ Ea þ R: Tm :ln

Kb :Tm h:A

 (12)

where Kb is the Boltzmann constant, h is the Plank constant and Tm is the DTG temperature peak. The entropy of the process was then calculated as:

DS ¼

DH
DG Tm

(13)

3. Results and discussion 3.1. Biomass characterization Table 1 shows the results obtained for proximate, elemental and immediate analysis, lignocellulosic contents, bulk density, calorific value and metal contents of DCB. This biomass presented low moisture value (6.5%) in comparison to those found for coffee residues such as coffee husks (10%), spent coffee grounds (80e10.9%) and coffee parchment (8.86), and wood biomass (8.5e60%) commonly used as energy resources [10,11,27,28],. Biomass containing less than 10% moisture is deemed good for pyrolysis and combustion. This is an interesting feature, given that the higher the water content of biomass, the higher the amount of energy required to initiate pyrolysis. Also, increases in moisture content reduce the calorific value and, consequently, decrease the total combustion efficiency. Another interesting feature is a high content of volatiles, because they are easier to ignite and burn despite the difficulty of process control [29]. Volatiles correspond to the amount of biomass that will volatilize during heating. Although such gases are of interest for the initial decomposition reactions, high amounts can lead to charcoals that burn too fast, thus hindering pyrolisis and combustion control. The value experimentally determined for DCB (69.18%) is similar to the ones reported in the literature for sugarcane bagasse (70.79%), rice husks (65.47%) and parchment (63.52%) [30,31], soybean processing residues, 69.6% and coconut shells, 70.5% [32], residues that are commonly employed as alternative biomass

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Table 1 Proximate, elemental and immediate analysis (%), lignocellulosic composition (%), bulk density and calorific value of defective coffee beans. Analysis

Dry biomass

Proximate analysis (wt.%) Moisture content Volatile matter Fixed carbon Ash content VM/FC

6.5 ± 0.05 69.18 ± 0.25 21.16 ± 0.22 3.14 ± 0.31 3.26

Ultimate analysis (wt.%) C H N O H/C O/C

45.95 ± 0.48 6.62 ± 0.61 2.38 ± 0.04 45.02 1.74 0.74

Lignocelullosic content (wt.%) Hemicellulose Cellulose Total lignin Soluble lignin Insoluble lignin Hollocelulose

6.05 28.83 3.41 0.90 ± 0.01 2.51 ± 0.38 40

Extractive content (wt.%) Hot water Solvent (Petroleum ether) Total (water þ petroleum ether)

39.77 ± 4.28 12.77 ± 2.38 52

Bulk density (Kg/m3) Bulk density in dry basis (Kg/m3)

562 ± 0.11 526

Gross (higher) calorific value (MJ/Kg) Net (lower) calorific value (MJ/Kg)

19.40 ± 0.47 19.06

Mineral contents (%) Ca K Mg P

1.98 35 3.5 2.8

for energy purposes. DCB presented low ash content (3.14%) when compared to coal (20%), sugarcane bagasse (11.30%) [33], soybean processing residues, (5.1%) [32] and rice husks (23%) [33]. In thermochemical processes in general, high ash contents can promote clogging and encrustations, causing damage to the machinery. Gasification technologies require a feedstock with less than 5% ash (preferably < 2%), in order to prevent the formation of clinkers [34]. The fixed carbon percentage establishes the amount of heat generated. The higher this value, the slower the fuel will burn. Thus, the obtained value of 3.14% is satisfactory, demonstrating thermal stability [30]. Ultimate analysis indicated carbon as the main constituent (45.95%), followed by oxygen (45.02%), hydrogen (6.62%) and nitrogen (2.38%). The ratios of H/C and O/C are important for understanding and predicting the thermochemical conversion process. Usually biomass have higher values of O/C and H/C ratios in comparison to fossil fuels due to the presence of cellulose and lignin in its constitution [34,35]. The studied biomass presented H/ C and O/C ratio values of 1.74 and 0.74, respectively, and such values are higher than those reported for eucalyptus and sugar cane bagasse (H/C ¼ 1.43 and O/C ¼ 0.63) [34]. The higher ratios can be associated to the high volatiles content, indicating that sample can be easily ignited and subsequently converted into products. The high amount of carbon confirms the potential of this biomass for energy purposes. Furthermore, low nitrogen levels are an

advantage in comparison to fossil fuels. Lingnocellulosic analysis indicated that DCB presented 28.83% cellulose content, which is similar to the ammounts reported for other biomass with energy potential including sawdust (31.5%), rice hulls (34%) and wheat hulls (38.2%) [36]. DCB presented low hemicellulose content (11.93%) when compared to other energy biomass, such as pine pellets (16%), sawdust (21.4%), rice hulls (27.3%), wheat husks (31.9%) and sugarcane bagasse (16e32%) [36]. When it comes to targeting production of solid products in thermochamical processs (e.g., torrefaction), biomass with contents of lignin and celulose higher than of hemicellulose are more advantageous since, at the commonly employed temperature ranges in such processes (e.g., ~200e300  C), the solid yield is significantly lower for hemicelluloses than for the other two componentes [37,38]. The content of lignin in DCB was low (3.41%) [39]. stated that lignin is directly related to the gravimetric yield of the carbonization and to the quality of the produced coal. These correlations are due to the complex chemical nature of the molecule, which, in addition to presenting greater resistance to thermal decomposition, also presents a higher percentage of elemental carbon in its composition when compared to the cellulose and hemicellulose molecules. In species typically used for energy production, including pinus, eucalyptus and sugar cane bagasse, lignin contents of 15e30% are observed. Despite the low lignin value found for DCB, it did not seem to be directly related to the percentage of fixed carbon in the sample, which presented a satisfactory value for energy purposes. DCB presented high ammounts of total extractives (52%) in comparison to other energy biomass including coconut husks (8.4%) [40] and sugarcane bagasse (16.6%) [36]. Considering wood, the most commonly employed material in the production of biochars, the ammount of extractives varies considerably. Reported values include 1e2% for Noruega abeto, 6e9% for Eucalyptus sp. and 40% for Schinopsis balansae [41]. The high ammount of extractives in DCB in in comparison to other biomasses is an interesting feature. Combustion energy varies considerably with the chemical composition of the material, with a high correlation between the heat value and the ammount of extractives, given that these substances are highly flammable, and have low molecular weight and activation energy [42]. Bulk density values (see Table 1) were high in comparison to several wood species, including Pinus (296.27 Kg.m
3), Shorea robusta (321.91 kg Kg.m
3) and Areca catechu (248.56 Kg.m
3) [43], which is an interesting characteristic. Density is directly related to operational costs including transportation and storage. Furthermore, it also affects handling of the biomass, equipment sizing and operation conditions. The obtained values for heating value (~19 MJ/kg) were high in comparison to other types of biomass including sugarcane, rice husks and corn cobs [30,35]. The differences observed for gross (GCV or higher heating value - HHV) and net calorific values (NCV or lower heating value - LCV) is related to the amount of energy associated to condensation of water during combustion. For GCV the water of combustion is condensed and the heat contained in the water vapor is recovered whereas for NCV the combustion products contain the water vapor and the heat in the water vapor is not recovered The obtained heating values are also high in comparison to other coffee processing residues such as coffee parchment (16.81 MJ/kg) [31] and husks (12 MJ/kg) [44]. Although values are smaller in comparison to those reported for spent coffee grounds, SCG (25.24 MJ/kg) [45], the use of pure SCG as fuel leads to lower boiler efficiencies and an increase in particle and gas emissions when compared to those obtained for wood pellets, for example [46].

C.M. Santos et al. / Renewable Energy 147 (2020) 1275e1291

3.2. FTIR analysis Fig. 1 presentes the FTIR spectrum for the studied biomass, i.e., defective coffee beans. Characteristc absorbance peaks commonly observed in spectra for coffees are clearly present, such as 2923 and 2854 cm
1, which are commonly attributed to asymmetric stretching of CeH in eCH3 groups of caffeine and of lipids [47]. The large band about 3200 cm
1 is attributed to the stretching of either eOH, mostly from water, phenolics and alcohols [48], or eNH, which is present in a diversity of nitrogenous compounds in coffee, e.g., amino acids and amines. The large peak in the region 22501850 cm
1 is most probably due to atmosferic CO2 interference, since natural crude coffee contains significantly small quantities of aromatic compounds to produce such a large band. The peaks in the 1800-1680 cm
1 region are attributed to the stretching of carbonyl groups (C]O), which are present if small quantities of native ketones and aldehydes in coffee and heavily present in compounds of the coffee lipidic [47,49] and carbohydrate fractions (e.g., lignin) [50]. Peaks at 1570-1500 cm
1 e 1380-1300 cm
1 can be attributed to the amide groups present in the nitrogenous compounds in coffee (e.g., protein). The bands at 1300-1000 cm
1 are related to the stretching vibration of CeO groups, which could be associated to stretching of glycosidic linkages in cellulose and hemicellulose at 1160 cm
1, to stretching of CeO in primary and secondary alcohols and to vibrations of aromatic CeOeC [48,51]. The preak at 900 cm
1 is attributed to the glycosidic b-(1e4) linkages in cellulose and galactomannans in coffee [52]. . 3.3. Thermogravimetric analysis Biomass decomposition can be divided into different stages, associated to water loss and degradation of hemicellulose, celulose and lignin. Such stages can be identified by thermogravimetric analysis (TG), specifically by observing the derivative curves (DTG), where each decomposition stage can be related to the peaks, given that their size is related to the reactivity of the substances [53]. Fig. 2 presents the thermogravimetric (TG) curves (black lines) and their derivatives (DTG e red lines) obtained for DCB in (a) oxidizing and (b) inert atmospheres, respectively, from 298.15 K to 898.15 K, with a heating rate of 15 K min
1. It is possible to observe four distinct stages of degradation (Fig. 2A). The first stage, up to about 408 K, is related to humidity loss. In the second stage, between

Fig. 1. Fourier transform infrared spectrum by the DRIFT's method of defective coffee beans.

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454 K and 594 K, as the temperatures rises (active pyrolysis), there is intense degradation of biomass with great mass loss, characteristic of the decomposition of cellulose and hemicelluloses, besides the volatilization of low-molecular-weight extractives [54,55]. The DTGs overlapping peaks show components with different reactivity levels degrading in the same temperature range, and the peak intensities indicates a high percentage of lignocellulosic and carbonaceous components (char) [53,56], as well as thermal stability [57]. In the third stage, from 569 K to 688 K, there are events with low decomposition rates (passive pyrolysis), characteristic of the continuation of the lignin decomposition, which began in the previous stage [58]. Step 4, which starts at 688 K, corresponds to the decomposition of the lignin and combustion of the char formed in previous steps. At the end of the process, approximately 3% of ashes are present, composed of inorganic remnants of the sample identified mostly as potassium, calcium, magnesium and phosphorus. In an inert atmosphere (Fig. 2B) it is also possible to identify the different stages of degradation considering the thermal behavior of extractives, cellulose, hemicelluloses and lignin, with 25% of final product. 3.4. Heating rate effect on biomass decomposition and burning The thermal degradation profile of DCB was obtained by TG analysis, under six different heating rates (from 5 to 30 K min
1). The TG and DTG curves obtained under oxidizing and inert atmospheres are shown in Fig. 3. Evaluation of the DTG curves in air (Fig. 3B) indicates superposition of two peaks in the range of 500e600K. This is particularly evident for the curves obtained at higher heating rates (15 K min
1) and attributed to the decomposition of cellulose and hemicelluloses. The main peak intensities ranged from 0.01 to 0.10 mg s
1 (see Table 3), and results indicate that peak intensity seems to be directly correlated to the heating rate. An increase of 25K in heating rate (from 5 to 30 K min
1) lead the peak intensity to increase 10 times. High heating rates result in a greater heat delay of the heat transferred to the sample particles, thus retarding the combustion of volatile particles and char [54]. The second peak, between 693.2 K and 706.1 K, refers to the final combustion of lignin and carbonaceous material, and present intensity values ranging from 0.01 to 0.04 mg s
1. Peak intensity does not seem to be related to heating rate, indicating that this parameter does not affect the combustion of these specific components. The formation of a shoulder at the second peak is noted, that increases with the heating rate, becoming prominent mainly at the rate of 10 K min
1, and being attributed to combustion of hemicelluloses and extractives. A first peak with high intensity followed by a shoulder in the second is usually attributed to cellulose recalcitrance [59]. Table 3 shows that peak temperatures, both first and second, increased as the heating rate increased. According to € Kok and Ozgür [53], at high heating rates there is a thermal delay of heat transferred to the particles of the material, causing the combustion to be slower. It is observed that for DCB the thermal delay is light, given that the maximum peak temperature did not vary significantly with heating rate. It is also possible to observe the thermal delay due to late intraparticle heat transfer and it can be inferred that surface oxidation dominates the biomass combustion at high heating rates, whereas heat transfer plays an important role at the lower heating rates. It is also possible to observe the thermal delay due to late intraparticle heat transfer. It is also possible to identify different degradation stages in inert atmosphere (Fig. 3C and D) considering the thermal behavior of the main constituents of the biomass. There are two apparent peaks, the first related to the degradation of cellulose and hemicellulose and the second related to the degradation of lignin. At rates of 5e10 K min
1 it is possible to visualize, in the first peak, the

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C.M. Santos et al. / Renewable Energy 147 (2020) 1275e1291

Fig. 2. TG and DTG curves of the defective coffee beans at heating rate of 15K.min
1 in an oxidizing (A) and inert (B) atmosphere to a temperature of 873.15K, under atmospheric pressure.

presence of overlapping, indicating the existence of different components with different reactivities. Evaluation of the DTG curves indicate that the maximum temperature of the peaks (listed on Table 3) increases with the increase in heating rate. The increase in maximum temperatures from the lowest to the highest heating rates were 8 and 5% for the oxidizing and inert atmospheres, respectively. Such behavior has been reported in previous studies employing fuel woods, with similar values: 11% increase for Prosopis juliflora [60], 8.5% for pine tree (Pinus ponderosa) and 8.2% for sal tree (Shorea robusta) [43]. 3.5. Ignition and burnout temperatures Ignition temperature (Tig) is defined as the minimum temperature at which a fuel spontaneously combusts in an environment

without an external source of ignition. Burnout Temperature (Tb) refers to the temperature at which the fuel is almost completely consumed, indicating its degree of reaction. The higher the burnout temperature, less combustible components are left in the remaining product [61]. The intersection method described by Lu and Chen [18] was used for evaluation of Tig and Tb, based on the data obtained in air (Table 4). Values obtained for the ignition temperature, between 501.5 K and 550.0 K, are within the range reported for biomass in general [62e66]. As the temperature of the maximum DTG peak increased due to the increase of the heating rate, the increase in Tig was observed, due to late intraparticle heat transfer. Burning temperature values are between 701.9 K and 749.4 K. As observed for Tig, Tb increased as the heating rates increased, implying that rapid heating of the surface of the material leads to a thermal delay, resulting in longer and therefore more

C.M. Santos et al. / Renewable Energy 147 (2020) 1275e1291

1281

Fig. 3. TG (A,C) and DTG (B,D) analyzes in oxidative (A,B) and inert (C,D) atmospheres.

Table 2 Some mathematical models used for description of reaction kinetics (adapted from Vyazovkin et al. [21]]. gðaÞ

f ðaÞ

Code

Reaction model

P4

Power law

P3

Power law

P2

Power law

P2/3

Power law

2 =3a
1=2

D1

One-dimensional diffusion

1 =2a
1

F1 A4

Mampel (first order) Avrami-Erofeev

1
a

a 2 a2

4ð1
aÞ½
lnð1
aÞ3=4

½
lnð1
aÞ1=4

A3

Avrami-Erofeev

3ð1
aÞ½
lnð1
aÞ2=3

½
lnð1
aÞ1=3

A2

Avrami-Erofeev

2ð1
aÞ½
lnð1
aÞ1=2

½
lnð1
aÞ1=2

D3

Three-dimensional diffusion

3=2ð1
aÞ2=3 ½1
ð1
aÞ1=3 
1

½1
ð1
aÞ1=3 2

R3

Contracting sphere

3ð1
aÞ

1
ð1
aÞ1=3

R2

Contracting cylinder

2ð1
aÞ1=2

D2

Two-dimensional diffusion

=

a

3a

23

14

=

a

2a

12

a

=

4a

34

13

=

12

=

=

3

=


lnð1
a)

2=3

½
lnð1
aÞ


1

1
ð1
aÞ1=2 ð1
aÞlnð1
aÞ þ a

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C.M. Santos et al. / Renewable Energy 147 (2020) 1275e1291

Table 3 Temperature and intensity of the DTGs maximum peaks at six heating rates. Heating rate (K.min
1)

Atmosphere Peak

5.0

First Second

553.4 693.2

First Second

0.01 0.01

First Second

570.5 645.6

First Second

0.006 0.002

Air

N

10

15

20

Temperature (K) 566.9 572.1 570.9 696.2 698.1 700.2
1 Intensity (mg.sec ) 0.02 0.05 0.06 0.03 0.04 0.04 Temperature (K) 575.4 581.1 580.9 653.5 660.6 675.0
1 Intensity (mg.sec ) 0.01 0.02 0.03 0.007 0.01 0.01

25

30

571.9 700.6

575.0 706.1

0.08 0.04

0.10 0.04

581.9 670.9

585.5 680.7

0.04 0.01

0.04 0.02

Table 4 Ignition and burnout temperatures of the biomasses at seven heating rates. Heating rate (K.min
1)

Tig (K) Tb (K)

5.0

10

15

20

25

30

501.5 699.6

512.3 719.8

528.0 725.0

531.4 733.9

541.0 741.8

550.0 749.4

complete combustion. With the increase of the heating rate, it was observed the displacement of both Tig and Tb to a zone of higher temperatures. One possible explanation is the occurence of thermal delay due to the intraparticle heat transfer of the biomass, since biomass is a poor heat conductor. 3.6. Kinetic analysis Fig. 4 presents the conversion degree curves (%) vs. Temperature (K) for different heating rates obtained in air (A) and nitrogen (B) atmospheres, where the presented profiles can be used for a better understanding of the biomass behavior in these different thermal processes. In air atmosphere, the process presents two visibly defined stages, whereas in nitrogen there is only one step observed in a sigmoidal curve. Up to approximately 600 K, both processes are similar, so the thermal transformations that occur probably are the same. Above this temperature, in the air plots, there is greater loss of mass, and consequently a marked change in the aspect of the curves, characteristic of oxidation processes. In these processes the chemical bonds are broken and the atoms and electrons are rearranged to form the products, with synergy among the compounds, which makes the process highly complex and difficult to understand. At the same time the curves obtained in nitrogen decrease more subtly, and the samples continue to degrade with the formation of char [67]; Chen and Wang 2007, [57]. Whether in air or nitrogen, a minimum activation energy is required for transformations to occur. It is necessary to overcome the energy barrier imposed by both physical and chemical conformations so that the reactions can take place. The activation energy (Ea) can be determined by estimating the energy released by the transformations that occur, and it is directly proportional to the stability of the material. Thus, to ensure the decomposition of the most stable structures, the energy input is essential [68]. The activation energy depends on the reaction mechanism, and high Ea values are indicative of slower reactions. According to Ref. [69], fuel reactivity can be measured directly from the activation energy, and thus knowledge of this parameter is fundamental in the evaluation of pyrolysis. Furthermore, it also provides data for optimization of process parameters and equipment design. In the present study,

KAS, OFW and Friedman models were employed for calculation of the minimum energy required. The regression lines, obtained by the previously mentioned isococonversional methods based on air and inert atmospheres, are plotted in Fig. 5A and B, respectively. An evaluation of the lines indicated that all methods provided a good description of decomposition kinetics for inert atmosphere (Fig. 5B). An evaluation of the plots in Fig. 5A indicates that the kinetic models could only provide a fair description at the beginning and at the end of the process. The distribution of the activation energy is presented in Fig. 6. The values of Ea calculated by the OFW and KAS methods are very similar. The small deviation between the results is due to the fact that the different approximations are used to calculate the temperature integral. A slightly larger distance was observed for Ea by the Friedamn method, because the method does not calculate the temperature integral. The correlation coefficient (R2), activation energy (Ea) and preexponential factor (A) are listed in Table 5. In the case of inert atmosphere, high values of correlation coefficient were observed for all the tested models (R2 > 0.98) and the values of the estimated parameters are in the same range, with similar estimates for OFW and KAS. In the case of air atmosphere, there is a significant variation in correlation coefficients, with acceptable values only for small conversions (a < 0.4). The results show that the activation energy varies with conversion values, reflecting the kinetic complexity in both combustion (air atmosphere) and pyrolysis (inert atmosphere) of the samples. In inert atmosphere the decomposition of cellulose and lignin continues whereas in air atmosphere the sample can also undergo combustion due to the presence of oxygen. For inert atmospheres, the Ea values are about 185 kJ/mol for species that contain lower quantities of extractives and Ea values are approximately 175 kJ/mol for biomass that contain higher extractive contents [70]. In the present study, Ea values ranged from 144 to 180 kJ/mol, depending on the employed model, and these values are in the lower range of those reported in the literature. This is attributed to the fact that the extractives can promote the degradation of biomass at relatively low temperatures, thus reducing its thermal stability. For inert atmosphere, the apparent activation energies in the 0.2e0.75 fraction range have values of 110.14e161.81 kJ/mol, 106.95e159.86 kJ/mol and 112.43e129.72 kJ/ mol, according to OFW, KAS and Friedman methods, respectively. In the 0.75e1 fraction range of degradation of DCB, values are in the following ranges: 161.81e157.38 kJ/mol (OFW), 159.86e153.78 kJ/ mol (KAS) and 129.72e172.79 kJ/mol (Friedman). Activation energy values have been reported to be in the following ranges: 223e246 kJ/mol, for cellulose decomposition and 105e198 kJ/mol for hemicellulose decomposition (Grønli et al., 2002). The different activation energies of DCB at different fractional conversions confirm the multistage characteristic of the pyrolysis. Ea values calculated according to Friedman's model are higher in comparison to the ones obtained using OFW and KAS models, which was also observed in oxidative atmosphere. For the process in air atmosphere a significant variation of the Ea with conversion of sample indicates a kinetically complex combustion process, i.e. the dependence of Ea with the conversion rate in dynamic experiments due to the change in the oxidation mechanism. In this sense, the dependence of Ea on a can be separated into three distinct regions for sample. The first region, in which iso-conversional Ea (in range 300e598K) can be considered stable, is defined as 0.1 < a < 0.5, corresponding to the devolatilization stage. The second region, in which a different variation and negatives values of Ea (in the range 598e678K) has been observed, is defined by 0.5 < a < 0.74. The fluctuating behavior can be associated with different oxidation mechanisms and the intense

C.M. Santos et al. / Renewable Energy 147 (2020) 1275e1291

Fig. 4. Conversion degree curves (a) vs. Temperature (K) for different heating rates.

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C.M. Santos et al. / Renewable Energy 147 (2020) 1275e1291

Fig. 5. Plot of isoconversional methods for sample in inert and air atmosphere showing the linear fits obtained.

burning of volatile matter. In the third region, for a between 0.74 and 0.9, there is a decrease in Ea (in the range 678e870K) with the conversion factor. Thus, the significant decreasing of the activation energy (for a > 0.74) could be associated with an intense burning of char (pyrolytic coal). Friedman's model provided the highest estimates for activation energy in both inert and oxidative atmosphere. One could state that, given that this model is associated to the simple differential form of the kinetics rate and does not rely on approximations, it should provide more reliable results in comparison to OFW and

KAS. However, there are errors associated to numerical differentiation as well as the use of DTG data and TG smoothing. Nonetheless, for all the employed models there is a compensation effect on the calculation of the pre-exponential fator (A) based on the linear form of the rate equation. Before the determination of the pre-exponential factor for each isoconversional model, the Criado [22] method was applied to determine the thermal degradation mechanisms as listed in Table 5, since according to isoconventional methods the activation energy can be determined without any knowledge of the function.

C.M. Santos et al. / Renewable Energy 147 (2020) 1275e1291

1285

Fig. 5. (continued).

Experimental and theoretical master plots for the most representative functions (F1, F2, F3, D3, R3, R2 and D2) are plotted as a function of conversion in Fig. 7. In inert atmosphere (Fig. 7A), the plots indicate that the biomass reaction is not the same depending on the conversion value. For all the evaluated heating rates, the experimental curves indicate that the two-dimensional diffusion reaction model (D2) provides a satisfactory description for low conversions (0.35). For higher conversions the one-dimensional diffusion reaction model (D3)

provides a better fit. Regardless of the conversion, results indicate that diffusion takes an important role in thermal decomposition. In oxidizing atmosphere (Fig. 7B), the decomposition mechanism is quite complex regardless of the heating rate. At low conversions (0.30), as also observed for inert atmosphere, the twodimensional diffusion reaction model (D2) provides the best description. For high conversions (0.90) the one-dimensional diffusion reaction model (D3) provides a better fit. No model could provide a satisfactory description of the experimental curves

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C.M. Santos et al. / Renewable Energy 147 (2020) 1275e1291

Fig. 5. (continued).

for intermediate conversion values. This can be associated to the multiple phenomena occurring simultaneously during this fase. During the oxidative phase of biomass decomposition (second peak at the DTG curve), gases and carbon are released and consumed simultaneously. Thus, there is a need for a more representative matematical model for this phase, taking into account parallel reactions [34]. Given the previously discussed tendencies, the twodimensional diffusion reaction model (D2) was employed for evaluation of the pre-exponential factor. With the calculated parameters (Ea and A), a comparison between experimental and calculated (by Friedman method) curves for DCB at 5 K min
1 for air and inert atmospheres is shown if Fig. 8. For inert atmosphere, it is possible to observe good agreement between the simulated and experimental curves, which shows the reliability of the parameters calculated for the pyrolysis of sample. However, for oxidative

atmosphere, there is a significant difference between the calculated and experimental curves in the 0.4e0.7 conversion range, showing that isoconversion methods do not provide reliable kinetic parameters for the decomposition of DCB in oxidative atmosphere. Although the employed models are commonly used for description of kinetics of thermal decomposition under inert atmosphere, recent studies have employed them in combustion processes. Chen et al. [71] evaluated the kinetics of oxy-fuel combustion of microalgae Chlorella vulgaris employing thermogravimetric analysis and both OFW and KAS models. They reported high correlation values for both models throughout the entire conversion range (0.1 < a < 0.9). They also reported an increase in activation energy with O2 concentration [72]. evaluated the oxy-fuel combustion of coals and chars, employing thermogravimetric data. The authors reported that the Friedman model provided a satisfactory description of the process, with Ea varying considerably

C.M. Santos et al. / Renewable Energy 147 (2020) 1275e1291

1287

Fig. 6. Activation energy determined by three different isoconversional methods for the thermal decomposition of sample in air (A) and inert (B) atmospheres.

especially at higher heating rates, indicating that process control changed from chemical to diffusive [73]. reported that the KAS model was appropriate for description of combustion kinetics of swine manure. In the recent study by Ref. [74], air and oxygen combustion parameters of coals and wood biomass (eucalipt and pinus) were evaluated according to OFW and KAS models. They reported high correlation values for both models in the case of air combustion. For oxygen combustion, high R2 values were only obtained for the initial conversion range (0.1 < a < 0.4), this being attributed to a violent exotermic reaction due to the high concentrations of oxygen. Unlike some of the reported data in the literature, in our study we observed that the evaluated models are not appropriate for description of air combustion throughout the entire conversion range, being acceptable only for the lowest conversion range. These

results are similar to those reported by Mureddu et al. [74] for oxygen combustion. The activation energy values (Ea) evaluated according to OFW, KAS and Friedman modelsd are displayed in Table 5. Calculated Ea values in the same range of other types of biomass reported in the literature. The kinetics energy involved in each process should vary according to both temperature and biomass composition. Therefore, the observed variations were expected. Furthermore, calculations will vary according to the experimental conditions and employed models. Thus, the obtained results are deemed quite reasonable. 3.7. Thermodynamic parameters The thermodynamic parameters were calculated for T ¼ Tm, the

C.M. Santos et al. / Renewable Energy 147 (2020) 1275e1291


17.93 98.64 285.95 386.62 406.74
1344.96 282.49
373.01
322.79
110.12
107.44
60.00
26.27
8.68
12.26
123.05
91.12
95.03 158.04 158.13 158.71 164.87 173.46 172.69 252.59 176.03 185.12 164.67 169.85 173.42 177.57 182.66 189.59 194.47 204.96 221.40 147.71 214.97 323.47 387.64 407.82
602.27 415.36
38.89
0.87 101.22 107.94 138.85 162.44 177.65 182.53 123.57 152.45 166.64 1011 1018 1028 1033 1034 10
57 1028 10
6 10
3 106 107 1010 1012 1013 1013 107 109 109 0.9896 0.9920 0.9750 0.8588 0.6606 0.4464 0.5465 0.8589 0.9506 0.9813 0.9866 0.9844 0.9799 0.9830 0.9937 0.9947 0.9914 0.9864
56.59 9.76 143.61 265.98 188.50
903.07 158.69 148.53
169.94
96.57
117.01
101.07
73.66
45.09
24.82
77.82
111.33
124.96 159.88 161.01 161.46 164.91 171.18 174.77 241.60 247.56 212.15 164.03 169.88 173.04 176.42 181.41 189.32 198.55 202.99 219.63 127.28 166.64 244.21 318.17 279.79
345.57 333.04 333.15 114.23 108.39 102.46 114.81 133.98 155.42 175.02 153.71 138.84 147.63 109 1013 1020 1027 1023 10
34 1022 1022 105 108 107 108 1010 1012 1013 1010 109 108 131.30 171.09 248.86 322.97 284.78
340.19 339.01 339.32 120.67 112.41 106.95 119.50 138.83 160.49 180.52 159.86 144.99 153.78 0.9906 0.9927 0.9767 0.8649 0.6441 0.4581 0.5580 0.8658 0.9607 0.9851 0.9879 0.9852 0.9836 0.9832 0.9887 0.9899 0.9954 0.9952
40.29 26.05 160.08 282.44 205.13
886.27 175.07 164.99
153.73
80.36
100.79
84.86
57.36
28.72
8.52
61.61
95.11
108.83 128.88 166.56 240.50 310.99 274.71
319.47 326.99 327.57 119.87 110.89 105.65 117.68 136.14 156.73 175.59 155.66 142.08 151.23 1010 1014 1021 1028 1024 10
33 1022 1022 106 108 107 109 1010 1012 1013 1010 108 1010 N

Air

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

132.90 171.01 245.15 315.79 279.70
314.09 332.96 333.74 126.31 114.91 110.14 122.37 140.99 161.80 181.09 161.81 148.23 157.38

152.09 151.54 148.27 148.25 156.52 191.20 226.11 232.50 208.45 157.19 163.73 166.57 169.19 173.28 180.50 191.16 196.88 213.94

R mol.K)

DH (KJ/ mol) A (s⁻1) Ea (KJ/ mol)

DG (KJ/ mol)

DS (J/

151.73 219.42 328.12 392.44 412.81
596.89 421.33
32.72 5.73 105.24 112.43 143.54 167.29 182.72 188.03 129.72 158.60 172.79

mol.K)

Ea (KJ/ mol) Ea (KJ/ mol)

KAS

2

OFW

Atmosphere Conversion degree ( a)

Table 5 Activation energy and thermodynamic parameters of defective coffee beans in air and nitrogen atmosphere.

A (s⁻1)

DH (KJ/ mol)

DG (KJ/ mol)

DS (J/ mol.K)

R

2

Friedman

A (s⁻1)

DH (KJ/ mol)

DG (KJ/ mol)

DS (J/

R2

0.9915 0.9896 0.9510 0.7846 0.4310 0.1763 0.2413 0.2547 0.7552 0.9878 0.9870 0.9861 0.9820 0.9861 0.9904 0.9954 0.9308 09002

1288

maximum peak temperature of the DTG, considering an intermediate heating rate of 15 K/min. Table 5 presents the thermodynamic parameters for the different degrees of conversion in both air and nitrogen, based on the different models. For reactions in solids, the pre-exponential factor is expected to range between 106 and 107, approximately, with these values being directly related to the structural conformation of the material [57]. The pre-exponential factor values found for the DBC varied for the different conversion rates, ranging from 10
33-1028; 10
34-1027; 10
57-1034 for experiments conducted in air and 107-1013; 107-1012; 106-1013 for those performed in nitrogen, based on the OFW, KAS and Friedman models, respectively. The negative results as well the wide range observed for the experiments in oxidative atmosphere confirm that the models do not provide a good fit for the entire conversion range. Results obtained in inert atmosphere are similar to those reported for other types of biomass including straw and rice bran (107-1012 and 107-1010 e OFW), chicken manure (107-1013 - OFW); and spent shitake substract [26]. These values are indicative of first-order kinetics, characterizing a highly complex structure with multiple reactions [57,75]. Low values of the preexponential factor and the activation energy are observed at the beginning and at the end of the thermal process, both in air and in nitrogen, indicating that the degradation process is easier and faster at the beginning and at the end [68], with the intermediate phase being the most complex and where the greatest range of reactions occur. The observation that the thermal process has different steps shows that the biomass has a heterogeneous and multifaceted structure. The low values of Ea and A may also indicate a porous material, which burns more easily under an oxidative atmosphere and has its degradation facilitated in an inert atmosphere [57]. The variations of enthalpies indicate energy differences between the reactants and the activated complexes formed, in agreement with the values of Ea obtained [57,76]. As the observed differences were small, for both processes, the formation of the complexes was favored, because the required energy is low [75]. The negative entropies indicate that the degree of disorder of the final product is lower when compared to the beginning of the process. It was observed, for the inert process, a pattern in the values, which are in agreement with the values of A and Ea, where the lowest values are obtained at the beginning and at the end of the thermal procedure, indicating that in the beginning the material is closer to its thermodynamic equilibrium, presenting low reactivity and low rate of formation of activated complexes. With the increase in temperature, there is an output of volatiles and molecular rearrangement, with formation of activated complexes in short periods of time, demonstrating that in this range the material presents high reactivity. At the end, with the exit of the less volatile elements and the end of the components degradation the material again has higher thermodynamic stability and lower reactivity [57,75]. This was not observed for the air process, where oxidation takes place, confirming that these conventional models can not be applied to the entire process. 4. Conclusion The present study demonstrated that defective coffee beans have intrinsic characteristics of interest for its application as solid biofuel such as low moisture, high volatile content and thermal stability. Its kinetic and thermodynamic parameters of burning and decomposition were determined by the mathematical model of Ozawa-Flynn-Wall, Kissinger-Akahira-Sunose and Friedman. The kinetics data obtained in inert atmosphere can be used for design, development and optimization of pyrolysis reactors. However, the data obtained in oxidative atmosphere indicate that a more reliable

C.M. Santos et al. / Renewable Energy 147 (2020) 1275e1291

Fig. 7. Theoretical ln½ðda=dtÞ=f ðaÞ dependencies for inert (A) and air (B) atmospheres. The codes correspond to the model numbers from Table 2.

1289

1290

C.M. Santos et al. / Renewable Energy 147 (2020) 1275e1291

Fig. 8. Comparison between experimental (*) and calculated (
) curves for defective coffee beans at 15K.min
1 for air (red) and inert (black) atmospheres. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

kinetics model is needed, that takes into account the simultaneous occurrence of pyrolysis and combustion reactions. Results indicated that this biomass presents a highly complex structure, with multiple reactions of decomposition. Given these results and the obtained gross calorific value of 19.4 MJ/kg, this study confirmed that this biomass presents potential to be used as a solid biofuel for energy production.

[11]

[12] [13] [14]

Acknowledgements

[15]

The authors acknowledge financial support from Conselho
gico e CNPq and Nacional de Desenvolvimento Científico e Tecnolo ~o de Amparo a  Pesquisa do Estado de Minas Gerais e Fundaça FAPEMIG.

[16]

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