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Thermal decomposition, kinetics and combustion parameters determination for two different sizes of rice husk using TGA
Engineering in Agriculture, Environment and Food xxx (xxxx) xxx
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Thermal decomposition, kinetics and combustion parameters determination for two different sizes of rice husk using TGA Saad El-Sayed Mechanical Power Engineering Dept., Zagazig University, Al-Sharkia, Egypt
A R T I C L E I N F O
A B S T R A C T
Keywords: Rice husk TGA Thermal pyrolysis Chemical kinetics Combustion characteristic parameters
The present study concerns the thermal pyrolysis kinetics of sieved rice husk that was classified into two sizes (38–200 μm) and (200–1000 μm) by using Thermo-Gravimetric analysis (TGA) at different heating rate (HR) values under N2. The thermal pyrolysis analysis was presented and kinetic parameters as activation energy (E), frequency factor (A), and order of reaction (n) were determined by using three different kinetic models. The effect of heating rate (HR) and particle sizes on the chemical kinetic parameters were presented and discussed. Direct method gave lower values of E and A compared to the integral method. Results showed that as particle size increases, values of the activation energy (E) and frequency factor (A) nearly increase. The combustion char acteristic parameters such as ignition, burnout and peak temperatures and their corresponding times were determined. It found that larger sizes (200–1000 μm) have a relatively lower ignition temperature, higher activation energy and noticeably lower ignition times as compared to the smaller sizes (38–200 μm).
1. Introduction Biomass was referred to all living matter, especially uses as a source of energy and fuel. It includes forest, agricultural species, some munic ipal solids, some industrial wastes, and animal by-products such as manure and fats. The analysis of biomass is done by using different analytical techniques such as TGA DTA, etc. This technique is applied to reactions where significant volatilization of a substance sample mass occurred. Several studies have addressed the pyrolysis kinetics of biomass. Skodras et al. (2006), investigated the pyrolysis and combus tion behavior of 10 biomass of 150 250-μm particle size and waste materials using TGA at different heating rates. They used independent parallel first-order reaction model for the kinetic analysis of the pyrol ysis results. Caballero et al. (1997), studied the kinetics of thermal decomposition of two lignocellulosic materials (olive stones and almond shells) using TGA at different heating rates. Different kinetic models were tested and the best results were obtained with a model that con siders the biomass decomposes via three independent reactions. Manya et al. (2003), examined the thermal decompositions of sugarcane bagasse and waste-wood samples using TGA. Three independent parallel decompositions corresponding to three pseudocomponents related to the hemicellulose, cellulose, lignin, and the weight loss related to the pyrolysis process was simulated. Fisher et al. (2002), used TGA coupled with a differential scanning calorimeter (DSC) and mass spectrometer
(MS) to pyrolyze materials such as cellulose, xylan and other carbohy drates. Also, a pyroprobe interfaced with a gas chromatograph (GC) and mass spectrometer for product identification were used. From their analysis, the DTG results at 20 � C/min heating rate show that the tem perature differences between the peak temperatures of the two decom position steps are approximately 70, 190, and 200 � C for cellulose, pectin, and xylan, respectively. Heikkinen et al. (2004), used TGA to examine pyrolysis of individual waste components (low stability organic, lignocellulosic and plastic materials) and waste mixtures. They showed that the weighed sum method can give a sign of the mixture composition. Biomass pyrolysis is a fundamental thermochemical con version process, which is of both industrial and ecological importance Slopiecka et al. (2012). Damartzis et al. (2011) investigated the pyrolysis kinetics of biomass and other related lignocellulose materials. In general, pyrolysis of biomass can be regarded as a superposition reaction of its means three components; hemicellulose, cellulose, and lignin as stated in Koufopanos et al. (1989). Meszaros et al. (2004), investigated the products of a Hungarian experimental plantation for energy crops. Three plant types and four various heating programs were estimated simultaneously by the method of least squares using the model of independent pseudo components. Miller and Bellan (1997), modeled the pyrolysis of general biomass materials through a superposition of cellulose, hemi-cellulose and lignin kinetics. All biomass components are modeled with
E-mail addresses: [email protected], [email protected]. https://doi.org/10.1016/j.eaef.2019.08.002 Received 4 March 2019; Received in revised form 31 July 2019; Accepted 26 August 2019 Available online 30 August 2019 1881-8366/© 2019 Asian Agricultural and Biological Engineering Association. Published by Elsevier B.V. All rights reserved.
Please cite this article as: Saad El-Sayed, Engineering in Agriculture, Environment and Food, https://doi.org/10.1016/j.eaef.2019.08.002
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Engineering in Agriculture, Environment and Food xxx (xxxx) xxx
methods. Gai et al. (2013) studied the pyrolysis kinetics of agricultural wastes, corn straw (CS) and rice husk (RH) in TGA under inert condi tions at different heating rates. Luangkiattikhun et al. (2008) investi gated a non-isothermal TGA of oil-palm solid wastes based on the observation of sample mass loss as a function of time or temperature at a specific heating rate. Tsamba et al. (2006) studied a pyrolysis charac teristic and kinetics of coconut and cashew nut shells. El-Sayed and Mostafa (2014, 2015) studied the pyrolysis kinetics of bagasse and cotton stock, and determined kinetic parameters using TGA/DTG and DTA techniques. El-Sayed and Khairy (2015) studied the thermal degradation of a corncob, wheat straw, and wheat dust and determined kinetic parameters using TGA/DTG data. The motivation of the present work is looking for deeper under standing the thermal decomposition mechanism of a sieved rice husk that classified into two different sizes. The first range (38–200 μm) used by most of TGA researcher and the second range is used to study the effects of using higher size (200–1000 μm) on the thermal pyrolysis process. To achieve that, the current study is focused on; (a) determining the kinetic parameters using different analytical kinetic models; (b) study the effect of the heating rates on thermal degradation and kinetic parameters values of the investigated material; and (c) determining the most important combustion characteristic parameters such as ignition, burnout, and peak temperatures using the weight loss profiles.
Table 1 Ultimate and proximate analysis and heat of combustion of rice husk dust. Ultimate analysis (wt. %, ad) C, % 39.87 H, % 5.92 N, % 1.33 S, % 0.43 Oa, % 52.45 H/C, 0.148 O/C 1.3 Proximate analysis (wt. %, ad) for sizes (38–200 μm) and (200–1000 μm), respectively. M, % 6.6–7 VM, % 63.3–68.12 Fc, % 14.26–17.91 Ash, % 10.62–12.19 332–641 Density, kg =m3 HHV (measured), kJ =kg
HHV (calculated) [30]b, kJ =kg
13988.5–14394.8 14145-14154
a
Oxygen content was calculated by difference, ad: air dry basis. The formula used to calculate HHV ¼ 19:914 ð0:2324 �AshÞ (based on proximate analysis). b
multi-step kinetics including both competitive initial pyrolysis and tar decomposition reactions. Moghtaderi (2006), presented a review of the pyrolysis models of lignocellulosic (wood-based) charring solid fuels developed over the past 60 years in order of increasing complexity. Blasi Di (2008), reviewed the state of the art in modeling chemical and physical processes of wood and biomass pyrolysis. Chemical kinetics are discussed in relation to primary reactions, characterized by one- and multi-component (or one- and multi-stage) mechanisms, and secondary reactions of tar cracking and polymerization. Chen and Kuo (2010), investigated torrefaction processes of bamboo, willow, coconut shell and wood (Ficus benjamina L.), using TGA. They found that when the light torrefaction was used, the results showed that the hemicellulose contained in the biomass was destroyed in a consid erable way, while cellulose and lignin were slightly affected. Li et al. (2008), studied the thermal decomposition of corn stalks skins, corn stalks cores, corn bracts and corn leaves using TGA. They found that the maximum pyrolysis rates increased with increasing the heating rate and the peak temperature (at maximum mass loss rate) also increased. Orfao et al. (1999), investigated the behavior of cellulose, hemicellulose and lignin using TGA and compared with the pyrolysis kinetics of cellulose specified using a first order kinetic model. Varhegyi et al. (1997), dis cussed the thermal decomposition of lignocellulosic biomass materials and their major components. Pseudo-first order models, parallel, suc cessive and competitive reaction schemes and complex reaction net works were used in their modeling. Thermochemical conversion processes such as pyrolysis and gasification are a viable means for the utilization of biomass residues as in Bridgwater (2003), Sami et al. (2001). A major step in these processes is devolatilization. It is consid ered as a basic mechanism for all these thermochemical processes, especially, for biomass that contains a large amount of volatile matter. Prins et al. (2006) studied the pyrolysis of cardoon via TGA under isothermal and non-isothermal conditions, respectively. The TGA are valuable in observing the influence of a single heating rate or varied heating rates, devolatilization temperature, and volatile matter released during biomass decomposition. However, the use of multiple heating rates has recommended for improving the accuracy of non-isothermal techniques as stated by White et al. (2011). Mansaray and Ghaly (1998) investigated the thermal degradation of four types of rice husk varieties at three heating rates in N2 atmosphere without determination of kinetic parameters or combustion character istic parameters. Biagini et al. (2008) examined thermal degradation of three biomass residues (rice husks, olive cake, and cacao shells) at different heating rates. The kinetic parameters of the investigated biomass materials are obtained by applying traditional isoconversional
2. Material and methods 2.1. Material properties Al-Sharkia Governate is considered one of the highest producers of rice in Egypt. The rice moisture after the harvest process was 18%. Rice leaves to dry under the sunrise in the fields before storage and the moisture does not exceed 14%. Milling (Whitened) of the rice is a combined series of mechanical processes that includes the removal of the hull, the outer layer, and the embryo of rice grain. The products from the milling process (i.e rice husk) were taken and sieved into two different sizes (38–200 μm and 200–1000 μm) and kept in sealed plastic bags in a dry area in the lab before doing the experiments. Proximate and elemental (ultimate) analyses of the rice husk are given in Table 1. Moisture was determined by the standard oven dry method with aiding of balance. The sample was placed in an uncovered crucible in the oven (about 4–5 h) at 105�5 � C. The dried sample was cooled to room temperature. The sample mass before drying and after drying were measured, then the percent of moisture content had been calculated. Volatile matter was determined by placing a dried sample in a covered crucible in a muffle furnace (Model; Ney Vulcan D-130, USA) at 950 þ 20 � C and kept exactly for 7 min. Then, the crucible is cooled rapidly in a desiccator to room temperature. The recorded initial sample mass and the final sample mass were used to calculate the volatiles content. Ash content of the samples was determined according to stan dard procedure of heating in the furnace at 700 � C � 25 � C. Content of fixed carbon was determined by difference. The proximate fuel prop erties were determined according to the ASTM standards; D3173-87 (2003) for moisture, D3175-07 (2007) for volatile matter (VM) and D3174-04 (2004) for ash. The HHV of the samples were determined using Barr oxygen bomb calorimeter Model 1341 EE according to ASTM D-2015. Calculations of the theoretical HHV values have also been done for the sake of comparison with the measured by a formula introduced by Manya et al. (2003). Bulk density of the two sizes was calculated according to ASTM D1895B. as shown in Table 1. The elemental composition of the rice husk was carried out using the CHNS analyzer (Model: vario MICRO cube Elementar Analysensysteme GmbH, Germany). The analytical procedure was based on the ASTM D5291-10 (2015) standard for determining the elemental CHNS (Carbon, Hydrogen, Ni trogen, and Sulphur, respectively) composition of fuels. All tests were repeated three times to ensure the accuracy and reliability of the mea surements, which are presented as average values in Table 1. Oxygen 2
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content was then calculated by difference.
as
The same notations Y and X can be used also in equations (7) and (8) (
2.2. Thermogravimetric analysis A TGA (Shimadzu TGA-50) is used for all tests. The temperature range is from room temperature up to 1000 � C at heating rates of 10, 15, and 20 � C⁄min under a nitrogen flow rate of 30 ml⁄min. Samples of 4.026 and 5.709 mg for sizes 38–200 μm and 200–1000 μm, respectively were used in tests.
Where: A is the pre-exponential or frequency factor, min 1 ; E is the activation energy of the decomposition reaction, kJ/mole; R is the universal gas constant, kJ/mole.K; T is the absolute temperature, K, t is the time, min 1; ƒ(α) is function, the type of which depends on the re action mechanism, and α is the degree of conversion. α and ƒ(α) are defined by the following expressions as m α¼ i mi
mt and ƒðαÞ ¼ ð1 mf
αÞn
E RT
ð1
dα A ¼ e dT β
ð1
(2)
According to Kissinger (1957), the reaction rate dα/dt will reach a maximum when the temperature rises during the reaction, and then returns to zero as the reactant is exhausted. Thus, the differentiation of Eq. (4) w.r.t. (t) or (T) equals zero when the maximum value of the re action rate occurs: � � � �� � d dα dα βE E ¼0 (13) Anð1 αÞn 1 exp ¼ 2 dt dt RT dt RT Therefore, βE ¼ Anð1 RTm 2
(4)
n
αÞ
ð1
(6)
�
� � � lnð1 αÞ AR ¼ ln 2 βE T
� E ðForn ¼ 1 RT
αm Þ ¼ n1=ð1
� ¼ Km nð1
αm Þn
1
(14)
(15)
nÞ
(16)
αm Þ�
n ¼ 0:1368 exp½5:365ð1
From Eq. (2), Eq. (12), Eq. (14) and Eq. (15), the following equations of E and A can be obtained as follows: � � dα E ¼ R Tm2 ð1 αm Þ n (17) dT m
2.3.2. The integral method The integral method developed by Coats and Redfern (1964). It was used to evaluate kinetic data from TG curves. The following equations are used for this analysis ( ) ( ) ! 1 ð1 αÞ1 n AR E � ln ¼ ln ðForn ¼ 6 1 (7) βE RT T2 1 n ln
E RTm
The term of (1- αm ) obtained from TGA results, and then (n) can be further determined by the use of Eq. (15). Therefore, (n) can be easily determined by thermogravimetric information, when the thermal decomposition of solid is regarded as a simple nth-order reaction (n – 1). Furthermore, for the purpose of easy application of (1- αm ), an expo nential regression equation was proposed to determine (n) conveniently from the experimental result of (1- αm ). This regression equation is
the form E X R
�
αm Þn 1 exp
The combination of Eq. (12) and Eq. (14) followed by rearrangement gives:
2.3.1. Direct Arrhenius plot method Taking the logarithm of both sides of Equation (4) the following equation is obtained in the form � � � � 1 d A E ln ¼ ln (5) � ð1 αÞn dT β RT � � dα Setting Y ¼ ln ð1 1αÞn dT and X ¼ T1 , then equation (5) can be put in
Y ¼ lnA
(10)
2
In non-isothermal TGA experiments, the heating rate (HR) is varied dα dt as a function of time as: dT ¼ ddtα � dT . Taking a linear heating rate β ¼ dT , dt Equation (3) can be rewritten as in the final form as � � E RT
(9)
plications, so the integral term can be approximately solved as: RTE exp ( RTE) (Vyazovkin (1997); Otero et al. (2008)), which allow Eq. (11) to be: � � � � 1 1 ART 2 E KRT 2 ¼ 1 ¼ ¼ exp (12) n 1 ð1 αÞ RT βE βE
(3)
αÞn
! 1 and X ¼ ðForn 6¼ 1 T
The integral term in the right-hand side of Eq. (11) does not have an exact analytical solution, but a large number of approximate equations have been proposed in the literature for performing the kinetic analysis of solid-state reactions Ortega (2008). Generally, (E)»2RT for most ap
where: mi ¼ The initial mass of the sample, mg; mf ¼ The final mass of the sample, mg; mt ¼ The sample mass at temperature T, mg, and n ¼ degree of reaction. Substituting an expression f (αÞ into equation (1) gives the expression of reaction rate in the form � � dα ¼Ae dt
)
A sequential method is proposed by Huang et al. (2011) for the purpose to determine the kinetic parameters of biomass pyrolysis quickly and accurately. By applying this method, rearrangement of Eq. (4) can be integrated as follows: 2 3 Z α Z dα 1 4 1 A T RTE 15 ¼ e dT (11) n ¼ n n 1 ð1 αÞ β 0 αÞ 0 ð1
(1)
ƒðαÞ
n
2.3.3. Sequential method
Generally, the thermal decomposition of biomass is expressed by the following equation: � � E RT
ð1 αÞ1 � 2 T 1 n
� � � lnð1 αÞ 1 Y ¼ ln and X ¼ ðForn ¼ 1 2 T T
2.3. Kinetics parameter study
dα ¼Ae dt
1
Y ¼ ln
� A¼ n
n 1 n
β
dα dT
�
"
� n 1 n
exp n Tm m
dα dT
� # (18) m
Using TG data at the maximum reaction rate (i.e., (1 - αm ) and (dα/ dT)m), both the activation energy (E) and the pre-exponential factor (A) can be obtained without a complicated calculation and process.
(8)
3
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VM ¼ 48–86 wt%; A ¼ 0.1–45 wt%, and FC ¼ 1–38 wt %, Vassilev et al. (2010). Rice husk contains high carbon and low hydrogen; this is favorable for combustion applications and contribute to the high heating value of biomass as shown in Obernberger and Thek (2004). The value of N content is so low in biomass fuel, which gives an indication that emissions of NOx can be traced through the combustion process. Sulphur content is low and this so favorable in combustion devices to minimize corrosion and reduce pollutant emission. Lastly, the higher heating value (HHV) is within the range of values 14–22 MJ/kg reported for other biomass in the literature, Vassilev et al. (2015). The bulk density of biomass fuels is important in terms of storage and transportation of the feed stock. Low bulk density is not attractive since it shows a negative effect on energy density, trans portation cost and storage capacity for both producers and end users as shown in Varhegyi et al. (1997). As a fuel, biomass burning rate was reported being depended on the bulk density as in Bridgwater (2003) and sawdust contains the lower bulk density. 3.2. Thermal degradation of the rice husk A detailed analysis of the characteristic thermal decomposition profiles in Fig. 1 show that both samples depict four peaks. Fig. 1a shows TG/DTG of HR; 10, 15, and 20 � C min 1 for (38–200 μm) particle sizes. TG analysis showed a progressive loss of mass as depicted by the downward sloping curves at various heating rates. This observation in dicates that the increase in temperature resulted in step-wise thermal degradation of the lignocellulosic components of the samples. Further more, there was a shift in the TG plots for size (38–200 μm) to the right with increasing heating rate from 10 � C/min to 20 � C/min, which is typically ascribed to the thermal time lags that occur during the thermal decomposition of biomass at different heating rates Slopiecka et al. (2012). A detailed description of the reaction pathway for the pyrolytic decomposition of the two sizes of the rice husk was deduced through the DTG plots as shown in Fig. 1a. The pyrolysis process of the lignocellu losic biomass is notably composed of regions that describe the moisture and very light volatile components removal; degradation of hemicellu lose; lignin and cellulose decomposition and lignin degradation (Yang et al., 2007; Sanchez-Silva et al., 2012). The four peaks in Fig. 1a are at 53.64 � C, 211.29 � C, 300 � C, and 469 � C and can be classified into three regions according to the slope of DTG curve; drying region (moisture and light volatile release), active pyrolysis (volatile release) region, and passive pyrolysis and probably showed a slow rate of carbonaceous matter decomposition region. The moisture and simple volatile loss region; it ranged between 39 � C and 180 � C, and the DTG shows a first peak in this drying stage of the thermal process, which was attributed to the moisture loss and also some light volatile may start to release. The following stage (zone 1) can divided into three regions as: (1) A first region starts at 180 � C–211 � C showed a very slight mass loss (5.4%). This region is identified as the starting of the decomposition of cellulose and hemicellulose. (2) A second region starts at 211 � C–270 � C where the mass loss in this region is more rapid than the previous one (28.4%), which can be referred as the starting of the active pyrolysis step. (3) A third region starts at 270 � C–325 � C. The mass loss in this third stage is highly rapid (see the slope of the DTG curve) (31%) and the maximum peak (300 � C) appears in this region. These three regions starting from 180 � C to 325 � C were taken as the first zone in the analysis of determining chemical kinetic parameters. It is shown that the most notable peak in the active pyrolysis stage (zone 1) is related to complete pyrolysis of hemicellulose, which takes place up to 325 � C. Nonetheless, the pyrolysis of cellulose occurred at 250 � C–520 � C where a peak at 469 � C was seen in DTG profile. This matched with the results that have been indicated in Mansaray and Ghaly (1998), and Yang et al. (2007). Since the cellulose compound has the structure branching of a chain of polysaccharides and no aromatic components, that’s why their volatilization is easy Gani and Naruse (2007). Ceylan and Topcu (2014), stated that zone is considered as an
Fig. 1. TG/DTG for rice husk at different particle sizes and heating rates.
3. Results and discussions The fuel characterization of the rice husk is presented in terms of its physicochemical, and thermal properties. The physicochemical proper ties are presented based on the elemental (ultimate), proximate, and calorific analyses. The thermal properties present the degradation behavior, characteristic temperature profiles, reaction mechanism, combustion characteristics, and kinetics data. 3.1. Physicochemical properties Table 1 presents the physicochemical (elemental, proximate, and calorific) fuel properties of rice husk based on average values from three experimental runs. Proximate analysis revealed high volatile matter (VM) and fixed carbon (FC) in addition to low moisture (M) and ash (A) contents for rice husk. A chemical energy stored in the biomass can be represented according to the proportion of fixed carbon and volatile matter. As the volatile/fixed carbon ratio increases, the available energy that biomass is able to be released increases. Also, the high VM and FC are an indication that the rice husk had a high content of condensable and non-condensable gases, which could be beneficial to the product yields and distribution during pyrolysis. However, the low MC and Ash indicate that the thermal conversion of rice husk could result in efficient conversion, low energy input, and operating costs as shown in Basu (2010). As the moisture percent increases in the biomass fuel, its high heating value (HHV) decreases, leading to an uneven overall energy balance in the combustors. Also, high values of ash content lead to an increase in operational costs. This may influence the burning rate during combustion and can even cause fouling and agglomeration behavior. In addition to this, biomass with high ash content (>10% wet basis) results in a poor combustion efficiency. The rice husk dust contains a high volatile matter (>60% wet basis) and the HHV. In comparison, the proximate properties are within the range M ¼ 3–63 wt%; 4
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Table 2 Devolatilization parameters at different heating rates for both sizes. Material size
β (� C/min)
(38–200 μm) rice husk
Tonset (� C)
10 15 20 10 15 20
(200–1000 μm) rice husk
Hemicellulose peak � � Tsh (� C) dm sh dt
136 147 150 136 150 150
0.13 0.193 0.265 0.175 0.053 0820
211.29 221.54 227.14 213.82 207.56 221.54
Cellulose peak � � dm peak dt 0.367 0.55 0.75 0.26 0.362 0.545
Toffset (� C) Tpeak ( C) �
300.08 307.13 311.09 309.3 311.42 317.58
567 625 613.64 550 531.8 550
Where Tonset is the extrapolated onset temperature calculated from the partial peak that results from the decomposition of the hemicellulose component (it identified � � dm the temperature at which the devolatilization can be considered to start, sh is the overall maximum of the hemicellulose decomposition rate, Tsh is the tem dt � � dm perature corresponding to the overall maximum of the hemicellulose decomposition rate, peak is the overall maximum of the cellulose decomposition rate, dt dm Tpeak is the temperature corresponding to the overall maximum of the cellulose decomposition rate and Toffset is the extrapolated offset temperature of the curves. dt This value describes the end of the cellulose decomposition (the temperature at which the main devolatilization can be considered complete). Table 3 Ignition, burnout and peak temperatures as well as their times for the tested samples of rice husk.
Rice husk (38–200)μm Rice husk (200–1000)μ m
β (� C/ min)
Tig (� C)
tig (s)
Tbo (� C)
tbo (s)
Tp (� C)
tp (s)
10 15 20 10 15 20
262 264 269 258 258 269
1341 888 709 1247 932 722
550 591 600 509 531 550
2240 1654 1266 1859 1337 1059
300 307 311 309 311 317
1522 1050 842 1542 1134 853
Where: Tig is the ignition temperature, tig is the ignition time, Tbo is the burnout temperature, tbo is the time of burnout temperature and tp is the time at peak.
active pyrolysis region because the main components, hemicelluloses, cellulose, and lignin decomposed. It is also a characterized by the high release of volatile matter. In the second zone (520 � C–1000 � C); the lignin decomposition was characterized by prolonged “tailing” but no peaks, as seen after the devolatilization region. This stage of the process pyrolysis can be partly ascribed to char degradation reactions, which occur over a wide temperature range from 80 to 900 � C and attributed to lignin decomposition. A slow degradation of biomass in this zone could be due to a thermal continuity of decomposition of lignin and other components of high molecular weight Vamvuka et al. (2003). The same behavior can be explained for the other heating rates and particle size except that the temperature at peaks increases with increasing the heating rate and particle size (see Table 2). 3.3. Effect of heating rate and particle sizes Fig. 2. TG profile for proximate analysis based on the (38–200 μm) and (200–1000 μm) thermal degradation.
Fig. 1 also, illustrates the effect of the heating rate 10, 15, and 20 � C min 1 and particle sizes (38–200 μm) and (200–1000 μm) on the ther mal degradation of rice husk. Few researchers have discussed the effect of these two parameters on the thermal decomposition of rice husk. It is shown in the figures, that the heating rate had an effect on the tem perature range of stages and on peak temperatures. Increasing the heating rate, lead to an increase in the peak temperature for the two sizes, but the range of temperature of each stage has been affected slightly, especially for the small size. This matched with the results found by (Sokoto and Bhaskar, 2018). The peak temperature gives the temperature value for the maximum volatile release rate. It can be observed that the two samples resembled each other in TG/DTG curves which presented two comparatively large mass losses, especially in the active pyrolysis stage (zone 1). Also, as the HR increases, the occurrence of the combustion reaction (mass loss peak) shifted gradually to higher
temperatures. This could be due to a decrease in heat transfer mecha nisms performance at higher heating rates. It was reported that at lower heating rates the temperature profile along the cross-section of the biomass could linear, as both the outer surface and the inner core of the biomass material attains the same temperature at a particular time when the lower heating rate is employed (Shuping et al., 2010). The heating of biomass particles occurs more gradually and leads to a better heat transfer to the inner parts of biomass (Damartzis et al., 2011; Chutia et al., 2013). At a higher heating rate, a substantial difference in tem perature profile exists along the cross-section of the biomass (Maiti et al., 2007). For the same HR, the 200–1000 μm sizes show a less slope of DTG curves for both two pyrolysis zones. This means that 200–1000 μm sizes 5
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Fig. 3. Ignition, burnout, and peak temperatures and times of different sized of rice husk at different heating rates. Table 4 Kinetic parameters of the direct method for the tested samples of rice husk at different heating rates. Rate
10
Zone
First zone
15 Second zone
Rice husk (38–200 μm) [Direct Arrhenius plot method] E (kJ =mole) 46.5 50
20
First zone
Second zone
First zone
Second zone
44.5
28
45
56.5
0.33
0.35
0.75
0.27
1.2
1.64
0.6 0.771985
0.8 0.781986
0.6 0.872176
0.5 0.822403
0.5 0.91661
0.9 0.801574
Rice husk (200–1000)μm Direct Arrhenius plot method E (KJ/mole) 67 34 0.5 0.2 A (min 1 )
60 0.9
42.5 0.5
52.5 1.1
55.5 1.3
n R2
0.5 0.9330
0.5 0.8471
0.3 0.9263
0.7 0.8530
A (min n R2
1
)
0.6 0.9664
0.4 0.9219
show a mass loss rate less than that of 38–200 μm sizes of 7.5% in the first zone and 4% for the second zone (see Table 3). The percentage of residual mass after the whole thermal degradation process is mainly determined by the total content of ash and fixed carbon as reported by Changdong and Azevedo (2005). Therefore, it can account for the greater mass loss extent of size 38–200 μm, since the total content of ash and fixed carbon for this size is smaller than that of size 200–1000 μm (see Table 1) and Fig. 2. The effect of HR on the thermal degradation of rice husk sizes of 38–200 μm is noted in Fig. 1a as compared with sizes 200–1000 μm in Fig. 1b. This may be due to insufficient time for completion of the thermal degradation reactions and larger heating rate may be required for these large sizes. Also, the poor thermal
conductivity exhibited by lignocellulosic substances impedes heat transfer within biomass particles and results in a particle temperature gradient. As the HR increases, the temperature gradient within the biomass particle increases, elevating the minimum temperature by which the pyrolysis process may progress Zhang et al. (2006). Another parameter that influences the whole thermal degradation process is the initial temperature (Ti) and the final temperature (Tf) for each zone. There is a fact that with increasing the HR, the time consumed in reaching the final temperature is less. Consequently, the heat transferred from the heating furnace within the TG instrument to the tested sample is limited, which may let there is a discrepancy between the two samples. 6
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Fig. 4. Y-X curves of the direct and integral methods at different values of n for the first and second zones of rice husk at heating rates 10 and 20 � C/min. Table 5 Kinetic parameters of the integral method for the tested samples of rice husk at different heating rates. Rate
10
Zone
First zone
15 Second zone
Rice husk (38–200)μm [Coats and Redfern (integral method)] E (kJ =mole) 63.5 61 A (min
1
)
n R2
n R2
1
)
Second zone
First zone
Second zone
62.5
62.7
72
75
0.6
0.5
0.9
0.7
2
1.2
1 0.9278
1 0.9493
1 0.9533
1 0.9599
1.2 0.9464
1.1 0.9639
Rice husk (200–1000)μm [Coats and Redfern (integral method)] E (kJ =mole) 75.5 101 A (min
20
First zone
78.5
89
74
84
0.7
1
0.96
0.98
1.1
1.2
0.9 0.9783
1.2 0.9647
0.9 0.9741
1 0.9629
0.8 0.9704
1 0.9673
As the HR increases, the mass losses are 44.4%, 49.3%, and 47.3% for the first zone and 30%, 29%, and 27.7% for the second zone. These fluctuations in mass loss % are matched with that reported by Gai e al. (2013). According to Jeguirim and Trouv� e (2009), this is because the total mass loss at any stage is mainly determined by the essential component of the tested sample, which is not affected by the HR. Therefore, the thermal behaviors of all the samples studied show that the differences found in the degradation of small and large particle sizes cannot only due to heat transfer or to the mineral matter content, but also to a possible segregation and change of the structure and compo sition during the milling process Marcillaa et al. (2013).
3.5. Ignition, burnout and peak temperature determination The important combustion characteristic parameters for the sieved rice husk are determined using the mass loss analysis (DTG profiles and data) under N2 as done in different previous works Vleeskens and Nandi (1986), Sahu et al. (2010). These parameters have an impact in relation to the residence time and furnace temperature. Ignition temperature (Tig) is identified as the temperature at which a sudden decrease in the mass loss on the DTG curves is noticed. The time at which ignition occurs is known as ignition time (tig). Burnout temperature (Tbo) is defined as the temperature at which the rate of mass loss consistently decreases to less than 1% per minute or when the mass loss rate becomes stable. The corresponding time is known as burning time (tbo). Peak temperature (Tp) is defined as the temperature that corresponds to the maximum rate 7
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Table 6 Kinetic parameters of the sequential method for the tested samples of rice husk at different heating rates. Rate
10
Zone
First zone
15
Rice husk (38–200)μm [Sequential method] 0.2611 ð1 αm Þ 9.06 � 10
ðdα=dTÞm
E (kJ =mole) A (min
1
5.34 � 10
7.79 � 10
E (kJ =mole) A (min
1
5.32 � 10
n n (experimental) [Eq. (25)]
0.3675
1.67 � 10
1.16 � 10
6
9.21 � 10
4.97 � 10
1.27 � 10
49.57 3
3.24 � 10
0.760 0.756
3
0.688 0.689
Heating rate, � C/min
Direct method
Integral method
Sequential method
Total activation energy Et (kJ/mole) 38–200 200–1000
10 15 20 10 15 20
96.5 72.5 101.5 101.0 102.0 108.0
124.5 125.2 147.0 175.5 167.5 158.0
0.2765
0.3308
6.56 � 10
6
4.09 � 10
7.23 � 10
96.87
71.20
0.96
4.68 � 10
0.975 0.96
0.649 0.654
0.2576 5
111.49 3
0.2197 5
1.59 � 10
Second zone
4.17 � 10
0.815 0.807
0.525 0.545
3
1.77 � 10
6
66.49
0.01146
0.2763 6
6.90 � 10
3
0.3170 5
9.47 � 10
111.34
96.88
0.01134
2.12 � 10
0.590 0.602
0.753 0.749
6
3
The (E) value can be determined from the slope of the fitted straight line (-E/R). The (A) value can be determined from the intercept of the line with the vertical axis. The effect of HR on the (E) values for particle sizes (38–200 μm), is fluctuated in a narrow range. However, for large particle size (200–1000 μm), as HR increases (E) value decreases for both zones. Also, (A) values increase with increasing HR for both particle sizes. It can be seen that as particle sizes increase, both (E) and (A) has nearly increased. It is also shown in the table that particle sizes (38–200 μm) have low (E) that means they are more reactive than the other sizes. Table 5 shows the results of the integral method for both stages as shown in Fig. 4c and d at HR 20 � C min 1 as an example. The slope and the intercept of the line with the vertical axis gives (-E/R) and A respectively can used to calculate E and A. This method gives higher values of (E) especially in the second zone compared with the direct method. Lower (E) and (A) values have been observed for both sizes compared to those obtained by the direct method. Table 6 presents the results obtained from the sequential method for both the first and second zones. The values of (n) here depend on (αm) or on (1- αm), but the values of (E) and (A) depend on the (dα/dT)m, at (Tm) and it is also depends on the HR and particle size. A deviation between the calculated (n), (E), and (A) using the experimental regression equation for (n) and so Eqs (17) and (18) for (E) and (A) did not exceed 5%. Comparing with the other two methods, the effects of HR on (E) are noted for both sizes. The values of (A) are lower than the values obtained from the other two methods. The values of kinetic parameters calculated for the two tested sam ples for the same method are varied from one zone to another which made these results more reliable and comparable with literature values Kalita et al. (2009). Also, these calculated values are varied for the two samples from one method to another for the same zone that made these results reliable and comparable with literature values Vimal et al. (2013). The values of the (E) for the two sizes calculated by the direct method are smaller than that calculated by the integral method. This is due to the approximate integration made in the integral method to reach its final form. Also, the integral method is often based on the introduc tory assumption of a certain reaction order and reaction model. So, the activation energy and pre-exponential factor are calculated simulta neously, not separately. So, these approaches are viewed as the model-fitting method. This kind of method usually used the single and simple reaction model during the reaction process, which can produce a substantial divergence; this discrepancy results from the difference be tween the ideal reaction model and actual heterogeneous reaction pro cess consisting of a series of parallel and sequential reactions. Therefore, adopting the model-fitting method may cause the highly uncertain values of the kinetic parameters, but a sequential method is proposed for the purpose to determine the kinetic parameters of biomass pyrolysis
Table 7 Total activation energy of the three methods. Rice husk sizes, μm
First zone
0.592 0.603
0.3633 6
Second zone
71.63 3
0.9978 0.982
0.3016 6
5
87.80 3
0.34 0.38
52.88
)
0.1945 27.19 3
n 0.54 n (experimental) [Eq. (25)] 0.55 Rice husk (200–1000)μm [Sequential method] 0.3188 ð1 αm Þ ðdα=dTÞm
First zone
3.38 � 10
6
51.33
)
Second zone
20
78.5 159.5 178.0 102.5 168.0 208.0
of mass loss (dm/dt)m due to volatilization. The corresponding time is known as peak time (tp). These temperatures and their corresponding times are a measure of combustibility and reactivity, respectively. The ignition characteristics for both sizes are recorded in Table 3 and plot in histograms in Fig. 3. It is observed that Tig, Tbo, and Tp are increased with increasing the HR as stated in Wilson et al. (2011). However, the burning rate (DTG)m or (dm/dt)m increases with increasing the HR, which was due to stronger thermal shock occurred in a short time, thus the reaction rate with oxygen in the fuel accelerated. One can also see from Table 3 that for 38–200 μm particle sizes, as HR increases the Tig so slightly increases and tig decreases because more heat is transferred to the powder. On the other side, for 200–1000 μm particle size, as the HR increases the Tig almost increases and the tig decreases. It is noticed that larger sizes (200–1000 μm) have relatively lesser Tig and noticeably higher tig except at HR ¼ 10 � C/min as compared with smaller sizes (38–200 μm). It is observed that for both sizes as the HR increases, the Tbo increases and the time reach it de creases Jau-Jang and Wei-Hsin (2015). It is noticed that the Tbo and its corresponding time for 200–1000 μm particle size are lower than those of 38–200 μm particle size. So, the 38–200 μm particle size is relatively less readily combustible compared with the other size. It is also shown that for 38–200 μm particle size, as the HR increases, the TP increases and the time to reach it decreases. The same trend can be seen for 200–1000 μm particle size. Low TP means that the smaller sizes are more reactive and easier to ignite compared to larger size Jun et al. (2016). 4. Analysis of kinetic data results Table 4 shows the results obtained from the direct Arrhenius method for both the first and second pyrolysis zones. The best value of (n) can be obtained by plotting Y vs. X for different values of (n) with the highest value of R2 as shown in Fig. 4a and b for (38–200 μm) and (200–1000 μm) for the two zones with HR 10, 0C/min as an example. 8
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quickly and accurately. So, the sequential method gives higher values of (E) than the other two methods. Finally, the (E) values obtained from the three kinetics methods lie within the range reported in other studies such as Gai et al. (2013), Yin and Goh (2011), Sevdalina et al. (2009) and Atul and Rao (1999). The total values of the activation energy for both stages are given in Table 7.
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5. Conclusions 1. DTG of both sizes of rice husk shows a peak in the drying region and the rate of mass losses for large sizes is lower. 2. Rice husk sizes (200–1000 μm) are less reactive than the sizes (38–200 μm) because the maximum peak during the devolatilization stage reached with a rate of mass losses 0.26, 0.362, and 0.545 mg/min of hemicellulose degradation after 25.78, 19, and 14.2 min, compared with 0.367, 0.55, and 0.75 after 25.37, 17.5, and 14 min. 3. It was observed that there is a slight lateral shift to higher temper atures for TG and DTG curves with increasing the heating rate. Meanwhile, Tig, Tbo, Tp, tig, tbo, and tp are increased with increasing the heating rate. 4. Larger sizes (200–1000 μm) have relatively lesser ignition tempera ture and noticeably lower ignition times as compared to the smaller sizes (38–200 μm). 5. The (E) values of two sizes calculated by the direct method are lower than those calculated by the integral method. The sequential method gives higher values of (E) than the other two methods. 6. It is shown that the most notable peak in the fast pyrolysis stage (first stage) is related to complete pyrolysis of hemicellulose which takes place up to 325 � C. But the pyrolysis of cellulose occurred at 250 � C to 520 � C. This matched with the results that have been reported in the literature. 7. As particle sizes increase, both (E) and (A) has nearly increased and particle sizes (38–200 μm) has a low (E) compared to particle sizes (200–1000 μm). 8. As HR increases activation energy (E) values decrease for large particle size (200–1000 μm). But frequency factor (A) values increase with increasing the HR for the two stages and both particle sizes Acknowledgment The author acknowledge the help of Eng. Mohamed E. Mostafa, PhD student in Huazhong University of Science and Technology in China, in drawing and table editing. References ASTM D1895. Standard test methods for apparent density, bulk factor, and pourability of plastic materials. https://www.astm.org/Standards/D1895.htm. Sharma, Atul, Rao, T. Rajeswara, 1999. Kinetics of pyrolysis of rice husk. Bioresour. Technol. 67, 53–59. https://doi.org/10.1016/S0960-8524(99)00073-5. Basu, P., 2010. Biomass Gasification and Pyrolysis: Practical Design and Theory. Academic Press (Elsevier), Burlington MA, USA. Biagini, E., Fanteib, A., Tognotti, L., 2008. Effect of the heating rate on the devolatilization of biomass residues. Thermochim. Acta 472, 55–63. https://doi. org/10.1016/j.tca.2008.03.015. Blasi Di, C., 2008. Modeling chemical and physical processes of wood and biomass pyrolysis. Prog. Energy Combust. Sci. 34, 47–90. https://doi.org/10.1016/j. pecs.2006.12.001. Bridgwater, A.V., 2003. Renewable fuels and chemicals by thermal processing of biomass. Chem. Eng. J. 91, 87–102. https://doi.org/10.1016/S1385-8947(02) 00142-0. Caballero, J.A., Conesa, J.A., Font, R., Marcilla, A.M., 1997. Pyrolysis kinetics of almond shells and olive stones considering their organic fractions. J. Anal. Appl. Pyrolysis 42, 159–175. https://doi.org/10.1016/S0165-2370(97)00015-6. Ceylan, S., Topcu, Y., 2014. Pyrolysis kinetics of Hazelnut husk using thermogravimetric analysis. Bioresour. Technol. 156, 182–188. https://doi.org/10.1016/j.biortech.201 4.01.040. Changdong, S., Azevedo, J.L.T., 2005. Estimating the higher heating value of biomass fuels from basic analysis data. Biomass Bioenergy 28, 499–507. https://doi.org/ 10.1016/j.biombioe.2004.11.008.
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Thermal decomposition, kinetics and combustion parameters determination for two different sizes of rice husk using TGA Saad El-Sayed Mechanical Power Engineering Dept., Zagazig University, Al-Sharkia, Egypt
A R T I C L E I N F O
A B S T R A C T
Keywords: Rice husk TGA Thermal pyrolysis Chemical kinetics Combustion characteristic parameters
The present study concerns the thermal pyrolysis kinetics of sieved rice husk that was classified into two sizes (38–200 μm) and (200–1000 μm) by using Thermo-Gravimetric analysis (TGA) at different heating rate (HR) values under N2. The thermal pyrolysis analysis was presented and kinetic parameters as activation energy (E), frequency factor (A), and order of reaction (n) were determined by using three different kinetic models. The effect of heating rate (HR) and particle sizes on the chemical kinetic parameters were presented and discussed. Direct method gave lower values of E and A compared to the integral method. Results showed that as particle size increases, values of the activation energy (E) and frequency factor (A) nearly increase. The combustion char acteristic parameters such as ignition, burnout and peak temperatures and their corresponding times were determined. It found that larger sizes (200–1000 μm) have a relatively lower ignition temperature, higher activation energy and noticeably lower ignition times as compared to the smaller sizes (38–200 μm).
1. Introduction Biomass was referred to all living matter, especially uses as a source of energy and fuel. It includes forest, agricultural species, some munic ipal solids, some industrial wastes, and animal by-products such as manure and fats. The analysis of biomass is done by using different analytical techniques such as TGA DTA, etc. This technique is applied to reactions where significant volatilization of a substance sample mass occurred. Several studies have addressed the pyrolysis kinetics of biomass. Skodras et al. (2006), investigated the pyrolysis and combus tion behavior of 10 biomass of 150 250-μm particle size and waste materials using TGA at different heating rates. They used independent parallel first-order reaction model for the kinetic analysis of the pyrol ysis results. Caballero et al. (1997), studied the kinetics of thermal decomposition of two lignocellulosic materials (olive stones and almond shells) using TGA at different heating rates. Different kinetic models were tested and the best results were obtained with a model that con siders the biomass decomposes via three independent reactions. Manya et al. (2003), examined the thermal decompositions of sugarcane bagasse and waste-wood samples using TGA. Three independent parallel decompositions corresponding to three pseudocomponents related to the hemicellulose, cellulose, lignin, and the weight loss related to the pyrolysis process was simulated. Fisher et al. (2002), used TGA coupled with a differential scanning calorimeter (DSC) and mass spectrometer
(MS) to pyrolyze materials such as cellulose, xylan and other carbohy drates. Also, a pyroprobe interfaced with a gas chromatograph (GC) and mass spectrometer for product identification were used. From their analysis, the DTG results at 20 � C/min heating rate show that the tem perature differences between the peak temperatures of the two decom position steps are approximately 70, 190, and 200 � C for cellulose, pectin, and xylan, respectively. Heikkinen et al. (2004), used TGA to examine pyrolysis of individual waste components (low stability organic, lignocellulosic and plastic materials) and waste mixtures. They showed that the weighed sum method can give a sign of the mixture composition. Biomass pyrolysis is a fundamental thermochemical con version process, which is of both industrial and ecological importance Slopiecka et al. (2012). Damartzis et al. (2011) investigated the pyrolysis kinetics of biomass and other related lignocellulose materials. In general, pyrolysis of biomass can be regarded as a superposition reaction of its means three components; hemicellulose, cellulose, and lignin as stated in Koufopanos et al. (1989). Meszaros et al. (2004), investigated the products of a Hungarian experimental plantation for energy crops. Three plant types and four various heating programs were estimated simultaneously by the method of least squares using the model of independent pseudo components. Miller and Bellan (1997), modeled the pyrolysis of general biomass materials through a superposition of cellulose, hemi-cellulose and lignin kinetics. All biomass components are modeled with
E-mail addresses: [email protected], [email protected]. https://doi.org/10.1016/j.eaef.2019.08.002 Received 4 March 2019; Received in revised form 31 July 2019; Accepted 26 August 2019 Available online 30 August 2019 1881-8366/© 2019 Asian Agricultural and Biological Engineering Association. Published by Elsevier B.V. All rights reserved.
Please cite this article as: Saad El-Sayed, Engineering in Agriculture, Environment and Food, https://doi.org/10.1016/j.eaef.2019.08.002
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methods. Gai et al. (2013) studied the pyrolysis kinetics of agricultural wastes, corn straw (CS) and rice husk (RH) in TGA under inert condi tions at different heating rates. Luangkiattikhun et al. (2008) investi gated a non-isothermal TGA of oil-palm solid wastes based on the observation of sample mass loss as a function of time or temperature at a specific heating rate. Tsamba et al. (2006) studied a pyrolysis charac teristic and kinetics of coconut and cashew nut shells. El-Sayed and Mostafa (2014, 2015) studied the pyrolysis kinetics of bagasse and cotton stock, and determined kinetic parameters using TGA/DTG and DTA techniques. El-Sayed and Khairy (2015) studied the thermal degradation of a corncob, wheat straw, and wheat dust and determined kinetic parameters using TGA/DTG data. The motivation of the present work is looking for deeper under standing the thermal decomposition mechanism of a sieved rice husk that classified into two different sizes. The first range (38–200 μm) used by most of TGA researcher and the second range is used to study the effects of using higher size (200–1000 μm) on the thermal pyrolysis process. To achieve that, the current study is focused on; (a) determining the kinetic parameters using different analytical kinetic models; (b) study the effect of the heating rates on thermal degradation and kinetic parameters values of the investigated material; and (c) determining the most important combustion characteristic parameters such as ignition, burnout, and peak temperatures using the weight loss profiles.
Table 1 Ultimate and proximate analysis and heat of combustion of rice husk dust. Ultimate analysis (wt. %, ad) C, % 39.87 H, % 5.92 N, % 1.33 S, % 0.43 Oa, % 52.45 H/C, 0.148 O/C 1.3 Proximate analysis (wt. %, ad) for sizes (38–200 μm) and (200–1000 μm), respectively. M, % 6.6–7 VM, % 63.3–68.12 Fc, % 14.26–17.91 Ash, % 10.62–12.19 332–641 Density, kg =m3 HHV (measured), kJ =kg
HHV (calculated) [30]b, kJ =kg
13988.5–14394.8 14145-14154
a
Oxygen content was calculated by difference, ad: air dry basis. The formula used to calculate HHV ¼ 19:914 ð0:2324 �AshÞ (based on proximate analysis). b
multi-step kinetics including both competitive initial pyrolysis and tar decomposition reactions. Moghtaderi (2006), presented a review of the pyrolysis models of lignocellulosic (wood-based) charring solid fuels developed over the past 60 years in order of increasing complexity. Blasi Di (2008), reviewed the state of the art in modeling chemical and physical processes of wood and biomass pyrolysis. Chemical kinetics are discussed in relation to primary reactions, characterized by one- and multi-component (or one- and multi-stage) mechanisms, and secondary reactions of tar cracking and polymerization. Chen and Kuo (2010), investigated torrefaction processes of bamboo, willow, coconut shell and wood (Ficus benjamina L.), using TGA. They found that when the light torrefaction was used, the results showed that the hemicellulose contained in the biomass was destroyed in a consid erable way, while cellulose and lignin were slightly affected. Li et al. (2008), studied the thermal decomposition of corn stalks skins, corn stalks cores, corn bracts and corn leaves using TGA. They found that the maximum pyrolysis rates increased with increasing the heating rate and the peak temperature (at maximum mass loss rate) also increased. Orfao et al. (1999), investigated the behavior of cellulose, hemicellulose and lignin using TGA and compared with the pyrolysis kinetics of cellulose specified using a first order kinetic model. Varhegyi et al. (1997), dis cussed the thermal decomposition of lignocellulosic biomass materials and their major components. Pseudo-first order models, parallel, suc cessive and competitive reaction schemes and complex reaction net works were used in their modeling. Thermochemical conversion processes such as pyrolysis and gasification are a viable means for the utilization of biomass residues as in Bridgwater (2003), Sami et al. (2001). A major step in these processes is devolatilization. It is consid ered as a basic mechanism for all these thermochemical processes, especially, for biomass that contains a large amount of volatile matter. Prins et al. (2006) studied the pyrolysis of cardoon via TGA under isothermal and non-isothermal conditions, respectively. The TGA are valuable in observing the influence of a single heating rate or varied heating rates, devolatilization temperature, and volatile matter released during biomass decomposition. However, the use of multiple heating rates has recommended for improving the accuracy of non-isothermal techniques as stated by White et al. (2011). Mansaray and Ghaly (1998) investigated the thermal degradation of four types of rice husk varieties at three heating rates in N2 atmosphere without determination of kinetic parameters or combustion character istic parameters. Biagini et al. (2008) examined thermal degradation of three biomass residues (rice husks, olive cake, and cacao shells) at different heating rates. The kinetic parameters of the investigated biomass materials are obtained by applying traditional isoconversional
2. Material and methods 2.1. Material properties Al-Sharkia Governate is considered one of the highest producers of rice in Egypt. The rice moisture after the harvest process was 18%. Rice leaves to dry under the sunrise in the fields before storage and the moisture does not exceed 14%. Milling (Whitened) of the rice is a combined series of mechanical processes that includes the removal of the hull, the outer layer, and the embryo of rice grain. The products from the milling process (i.e rice husk) were taken and sieved into two different sizes (38–200 μm and 200–1000 μm) and kept in sealed plastic bags in a dry area in the lab before doing the experiments. Proximate and elemental (ultimate) analyses of the rice husk are given in Table 1. Moisture was determined by the standard oven dry method with aiding of balance. The sample was placed in an uncovered crucible in the oven (about 4–5 h) at 105�5 � C. The dried sample was cooled to room temperature. The sample mass before drying and after drying were measured, then the percent of moisture content had been calculated. Volatile matter was determined by placing a dried sample in a covered crucible in a muffle furnace (Model; Ney Vulcan D-130, USA) at 950 þ 20 � C and kept exactly for 7 min. Then, the crucible is cooled rapidly in a desiccator to room temperature. The recorded initial sample mass and the final sample mass were used to calculate the volatiles content. Ash content of the samples was determined according to stan dard procedure of heating in the furnace at 700 � C � 25 � C. Content of fixed carbon was determined by difference. The proximate fuel prop erties were determined according to the ASTM standards; D3173-87 (2003) for moisture, D3175-07 (2007) for volatile matter (VM) and D3174-04 (2004) for ash. The HHV of the samples were determined using Barr oxygen bomb calorimeter Model 1341 EE according to ASTM D-2015. Calculations of the theoretical HHV values have also been done for the sake of comparison with the measured by a formula introduced by Manya et al. (2003). Bulk density of the two sizes was calculated according to ASTM D1895B. as shown in Table 1. The elemental composition of the rice husk was carried out using the CHNS analyzer (Model: vario MICRO cube Elementar Analysensysteme GmbH, Germany). The analytical procedure was based on the ASTM D5291-10 (2015) standard for determining the elemental CHNS (Carbon, Hydrogen, Ni trogen, and Sulphur, respectively) composition of fuels. All tests were repeated three times to ensure the accuracy and reliability of the mea surements, which are presented as average values in Table 1. Oxygen 2
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content was then calculated by difference.
as
The same notations Y and X can be used also in equations (7) and (8) (
2.2. Thermogravimetric analysis A TGA (Shimadzu TGA-50) is used for all tests. The temperature range is from room temperature up to 1000 � C at heating rates of 10, 15, and 20 � C⁄min under a nitrogen flow rate of 30 ml⁄min. Samples of 4.026 and 5.709 mg for sizes 38–200 μm and 200–1000 μm, respectively were used in tests.
Where: A is the pre-exponential or frequency factor, min 1 ; E is the activation energy of the decomposition reaction, kJ/mole; R is the universal gas constant, kJ/mole.K; T is the absolute temperature, K, t is the time, min 1; ƒ(α) is function, the type of which depends on the re action mechanism, and α is the degree of conversion. α and ƒ(α) are defined by the following expressions as m α¼ i mi
mt and ƒðαÞ ¼ ð1 mf
αÞn
E RT
ð1
dα A ¼ e dT β
ð1
(2)
According to Kissinger (1957), the reaction rate dα/dt will reach a maximum when the temperature rises during the reaction, and then returns to zero as the reactant is exhausted. Thus, the differentiation of Eq. (4) w.r.t. (t) or (T) equals zero when the maximum value of the re action rate occurs: � � � �� � d dα dα βE E ¼0 (13) Anð1 αÞn 1 exp ¼ 2 dt dt RT dt RT Therefore, βE ¼ Anð1 RTm 2
(4)
n
αÞ
ð1
(6)
�
� � � lnð1 αÞ AR ¼ ln 2 βE T
� E ðForn ¼ 1 RT
αm Þ ¼ n1=ð1
� ¼ Km nð1
αm Þn
1
(14)
(15)
nÞ
(16)
αm Þ�
n ¼ 0:1368 exp½5:365ð1
From Eq. (2), Eq. (12), Eq. (14) and Eq. (15), the following equations of E and A can be obtained as follows: � � dα E ¼ R Tm2 ð1 αm Þ n (17) dT m
2.3.2. The integral method The integral method developed by Coats and Redfern (1964). It was used to evaluate kinetic data from TG curves. The following equations are used for this analysis ( ) ( ) ! 1 ð1 αÞ1 n AR E � ln ¼ ln ðForn ¼ 6 1 (7) βE RT T2 1 n ln
E RTm
The term of (1- αm ) obtained from TGA results, and then (n) can be further determined by the use of Eq. (15). Therefore, (n) can be easily determined by thermogravimetric information, when the thermal decomposition of solid is regarded as a simple nth-order reaction (n – 1). Furthermore, for the purpose of easy application of (1- αm ), an expo nential regression equation was proposed to determine (n) conveniently from the experimental result of (1- αm ). This regression equation is
the form E X R
�
αm Þn 1 exp
The combination of Eq. (12) and Eq. (14) followed by rearrangement gives:
2.3.1. Direct Arrhenius plot method Taking the logarithm of both sides of Equation (4) the following equation is obtained in the form � � � � 1 d A E ln ¼ ln (5) � ð1 αÞn dT β RT � � dα Setting Y ¼ ln ð1 1αÞn dT and X ¼ T1 , then equation (5) can be put in
Y ¼ lnA
(10)
2
In non-isothermal TGA experiments, the heating rate (HR) is varied dα dt as a function of time as: dT ¼ ddtα � dT . Taking a linear heating rate β ¼ dT , dt Equation (3) can be rewritten as in the final form as � � E RT
(9)
plications, so the integral term can be approximately solved as: RTE exp ( RTE) (Vyazovkin (1997); Otero et al. (2008)), which allow Eq. (11) to be: � � � � 1 1 ART 2 E KRT 2 ¼ 1 ¼ ¼ exp (12) n 1 ð1 αÞ RT βE βE
(3)
αÞn
! 1 and X ¼ ðForn 6¼ 1 T
The integral term in the right-hand side of Eq. (11) does not have an exact analytical solution, but a large number of approximate equations have been proposed in the literature for performing the kinetic analysis of solid-state reactions Ortega (2008). Generally, (E)»2RT for most ap
where: mi ¼ The initial mass of the sample, mg; mf ¼ The final mass of the sample, mg; mt ¼ The sample mass at temperature T, mg, and n ¼ degree of reaction. Substituting an expression f (αÞ into equation (1) gives the expression of reaction rate in the form � � dα ¼Ae dt
)
A sequential method is proposed by Huang et al. (2011) for the purpose to determine the kinetic parameters of biomass pyrolysis quickly and accurately. By applying this method, rearrangement of Eq. (4) can be integrated as follows: 2 3 Z α Z dα 1 4 1 A T RTE 15 ¼ e dT (11) n ¼ n n 1 ð1 αÞ β 0 αÞ 0 ð1
(1)
ƒðαÞ
n
2.3.3. Sequential method
Generally, the thermal decomposition of biomass is expressed by the following equation: � � E RT
ð1 αÞ1 � 2 T 1 n
� � � lnð1 αÞ 1 Y ¼ ln and X ¼ ðForn ¼ 1 2 T T
2.3. Kinetics parameter study
dα ¼Ae dt
1
Y ¼ ln
� A¼ n
n 1 n
β
dα dT
�
"
� n 1 n
exp n Tm m
dα dT
� # (18) m
Using TG data at the maximum reaction rate (i.e., (1 - αm ) and (dα/ dT)m), both the activation energy (E) and the pre-exponential factor (A) can be obtained without a complicated calculation and process.
(8)
3
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VM ¼ 48–86 wt%; A ¼ 0.1–45 wt%, and FC ¼ 1–38 wt %, Vassilev et al. (2010). Rice husk contains high carbon and low hydrogen; this is favorable for combustion applications and contribute to the high heating value of biomass as shown in Obernberger and Thek (2004). The value of N content is so low in biomass fuel, which gives an indication that emissions of NOx can be traced through the combustion process. Sulphur content is low and this so favorable in combustion devices to minimize corrosion and reduce pollutant emission. Lastly, the higher heating value (HHV) is within the range of values 14–22 MJ/kg reported for other biomass in the literature, Vassilev et al. (2015). The bulk density of biomass fuels is important in terms of storage and transportation of the feed stock. Low bulk density is not attractive since it shows a negative effect on energy density, trans portation cost and storage capacity for both producers and end users as shown in Varhegyi et al. (1997). As a fuel, biomass burning rate was reported being depended on the bulk density as in Bridgwater (2003) and sawdust contains the lower bulk density. 3.2. Thermal degradation of the rice husk A detailed analysis of the characteristic thermal decomposition profiles in Fig. 1 show that both samples depict four peaks. Fig. 1a shows TG/DTG of HR; 10, 15, and 20 � C min 1 for (38–200 μm) particle sizes. TG analysis showed a progressive loss of mass as depicted by the downward sloping curves at various heating rates. This observation in dicates that the increase in temperature resulted in step-wise thermal degradation of the lignocellulosic components of the samples. Further more, there was a shift in the TG plots for size (38–200 μm) to the right with increasing heating rate from 10 � C/min to 20 � C/min, which is typically ascribed to the thermal time lags that occur during the thermal decomposition of biomass at different heating rates Slopiecka et al. (2012). A detailed description of the reaction pathway for the pyrolytic decomposition of the two sizes of the rice husk was deduced through the DTG plots as shown in Fig. 1a. The pyrolysis process of the lignocellu losic biomass is notably composed of regions that describe the moisture and very light volatile components removal; degradation of hemicellu lose; lignin and cellulose decomposition and lignin degradation (Yang et al., 2007; Sanchez-Silva et al., 2012). The four peaks in Fig. 1a are at 53.64 � C, 211.29 � C, 300 � C, and 469 � C and can be classified into three regions according to the slope of DTG curve; drying region (moisture and light volatile release), active pyrolysis (volatile release) region, and passive pyrolysis and probably showed a slow rate of carbonaceous matter decomposition region. The moisture and simple volatile loss region; it ranged between 39 � C and 180 � C, and the DTG shows a first peak in this drying stage of the thermal process, which was attributed to the moisture loss and also some light volatile may start to release. The following stage (zone 1) can divided into three regions as: (1) A first region starts at 180 � C–211 � C showed a very slight mass loss (5.4%). This region is identified as the starting of the decomposition of cellulose and hemicellulose. (2) A second region starts at 211 � C–270 � C where the mass loss in this region is more rapid than the previous one (28.4%), which can be referred as the starting of the active pyrolysis step. (3) A third region starts at 270 � C–325 � C. The mass loss in this third stage is highly rapid (see the slope of the DTG curve) (31%) and the maximum peak (300 � C) appears in this region. These three regions starting from 180 � C to 325 � C were taken as the first zone in the analysis of determining chemical kinetic parameters. It is shown that the most notable peak in the active pyrolysis stage (zone 1) is related to complete pyrolysis of hemicellulose, which takes place up to 325 � C. Nonetheless, the pyrolysis of cellulose occurred at 250 � C–520 � C where a peak at 469 � C was seen in DTG profile. This matched with the results that have been indicated in Mansaray and Ghaly (1998), and Yang et al. (2007). Since the cellulose compound has the structure branching of a chain of polysaccharides and no aromatic components, that’s why their volatilization is easy Gani and Naruse (2007). Ceylan and Topcu (2014), stated that zone is considered as an
Fig. 1. TG/DTG for rice husk at different particle sizes and heating rates.
3. Results and discussions The fuel characterization of the rice husk is presented in terms of its physicochemical, and thermal properties. The physicochemical proper ties are presented based on the elemental (ultimate), proximate, and calorific analyses. The thermal properties present the degradation behavior, characteristic temperature profiles, reaction mechanism, combustion characteristics, and kinetics data. 3.1. Physicochemical properties Table 1 presents the physicochemical (elemental, proximate, and calorific) fuel properties of rice husk based on average values from three experimental runs. Proximate analysis revealed high volatile matter (VM) and fixed carbon (FC) in addition to low moisture (M) and ash (A) contents for rice husk. A chemical energy stored in the biomass can be represented according to the proportion of fixed carbon and volatile matter. As the volatile/fixed carbon ratio increases, the available energy that biomass is able to be released increases. Also, the high VM and FC are an indication that the rice husk had a high content of condensable and non-condensable gases, which could be beneficial to the product yields and distribution during pyrolysis. However, the low MC and Ash indicate that the thermal conversion of rice husk could result in efficient conversion, low energy input, and operating costs as shown in Basu (2010). As the moisture percent increases in the biomass fuel, its high heating value (HHV) decreases, leading to an uneven overall energy balance in the combustors. Also, high values of ash content lead to an increase in operational costs. This may influence the burning rate during combustion and can even cause fouling and agglomeration behavior. In addition to this, biomass with high ash content (>10% wet basis) results in a poor combustion efficiency. The rice husk dust contains a high volatile matter (>60% wet basis) and the HHV. In comparison, the proximate properties are within the range M ¼ 3–63 wt%; 4
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Table 2 Devolatilization parameters at different heating rates for both sizes. Material size
β (� C/min)
(38–200 μm) rice husk
Tonset (� C)
10 15 20 10 15 20
(200–1000 μm) rice husk
Hemicellulose peak � � Tsh (� C) dm sh dt
136 147 150 136 150 150
0.13 0.193 0.265 0.175 0.053 0820
211.29 221.54 227.14 213.82 207.56 221.54
Cellulose peak � � dm peak dt 0.367 0.55 0.75 0.26 0.362 0.545
Toffset (� C) Tpeak ( C) �
300.08 307.13 311.09 309.3 311.42 317.58
567 625 613.64 550 531.8 550
Where Tonset is the extrapolated onset temperature calculated from the partial peak that results from the decomposition of the hemicellulose component (it identified � � dm the temperature at which the devolatilization can be considered to start, sh is the overall maximum of the hemicellulose decomposition rate, Tsh is the tem dt � � dm perature corresponding to the overall maximum of the hemicellulose decomposition rate, peak is the overall maximum of the cellulose decomposition rate, dt dm Tpeak is the temperature corresponding to the overall maximum of the cellulose decomposition rate and Toffset is the extrapolated offset temperature of the curves. dt This value describes the end of the cellulose decomposition (the temperature at which the main devolatilization can be considered complete). Table 3 Ignition, burnout and peak temperatures as well as their times for the tested samples of rice husk.
Rice husk (38–200)μm Rice husk (200–1000)μ m
β (� C/ min)
Tig (� C)
tig (s)
Tbo (� C)
tbo (s)
Tp (� C)
tp (s)
10 15 20 10 15 20
262 264 269 258 258 269
1341 888 709 1247 932 722
550 591 600 509 531 550
2240 1654 1266 1859 1337 1059
300 307 311 309 311 317
1522 1050 842 1542 1134 853
Where: Tig is the ignition temperature, tig is the ignition time, Tbo is the burnout temperature, tbo is the time of burnout temperature and tp is the time at peak.
active pyrolysis region because the main components, hemicelluloses, cellulose, and lignin decomposed. It is also a characterized by the high release of volatile matter. In the second zone (520 � C–1000 � C); the lignin decomposition was characterized by prolonged “tailing” but no peaks, as seen after the devolatilization region. This stage of the process pyrolysis can be partly ascribed to char degradation reactions, which occur over a wide temperature range from 80 to 900 � C and attributed to lignin decomposition. A slow degradation of biomass in this zone could be due to a thermal continuity of decomposition of lignin and other components of high molecular weight Vamvuka et al. (2003). The same behavior can be explained for the other heating rates and particle size except that the temperature at peaks increases with increasing the heating rate and particle size (see Table 2). 3.3. Effect of heating rate and particle sizes Fig. 2. TG profile for proximate analysis based on the (38–200 μm) and (200–1000 μm) thermal degradation.
Fig. 1 also, illustrates the effect of the heating rate 10, 15, and 20 � C min 1 and particle sizes (38–200 μm) and (200–1000 μm) on the ther mal degradation of rice husk. Few researchers have discussed the effect of these two parameters on the thermal decomposition of rice husk. It is shown in the figures, that the heating rate had an effect on the tem perature range of stages and on peak temperatures. Increasing the heating rate, lead to an increase in the peak temperature for the two sizes, but the range of temperature of each stage has been affected slightly, especially for the small size. This matched with the results found by (Sokoto and Bhaskar, 2018). The peak temperature gives the temperature value for the maximum volatile release rate. It can be observed that the two samples resembled each other in TG/DTG curves which presented two comparatively large mass losses, especially in the active pyrolysis stage (zone 1). Also, as the HR increases, the occurrence of the combustion reaction (mass loss peak) shifted gradually to higher
temperatures. This could be due to a decrease in heat transfer mecha nisms performance at higher heating rates. It was reported that at lower heating rates the temperature profile along the cross-section of the biomass could linear, as both the outer surface and the inner core of the biomass material attains the same temperature at a particular time when the lower heating rate is employed (Shuping et al., 2010). The heating of biomass particles occurs more gradually and leads to a better heat transfer to the inner parts of biomass (Damartzis et al., 2011; Chutia et al., 2013). At a higher heating rate, a substantial difference in tem perature profile exists along the cross-section of the biomass (Maiti et al., 2007). For the same HR, the 200–1000 μm sizes show a less slope of DTG curves for both two pyrolysis zones. This means that 200–1000 μm sizes 5
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Fig. 3. Ignition, burnout, and peak temperatures and times of different sized of rice husk at different heating rates. Table 4 Kinetic parameters of the direct method for the tested samples of rice husk at different heating rates. Rate
10
Zone
First zone
15 Second zone
Rice husk (38–200 μm) [Direct Arrhenius plot method] E (kJ =mole) 46.5 50
20
First zone
Second zone
First zone
Second zone
44.5
28
45
56.5
0.33
0.35
0.75
0.27
1.2
1.64
0.6 0.771985
0.8 0.781986
0.6 0.872176
0.5 0.822403
0.5 0.91661
0.9 0.801574
Rice husk (200–1000)μm Direct Arrhenius plot method E (KJ/mole) 67 34 0.5 0.2 A (min 1 )
60 0.9
42.5 0.5
52.5 1.1
55.5 1.3
n R2
0.5 0.9330
0.5 0.8471
0.3 0.9263
0.7 0.8530
A (min n R2
1
)
0.6 0.9664
0.4 0.9219
show a mass loss rate less than that of 38–200 μm sizes of 7.5% in the first zone and 4% for the second zone (see Table 3). The percentage of residual mass after the whole thermal degradation process is mainly determined by the total content of ash and fixed carbon as reported by Changdong and Azevedo (2005). Therefore, it can account for the greater mass loss extent of size 38–200 μm, since the total content of ash and fixed carbon for this size is smaller than that of size 200–1000 μm (see Table 1) and Fig. 2. The effect of HR on the thermal degradation of rice husk sizes of 38–200 μm is noted in Fig. 1a as compared with sizes 200–1000 μm in Fig. 1b. This may be due to insufficient time for completion of the thermal degradation reactions and larger heating rate may be required for these large sizes. Also, the poor thermal
conductivity exhibited by lignocellulosic substances impedes heat transfer within biomass particles and results in a particle temperature gradient. As the HR increases, the temperature gradient within the biomass particle increases, elevating the minimum temperature by which the pyrolysis process may progress Zhang et al. (2006). Another parameter that influences the whole thermal degradation process is the initial temperature (Ti) and the final temperature (Tf) for each zone. There is a fact that with increasing the HR, the time consumed in reaching the final temperature is less. Consequently, the heat transferred from the heating furnace within the TG instrument to the tested sample is limited, which may let there is a discrepancy between the two samples. 6
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Fig. 4. Y-X curves of the direct and integral methods at different values of n for the first and second zones of rice husk at heating rates 10 and 20 � C/min. Table 5 Kinetic parameters of the integral method for the tested samples of rice husk at different heating rates. Rate
10
Zone
First zone
15 Second zone
Rice husk (38–200)μm [Coats and Redfern (integral method)] E (kJ =mole) 63.5 61 A (min
1
)
n R2
n R2
1
)
Second zone
First zone
Second zone
62.5
62.7
72
75
0.6
0.5
0.9
0.7
2
1.2
1 0.9278
1 0.9493
1 0.9533
1 0.9599
1.2 0.9464
1.1 0.9639
Rice husk (200–1000)μm [Coats and Redfern (integral method)] E (kJ =mole) 75.5 101 A (min
20
First zone
78.5
89
74
84
0.7
1
0.96
0.98
1.1
1.2
0.9 0.9783
1.2 0.9647
0.9 0.9741
1 0.9629
0.8 0.9704
1 0.9673
As the HR increases, the mass losses are 44.4%, 49.3%, and 47.3% for the first zone and 30%, 29%, and 27.7% for the second zone. These fluctuations in mass loss % are matched with that reported by Gai e al. (2013). According to Jeguirim and Trouv� e (2009), this is because the total mass loss at any stage is mainly determined by the essential component of the tested sample, which is not affected by the HR. Therefore, the thermal behaviors of all the samples studied show that the differences found in the degradation of small and large particle sizes cannot only due to heat transfer or to the mineral matter content, but also to a possible segregation and change of the structure and compo sition during the milling process Marcillaa et al. (2013).
3.5. Ignition, burnout and peak temperature determination The important combustion characteristic parameters for the sieved rice husk are determined using the mass loss analysis (DTG profiles and data) under N2 as done in different previous works Vleeskens and Nandi (1986), Sahu et al. (2010). These parameters have an impact in relation to the residence time and furnace temperature. Ignition temperature (Tig) is identified as the temperature at which a sudden decrease in the mass loss on the DTG curves is noticed. The time at which ignition occurs is known as ignition time (tig). Burnout temperature (Tbo) is defined as the temperature at which the rate of mass loss consistently decreases to less than 1% per minute or when the mass loss rate becomes stable. The corresponding time is known as burning time (tbo). Peak temperature (Tp) is defined as the temperature that corresponds to the maximum rate 7
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Engineering in Agriculture, Environment and Food xxx (xxxx) xxx
Table 6 Kinetic parameters of the sequential method for the tested samples of rice husk at different heating rates. Rate
10
Zone
First zone
15
Rice husk (38–200)μm [Sequential method] 0.2611 ð1 αm Þ 9.06 � 10
ðdα=dTÞm
E (kJ =mole) A (min
1
5.34 � 10
7.79 � 10
E (kJ =mole) A (min
1
5.32 � 10
n n (experimental) [Eq. (25)]
0.3675
1.67 � 10
1.16 � 10
6
9.21 � 10
4.97 � 10
1.27 � 10
49.57 3
3.24 � 10
0.760 0.756
3
0.688 0.689
Heating rate, � C/min
Direct method
Integral method
Sequential method
Total activation energy Et (kJ/mole) 38–200 200–1000
10 15 20 10 15 20
96.5 72.5 101.5 101.0 102.0 108.0
124.5 125.2 147.0 175.5 167.5 158.0
0.2765
0.3308
6.56 � 10
6
4.09 � 10
7.23 � 10
96.87
71.20
0.96
4.68 � 10
0.975 0.96
0.649 0.654
0.2576 5
111.49 3
0.2197 5
1.59 � 10
Second zone
4.17 � 10
0.815 0.807
0.525 0.545
3
1.77 � 10
6
66.49
0.01146
0.2763 6
6.90 � 10
3
0.3170 5
9.47 � 10
111.34
96.88
0.01134
2.12 � 10
0.590 0.602
0.753 0.749
6
3
The (E) value can be determined from the slope of the fitted straight line (-E/R). The (A) value can be determined from the intercept of the line with the vertical axis. The effect of HR on the (E) values for particle sizes (38–200 μm), is fluctuated in a narrow range. However, for large particle size (200–1000 μm), as HR increases (E) value decreases for both zones. Also, (A) values increase with increasing HR for both particle sizes. It can be seen that as particle sizes increase, both (E) and (A) has nearly increased. It is also shown in the table that particle sizes (38–200 μm) have low (E) that means they are more reactive than the other sizes. Table 5 shows the results of the integral method for both stages as shown in Fig. 4c and d at HR 20 � C min 1 as an example. The slope and the intercept of the line with the vertical axis gives (-E/R) and A respectively can used to calculate E and A. This method gives higher values of (E) especially in the second zone compared with the direct method. Lower (E) and (A) values have been observed for both sizes compared to those obtained by the direct method. Table 6 presents the results obtained from the sequential method for both the first and second zones. The values of (n) here depend on (αm) or on (1- αm), but the values of (E) and (A) depend on the (dα/dT)m, at (Tm) and it is also depends on the HR and particle size. A deviation between the calculated (n), (E), and (A) using the experimental regression equation for (n) and so Eqs (17) and (18) for (E) and (A) did not exceed 5%. Comparing with the other two methods, the effects of HR on (E) are noted for both sizes. The values of (A) are lower than the values obtained from the other two methods. The values of kinetic parameters calculated for the two tested sam ples for the same method are varied from one zone to another which made these results more reliable and comparable with literature values Kalita et al. (2009). Also, these calculated values are varied for the two samples from one method to another for the same zone that made these results reliable and comparable with literature values Vimal et al. (2013). The values of the (E) for the two sizes calculated by the direct method are smaller than that calculated by the integral method. This is due to the approximate integration made in the integral method to reach its final form. Also, the integral method is often based on the introduc tory assumption of a certain reaction order and reaction model. So, the activation energy and pre-exponential factor are calculated simulta neously, not separately. So, these approaches are viewed as the model-fitting method. This kind of method usually used the single and simple reaction model during the reaction process, which can produce a substantial divergence; this discrepancy results from the difference be tween the ideal reaction model and actual heterogeneous reaction pro cess consisting of a series of parallel and sequential reactions. Therefore, adopting the model-fitting method may cause the highly uncertain values of the kinetic parameters, but a sequential method is proposed for the purpose to determine the kinetic parameters of biomass pyrolysis
Table 7 Total activation energy of the three methods. Rice husk sizes, μm
First zone
0.592 0.603
0.3633 6
Second zone
71.63 3
0.9978 0.982
0.3016 6
5
87.80 3
0.34 0.38
52.88
)
0.1945 27.19 3
n 0.54 n (experimental) [Eq. (25)] 0.55 Rice husk (200–1000)μm [Sequential method] 0.3188 ð1 αm Þ ðdα=dTÞm
First zone
3.38 � 10
6
51.33
)
Second zone
20
78.5 159.5 178.0 102.5 168.0 208.0
of mass loss (dm/dt)m due to volatilization. The corresponding time is known as peak time (tp). These temperatures and their corresponding times are a measure of combustibility and reactivity, respectively. The ignition characteristics for both sizes are recorded in Table 3 and plot in histograms in Fig. 3. It is observed that Tig, Tbo, and Tp are increased with increasing the HR as stated in Wilson et al. (2011). However, the burning rate (DTG)m or (dm/dt)m increases with increasing the HR, which was due to stronger thermal shock occurred in a short time, thus the reaction rate with oxygen in the fuel accelerated. One can also see from Table 3 that for 38–200 μm particle sizes, as HR increases the Tig so slightly increases and tig decreases because more heat is transferred to the powder. On the other side, for 200–1000 μm particle size, as the HR increases the Tig almost increases and the tig decreases. It is noticed that larger sizes (200–1000 μm) have relatively lesser Tig and noticeably higher tig except at HR ¼ 10 � C/min as compared with smaller sizes (38–200 μm). It is observed that for both sizes as the HR increases, the Tbo increases and the time reach it de creases Jau-Jang and Wei-Hsin (2015). It is noticed that the Tbo and its corresponding time for 200–1000 μm particle size are lower than those of 38–200 μm particle size. So, the 38–200 μm particle size is relatively less readily combustible compared with the other size. It is also shown that for 38–200 μm particle size, as the HR increases, the TP increases and the time to reach it decreases. The same trend can be seen for 200–1000 μm particle size. Low TP means that the smaller sizes are more reactive and easier to ignite compared to larger size Jun et al. (2016). 4. Analysis of kinetic data results Table 4 shows the results obtained from the direct Arrhenius method for both the first and second pyrolysis zones. The best value of (n) can be obtained by plotting Y vs. X for different values of (n) with the highest value of R2 as shown in Fig. 4a and b for (38–200 μm) and (200–1000 μm) for the two zones with HR 10, 0C/min as an example. 8
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Engineering in Agriculture, Environment and Food xxx (xxxx) xxx
quickly and accurately. So, the sequential method gives higher values of (E) than the other two methods. Finally, the (E) values obtained from the three kinetics methods lie within the range reported in other studies such as Gai et al. (2013), Yin and Goh (2011), Sevdalina et al. (2009) and Atul and Rao (1999). The total values of the activation energy for both stages are given in Table 7.
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5. Conclusions 1. DTG of both sizes of rice husk shows a peak in the drying region and the rate of mass losses for large sizes is lower. 2. Rice husk sizes (200–1000 μm) are less reactive than the sizes (38–200 μm) because the maximum peak during the devolatilization stage reached with a rate of mass losses 0.26, 0.362, and 0.545 mg/min of hemicellulose degradation after 25.78, 19, and 14.2 min, compared with 0.367, 0.55, and 0.75 after 25.37, 17.5, and 14 min. 3. It was observed that there is a slight lateral shift to higher temper atures for TG and DTG curves with increasing the heating rate. Meanwhile, Tig, Tbo, Tp, tig, tbo, and tp are increased with increasing the heating rate. 4. Larger sizes (200–1000 μm) have relatively lesser ignition tempera ture and noticeably lower ignition times as compared to the smaller sizes (38–200 μm). 5. The (E) values of two sizes calculated by the direct method are lower than those calculated by the integral method. The sequential method gives higher values of (E) than the other two methods. 6. It is shown that the most notable peak in the fast pyrolysis stage (first stage) is related to complete pyrolysis of hemicellulose which takes place up to 325 � C. But the pyrolysis of cellulose occurred at 250 � C to 520 � C. This matched with the results that have been reported in the literature. 7. As particle sizes increase, both (E) and (A) has nearly increased and particle sizes (38–200 μm) has a low (E) compared to particle sizes (200–1000 μm). 8. As HR increases activation energy (E) values decrease for large particle size (200–1000 μm). But frequency factor (A) values increase with increasing the HR for the two stages and both particle sizes Acknowledgment The author acknowledge the help of Eng. Mohamed E. Mostafa, PhD student in Huazhong University of Science and Technology in China, in drawing and table editing. References ASTM D1895. Standard test methods for apparent density, bulk factor, and pourability of plastic materials. https://www.astm.org/Standards/D1895.htm. Sharma, Atul, Rao, T. Rajeswara, 1999. Kinetics of pyrolysis of rice husk. Bioresour. Technol. 67, 53–59. https://doi.org/10.1016/S0960-8524(99)00073-5. Basu, P., 2010. Biomass Gasification and Pyrolysis: Practical Design and Theory. Academic Press (Elsevier), Burlington MA, USA. Biagini, E., Fanteib, A., Tognotti, L., 2008. Effect of the heating rate on the devolatilization of biomass residues. Thermochim. Acta 472, 55–63. https://doi. org/10.1016/j.tca.2008.03.015. Blasi Di, C., 2008. Modeling chemical and physical processes of wood and biomass pyrolysis. Prog. Energy Combust. Sci. 34, 47–90. https://doi.org/10.1016/j. pecs.2006.12.001. Bridgwater, A.V., 2003. Renewable fuels and chemicals by thermal processing of biomass. Chem. Eng. J. 91, 87–102. https://doi.org/10.1016/S1385-8947(02) 00142-0. Caballero, J.A., Conesa, J.A., Font, R., Marcilla, A.M., 1997. Pyrolysis kinetics of almond shells and olive stones considering their organic fractions. J. Anal. Appl. Pyrolysis 42, 159–175. https://doi.org/10.1016/S0165-2370(97)00015-6. Ceylan, S., Topcu, Y., 2014. Pyrolysis kinetics of Hazelnut husk using thermogravimetric analysis. Bioresour. Technol. 156, 182–188. https://doi.org/10.1016/j.biortech.201 4.01.040. Changdong, S., Azevedo, J.L.T., 2005. Estimating the higher heating value of biomass fuels from basic analysis data. Biomass Bioenergy 28, 499–507. https://doi.org/ 10.1016/j.biombioe.2004.11.008.
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