PYROLYSIS OF PINE BARK, WHEAT STRAW AND RICE HUSK: THERMOGRAVIMETRIC ANALYSIS AND KINETIC STUDY

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1 PYROLYSIS OF PINE BARK, WHEAT STRAW AND RICE HUSK: THERMOGRAVIMETRIC ANALYSIS AND KINETIC STUDY Ana Isabel Marques Ferreiro Department of Mechanical Engineering, Instituto Superior Técnico, University of Lisboa ABSTRACT This manuscript focuses on the pyrolysis of pine bark, wheat straw and rice husk. Thermogravimetric curves were obtained for the three biomass fuels for heating rates of 5, 10 and 15 K/min in an inert atmosphere of Argon to investigate the impact of the type of biomass in the pyrolysis behaviour under different heating conditions. Distinctive thermogravimetric and differential thermogravimetric curves were obtained owing to the different composition of the biomass fuels, but the impact of the heating rates was marginal. In order to better understand the impact of the biomass composition in the pyrolysis, their main components were estimated. Additionally, a two-step algorithm was used to optimize the global kinetic parameters of a single reaction model (SFOM) and of a three parallel reaction model (3PM). The activation energies obtained by fitting each model to the experimental data are within the values reported in the literature. For each biomass fuel, all heating rates were globally fitted, with errors of the order of 5%-7% for the SFOM and of ~ 2% for the 3PM. KEYWORDS Biomass, pyrolysis, thermogravimetry, kinetic study, optimization INTRODUCTION Biomass is one of the most abundant and used sources of renewable energy in the world with reduced impact on global warming. Biomass is becoming more relevant as an energy carrier due to its high diversity and availability. It includes plants, leftovers from agricultural materials and forestry processes, as well as organic industrial, animal and human wastes. Agricultural wastes, in particular, contain a high amount of organic constituents (cellulose, hemicellulose, lignin and minor amounts of other organics) and possess high energy content [1]. Biomass can be upgraded through pyrolysis, which is a form of thermal treatment that decomposes organic materials into liquid, solid and gaseous forms in the absence of oxygen. All three-output fractions have potential as fuels for transports, power generation and combined heat and power [2]. Most biomass upgrading processes are optimized to woody biomass [3]; however, due to economic and environmental reasons, only a part of the available forest biomass can be used. In this context, in addition to forest biomass, it is critical to use also agricultural residues for energy purposes. The extreme variability of the biomass feedstock demands for an extensive investigation on the impact of its composition in their pyrolysis behaviour. Thermogravimetric (TG) and differential thermogravimetric (DTG) curves allow studying the pyrolysis behaviour, relate it to the feedstock properties and isolated it from transport processes. There are several pyrolysis kinetic models with distinctive levels of complexity available in the literature. The simplest one is the single first order reaction model (SFOM) [4 7] that considers only one stage of decomposition. Another one is the three parallel reactions model (3PM) [3,4,8] that describes the global decomposition of cellulose, hemicellulose and lignin. The decomposition of extractives can be added, as in the work of Grønli et al. [9], who considered five parallel reactions (5PM). Complex predictive advanced models include the Bio-CPD, which considers additional molecular structural constrains during the pyrolysis process, and the Bio-PoliMi mechanism, a multistep devolatilization model with 43 species and 14 chemical reactions [10]. The later models were developed as predictive tools, and require extensive validation. The biomass contents of cellulose, hemicellulose and lignin are usually not available so methods to estimate them are needed. There are a few methods available that use the biomass proximate and ultimate analysis to predict the cellulose, hemicellulose and lignin contents. Sheng and Azevedo [17] developed a correlation method based on extensive literature data, and Cuoci et al. [18] developed the triangulation method that considers five different reference components, specifically for the Bio-PoliMi mechanism. Contrary to the simplistic and time consuming Arrhenius plot method, optimization methods based in the minimization of the error between an experimental curve and a predictive curve can produce better estimations of the relevant kinetic parameters [3,9,11,12]. Additionally, optimization methods can be used to fit multiple rate kinetic models that can better describe biomass degradation when compared to one global reaction [7,13 16]. 1

2 A number of recent studies complemented TGA with kinetic studies based in optimization methods of global kinetic models to evaluate the pyrolysis behaviour of different types of biomass [3,9]. Grønli et al. [9] obtained kinetic parameters for the pyrolysis of hardwoods and softwoods by fitting a 5PM to the respective DTG curves using a least-squares procedure. The authors showed that unified activation energies for the three biomass components are able to describe the pyrolysis behaviour with good accuracy. Burhenne et al. [3] applied the kinetic parameters derived from Di Blasi [4] and Grønli et al. [9], to model the pyrolysis of woody biomass (spruce + bark) and agricultural biomass (wheat and rape straw). In this work [3], the mass fractions of the three main biomass components were obtained by fitting the pyrolysis DTG curves using a least-squares procedure. These studies [3,9] show that the lignin content of any biomass feedstock is the main controlling factor in pyrolysis. The objective of this work is to study experimentally and kinetically the pyrolysis of pine bark, wheat straw and rice husk. TGA was performed for three heating rates (5, 10 and 15 K/min) in an inert atmosphere of Argon. The biomass composition in terms of cellulose, hemicellulose and lignin was estimated using the correlation method. The kinetic parameters were obtained by fitting the SFOM and 3PM to the experimental data using a two-stage optimization procedure. MATERIALS AND METHODS Table 1 lists the proximate and ultimate analysis for the three biomass fuels (pine bark, wheat straw and rice husk) used in this work. The correlation method [17] was used to estimate the contents of cellulose (CELL), hemicellulose (HCE) and lignin (LIG) of the present biomass fuels since their composition falls outside the PoliMi triangle. The mass fractions of cellulose and hemicellulose were calculated as follows: CELL = (O C) (O C) (H C) (H C) (VM) (VM) 2 HCE = (O C) (O C) (H/C) (H C) (VM) (VM) 2 (1) (2) where O/C and H/C are the molar fractions and VM is the volatile matter in wt.%, dry-ash-free (daf). The mass fraction of lignin was calculated by difference. Table 2 lists the composition range of the correlation method. The precision of the correlation for cellulose is 90% and for hemicellulose is 81% [17]. Table 1. Proximate and ultimate analysis of the biomass fuels studied. Parameter Pine bark Wheat straw Rice husk Proximate analysis (wt.%, ar) Volatile matter (VM) Fixed carbon (FC) Ash Moisture Ultimate analysis (wt.%, daf) C H N S O a a Calculated by difference, ar as received, daf dry-ash-free Table 2. Composition range of the correlation method [17]. O/C (molar ratio) H/C (molar ratio) VM (wt.%) Range Figure 1 shows the contents of cellulose, hemicellulose and lignin of the three biomass fuels estimated through Eqs. (1) and (2). The figure reveals that the pine bark is richer in hemicellulose, while both the wheat straw and rice husk are richer in cellulose. 2

3 Component (wt.%, db) PB WS RH Figure 1. Contents of cellulose, hemicellulose and lignin. Table 3 shows the comparison between the estimated composition of the three main components and values reported in the literature. The estimated composition of pine bark and rice husk is consistent with the literature data, but the lignin fraction of the wheat straw is slightly higher than that reported by other authors. Table 3. Comparison between the estimated composition of the three main components and literature values [3,19,20]. Biomass Correlation Literature CELL HCE LIG CELL HCE LIG Pine bark (wt.%, db) Wheat straw (wt.%, db) Rice husk (wt.%, db) The thermogravimetric tests were performed in a SDT 2960 simultaneous DSC-TGA (TA Instruments), under an atmosphere of Argon, with a constant flow of 100 ml/min. The samples were grinded to less than 1 mm and placed on a measuring crucible with an initial weight of 5 (± 1) mg. The drying stage consisted in heating each biomass to 383 K over 10 min, followed by a plateau of 30 min. When this stage was completed, the sample was heated to 1173 K, using heating rates of 5, 10 and 15 K/min. KINETIC STUDY 0.0 CELL HCE LIG The total weight loss of each component i (in the case of the SFOM it is just one component) is governed by a single reaction as function of time, temperature and mass loss history as follows [5]: dm i dt = k i(t)(vm i V g,i ) (3) where dmi/dt is the mass in weight percent of the i th component, VMi is the maximum volatile matter that can be loss and Vg,i is the total amount of volatile gases that have left the particle, both in wt.%. For the 3PM, the amount of VM is corrected using the fraction of char produced by the i th component, Ci, as follows [9]: VM i = VM(1 C i ) (4) The rate constant ki (T) is expressed by the Arrhenius equation as follows: k i = A i T p γ exp ( E i RT p ) (5) where R is the ideal gas constant (J.K -1 mol -1 ), Ai is the pre-exponential factor (s -1 ), Ei is the activation energy (J.mol -1 ) and γ is the temperature power coefficient [5]. The mass balance is integrated over the time considering the experimental duration using an explicit Euler method with a small enough time step to ensure sufficient accuracy. The experimental heating rates are imposed to the mass balance. The kinetic optimization procedure was programmed using an object-oriented structure with two steps: the genetic algorithm (GA) from MATLAB s global optimization toolbox and the MATLAB s least squares 3

4 OOP fitness function (LSQ). The error function to minimize, in other words, the objective function reads as [11]: δ = 1 β N βn (U exp (i,j) U pred(i,j) ) 2 i=1 j=1 (6) where β is the number of heating rates considered for the optimization, N is the number of time integrations at each rate, and Uexp and Upred are the experimental and predicted yields at each time step and rate, respectively. The objective function can have a number of local minima when multiple heating rates are considered. The GA was used to provide an initial guess of the global minimum, which was then fed to the LSQ to minimize further the error. Figure 2 shows the process flow of the kinetic optimization procedure. Biomass Properties Experimental Conditions Experimental Curves Genetic Algorithm Initial guess Least Squares Fitting Optimized Kinetics Figure 2. Kinetic optimization procedure. For both kinetic models considered, initial bounds were imposed in the GA following typical ranges from the literature (see below). The maximum number of generations was set to 100, with a limit of stall generation number to 50. The population size for each generation was set to 30. Finally, it was allowed 80% crossover between generations and 5% of mutation within an individual to ensure variety and new chromosomes in the next generation. Regarding the least squares function, the same bounds of GA were set for the 3PM, while for the SFOM no bounds were imposed. The maximum function evaluations and the maximum iterations number were both set to Both methods converge when the average change in the fitness value is less than The fitting program was run in a work computer, using MATLAB R2013b. Table 4 lists the computer specifications. Table 4. Computer specifications. CPU RAM Hard Drive Intel core i GHz 8.00 GB DDR MHz 1TB 7200 RPM 64MB Cache The kinetic parameters are E, A and γ in the case of the SFOM, and Ei, Ai, γi and Ci in the case of the 3PM. Table 5 shows the typical range of activation energies reported in the literature for the SFOM. In the case of woody biomass and wheat straw, the wide range of activation energies encountered can be a consequence of the different heating conditions used in the experiments and/or of the different biomass characteristics (particle size and composition) [21]. Table 6 shows the typical kinetic parameters for the 3PM and the fraction of char produced by each component. These parameters are typically used for woody biomass. Table 5. Typical range of activation energies for the SFOM [6,21 23]. 4

5 Biomass E (kj/mol) Woody biomass Wheat straw Rice husk Table 6. Typical kinetic parameters for the 3PM and fraction of char produced by each component [2,8,9,12,24]. ECELL EHCE ELIG ACELL AHCE ALIG CCELL CHCE CLIG To ensure consistency in the predicted results with the 3PM, it was estimated only one activation energy and one fraction of char produced by each component (cellulose, hemicellulose and lignin) for each biomass, regardless of the heating rate. These tests showed good agreement with the literature data for the SFOM, but for the 3PM it was necessary a larger universe for the activation energy of the cellulose, for the pre-exponential factor of the hemicellulose and for the char fraction produced by the three main biomass components. Regarding the activation energy of the cellulose the lower bound was set to 80 kj/mol, as for the pre-exponential factor of hemicellulose the upper bound was set to and for CCELL, CHCE and CLIG the new ranges were reset to , and , respectively. It is important to notice that since the Arrhenius plot method does not allow fitting the temperature exponents, the limits were imposed empirically from 10 to +10. RESULTS AND DISCUSSION Figure 3 shows the pyrolysis yields for pine bark, wheat straw and rice husk for the heating rates of 5, 10 and 15 K/min. At the end of all stages of decomposition, each biomass reaches a common value typical of the mass solid residue, regardless of the heating rate, as seen in other studies [13]. Independently of the heating rate, pine bark finishes decomposition at higher temperatures after rice husk and wheat straw that finish decomposition earlier, hence at lower temperatures. These results are consistent with the estimated composition. Pine bark has the highest amount of lignin after rice husk and wheat straw (cf. Figure 1), and it is the lignin content that controls the pyrolysis process - the higher the amount of lignin, the slowest is the decomposition of the biomass. Figure 4 shows the DTG curves as a function of the temperature for pine bark, wheat straw and rice husk for the heating rates of 5, 10 and 15 K/min. The results show that pine bark behaves differently than wheat straw and rice husk, which have similar weight loss curves. The first region of decomposition, referred to as such due to the overlap of the first and second stages of decomposition [25], corresponds to the hemicellulose and cellulose, respectively. The DTG curves also show that the hemicellulose decomposition (first stage) usually appears as a more or less pronounced shoulder instead of a distinct peak, as it happens for cellulose [9]. The second region (third stage) corresponds to the decomposition of the lignin that occurs in a wide range of temperatures until it reaches temperatures of 973 K [25]. Figure 3. Pyrolysis yields for pine bark (PB), wheat straw (WS) and rice husk (RH) for 5, 10 and 15 K/min. 5

6 Figure 4. DTG curves as a function of temperature for pine bark (PB), wheat straw (WS) and rice husk (RH) for 5, 10 and 15 K/min. Table 7 shows the characteristics of the first region of decomposition for the three biomass fuels. In the table Ti is the temperature where the decomposition of the first region begins, THCE is the hemicellulose maximum devolatilization rate, TCELL is the cellulose maximum devolatilization rate and Tf is the temperature where the decomposition of the first region ends. Table 7. Characteristics of the first region of decomposition for the three biomass fuels. Biomass β Ti (K) THCE (K) TCELL (K) Tf (K) Tf Ti (K) Pine bark Wheat straw Rice husk The decomposition characteristics are consistent with other studies [4,9,21]. The devolatilization maxima for the hemicellulose occur in between 498 K and 598 K and for the cellulose occur in between 598 K and 648 K. Also, the cellulose decomposition resulted in a much higher decomposition maximum compared to the hemicellulose and lignin decomposition [25]. Figure 4 and Table 7 reveal that as the heating rate increases, the DTG curves tend to be wider, because the pine bark are richer in lignin, Tf Ti is larger than the corresponding values for rice husk and wheat straw, underlining that the higher amount of lignin leads to the slowest decomposition [3]. Table 8 shows the kinetic parameters obtained by fitting the SFOM to the experimental data for the three biomass fuels. The obtained results are consistent with the literature data except for pine bark [6,21 23]. This may be because most of the literature results are relative to various types of pine wood, excluding generally the pine bark. Table 9 shows the activation energies and char fractions and Table 10 shows the pre-exponential factors and model constants, all obtained by fitting the 3PM to the experimental data for the three biomass fuels. Given the optimization procedure, through which the kinetic parameters were optimized specifically for each biomass, some variations were obtained for the activation energies of the cellulose, hemicellulose and lignin. Nevertheless, the values are very close when comparing all the biomass fuels. It is expected that the cellulose and the hemicellulose decomposed almost entirely due to its weaker structure making lignin the main contributor to the formation of char [3,26], as typified by CLIG in Table 9. 6

7 Table 8. Kinetic parameters obtained by fitting the SFOM to the experimental data for the three biomass fuels. Biomass E A (s -1 ) γ (kj/mol) δ (%) Pine bark Wheat straw Rice husk Table 9. Activation energies and char fractions obtained by fitting the 3PM to the experimental data for the three biomass fuels. Biomass ECELL EHCE ELIG CCELL CHCE CLIG δ (kj/mol) (kj/mol) (kj/mol) (wt.%) (wt.%) (wt.%) (%) Pine bark Wheat straw Rice husk * All the values 0 are smaller than Figures 6 to 8 show the TG, DTG and predicted curves for pine bark, wheat straw and rice husk, respectively. The SFOM captures the pyrolysis behaviour in a satisfactory way but only the 3PM reproduces it rather well, since it considers three stages of decomposition. When comparing the fitting errors obtained for the SFOM and for the 3PM (cf. Tables 8 and 9), it becomes obvious that the latter describes better the pyrolysis processes. Nevertheless, the fitting error of the SFOM is very satisfactory, evidencing the appropriateness of the optimization method. Only the 3PM is capable to predict correctly the maximum devolatilization rate for all biomass fuels, but in the case of pine bark there is a slight shift of +20 K. Table 10. Pre-exponential factors and model constants obtained by fitting the 3PM to the experimental data for the three biomass fuels. Biomass β (K/min) ACELL (s -1 ) AHCE (s -1 ) ALIG (s -1 ) γcell (-) γhce (-) γlig (-) Pine bark Wheat straw Rice husk

8 Figure 6. TG (top), DTG (bottom) and predicted curves for pine bark (PB). Figure 9 shows DTG and predicted curves using the 3PM for pine bark, wheat straw and rice husk, where the contributions of the cellulose, hemicellulose and lignin are plotted. Since the results are similar for all heating rates, only the DTG curves for 5 K/min are included in the Figure 9. For the cases of the wheat straw and rice husk the predicted devolatilization maxima of the cellulose and hemicellulose components show excellent agreement with the experimental curves. In the case of pine bark the predicted devolatilization maxima do not overlap. This may be due to an over estimation of the cellulose component as discussed previously. Figure 7. TG (top), DTG (bottom) and predicted curves for wheat straw (WS). 8

9 Figure 8. TG (top), DTG (bottom) and predicted curves for rice husk (RH). Figure 9. DTG and predicted curves using the 3PM for pine bark (PB), wheat straw (WS) and rice husk (RH) at 5 K/min. CONCLUSIONS The pyrolysis behaviour of pine bark, wheat straw and rice husk was studied by thermogravimetry using heating rates of 5, 10 and 15 K/min. The kinetic parameters were obtained by fitting the SFOM and 3PM to the experimental curves using a two-stage optimization procedure. The main conclusions of this work are as follows. The results reveal that for each biomass the variation of the heating rates had a small impact in the pyrolysis process, particularly in the total mass loss; the biomass decomposition, however, started earlier for the highest heating rate. The activation energies obtained in this work using the SFOM were 55.5, 79.6 and 87 kj/mol for pine bark, wheat straw and rice husk, respectively. The activation energies for cellulose, hemicellulose and lignin obtained in this work using the 3PM were, respectively, 152.5, 95.7 and 44.3 kj/mol for pine bark, 143.3, 83.6 and 37 kj/mol for wheat straw, and 163.8, and 37.2 kj/mol for rice husk. Overall, the optimized parameters for the SFOM resulted in a very satisfactory fitting error of 7.1%, 5.2% and 4.8% for pine bark, wheat straw and rice husk, respectively. The optimized 9

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