A Generalized Procedure for the Devolatilization of Biomass Fuels

An optimization procedure based on a summative law and first-order reaction model is ..... 4.1Application of the Procedure to Different Thermal Progra...
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A Generalized Procedure for the Devolatilization of Biomass Fuels Based on the Chemical Components Enrico Biagini* and Leonardo Tognotti Dipartimento di Ingegneria Civile e Industriale, Università di Pisa, Largo Lazzarino 1, 56122, Pisa, Italy ABSTRACT: The devolatilization is a basic mechanism for all thermochemical processes (pyrolysis, combustion, gasification), especially for biomasses that contain a large amount of volatile matter. Because of the wide variety in origin, structure, and composition, it is desirable to study these renewable fuels starting from their chemical composition, which is referring to the organic fractions of cellulose, hemicellulose, extractives, and lignin. An optimization procedure based on a summative law and first-order reaction model is validated in this work with uniform experimental data on 37 biomasses, such as woods, energy crops, and agricultural and food residues, selected as potential energy sources on a regional scale. The thermogravimetric (TG) weight loss and its derivative (dtg) curves are accurately predicted (discrepancy between 0.4 and 1.4%) in the entire temperature range with kinetic parameters for common biocomponents, defined as the unseparated fractions of the biomasses with the original content of ash. The kinetic parameters obtained are within the ranges of values obtained in the literature for the activation energy of synthesized components. The procedure developed here is also successfully extended to further tests under different heating rates and biomasses outside the data set used for the validation and literature data. Finally, it can be used for obtaining the chemical composition of lignocellulosic materials based on a simple TG run.

1. INTRODUCTION Many modeling approaches based on chemical composition have been proposed to simulate the pyrolysis of biomass fuels.1−5 An acceptable point is that the behavior of the lignincellulosic biomasses can be sufficiently well predicted by a weighted sum of the behavior of the chemical components cellulose, hemicellulose, extractives, and lignin (CHEL). It is assumed that these components react independently with negligible influence due to their interactions,4,6,7 so the devolatilization of the biomass can be represented by a set of parallel reactions. Simple as well as complex kinetic schemes were successfully applied.8,9 The problem moves to the realistic characterization of the single components and a reliable method to elaborate the kinetic parameters. The number of components or pseudocomponents used in the majority of the approaches is three (CHL), while in a few cases the contribution of extractives or more than one reaction stage in the decomposition of hemicellulose and/or lignin is also taken into account.10 The chemical composition of biomasses can be analyzed with traditional methods (for instance, leaching with hot water and/ or organic solvents for the extractives and sequential basic and acid washings for the hemicellulose, cellulose, and lignin fractions, according to the procedures11−13), which quantify the fractions of CHEL. As a matter of fact, these methods are not standardized, are scarcely reproducible and accurate, and give a poor closure of the balance; that is, the sum of the four fractions (on a dry and ash-free basis) can give much less than 100%. The fraction of the extractives is not often found in literature works, and sometimes it is added to that of the hemicellulose. As for this latter component, sometimes it is added to the cellulose, and only the holocellulose fraction is indicated. These issues represent strong limitations and give significant experimental errors due to the existence of different analytical methods, the presence of ash (that can be partially © 2014 American Chemical Society

removed or persist during the determination of a specific fraction), the imperfect separation of the organic polymers, or their modification during the treatments. Cellulose is a linear polymer with a very high molecular weight and forms fibers with different degrees of crystallinity.4 Hemicellulose is a more branched polymer with a relatively lower molecular weight that interconnects the cellulose fibers. Different monomeric units are recognized, such as xylose, galactose, and arabinose. It is hardly separated from the biomass without modifying its structure. Extractives are low molecular weight compounds such as terpenes, tannins, fatty acids, resins, sugars, and proteins. Finally, lignin is a reticulate polymer with a heterogeneous structure. Synthetic components or the ones separated from a particular biomass can be not representative of the lignocellulosic materials. For instance, it was proved that the lignin of hardwoods differs from lignin of softwoods in composition and reactivity.14 Analogously, some researchers6,15 found different devolatilization behaviors from the hemicelluloses of several woods. It depends on the chemical composition but also on the molecular structure. For instance, the higher the cellulose crystallinity degree, the lower the reactivity during the devolatilization.4 Furthermore, it is universally recognized the crucial role of ash,4,16−18 catalyzing decomposition or recombination reactions, in the devolatilization of “real” biomasses, although a quantification of its effect is not achieved yet. Also, the numerical methods applied to the experimental data can give significant differences in the results of the devolatilization studies and elaboration of kinetic parameters. So, in general, wide ranges of chemical composition and kinetic parameters can be found for biomasses and their Received: October 28, 2013 Revised: December 30, 2013 Published: January 1, 2014 614

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each biocomponent (a single first-order reaction model is assumed) and the chemical composition of all the biomasses in the data set to simulate the devolatilization. It is necessary to define a couple of kinetic parameters (A, E) for the four biocomponents, that is, eight unknowns, and the chemical fractions of all the biomasses inside the data set, that add (4−1)*37 = 111 unknowns, as the chemical composition is given on a dry and ash-free (daf) basis, so the sum of the fractions for each biomass must give 1. The experimental points of the TG weight loss and its derivative are used as reference data of the procedure described below and schematized in Figure 1 to set the equations and find all the unknowns. For the ith material, the experimental data are represented by a Ni × 4 matrix, where Ni is the number of experimental data registered by the TG instrument software. The raw data are normalized and cut in

components.10 Until now, a generalized procedure for these issues, though desirable, was not attained. The present work goes in this direction. It is important to develop methods starting from the chemical components in the same form they are in the virgin biomass, with their natural structure and the presence of the original ash in it. The term “biocomponents” will be used hereafter to circumscribe these characteristics. It is not the case of synthesized materials that have a different structure and ash content with respect to the unseparated ones. Also, the chemical and physical alterations introduced during the separation procedure and the impossibility to reproduce the interactions among the components10 give significant differences between single components and those effectively present inside the biomass. Besides, several celluloses, lignins, and surrogated hemicelluloses (such as xylans) exist in commerce or can be separated from different biomasses, so that the characteristics obtained from them (kinetic parameters, for instance) can represent only the components of specific biomasses, with no possibility of generalization. Here a simple weighted sum model (based on the CHEL biocomponents) is coupled with an optimization procedure to find the generalized kinetic parameters of decomposition and the chemical fractions of “real” biomasses. The procedure is based on the TG biomassdevo database containing the results of thermogravimetric (TG) tests of around 50 biomass materials. A selection of 37 biomasses is operated among those potentially valuable as energy sources on a regional area, as this work is developed in the frame of the Italian Regional Project “BPT-BioPower in Tuscany”. The aim of the project is to study the possibility of producing biomasses (from energy crops and agro-food residues) in a regional area for the distributed generation through small−medium size gasifiers and combustors, and the reconversion of a power plant. The activity described in the present work aims at providing a tool for predicting the composition and kinetics of every biomass used in the plants, even in blends.

2. METHODOLOGY 2.1. Selection of the Experimental Data. The TG biomassdevo database developed at DICI-UNIPI contains the results of thermogravimetric tests performed in the last 10 years on biomass materials. Different TG balances (Mettler TA-3000, Netzsch STA 409/C, TA Instruments TG Q500 V6.1), different gas environments (nitrogen, air, O2/N2 mixtures) and thermal programs (heating rate from 5 to 80 K/min) were used. For the application of the procedure described in the next section, a restricted number of tests in the global database were selected to constitute a data set of uniform data. They were carried out in the same TG balance (Q500 TA Instruments) with the same thermal program (from 400 to 1070 at 20 K/min) under an inert flow of nitrogen (100 mL/min). The biomasses were previously dried, pulverized, and sieved (90−150 μm), and an initial mass of the samples around 10 mg was tested to limit the thermal gradients in the conditions used. For each material, the data are the average values of at least two TG tests under the same conditions. No significant differences are observed in the replication of the tests for the materials in the selected data set. As for the biomass selection, 37 materials were chosen among those available on the regional area and potentially usable as energy sources. Some derive from energy crops or are potential energy crops in Tuscany; others are residues of agricultural/food activities or wood industry. The entire harvested plant as well as parts of it are tested. In all cases, they are untreated biomasses. 2.2. Development of the Optimization Procedure. The optimization procedure aims at providing the kinetic parameters of

Figure 1. Scheme of the optimization procedure for calculating the chemical composition of biomasses and the kinetic parameters of devolatilization for the biocomponents. 615

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xi , j ≥ 0, ∀ i , j

the same temperature range (400−1070 K), so that the four columns are time t (s), temperature T (K), normalized weight loss (α), normalized derivative weight loss dtg (−dα/dt, s−1), where α is defined as m − m∞ α= m0 − m∞ (1)

Given the values of the kinetic parameters and once ith is fixed, the four fractions xi,j can be determined by minimizing the following chisquared function: χi2 = function (xi , j) =

where m is the actual mass of the sample registered by the TG balance, and m0 and m∞ are the initial and final mass, respectively. It is worth noting that the initial mass is taken at T0 = 400 K, that is, after the drying step, while the final mass is taken at the end of the devolatilization step Tf, that is, before the incineration step. The dtg values are positive, so the respective curves will be represented in the I quadrant of plots shown in the next sections. For the ith material, the parameter Ti,max is defined as the temperature for the maximum value of the dtg (or alternatively the maximum rate of devolatilization) in the temperature range considered. The mean temperature Tmean is defined as the average value of all the Ti,max. For the selected data set Tmean was 620 K. This value depends on the biomasses considered as well as the thermal program used. In general, the higher the heating rate used in the TG balance, the higher the temperature for the maximum rate of decomposition.19 All the dtg experimental data are then translated with the following transformation of the temperature:

⎧ θ = Ti − Ti ,max + Tmean ⎪ i ⎨ ⎪ ⎩ dtgi ,exp = dtgi ,exp

dαj dt

The approximation of Coats and Redfern analytically solve the single differential eq 3:

(dtgi ,exp − dtgi ,mod)2 dtgi ,exp

(9)

where the sum is extended to all the Ni experimental points available for the ith biomass. The optimization is based on the differential measurements because the details of the devolatilization submechanisms are better recognized. The matrix xi,j(0) of the chemical composition is then obtained for all the biomasses. With these compositions, the kinetic parameters of the subsequent iterative step can be calculated by minimizing the following overall function: χ 2 = function (A j , Ej) =

∑ χi2 n

(10)

where the sum is extended to all the n biomasses of the data set. The and E(1) for the four biocomponents are then set of kinetics A(1) j j obtained. The iterative procedure is repeated according to the scheme of Figure 1. The stopping condition of the procedure is reached when the relative difference between χ2(k) in the kth step and the value in the previous step is lower than 0.005 for at least five consecutive times. In other words, the final step is reached when the following expression:

χ 2(k) − χ 2(k − 1)

(2)

= dtg j,SFOR = A je−Ej / RT (1− α)

∑ Ni

χ 2(k)

In such a way, all the materials are shifted along the temperature axis to have the maximum derivative weight loss curve at the same temperature. It is necessary to ensure that all the cellulose decomposition peaks coincide. As stated in the introduction, four chemical components (instead of the classical three components model) are considered: cellulose, hemicellulose, extractives, and lignin. The extractives are included here because many agricultural and food residues contain some organic matter that decomposes at very low temperature. A single first-order reaction (SFOR) model is assumed for the devolatilization of the jth component, with the Arrhenius parameters Aj and Ej (here more properly called pre-exponential and exponential factors, respectively, instead of frequency factor and activation energy, because they are applied to a complex chemical mechanism instead of a single reaction): −

(8)

< 0.005 (11)

is verified for five consecutive iterations.

3. RESULTS 3.1. Convergence and Optimized Kinetic Parameters. With the selected data set and the initialization assumptions, 12 iterations were sufficient to match the stopping criterion described above. The convergence results are shown in Figure 2. The overall chi-squared curve (sum of the chi-squared

(3) 20

is assumed to

⎛A R⎛ ⎞⎞ E ⎛ − ln(α) ⎞ j ⎜⎜1 − 2RT ⎟⎟⎟ − j ⎟ = ln⎜⎜ ln⎜ 2 ⎟ ⎝ T ⎠ Ej ⎠⎠ RT ⎝ βEj ⎝

(4)

where β is the constant heating rate during the TG test, thus assuming a linear dependency between time and temperature:

T = T0 + βt

(5)

Figure 2. Evolution of the exponential factor for the decomposition of the four biocomponents and the overall chi-squared function (extended to all biomasses in the data set) during the iterations of the optimization procedure.

A(0) j

and The optimization procedure starts with the initial values of adopted from literature works10 and common values for the E(0) j massive fraction of the jth component in the ith biomass xi,j. Therefore, the dtg of the ith biomass can be expressed as the weighted sum of the dtg of the four biocomponents according to

functions of all the biomasses inside the data set) varies strongly in the first four iterations and then levels to an asymptotic value. No oscillation around this value is observed. In the same figure, also the variations of the exponential factor E for the four components are plotted against the number of iterations. The largest variations with respect to the initial assumptions are experienced by cellulose and extractives. All the parameters tend to a substantial constant value in the

4

dtgi ,mod =

∑ xi ,jdtgj ,SFOR j=1

(6)

with the constraints: 4

∑ xi ,j = 1, ∀ i j=1

(7) 616

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Table 1. Chemical Composition (daf basis) Obtained with the Optimization Procedure for the Biomasses Inside the Data Set and Average Discrepancy Ave Da between Predicted and Experimental Weight Loss Data biomass minimum value mean value maximum value Arundo donax Arundo donax beech carthamus carthamus lignin linum linum miscanthus miscanthus miscanthus miscanthus olive olive olive olive olive pine pine poplar poplar poplar poplar rice rice rice sorghum sorghum sunflower seeds sunflower seeds sunflower seeds vitis wheat straw wood wood wood wood

part/notes

leaves stems wood stems husks from steam explosion stems husks flowers leaves stems cake A cake B cake C cake D cake E wood shells biennal triennal leaves stems husks A husks B husks C leaves stems cake A cake B cake C stems pellet pellet pellet pellet

A B C D

cellulose

hemicellulose

extractives

lignin

ave Da

0.282 0.452 0.588 0.376 0.407 0.541 0.474 0.373 0.418 0.541 0.399 0.555 0.433 0.429 0.508 0.382 0.406 0.405 0.441 0.341 0.465 0.509 0.449 0.471 0.282 0.439 0.455 0.529 0.569 0.466 0.342 0.444 0.456 0.447 0.415 0.588 0.457 0.495 0.543 0.466

0.057 0.187 0.279 0.138 0.196 0.247 0.133 0.206 0.057 0.108 0.08 0.241 0.279 0.217 0.21 0.219 0.225 0.21 0.192 0.168 0.241 0.187 0.215 0.235 0.13 0.173 0.247 0.235 0.176 0.164 0.089 0.177 0.164 0.188 0.208 0.148 0.224 0.115 0.266 0.222

0 0.043 0.272 0.067 0.111 0.052 0 0 0.111 0 0 0.034 0.013 0.016 0.028 0 0.001 0 0.01 0.054 0.043 0.041 0.121 0.094 0.018 0.067 0.01 0 0 0.006 0.272 0.055 0.051 0.051 0.023 0 0.034 0.096 0.033 0.068

0.158 0.318 0.57 0.419 0.287 0.16 0.393 0.421 0.414 0.351 0.522 0.169 0.276 0.337 0.254 0.399 0.369 0.385 0.357 0.436 0.251 0.262 0.216 0.2 0.57 0.321 0.287 0.235 0.254 0.364 0.297 0.325 0.329 0.314 0.354 0.265 0.284 0.294 0.158 0.244

0.00366 0.00755 0.01407 0.00945 0.00600 0.00366 0.01234 0.01086 0.00791 0.01234 0.01407 0.00462 0.00520 0.00805 0.00387 0.00840 0.00837 0.00896 0.00817 0.00884 0.00562 0.00459 0.00618 0.00522 0.01320 0.00553 0.00391 0.00393 0.00444 0.00647 0.00574 0.01330 0.01358 0.01185 0.00668 0.00760 0.00545 0.00679 0.00410 0.00409

last iterations. A similar trend is observed for the preexponential factor A (plot not reported here). The exponential factor for the cellulose was 155 MJ/kmol, which is relatively low with respect to common values found in literature works (based on separated components): Antal et al.21 noted a range of activation energy between 210 and 250 MJ/kmol for decoupled tests (that is, sample and instrument thermocouples are not coupled), although similar values are found by Milosavljevic and Suuberg22 that recognized a range between 128 and 160 MJ/kmol and recommended a value of 140 MJ/kmol for relatively high temperature (>600 K) and heating rate (>10 K/min) runs. The same Antal et al.21 noted a range between 135 and 210 MJ/kmol for gas coupled tests. The exponential factor for the hemicellulose was 105 MJ/kmol, which is within the common values found in literature works: Di Blasi10 indicated a range of values between 80 and 116 MJ/ kmol. The exponential factor for extractives was 90 MJ/kmol, which is within the range indicated for the hemicellulose to

which they are often joined. Finally, the exponential factor for the lignin was 32 MJ/kmol, which is within the common ranges found in literature works: Di Blasi10 indicated a range of values between 18 and 65 MJ/kmol, while Varhegyi et al.23 reported a relatively higher range (34−65 MJ/kmol). 3.2. Optimized Chemical Composition of Biomasses. The results of the chemical composition (daf basis) obtained at the end of the optimization procedure are given in Table 1. The average difference ave Da between the experimental data αexp and the prediction of the model αmod is also listed in the table. For the ith biomass it is defined as ave Da =

∑ N |αexp − αmod| i

Ni

(12)

This parameter quantifies the adequacy of the fitting procedure, as the higher the value of ave Da, the lower the accuracy of the model prediction. It represents the average discrepancy that can be expected when predicting the 617

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Figure 3. Comparison of the predicted and experimental dtg curves, superimposition of dtg curves of biocomponents with the optimized composition of mischantus (20 K/min).

Figure 4. Comparison of the predicted and experimental normalized weight loss curves for mischantus (20 K/min), superimposition of curves of biocomponents decomposition.

poses at 598−648 K (in the present work, the temperature range is 567−644 K), hemicellulose decomposes at 498−598 K (here 517−597 K), and lignin decomposes gradually over the range 523−773 K (here from 470 to 926 K). Therefore, a good agreement is found for the decomposition temperatures of cellulose and hemicellulose. As for the lignin, there is some discordance, although wider ranges of decomposition temperature can be found in the literature: Orfao et al.5 observed a pyrolysis range for the lignin almost up to 1170 K. As for the chemical fractions, significant differences can be observed for various parts of the same plant (see, for instance, the different parts of miscanthus), for the same biomass of different origins (see, for instance, the different samples of olive cakes) or preparations (see, for instance, the different samples of wood pellets). The minimum, mean, and maximum values for the four biocomponents in the data set are also listed in Table 1. A common value around 27% is the maximum value predicted for both the hemicellulose and extractives of the biomasses inside the selected data set. The results are reasonable, although higher values can be found in the literature for these two components. Maximum values up to 59% and 57% for the cellulose and lignin fractions, respectively, are obtained. Such high values for these fractions can be indeed found in literature works. However, a possible superimposition of two close dtg subpeaks could have made the deconvolution procedure difficult. For instance, common values of wheat

normalized weight loss for a specific biomass. A maximum discrepancy of 1.4% can be observed for linum husks, while the maximum accuracy is for beech wood (an average discrepancy of only 0.37% can be observed). The average discrepancy is less than 0.8%, which is a good fit for the biomasses in the selected data set and the temperature range considered. As an example, the experimental dtg curve of a biomass, the predicted dtg curve, and the subpeaks of the four biocomponents (each one weighted for the respective chemical fraction) are superimposed in Figure 3 for the case of miscanthus. A good agreement for the dtg curves and a good deconvolution of the subpeaks can be observed. Similar graphs can be plotted for all the biomasses in the selected data set. It is worth noting that the kinetic parameters of the biocomponents are the same in all the cases. For the same material, the experimental weight loss curve and that predicted by the model are compared, as observable in Figure 4. The agreement between the model predictions and the experimental data is good for the specific case as well as the other biomasses of the data set. In the same figure, the weight loss curves of the biocomponents are also superimposed to appreciate the devolatilization behavior of the different fractions. The typical temperature range of decomposition and the different reactivity of the components can be compared. As stated by Di Blasi10 in her review, for heating rates at sufficiently slow temperatures, the cellulose decom618

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Table 2. Comparison of the Chemical Composition Predicted by the Model and Those Found in Literature for Similar Biomassesa model predictions

a

literature results

fuel

C

H

E

L

C

H

E

L

note/ref

poplar wood

0.45

0.21

0.094

0.25

beech wood

0.54

0.25

0.05

0.16

miscanthus

0.555

0.241

0.034

0.169

Arundo donax rice husks

0.407 0.52

0.196 0.22

0.111 0.003

0.287 0.26

olive cake

0.395

0.203

0.013

0.389

0.455 0.48 0.49 0.48 0.441 0.45 0.438 0.313 0.361 0.339 0.284 0.24 0.22

0.19 0.3 0.22 0.28 0.178 0.3 0.274 0.243 0.197 0.165 0.203 0.236 0.21

0 0 0 0 0.032 0 0.065 0 0 0 0 0 0

0.25 0.22 0.24 0.24 0.216 0.21 0.168 0.143 0.194 0.214 0.281 0.484 0.45

* 24 24 * 24 24 * 24 * 24 * 25 * 24 * 24 * 24 * 24 * 24 * 24

The note * indicates that the sum of components is less than 98%.

straw24 are in the range 32−44% for cellulose and 22−32% for hemicellulose, while those found here are 58.8 and 14.8%, respectively. The single fractions disagree, although their sum (73.6%) is in line with mean values found in the literature (around 74%). The results of traditional analysis for the selected biomasses are not available in the TG biomassdevo database, so a direct comparison with the chemical composition obtained in this work is not possible. However, the chemical composition of similar biomasses can be found in literature works, and these values will be compared with those predicted by the model. This will give also an idea of the range of values that can be expected for the chemical composition of a specific biomass (see more exhaustive case studies in ref 9 for different varieties of the same biomass). It is worth noting that (i) The comparison is only indicative as the samples are not the same, and divergences due to the different origin, composition, and ash content are unavoidable. (ii) The chemical analysis is affected by experimental errors that depend on the method used, as mentioned in the introduction. (iii) The balance from the traditional chemical analysis may close with a large discrepancy (sum of CHEL fractions of 80− 90% or less) due to loss of material, uncertainties, and presence of ash, and it is not appropriate to normalize the results or ascribe the complement to one fraction or another. (iv) Differences between the results of the traditional analysis and the procedure developed here to find the chemical composition of biomasses may derive from the fact that this latter does not require the separation of the components so that they are analyzed in the original form they are in the virgin biomass, while they are separated and in some cases also modified during the traditional analysis. (v) Interconnections between the biopolymers and interactions among them during the decomposition are accounted for only if no separation is operated. Some representative examples are given in Table 2, where the results obtained for some biomasses inside the selected data set and the chemical composition of similar biomasses found in the literature are compared. It is worth noting that if zero is indicated for the fraction of extractives, it is likely that they are not analyzed in that work. The note * means that the balance does not close; that is, the sum of CHEL is less than 98%.

Many data are taken from the Phyllis2 database for biomass and waste by ECN.24 The first material considered is poplar wood. Excluding leaves, the mean composition of poplar wood samples studied here is in good agreement with the first reference, but on the extractives. The second reference is quite in line with the results obtained here if the fractions of hemicellulose and extractives are added. The second example is for beech wood. In this case, the cellulose fraction predicted by the model is higher, and the lignin one is lower than those found in the two references shown in Table 2. As for miscanthus, the cellulose fraction predicted by the model is overpredicted by the model, while the lignin is quite lower compared to those found in the literature with traditional analysis. The chemical composition of Arundo donax is in line with those found in the literature25 for the cellulose and the sum (hemicellulose + extractives), while a remarked difference can be seen for the lignin. The average composition of rice husk samples found here differs significantly from those shown in Table 2. This may depends on the variety of these residues, as also Mansaray et al.26 found wide ranges of cellulose (34−43% daf), hemicellulose (24−26%), and lignin (30−42%). In all the cases above, the balances are very low (70−75%), so only a qualitative agreement with the model predictions can be seen. Finally, the average composition of olive cake samples obtained here is quite in line with the first reference, although the balance is only at 77%, while the comparison with the other two references gives a lower fraction of cellulose.

4. APPLICATION OF THE CHEL PROCEDURE TO FURTHER DATA The optimization procedure developed in the previous section gave the chemical composition of the biomasses and generalized kinetic parameters for the devolatilization of the CHEL biocomponents. The application of the procedure was good for all the biomasses in the selected data set, as seen above. The aim of this section is to extend the procedure to further data and verify the adequacy of the fit. In particular, the data of interest are 619

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Figure 5. Comparison of the CHEL model predictions and experimental data for beech wood (biomass inside the data set) under different heating rates: (a) normalized weight loss and (b) dtg curves.

Figure 6. Comparison of normalized weight loss (thin dotted black curve) and dtg (thin dotted gray curve) predicted by the CHEL model and the experimental weight loss (thick black curve) and dtg (thick gray curve) for (a) hazelnut shells and (b) cacao shells (biomasses not included in the data set).

(1) Same biomasses of the data set tested under different thermal programs: in this case, the same composition obtained in the previous section is adopted. (2) “New” materials or materials tested in different instruments: in this case, the generalized kinetic parameters obtained in the previous section are adopted to find the chemical composition of these materials by minimizing the mean difference between experimental and model results. (3) Data from literature works: also in this case, the generalized kinetic parameters are adopted to find the chemical composition of these materials by minimizing the mean difference between experimental and model results. 4.1. Application of the Procedure to Different Thermal Programs. The TG biomassdevo database contains further data on some biomasses included in the selected data set but tested under different heating rates. The CHEL procedure is applied to these data by adopting the generalized kinetic parameters of the biocomponents and the respective chemical fractions obtained in the previous section, and simply changing the value of β in the kinetic expression (eq 4). The comparison of TG and dtg curves obtained with the model and the experimental ones can be observed in Figure 5 for the case of beech wood devolatilized under 10 and 40 K/min. A good agreement can be observed in the entire range of temperatures except for the last interval at high temperature (corresponding to the devolatilization of lignin). The average discrepancy ave Da for the results under 10 K/min is 0.0138, and the one under 40 K/min is 0.0266. The value of ave Da in the reference case (20 K/min used in the optimization procedure) was 0.0037, and thus a worsening of the performance can be noted, although the fit can be considered good. Similar results can be found for other materials tested under different heating rates. For instance, the ave Da value for

miscanthus was 0.0174 for the TG results under 10 K/min, 0.0295 for those under 40 K/min, and 0.045 for those under 60 K/min, while the accuracy in the reference case (20 K/min) was quite better (ave Da 0.0046). In general, the accuracy in predicting the late stages for different heating rate tests is weaker with respect to the reference case. Therefore, a better representation of the lignin subpeak could give a substantial improvement for predicting the devolatilization in the entire temperature range. Also the introduction of secondary reactions could be useful for simulating the late stage of devolatilization. 4.2. Application of the Procedure to New Materials. Among the “homemade” experimental data available, two materials not included in the selected data set were tested under the same thermal program (20 K/min) but in a different TG balance (Netzsch STA 409/C). So, to apply the optimization procedure it is necessary to define the chemical composition by adopting the kinetic parameters obtained from the previous section and minimizing the χ2 of each biomass to find the relative fractions. In the first case of hazelnut shells, the chemical composition (daf basis) obtained is 0.416 cellulose, 0.283 hemicellulose, 0.031 extractives, and 0.269 lignin. The comparison of the weight loss and its derivative curves for the model prediction and the experimental data is shown in Figure 6a. The agreement is good, with a discrepancy ave Da 0.010 on the weight loss, although a visible difference can be observed in the subpeaks of hemicellulose and cellulose that are likely to balance each other. In the second case, the residue of the cacao production is considered. The dtg curve is peculiar with three close relative maxima (see Figure 6b). The absolute maximum of the dtg takes place at a relatively low temperature corresponding to the decomposition of extractives and not to the decomposition of 620

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Figure 7. (a−f) Comparison of normalized weight loss (thin dotted black curves) and dtg (thin dotted gray curves) predicted by the CHEL model and the experimental normalized weight loss (thick black curves) and dtg (thick gray curves) for different biomasses studied in literature works.

cellulose, as occurred in all the biomasses studied so far. This occurrence can make the optimization procedure less automatic, as the translation of the dtg curve for the optimization should be done with the right term, which is

referring to the cellulose subpeak. The chemical composition (daf basis) obtained is 0.153 cellulose, 0.145 hemicellulose, 0.132 extractives, and 0.570 lignin. Although the large fraction of the extractives makes the devolatilization start early, with a 621

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Figure 8. Scheme of the procedure for obtaining the chemical composition of biomass materials.

components: for instance, the chemical composition of legume straw obtained by the authors was CHEL = 0.28, 0.34, 0.02, 0.34, while that obtained with the procedure developed here is CHEL = 0.45, 0.16, 0.0, 0.38. The comparison is good only if the holocellulose fraction is considered. Similar conclusions derives from the second biomass tested by Li et al.28

significant discrepancy in the initial temperatures, the main devolatilization step is well predicted. The largest discrepancy is actually at a high temperature, that is, during the decomposition of lignin: this could be attributed to experimental errors or secondary reactions as the experimental dtg curve does not end asymptotically to the axis. Globally, the fit is quite good with an average discrepancy ave Da 0.027. 4.3. Application of the Procedure to Literature Data. Among the numerous data found in literature works, some examples have been selected to verify the applicability of the optimization procedure. The selected papers provide sufficient information on the thermal profile used during the devolatilization test (initial and final temperatures, heating rate, carrier gas), data or plots of both the TG and dtg as functions of time or temperature, composition of the biomass, especially to normalize the raw data. A precious source of reliable data was found in the work by Gaur and Reed,27 where a lot of uniform TG tests are reported for a variety of biomasses. In all the tests, the heating rate was 10 K/min. It is interesting to apply the CHEL procedure to some biomasses that have different origins with respect to those included in the selected data set, based essentially on biomasses found on a regional area. As in the previous point, the method is applied by translating opportunely the dtg data, adopting the kinetic parameters of section 3.1 and thus minimizing the χ2 of each biomass to find the chemical composition. The fit is generally good: some examples are shown in the first plots of Figure 7a−d, where the predicted and experimental curves are compared. Also some biomass materials studied by Raveendran et al.4 are considered for comparison. In these tests, the heating rate was 50 K/min. Two examples are shown in Figure 7e,f, where the model and experimental curves are compared. The agreement is good for rice straw (ave Da 0.013), with a significant divergence in the late stage of the devolatilization, that is, for temperatures above 670 K. The agreement is less good for coir pith (ave Da 0.026) with a more evident discrepancy in the same temperature range. Finally, the TG tests under 10 K/min for two biomasses studied by Li et al.28 are also considered for comparison. The method is applied with a good agreement between model and experimental data, as shown in Figure 7g,h. In both cases, an undervaluation of the lignin decomposition can be observed, thus giving some discrepancy at high temperature. Globally, the accuracy is good, with values of ave Da around 0.015. In that work, the results of the traditional chemical analysis are also provided. The agreement is good for lignin and extractives, while remarked differences can be observed for the first two

5. DISCUSSION AND CONCLUSIONS An optimization procedure was developed starting from the experimental data of TG devolatilization tests performed under uniform conditions (same instrument, same thermal program) on 37 biomasses valuable as energy sources in the Italian region of Tuscany. The procedure gave the kinetic parameters of the four CHEL (cellulose, hemicellulose, extractives, lignin) biocomponents and the chemical composition. The agreement of model predictions and experimental data was accurate for all the biomasses inside the selected data set, with an average discrepancy around 0.8%. The CHEL procedure was used also for tests under different thermal programs (with heating rates in the range 10−60 K/ min) and biomasses outside the selected data set (other homemade materials as well as literature works). In general, the agreement with “external” data is good under the following specifications: (i) The late stage in devolatilization is sometimes predicted with a scarce accuracy, and this can be due to secondary reactions (not accounted for in the model), effect of ash at high temperature (not quantified in the model), or the extreme complexity of lignin in specific biomasses. The introduction of more complex kinetic models (for instance, consecutive reactions) or the inclusion of a further component (for instance two kinds of lignins) could give a more accurate prediction, though the complexity of the procedure would increase. (ii) It is possible that some compensation effects could have played a role during the optimization procedure that was based on data obtained under the same thermal program, although the data set was formed of a lot of different tests. The approach could be repeated with minor modification to the optimization procedure, by including also the data of biomasses studied under different thermal programs. This was not done here because these data at present do not cover a sufficient wide range of biomasses. (iii) Although some agreements have been verified, the results for the chemical composition obtained with the CHEL procedure may differ from that obtained with the traditional separation treatments. This is plausible if one considers that the approaches are different. As said above, the separation 622

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procedures or those based on synthetic components can be scarcely reproducible, give incomplete balance closure, modify the structure and reactivity of the components, and do not consider the effect of ash and interactions of components. The adequacy of the procedure developed in this work and based on the four biocomponents (avoiding the above-mentioned limitations) was evidenced in the examples shown above and is proposed as a valid alternative to the traditional chemical analysis. Indeed, the set of kinetic parameters (that are within the ranges of values obtained in the literature for the activation energy of synthesized or separated components) for the four biocomponents and the optimization procedure developed in this work can be used for obtaining the chemical composition of any new material (Figure 8). The overall optimization procedure is described in this work and gives the couples of (E, A) for the four biocomponents. These parameters can be used in the specific optimization procedure to determine the chemical composition of a “new” material. It is necessary to provide only the derivative weight loss curve from a TG devolatilization run, instead of complex and onerous treatments to separate the chemical fractions according to the traditional chemical analysis. The chemical fractions can be obtained by minimizing the chi-squared function, which compares the model and experimental dtg points in the entire temperature range, according to the steps described in section 4.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was funded by the Project “BPT - BioPower in Tuscany”, POR CREO FESR 2007/2013 − Bando Unico R&S − Anno 2012 − Regione Toscana.

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REFERENCES

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