Pyrolysis Characteristics of Biomass and Biomass Components

ELSEVIER PH: S0016-2361(96)00030-0

Fuel Vol. 75 No. 8, pp. 987-998, 1996 Copyright © 1996 Elsevier Science Ltd

Printed in Great Britain. All rights reserved 0016-2361/96 $I 5.00 + 0.00

Pyrolysis characteristics of biomass and biomass components

K. Raveendran, Anuradda Ganesh and Kartic C. Khilar* Energy Systems Engineering, Department of Mechanica/ Engineering, *Department of Chemica/ Engineering, Indian Institute of Technology, Bombay--400 076, India e-mai/." [email protected]/n (Received 22 May 1995; revised 2 January 1996)

Biomass pyrolysis studies were conducted using both a thermogravimetric analyser and a packed-bed pyrolyser. Each kind of biomass has a characteristic pyrolysis behaviour which is explained based on its individual component characteristics. Studies on isolated biomass components as well as synthetic biomass show that the interactions among the components are not of as much significance as the composition of the biomass. Direct summative correlations based on biomass component pyrolysis adequately explain both the pyrolysis characteristics and product distribution of biomass. It is inferred that there is no detectable interaction among the components during pyrolysis in either the thermogravimetric analyser or the packed- bed pyrolyser. However, ash present in biomass seems to have a strong influence on both the pyrolysis characteristics and the product distribution. Copyright © 1996 Elsevier Science Ltd.

(Keywords: biomass; biomass components; pyrolysis)

For an agriculture-based economy like that of India, the prospect of being able to convert widely available biomass materials into various forms of fuel is most attractive. In every thermochemical conversion route, pyrolysis plays a vital role. Hence there is renewed interest in understanding the complex pyrolysis process. In the past few decades much work has been done to study the influence of the operating parameters, such as temperature, heating rate, pressure and residence time, on pyrolysis 1. However, there still remains a need to study the effect of the feedstock properties on the process. Different kinds of biomass, although consisting of the same major constituents, have different com- positions. The influence of biomass composition and ash content and composition on pyrolysis characteristics is the object of the present study. This paper discusses the pyrolysis characteristics and product distribution of biomass and their correlation with those of the individual components.

Most of the pyrolysis characteristics reported in literature are for woody materials. A few attempts have been made at correlating the pyrolysis characteristics of biomass with those of its constituents, viz. cellulose, hemicellulose, lignin and extractives 1. Thermal analysis curves for wood often exhibit three peaks and have led researchers to believe that the mechanism of wood pyrolysis is a superposition of the mechanisms of its components 2-7. Shafizadeh and McGinnis 8 argued that qualitatively the thermal behaviour of wood reflected the behaviour of its components.

Beall 9 showed that the same major components isolated from wood by different methods behave

differently; and Roberts 1° showed that the structural properties of the components influence the pyrolysis characteristics, so he did not attempt to correlate the pyrolysis behaviour of biomass with that of its components. Other workers/1-13 attempted to correlate biomass pyrolysis characteristics with those of its components using simple overall pyrolysis kinetic models. Though good agreement was seen at a particular heating rate, specific for each biomass, the agreement was not satisfactory at other heating rates. This discrepancy was attributed to the influence of heating rate on char formation / . Antal 1, in his exhaustive review on pyrolysis of biomass, addressed the problem of 'whether the pyrolysis of lignocellulose can be represented as a simple superposition of the behaviour' of its components'. He proposed that a mathematical superposition of the components' t.g.a, curves should explain their interaction adequately.

It is appropriate to mention here some of the work carried out on copyrolysis of various biomass and coal mixtures which give insight into the interaction of biomass during pyrolysis. Klose and Stuke 14 reported no interaction between coal and biomass during copyrolysis in t.g.a. However, Nikkhah et al. 15, in their detailed copyrolysis studies of various biomass and coal mixtures in a batch reactor, reported increased gas yields, as well as increased heating value and hydrocarbon content of the pyrolysis gases. McGee et a l ) 6 reported copyrolysis studies of the mixtures of poly(vinyl chloride) (PVC) and wood/straw, to simulate municipal solid waste pyrolysis char. They found that the interaction between PVC and wood/straw increased the char

Fuel 1996 Volume 75 Number 8 987

Pyrolysis characteristics of biomass and biomass components: K. Raveendran et al.

yield but reduced the char reactivity. Copyrolysis studies conducted by Khan et al. ~7 on mixtures of coals and heavy petroleum residues and by Saxby and Sato ~8 on Australian oil shale and lignite, all in a packed-bed pyrolyser (PBP), revealed the prevalence of synergetic effects; they also showed that the initial composition of the feedstock mixture had a direct bearing on the product distribution and properties.

On critical analysis of these studies, the interesting fact emerges that most of the studies conducted in a t.g.a. show no interaction among biomass components or between biomass and coal, whereas the studies conducted in packed-bed batch reactors show interaction in both cases.

Given the range of knowledge reported above, the following objectives were set for the present study:

(1) to investigate comprehensively the pyrolysis characteristics and product distribution of various kinds of biomass and their components in both t.g.a. and PBP, and to compare the results to attempt to reconcile the discrepant information and inferences reported in the literature;

(2) to develop a mathematical model to predict the pyrolysis characteristics and product distribution of biomass from the characteristics of the individual components;

(3) to study the influence of the composition of the ash as a parameter within (1) and (2).

EXPERIMENTAL

Feedstock Biomass. Fourteen commonly available kinds of

biomass in the region were selected for the present study. These included a variety of potential biomass fuels available from energy crops (wood) as well as residues of agricultural (husks, straw etc.) and food processing activities (bagasse, nut shells etc.). The chemical composition of these materials was determined by methods described in the literature ~9. The composition of ash

was determined using standard geological rock analysis methods2°'2l; the results are presented in an earlier paper 22 .

All biomass samples taken for both t.g.a, and PBP studies were ground <250 #m.

Isolated biomass components and synthetic biomass also were studied. Holocellulose, cellulose, hemicellulose, lignin and extractives were isolated from wood by methods described in the literature ~9. Synthetic biomass was prepared to simulate the results of original whole biomass (with and without ash).

Synthetic biomass preparation. Synthetic biomass samples were prepared by mixing each of the individual biomass constituents (cellulose, xylan, lignin, extractives and ash) proportionately. The proportions of individual constituents were obtained from summative analysis. Standard cellulose, lignin and xylan samples were obtained commercially for this purpose. Extractives were isolated from each kind of biomass according to TAPPI standard Tllm. The ash was obtained by combusting the corresponding biomass in a muffle furnace. To simulate deashed biomass, ash was excluded from the mixture of other constituents.

Pyrolysis To fulfil the objectives of the study, pyrolysis

experiments were conducted in both a t.g.a, and a PBP. In the t.g.a, experiments a small sample (5-10mg) was taken, thus reducing the effect of secondary reactions and heat and mass transfer on the product yield, whereas in PBP experiments a much larger sample was used (10-25 g), so that secondary reactions and other heat and mass transfer effects played greater roles.

T.g.a. studies. A DuPont 9900 thermal analyser was used for all the pyrolysis studies. Dynamic t.g.a, studies were carried out at linear heating rate of 50Kmin -1, covering the temperature range from atmospheric to 1273K. All the studies were carried out in an inert atmosphere of flowing nitrogen (50 cm 3 min-1).

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Figure 1 Schematic of the packed-bed pyrolyser test set-up

988 Fuel 1996 Volume 75 Number 8

Pyrolysis characteristics of biomass and biomass components." K. Raveendran et al.

Table I Pyrolysis characteristics of biomass components in t.g.a. ~

Yield (wt% daf)

Volatiles Char

Max. rate

(wt% K -l)

Temp. at max. rate (K)

Initial decomp.

temp. (K)

Temp. at involution point (K)

Whatman cellulose 97.5 2.5 1.7 682 573 713 Wood cellulose 86.0 14.0 1.7 667 573 697 Alkali lignin 59.4 40.6 0.3 695 413 773 Acid lignin 52.9 47.1 0.4 693 473 700 Hemicellulose 68.0 32.0 0.5 573 448 550 Xylan 70.0 30.0 0.9 584 463 500 Extractives 73.0 26.9 0.4 668 393 575

a Heating rate 50 K min -1

Untreated biomass, isolated biomass components and synthetic biomass were all subjected to these analyses.

PBP studies. Experiments were conducted in a packed-bed pyrolyser designed for the purpose, with provision for collecting the pyrolysis products. Figure 1 shows a diagram of the pyrolysis reactor set-up, consisting of an electrically heated stainless steel tubular pyrolysis reactor and another electrically heated stainless steel tubular gas heater. Nitrogen from the cylinder was first heated to the operating temperature in the gas heater and entered the pyrolysis reactor tangentially at the top, flushed the pyrolysis vapour and left through the bottom of the reactor through a double-walled glass condenser tube to a train of wash-bottles immersed in an ice bath. Cold water was circulated through the condenser. A programmable PID controller was used to control the temperatures of the reactor and heater. Thermocouples were incorporated at three places in the reactor and one in the sample boat to monitor the sample temperature.

Five representative kinds of biomass were chosen for investigations in this reactor. Isothermal experiments 773K in an inert atmosphere of flowing nitrogen (500cm 3 min -1) were conducted for each of these five biomass samples, isolated biomass components and mixtures of the components.

A known weight of ~ 1 0 - 2 5 g of powdered samples was taken in the stainless steel mesh 'boat ' and placed in the furnace, which was already maintained at the predetermined temperature of 773 K with the nitrogen flow. The volatiles evolved were collected and quenched, and the non-condensable gases were passed through a flow meter. The experiments were continued until the evolution of gases ceased.

The furnace was switched off and the flow of nitrogen was maintained until the sample attained room temperature. The char remaining in the furnace was carefully removed and weighed. The yield of liquids was obtained by weighing the wash-bottles before and after the experiment, and the yield of gas was obtained by difference. Repeatability was ensured by repeating the experiments until the same product yield fractions were obtained with a precision of 4-2%.

RESULTS AND DISCUSSION

Pyrolysis characteristics of biomass components in t.g.a. Figure 2 presents the integral and differential thermo-

grams of the basic components. The pyrolysis data are

1,00

0,75

g u

~ 0,50

r r r o + Acid l i gn in

AIk=tl llgnin 0 Extractivcs If w h a t r n a n ce( lu los¢ • Wood ce l lu lose • Herni cel lulose • X y l o n

o,

0.25

.==. I=, ~ t

0.00

1.50

1.25

1.00

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0.75

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Q

0'25

DTG + Acid tignin O Alkal i l ignin o Extmct lves A Whatmon cellutos~ v Wood cellulose • Hemi cellulose • Xy lan

0.00 150 250 350 450 550 650 750 850

Temperature ( C )

Figure 2 T.g.a. and d.t.g, curves of biomass components

presented in Table 1. Several observations can at once be highlighted:

(1) cellulose decomposes within a narrow temperature range, 573-703 K;

(2) cellulose decomposition rate is the highest and char yield the lowest;

(3) Whatman cellulose has a higher rate as well as a narrower temperature range for decomposition than cellulose extracted from wood;

(4) lignin on the other hand decomposes over a wider temperature range, 523-823 K;

(5) the char yield from lignin is the highest, in the region of 45-50 wt%;

(6) hemicellulose and xylan are thermally the most unstable and start decomposing at a much lower temperature than every other component, with ~30 wt% char yield;



Table 2 Pyrolysis characteristics of whole biomass in t.g.a, a

Yield (wt% daf)

Volatiles Char

Max. Temp, Initial Temp. at

rate at max. decomp, involution

(wt% K-l) rate (K) temp. (K) point (K)

Bagasse 79.7 20.3 0.9 677 483 688

Cashewnut shell 81.1 18.9 0.6 638 473 673

Coconut coir 69.8 30.2 0.8 672 513 673

Coconut shell 70.7 29.3 0.8 615 518 678

Coir pith 56.8 43.2 0.6 622 483 663

Corn cob 73.5 26.5 1.1 603 533 653 Corn stalks 70.9 29.1 1.1 634 498 653

Cotton gin waste 80.6 19.4 1.3 679 523 688

Groundnut shell 68.7 31.3 0.7 662 493 683

Millet husk 70.1 29.9 0.9 653 523 663

Rice husk 70.0 30.0 0.8 666 518 663

Rice straw 74.7 25.3 1.0 651 518 673

Subabul wood 76.3 23.7 0.9 683 498 663

Wheat straw 72.8 27.2 0.9 604 493 663

Heating rate 50 K min-1

Table 3 Distribution of volatiles released during biomass pyrolysis in t.g.a, a

Moisture

Zone I Zone II <373K 373-523 K

Distribution of volatiles (temp. ranges)

Zone III Zone IV Zone V Total 523-623 K 623-773 K >773 K volatiles

Bagasse 5.6 3.2 27.2 41.8 4.5 79.7

Cashewnut shell 1.3 4.7 37.7 27.2 11.5 81.1

Coconut coir 8.3 2.2 25.0 35.1 7.6 69.9

Coconut shell 7.4 2.8 30.3 33.1 4.4 70.7

Coir pith 14.6 1.0 22.0 30.6 3.2 56.8 Corn cob 5.5 2.5 28.6 38.9 3.5 73.5

Corn stalks 5.4 2.3 27.2 30.4 11.0 70.9

Cotton gin waste 7.4 1.7 27.6 42.8 8.6 80.7

Groundnut shell 8.0 4.4 20.9 36.7 6.8 68.7

Millet straw 8.3 4.2 30.6 30.4 4.9 70.1

Rice husk 7.7 6.2 22.7 37.5 8.2 70.0

Rice straw 9.7 5.1 24.1 40.3 5.2 74.7 Subabul wood 6.2 3.6 24.2 44.5 4.0 76.3

Wheat straw 6.5 3.2 36.0 24.3 6.3 72.8

a Moisture, wt% ash-free biomass; volatiles, wt% daf biomass; heating rate 50 K min-l

(7) extractives decompose in a similar way to l ignin bu t at a slightly higher rate and at a slightly lower temperature.

F r o m the above observations, a ' zona t ion ' scheme can be envisaged, relating to the decomposi t ion status of each component . The 'zones ' are postulated as:

zone I: <373 K main ly mois ture evolut ion zone II: 373-523 K extractives start decomposing zone III: 523-623 K predominantly hemicellulose decom-

posi t ion zone IV: 623-773 K main ly cellulose and l ignin decom-

posi t ion zone V: >773 K mainly l ignin decomposi t ion

Pyrolysis characteristics of biomass in t.g.a. The fourteen potent ia l biomass fuels selected for this

study were subjected to pyrolysis in the t.g.a. The results are presented in Table 2. In accordance with the zona t ion hypothesis, the volatiles released on pyrolysis were classified as shown in Table 3. Tha t each kind of biomass has its own typical decomposi t ion characteristics is evident from these tables. F o r example coir pi th releases only 56w t % as volatiles, whereas bagasse and cot ton gin waste each release ~ 8 0 w t % . The m a x i m u m rate of decomposi- t ion of co t ton gin waste is more than twice that of coir pith. Even though it is evident f rom Table 3 that most of the volatiles are released in zone IV, the


Pyrolysis characteristics of biomass and biomass components. K. Raveendran et al.

t .O0

O '~ 0.75 ?,

0.50

. C Oa

'~' 0.25

0 .00

A

8 o.75 u

O

u. 0,50

0.zs "~,

0 ,00 150

. \ l l I . . . . Untreated . . \ \ , ~ /~l I " \ i ~

- , , } ' \ I COR.CO .

-- - Un t r zo t zd - ~ \ / , l

COfR PITH

___¢' ," ,

3 SO 550 750~150 T e m p e r a t u r e (C )

i

• S y n t h e t i c

- - U n t r e a t e d

RICE STRAW

350 T e m p e r a t u r e ( C )

J ~ ' . ' N " ~ . I , J~"

Synthet ic \ x . ~ Synthet ic

o

i

. . . . Un t rea ted , I I -Un t rea ted m

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,ti', o.

- - ~ _ _ _ / I , .

150 350 550 750 A o Temperature ( C )

0 3 5

0.50

0 .25 .~ >o

0.00 SSO SO

Figure 3 T.g.a. and d.t.g, curves of synthetic and untreated biomass

100

U

0.75

0.50

0.25 >

>

1.00 o

numerical values range from 28wt% for coir pith to almost 81 wt% for corn stalk.

The above observations bring about the specificity of individual biomass types. This specificity is held to be attributable to the differences in composition of the biomass. To investigate whether the effects of the individual components of a biomass are simply additive, synthetic biomass prepared by mixing the relevant components was studied. Figure 3 presents the weight loss and derivative weight loss curves for synthetic biomass samples (the curve for fresh biomass is also shown for comparison).

It is appropriate to mention here that the curves of the synthetic biomass matched well (with minor deviations explained later) the curves for fresh biomass only when wood cellulose was used for preparing the synthetic samples. The deviation of the fresh biomass curve was much greater for synthetic biomass containing Whatman cellulose. To explain this, X-ray diffraction (XRD) analyses were carried out to determine the crystallinity of celluloses isolated from various kinds of biomass. The crystallinity index (Cr-I) was calculated by the method suggested by Segal and co-workers 23' 24. Table 4 gives the Cr-I values obtained for different types of cellulose. It may be noted that crystallinity of cellulose extracted from biomass lies between the Cr-I values of Whatman cellulose and chromatograph cellulose. It is known 25-28 that crystallinity influences the pyrolysis characteristics. Hence, to synthesize biomass it is necessary to use cellulose having a crystallinity similar to that in the natural biomass. Hence wood cellulose was used.

The above results indicate the interesting fact that the components themselves play individually significant roles in determining the pyrolysis characteristics of biomass. Also, the basic structure or degree of poly- merization of the biomass is less significant than its composition. In other words, the way in which components are bound (chemically) is not as important as the actual amounts of individual components present in a particular biomass. However, the chemical structure of the individual components, for example the type of

Table 4 Crystallinity index of cellulose

Cellulose Cr-I

Wha tman 34.4 Chromatograph 91.5 Wood 68,9 Coir 49,6 Corn cob 68.9 Rice husk 56.5 Rice straw 52.9 Groundnut shell 54.7

cellulose (defined in terms of Cr-I, which incidentally is of the same order of magnitude for all types of biomass), has an effect on the pyrolysis characteristics of biomass.

Mathematical correlation for pyrolysis in t.g.a. An attempt was made to represent mathematically the

correlation between the pyrolysis characteristics of biomass and those of its components. This simple additive correlation assumes that the overall pyrolysis behaviour ofa biomass is the weighted sum of the partial contributions of its components, the relative proportions in the total composition defining the respective weight losses of the components:

[AWbIT]C = AWc]Z ° Xc + AWhIT" Xh + AW]IT" Xl

+ AWeIT'Xe (1)

where [AWbtr]c is the correlated weight loss for any given biomass and X¢, Xh, Xl, Xe are the initial fractions of cellulose, hemicellulose, lignin and extractives, respectively, present in the corresponding biomass. The values of local weight loss at a temperature T for biomass and for all components, designated as AWblT, Awcl T, Awh[ T, AWl[ T and AwelT respectively, are obtained from individual t.g.a, curves.

The correlated data for biomass are obtained through the following steps based on Equation (1):

(1) The t.g.a, weight loss curve for each component is accurately read in a fixed temperature range to define



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~0.S0- / ~ ] [ ~ .. i~!:! _ _ . . . . Correlated, Eqns.(3)~,. (4) 0.50 =~n $E1- Standard error, Eqn$. (1) ~, (2)

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o o o . - - - , --. 150 350 550 750~150 350 SSO 750

Temperature ( C ) Temperature( C )

Figure 4 T.g.a. and d.t.g, curves of untreated biomass, and correlated data

the weight loss at that temperature; these weight losses are used as weightings.

(2) The weighted sum of the weight loss of biomass over the specified temperature range is computed by multiplying the weight losses of the individual components obtained in step (1) by the corresponding initial fractions of the components present in the biomass and summing.

(3) The resulting weighted sum is taken as a measure of weight loss at the specified temperature for the total biomass, which is summed over the temperature range 423-1023 K to obtain the complete weight loss curve.

Figure 4 compares the calculated weight loss with typical t.g.a, curves for few kinds of biomass, as well as the derivative curves for these cases. The equation for the derivative of weight loss curve is as follows (as may be deduced from Equation 1):

dw dw

q- (~)1T'XI-}- (~tt)e T "Xe (2)

where [(dw/dt)b[7-]c is the correlated derivative weight loss for any given kind of biomass and the values of the derivative weight loss at temperature T for the biomass and for all its components at a temperature are designated a s (dw/dt)blT, (dw/dt)clT, (dw/dt)llT, (dw/dt)hlr, and (dw/dt)elr, and are directly obtained from the individual d.t.g, curves.

As can be observed, the correlated data are comparable with the experimental data, with a standard error of 0.13-0.18. The deviation in the correlated data is mainly in the region where the devolatilization rate is high and where a major fraction of the devolatilization occurs. This deviation may be attributed to the influence of ash elements, as shown below, besides some unaccountable factors such as density and porosity.

To calculate the weight loss data using Equations (1) and (2), wood cellulose t.g.a, data were used (Figure 2). It is important to mention here that the cellulose chosen for study should have similar crystallinity, as discussed above. When Whatman cellulose data were used in Equations (1) and (2) the calculated weight losses did not match the experimental data. Figure 5 shows the experimental and calculated (using Whatman cellulose) t.g.a, data. As can be seen, the deviation obtained is quite significant, which is mainly attributed to the difference in the cellulose crystallinity.

To put the above inferences on a firmer basis, correlation studies were also conducted with synthetic biomass samples prepared with and without ash; the results are shown in Figures 6 and 7. It is interesting and encouraging to find that the correlated data match very well with the calculated data in the case of synthetic biomass without ash. The deviation seen in the case of synthetic biomass with ash is obviously due to the influence of ash.

To account for the influence of ash, a multiplicative correction factor was introduced in the above correlations:

[eXWbl r]E = [aWb[r]C

x A - ~ XdT: xexp

(3)

[(~-~)b TJE= [ ( -~ )b TIC

X (A- [()(a~nlxnz] Slj

(4)

where [AWbIT]E and [(dw/dt)blr]E are the experimental weight loss data and derivative weight loss data for any given biomass, Xa, Xsi and X1 are the initial silica-free



1.00

0,75

i 0,50

~ 0.25

0,00

c 0,75 o

"= o.s,

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T, 0.2

0,00

i ! $E -- 0 o2241 ~ \ ^ 5 E = 0 , 2 2 5 3 N SE = 0"2656

, , - l - _

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- - O.2S .~

i 0 - 0 0

Temperature (C)

0,75 u Experimental) Eqns. (1) ~. (2)

Correlated (Whatmo Cellulose) 0.50 .~ SE-Standard error

o.2s -~

0 , 0 0 150 350 SSO 750,150 350 SSO 750

Temperature ( C ) Temperature (C)

Figure 5 T.g.a. and d.t.g, curves of untreated biomass, and correlated data

1.0

g o.7s

u~ -- O.SO

~ ~ ~ t,oo 5E = 0,0683 5E = 0.0732 5E=0.0142

Synthetic corn cob Synthetic rice husk " Synthtti¢ wood " 0.75 wdhout ash

with out ash without ash I

.... ., I 0.25 °

o.oo

5E : 0"0 473 1 !SE : 0.0732 ~so "-¢'~ 350Temperature(cSSO ) 7SO

Synthetic coir pith " ' I Synthetic rice straw" 0.?S ;" without ash I I without ash

- 050

0-25

0.00

"~ 0.7 o u o

~ O.S,

E en 0.25

0.00 F=- iS0

\ - ~ _ / •

350 550 "/5OptSO 350 SSO Tempera ture(C) Temperature ( C )

0.25

~ ' 0 . 0 0 7 5 0

Figure 6 T.g.a. and d.t.g, curves of synthetic biomass without ash, and correlated data

==

.=

r~

Experimental . . . . Correlated, Eqns. ( t l&(2)

SE - Standard error

ash, silica and lignin fractional contents in dry biomass on a weight basis, and A = 0.5, n 1 = 8.5 and n2 = 7.0.

Constants A, n 1 and n2 were obtained by non-linear optimization, using least-squares criteria for con- vergence, using the Box complex algorithm 29. As can be seen from Figures 4 and 7, the weight loss data predicted using Equations (3) and (4) match the experimental results well.

Equations (3) and (4) show the combined effect of ash elements and biomass constituents, particularly the lignin component. This may be explained in a similar manner to that in the earlier paper on the influence of ash 22, that lignin present in biomass forms more char during pyrolysis and this char is gasified by catalytic

action of certain ash elements in the presence of pyrolysis products such as water and carbon dioxide. The inorganic ash elements are known to play a catalytic role in gasifying char. Silica in ash does not play any catalytic role; however, it alters the thermal properties and the pore structure of char and hence its reactivity.

Mathematical correlation for pyrolys& in PBP: prediction of product distribution

To investigate further, studies were carried out in the packed-bed pyrolyser with five representative types of biomass and also the standard biomass components such as cellulose, xylan and lignin. This was intended to provide insight into the interaction, if any, among the



1,00 - ~ ~ n -- "~"~ I I ~ r i 1.00

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~tl.~, 1, Synthetic corn ¢ob ~ / ~ Synth¢tic ricehusk '~ ! Synth¢tic woo¢l -- k ~1 ~'l w,,h a.h j~ll w.h ash e,\.~L ., ,h a.h

o.,o- t " t i \ 4 - o s o _ .'. i " . . .

o~s- ) . i I " - ~ . . . . . . /..:,, - ~ / - i ~ ~ - ~ o

- J , " ~ " ~ - - - - - - o o o ~ °°°L ,_- o. o . , . ,,o ,o

.9 / ~ ~ Synthetic coir pith ~ Synthcticrice straw 3.75 Expcrimentol ~" / A~'~ w,l, a.~ / ~ * " " ° ' " z - - - ¢ o r r e , a , e d . E , . s . . ~ = C2~

0150 I- t ~ _ -- ~ I - 0.50 "~ ...... Correlettetl, Eqll,, (3)& (/~-) SE1- StandarderrorpEqBs(1)&(?.)

~ 0.25[ / / I ' " ' ' ~ / / i : ~ : SE2-Standard error, EqBS (3 '& '&)

; t ..#..-5' k " i . . . . 0.,, ._.

0.00 ~ I I ~ I ~ 13 13n 150 350 550 750,150 350 550 750

Temperature(C) Temperature(C)

Figure 7 T.g.a. and d.t.g, curves of synthetic biomass with ash, and correlated data

Table 5 Pyrolysis product yields (wt% daf) in PBP experiments

Volatiles Char Liquids Gas

Biomass Coir pith 70.5 29.5 29.5 41.0 Corn cob 79.9 20.1 37.4 42.5 Groundnut shell 72.9 27.1 40.5 32.5 Rice husk 82.7 17.3 41.2 41.5 Rice straw 78.8 21.2 47.0 31.8 Subabul wood 80.7 19.3 22.6 58.1

Components Cellulose 88.9 11.1 46.8 43.0 Lignin 58.3 41.7 26.8 30.5 Xylan 79.3 20.7 40.5 38.8

components during pyrolysis. The information on product distribution would be useful in design and development of any pyrolysis process.

Biomass and components pyrolysis product distribution. Table 5 presents the product distribution obtained for biomass on pyrolysis in the PBP. The char yields from biomass vary from ~17wt% for rice husk to 30wt% for coir pith. The liquid yields vary from ,,~22wt% for wood to almost double that figure for groundnut shell and rice husk. The gas yields vary from ~30wt% for groundnut shell to ~58wt% for wood. As seen earlier in the t.g.a, studies, here also biomass showed specificity in terms of product distribution. (Note: for the sixth set of data, for rice straw, see below.)

To gain more information to explain the specificity of biomass or the variation in product distribution, experiments with biomass components were conducted with the individual isolated components. The product distributions obtained for the biomass components studied are also presented in Table 5. As can be seen, wood cellulose gives the lowest char yield and the highest liquid and gas yields, whereas lignin gives the highest char yield and the lowest liquid and gas yields. The product distribution from xylan lies between that of cellulose and lignin. It is also interesting to note that the

product distributions obtained from most of the biomass types are intermediate between those obtained with cellulose and lignin. In other words, the product yields obtained with cellulose and with lignin hold as the upper and lower limits respectively for the product yields that can be obtained with most types of biomass, for a given operating condition. This is an important criterion to be considered for any biomass pyrolysis process design.

Additive correlation for product distributhgn. Similar to the correlation discussed above for t.g.a, data, an additive model is suggested for predicting the product distribution obtained in the PBP. On similar lines as explained earlier, this model also assumes that the additive overall product distribution of biomass is the weighted sum of the partial contributions of its components, with the relative proportions in the total composition defining the respective weights of the components:

(rb, i)c =rc, i . x c + rx, i . ( x h + x o ) + rl,;.(xL) (5) where Y is the product yield and subscripts b, c, x, h, and 1 denote the yields from biomass, cellulose, xylan, hemicellulose and lignin as obtained by additive analysis, on the dry and ash-free basis; subscript i denotes char, liquid or gas accordingly.

The extracts and hemicellulose are seen to behave similarly, especially in terms of char yield (cf. Figure 2). Hence extractives can be combined with hemicellulose (Xh + Xe) for calculating the product yield.

Table 6 presents the theoretical product distribution calculated by Equation (5) and the deviation from the experimental values. As can be seen, the deviation is greatest for wood and least for groundnut shell. To explain the deviation between the experimental and calculated product yields, the possible influence of two factors was considered: (1) interaction among the components; and (2) influence of ash elements. Each factor was investigated separately.

(1) Interaction among components. Since cellulose and lignin together form ,,~70 wt% of the biomass, these two



Table 6 Calculated product yields a

Char Liquid Gas

Calc. Diff. Dev. Calc. Diff. Dev. Calc. Diff. Dev.

Coir pith 24.4

Corn cob 21.1

Groundnut shell 24.0

Rice husk 21.2

Rice straw 21.1

Wood 21.9

-5 .2 - 17.5 46.8 17.3 58.9 36.5 -4 .5 - 10.9

1.0 5.2 42.4 4.9 13.1 40.7 - 1.8 -4 .3

-3.1 - 11.4 38.8 - 1.7 -4 .5 38.0 5.5 17.1

3.9 22.4 41.5 0.3 0.7 39.9 -1 .7 -4 .0

-0.1 -0 .5 41.8 -5 .2 -11.0 39.0 7.2 22.7

2.6 13.3 39.2 16.6 73.2 38.0 -20.0 -34.5

a Calc., calculated product yield (wt% daf) by Equation (5); Diff., difference between experimental and calculated values (wt% daf); Dev., relative deviation from experimental data (%)

Table 7 Pyrolysis product yields (wt%) of component mixtures

Cellulose: lignin ratio Volatiles Char Liquids Gas

3:1 81.9 18.1 40.5 41.3 1 : 1 70.2 29.8 33.1 37.0 1:3 63.9 36.1 31.5 32.8

60

. . , / .

',.9 L I J

~= 4O

Q ,..I _w

~ 20 U

o O.

I I I I I t

(C-" L) -CELLULOSE °LIGNIN RATIO

( 0 : 1 ) ( 1 : 3 ) (1:1) ( 3 ; 1 ) ( 1 : 0 )

t, - GAS Y I E L D

~ , , , ! . . . . i . . . . i . . . . i . . . . I . . . . i . . . .

- 20 0 20 40 60 80 100 120

CE LLU L O S E ( ' / , WEIGHT)

I . l l , i . . . . I ) i , i I . . . . I , i i , I . . . . i . . . . i

120 100 80 60 40 20 0 - 2 0

LIGNIN 1% W E I G H T )

Figure 8 Product yields of cellulose-lignin mixtures vs. cellulose and lignin contents

major components were mixed in different proportions. The experimental product distributions obtained are presented in Table 7.

As seen for biomass product distribution above, here also the product yields obtained lie between the upper and lower limits obtained from cellulose and lignin. It can also be readily seen from Table 7 that as the cellulose content of the mixture decreases, the char yield increases and the liquid and gas yields decrease. Figure 8 presents the product distribution of mixtures as a function of their cellulose or lignin content. It is clear that the product yields are direct functions of the initial composition of the mixture. Hence it may be said that there is no detectable effect of interaction among the components on pyrolysis product distribution.

(2) Influence of ash. To explain the influence of ash, a similar approach to that used for the correlation in the t.g.a, study was used here. The influence of ash was found to be a function of the lignin, silica-free

ash and silica contents of the biomass, is in accord with previous studies 22. The following relation describes this function:

(Yb, i)E ----= (Yb, i)C x [A XI nl j[-~2 Xsn3] (6)

where ( Yb, i)E is the product yield obtained experimentally and (Yb, i)C is defined by Equation (5).

Figure 9 shows a plot of %,i vs. [A XI nl Xa n2 Xs~3], where 'ffb, i = (Yb, i)E/(Yb, i)C and the values of nl, n2, n3 and A are presented in Table 8 along with standard error and R 2 values. These values of nl, n2, n3 and A are difterent for the char, liquid and gas yields. The above correlation was developed using data obtained for the five biomass materials used in the PBP experiments. To validate the correlation, experiments were conducted for one more kind of biomass, namely rice straw; yields of char, liquids and gases matched well with those obtained from the correlation.

It can be inferred from the results of both t.g.a, and PBP experiments that the secondary reactions or any other effect due to the increased sample mass mainly influence the product distribution and that the interaction of the individual components remains unaffected. This is clear from the fact that the t.g.a, and PBP results can be used to develop correlations to obtain pyrolysis characteristics. The differences lie in the correlation coefficients and not in the observed trend.

Simplified overall correlation. To be able to predict the biomass pyrolysis product yields using Equation (6), it is necessary to know not only the chemical composition of the biomass but also the pyrolysis product yields from the individual components at a specified temperature. To simplify this further, another correlation was attempted, keeping in view the influences of ash and of cellulose and lignin, which contribute the major fraction by weight. This is expressed as:

where the constants A, nl, n2 and n 3 were obtained by regression analysis and are presented in Table 8 along with the standard error and R 2 values obtained. The correlations were developed with the experimental data obtained for five kinds of biomass and were verified with the data obtained for rice straw. Figure 10 shows the product yields as a function of composition, and as can be seen, the calculated yields are in good agreement with the experimental values.



1'60

1,4,0

~ 1.20

~,~ 1.00

"80

.0"8 8 .g

~. 0,92

WD

GS ~ A ~ C P . . . . .

l I ', I L I , . 2 -° ' 'u o.s 0"8 1-o 1-2 1.4

1 o , 5 . . . . . . . . . . . . . . . S o R H

0'56 o CP I W D I I i ! If I

1'300 0'6 0'8 1'0 CP- COIR PITH CP CB-CORN COB f o G S -GS- GROUNDNUT SHELL

o I RH I

0"70 i ~ i I I I I I I I 0,75 -85 -95 1"05 1.15

n 1 n2 n3 X t .Xa "Xsi C

1.10

J ¢ u

0.90

Gs cP

. . . . /2~- - - 6 / "CB

I I I i I I I

0.4. 0'6 0'8 1"0 1"2 1,4- 1"6

. . . . . . . . . . .

o c / c o p e

aWD I I I I

0"2 0'6 0'6 0'8 1'0 1"2 1"4. RH-RICE HUSK CP RS-RICE STRAW o j

-wo -WOOD s

. . . . . . . . . 3; f-oG8 WD / I -

R H o , , , ~ / Jl I

I I I II ] I I I 0"8 "9 1"0 1-1 1'2

- X • -Xsi . C

1 .5

Figure 9 Ratio of product yields (experimental and correlated) vs, cellulose, lignin, ash and silica contents

Table 8 Regression coefficients for Equations (6) and (7)

Correlation coefficients

A n 1 n 2 n 3

Standard

error R 2

Additive correlation (Eqn 6)

Char 2.584 0.378 0.343 -0.048 0.13 0.79

Liquid 1.888 0.694 0.273 0.030 0.23 0.79

Gas 0.349 -0.562 -0.363 0.001 0.15 0.86

Overall correlation (Eqn 7)

Char 49.90 -0.0258 0.258 -0.044 0.03 0.99

Liquid 102.86 0.7028 0.335 0.033 0.16 0.90

Gas 13.93 -0.4168 -0.337 -0.002 0.15 0.87

Char (t.g.a.) 56.43 -0.2664 0.079 0.048 0.12 0.91

The above correlations give a good fit for char yields obtained in the t.g.a, studies also. The correlation coefficients obtained for t.g.a, data are also presented in Table 8. As can be seen from Figure 10, the t.g.a, data also follow a similar trend to that of the PBP data.

CONCLUSIONS

Thermogravimetric studies show that each kind of biomass has unique pyrolysis characteristics, by virtue of the specific proportions of the components present in it. A simple additive correlation to define this phenomenon has been developed, which suggests that there is no detectable interaction among the components during pyrolysis. These results are supported by

synthetic biomass studies. The influence of ash is reflected in the correlation developed.

It is also shown that the individual components taken for study should be similar to those present in biomass. In this respect the crystallinity index is found to be a useful measure for comparing various celluloses.

The specificity of biomass is also seen in the product distribution obtained in the packed-bed pyrolyser, which is further explained by the product distribution from the individual components. In this case also, a simple additive correlation incorporating the influence of ash has been developed which describes the product distribution well. These results are supported by studies on mixtures of components. The results re-emphasize the fact that the feedstock composition and properties play a


Pyrolysis characteristics of biomass and biomass components. • K. Raveendran et al.

60

~" so

40

>.

30

20 SO

o.. 4(]

i/I

' ~ 313

>-

2 (

30

m 2S 13. "E

~ 20

15

15 45

40 ,¢ p. i- o

~ 30 >-

WD

i i i !o ,'o s'o ' ,o

GS R H H ~ "

I ;tl5 I 3iS ! 415 20

o G S

o R H ' ~ * oWO

I ,119 I I I 217 I 23 1S

CP- COIR PITH CP • CB-CORN COB o

G5- GROUNDNUT SHELL

S RH

! ~ ' ~0 : 510 i 30 4 60

1 I 30 40 5b

I I I I I I I 19 23 27 30

RH-RICE HUSK RS-RICE STRAW CP o WD-WOOD

20 J 2,5 i ~ i i w 20 3 35 37.5 20

x l. x 2. n3 r x q "1 x2.2 x, 3. c x , i . c L~?J "

Figure 10 Product yields as a function of cellulose, lignin, ash and silica contents

I 37.5

I I t / I ! 25 30 35

significant role in determining pyrolysis characterist ics and p roduc t dis t r ibut ion, and these are generically specific for each k ind o f b iomass , under given opera t ing condi t ions .

Resul ts o f bo th t.g.a, and PBP exper iments show tha t secondary react ions or any o ther effect due to an increase in sample mass main ly influence the p roduc t d is t r ibu- tion; the in te rac t ion of the ind iv idua l compone n t s remains unaffected.

A C K N O W L E D G E M E N T S

The au thors wish to t hank Professor W o l f g a n g Klose, F G T h e r m o d y n a m i k , Kasse l Univers i ty , G e r m a n y , for useful discussions and va luable suggestions. They also t hank D r K. I. G a s n a s e k a r a n and D r A. Sundaresan , Research Scholars in the Chemis t ry D e p a r t m e n t , I . I .T. , Bombay , for their help in ob ta in ing the X R D pat terns .

R E F E R E N C E S

Antal, M. J., Jr. In 'Advances in Solar Energy', Vol. 2 (Eds K. W. Boer and J. A. Duffle), American Solar Energy Society, New York, 1983, pp. 175-239

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473 6 Tang, W. K. Forest Service Research Paper FPL 71, US Dept.

of Agriculture, 1967 7 Shafizadeh, F. and DeGroot, W. F. In 'Fuels and Energy from

Renewable Resources' (Eds F. Shafizadeh, K. Sarkanen and D. Tillman), Academic Press, New York, 1977

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9 Beall, F. C. Wood Sci. Technol. 1971, 5, 156 10 Roberts, A. F. In 'Thirteenth Symposium (International) on

Combustion', The Combustion Institute, Pittsburgh, 1971, p. 893

11 Mellotte, H. and Richard, J. R. In 'Energy from Biomass', EUR 8245, Commission of the European Communities, 1983, pp. 523-529

12 Vovelle, E. and Mellotte, H. In 'Energy from Biomass', EUR 8245, Commission of the European Communities, 1983, pp. 925-930

13 Ward, S. M. and Braslaw, J. Combust. Flame 1985 21,261 14 Klose, W. and Stuke, V. Fuel Process. TechnoL 1993, 36, 283 15 Nikkhah, K., Bakhshi, N. N. and MacDonald, D• G. In 'Energy

from Biomass and Wastes XVI' (Ed. D. L. Klass), Institute of Gas Technology, Chicago, 1993

16 McGee, B., Norton, F., Snape, C. E. and Hall, P. J. Fuel 1995, 74, 28



17 Khan, M. R., Heshieh, F. Y. and Heaclky, L. Am. Chem. Soc. Div. Fuel Chem. Preprints 1989, 34, 1167

18 Saxby, J. D. and Sato, S. Fuel 1990, 69, 1109 19 Browning, B. L. 'Methods of Wood Chemistry', Vols I & II,

Wiley, New York, 1970 20 Shapino, S. and Brannock, W. W. 'Rapid Analysis of Silicate,

Carbonate and Phosphate Rocks', US Geological Survey Bulletin 1144-A, Washington, 1962

21 Boar, P. L. and Ingram, L. K. Analyst 1970, 95, 124 22 Raveendran, K., Ganesh, A., Khilar, K. C. Fuel 1995, 74, 1813

23 Segal, L., Creely, J. J., Martin, A. E., Jr and Conrad, C. M. Tex. Res. J. 1959, 21, 786

24 Loeb, L. and Segal, L. Tex. Res. J. 1955, 21, 516 25 Chatterjee, P. K. and Conrad, C. M. Tex. Res. J. 1966, 36, 487 26 Kilzer, F. J. and Broido, A. Pyrodynamics 1965, 2, 151 27 Broido, A. and Weinstein, M. Combust. Sci. Technol. 1970, 1,

279 28 Weinstein, M. and Broido, A. Combust. Sci. Technol. 1970, 1,

287 29 Box, M. J. Computer J. 1965, 8, 42


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Pyrolysis Characteristics of Biomass and Biomass Components