Bioresource Technology 101 (2010) 359–365

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Bioresource Technology

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Pyrolysis characteristics and kinetics of the marine microalgae Dunaliella tertiolectausing thermogravimetric analyzer

Zou Shuping a,b, Wu Yulong a,*, Yang Mingde a,*, Li Chun c, Tong Junmao d

a Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, Chinab School of Chemical Engineering and Technology. Tianjin University, Tianjin 300072, Chinac School of Life Science and Technology, Beijing Institute of Technology, Beijing 100081, Chinad Food College, Shihezi University, Shihezi 832000, Xijiang, China

a r t i c l e i n f o

Article history:Received 7 May 2009Received in revised form 29 July 2009Accepted 6 August 2009Available online 31 August 2009

Keywords:Dunaliella tertiolectaPyrolysisThermogravimetricMaster-plots method

0960-8524/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.biortech.2009.08.020

* Corresponding authors. Tel.: +86 010 897960(Y. Mingde).

E-mail addresses: wylong@tsinghua.edu.cn (W.edu.cn (Y. Mingde).

a b s t r a c t

A genus of unicellular green marine microalgae, Dunaliella tertiolecta, was pyrolysed in a thermogravi-metric analyzer from room temperature to 900 �C in a highly purified N2 atmosphere at different heatingrates of 5, 10, 20, and 40 �C/min. The results showed that three stages appeared in this thermal degrada-tion process, with increasing temperature, initial temperature, and peak temperature of pyrolysis shiftingto a higher value as the heating rate increased. The increased heating rate also resulted in increased totalvolatile matter. The kinetic analysis of the main pyrolysis process used a composite procedure involvingthe iso-conversional method and the master-plots method. The iso-conversional method indicated thatthe pyrolysis reaction should conform to a single reaction model with an activation energy of145.713 kJ mol�1 using Kissinger’s method and 146.421 kJ mol�1 using Flynn–Wall–Ozawa’s method,respectively. The master-plots method suggested that the most probable reaction mechanism wasdescribed by an Fn model. Finally, it was estimated that the pre-exponential factor was A = 2.28 �1013 s�1, the kinetic exponent was n = 2.4, and the reaction model function was f(a) = (1 � a)2.4. Theresults of this study provide useful information for designing a pyrolytic processing system using micro-algae D. tertiolecta as feedstock.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Recently, biomass has been considered as an alternative energysource because it is a renewable resource and fixes CO2 in theatmosphere through photosynthesis. Moreover, because fuels frombiomass have low sulfur and nitrogen contents, its energy utiliza-tion creates less environmental pollution than does fossil fuels.Microalgae seems to be an especially ‘promising biomass’ becauseof its higher photosynthetic efficiency, higher biomass production,and faster growth than other biomasses (e.g., trees) (Calvin andTaylor, 1989). Moreover, microalgae can be farmed using fresh ormarine waters and avoiding agricultural land; hence, there willbe no competition with food production. If fuel is recovered effi-ciently from microalgae, the microalgae can be used as second-generation biofuels instead of using fossil fuel.

Recently, many efforts have been put into producing fuel frommicroalgae. Nagle and Lemke (1990) and Milne et al. (1990) havestudied production of diesel fuel and gasoline through the transe-

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88; fax: +86 10 69771464

Yulong), yangmd@tsinghua.

sterification and catalytic cracking of lipids accumulated in algalcells; however, the raw material in their methods is restricted tomicroalgae that have high lipid content. Some researchers havestudied producing fuel from microalgae through direct liquefactionin pure water in conditions close to its critical state (Minowa et al.,1995; Sawayama et al., 1999; Yang et al., 2004). However, technicalproblems may arise because of the high pressure and corrosive ef-fects of water.

Pyrolysis produces energy fuels with high fuel-to-feed ratio,making it the most efficient process for biomass conversion, hasbeen widely applied to a number of biomass species. In recentyears, the pyrolysis process for microalgae biomass has attracteda great deal of attention. Miao et al. (2004) have reported that abio-oil product from fast pyrolysis of autotrophic microalgae, Chlo-rella protothecoides and Microcystis aeruginosa, is characterized bylow oxygen content, a high heating value of 29 MJ kg�1, and alow viscosity of 0.10 Pa/s. Bio-oil produced from microalgae makesit more suitable for fuel use than oils by fast pyrolysis from ligno-cellulosic materials. Miao and Wu (2004) also found that the yieldand quality of bio-oil produced from heterotrophic C. prototheco-ides cells is higher than from autotrophic cells by fast pyrolysis.This process represents a valuable contribution to creating anindustrial system that produces liquid fuel from microalgae.

Table 2The ultimate analysis results of Dunaliella tertiolecta (on dry basis).

C (%) H (%) Oa (%) N (%) S (%)

39 5.37 53.02 1.99 0.62

a By difference.

Table 3The chemical content analysis results of Dunaliella tertiolecta (on dry basis).

Crude protein Crude lipid Crude carbohydrate

61.32 2.87 21.69

360 Z. Shuping et al. / Bioresource Technology 101 (2010) 359–365

For the proper design and operation of the pyrolysis conversionsystems, a thorough knowledge of the thermal behaviour andpyrolysis kinetics of biomass are required. Thermogravimetricanalysis (TGA) was selected for the thermal decomposition pro-cess. The kinetic data obtained from TGA are very useful in helpingus understand the thermal degradation processes and mecha-nisms; these data also may be used as input parameters for a ther-mal degradation reaction model. Extensive literature has beenpublished on the experimental and mechanistic aspects of ligno-cellulosic biomass, such as waste woods, agricultural residues,and municipal solid wastes (Vamvuka et al., 2003; Mangut et al.,2006; Hu et al., 2007; Wang et al., 2008; Grammelis et al., 2009;Park et al., 2009). However, very little information is available onthe pyrolysis kinetics of microalgae.

Peng et al. (2001) investigated the pyrolytic characteristics oftwo kinds of microalgae—Spirulina platensis and C. prototheco-ides—by thermogravimetric analysis. The authors presented thepyrolysis reaction model mainly by nth-order expressions. How-ever, other kinetic equations such as nucleation and the nucleus-growing phase-boundary reactions, diffusion, or power law canalso be used to describe pyrolytic process more exactly.

Dunaliella tertiolecta, a genus of green halophilic marine micro-algae, accumulates b-carotene at more than 10% of the algal dryweight. Because the technology for mass cultivation of Dunaliellato obtain b-carotene has been established, if energy can be recov-ered from Dunaliella in the form of oil by pyrolysis, an energy pro-duction system from mass cultivated Dunaliella can be created.

In this article, pyrolysis of D. tertiolecta was investigated withTGA, and the characteristics of the thermal degradation of thesemicroalgae at different heating rates were studied. The objectivewas to obtain the kinetic parameters of decomposition via the Kis-singer–Akahira–Sunose (KAS) method and Flynn–Wall–Ozawa(FWO) method and determine the degrade mechanism by usingthe master-plots method.

2. Methods

2.1. Material

The powder of microalgae, D. tertiolecta, was provided by theTanjing Microalgae Biotechnologies Co., Ltd. (Tianjin, PR China).The proximate analysis and calorific value measurements of D. ter-tiolecta were carried out according to ASTM standards; the resultsare presented in Table 1. The ultimate sample analysis was carriedout using an EAI CE-440 elemental analyzer (Table 2). The contentsof crude protein, crude fat, and carbohydrate were determined bythe Kjeldehl method, the Soxhlet extract method, and the phe-nol–sulfuric acid method, respectively (Table 3). The powder ofD. tertiolecta in this study was dried at 105 �C for 12 h, ground pre-viously, and sieved to obtain a < 100 lm fraction.

2.2. Thermogravimetric analysis

The experiments were carried out in a Seiko InstrumentsEXSTAR 6000 thermogravimetric analyzer. In each experiment,10 mg of D. tertiolecta sample was spread uniformly on the bottomof the alumina crucible of the thermal analyzer. The pyrolysisexperiments were performed at heating rates of 5, 10, 20, and40 �C/min in a dynamic high purity nitrogen flow of 50 ml/min�1.

Table 1The proximate analysis results of Dunaliella tertiolecta (on dry basis).

Moisture (wt.%) Volatiles (wt.%) Fixed carbon (wt

4.98 54.48 27.00

The temperature of the furnace was programmed to rise from roomtemperature to 900 �C. The experiments were replicated at leasttwice to determine the irreproducibility, which was found to bevery good.

2.3. Kinetic methods

In the non-isothermal experiments carried out with a thermobalance, the sample mass was measured as a function of tempera-ture. The rate of degradation or conversion, da/dt, is a linear func-tion of a temperature-dependent rate constant, k, and atemperature-independent function of conversion, f(a):

dadt¼ jf ðaÞ ð1Þ

The reaction rate constant, k, has been described by the Arrhe-nius expression

j ¼ A exp � ERT

� �ð2Þ

where A is the pre-exponential factor, E is the activation energy, R isthe gas constant, and T is the absolute temperature. The combina-tion of Eqs. (1) and (2) gives

dadt¼ A exp � E

RT

� �� f ðaÞ ð3Þ

If the temperature of the sample is changed by a controlled andconstant heating rate, b = dT/dt, the rearrangement of Eq. (3) gives

dadT¼ A

bexp � E

RT

� �f ðaÞ ð4Þ

The integrated form of Eq. (4) is generally expressed as

GðaÞ ¼Z a

0

daf ðaÞ ¼

Ab

Z T

T0

exp�ERT

� �dT ð5Þ

where G(a) is the integrated form of the conversion dependencefunction f(a). Based on these equations, different kinetic methodswere applied in this study.

2.3.1. Iso-conversional methodIt is well known that the iso-conversional method easily gives

an estimate of activation energy regardless of the reaction mecha-nism. Two kinds of iso-conversional methods are applied in thisarticle.

.%) Ash (wt.%) Gross calorific value (MJ kg�1)

13.54 14.24

Z. Shuping et al. / Bioresource Technology 101 (2010) 359–365 361

KAS (Kissinger, 1957) equation

lnb

T2

� �¼ ln

AREaGðaÞ �

Ea

RTð6Þ

FWO (Flynn and Wall, 1966; Ozawa, 1965)

ln b ¼ ln0:0048AEa

RGðaÞ � 1:0516Ea

RTð7Þ

Using Eqs. (6) and (7), the linear representation of ln (b/T2), ln bvs. 1/T allows us to determine the activation energy with a givenvalue of the conversion.

2.3.2. Master-plots methodGenerally, decomposition reactions are very slow at sub-ambi-

ent temperatures; therefore, the lower limit of the integral onthe right side of Eq. (5), T0, can be approximated to be zero. Theintegrated form of Eq. (4) gives

GðaÞ ¼ Ab

Z T

T0

exp�ERT

� �dT � A

b

Z T

0exp

�ERT

� �dT ¼ AE

bRPðuÞ ð8Þ

where the temperature integral, PðuÞ ¼R u1 �ðe�u=u2Þdu (u ¼ E=RT),

has no analytical solution and can be expressed by an approxima-tion. The rational approximation of Doyle (1962) gives sufficientlyaccurate results:

PðuÞ ¼ 0:00484 � expð�1:0516uÞ ð9Þ

For a single-step process with an invariant G(a) expression, ananalysis using the master plots delivers an unambiguous choiceof the appropriate kinetic model (Gotor et al., 2000; Perez-Maque-da et al., 2002). Taking account into a single-step process, the ki-netic triplets (i.e., the kinetic model), A and E, are invariable.Using a reference at point a = 0.5 and according to Eq. (8), one gets

Gð0:5Þ ¼ AEbR

� �Pðu0:5Þ ð10Þ

where u0.5 = E/RT(0.5). The following equation is obtained by dividingEq. (8) by Eq. (10)

GðaÞGð0:5Þ ¼

PðuÞPðu0:5Þ

ð11Þ

Table 4Most frequently used mechanisms of solid state processes.

Mechanisms Symbol

Order of reactionFirst-order F1

Second-order F2

Third-order F3

DiffusionOne-way transport D1

Two-way transport D2

Three-way transport D3

Ginstling-Brounshtein equation D4

Limiting surface reaction between both phasesOne dimension R1

Two dimensions R2

Three dimensions R3

Random nucleation and nuclei growthTwo-dimensional A2

Three-dimensional A3

Exponential nucleationPower law, n = 1/2 P2

Power law, n = 1/3 P3

Power law, n = 1/4 P4

Plotting G(a)/G(0.5) against a corresponds to theoretical masterplots of various G(a) functions (Table 4). To draw the experimentalmaster plots of P(u)/P(u0.5) against a from experimental data ob-tained under any heating rates, the experimental master plot isindependent of the heating schedule. Eq. (11) indicates that, for agiven a, the experimental value of P(u)/P(u0.5) and theoreticallycalculated values of G(a)/G(0.5) are equivalent when an appropri-ate kinetic model is used. This integral master-plots method canbe used to determine the reaction kinetic models of decompositionreactions.

3. Results and discussion

3.1. Thermogravimetric experiments

3.1.1. Thermal degradation processFig. 1 shows the weight loss and the rate of weight loss curve

obtained during the pyrolysis of D. tertiolecta sample under inertatmosphere at a heating rate of 10 �C/min. Peng et al. (2001) gavecurves of a similar shape for S. platensis and C. protothecoides. Dur-ing thermal degradation of D. tertiolecta samples, three stages canbe distinguished during the heating process of the samples. Thefirst stage (I) goes from room temperature to 165 �C; a slightweight loss in the weight loss curve and a small hump in the rateof weight loss curve is observed. This could be due to the loss ofwater and light volatile compounds. The second stage (II) goesfrom 165 �C to 342 �C. Stage II is characterized by a major weightloss, which corresponds to the main pyrolysis process. Most ofthe organic materials are decomposed in this stage. The mass lossof this stage is more than 50% of total volatiles. There is a strongpeak in the rate of weight loss curve, with a peak temperature at275.3 �C, at which the rate of weight loss attains maximum. Thethird stage (III) goes from 342 �C to the final temperature(900 �C). In this third stage, the carbonaceous matters in the solidresiduals continuously decomposed at a very slow rate. A slightcontinued loss of weight is shown in the weight loss curve.

The analysis of the rate of weight loss curve shows that, duringthe main pyrolysis process, only one strong peak and, therefore onedecomposition process corresponding to the degradation of crudeprotein, was observed. It is obviously different from the usualtwo decompositional processes in corresponding to the degrada-tion of cellulose and semicellulose for lignocellulosic materials

f(a) G(a)

1 � a �ln(1 � a)(1 � a)2 (1 � a)�1 � 1(1 � a)3 [(1 � a)�2 � 1]/2

0.5a a2

[�ln(1 � a)]�1 a + (1 � a)ln(1 � a)1.5(1 � a)2/3[1 � (1 � a)1/3]�1 [1 � (1 � a)1/3]2

1.5[(1 � a)1/3 � 1]�1 (1 � 2a/3) � (1 � a)2/3

1 a2(1 � a)1/2 1 � (1 � a)1/2

3(1 � a)2/3 1 � (1 � a)1/3

2(1 � a)[�ln(1 � a)]1/2 [�ln(1 � a)]1/2

3(1 � a)[�ln(1 � a)]2/3 [�ln(1 � a)]1/3

2a1/2 a1/2

3a2/3 a1/3

4a3/4 a1/4

0 100 200 300 400 500 600 700 800 9000

20

40

60

80

100

Temperature / °C

Wei

ght l

oss

/ %

Ι ΙΙ III

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

Rat

e of

wei

ght l

oss

/ %°C

-1

Fig. 1. Typical TG–DTG curves of Dunaliella tertiolecta at a heating rate of 5 �C/min.

362 Z. Shuping et al. / Bioresource Technology 101 (2010) 359–365

(Meszaros et al., 2004). The reason for this result is most likely thatthe thermal degradation process of biomass is influenced directlyby the raw material composition. Microalgae materials are mainlycomposed of protein (protein contents exceed 60%), whereas ligno-cellulose materials are mainly (95%) composed of cellulose, semi-cellulose, and lignose. The degradation characteristics of the D.tertiolecta sample with respect to devolatilisation temperaturesand total volatile matter content are given in Table 5.

Table 5 also shows that the pyrolysis of D. tertiolecta under inertconditions occurred at lower temperatures than those obtained forother microalgae and wood samples in the same TGA experiments(Peng et al., 2001; Orfao et al., 1999). Specifically, the maximummass loss rate of D. tertiolecta decomposition was obtained at low-er temperatures (260–300 �C) than the that of two other microal-gae samples, S. platensis and C. protothecoides (330–360 �C) (Penget al., 2001). In addition, Orfao et al. (1999) found that, in a similarexperiment, the peak temperature of pine wood decompositionwas 330 �C. Besides, the total mass losses of volatile matter for D.tertiolecta were lower than those obtained for microalgae S. platen-sis and C. protothecoides samples (Peng et al., 2001).

3.1.2. Effect of heating rateFig. 2 shows the weight loss and the rate of weight loss curves

obtained from the pyrolysis of D. tertiolecta at different heatingrates (5, 10, 20, and 40 �C/min). The general shift to higher temper-atures occurs when the heating rate is increased. This is typical forall non-isothermal experiments. The main reason for these shifts isthat biomass is a poor conductor of heat, there exists a tempera-ture gradient throughout the cross-section of the biomass. At lowerheating rate, the temperature profile along the cross-section can beassumed linear as both the outer surface and the inner core of thebiomass material attains same temperature at a particular time assufficient time is given for heating. On the other hand, at a higherheating rate, a substantial difference in temperature profile existsalong the cross-section of the biomass (Maiti et al., 2007). Theother reason may be due to the effect of heating rate on secondary

Table 5Characteristic devolatilisation temperatures and total volatile matter content for Dunaliell

Heating rate (�C/min) Ti (�C)b Te (�C)c

5 155 32010 165 34220 185 37240 200 425

a Tmax was determined from the respective DTG curve peaks.b Ti is the temperature of initial decomposition.c Te is taken as the point of equal value of instantaneous thermal degradation rate tod VM is the total volatile matter evolved in the second stage.

reactions of the primary pyrolysis products (tar and high-molecu-lar-weight compounds), as some authors have reported (Juntgen,1984). On the other hand, Table 5 shows the total % mass lossesin the second stage were 52.34%, 56.22%, 59.53%, and 66.71% onoriginal basis at the heating rates of 5, 10, 20, and 40 �C/min,respectively. It is evident from the results that the increased heat-ing rate resulted in increases in the total volatile matter. Lowerheating rates results in longer residence times inside the reactorand favor secondary reactions such as cracking, re-polymerizationand re-condensation, which ultimately lead to the formation of thesolid char (Maiti et al., 2007). The other reason may be due to someresistances to mass or heat transfer in the complex matrix of sam-ple at low heating rates, however, an increase in heating rate mayovercome these resistances by means of strengthened drivingforces of mass and heat transfer inside the particles of biomass,and lead to a higher conversion.

3.2. Kinetics

3.2.1. Iso-conversional method for estimating activation energyAccording to Eqs. (6) and (7), a plot of ln (b/T2), ln b against 1/T

should be a straight line. From the slot of line, we can estimateapparent activation energy of the dynamic degradation at variousconversion a. Fig. 3 shows the representative plots for the mainstage of weight loss (i.e., stage II). The activation energies calcu-lated by the KAS and FWO methods are listed in Table 6.

As shown in Table 6, all plots had fairly high linear correlationcoefficients greater than 0.99, and the average values of activationenergy worked out through the KAS and FWO methods varyslightly, so the results are credible. The activation energies hardlyvaried with the degree of conversion, indicating that there existsa high probability for the presence of a single-step reaction(Vyazovkin, 2000).

Compared with other methods, the KAS method and the FWOmethod have the advantage that they do not require previousknowledge of the reaction mechanism for determining the activa-tion energy. Because the average values of activation energyworked out by the KAS and FWO methods were very close, wechose the average values of two (i.e., 146.067 kJ mol�1) as the valueof activation energy used in the master-plots method.

3.2.2. Master-plots method for determining kinetic modelUsing the predetermined value of E, along with the temperature

measured as a function of a, P(u) can be calculated directly accord-ing to Eq. (9). Then, the experimental master plots of P(u)/P(u0.5)against a under various heating rates from experimental datacan be drawn. Plotting G(u)/G(0.5) against a corresponds to theo-retical master plots of various kinetic functions. Both of the exper-imental master plots and theoretical master plots of various kineticfunctions (Table 4) are shown in Fig. 4. The experimental masterplots of the different heating rates are practically identical, indicat-ing that the kinetics degradation process of D. tertiolecta could bedescribed by a single kinetic model (Tanaka, 1995). The compari-

a tertiolecta, obtained at different heating rates.

Tmax (�C)a (dw/dt)max (% min�1) VM (%)d

266.5 5.58 52.34275.3 14.24 56.22285.2 31.66 59.53299.4 59.34 66.71

Ti.

150 200 250 300 350 400 45030

40

50

60

70

80

90

100

Temperature / °C

Wei

ght l

oss

/ %

12

34

1 ⎯ 5 °C min-1

2 ⎯ 10 °C min-1

3 ⎯ 20 °C min-1

4 ⎯ 40 °C min-1

a

150 200 250 300 350 400 450-70

-60

-50

-40

-30

-20

-10

0

Temperature / °C

Rat

e of

wei

ght l

oss

/ %m

in-1

1 ⎯ 5 °C min-1

2 ⎯ 10 °C min-1

3 ⎯ 20 °C min-1

4 ⎯ 40 °C min-1

1

2

3

4

b

Fig. 2. (a) TG curves of Dunaliella tertiolecta in different heating rate against temperature and (b) DTG curves of Dunaliella tertiolecta in different heating rate againsttemperature.

0.0016 0.0017 0.0018 0.0019 0.0020

-11.0

-10.5

-10.0

-9.5

-9.0

-8.5

0.7 ( )0.8 ( )

0.5 ( )0.4 ( )

0.6 ( )

0.3 ( )0.2 ( )

ln(β

/Τ2)

a

1 / T

0.0016 0.0017 0.0018 0.0019 0.0020

-11.0

-10.5

-10.0

-9.5

-9.0

-8.5

ln( β

/ T

2)

b

0.7 ( )0.8 ( )

0.5 ( )0.4 ( )

0.6 ( )

0.3 ( )0.2 ( )))

1 / T

Fig. 3. (a) Plots for determination of activation energy of the dehydration at different a by KAS methods and (b) plots for determination of activation energy of thedehydration at different a by FWO methods.

Table 6The activation energies obtained by TG data at different rates.

Conversion rate (a) KAS method FWO method

E (kJ mol�1) Related coefficient E (kJ mol�1) Related coefficient

0.2 139.639 0.99663 141.331 0.997070.3 147.957 0.99520 149.334 0.995780.4 152.713 0.99474 152.942 0.995360.5 151.598 0.99296 152.954 0.993790.6 150.417 0.99213 151.903 0.993070.7 138.953 0.99152 141.104 0.992620.8 131.715 0.99854 134.371 0.99876

Average 145.713 146.421

Z. Shuping et al. / Bioresource Technology 101 (2010) 359–365 363

son of the experimental master plots with theoretical ones indi-cates that no existing theoretical master plots match the experi-mental ones perfectly. However, the Fn model, G(a) = [(1 �a)1�n � 1]/(n � 1), should describe the kinetic process for thermaldehydration of D. tertiolecta because the experimental master plotslay between the theoretical master plots F2 and F3.

3.2.3. Evaluating pre-exponential factor and kinetic exponentThe accommodated Fn model with a non-integral exponent n

was used for estimating the kinetic exponent n and A. The expres-sion of Fn is introduced into Eq. (8), Eq. (12) is obtained

GðaÞ ¼ AEbR

PðuÞ ¼ ð1� aÞ1�n � 1n� 1

ð12Þ

To further make certain optimal value of n, we increased n from2.0–3.0 with increments of 0.1 and a plot [(1 � a)1�n � 1]/(n � 1)vs. EP (u)/bR using linear least-square fitting procedure. The mostprobable value of n is that for which the intercept is closest to zeroand correlation coefficient r is the highest. Results show thatn = 2.4 and A = 2.28 � 1013 s�1 for E = 146.067 kJ mol�1. The plotsof [(1 � a)1�n � 1]/(n � 1) vs. E/bR(P(u)) at n = 2.4 at various heat-ing rates and their linear-fitting drawing through the zero pointare shown in Fig. 5, respectively.

Comparison of kinetic parameters of pyrolysis for microalgae isshown in Table 7. The activation energy for the pyrolysis ofD. tertiolecta was higher than that of other two kinds of microalgae(S. platensis and C. protothecoides) (Peng et al., 2001) and cornstover (Ajay et al., 2008) Moreover, the reaction order and

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00

1

2

3

4

5

6

7

8

0

1

2

3

4

5

6

7

8

α

P(u

) /P

(u0.

5)

F3F2

D3

D4

D2

F1D1R3R2

A3 P2P3P4

R1A2

G(α

) /G

(α0.

5)

Fig. 4. Theoretical master plots of G(a)/G(0.5) against a for various reaction models(Table 4) and experimental master plots of P(u)/P(u0.5) against a from experimentaldata obtained at heating rates 5 �C/min�1(h), 10 �C/min (d), 20 �C/min (D), 40 �C/min (.).

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.00

2

4

6

8

10

12

1013× EP (u)/βR

[(1-

a)-1

.4-1

]/1.

4

Fig. 5. Plotting [(1 � a) 1�n � 1]/ (n � 1) against 1013 � EP (u)/bR at n = 2.4 atvarious heating rates 5 �C/min (j), 10 �C/min (D), 20 �C/min (N), 40 �C/min (h) andtheir linear-fitting drawing (solid line).

Table 7Comparison of various kinetic parameters of pyrolysis for different biomass materials.

Biomass materials E (J mol�1) A (min) n

D. tertiolecta 1.46 � 105 2.28 � 1013 2.4S. platensis 7.62–9.7 � 104 2.44–7.62 � 106 1.55–1.98C. protothecoides 4.22–5.25 � 104 3.52–11.1 � 103 1.29–1.88Corn stover 5.8–6.26 � 104 1.35–7.33 � 104 0.74–0.83

364 Z. Shuping et al. / Bioresource Technology 101 (2010) 359–365

pre-exponential factor obtained from different biomass are obvi-ous different (Table 7). The results suggest, the thermal behaviouris influenced greatly by composition of biomass materials, andthere may be obvious differences in pyrolysis kinetics among thesimilar species of biomass. The results of our study provide usefulinformation for designing a pyrolytic processing system using mic-roalgae D. tertiolecta as feedstock.

4. Conclusions

The thermogravimetric analysis of showed that three stages canbe distinguished during the heating process. The moisture is re-

moved in the first stage; the second stage is the main pyrolysisprocess and most of the organic materials are decomposed in thisstage; and the solid residual slowly decomposed in the third stage.The initial temperature of pyrolysis and the temperature at whichthe pyrolysis rate reaches the peak value shift to the higher tem-perature as the heating rate increasing. The increased heating ratealso resulted in small increases in the total volatiles release.

To model the main stage of weight loss (i.e., stage II), the iso-conversional method was applied to estimate the apparent activa-tion energy E; the most probable mechanism function f(a) wasdetermined by using the master-plots method. The result showedthat the most probable mechanism is the simple order reactionmodel function, f(a) = (1 � a)2.4, A = 2.28 � 1013 s�1, E = 146.067 kJmol�1.

Acknowledgements

The project was supported by the National High Technology Re-search and Development Program of China (863 Program) (No.2008AA06Z342) and State Key Basic Research and DevelopmentProgram of China (973 Program) (No. G2006CB705809).

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