Wood Fast Pyrolysis(2)

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Wood Fast Pyrolysis: Comparison of Lagrangian and Eulerian Modeling

Approaches with Experimental Measurements

Olivier Authier,*, Monique Ferrer, Guillain Mauviel, Az-Eddine Khalfi, and Jacques Lede

LSGC, ENSIC, CNRS-Nancy UniVersite, 1 rue GrandVille, BP 20451, 54001 Nancy cedex, France, and FluidMechanics, Energy and EnVironment Department, EDF R&D Chatou, 6 quai Watier, BP 49,

78401 Chatou cedex, France

The aim of the present paper is to validate Lagrangian and Eulerian modeling approaches of biomass fastpyrolysis from comparison with experimental measurements. Wood samples are submitted during measuredtimes to a controlled and concentrated radiation delivered by an image furnace. The heat flux densities areclose to those encountered when wood is surrounded by hot bed particles in a dual fluidized bed (DFB)gasifier. In the image furnace, the sample is placed inside a transparent quartz reactor fed by a cold carriergas. The volatile matter (condensable vapors and gases) released by the solid is quenched inside the reactor.It is hence possible to selectively study primary pyrolysis phenomena occurring at the solid level. All thepyrolysis products (char, vapors, and gases) are recovered, and their masses are measured as a function of theflash time allowing the assessment of mass balances. The yield of vapors does not significantly depend onthe available heat flux density, unlike the gases and char yields. The experimental results are compared todata derived from two different modeling approaches. Their basic assumptions are discussed from characteristic

time values which reveal the controlling phenomena. Mass transfer limitations are neglected in comparisonwith heat transfer and chemical phenomena. The first type of pyrolysis model relies on an original Lagrangianapproach where mathematical equations of heat and mass balances are written with the assumptions thatwood and char form two distinct layers. In the second one, a classical Eulerian approach is considered: equationsare directly written at the whole particle level. The results of the two models as well as the experimental data(sample mass losses and product yields) are in quite good agreement.

1. Introduction

Among the possible biomass conversion technologies, gas-ification represents an interesting and cost-effective route wherebiomass is transformed into a syngas at high temperatures inthe presence of an oxidizing agent. The gasifiers may be dividedinto three main groups: entrained flow, fluidized bed (bubbling/

circulating), and fixed bed.1 A high-quality gas can be producedby using a dual fluidized bed (DFB) system with steam as thegasification agent.2,3 The process (Figure 1) includes tworeactors in series with circulation of the hot fluidizing particles(catalysts): a gasification reactor (gasifier) inside which virginbiomass is fed, and a combustion reactor (riser). Combustionof residual char and of additional fuels inside the riser providesthe energy required for the endothermic reactions occurring inthe gasifier.4

A good understanding of the chemical and physical phenom-ena involved in the biomass degradation inside the gasifier isrequired for the development, optimization, and scale-up of theprocess. Typically, biomass samples enter the fluidized bed

inside which they undergo heating and physicochemical trans-formations which depend on the intensity of heat transferefficiencies (hot particle collisions, convection, and radiation).After drying,5 the biomass temperature increase induces py-rolysis with char formation (carbon-rich residue) and releaseof volatile matter, resulting in up to 80% biomass weight loss.6

Volatile matter is composed of gases (low molecular massproducts: H2, CO, CO2, C1-C3) and vapors (products that are

liquids at room temperature). The structure and composition ofthe char, which undergoes subsequent gasification reactions,depend on biomass properties and process operating conditions.At the same time, primary volatile products undergo secondarycracking/reforming reactions. Their release may also cause thefragmentation of the pyrolyzing biomass particles because of

inner pressure increase during pyrolysis.

7

In addition, particleattrition due to the mechanical influence of bed particles mayoccur, resulting in the separation of small char fragments.8

Analysis of all these phenomena is made complicated by thefact that they all occur simultaneously inside the reactor. It isthen difficult to study them separately. Unfortunately, math-ematical modeling for reliable scale-up of the gasifier requiresthe knowledge of all these involved steps. That is why the maininvolved elementary phenomena such as chemical reactions andtransfer phenomena should be decoupled and studied indepen-dently of each other.

There are few available models in the literature taking intoaccount drying, pyrolysis, gasification, and homogeneous andheterogeneous reactions in competition with heat and mass

transfers inside a biomass fluidized bed gasifier.9,10 In severalcases,thepyrolysisprocessisalsoassumedtobeinstantaneous9,11-13

* To whom correspondence should be addressed. Tel.: +33 (0)3 8317 52 82. Fax: +33 (0)3 83 32 29 75. E-mail: [email protected].

CNRS-Nancy Universite. EDF R&D Chatou. Figure 1. Schematic representation of a dual fluidized bed process.2

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as soon as the biomass particles enter the bed, i.e., before mixingwith bed material. However, the validity of such an assumptiondepends on the particle size and is only valid for thin particles.14

Under high heat transfer efficiency conditions, the pyrolysis timecannot be neglected for particles a few millimeters in size,resulting in internal temperature gradients.15-17 Moreover,kinetics of fast primary pyrolysis should be determined, so far

as possible, under thermal conditions similar to those encoun-tered in a fluidized bed. Unfortunately only a few reliable worksmade under such conditions have been published. For example,the models are often written on the basis of experimentsperformed in thermogravimetric devices18,19 which are notrepresentative of fast pyrolysis.

The aim of the present paper is to validate Lagrangian andEulerian modeling approaches of biomass fast pyrolysis underthermal conditions close to those prevailing in a DFB. For thatpurpose, experiments are performed with an image furnacedelivering a concentrated radiation under clean conditions ofheat flux densities which can be controlled inside largedomains.16,20 Experimental data (rate of sample mass loss; gases,

vapors, and char masses as a function of the flash time) arethen compared to the results of the models. Two approaches(i.e., Lagrangian and Eulerian) are considered for pyrolysismodeling of a biomass particle. The Lagrangian referenceconsiders a wood/char interface moving through space and timeinside the particle, whereas the Eulerian reference considerschanges as they occur at a fixed point. In the Eulerian approach,classical with regard to other pyrolysis models from theliterature, equations are directly written on the whole particle.In the original Lagrangian approach representative of experi-mental observations of the present work, mass and heat balancesare written in the assumptions that wood and char form twodistinct layers. These models, written on the basis of thecontrolling phenomena determined from the calculation of

characteristic times, are hence representative of biomass fastpyrolysis under specific conditions.

2. Experimental Section

2.1. Heat Transfer in Fast Pyrolysis. The literature oftenreports experimental data related to pyrolysis performed as afunction of a reference temperature and/or heating rate. Unfor-tunately, these parameters are difficult to define. They cancorrespond to those of the heat source or of the sensor, and notnecessarily to those of biomass itself.20 That is why availableheat flux density at the particle level has been suggested as amore reliable criterion to characterize the pyrolysis thermaloperating conditions.21

In a fluidized bed, heat is transferred to biomass by threepossible mechanisms: gas convection, radiation, and contact with

the material bed particles. The heat transfer coefficients dependon a great number of physical properties (particles and gas) andof reactor characteristics. They may vary along the bed height.Nevertheless, global heat transfer coefficients in a fluidized bedcommonly range from 200 to 800 W m-2 K-1.22-24 Moreover,due to efficient mixing and intensive heat transfer betweenparticles, the material temperature can be considered as practi-cally uniform in the whole fluidized bed volume. Assuming amaterial bed temperature of about 1120 K,4 the available heatflux density at the biomass particle surface during pyrolysis canbe considered as ranging from about 0.2 to 0.8 MW m-2.

2.2. Image Furnace: Source of Controlled Heat Flux

Density. Experiments are performed in an image furnace (Figure2) already used to study the elementary processes of biomass

fast pyrolysis.6,16,20,21,25-30 The principle of the image furnacehas been already described in details in the works of Boutin et

Figure 2. Image furnace: qualitative scheme of the experimental setup used for studying the primary steps of biomass fast pyrolysis.

Table 1. Sample Ultimate Analysis (wt %)a

sample C (wt %) H (wt %) O (wt %) Ash (wt %)

oak 49.1 5.8 44.2 0.3

char (1) 90.2 1.7 3.7 0.4char (2) 82.6 2.5 11.1 1.7

a The values given for char are obtained after total wood conversionand for two values of the available heat flux density (1 ) 0.8MW m-2, 2 ) 0.3 MW m

-2). Values are known with absoluteaccuracies close to (0.5%.

Table 2. Typical Examples of Mass Balances and Gas Compositionsfor Two Values of the Available Heat Flux Density (1 ) 0.8MW m-2, 2 ) 0.3 MW m

-2)a

fluxflashtime(s)

mass andyield (wt %)

of gases


of vapors


of charmassloss

massbalance(wt %)

1 3.33 12.9 29.3 6.0 45.1 9425 57 12

2 13.25 7.1 36.1 11.8 47.6 9312 61 20

flux flashtime(s)

molar fractions (in %) of gas species (excluding N2)

H2 CO CO2 CH4 C2H6 C2H4 C2H2 C3H8

1 3.33 17.3 52.6 13.4 9.0 0.6 5.4 1.0 0.72 13.25 6.0 57.5 18.3 12.0 0.7 4.4 0.3 0.8

a All masses are in 10-6 kg.

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al.20,25,26 Hence, the experimental setup and procedure are onlybriefly described.

Wood samples are submitted to the flux of a controlled andconcentrated radiation chosen in order to be representative offast pyrolysis. The radiation is delivered by a 5 kW high pressurexenon arc lamp (Osram XBO 5000 W/H CL OFR) placed atthe first focus (F1) of a first elliptical mirror. In its originalconfiguration,21,25,26 the sample was placed at the first focus(F2) of a second elliptical mirror; both mirrors have the samesecond focus (F3). In the present study, the sample is simplyplaced at the focus F3. The available heat flux densities can be

varied and controlled by using specific metallic grids interceptingthe radiation issued from the first mirror. It is measured through

the use of a specific device relying on temperature measure-

ments.25 The experimental results reported in this study have

been obtained under two values of flux densities: 0.8 MW m-2

(1) and 0.3 MW m-2 (2). The flash times are controlled

through a moving pendulum20 intercepting the light beams and

placed downstream of the grid. Sensors fixed on the pendulum

are connected to a computer in order to determine the value of

the flash time which can be varied from 1 to 20 s (accuracy of

about 0.01 s).

2.3. Pyrolysis Reactor and Product Recoveries. The wood

sample which absorbs radiation is placed inside a cylindricaltransparent quartz reactor (inside diameter, 30 10-3 m; height,

Figure 3. Variations of mass losses, and gases, vapors, and char masses as a function of the flash time for two values of the available heat flux density ( 1) 0.8 MW m-2, 2 ) 0.3 MW m

-2).

Figure 4. Molar fractions (in %) of pyrolysis gases (a, CO, CO2, H2; b, hydrocarbons) as a function of the flash time for two values of the available heatflux density (1 ) 0.8 MW m

-2, 2 ) 0.3 MW m-2).

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50 10-3 m) fed by two flows of N2 (overall flow rate, 3.7 10-5 STP m3 s-1). The injector nozzles are set up on bothsample sides and at its bottom vicinity, in order to prevent vapordeposition on the reactor walls. The reactor position is adjustedwith a three-axis translation device, and its precise alignmentat the focus F3 is obtained with two laser beams.

In usual pyrolysis reactors, the condensable vapor yields maydepend on their residence time in the hot zone because of secondarycracking reactions. Studying primary pyrolysis occurring at the solid

level requires fast cooling of the vapors. This is another advantageof the image furnace where the condensable vapors and gasesreleased from the surface of the biomass sample are quenchedinside the reactor by mixing with the cold nonabsorbing carriergas (N2). The gas mixture temperature in the reactor is hence closeto room temperature, while only biomass is heated by absorptionof the concentrated radiation.

After leaving the reactor under atmospheric pressure, con-densable vapors are trapped by a quartz fiber filter (WhatmanGF/A) and two cartridges (diameter, 1 10-2 m; length, 20 10-2 m) packed respectively with zeolite particles (SiliporiteG5 pellet 1.6 10-3 m) and glass wool. All the permanentgases are then recovered in a sampling bag through a solenoid

valve. The sampling time is adjusted according to the flash timeand the residence time of the gases between the reactor and thebag. The carrier gas bypasses the bag before and after eachexperiment.

2.4. Sample Preparations and Measurements. The experi-ments are performed with oak samples cut out from thelongitudinal plane of a wooden board. The samples are cylinders(radius, 5 10-3 m; thickness, 3 10-3 m; mass, 125 10-6 kg) prepared with a lathe. Before pyrolysis, the samplesare dried for 1 day in an oven at 378 K. A new one is preparedfor each experiment.

The mass loss of the sample is determined by weighing itbefore and after the flash time. The mass increases of the reactor,

the filter, and the cartridges give the mass of condensable vapors(including water), whereas the mass of gases is calculated fromtheir composition, the mass flow rate of inert gas, and thesampling time. The composition of the gaseous products (i.e.,CO, CO2, H2, CH4, C2H6, C2H4, C2H2, C3H8) which are highlydiluted in the carrier gas is determined by gas chromatography,with a Varian CP-3800 using FID (CP-Poraplot U type capillarycolumn with silica packing, 27.5 0.63.10-3 m) and TCD(carbosphere column, 2 2.10-3 m) detectors, and a Varian3900 using a TCD detector. The mass of char accumulated onthe sample surface is simply determined by scraping the sampleafter the flash time. All the weighed masses are obtained withan accuracy of about 10-7 kg. The solids ultimate analysis (C,H, O, and ash mass fractions) is carried out at the ServiceCentral dAnalyse of CNRS (Solaize, France). The relatedmethods are given in Lede et al.31 The char ultimate analysis isperformed after complete wood conversion, once for each heatflux density (Table 1).

Sample mass losses and masses of all the pyrolysis productsare studied as a function of the flash time. The yields of pyrolysisproducts are calculated by comparing the masses of each productwith respect to the reacted biomass (mass loss + mass of char).It is hence possible to determine mass balances by summingthe yields of pyrolysis products.

2.5. Experimental Results and Discussion. Typical ex-amples of mass balances are reported in Table 2. Mass balanceclosures range from about 90% to 95%. Results obtained for

both available heat flux densities are given in Figures 3 and 4.In a first approximation, all the masses increase linearly with

the flash time once pyrolysis has begun. The shrinking orswelling of the char layer that slightly modifies the accuratesample surface adjustment at the focus F3 does not seem toinduce a deviation from the linearity. This linear tendencyespecially for the mass loss is also mentioned in the case of themain wood components of fast pyrolysis performed under ahigher available radiant flux density (7.4 MW m-2), for

cellulose26

and lignins.27

For the heat flux density 1 (0.8 MW m-2), Figure 3 shows

that the formation of all products begins for a flash time, calculatedfrom linear regression, of approximately 0.7 s. This periodcorresponds to the preheating of the biomass until the pyrolysistemperature is reached at the surface. Let us define the specificrate of mass loss as the ratio of the slope of the mass loss straightline calculated from linear regression with respect to the value ofthe sample cross section submitted to the radiant flux. For 1, aspecific mass loss rate of about 0.163 kg m-2 s-1 can be calculated.For the lower heat flux density (2), the mass loss begins after aheating period of about 3.2 s and the related specific rate of massloss is 0.055 kg m-2 s-1. The specific mass loss rate clearly

increases with the available heat flux density, whereas the pyrolysisstarting time decreases. The specific mass loss rates are also lowerthan those obtained under higher available radiant flux (7.4MW m-2) for cellulose samples26 (1.190 kg m-2 s-1; radius, 2.5 10-3 m) and Organocell lignin samples27 (0.395 kg m-2 s-1;radius, 5 10-3 m).

For the heat flux density 1, it is not possible to distinguishsignificant differences between the starting times for eachpyrolysis products, showing that gas and vapor release, and charformation occur at the same time. This observation is alsomentioned for lignin,27 whereas, in the case of cellulose,26 atransient period with the formation of a layer of intermediateliquid compound (ILC) is observed. For the heat flux density2, gas formation seems to occur shortly after the beginning of

the mass loss and char formation but the experimental accuracyis not sufficient to confirm this tendency.

Table 3. Average Values of Pyrolysis Products Yields (wt %)Obtained for Two Values of the Available Heat Flux Density (1 )0.8 MW m-2, 2 ) 0.3 MW m

-2)a

flux gases (wt %) vapors (wt %) char (wt %)

1 21 ( 7 62 ( 5 11 ( 22 8 ( 4 61 ( 6 22 ( 9

a Because of linear tendencies (Figure 3), the average value for eachproduct is obtained by dividing the sum of the yields at different flashtimes by the number of experiments; standard deviation is then

calculated.

Figure 5. Simplified kinetics pathway representing the primary steps ofbiomass pyrolysis according to Shafizadeh and Chin.39

Table 4. Reaction Kinetic Rate Constants (Associated with theSimplified Kinetics Pathway Reported in Figure 5) Used in theModels According to Chan et al.47

kineticparameter (s-1)

activation energy(J mol-1)

preexponentialfactor (s-1)

kG 140 000 1.3 108

kV 133 000 2.0 108

kC 121 000 1.08 107



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Figure 4 shows that the fractions of CO, CO2, and CH4,slightly vary with the flash time once the pyrolysis has begun.In each case, the most abundant gaseous product is CO. Themolar fraction of H2 is quite low at the beginning of pyrolysisand then increases. The light hydrocarbon mole fractions containmainly CH4 and C2H4 with minor fractions of C2H2, C2H6, and

C3H8. The gas composition obtained in the present study is alsoclose to that obtained by Le Dirach et al.6 for oak samplessubmitted to a higher heat flux density. The noticeable variabilityof the amount of hydrogen is generally explained by secondarythermal cracking reactions.32-34 However, several authors haverather suggested that hydrogen formation may result fromreactions occurring inside the reacting sample at the level ofILC21,27,35 formed at the beginning of biomass pyrolysis.

The average product yields and their standard deviations arereported in Table 3. The yields of vapors do not significantlydepend on the heat flux density, contrary to the yields of gasesand char that respectively increase and decrease with the heatflux density (see also Table 2). Regarding the evolution of wood

pyrolysis products as a function of the heat flux density, Gronliand Melaaen36 report that the char and vapor final yieldsexperimentally decrease while the gas yield increases when theheat flux density increases from 0.080 to 0.130 MW m-2. Intheir experiments performed using a xenon arc lamp as a radiantheat source,36 such a phenomenon could result from a highertemperature in the char layer for higher heat flux densities thatfavor secondary reactions. Indeed, Gronli and Melaaen36 studiedthe pyrolysis of thick samples (length, 30 10-3 m) incomparison to ours (length, 3 10-3 m) that greatly increasesthe intraparticle resistance to mass transfer and vapors crackingby contact with a thick char layer. Experiments of Di Blasi etal.37 performed in a furnace with tubular quartz infrared lampsalso showed that product distribution during pyrolysis dependson heating conditions. For lower applied heat flux densities inthe range 0.040-0.080 MW m-2, the same trends as those ofthe present work are obtained for several wood species (length,40 10-3 m); vapor yield is also nearly constant whereas thefinal yields of char and gases respectively decrease and increaseas the heat flux density increases. It is interesting to notice thatthe increase of gas yield does not seem to result from gas phasecracking reactions since the vapor yield is quite constant. Thepyrolysis product distribution could be explained by intrapar-ticular secondary reactions, which could be of less importancein the case of thin biomass samples.

The results of the solids ultimate analysis are reported in Table1. The oak ultimate analysis is quite similar to typical wood

ultimate analysis.38 For char samples, the carbon and oxygenfractions respectively increase and decrease with the heat flux

density. By contrast, the absolute hydrogen amount in the char

does not vary significantly. The difference of ultimate analysisbetween both char samples may be due to the differences oftemperatures reached by the char.

3. Theoretical Section

The purpose of this section is to compare two different modelsrepresenting the pyrolysis of a wood sample submitted to a givenheat flux density. The choice of the simplified kinetic modelused, as well as the main assumptions (relying on the values ofseveral characteristic times) are first discussed. Then, the resultsof both models are compared and discussed with regard to thosereported in the Experimental Section.

3.1. Kinetic Scheme of Biomass Pyrolysis. Because kineticmodels based on a one-step global reaction are not valid to predictthe variations of pyrolysis product distribution, several authors usea kinetic scheme relying on three parallel reactions as representedin Figure 5. However, such a kinetic scheme is undoubtedly asimplification and is not representative of the true chemical behaviorof wood pyrolysis that includes a great number of reactions ofdepolymerization, dehydratation, decarboxylation, etc. The firstcomprehensive models have been developed for pure cellulosewhich is the major component of wood and because its propertiesare well-known. It has been, for example, clearly shown thatcellulose pyrolysis gives primary rise to an ILC.20,40 More recently,Luo et al.41 proposed a comprehensive pathway taking into accountILC. Lignin, another component of wood, is also known to pass

through a liquid phase.27,42 In addition, the fusionlike phenomenonof wood first identified by Lede et al.43,44 results from the fact that

Table 5. Physicochemical Parameters of Wood and Char Chosen for the Models

parameter wood reference char reference

r (m) 5 10-3 measured 5 10-3 -L (m) 3 10-3 measured 3 10-3 -F (kg m-3) 540 measured 170 measuredCp (J kg

-1 K-1) -436.3 + 5.6856T measured 420 + 2.09T - (6.85 10-4)T2 59 (W m-1 K-1) 0.35 60 0.10 36R 0.63 measured 0.06 measuredKf (m

2) 10-14 50 10-12 50

parameter value reference

Df (m2 s-1) 10-6 typical value at 500 K56

f (Pa s) 10-5 typical value at 500 K56

Pf (Pa) 105 volatile matter over pressure may be higher56

hcg (W m-2 K-1) 20 gas flow parallel to a vertical plate61

T (K) 293 -Hj (J kg

-1) 418 103 47

Table 6. Characteristic Times and Dimensionless Numbers of theMain Processes Involved during Fast Pyrolysis of Wood

characteristictimes (s)

wood char Tref (K) i (MW m-2)

thc 23.4 21.8 600tdm 9.0

tcm 9.0

10

-2

9.0

10

-4

tht 4.6 1.2 600 0.8- 0.7 1800 0.88.6 2.3 600 0.3

- 1.0 1800 0.3tp 1.6 - 800

2.910-2 - 1000

dimensionlessnumber

orders ofmagnitude

Pe 102-104

Le 10-5-10-3

Bi 1-10Th 1 -103



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wood passes through a liquid phase at a rather constant temperatureduring fast pyrolysis. Lede et al.40 have shown that ILC resultingfrom wood primary decomposition cannot be ignored in conditionsof fast pyrolysis. However, it is difficult to use the standardBroido-Shafizadeh type model45 because the relative fractions ofchar and gas may vary according to the experimental conditions.

For the sake of simplification, the three competitive reactionsscheme is however chosen in the present paper because it cansimply account for different product selectivities according tothe experimental conditions. Unfortunately, there is no consensusconcerning the kinetics parameters to be used for each of thesethree parallel reactions, despite the numerous works reportedin the literature.46-49 The predictions of the various kinetic datasets deeply differ6,50 certainly because of the multiplicity ofwood types used, experimental techniques, operating conditions,inaccurate models, and poor knowledge of physical parameters.In the present study, the kinetic parameters of Chan et al.47

(Table 4) are used for the modeling because they have alreadyproved to be appropriate under pyrolysis conditions controlledby heat transfer.36,50

3.2. Characteristic Times Analysis. According to experi-mental conditions and to wood and char physicochemicalproperties, pyrolysis can be controlled by chemical reactions,heat transfer, and/or mass transfer. If the rate-controllingphenomena are not identified, mathematical models should take

into account complex couplings between conservation of mass,momentum, and energy in the solid matrix.5,36,51,52

The estimation of characteristic times is a useful tool fordefining the controlling steps and thus solving simplified models.However, such an analysis is only qualitative because manyparameters vary during pyrolysis (solid properties such asthermal conductivity, heat capacity, density, porosity, perme-ability, diffusivity). Furthermore, the choices of referencetemperatures may be arbitrary. The characteristic time analysisfor biomass thermal reactions has been already used by severalauthors.14,47,53-57 The main characteristics times associated withpyrolysis are listed as follows:

internal heat transfer by conduction: thc )FCpL

2

(1)

internal mass transfer by diffusion: tdm )L

2

Df(2)

internal mass transfer by convection: tcm )L

2f

PfKf(3)

external heat transfer: tht )FCpL

h(4)

pyrolysis time for a three parallel reactions scheme:

tp )1kP

)1

kG + kV + kC(5)

The characteristic length L is the ratio of the sample volumeto the surface exposed to external heating. For a cylinder inour case, it is simply equal to the particle thickness.

For the calculation of the pyrolysis time, the referencetemperature is chosen as the local temperature of the pyrolysiszone, which is the small volume inside which reactions occur.The choice of a relevant temperature is critical for thecalculation accuracy. Yet, few experimental studies have beendevoted to determine its appropriate value during fastpyrolysis.44,58 Its value is representative of the reactingbiomass temperature that varies inside relatively narrow limits(800-1000 K from the model predictions, see section3.6.2). The chemical rate constants are supposed to obeyArrhenius-type laws:

k)A exp(-E

RT) (6)For the external heat transfer time calculation, the reference

temperature value is the temperature at the surface (virginbiomass at the beginning and then char) exposed to the incidentradiation (600-1800 K from the model prediction, see section3.6.2). By applying blackbody laws, the available heat fluxdensity is related to the temperature of the xenon arc lampdelivering the radiation and to the Stefan-Boltzmann constant,

as follows:

i)Ti4 (7)

The heat source temperature is calculated from eq 7:

Ti (i)14

(8)

In the case of the image furnace, the external radiant heattransfer coefficient is then equal to

h ) (Ti + Tref)(Ti2

+ Tref2) (9)

The parameters used for the calculation of characteristic timesare given in Tables 4 and 5 (see section 3.5 for details about

the values of the physicochemical parameters). The results aresummarized in Table 6.

In order to examine the relative importance of the mainphysicochemical phenomena occurring during wood pyrolysis,let us consider the following dimensionless numbers relying onthe characteristic times and whose orders of magnitude are givenin Table 6:

Peclet number: Pe )PfKf

Dff)

tdm

tcm(10)

Pe is much greater than 1, showing that volatile speciestransfer by diffusion is slower than by convection. As aconsequence, a model taking into consideration volatile release

could disregard diffusion in the gas mixture phase comparativeto convective mass transfer. The mass transfer importance

Figure 6. Schematic representation of a sample submitted at one side to auniform radiant flux: layers Lagrangian model (LM) and global Eulerianmodel (GM).



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comparative to heat transfer is then determined by the Lewisnumber value.

Lewis number: Le )f

PfKfFCp)

tcm

thc(11)

Le is much less than 1, showing that the pyrolysis modelshould mainly focus on internal heat transfer rather than mass

transfer. The volatile release from the particle can be assumedas an instantaneous process. Internal pressure increase duringpyrolysis5 also induces the formation of larger pores in the charlayer, making mass transfer easier.

Biot number: Bi ) hL

) thctht

(12)

Table 7. Balance Equations of the LM

mass balance heat balance

Layer (I)

shrinking velocity: temperature:

uB

x) -

j

kjT

t)

B

FBCp,B

2T

x2

+ (uB + 1FBCp,BdB

dT

T

x) Tx - 1Cp,Bj kjHj

uB(x,t)0) ) 0 T(x,t)0) ) T0uB(x)0,t) ) 0

product masses:

dmB

dt) uB(x)LB)FBS

mB(t)0) ) mB,0

dmj

dt) FBS0

LBkj(x) dx

mj(t)0) ) 0

length:

LB )mB

FBS

Layer (I)

length: temperature:

LC )mC

FCST

t)

C

FCCp,C

2T

x2

+1

FCCp,C

dC

dT(T

x)2

(ifLC > 10-6 m)

Interface (I)

-

(B

T

x)x)LB-

)

(C

T

x)x)LB+

(ifLC > 10-6 m)

Side (II)

-

(BT

x)x)0 ) -B(T4

- T4) - hcg(T- T) (if LB > 10

-6 m)

(CT

x)x)0 ) -C(T4

- T4) - hcg(T- T) (if LB < 10

-6 m)

Side (III)

-

(

B

T

x)x)LB

) (1 - RB

) - B

(T4 - T

4) - hcg

(T-

T) (if LC < 10-6 m)

(CT

x)x)LB+LC ) (1 - RC) - C(T4

- T4) - hcg(T-

T) (if LC > 10-6 m)



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Bi is greater than 1: internal heat transfer by conduction iscontrolling. Because of intraparticle thermal gradients, thetemperature distribution in the whole particle volume is notuniform.

Thiele number: Th )FCpL

2kp

)

thc

tp(13)

Th is greater than 1, showing that the internal heat transferproceeds slower than the pyrolysis reaction. Consequently,

pyrolysis occurs inside a thin zone which moves from thesurface of the sample through the particle.53

In summary, for a thin pellet submitted to a high heat fluxdensity, the times of intrinsic pyrolysis and of volatile masstransfer within the pores are significantly smaller than the timescale of heat transfer inside the solid matrix and internal heattransfer is rate-controlling.

3.3. Model Assumptions. In order to facilitate the imple-mentation of the pyrolysis model into a global gasifier model,some of the following assumptions are made according to theabove-mentioned elements. The main assumptions and charac-teristics are as follows:

(a) The model is one-dimensional.(b) Volatiles leave instantaneously the sample, and gaseous

transport phenomena through the porous char matrix areneglected.

(c) Heat is transferred by conduction inside the solid assumedto behave as a homogeneous medium.

(d) The simultaneous absorption of heat flux density (providedby the xenon arc lamp), radiative losses, and convective carrier

gas cooling are considered on the heated side. Convective andradiative losses are considered on the opposite nonexposed side.

(e) The available heat flux density is uniform on the exposedsurface.

(f) The specific heat capacity is assumed to be a function of

the local temperature, whereas wood and char densities areconstant and independent of the temperature.

(g) Wood and char are gray bodies whose surfaces spectralproperties do not depend on wavelengths and temperature. As

for cellulose, their transmittance is neglected.62 Spectral reflec-tivity and emissivity are connected by

1 - R ) (14)

(h) Despite the uncertainties revealed by the literature onpyrolysis heats (negligible, endothermic, or exothermic), theirvalues may be of importance in thermal effects modeling,

causing an impact on temperature profiles.42 Other results havebeen reported about the minor effect of heat pyrolysis.55 The

heat of pyrolysis is considered endothermic, and the same isconsidered for the three primary reactions.47

Table 8. Balance Equations of the GM

mass balance heat balance

Volume (I)

product masses: temperature:

mB

t) -

j

kjFBSdxT

t)

Sdx

k

mkCp,k(

2T

x2

+d

dT(T

x)2

-j

kjFBHj)

mB(t)0) ) mB,0 T(x,t)0) ) T0

mj

t) kjFBSdx

parameters:

mj(t)0) ) 0 S ) r2

length: )k

kk

L

t)

1

S

k

1

Fk

mk

tk)

mk

k

mk

)k

kk

Side (II)

-

(T

x)x)0 ) -(T4

- T4) - hcg(T- T)

Side (III)

-

( Tx)x)L ) (1 - R) - (T4 - T4) - hcg(T- T)



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(i) The volume of the sample decreases (structural changeby solid shrinkage) according to the release of volatiles andgases and to both wood and char densities.

Note that, for a use in a fluidized bed, some adaptations willhave to be made in the pyrolysis model assumptions, forextension to nonconstant heat flux and to larger diameterparticles (e.g., two-dimensional model).

3.4. Basic Balance Equations. Two approaches are consid-ered for the pyrolysis models of a single biomass particle. Thefirst one relies on an original Lagrangian approach (LM, layersmodel) where mathematical equations of mass and heat balancesare written in the assumptions that wood and char form twodistinct layers. Thus, the char layer is separated from the virginwood by an interface that propagates through the sample. Inthe second one, a classical and global Eulerian approach (GM,global model) is considered: mass and heat equations arewritten on the whole particle. At any time, a partially pyrolyzedelement in the particle is considered as a mixture of both charand wood. A schematized representation of both approaches isgiven in Figure 6.

3.4.1. Balance Equations of the Layers Model (LM).The LM relies on concepts similar to those developed inprevious papers6,26,30,53 and on experimental observations madeon wood samples having partially reacted in fast pyrolysisconditions, showing the presence of a distinct layer of charsurmounting the unreacted biomass. The particle is simplydivided into two zones (I) and (I) (Figure 6) correspondingrespectively to the wood and char layers. The model can alsobe compared with the unreacted-core-shrinking approximationused for wood degradation modeling.63 The mass and energyconservation equations for both layers are reported in Table 7.The boundary conditions are written on the surface receivingthe incident radiation (III), on the other nonexposed side (II),and at the interface between wood and char (I). The transition

between wood and char layer is arbitrarily chosen as corre-sponding to a small assigned char thickness equal to 10 -6 m.26

3.4.2. Balance Equations of the Global Model (GM).

Such an approach has been already carried out with thesimplifying assumption of an instantaneous outflow of volatiles

out of the solid and internal heat transfer limitation.54,64-66

Themass and energy conservation equations are written on the wholeparticle (I) (Figure 6) and are given in Table 8. The boundaryconditions are written on the surface receiving the incidentradiation (III) and on the other nonexposed side (II). The samplephysical properties are calculated by linear interpolation of theproperties of wood and char.

3.5. Values of the Parameters Used in the Models. Thevalues of the parameters required for solving the models arereported in Tables 4 and 5. Some values of the physicalconstants of wood and char used in the models have beenmeasured. The apparent density of char is determined with apycnometer with capillary cap (volume 25 10-6 m3). The

samples are previously covered by a thin layer of varnish inorder to make easier their contact with water and to avoid waterpenetration. The heat capacity is measured with a differentialscanning calorimeter (Pyris 1 DSC PerkinElmer). Linearrelationships are obtained as a function of temperature between293 and 373 K. However, because the measurement of heatcapacity at higher temperatures is difficult, a correlation fromthe literature has been retained for char.59 The reflectivities ofwood and char have been measured by hemispherical directionalreflection with a spectrophotometer (Cary 500) and integratingsphere, at ambient temperature and for wavelengths between0.35 10-6 and 2.5 10-6 m. The values of the thermalconductivities established at ambient temperature are obtained

from the literature.

36,60

3.6. Modeling Results. Both models have been solved inthe case of two available heat flux densities (1 and 2) andcompared to the experimental results. The systems of differentialequations are solved by the method of lines67 with the DDASSLsolver. For both heat flux densities, the simulation time is chosenas the highest experimental flash time.

3.6.1. Comparison with the Experimental Results and

Discussion. Let us define a criterion to quantify the differencebetween model predictions and experimental results. Experi-mental and theoretical masses (i.e., mass loss, gas, vapor, andchar masses) may be compared according to

Jl ) 1nz)1

n

(m

l,z

E (tz

) - ml,z

M(tz

)

ml,zE (tz) )

2

for ml,zE (tz) > 0 (15)

Figure 7. Comparison of experimental and theoretical (LM and GM) variations of mass losses as a function of the flash time for two values of the availableheat flux density (1 ) 0.8 MW m

-2, 2 ) 0.3 MW m-2).

Table 9. Values of the Criterion J (LM and GM) Obtained for TwoValues of the Available Heat Flux Density (1 ) 0.8 MW m

-2, 2 )0.3 MW m-2) and Calculated by eq 15

1 (n ) 16) 2 (n ) 11)LM GM LM GM

mass loss 0.06 0.05 0.03 0.11gas mass 0.27 0.19 2.26 1.36vapor mass 0.18 0.10 0.06 0.14char mass 0.89 0.42 0.07 0.14



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Figure 7 shows that the time evolution of the mass loss andthe starting time of pyrolysis are quite well represented by thetwo models and for both available heat flux densities. For thehighest value (1), differences appear for times above about4 s. In the case of the lower heat flux density (2), the massloss predicted by the GM is higher than by the LM for flashtimes above about 10 s. It may be noticed that the value ofcriterion J is hence higher for GM than for LM (Table 9).

The distribution of pyrolysis products is given in Figure 8.Both models are in agreement with experiments for the vapors(Figure 8b and Table 9). Gas masses (Figure 8a) are overesti-mated by both models in the case of the lower heat flux density

(2). On the other hand, they are underestimated for the higherone (1). In both cases, the value of the criterion J is higher

(Table 9) than its value for vapors. The char mass (Figure 8c)is significantly overestimated for the higher heat flux (1) (Table

9). The starting times of the phenomena are always quite wellestimated for each product. The average yields of pyrolysis

Figure 8. Comparison of experimental and theoretical (LM and GM) variations of pyrolysis product masses (a, gases; b, vapors; c, char) as a function of

the flash time for two values of the available heat flux density (1 ) 0.8 MW m-2, 2 ) 0.3 MW m-2).

Table 10. Average Values (LM, GM, and Experimental) of PyrolysisProduct Yields (wt %) Obtained for Two Values of the AvailableHeat Flux Density (1 ) 0.8 MW m

-2, 2 ) 0.3 MW m-2)

flux approach gases (wt %) vapors (wt %) char (wt %)

1 LM 16 ( 1 65 ( 1 19 ( 2GM 17 ( 1 65 ( 1 18 ( 3experimental 21 ( 7 62 ( 5 11 ( 2

2 LM 15 ( 1 64 ( 1 21 ( 1GM 15 ( 1 65 ( 1 20 ( 1experimental 8 ( 4 61 ( 6 22 ( 9



11/14


12/14

and model predictions is quite satisfactory. It can also be noticedthat, in the case of the biomass fast pyrolsis performed in afluidized bed, Kersten et al.50 reports a good agreement betweenexperimental vapor yields and a model prediction relying onChan et al. kinetic constant values.47

3.6.2. Variations of Sample Length and Temperatures.

The variations of the length and temperatures of samples aspredicted by both models are reported in Figure 9. Shrinkageof the sample is observed for both models (Figure 9a). In the

case of GM, let us quantitatively define the pyrolyzing zone asthe region where the wood conversion (i.e., the ratio, inside anelementary volume, of the mass of reacted biomass to the initialbiomass mass) is in the range 0.1-0.9. As mentioned incalculation of the Thiele number in the characteristic timeanalysis, the reaction occurs in a thin reaction zone thatpropagates through the solid as the reaction proceeds. For thehigher heat flux (1), the pyrolyzing zone thickness varies fromabout 100 10-6 to 225 10-6 m. For the lower heat flux(2), it is significantly larger and varies from about 180 10

-6

to 750 10-6 m (Figure 9a). Therefore the model predicts thatthe pyrolysis zone thickness decreases with the increase of theexternal heat supply. In the case of 2 and above about 16 s,

the thickness of the pyrolyzing zone suddenly decreases whenits back temperature reaches the nonexposed side temperature(Figure 9b,c). The sample thicknesses are also similar for bothmodels from the beginning to the end of pyrolysis.

The experimental measurement of the particle temperatureis difficult. Indeed, a thermocouple located in the vicinity ofthe focus of the image furnace cannot accurately give the truetemperature of the solid surface because of possible incidentradiation absorption. Pyrometric measurement is also madedifficult. The measurement could only be performed in a specificrange of wavelengths and could be largely disturbed by thevigorous release of volatile matter from the surface. It is hencedifficult to confirm the following model previsions.

Predicted time evolutions of temperatures at different posi-tions in the solid are given in Figures 9b and 9c. The resultsshow that the temperature of the exposed side increases due toabsorption of the heat flux (Figure 9b). When the surfacetemperature becomes high enough, pyrolysis begins at the solidsurface. A sharp increase of the temperature is observed whenthe char layer is formed in the LM because char has a higherabsorptivity than wood. The transition time is longer for theGM because global optical properties are calculated in this casefrom the mass fraction and optical properties of both wood andchar. It is noticed that the starting times of this sharp temperatureincrease and of product formation are similar (Figures 7, 8, and9b). The temperatures of the nonexposed side also show goodagreement for both models.

The temperatures of the wood-char interface (LM) and insidethe pyrolyzing zone (GM) are deduced from the modeling(Figure 9c). For the GM and after an induction period due tobiomass preheating, the temperatures slightly decrease duringpyrolyis from 1180 to 780 K (stabilization phase between about780 and 900 K) for the higher heat flux density (1), and from1000 to 760 K (stabilization phase between about 760 and 900K) for the lower one (2). Pyrolysis temperatures are in goodagreement for both approaches. These variations of temperatures,despite an intensive heat supply, may be explained by thecompetition and equilibrium between the heat demand forpyrolysis and the heat density absorbed by the sample, inagreement with the fusion-like behavior of wood pyrolysis.44

Similar trends are clearly obtained by both approaches of thepyrolysis models. LM and GM suitably predict the behavior of

biomass submitted to high heat flux densities with few discrep-ancies between both theoretical approaches. Differences betweenexperimental measurements and models may result fromuncertainties of physicochemical properties and kinetic parameters.

4. Concluding Remarks

Theoretical and experimental studies of biomass fast pyrolysismade under controlled heat flux densities similar to thoseencountered in a fluidized bed have been compared.

The experiments performed in an image furnace have shownthat, under our operating conditions, the yields of vapors donot significantly depend on the value of the heat flux density,in the range 0.3-0.8 MW m-2. However, the gas and charyields as well as their compositions clearly depend on thesethermal conditions.

The calculations of characteristic times have pointed out thatmass transfer limitations could be neglected comparative tocompetitive heat transfer and chemical reactions. On this basis,two theoretical approaches have been considered for modeling:Eulerian and Lagrangian approaches. The original Lagrangianapproach relies on experimental observations under fast py-rolysis conditions showing the presence of two layers in the

partially reacted sample (i.e., the unreacted biomass surmountedby a distinct layer of char). The Eulerian approach is classicalwith regard to other pyrolysis models from the literature.

Both approaches give similar results for predicting massevolutions and are in quite good agreement with the experi-mental results. Consequently, they are both valid to model thepyrolysis of a biomass particle under thermal conditions closeto those prevailing in a DFB. The agreement between theoreticalpredictions and measurements is very satisfying considering thefuture use of the pyrolysis model in a comprehensive gasifiermodel.

Acknowledgment

This research has been performed thanks to EDF R&Dfinancial support. The authors are also grateful to J.-P. Corriou(LSGC, ENSIC, CNRS-Nancy Universite) for his relevantadvice in the use of the DDASSL solver employed for theresolution of the differential equations systems, M. Bouroukba(LTMP, ENSIC, CNRS-Nancy Universite) for his help in theheat capacity measurements, and B. Monod (LEMTA, ENSEM,CNRS-Nancy Universite) for the reflectivity measurements.

Note Added after ASAP Publication: The version of thispaper that was published on the Web April 14, 2009 had anerror in Table 8. The corrected version of this paper was repostedto the Web April 17, 2009.

Nomenclature

A ) preexponential factor (s-1)

Cp ) heat capacity (J kg-1 K-1)

D ) diffusivity (m2 s-1)

E ) activation energy (J mol-1)

h ) heat transfer coefficient (W m-2 K-1)

H ) reaction heat (J kg-1)

J ) criterion used to quantify the difference between models and

experiments

k ) kinetic rate constant (s-1)

K ) permeability (m2)

L ) thickness, characteristic length (m)

m ) mass (kg)n ) experiment number



13/14

P ) pressure (Pa)

r ) radius of sample cross section (m)

R ) gas constant, 8.314 J mol-1 K-1

S ) surface (m2)

t ) time (s)

T ) temperature (K)

u ) shrinking velocity (m s-1)

x ) axis (m)

Greek Symbols

R ) reflectivity

) emissivity

) solid fraction

) available flux density (W m-2)

) thermal conductivity (W m-1 K-1) ) viscosity (Pa s)

F ) mass density (kg m-3)

) Stefan-Boltzmann constant, 5.67 10-8 W m-2 K-4

Subscripts

) ambient

0 ) initialB ) biomass

C ) char

cg ) cooling gas

cm ) internal mass transfer by convection

dm ) internal mass transfer by diffusion

f ) volatile matter (gases and vapors)

G ) gases

hc ) internal heat conduction

ht ) external heat transfer

i ) heat source

j ) gases, vapors, char

k ) biomass, char

l ) mass loss, gases, vapors, char

P ) pyrolysisref ) reference

V ) vapors

Superscripts

E ) experimental

M ) model

Dimensionless Numbers

Bi ) Biot number

Le ) Lewis number

Pe ) Peclet number

Th ) Thiele number

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ReceiVed for reView December 03, 2008ReVised manuscript receiVed February 19, 2009

Accepted March 13, 2009

IE801854C


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