Modeling chemical and physical processes of wood and biomass

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Progress in Energy and Combustion Science 34 (2008) 47–90

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Modeling chemical and physical processes of wood andbiomass pyrolysis

Colomba Di Blasi�

Dipartimento di Ingegneria Chimica, Universita degli Studi di Napoli ‘‘Federico II’’, P.le V. Tecchio, 80125 Napoli, Italy

Received 5 February 2005; accepted 7 December 2006

Available online 23 April 2007

Abstract

This review reports the state of the art in modeling chemical and physical processes of wood and biomass pyrolysis. Chemical kinetics

are critically discussed in relation to primary reactions, described by one- and multi-component (or one- and multi-stage) mechanisms,

and secondary reactions of tar cracking and polymerization. A mention is also made of distributed activation energy models and detailed

mechanisms which try to take into account the formation of single gaseous or liquid (tar) species. Different approaches used in the

transport models are presented at both the level of single particle and reactor, together with the main achievements of numerical

simulations. Finally, critical issues which require further investigation are indicated.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: Pyrolysis; Wood; Biomass; Chemical kinetics; Transport models

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

2. Chemical kinetics of biomass pyrolysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

2.1. Measurements of primary pyrolysis rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

2.2. One-component mechanisms of primary pyrolysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

2.3. Multi-component devolatilization mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

2.4. Multi-component mechanisms of primary pyrolysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

2.5. Secondary reactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

2.6. Outline of multi-step mechanisms of cellulose pyrolysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

2.7. Distributed activation energy (DAE) models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

2.8. Conclusions and further developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

3. Transport models of biomass particle pyrolysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

3.1. Transport models with volumetric decomposition rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

3.2. Intra-particle transport phenomena. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

3.3. External heat transfer coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

3.4. Extra-particle processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.5. Unreacted-core-shrinking models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.6. Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

3.7. Experimental validation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

3.8. Empirical correlations and apparent kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79


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4. Models of pyrolysis reactors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.1. Fixed-bed reactors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.2. Fast pyrolysis reactors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82


Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

1. Introduction

Sustainable heat and power generation from biomass areat the center of scientific and industrial interest owing tothe increasing awareness about the continuous diminutionin the availability of fossil fuels and the higher sensibilitytoward environment preservation from pollutants gener-ated by conventional energetic systems. Biomass is a termfor all organic material that stems from plants [1], withwood as the main representative. From the chemical pointof view, it is a composite material, constituted by a mixtureof hemicellulose, cellulose, lignin and extractives [1–5], withproportion and chemical structure affected by variety.Inorganic matter (ash) is also part of the biomasscomposition, with a content ranging from less than 1%in wood to 15% in herbaceous biomass and feedstocks [6]and up to 25% in some agricultural residues [7]. Potassium,calcium, sodium, silicon, phosphorus and magnesium arethe main ash constituents [8–11]. Chlorine is also found insignificant concentrations in herbaceous biomass [10,11].

The classification techniques developed for coal, that is,proximate and ultimate analyses, are also applied for thecharacterization of biomass fuels. These are generally lowin carbon (roughly between 40% and 50% on dry, ash-freebasis) and high in volatile matter and oxygen [1,12,13],which result in low calorific values. A significant advantageof biomass with respect to coal is that the contents ofnitrogen and especially sulfur are low.

From the physical point of view, biomass presents acomplex structure, accurately characterized for hardwoodsand softwoods [3], which gives rise to anisotropic proper-ties [14–16]. In this case, the thermal conductivity acrossand tangential to the grain direction is approximately one-third that along the grain, whereas the permeability to gasflow across the wood grain is much lower (up to a factor of104) than that along the other two directions. Moreover,for conditions of practical interest, biomass always presenta non-negligible content of moisture. In freshly harvestedwood, moisture can exist in three forms: water vapor in thepores, capillary or free (liquid) water in the pores andhygroscopic or bound water in the solid structure [16]. Thewater vapor can be assumed to be in the same potentialenergy state as it would exist outside the solid. Neglectingthe capillary-water attractive force, the same assumptionholds for capillary water. The bound water consists ofwater molecules absorbed into the cellulose molecule byhydrogen bounding at the hydroxyl locations. When allavailable sites are occupied with water molecules, themedium is at the fiber saturation point (FSP). For many

types of wood, this contribution can reach about 30% ofthe dry weight.Pyrolysis, consisting of solid thermal degradation in the

absence of oxidizing agents, is a possible thermochemicalconversion route, resulting in the production of a hugenumber of chemical compounds. However, for engineeringapplications, reaction products are often lumped into threegroups: permanent gases, a pyrolytic liquid (bio-oil/tar)and char [17], or simply into volatiles and char. They resultfrom both primary decomposition of the solid fuel andsecondary reactions of volatile condensable organic pro-ducts into low-molecular weight gases and char, as they aretransported through the particle and the reaction environ-ment. Numerous factors affect the pyrolysis rate and theyields, composition and properties of the product classes.Temperature, pressure and heating rate are the chiefoperating parameters. In addition, biomass properties(chemical composition, ash content and composition,particle size and shape, density, moisture content, etc.)also play an important role.Permanent gases comprise CO2, CO, CH4 and lower

amounts of H2 and C2 hydrocarbons (for instance, see[18–29]). The composition of the liquid is highly dependenton the severity of the thermal treatment, that is,temperature and residence of the tar vapors in the hotreaction environment [30–37], and the presence of char[38]. Primary vapors (oxygenates) are associated withreaction temperatures below 673–773K, followed byhydrocarbon or secondary tars for temperatures up to1123K and aromatic or tertiary tars above 1123–1273K.The composition increases in the order of mixed oxyge-nated compounds, phenolic eters, alkyl phenolycs, hether-ocyclic ethers, polyaromatic hydrocarbons (PAH), andlarge PAH [30,31]. Liquid products also contain anappreciable proportion of water originated from both themoisture content of the solid fuel and the decompositionreactions.Reviews are available on the operating conditions and

the reactor configurations which maximize the yields ofcondensable products [39–43] or char [38,44,45] frombiomass pyrolysis. In the former case, the conversionprocess is indicated as fast pyrolysis and, after cooling andcondensation, bio-oil is obtained. This is a renewable fuel,which can be easily stored and transported and can also beused for the production of chemicals [46]. Following thedefinition of fast pyrolysis given in [43], specific for themaximization of the liquid products, the following condi-tions should be met: (a) high heating and heat transfer ratesat the reaction zone, (b) primary conversion temperature of

ARTICLE IN PRESSC. Di Blasi / Progress in Energy and Combustion Science 34 (2008) 47–90 49

about 770K and vapor phase temperature of 670–730K,(c) short residence time of products in the vapor phase(below 2 s), (d) rapid cooling of the vapor-phase productsto obtain pyrolysis liquid. Elimination of points (c) and (d),through an increased activity of secondary reactions of tarvapors, favor the formation of gaseous (cracking) and/orsolid (polymerization) species. Conversely, the eliminationof point (a) is the key feature for the so-called conventional(or slow) pyrolysis, which produce comparable yields of thethree lumped classes of products.

The char product is useful as a renewable fuel or forother applications, such as metal reductant, soil amenderand the production of activated carbon and biocarbonelectrodes [38]. As a measure of the efficiency of thecarbonization process of a given feedstock, a comparison issuggested between the fixed-carbon yield of char and thetheoretical yield of carbon, predicted by thermochemicalequilibrium calculations. Slow heating rates coupled with alow-temperature peak, high pressures (also in the presenceof relatively high heating rates, typically 1–2K/s) and longresidence times of the vapor phase products within thereaction environment are indicated [38] as the chiefconditions for maximizing the yields of this product.

Despite the low values, a key constraint in the thermalconversion of biomass arises from the presence of nitrogen.Nitrogen containing species, released during biomasspyrolysis, include [47] hydrogen cyanide (HCN), ammonia(NH3) and isocyanuric acid (HNCO). The first twocompounds are quantitatively more important and theirrelative share is affected by the thermal conditionsestablished during conversion (temperature and particlesize) and the fuel type [47,48]. At low temperatures, a largeportion of the nitrogen is retained in the char and NH3 isthe main gaseous nitrogeneous species. At high tempera-tures, more nitrogen is released in the gas phase with HCNbecoming the most important product. Hence, two mainroutes have been identified: one leading to NH3 and charnitrogen and the other leading to volatile cyclic amideswhich, following cracking, give rise to HCN and HNCO.

High amounts of inorganic constituents, especiallypotassium, in some residues and herbaceous biomass oftencontribute to adverse impact on the different elements of theconversion systems through fouling, slagging and, in thecase of fluidized-bed reactors, bed agglomeration [49].Chlorine is a facilitator of alkali volatilization and, likesulfur, is an important contributor to corrosion, metalwastage and pollution [49]. It is not possible [50,51] toseparate the chlorine products into either the char residue orthe volatile products during pyrolysis. Indeed, for someherbaceous biomass and residues, about 20–50% is releasedat temperatures between 573 and 673K, whereas about30–60% is still retained by the char up to temperatures of1173K [50]. Water washing (leaching) is a mean to eliminatealkali metals from herbaceous biomass, thus improving thebehavior during thermal treatments [10,52].

Technical enhancements in the contribution of biomassto commercial energy needs are focused on improving both

the efficiency and environmental impacts of conversionprocesses. Large-scale development and optimizationrequire mathematical modeling which, allowing quantita-tive representation of various phenomena, is a powerfultool for process design, prediction of reactor performances,understanding of pollutants evolution, analysis of processtransients and examination of strategies for effectivecontrol. Thermo-chemical conversion of biomass in prac-tical systems results from a strong interaction betweenchemical and physical processes at the levels of both thesingle particle and the reaction environment. Thus, in thisreview the current state of the art of wood and biomasspyrolysis models is analyzed in relation first to chemicalkinetics and then to transport phenomena. It is worthnoting that a large part of the models and results reviewedhere may also be of interest in fire safety science wherepyrolysis of lignocellulosic fuels is an important step for theinitiation and growth of both forest and building fires.

2. Chemical kinetics of biomass pyrolysis

Pyrolysis kinetics, coupled with the description oftransport phenomena, produce advanced computationaltools for the design and optimization of chemical reactorsapplied for thermochemical conversion of wood andbiomass. Also, the knowledge of the fuel reactivity isneeded for the formulation of simple design and scalingrules. Several reviews on the chemical kinetics of ligno-cellulosic materials are already available, which includethose of Refs. [17,53–59]. In this section, after a briefpresentation of the problems encountered in carrying outmeasurements of weight loss under a pure kinetic control,the literature results on the chemical kinetics of wood andbiomass are reviewed, giving special consideration to workrecently published or not previously examined. Wood/biomass components are not explicitly considered, apartfrom a brief outline of the most recent findings aboutcellulose pyrolysis, with special emphasis on the formationrates of the main decomposition products. It should bementioned that the mechanisms and kinetic constants forthe decomposition of xylan (representative of hardwoodhemicelluloses) and lignin are summarized in the literaturereview reported in the studies [60,61], respectively.

2.1. Measurements of primary pyrolysis rates

On an indicative basis, in thermogravimetry (slowheating rates for a sufficiently small mass of the sample,so that a kinetic control is established), primary degrada-tion of biomass starts at about 500K, fast rates areattained at about 573K and the process is practicallyterminated at 700–750K [55,62,63]. Weight loss resultsfrom the activity of numerous reactions. Therefore,thermogravimetric curves, measured for dynamic or static(isothermal) conditions, are useful simply for the formula-tion of global or semi-global mechanisms, which caninclude the effects of reaction parameters and sample


properties. Several studies (for instance, see [64–67])suggest that primary decomposition rates of biomass canbe modeled taking into account the thermal behavior of themain components and their relative contribution in thechemical composition. For heating rates at sufficiently slowor moderate temperatures, several zones appear in theweight loss curves, which can be associated with com-ponent dynamics. Indeed, hemicelluloses decompose at498–598K, cellulose at 598–648K, whereas lignin decom-poses gradually over the temperature range of 523–773K[63]. As the heating rate is increased, given that the range ofthe degradation temperatures of components is relativelynarrow, the different peaks in the degradation rate tend tomerge and the characteristic process temperatures tend tobecome progressively higher. Furthermore, if temperaturesare sufficiently high, significant degradation rates aresimultaneously attained by all the components.

The term ‘‘pseudo-component’’ is more appropriate as itis impossible to avoid overlap between the differentcomponents in the measured weight loss curves. In otherwords, although for each zone a main contributor can beidentified as hemicellulose, cellulose and lignin, respec-tively, the simultaneous participation of the other compo-nents cannot be avoided with an extent that depends on thebiomass characteristics and the severity of the conversionconditions. On the other hand, results obtained from theanalysis of single components cannot be directly applied tobiomass because of chemical and physical alterationsintroduced in the separation procedure, the impossibilityto reproduce component interactions and the presence ofash [55,67]. Ash constituents, especially potassium, sodiumand calcium, act as catalysts for the decomposition processand favor char formation [52,68]. As chlorine andpotassium in biomass are water soluble, they can largelybe removed through leaching, thus mitigating their impacton the high-temperature conversion devices [10,11]. Inreality, water or mild acid washing also introducesignificant modifications in the biomass decompositioncharacteristics [52,69–73]. In particular, this procedure iseffective for separating and sharpening the peaks of therate curves, thus facilitating, for instance, in situ investiga-tion of cellulose and hemicellulose decomposition kinetics.An increase in reaction temperature and amount of volatileproducts is also observed.

Despite the numerous weight loss measurements availablein the literature, a systematic classification of biomass fuels,based on thermogravimetric analysis, and general mechan-isms to interpret such measurements are not available.Indeed, the differences in the experimental apparatus andconditions and the lack of reference materials make this taskextremely difficult. Quantitative differences between ther-mogravimetric characteristics are caused by several factorswhich, in addition to the wood or biomass species, include,even for the same sample, the geographical origin, the age orthe specific part of the plant [3,73].

A difficulty in kinetic analysis also exists in separatingthe effects of chemistry and transport phenomena. One of

the key points, in relation to intrusion of heat and masstransfer processes in kinetic analysis, is the sample size/mass during pyrolysis which cause spatial gradients oftemperature (a process taking place under non-negligibleeffects of internal heat transfer) or significant differences oftemperatures between the sample and the controllingthermocouple, especially when these are not in closecontact (non-negligible external heat transfer resistance).The latter is the most common case given that, in theexperiments motivated by kinetic analysis, usually thesample mass is small and the rate/temperature of heating iskept at low levels. Owing to sample thermal inertia and/orreaction energetics, significant differences, often indicatedas ‘‘thermal lag’’ [74], may be established between thesample and the controlling (external) thermocouple. Theseeffects, extensively discussed in previous reviews [53,55],are quite high for cellulose [72,74–78] as a consequence ofthe strong endothermicity of the decomposition processwhen pressures are low and mass transfer resistancenegligible. The most visible effects of such a drawbackare a shift of the mass loss peak to higher temperatures andan increase in the yields of char as the sample mass isaugmented.In contrast with the pyrolysis of cellulose, the sample

mass is shown to have a negligible influence on the positionof the maximum rate of mass loss and the yield of char forstraw and washed straw [72]. This result is likely to holdalso for other biomasses and stems mainly from lowereffects of reaction energetics. Several studies [79–82] clearlyshow that exothermicity in biomass pyrolysis is associatedwith the formation of char. It can be understood that,owing to the higher yields of char generated from biomassin comparison with cellulose, the positive and negativeeffects in the reaction heats tend to become nearlyequivalent.Char is generated from both primary and secondary

reactions [38]. In the latter case, it is a coke derived fromthe decomposition of organic vapors (tars) on thecarbonaceous solid, which acts as a catalyst. Beneficialeffects are reported of both prolonged vapor-phaseresidence times and increased concentrations of vaporson the carbonization chemistry [80]. Under pressure, tarryvapors have a smaller specific volume, so that their intra-particle residence time is prolonged, favoring their decom-position, as they escape the biomass particle. Also theconcentration (partial pressure) of tarry vapors is higher,thus increasing the decomposition reaction rate. Hence,pressure and flow rate [80] and, in general, mass transferlimitations [80,81] are key parameters for both the yields ofchar and the exothermicity of the pyrolysis process. Indeed,a linear relation between heat of pyrolysis reactions andchar yields is found [80–82]. In quantitative terms, for theexothermic formation of char, values are reported of 3.6[80], 2 [81] and 3.5–3.8 [82] kJ/g of char formed. Thereactions forming tar precursors are estimated to be nearlythermo neutral [81] and the main enthalpy sink isattributed to tar evaporation [80,81], which is favored by

ARTICLE IN PRESS

Fig. 1. One-component mechanism of primary wood pyrolysis proposed

by Shafizadeh and Chin [88] (activation energies are expressed as cal/mol).

C. Di Blasi / Progress in Energy and Combustion Science 34 (2008) 47–90 51

low pressures and high flow rates. The endothermicity oftar formation is estimated to be comprised between 0.9 and1.3 kJ/g of tar formed [82]. Finally, it should also be notedthat, for the conditions of thermal analysis, the processes oflignin and cellulose decomposition are considered [83–85]to be globally exothermic and endothermic, respectively.

Another important aspect to be taken into account,when investigating primary decomposition, is that theeffects of secondary reactions should be kept at aminimum. Although it is not always possible to separatethe activity of the two classes of reactions, small size/massof the sample is highly advisable because of the consequentreduction in the residence times of the vapor phaseproducts in the hot reaction environment. A furtherkey parameter in this matter is the reaction temperaturewhich, to limit the activity of secondary reactions of tarcracking, should be maintained at least below 773K[18–22,27,28,30].

When coupled with the description of transport phe-nomena, chemical kinetics should be able to predict: (1)conversion time, and (2) product distribution, as theoperating conditions are varied. Associated with theseglobal parameters, details about the degradation dynamicsare also obtained. A simplified description of primarydecomposition processes, usually adopted for isothermalconditions or fast heating rates, is based on a one-component (or one-stage) reaction process. In this case,weight loss curves are often associated with additionalmeasurements concerning the yields of the three productclasses, in order to evaluate the related formation rates.Multi-component (or multi-stage) reaction mechanisms arealso proposed where each reaction takes into account thedynamics of several zones or pseudo-components in themeasured curves of weight loss. Devolatilization reactionsare essentially considered, with only a very few exceptionswhere both devolatilization and charring are included. Thekinetic models make use of an Arrhenius dependence ontemperature, thus introducing the parameters activationenergy and pre-exponential factor, and a linear or power-law dependence on the component mass fraction, whichmay lead to additional parameters (the exponents).Contrary to the case of coal, models based on a Gaussiandistribution of activation energies are scarce. A mentionshould also be made of isolated applications of peculiarmodels, not discussed in detail here, which include thedeactivation model proposed in [86] for beech wood and afew agricultural residues, the use of artificial neuralnetwork methods for cellulose and lignin decomposition[87] and other treatments as discussed in [58].

2.2. One-component mechanisms of primary pyrolysis

As already observed, biomass weight loss curves,obtained under dynamic or isothermal conditions, presentdifferent reaction zones mainly corresponding to compo-nent decomposition, which tend to merge as the heatingconditions become more severe. Hence, when fast heating

rates or high temperatures are established, the majority ofkinetic mechanisms consists of a single or three parallelreactions for the formation of the main product classes(one-stage or one-component mechanisms) following theproposal by Shafizadeh and Chin [88] for wood (Fig. 1).The separate formation rates of different product classes

introduced by the reaction mechanism of Fig. 1 may bequestionable from the point of view of analytical chemistry[53,54]. However, as previously observed [17,89,90], thecomparable activation energies of the three reactions donot allow the selectivity to be displaced toward only one ofthe products. For negligible activity of secondary reactions,product distribution from cellulose pyrolysis carried out atatmospheric pressure (in accordance with the mechanismproposed by Bradbury et al. [91]) indicates that both charand gas yields decrease as the reaction temperature isincreased (that is, their formation is linked) whereas, inprimary wood pyrolysis, both liquid and gas yieldscontinuously increase at the expense of char [18–22,27,29]. It can be postulated that, during fast heating, giventhat holocellulose is converted mainly into liquids, theother two product classes (gases and char) are mainly dueto lignin degradation. Hence, at low temperatures, on aglobal basis, there is a competition between liquid(holocellulose degradation) and char (lignin degradation)formation, with the former becoming successively morefavored. At high temperatures, gas formation rates tend toincrease, owing to the predominance of devolatilization(versus charring) rates of lignin decomposition.Table 1 and Fig. 2 present a summary of the one-

component mechanisms of wood/biomass pyrolysis pro-posed on the basis of experiments carried out underisothermal or fast heating rate conditions. At a first glance,it appears that the activation energy of the global reactionrate (k) presents widely variable values, roughly comprisedbetween 56 and 174 kJ/mol. This can be the result of thedifferent heating conditions established in the experimentaldevices, which include tube furnaces, entrained and fluidbed reactors, screen heaters, drop tubes and classicalthermogravimetry, the different sample characteristics (sizeor mass and wood/biomass variety) and the mathematicaltreatment of the experimental data.A more careful examination of the data, taking into

account the temperature range investigated, produces tothe following main groups:

(a1)
high-temperature data (up to 1400K) withE ¼ 69–91 kJ/mol [93,94,98];
(a2)
low-temperature data (below 700–800K) withE ¼ 56–106 kJ/mol [92,95,96,99];

ARTICLE IN PRESS

Table 1

Kinetic constants for one-component mechanisms of wood/biomass pyrolysis (same data as in Fig. 1)

Author (Ref.) Feedstock

(variety, size, mass)

Experimental

system

Tr Reaction mechanism Kinetic constants: E (kJ/mol),

A (s�1)

Thurner and

Mann [92]

Oak, 650mm Isothermal tube

furnace

573–673K dY

dt¼ � kY

k ¼ kC þ kG þ kL

k ¼ 2.47� 106 exp(�106.5/RT)

kL+kG ¼ 1.73� 106 exp(�106.5/RT)

kC ¼ 7.4� 105 exp(�106.5/RT)

kL ¼ 4.12� 106 exp(�112.7/RT)

kG ¼ 1.43� 104 exp(�88.6/RT)

Gorton and

Knight [93]

Hardwood,

300–350 mmIsothermal

entrained-flow

reactor

677–822K dY

dt¼ �kY k ¼ 1.483� 106 exp(�89.52/RT)

Ward and

Brashlaw [64]

Wild cherry Isothermal tube

furnace

538–593K dY

dt¼ � kðY � YC1Þ

YC1 ¼ 0:25� 0:30

k ¼ 11.9� 1011 exp(�173.7/RT)

Nunn et al. [94] Sweet gum,

hardwood,

45–88 mm, 100mg

Screen heater

(1000K/min)

600–1400K dYV

dt¼ kðYV1 � YVÞ

YV1 ¼ 0:93

kL+kG ¼ 33.38� 104 exp(�69/RT)

Chan et al. [14] — — — dY

dt¼ � kY

k ¼ kC þ kT þ kG þ kW

kC ¼ 1.08� 107 exp(�121/RT)

kT ¼ 2� 108 exp(�133/RT)

kG ¼ 1.3� 108 exp(�140/RT)

kW ¼ 5.13� 106 exp(�92.1/RT)

Font et al. [95] Almond shells,

300–500 mm, 2mg

Pyroprobe 100 733–878K dY

dt¼ �kY k ¼ 1.885� 106 exp(�108/RT)

kC ¼ 2.98� 103 exp(�73/RT)

kL ¼ 5.85� 106 exp(�119/RT)

kG ¼ 1.52� 107 exp(�139/RT)

Samolada and

Vasalos [96]

Fir wood,

300–425 mm, 2 g

Isothermal batch

fluid-bed

673–773K dY V

dt¼ kðY V1 � Y V Þ

Y V1 ¼ 0:005

kG+kL ¼ 2.40� 104exp(�94/RT)

Wagenaar et al.

[97]

Pine, 100–125 mm TGA 553–673K dY

dt¼ � kY

k ¼ kC þ kG þ kL

k ¼ 1.4� 1010 exp(�150/RT)

Drop tube 773–873K kC ¼ 3.05� 107 exp(�125/RT)

kL ¼ 9.28� 109 exp(�149/RT)

kG ¼ 1.11� 1011 exp(�177/RT)

Reina et al. [98] Forest waste,

p1000mm, 25mg

Isothermal TGA 498–598K dY

dt¼ �kðY � YC1Þ;YC1 ffi 0:25 k ¼ 7.68� 107 exp(�124.87/RT)

973–1173K dY

dt¼ �kðY � YC1Þ;YC1p0:15 k ¼ 6.33� 102 exp(�91.53/RT)

Di Blasi and

Branca [99]

Beech,o80 mm, 9mg Tube furnace 573–708K dY

dt¼ �kY (a) k ¼ 2.4� 105 exp(�95.4/RT)

(b) k ¼ 4.4� 109 exp(�141/RT)

kL+kG ¼ 1.5� 1010 exp(�149/RT)

kC ¼ 3.3� 106 exp(�112/RT)

kG ¼ 4.4� 109 exp(�153/RT)

kL ¼ 1.1� 1010 exp(�148/RT)

C. Di Blasi / Progress in Energy and Combustion Science 34 (2008) 47–9052

(a3)
low-temperature data (below 700–800K) withE ¼ 125–174 kJ/mol [64,97–99].
In the presence of non-negligible heat and mass transferlimitations, the analysis and interpretation of measure-ments traduce in apparent kinetics, characterized byactivation energies much lower than the true values and

lower rates than those determined in classical thermo-gravimetry [55]. This appears to be the case of the kineticconstants estimated by means of the high-temperature dataof the group a1 (for instance, screen heaters or entrainedbed reactors), which may be affected by significant heat/mass transfer intrusions. The kinetic models of Refs.[93,94] are essentially proposed as correlations for specific

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1.00 1.20 1.40 1.60 1.80 2.00

-12

-10

-8

-6

-4

-2

0

2

[95]

[94] [99b]

[98]

[64]

[99a][96]

[92]

[97]

[93]

ln k

1000/T [K-1]

Fig. 2. Arrhenius plot for the global decomposition rate of wood/biomass

based on one-component mechanisms (see Table 1 for kinetic parameter

values).

525 575 625 675 7250

20

40

60

80Ref.

[14]

; [64]

; [92]

; [97]

; [98]

; [99]

Char

Yie

ld [w

t%]

T [K]

Fig. 3. Predicted (lines) and measured (symbols) of char yields on

dependence of the reaction temperature (kinetic control and one-

component mechanisms).


experiments. The actual particle temperatures are unknownin [93], whereas screen heater measurements [94] areaffected by significant practical problems [100]. In thisapparatus, it is difficult to achieve a good control of thesample temperature, the recorded value is strongly affectedby thermocouple positioning and the effects of reactionenergetics are not taken into account. Finally, the lowvalues of the kinetic constant predicted with the model ofRef. [98] in the high-temperature range (973–1173K, notshown) can again be due to a lack of kinetic control.

The impossibility to get high temperatures without goingthrough the low-temperature region also excludes a changein the reaction mechanism. These considerations lead Antal[54] to write that ‘‘accurate estimates of high-temperaturebiomass pyrolysis reaction rates can be best obtained byextrapolating low-temperature kinetic data applicable tothe reaction pathways of interest at high temperatures’’.However, it can be noted that, for low-temperature data,both a low-activation energy group (a2) and a high-activation energy group (a3) of kinetic constants exist.

In order to estimate the global rate constant, k, for theisothermal process described by the three reactions ofFig. 1, two different treatments can be used [99], thoughassuming in both cases that only the central part of theweight loss curves, the most important from the quantita-tive point of view, has to be described. That is, the massconservation equations can be integrated over the entireduration (time) of the process, or specifically over the timecorresponding to the central part of the weight loss curve.In fact, zones with different slopes in the Arrhenius plotmake evident the existence of several sequential reactionsteps. Then, the usual Arrhenius plot and a least-squareanalysis give the activation energy and the pre-exponentialfactor of the global pyrolysis kinetics. It is found [99] thattwo sets of kinetic constants can be obtained (Table 1:E ¼ 95.4 kJ/mol (model a), and E ¼ 141.2 kJ/mol (modelb)) for the same group of isothermal experiments. Fig. 2

shows that both sets are comprised in the range ofliterature values. The kinetic constants of the model ofRef. [99] (model a) and those of Refs. [92,96] are roughlythe same. The kinetic constants are also roughly the samefor the model of Ref. [99] (model b)) and those of Refs.[97,98] (Ref. [98] for a temperature range of 498–598, forestwaste).The results of Ref. [99] for the models (a) and (b), plotted

in Fig. 2, indicate that the mathematical treatment of thecentral part of the same weight loss curves affectssignificantly the activation energies. At low temperatures,the model (a) predicts degradation rates faster than thoseof the model (b) but fails to predict the fast increase of thewood degradation rate with temperature, which is duemainly to the activity of components in the central part ofthe isothermal weight loss curves. Hence, the model (b) andthose of group a3, introduced above, appear to be moreappropriate than the model (a) (and those of group a2) forpredicting the behavior of chemical reactors in practicalapplications.The kinetic constants, plotted in Fig. 2, are inversely

proportional to the characteristic (chemical) times of woodpyrolysis. If product yields are among the desired modeloutputs, the kinetics for the formation rates of the differentproduct classes should be estimated. For isothermal data,this requires the knowledge of the corresponding yields foreach reaction stage of the mechanism [89,99]. Given thataccurate measurements of the volatile fractions cannot beaccomplished for the small sample quantity used inthermogravimetric analysis, and laboratory scale reactorsonly allow the total final yields of products to be obtained,alternative formulations are difficult to accomplish withrespect to a one-component mechanism of wood orbiomass primary degradation including the productformation rates such as in the proposal of Fig. 1.The kinetic constants for the formation rates of char, gas

and liquids (or for volatiles and char), where available, are

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525 575 625 675 7250

20

40

60

80

100

Ref.

Liquid

Gas

[14]

[92]

[97]

[99]

Yie

lds [w

t%]

T [K]

Fig. 4. Predicted liquid and gas yields on dependence of the reaction

temperature (kinetic control and one-component mechanisms).

Fig. 5. Multi-component devolatilization mechanisms (Ci is the volatile

fraction of the ith pseudo-component).


also listed in Table 1. The corresponding predictions of theproduct yields for wood/biomass, obtained under kineticconditions and following [99], are summarized in Fig. 3(char) and Fig. 4 (gas and liquids) where, for completeness,the experimental data are also included. In all cases, themeasured char yields decrease as the temperature isincreased. Good agreement (Fig. 3) is observed betweenpredictions and measurements [64,98,99] for low tempera-tures. The Thurner and Mann [92] mechanism does notreproduce the experimental trends even from the qualita-tive point of view, whereas quantitative differences are highbetween the other mechanisms. In particular, only thekinetic constants of Refs. [97,99] predict a strong influenceof temperature (quantitative differences are probably dueto the use of softwood and hardwoods, respectively). Inother cases, the char yields remain high (20–30%).Consequently, the related kinetics are not applicable forfast pyrolysis, where typical char yields are below 15%.

For liquid and gas formation, differences betweenkinetics (Fig. 4) exist from both the qualitative and thequantitative point of view. Kinetic constants of Refs.[14,99], in qualitative agreement with experimental mea-surements (for instance, [18–22,25–29]), indicate that bothliquid and gas yields increase with temperature. The gasyields attain about the same values, but liquid yields arehigher in [99] (lower char yield). The other two sets of datado not give predictions in qualitative agreement withexperimental observation. Indeed, the gas [92] or the liquid[97] yields decrease as the temperature is increased (though,in the latter case, this is hardly evident).

It is difficult to explain the differences between theproposed kinetics, but the use of thick particles [92] clearlygives rise to heat and mass transfer limitations, whichappear as high char yields. Other critical aspects are thevery narrow range of temperatures investigated, theevaluation of product yields at high temperature [97],when secondary reaction activity is not negligible, and theabsence of temperature measurement/control [92,97].

Finally, the results may be affected by the wood orbiomass species (for instance, at low temperatures, veryhigh yields of char are predicted [90] in the case of almondshells [95]).

2.3. Multi-component devolatilization mechanisms

The majority of multi-component mechanisms simplyconsist of devolatilization reactions, which can be appliedto predict only the rate of weight loss, provided that thetotal amount of matter to be released in the gas/vaporphase is already known (assigned or measured). Dynamicexperiments are generally carried out by means of classicalthermogravimetry. The most used mechanisms usuallycomprise parallel reactions (Fig. 5) for the decompositionof the volatile fractions of pseudo-components, althoughconsecutive reactions can also be applied [101], owing tosignificant overlap between the different evolution times. Inthe former case, each pseudo-component, whose volatilefraction is among the model parameters, acts as if therewere no interactions. The number of pseudo-componentsor zones, in the majority of the cases, is three and againcoincides with hemicellulose, cellulose and lignin (three-component devolatilization mechanisms). In a few cases,the contribution of extractives or more than one reactionstage in the decomposition of components, especiallyhemicellulose and lignin, are also taken into account.An important aspect is represented by the mathematical

treatment of the experimental data to formulate reactionmechanisms and to estimate the related kinetic parameters.The use is recommended [59,101] of differential (versusintegral) measurements because the details of the devola-tilization process are better shown. Furthermore, linearforms of the mass conservation equations, usually com-bined with analytical methods for the evaluation of thekinetic constants, may present serious drawbacks derivingfrom data manipulation and applicability limited to singlemeasurements [59]. Instead, numerical solutions of theconservation equations coupled with minimization meth-ods of objective functions, adequately defined, are advised[57,59].The three-component mechanism with linear or non-

linear dependence on species concentrations, for thevolatile fractions of the pseudo-components hemicellulose,cellulose and lignin, is widely applied [55,57,69,70,73,102–109] to describe dynamic thermogravimetric curvesof wood/biomass devolatilization. In several cases[55,57,69,73,104,105,107] dynamic measurements and thecorresponding kinetic analyses examine one heating rateonly, generally below 10K/min. Process simulations showthat the pseudo-components hemicellulose and cellulosedecompose independently of one another, the former

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400 500 600 700 8000.0

0.2

0.4

0.6

0.8

1.0

lignin

hemicellulosecellulose

-dY

/dt

x 1

03 [

s-1

]

T [K]

Fig. 6. Comparison between the observed (symbols) and simulated

differential curves (solid line) for beech wood heated at 5K/min, by

means of a three-component devolatilization model (parameters estimated

for a single heating rate [107]). Lines with various styles denote the

predicted volatile evolution from the different pseudo-components.


associated with the shoulder and the latter with the peak ofthe rate curves, whereas the lignin pseudo-componentdecomposes slowly over a very broad range of tempera-tures. An example is provided in Fig. 6, for beech woodand a heating rate of 5K/min [107]. The agreementbetween the kinetic parameters, estimated by means ofdifferential curves, is acceptable. Activation energies varybetween 80 and 116 kJ/mol for hemicellulose, 195–286 kJ/mol for cellulose, and 18–65 kJ/mol for lignin. Further-more, the component contributions, expressed as percentof the total mass fraction, are roughly 20–30% forhemicellulose, 28–38% for cellulose and 10–15% for lignin.They are obtained as a result of the optimization procedure(based on total mass conservation, the complement to 100is represented by char).

Compared with the case of one-component reactionmechanisms, the use of classical thermogravimetric systemswith very slow heating rates and the application ofnumerical methods for parameter estimations certainlycontribute to reduce the differences between the estimatedvalues of the kinetic constants. The effects of the highlyheterogeneous material, however, still remain and generalmechanisms with a wide range of applicability are notavailable. Attempts in this direction are the resultspresented in [107], in relation to nine wood species whosechemical composition is comprised within the widest rangeindicated for the hardwood and softwood categories, andin [73] for chestnut, which presents large deviations in thechemical composition compared with standard hardwoodcategories. It is reported that, at least for one slow heatingrate, the same set of activation energies can be applied in allcases. The differences in the characteristic reactiontemperatures and the yields of char between species aretaken into account by pre-exponential factors and stoichio-metric coefficients. However, in the case of chestnut, theaccuracy in the predictions is acceptable only for engineer-

ing applications. The same considerations apply for theeffects of sample geographical origin and pretreatments(hot water or acetone extraction) [73].When the kinetic constants are estimated by means of

one experiment only, compensation effects [110,111] arenot avoided, that is, the possibility of different couples ofpre-exponential factor and activation energy to describereasonably well the same weight loss curve. Indeed, onlyone set of data can predict the behavior of the material atseveral heating rates, consisting of the displacement of theweight loss curves toward successively shorter times forthermal conditions successively more severe [110]. Numer-ical simulations [74,77], based on simple models ignoringspatial gradients and for the conditions (sample size andheating rate) typical of thermal analysis, show the existenceof a thermal lag between the sample and the external(heating) temperature. It is a consequence of the endother-micity of biomass decomposition and has been related byNarayan and Antal [74] to compensation effects. If thethermal lag is non-negligible, estimated values of theapparent activation energy and pre-exponential factor willbe less that their true (intrinsic) values. Conesa et al. [59], intheir literature review, also agree with this explanation.Hence, in order to determine the intrinsic reaction kinetics,such effects should be avoided. It is suggested that [59] ‘‘thefitting of various runs performed in different conditions(different heating rates, or different temperature programin general) at the same time, using the same kinetic modeland parameters, could be a method valid for distinguishingan actual model and for solving the compensation effect. Amodel involving a change in kinetic constants with theheating rate and/or the extension of the reaction can onlybe considered a correlation model very far from the actualkinetics’’. The inclusion of several heating rates, especiallythe higher values, in kinetic analysis of wood/biomassdevolatilization is also important from the practical side.Indeed, the fuel particles in industrial systems usuallyexperience widely variable heating rates, which often arehigher than those typical of thermal analysis.In some cases, thermogravimetric curves of biomass

decomposition are measured at several heating rates butthe kinetic analysis is incomplete as no effort is made toproduce a general kinetic model applicable for the differentthermal conditions (for instance, see [66] for rice husks).The separate analysis of each curve, based on analyticalsolutions, usually produce sets of kinetic parameters highlyvariable with the heating rate. This result can be partlyattributed to non-negligible effects of transport phenom-ena, especially at the higher heating rates, and partly to thesimplified method for extracting kinetic parameters fromthe measured curves. In any case, such kinetics should notbe incorporated in transport models for the description ofpractical conversion systems.The simultaneous evaluation of multiple thermogravi-

metric curves for different heating rates is examined inseveral studies, which include 2–25K/min for olive stonesand almond shells [102], 3–100K/min for untreated and

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400 500 600 700 800

0.0

0.2

0.4

0.6

0.8

1.0

lignin

cellulosehemicellulose

-dY

/dt

x 1

03 [

s-1

]

T [K]

Fig. 7. Comparison between the observed (symbols) and simulated

differential curves (solid line) for beech wood heated at 5K/min, by

means of a three-component devolatilization model (parameters estimated

for heating rates between 5 and 104K/min [109]). Lines with various styles

denote the predicted volatile evolution from the different pseudo-

components.


water washed rice husks [103], 0.5–108K/min for water-washed beech wood [106] and 5–20K/min for waste woodand other residues [108]. Apart from the different fuels andpre-treatments examined, the comparison between theresults is difficult owing to power-law dependence on themass fraction [102,106,108], the use of integral data[103,106] and the selection of widely different values ofthe component fractions. The overall trend is that thekinetic evaluation of cellulose devolatilization remainsroughly unchanged (activation energies of 192–250 kJ/mol, which compare well with single-curve results), buthigher activation energies of hemicellulose degradation arereported (154–200 kJ/mol). As for the reaction of the thirdpseudo-component, activation energies of 36 kJ/mol [103],associated with a strong dependence of the correspondingpre-exponential factor on the heating rate, 188–219 kJ/mol[102,106], and 54–61 kJ/mol [108] are given (in [106], thedevolatilization of this component takes place along thelow-temperature region typical of hemicellulose).

The recent study by Branca et al. [109] tries to explainthe differences in the kinetic constants when the three-component mechanism, with rates linearly dependent onthe volatile content, is applied for the interpretation ofthermogravimetric curves obtained at several heating rates(range 3–100K/min). It is found that the set of activationenergies estimated in [107] (100, 236 and 46 kJ/mol,respectively) and representative of values obtained whenonly one differential thermogravimetric curve is processed,gives rise to very high deviations between predicted andmeasured rate curves. The agreement is highly improved bya new set of data consisting of activation energies of 147,193 and 181 kJ/mol, respectively, for the pseudo-compo-nents hemicellulose, cellulose and lignin. An example of thepredicted dynamics is shown in Fig. 7 for beech wood anda heating rate of 5K/min. A comparison with the kineticsdetermined with the use of one heating rate only (Fig. 6)shows that the overlap is reduced between the devolatiliza-tion rates of the three pseudo-components whose chemicalcomposition is also modified. The amount of volatilesreleased in the second (cellulose) stage is increased(46–49% versus 38–44%) at the expenses of that associatedwith the first (hemicellulose) stage (about 18–24% versus23–30%). Furthermore, instead of a slow decompositionrate over a broad range of temperatures, the activity of thethird reaction (lignin devolatilization) is mainly explicatedalong the high-temperature (tail) region of the weight losscurves.

Sometimes the three-component mechanism is modifiedto include additional steps for improving the accuracy ofthe predictions, as in [112] for isothermal data, and[107,113,114] for dynamic data. In [112] wood (pine,chestnut and pine bark), devolatilization is modeled bymeans of six parallel reactions corresponding to differentvolatile fractions. The first two, quantitatively moreimportant, are associated with hemicellulose and cellulose,respectively (the corresponding activation energies of 83and 146 kJ/mol are in good agreement with the values

reported for single dynamic curves of the weight loss rate).The other components are assumed to correspond mainlyto parts of the lignin macromolecule (activation energiesbetween 60 and 130 kJ/mol).Two additional reactions, in the low-temperature region

(below 553K) associated with extractive decomposition,are considered in [107] for both hardwood and softwoodspecies. In [113], thermogravimetric curves of wood(hornbeam, walnut, pine) are interpreted using five parallelreactions, with a power-law dependence on the volatilemass fraction, with parameters dependent on the woodspecies. Two fractions are considered for both thehemicellulose (activation energies in the ranges 101–175and 257–272 kJ/mol, respectively) and cellulose (activationenergies 93–99 and 181–183 kJ/mol, respectively) pseudo-components and one for lignin (activation energies of93–105 kJ/mol), though it is pointed out that the evalua-tions for the small fraction of cellulose, characterized by alow activation energy, are uncertain. Reaction orders arecomprised between 0.8 and 1.8. It can be noted that thisstudy again confirms high activation energies for thedecomposition of the cellulose component.It is also found [114] that, to obtain accurate predictions

of the devolatilization rates of wood (poplar, black locustand willow) at different heating programs (heating ratesbetween 20 and 40K/min), it is necessary to use at least sixor four reactions depending on the assumption of a linearor a power-law dependence on the volatile fraction. This isan indirect confirmation of the large inaccuracies intro-duced in multiple-curve predictions by the set of kineticparameters of the three-component mechanism as esti-mated for single curves. The use of additional reactions orparameters, compared with previous slow heating ratesanalysis, is justified [114] with the consideration that a‘‘wider range of experimental conditions reveals more of

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Fig. 8. The multi-component pyrolysis mechanism proposed in [117] (A is

the fuel, B and D are reaction intermediates).


the chemical inhomogeneities of the biomass components’’.The pseudo-component cellulose is still characterized by aquite high value of the activation energy (188 kJ/mol),whereas a wide range of activation energies is proposed forthe remaining reactions (84–241 kJ/mol).

Although the differences in the kinetics of wood/biomassdevolatilization may be a consequence of model featuresand procedure of parameter estimation (in addition to thechemical properties of the samples), a round robin witheight participants [115], carried out to evaluate thechemical kinetics of Avicel PH 105 microcrystallinecellulose, clearly puts into evidence the existence of someflaws in the temperature measurements even for standardthermogravimetric systems. It shows that, for low heatingrates (5K/min), the agreement is acceptable (scatter in thetemperature measurements of about 17K and char yieldsbetween 4% and 10.9%) and, in terms of kineticparameters, the differences can be accounted for by someuncertainty on the pre-exponential factor of the one-stepfirst-order reaction (devolatilization). At higher heatingrates (40K/min), the impact of thermal lag is larger andappears as reduced values of activation energies and pre-exponential factors. The authors also point out that thescatter in the experimental data and the instrumental errorswell represent the performances of the state-of-the-artinstruments, so that they ‘‘are inclined to acknowledge thefact that biomass pyrolysis kinetics are inherently difficultto study by any technique, and these difficulties contributesignificant uncertainties’’ in the ‘‘understanding of thephenomena’’.

2.4. Multi-component mechanisms of primary pyrolysis

A very few multi-component (or multi-stage) mechan-isms of wood/biomass pyrolysis are available, that is,mechanisms for predicting the formation rates and theyields of reaction products or solid- and gas-phaseintermediates. Examples based on series reactions, whichtry to take into account the presence of several zones in theisothermal weight loss curves, are given in [116,117] forstraw and beech wood, respectively. The mechanism ofwood pyrolysis, proposed by Miller and Bellan [118], alsobelongs to this category, which incorporates multi-stepmechanisms for the main components and uses thesuperposition of the different contributions, taking intoaccount chemical composition. It can be considered a re-examination of the mechanisms originally proposed in[64,65].

A three-stage series mechanism (Fig. 8) is proposed in[116,117], which takes into account the competitiveformation of classes of compounds belonging to eitherthe gas (vapor) or the solid phase. However, kineticparameters are estimated only for each global reaction(simultaneous charring and devolatilization processes) ofthe three stages. In the case of beech wood, estimations arecarried out in the temperature range 528–593K (k1),528–708K (k2) and 603–708K (k3), for three zones clearly

visible in the isothermal weight loss curves and correspond-ing to the main pseudo-components introduced in Fig. 8.The inclusion of low-temperature (528–573K) data in theArrhenius plot does not result in significant changes in thekinetic parameters for the central part of the reaction zone(an activation energy of 143 kJ/mol against 141 kJ/molpreviously estimated [99] for temperatures above 573K),which are also close to those of other isothermal analyses[64,97,98,112]. These values are, however, lower than thoseestimated for the devolatilization mechanisms based ondynamic measurements. As for the hemicellulose stage, theactivation energy (76 kJ/mol) reported in [117] is in therange of those obtained from the evaluation of singledynamic curves and the isothermal analysis in [112]. Thelow activation energy for the third (lignin) stage also agreeswith the results obtained from evaluation of single dynamiccurves.In the light of the criticism raised by the recent analysis

by Branca et al. [109] about the kinetic constants of thethree-component devolatilization mechanism estimated bythe use of single curves (also taking into account possibleflaws in the thermogravimetric measurements [115]), it canbe argued that the kinetic constants estimated by means ofisothermal curves also produce poorly accurate predic-tions. Moreover, the consecutive reactions of Fig. 8 sufferfrom a higher overlap between the degradation rates of themain components compared with slow heating ratedynamic analyses. On the other hand, if the formationrate of the product classes for each stage should bedetermined, the corresponding final yields should bemeasured. However, this is not possible because, as alreadypointed out, only integral data concerning completeconversion can be measured from chemical reactors.A multi-component mechanism, taking into account the

decomposition rate of hemicellulose, cellulose and lignin, isproposed in [118], as already in [64–65]. The model in notbased on a specific set of experiments but relies upon a re-examination of literature data. It is assumed that themechanism of cellulose pyrolysis by Bradbury et al. [91]is also applicable for the other two main components(Table 2). The reaction rates present the usual Arrheniusdependence on temperature and are first order in thereactant mass fraction. The depolymerization step does notintroduce any change in the chemical composition but it issuggested to modify the physical properties, for instance,porosity. The kinetic constants estimated by Bradbury etal. [91] are used for cellulose. The corresponding initialestimates for the activation step of the hemicellulose andlignin components are derived from [64]. The celluloseparameters reported in [64] are also assigned for the other

ARTICLE IN PRESS

Table 2

Multi-component mechanism and kinetic constants for wood pyrolysis based on the contribution of the three main components [118]

TAR

νC CHAR+(1- νC)GAS

k2

k3

k1C A

C Kinetic constants: A (s�1), E (kJ/mol)

Cellulose Hemicellulose Lignin

k1 2.80� 1019 exp(�242.4/RT) 2.10� 1016 exp(�186.7/RT) 9.60� 108 exp(�107.6/RT)



nc 0.35 0.60 0.75

Fig. 9. A global mechanism for the secondary reactions of vapor-phase

tarry species as proposed by Antal [120,121].


steps of the hemicellulose mechanism whereas those for thelignin component are derived from [65] disregarding thepower-law dependence for the solid mass fraction. Thefinal parameter values are estimated so as to get the best fitwith the char yields obtained in [65,119] for beech wood(TGA tests executed with heating rates of 5–80K/min) andlignin (isothermal tests for temperatures in the range673–1073K for small-sized particles) and in [21] forsmall-sized maple wood particles (isothermal tests in afluidized-bed reactor with temperatures in the range673–1073K). Extractive and ash contents are incorporatedin the hemicellulose component. The resulting set of kineticparameters is reported in Table 2. Comparison with otherexperiments is made a posteriori without any readjustmentof the parameters. Good qualitative agreement is attainedwith the expected behavior of the three components for therange of pyrolysis temperatures and both location andmagnitude of the peak rates. The predicted char yieldsdecrease as the temperature is increased for all thecomponents: the largest values are obtained for lignin,the minimum for cellulose and hemicellulose yields arebounded by the former two. A slight delay in the beginningof the devolatilization process occurs but it is related to aregion of small weight loss. Although not extensivelytested, this remains one of the few attempts to produce ageneral mechanism of biomass pyrolysis.

2.5. Secondary reactions

At high temperatures and given sufficiently longresidence times, secondary reactions of primary tar vaporsalso become active [30,120,121]. These alter both the yieldsand composition of the wood/biomass pyrolysis products.They may occur in the pores of the particles, whileundergoing primary degradation, homogeneously in the

vapor phase and heterogeneously over the char surfacesand the extra-particle surfaces. The latter aspect is notconsidered here, but extensive research on biomassgasification confirm the catalytic effects exerted bydifferent materials on the cracking of tarry components(see, for instance, the reviews [122,123]).Secondary reactions of tar vapors are classified as

homogeneous and heterogeneous and include processessuch as cracking, partial oxidation, re-polymerization andcondensation [36]. The complex chemical composition oftarry products would require a huge number of chemicalreactions to describe the details of the transformations.However, despite the quantitative understanding about thechemical composition of this class of products, the mostcited mechanism simply consists of two competing reac-tions [120,121] as reported in Fig. 9. The reactive volatilematter is assumed to be consumed by two competitivereactions leading to the formation of permanent gases anda refractory condensable material. The existence of thesecond reaction is inferred from the gas yield data, whichdisplay an asymptotic behavior (after residence times ofabout 5 s) that is strongly dependent on temperature.Higher temperatures result in dramatic increases in theasymptotic yields of all the light permanent gasesproduced. The temperature-dependent asymptotes requirethe existence of the second reaction in order to explain thedisappearance of carbon atoms in the gas phase when thegas phase temperature is reduced. However, the kinetic


models usually neglect this competition because either thetemperatures are sufficiently high so that the amount ofrefractory tar formed is small or a ‘‘devolatilization’’process is considered (similar to devolatilization versuspyrolysis for the mechanisms of primary reactions). In theformer case, the rate of tar cracking is generally describedby a global reaction, with a rate linearly dependent on themass concentration of the vapor-phase tar (rV) and theusual Arrhenius dependence on temperature. In alterna-tive, the cracking rate is linearly dependent only on thereactive fraction of the primary tar (rVT). In a few cases,the existence of tar fractions with different reactivities isexplicitly acknowledged.

The competition between the chemical paths of gas andrefractory tar formation (Fig. 9) has important implica-tions from the point of view of process development. Thethermal stability of tars for temperatures below 773K[18–22,27,28,30] is a key issue in the fast pyrolysis processesaimed at bio-oil production, as extensively discussed inseveral technology reviews [39–43]. The kinetics ofsecondary tar reactions is also of paramount importancein biomass gasification. The amount of tar produced andits composition depend on the type of gasifier and theprocess conditions. In principle, producer gas with a lowtar content can be obtained if a high-temperature zone canbe created where the volatile products of pyrolysis areforced to reside sufficiently long to undergo secondarygasification. However, the discover of a refractory tarproduct [120,121] of secondary reactions has motivatedextensive research activities on catalytic pyrolysis for thevapor phase products which, as anticipated, have beenreviewed.

Compared with primary reactions, secondary reactionsare less investigated and evaluations of the kineticconstants are essentially available only for the crackingprocess. This information, together with the range ofexperimental conditions, is summarized in Table 3. Fig. 10provides the corresponding Arrhenius plot. It can be seenthat the estimated activation energies vary between 66 and123 kJ/mol though, for the majority of the studies, therange is narrower and roughly corresponds to 80–100 kJ/mol, with the exception of cellulose tars [120]. It is plausiblethat the comparable values of the cracking rates, reportedin all cases, are the result of the high simplificationrepresented by the global one-step reaction applied tomodel the cracking process and the assumption of idealplug-flow behavior for the gas/vapor phase. Indeed,experimental conditions (thermal and fluid-dynamic con-ditions of primary and secondary degradation), samplecharacteristics (biomass/wood, size, shape, ash and moist-ure content), and mathematical treatments of the data arehighly different among authors.

Experiments consider either one reactor [124,127,129,133] for both primary and secondary reactions, ortwo reactors (or zones) in series [36,120,121,125,128,132,135]. The variety of experimental conditionsestablished during primary pyrolysis (nominal heating

rates from a few to thousands K/min with final tempera-tures between 723–1327K) results in different composition(and reactivity) of tarry species. In other words, the activityof secondary reactions is also explicated, at differentextent, during the process of primary decomposition ofthe starting material. Moreover, apart from the cases wherecontinuous systems are used [36,127], the composition ofthe vapor stream undergoing secondary reactions is alsohighly variable with time and may be affected by thelocation and type (total gas or single gaseous species, liquidyields, etc.) of measurements [135]. In addition to thedifferences in the temperature and residence times of thevapor phase, the presence of reactive species, such as steam(also from primary decomposition), and char may have animportant impact on the tar decomposition rates. Forinstance, char freshly formed is reported [136] to cause theheterogeneous conversion of about 14% of the primary tarproduct. The catalytic role exerted by charcoal on tarconversion is also recognized in biomass gasification. Toexploit this feature, peculiar reactor design schemes havebeen proposed, such as for the downdraft gasifier, modifiedto include internal recycle of vapor-phase tars over theglowing char and combustion of pyrolysis gases [137], andtwo-stage fixed-bed gasifiers [138]. Also, the catalyticaction exerted by charcoal on tar conversion is confirmedfor supercritical water gasification of biomass [139].Given that the presence of specific compounds in the

pyrolysis liquids is directly related to the decomposition ofthe main constituents of wood/biomass and also affectedby the chemistry of the ligninic fraction [37], it can beunderstood that differences in the reactivity are also causedby the type of feedstock. Different woods [36,120,121,125,127,128,133,135] are examined together with cellulose[120,121], biomass [129,134], municipal solid waste(MSW) [124], refuse-derived fuel (RDF) [131] and lignin[132].Differences in the rate expressions also produce varia-

tions in the estimated values of the kinetic parameters. Asanticipated, the global cracking rate is assumed to belinearly dependent on the mass concentration of the vapor-phase tar (rV). Exceptions are represented by the studies ofRefs. [36,125,128]. Boroson et al. [128] assume that it islinearly dependent only on the reactive fraction of theprimary tar (rVT) which, for the experimental conditionsinvestigated and sweet gum hardwood, corresponds toabout 94%. The same kinetic equation and activationenergy reported in [128] are also used in [140] for predictingthe decomposition of beech wood tar in a thermogravi-metric system and a muffle furnace. The reactive fraction oftar is, however, lowered to 78% and the pre-exponentialfactor is modified to become A ¼ 105.14 s�1.Tar vapors are also assumed to consist of two [124] or

three [125] fractions with different reactivities. Garcia et al.[124] use either one or two parallel reactions with rateslinear in the total tar concentration. It is found that thelatter mechanism is associated with significantly highervalues of the activation energies (150–290 kJ/mol). More-

ARTIC

LEIN

PRES

S

Table 3

Kinetic constants for the global reaction of tar cracking, where rV and rVT are the total and reactive mass concentration of vapor-phase tar, respectively (same data as in Fig. 10)

Author (Ref.) Reactor; atmosphere Primary pyrolysis: material; heating

conditions

Secondary pyrolysis: temperature;

vapor residence time

Kinetic law A (s�1); E (kJ/mol)

Antal [120,121] Tubular, two zone, batch reactor; steam 100–300mg of cellulose, cherry wood,

yellow pine powder; 773K

773–1023K; 0.5–12 s r ¼ �3.57� 1011 exp(�204/RT)rT;(cellulose); E (cherry wood) ¼ 98.6;

E (yellow pine) ¼ 101

Diebold [126] Continuous vortex reactor+tubular

reactor; steam

Softwood sawdust; 898K 923–1098K; o1 s r ¼ �1.55� 105 exp(�87.6/RT)rT

Liden et al. (1988) [127] Continuous, bubbling fluidized-bed

reactor; nitrogen

0.6–1.1mm thick poplar; 723–823K 723–823K; 0.5–0.7 s r ¼ �4.28� 106 exp(�107.5/RT)rT

Boroson et al. [128] Series-connected, tubular reactors;

helium

20mm deep bed of sweetgum hardwood

powder; 0.2K/s up to 723K

773–1073K; 0.9–2.2 s R ¼ �104.8 exp(�93.3/RT)rTV;yNR ¼ 6%

Font et al. [129] Batch, fluidized-bed reactor; nitrogen 3–4 g of almond shells, 978–1123K r ¼ �4.5� 106exp(�110.1/RT)rT0.3–0.5mm thick;

978–1123K

Graham et al. [130] Continuous ultrapyrolysis reactor;

nitrogen

Cellulose powder; 923–1173K 923–1173K; 0.05–0.9 s r ¼ �1.1� 106 exp(�100.8/RT)rTV

Cozzani et al. [131] Two-zone tubular reactor; helium 15 g of milled RDF; 773–1173K 773–1173K; 6–22 s r ¼ �4.1� 104 exp(�102.3/RT)rTVGarcia et al. [124] Batch, fluidized bed reactor; nitrogen 0.8–5 g of MSW; 973–1073K; r ¼ �1.9� 106 exp(�99.5/RT)rT

973–1123K o5 s

Caballero et al. [132] Pyroprobe 1000+tubular reactor

packed with quartz particles; helium

1mg of Kraft lignin (Ecalyptus wood)

powder; 300K/s up to 973K

673–1033K; r ¼ �4.138� 103 exp(�84.7/RT)rT

Lede [133] Cyclone reactor; mixture of helium/

argon and steam

0.2–1mm thick beech wood particles;

793–1327K

793–1327K; 0.04–0.15 s r ¼ �5.9� 107 exp(�123.5/RT)rT

Fagbemi et al. [134] Tubular reactor+packed-bed (metallic

rings) reactor; helium

20–30 g of wood, straw, coconut shells

powder; 673–1173K

673–1173K; 0.3–0.4 s r ¼ �4.34 exp(�23.4/RT)rT

Rath and Staudinger [125] TGA+tubular quartz reactor; nitrogen 500mg of spruce wood (0.5–1mm thick

particles); 5K/min up to 1323K

873–1073K; 0.5–2 s rI ¼ �3.076� 103 exp(�66.3/

RT)rT(I); rII ¼ 1.13� 103 exp(�109/

RT)rT(II); yNR ¼ 22%

Morf et al. [36] Continuous fixed-bed reactor+tubular

reactor; nitrogen

10–40mm thick spruce and fir particles;

653K

773–1273K; o0.2 s r ¼ �4.0� 104 exp(�76.6/RT)rT

Baumlin et al. [135] Tubular reactor+perfectly stirred

reactor; argon

1 g of beech wood sawdust; 820K 836–1303K; 0.3–0.5 s r ¼ �1.9� 103 exp(�59/RT)rT

C.

Di

Bla

si/

Pro

gress

inE

nerg

ya

nd

Co

mb

ustio

nS

cience

34

(2

00

8)

47

–9

060

ARTICLE IN PRESS

Table 4

Mechanism and kinetic constants for 5-hydroxymethylfurfural, levoglu-

cosan and hydroxyacetaldehyde proposed in [141]

Primary�!k1

Secondary�!k2

Tertiary

E1 (kJ/mol) E2 (kJ/mol) A1 (s�1) A2 (s

�1)

5-Hydroxymethyl

furfural

76 117 3.3� 104 1.7� 107

Levoglucosan 185 228 3.5� 1010 7.0� 1012

Hydroxyacetaldehyde 218 — 1.2� 1012 —

0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4

-6

-4

-2

0

2

4

6

8

[125 ]

[135]

[131][132]

[130]

[134]

[36]

[125]

[133]

[124]

[129]

[128]

[127]

[126]

[120]

lnk

1000/T [K-1]

Fig. 10. Arrhenius plot for the global reaction of vapor-phase tar cracking

(see Table 3 for the values of the kinetic parameters).


over, for the specific experimental conditions, around70–80% of primary tar generates permanent gases, whereasthe remaining fraction is a refractory matter or char, aconfirmation of varying reactivities among the tar frac-tions. From the mathematical point of view, when a multi-step process is fitted by a single step reaction model, theestimated activation energy is lower than the intrinsic(actual) values of the different steps. However, although in[125] primary tar is assumed to consist of two reactive andone unreactive fraction, kinetic evaluation for the twoparallel reactions still produces low values of the activationenergies (Table 3). A competition between the reactionsleading to the formation of permanent gases and arefractory tar is explicitly postulated in [120,121] wherean evaluation is also provided of the kinetic parameters forthe polymerization process.

Little work has been made on the kinetics of thereactions undergone by primary products of wood/biomasspyrolysis. Levoglucosan has often been considered[79–81,141] as a model compound for tar. The results ofa comprehensive study on the influences of the gas flow atvarious pressures on the yields of char generated fromcellulose are summarized [80] by a conceptual mechanism,which includes both primary and secondary decomposi-tion. Low-temperature paths are evidenced (formation ofanhydrocellulose and char), which are not of greatimportance because associated with slow heating rates,that are uneconomical for practical applications [38]. Themost important results concern the pathway associatedwith the formation of levoglucosan, favored by fastheating, and its evolution. At high flow rates and pressuresbelow 1MPa, an endothermic zone is observed andassociated with levoglucosan evaporation. However, atlow flow rate, the endothermic zone is replaced by astrong exothermic zone, attributed to decompositionreactions with formation of gas and carbonized levogluco-san. The decomposition of this compound occurs via two

competing reactions affected by the heating rate (highvalues of these favor gas formation). Kinetic constants forthe different reaction paths are not available, so thismechanism has never been incorporated in transportmodels.Information is also available [141] on the global kinetics

for the formation of primary, secondary and tertiaryproducts from the decomposition 5-hydroxymethylfurfural(5-HMF), levoglucosan and hydroxyacetaldehyde. Mea-surements are carried out by means of a tubular gas phasepyrolysis reactor coupled to a molecular-beam massspectrometer (MBMS). Multi-variate data analysis is usedfor the estimation of kinetic constants for the reactionmechanism formulated by analysis of the temporal profilesof the pyrolysis products. For 5-HMF and levoglucosan,two sequential reactions are proposed (Table 4) fordescribing primary, secondary and tertiary products(primary components are the evaporated reactants andtheir fragment ions). Products from hydroxyacetaldehyde(Table 4) only require a one step reaction.Recent investigations [142–145] on the low-temperature

devolatilization (evaporation and cracking) of pyrolysisliquids produced from different fuels and variable heatingconditions confirm the importance of polymerizationversus cracking reactions. For fast pyrolysis liquids and adevolatilization process carried out under the conditions ofthermal analysis, secondary char retains about the half ofthe initial carbon content of the liquid. Moreover, highyields are obtained especially for liquids produced fromcellulose, indicating the important role played by sugarsand not only by the products of lignin decomposition.However, while the global devolatilization kinetics isprovided [144,145], further experimentation is still needfor an evaluation of the secondary char formation rate.

2.6. Outline of multi-step mechanisms of cellulose pyrolysis

Given its large share in wood and biomass composition,cellulose pyrolysis is the subject of a significant number ofstudies. Reviews [53–56] are also proposed. The mostrecent modifications are discussed here of the mechanismproposed by Bradbury et al. [91] extended to include thereaction of tar cracking (Fig. 11), which is often coupledwith transport equations. The first step, not detected bythermogravimetric systems and associated with the forma-


tion of a molten-phase intermediate (active cellulose), is thesubject of research studies [146]. Recent experiments [147]carried out for very short residence times (35–75ms) showthat it is possible to selectively depolymerize pure cellulosepowder, which passes through a molten phase, to givesignificant yields of anhydro-oligosaccarides in the rangeC2–C7. The active cellulose decomposes via two competi-tive reactions associated with weight loss. The first is adepolymerization process, favored at high temperatures(high activation energy, Bradbury et al. [91]), and leadingto the formation of volatile species (following the studiescarried out by Shafizadeh et al. [148] and Shafizadeh [149],mainly levoglucosan), which may also undergo secondaryreactions. The second pathway, favored at low tempera-tures, foresees the formation of char, carbon dioxide andsteam.

Extensive analyses on the products of cellulose pyrolysisand their dependence on temperature [150–155] support thespeculation that hydroxyacetaldehyde is a product ofprimary decomposition, specifically through a process ofring scission which becomes progressively more importantat high temperatures. Furthermore, it is noted that whenlarge amounts of hydroxyacetaldehyde are formed, theformation of char (favored at low temperatures and/or bythe presence of ionic substances) is reduced. As a result ofthese studies, Piskorz et al. [151] propose a modifiedversion (Fig. 12) of the mechanism by Bradbury et al. [91].The initial stage takes into account the competitiveformation of char (with carbon dioxide and steam), whichis again favored by low temperatures, and a rapid

Fig. 12. The mechanism of cellulose pyro

Fig. 11. The mechanism of cellulose pyrolysis proposed by Bradbury et al.

[91].

reduction in the degree of polymerization associated withthe formation of active cellulose. Successively, a furthercompetition is established between two pathways. The firstis ring fragmentation (decarbonylation, dehydration) withthe formation of hydroxyacetaldehyde (and other productsincluding formic acid, acetic acid, glyoxal, methylglyoxal,etc.), which is favored by high temperatures and catalyzedby metallic compounds. The low-temperature process,favored by the absence of impurities in the substrate, isdepolymerization by transglycosylation with the formationof levoglucosan, cellobiosan (and other sugars, such asglucose, fructose) in high yields. Possible decomposition ofcellobiosan is indicated as the major route for formalde-hyde production.More recent studies [156–158] largely confirm the

findings summarized by the conceptual mechanism byPiskorz et al. [151] and contribute in the determination ofthe kinetic constants for the formation of some com-pounds. Baniasz et al. [156,157] measure the release curvesof formaldehyde, hydroxyacetaldehyde, carbon monoxideand carbon dioxide during the rapid pyrolysis of cellulosein the temperature range 673–1073K. The yields offormaldehyde, hydroxyacetaldehyde and carbon monoxideare observed to increase as the reaction conditions aremade more severe, whereas the yields of carbon dioxidedecrease. Independent mechanisms are proposed for theinterpretation of the different dynamics, but the estimationof the kinetic constants points out that inter-relations existbetween the examined products. The proposed mechanismis reported in Fig. 13 together with the kinetic constants.The pathway leading to intermediates is the limiting stepfor the formation of formaldehyde and carbon monoxide,which are indicated as products of secondary reactions.Levoglucosan is considered the major contributor (withcarbon dioxide) among the competing species for theformation of hydroxyacetaldehyde. The difference, withrespect to the mechanism proposed by Piskorz et al. [151],

lysis proposed by Piskorz et al. [151].

ARTICLE IN PRESS

Fig. 13. The mechanism of cellulose pyrolysis proposed by Banyasz et al. [156,157] (activation energies are expressed as cal/mol).


is that the formation of hydroxyacetaldehyde occursthrough the formation of reaction intermediates.

2.7. Distributed activation energy (DAE) models

The chemical complexity of both the biomass and therelated pyrolysis products motivate the introduction ofkinetic models based on kinetic laws different from thosepresented above. A few cases, not considered in [58] andspecifically formulated for biomass fuels, are examinedhere [128,159–162]. A DAE model is always proposedstarting from treatments already used for coal. Thisapproach avoids the low values of the activation energieswhich result when a single-step reaction is applied to fit atemperature dependence that arises from the occurrence ofdifferent reactions in different temperature intervals [128].

Chen et al. [159] combine a functional group (FG) modelfor gas evolution and a statistical depolymerization,vaporization and crosslinking (DVC) model for tar andchar formation. The evolution of each FG is described by afirst-order Arrhenius reaction with a DAE of width s.Measurements are made using thermogravimetric analysiscoupled with Fourier transform infrared spectroscopy(TG-FTIR) for cellulose, wood and some biomasses.Compared with coal, the number of FGs is lower and,apart from methane, each gaseous species (tar, CO, CO2,H2O) evolves in the form of a single peak. The estimatedvalues of the average activation energies are of the order of167–250 kJ/mol (in the case of cellulose up to 318 kJ/mol).The agreement is good between measurements and predic-tions for moderate heating rates, whereas it becomes poorwhen the screen heater measurements by Nunn et al. [94]are considered. The same modeling approach and compar-able values of the activation energies are reported in [160]for wood and Miscanthus pyrolysis, but the analysis isextended to 15 species, including problematic compoundssuch as HCN and HNCO (the release rate of nitrogencompounds, in biomass combustor models, is usuallyassumed to be directly proportional to the rate of soliddevolatilization, although thermogravimetric measure-ments indicate a temporal lag between the two processes[48]), and makes use of one to three FGs for each species.Difficulties are again found for the correct predictions of

the gaseous species release curves for different heatingrates, probably in consequence of a lack of competitionbetween tar and gas formation in the proposed model(comparable values of the kinetic parameters). Rostami etal. [161] modify the DAE model to facilitate its couplingwith mathematical descriptions of transport phenomena.Although special care [160] may be taken in reducing the

activity of secondary reactions, the results of the kineticanalyses discussed above combine the activity of bothprimary and secondary reactions in the estimated para-meters. Specific for the secondary reactions is the DAEmodel proposed by Boroson et al. [128], to take intoaccount the complex chemistry of tarry products and toextend the range of applicability of the proposed kinetics.This treatment assumes that each species is the result of alarge number of independent parallel first-order reactionswith invariant pre-exponential factor and activationenergies described by a continuous distribution function.The estimation procedure assumes a constant pre-expo-nential factor (1013 s�1) and produces an activation energyof 234 kJ/mol (standard deviation 21 kJ/mol and unreactivetar fraction 4.77%), which is about 2.5 higher than that ofthe single reaction previously considered. A DAE model isalso used in [162] to evaluate the kinetics of tar cracking,based on measurements carried out with a fluidized-bedreactor with variable freeboard height (approximated by aplug-flow reactor) for cellulose, MSW and birch woodpyrolysis (particles of 0.08–0.15mm, temperatures in therange 673–973K and vapor residence times of 0.25–3 s).However, the average activation energies (81 (cellulose), 73(birch wood) and 89 (MSW) kJ/mol) are much lower thatthan those reported by Boroson et al. [128].

2.8. Conclusions and further developments

One-component or multi-component mechanisms ofprimary pyrolysis have been proposed based on theanalysis of experimental data on wood/biomass pyrolysis,obtained for isothermal conditions or fast heating rates.One-component mechanisms generally consist of threeparallel reactions, as proposed by Shafizadeh and Chin[88], for the formation of the three classes of pyrolysisproducts: char, gas and tars (or liquids). The activation


energies of the global pyrolysis rate lie within two separateranges: 60–90 and 124–174 kJ/mol, respectively. It appearsthat the former range of low values results from eithersevere heat and mass transfer limitations in the experi-ments, owing to high temperatures/heating rates, orsimplifications in the reaction mechanism, that is, the useof a single global reaction for the description of the entirecurves of weight loss. The second set of high activationenergies is produced from measurements carried out undermoderate thermal conditions and the description of thecentral part of the weight loss curves only, where thecontribution of the cellulosic component is predominant.In this case a fast increase of the decomposition rate withtemperature, as observed from the measurements, ispredicted and therefore this set of data appears to be moreappropriate for inclusion in transport models for thesimulation of particle and/or reactor dynamics.

The kinetic parameters for the formation rates of thethree products classes from wood/biomass pyrolysis (one-component mechanisms) have also been estimated. In somecases even the qualitative trends, on dependence oftemperature, are not predicted and the final char yieldsremain high, presumably again as a consequence oftransport phenomena control or too low reaction tempera-tures. Only in two cases [97,99], a strong dependence of theyields of products on temperature is predicted and thusthese kinetic parameters appear to be suitable, in principle,also for modeling fast pyrolysis processes. Another criticalpoint of high-temperature experiments is that the yields ofproducts, needed in combination with weight loss measure-ments for parameter estimation, may result from theactivity of both primary and secondary degradation.

The advantage of one-component pyrolysis mechanismsis that, when coupled with transport equations, both theyields of products and the decomposition rate (conversiontime) can be predicted. However, the assumption of one-component behavior for composite fuels, such as wood andbiomass, unavoidably produces inaccuracies in the detailsof the decomposition rates (and conversion time). The fewmulti-component mechanisms of biomass/wood pyrolysis,based on the description of different zones in theisothermal weight loss curves (for instance, [117]) or onthe combination of multi-step mechanisms for the decom-position of the pseudo-components hemicellulose, celluloseand lignin (treatment proposed in [118]), have beenformulated with the scope of improving these aspects.However, in the first approach, the product formation ratescannot be predicted for the different stages given thatproduct yields can be measured only on an integral basis.In the second case, although the combination of multi-stepkinetics for components is capable of predicting bothconversion times and yields of products, the generalvalidity of this approach is not supported by reliable andextensive experimentation and, on the other hand, thedifficulties and inaccuracies associated with the use ofcomponent degradation rates are well known to theresearches of this field.

Multi-component mechanisms, for the large majority,simply describe the devolatilization process (the globaldevolatilization rate on dependence of time). That is, thefinal char yields should be known or assigned a priori andproduct distribution cannot be predicted. Usually, threeparallel, first-order reactions in the amount of volatilesreleased from the pseudo-components hemicellulose, cellu-lose and lignin are considered. The analysis of singledynamic thermogravimetric curves assumes that hemicel-lulose and cellulose are associated with the shoulder andthe peak of the rate curves, respectively, whereas lignindecomposes slowly over a very broad range of tempera-tures. The activation energies vary between 80 and 116 kJ/mol for hemicellulose, 195–286 kJ/mol for cellulose, and18–65 kJ/mol for lignin. Analyses based on the simulta-neous evaluation of thermogravimetric curves, obtained forseveral heating rates, needed to avoid compensation effectsin parameter estimation, confirm only the kinetics for thecellulosic components. Although the comparison betweendifferent results is difficult owing to variations in theexperimental conditions, mathematical treatment ofthe data, nature of the fuel and possible flaws in themeasurements, it appears that the heating rate effects,when assuming first-order reactions, result in higheractivation energies for the devolatilization of the pseudo-components hemicellulose (147 kJ/mol) and lignin (181 kJ/mol) [109]. Furthermore, the activity of the lignindevolatilization reaction is mainly explicated along thehigh-temperature (tail) region of the weight loss curves.Devolatilization mechanisms are also available whereadditional reactions are introduced, to describe volatileformation from minor components, such as extractives, orto take into account different steps in the volatile releasefrom the chief biomass components, or power-law depen-dence on the mass fractions are assumed. It can beunderstood that the accuracy in the predictions of theweight loss characteristics is improved as the number ofmodel parameters is increased. However, simplicity isalways desired for the global reaction mechanisms espe-cially in the view of inclusion in transport models.Secondary degradation of tar products has been

observed [120,121] to take place according to twocompetitive reactions for the formation of permanent gases(cracking) and refractory condensable materials which,depending on pressure, temperature and flow rate condi-tions, may also lead, in addition to further gas, to(secondary) char formation. The large majority of kineticstudies disregards this competition and simply assumes aglobal cracking rate, proposing a convergence towardactivation energies roughly comprised between 80 and100 kJ/mol. In a few cases, the recognition of different tarfractions with variable reactivity and the use of two orthree reactions to describe the cracking process give rise tohigher activation energies. Levoglucosan has often beenused as a model compound (more recently also 5-HMF andhydroxyacetaldehyde) of tar to investigate secondaryreaction chemistry. However, the understanding of the


reaction paths of refractory tar and secondary charformation is still only qualitative.

The chemical characterization of the gas and vaporphase products generated from pyrolysis of cellulose hasalso been used to formulate conceptual mechanisms for theformation of single products (levoglucosan, hydroxyace-taldehyde, hydroxy-2-propanone), starting from the pro-posal by Bradbury et al. [91]. The main findings,summarized by Piskorz et al. [151], are supported by morerecent experiments and preliminary evaluations of the rateconstants [156–158]. However, no quantitative informationis available for wood or biomass, even with reference to themain components of the pyrolysis liquid, for instancewater, which is an important parameter for the quality ofthis product. Moreover, only a few attempts have also beenmade to develop DAE models.

Although the performances of the mechanisms forprimary and secondary pyrolysis can be fully evaluatedafter combination with the transport equations to describeapplications at a laboratory or industrial scale, it can beconcluded that, even for a kinetic control, only in a veryfew cases the correct trends are predicted. The under-standing of the pyrolysis kinetics is essentially qualitative(for instance, in relation to the influences of some variables,such as, ash content/composition, pressure, gaseousenvironment and gas flow rate). The large scatter in thepredictions is caused by the fuel type, the mathematicaltreatment of the data and possible errors in the measure-ments. Hence, extrapolations to conditions different fromthose of the experiments is highly questionable.

Following these conclusive remarks on the biomasspyrolysis kinetics, aspects which deserve further investiga-tion and/or consideration are related to the formulation ofboth (a) lumped-reaction mechanisms and (b) detailedreaction mechanisms taking into into account the dynamicsof single-product species, valid for widely variable thermalconditions and biomass/wood varieties. While lumpedkinetics, referred to product classes (either char, gas andliquid or, simply, volatiles and char), can be very useful forthe design of different reactor types capable of achieving,for instance, fast pyrolysis or flash carbonization, detailedkinetics are needed for selecting optimal operating condi-tions apt to maximize the yields of specific compounds.Further efforts in both directions are highly desirable. Toimprove kinetic models, experimental data should beproduced on the quantitative aspects of pyrolysis productyields and composition and their dependence on sampleproperties and conversion conditions.

As for the lumped-reaction mechanisms, although thedegree of detail may be determined by the final application,the validity of one-component versus multi-componentmechanisms for the quantitative prediction of both theconversion time and decomposition rates should beassessed. Another aspect of great importance concernsthe extension of multi-component mechanisms to predictproduct distribution. As for detailed mechanisms, signifi-cant experimental effort is needed to produce quantitative

data on the chief tar components and their dependence onthe reaction conditions and the wood/biomass species. Thiscould provide the basis for the successive formulation ofkinetics for tar component formation, for improving thequality of fast pyrolysis oils, and destruction, for theoptimization of the gasification processes.The influences of the heating rate on the kinetic

constants of the multi-component mechanisms or, in otherwords, the verification of the absence of compensationeffects in the kinetic constants currently available are onlypartially addressed. Moreover, it is often pointed out thatkinetic analysis does not use thermal conditions compar-able with those of practical applications. This is a verycritical issue. Indeed, to produce experimental datarepresentative of the intrinsic kinetics of primary reactionsat high temperatures, adequate consideration should begiven to the separation between chemistry and transporteffects, on one side, and between primary and secondaryreaction processes, on the other. In reality, for correctmeasurement of high-temperature data, there could be aneed to introduce innovative concepts in experimentdesign. Another aspect, often overlooked despite of theclear evidence shown by accurate investigations in thissector, is the presence of errors in the measurements evenwhen carried out with commercial thermogravimetry.Kinetic analysis of pyrolysis/devolatilization processes

has been mainly focused on wood species and, in a fewcases, on agricultural residues. Thus, the search of generalreaction schemes applicable for the different varieties ofbiomass fuels has not yet been extensively pursued. Giventhe wide variability among the chemistry of the differentspecies and the influences of sample origin, this issue is ofparticular practical importance.The application of mathematical modeling of conversion

systems also requires sub-models for the release of volatilenitrogen species and other minor compounds, such aschlorine, during pyrolysis. Simplified treatments, in trans-port models of biomass combustors, usually assume thatthese compounds are released together with gases and tarvapors. Further effort is needed on these aspects as resultsof experimental investigations have not yet been inter-preted by kinetic models.

3. Transport models of biomass particle pyrolysis

In practical conversion systems, wood or biomassconversion takes place as a result of a strong interactionbetween chemistry and transport phenomena at the level ofboth the single particles and the reaction environment.When exposed in a high-temperature environment, theparticle is initially interested by transient heat conduction.Then the process of moisture evaporation occurs, which ishighly endothermic. Depending on the initial moisturecontent, capillary flow of free water through the voids,bound water diffusion and convective and diffusivetransport of water vapor are controlling. Successively, thealready dried portion of the particle, in the neighborhood


of the heated surface, undergoes thermal degradation.When all the volatile species are removed from the solid, achar layer is formed. Therefore, the following spatial zonesappear during the process transients: an inert char layer, apyrolysis region, a drying region and the virgin moist solid.Water vapor and volatile pyrolysis products, partly leavethe particle flowing across the heat-exposed surface. Afraction may also migrate toward low-temperature regions,where re-condensation may occur. The flow of products,owing to larger permeabilities, mainly occurs toward theheated surfaces and, because of the high temperature,secondary reactions of tar degradation may occur. Inaddition to heat, momentum and mass transfer, changes inthe physical structure of the reacting solid are observedwith the development of a network of cracks in the alreadypyrolyzed region, surface regression, internal shrinkageand/or swelling and, in some cases, (primary) fragmenta-tion. The reaction environment affects the particle pyr-olysis, essentially through the rate of external heating, andalso the final yields of products. Indeed, secondaryreactions of primary tar vapors also take place outsidethe wood/biomass particle given temperature and residencetimes sufficiently high.

The most important achievements in transport models ofwood/biomass pyrolysis, which couple the chemical ki-netics with mathematical descriptions of physical processesare reviewed in [16,17] and, with special emphasis onmaterial flammability characteristics, in [163]. Paperspublished afterwards, which include innovative featuresin relation to model development or present significantimprovements in the understanding of wood pyrolysisprocesses, are reviewed here.

Transport models for single wood particles/logs assumethat the porosity is fine and uniformly distributed so thatthe material is a homogeneous medium, where gas andsolid are in good thermal contact. The detailed modelscouple kinetics of primary and secondary reactions for thethree classes of lumped products with the description of therelevant physical processes. Transport models are alsoavailable consisting of more simplified kinetics withdescriptions of transport phenomena using differentdegrees of approximation. In contrast with the assumptionof volumetric decomposition rates, a further category ofsimplified models uses the unreacted-core-shrinking treat-ment, where decomposition takes place at an infinitely thinsurface with either infinite- or finite-rate kinetics. Finally,the empirical formulae for the conversion time and theparameters of apparent global kinetics, useful for thedesign of conversion units, are briefly examined.

The numerical simulation of biomass pyrolysis, oftencontrolled by intra-particle heat conduction, usually doesnot present specific computational problems. Standardnumerical methods, for instance finite-difference schemeswhich, in addition to the properties of convergence,consistence and accuracy, guarantee the absence of phaseand amplitude errors (causing unphysical oscillations orinstabilities), also preserve conservation of mass, momen-

tum and energy of the discretized version of the conserva-tion equations. Critical conditions, which require a carefulselection of time and space steps, may be shown by fastpyrolysis, where gradients of temperature, chemical speciesand flow velocity become very high.

3.1. Transport models with volumetric decomposition rates

Detailed models couple mechanisms of primary andsecondary reactions for the three main product classes(liquids, gas and char) with the conservation equations ofmass, momentum and energy. This model categoryincludes the work presented in [77,118,164–181]. The one-component mechanism of primary wood degradation,based on three parallel reactions for the formation ofprimary pyrolysis products, as proposed by Shafizadeh andChin [88], is generally used in the transport models wherepredictions of product yields are of interest. The kineticconstants estimated by Thurner and Mann [92] are used in[166,178,179,181], those by Chan et al. [14] in [16,170,172,176], those by Di Blasi and Branca [99] in [180], thoseby Wagenaar et al. [97] in [177]. A comparison is providedin [166] between the kinetic constants estimated in[14,92,95], whereas different description of chemicalkinetics are also considered in [173]. A three-componentmechanism, accounting for the competitive formation oftar and linked gas and char, is used in [167]. Given itsimportant contribution in wood composition, cellulose isconsidered as the starting material in several cases[77,165,167,168,171], where the chemical kinetics aredescribed as in Bradbury et al. [91]. Secondary reactionsof tar cracking are generally described according to thekinetics by Liden et al. [127], but several rate constants arecompared in [180]. Tar polymerization is modeled in[167,178,179,181] with kinetic constants as in [182].Associated with primary and secondary degradation,

enthalpy variation takes place. Janse et al. [177] assumethat the formations of the primary products from woodpyrolysis are endothermic processes, whereas in[118,167,174,180] primary char formation is an exothermicprocess and tar formation/vaporization is an endothermicprocess. Secondary tar cracking is always modeled as aweakly exothermic process. In the Bradbury et al. [91]mechanism, the formation of the active intermediate, whichis however not rate-limiting, is described as an isothermalprocess [77,165,168].Some transport models make use of simplified kinetics of

devolatilization or decomposition without secondary reac-tions. These include the works of Refs. [85,183–193]. Aone-step pyrolysis reaction is used with assigned yields ofvolatile and solid products and guessed values of thekinetic parameters in [183,184,186,188,190] or with differ-ent sets of kinetic constants referred to the cellulosiccomponent in [192]. Two parallel reactions for theformation of volatile species and char are proposed in[191], with parameters previously determined [194] andrepresentative of apparent kinetics. Bilbao et al. [85,189]


use kinetic constants for the devolatilization processextracted from thermogravimetric analysis [195,196]. Theseforesee a low- and a high-temperature step, with ademarcation temperature of 563K. The first endothermicstep, associated mainly with cellulose devolatilization,presents a kinetic constant independent of temperaturebut affected by the heating rate. The second exothermicstep is related to lignin decomposition and presents theusual Arrhenius dependence on temperature. Saastamoi-nen and Richard [187] use a global decomposition reaction,where the yield of char is assigned by means of an empiricalcorrelation to account for the temperature effects. Mod-ifications in the kinetic law would require, as pointed outby the authors, a congruent evaluation of the kineticparameters. However, the estimations provided by Nunn etal. [94] are used. Boutin et al. [193] disregard charformation in the mechanism by Bradbury et al. [91] ofcellulose pyrolysis, to describe pellets behavior under fastexternal heat transfer rates.

3.2. Intra-particle transport phenomena

In the description of intra-particle transport phenomena,the majority of the models [77,164–170,172–174,176–181,184,185,187,189,190–193] uses a one-dimensionalsystem with the following assumptions: local thermalequilibrium, perfect gases, negligible kinetic and potentialenergy and replacement of internal energy with enthalpy,negligible enthalpy flux due to species diffusion and bodyforces. Sometimes diffusive transport of species within thepores and accumulation of energy in the gas phase[178,179,181,185,190] and pressure variations [185,187,189,190] are neglected. The most advanced models take intoaccount the following physical processes [77,118,165–181,183,184,188]: heat transfer by convection, conduction andradiation, convective transport of volatile species, gaspressure and velocity variations. The latter variables aredescribed by means of the Darcy law, except in [118,167],where the conservation equations for the gas phasemomentum are written assuming that the gaseous mixtureflows through individual channels (pores) within theparticle. Convective transport of enthalpy is simplified in[189] by means of a global term at the particle surface usinga finite-difference balance, to express the boundarycondition. The assumption of one-dimensional system isremoved in a few cases [85,171,175,183,184,186,188,191].These are simplified models with the exception of the modelpresented in [171] which couples the kinetics of primaryand secondary reactions with the description of the chiefheat, mass and momentum transfer processes. Thermalconductivity and permeability along the two directions areassigned so as to reproduce those parallel and perpendi-cular to wood fibers, respectively. The same features in thedescription of physical processes are presented by themodels developed in [183,184,188]. Bilbao et al. [85]consider a two-dimensional unsteady equation for enthalpyconvection and conduction, formulated for constant-

volume (spherical) particle at a constant pressure. In othercases, a simple unsteady two-dimensional heat conductionequation is introduced [186,191]. To take into account theeffects of wood anisotropy on heat transfer, a simplifieddescription of convective transport along the wood fibers isalso proposed in [175] in conjunction with a comprehensivedescription of the processes for the perpendicular direction.Volume variation is modeled in relation to the shrinkage

of the particle [166,172,173,175,178–181,191]. A generalmodel is proposed in [166] and used in [172,173,175,180].Equations describing the time evolution for the volumeoccupied by the solid and the gas are written. It is assumedthat the volume occupied by the solid decreases linearlywith the wood mass and increases with the char mass, by achosen shrinkage factor, a, as devolatilization takes place.Hence, the volume occupied by the gas is made by twocontributions, the first due to the initial volume occupiedby volatile species and the second by the fraction, b, ofvolume left by the solid as a consequence of thedevolatilization process. In order to account for possiblestructural changes during devolatilization, the initialvolume of volatiles may also vary linearly with thecomposition of the degrading medium, from an initialvalue, determined by the initial solid porosity, to a finalvalue taken as a fraction, g, of the initial one. Theparameters a, b, g, which vary from 0 (total disintegrationof the particle) to 1 (no shrinkage), should be assigned.In [178,181], shrinkage is defined as the ratio between the

current and the original size of the computational cell ofthe one-dimensional integration domain. It is assumed tovary linearly with the composition of the solid and the finalsize of the sample should be assigned. In the two-dimensional cylindrical domain modeled in [191], particleshrinkage is made to occur simply by varying the size of theelementary control volumes of the discretized integrationdomain in proportion to the averaged conversion, withoutmodifications in the formulation of the conservationequations. Three models are proposed corresponding touniform shrinkage, shrinking shell and shrinking cylinder,which produce the shrinkage factors along the radial andaxial directions. The models differ in the way they averagethe conversion. The final shrinkage is always calculated bymeans of an empirical formula obtained considering datafor cylindrical particles (60–650mg) exposed to furnacetemperatures of 586–673K. Bharadwaj et al. [192] examinethe effects of particle shrinkage according to two limitapproximations corresponding to constant volume (noshrinkage) and constant density (shrinkage proportional tothe mass of volatile products). The model equations do notinclude any term related to volume variations and thus it islikely that variations in the grid size are used to describethis process.Volume variation should also be accounted for in the

case of ablative pyrolysis. Di Blasi [168] describes charablation and the chief transport phenomena through theporous solid and the molten layer (adjacent to the hotplate) whose properties and size vary during the thermal


degradation. The early stages of cellulose pellet pyrolysissubjected to image furnace heating (high, controlled heatflux intensities delivered for assigned period of time) aremodeled in [193] by describing heat conduction through thesolid and the liquid phase and neglecting char formation.

Models of high-temperature evaporation of moisture inwood are available. In some cases, a detailed description ofthese processes is made [197], also coupled [16,170,172,173,175] with the description of the main processes of woodpyrolysis. The mathematical formulation is based on theconservation equations for enthalpy, mass and momentumfor the solid, the liquid and the gas phase. Phenomena ofmoisture transport include water vapor convection anddiffusion and capillary water convection in the pores of theparticle, and bound water diffusion in the solid wood.Momentum transfer, for the liquid and the gas phase, isdescribed according to the multi-phase Darcy law. Localthermodynamic equilibrium is assumed, with a sorptionisotherm that couples the moisture contents in the solidand the gas phase.

In simplified models of moisture evaporation, thetransport phenomena of liquid-phase water and vapordiffusion are assumed to be negligible [85,179,183–187,189]. In [183,184] the moisture evaporation rate is derivedfrom the saturation vapor pressure, whereas Bilbao et al.[189] adopt the treatment originally proposed in [198]. Thedrying rate is controlled by heat supply and takes place atthe normal boiling point of water or, for low moisturecontents, at a temperature as empirically determined. Inthis way, the vapor pressure depression is treated as a risein the moisture boiling point, defined as the temperature atwhich moisture is in equilibrium with water vapor atatmospheric pressure.

An Arrhenius law temperature dependence of themoisture evaporation rate is proposed in several cases[179,186,199]. In [179], moisture evaporation begins onlyfor temperature above 368K and the corresponding rate isapproximated by a first-order Arrhenius law. The activa-tion energy is the same as proposed in [14] whereas thecorresponding pre-exponential factor is increased by afactor of 104 to provide a plateau between 373 and 393K inthe case of thick wood. Re-condensation of water vapor ismodeled assuming supersaturated state conditions with acondensation rate proportional, through an empiricalparameter, to the flow rate. In [199], the pre-exponentialfactor of Ref. [14] is again modified but re-condensation isnot taken into account. This approach allows for a finitethickness of the evaporating region and also eliminates thecomplications in the numerical solution [197], due to thepresence of the unknown moisture evaporation rate withan empirical expression for the vapor pressure, instead ofan evolution equation or a simple production term. Themain qualitative features of the process are also retained,that is, the temperatures rises continuously into and out ofa drying plateau [179].

Moisture evaporation takes place at an infinitely thinfront at constant temperature in the transport models of

wood pyrolysis proposed in [85,187,192]. The process iscontrolled by heat transfer, that is, the heat transferred atthe front is entirely used for moisture evaporation. Theevaporation temperature is assumed to coincide with thenormal boiling point of water or with a close value. Inparticular, in [192], when locally (at a certain controlvolume of the computation grid) attained, this temperatureis assumed to remain constant as long as the moisturecontent is different from zero. The steady-state formulationof the enthalpy conservation equation is used to evaluatethe evaporation rate. This treatment avoids the numericalcomplications due to the assumption of an infinitely thinfront of moisture evaporation at constant temperature. Onthe other hand, it does not introduce any specialcomplication in unreacted-core-shrinking models of woodpyrolysis, as discussed in the following.An important aspect of transport models is represented

by the description of physical properties and theirvariations during the conversion process. A comprehensivereview is given by Gronli [16] of correlations and values ofthe thermal conductivities, gas permeability, specific heatsand other properties of several wood species and char. Inthe majority of transport models, thermal conductivity,mass diffusivity and permeability are assumed to varylinearly with conversion between the values for the virginsolid and the char (for instance, [16,77,164–181]). Effectivevalues are used for the thermal conductivity, which alsoincludes a radiative contribution dependent on tempera-ture. In a few cases, correlations explicitly incorporating atemperature dependence are used [183,184]. Specific heatsfor the virgin solid and the pyrolysis products, anddynamic viscosity are assumed to vary with the tempera-ture [170,178–180,183,184] or to remain constant. Model-ing results of inert char heating [200] show that a betteragreement between predicted and measured temperatureprofiles is obtained when a constant value of the thermaldiffusivity is used. A possible compensation is suggestedbetween the simultaneous increase in the specific heat andthe effective thermal conductivity with temperature. Thevoid fraction is assumed to vary with the solid density[178,179,181], the conversion [77,164–166,168–176] or toremain constant [192].Primary fragmentation of wood particles is attributed

[201,202] to the pressure build-up, when the rate ofvolatiles production internally is faster than their escaperate through the pores in the charred wood. A mechanicalmodel for this process is available [202]. The heating rate,the rate and heat of reactions and the thermophysicalproperties are indicated as the factors responsible for thedetails of structural behavior. At both low and hightemperatures, the pressure peaks before the center tem-perature exceeds the external temperature. Then, particlebreak-up suddenly occurs and the pressure drops quicklyto the ambient value. It is worth noting that fragmentationof coal particles, fed to fluidized-bed combustors, is alsoconcentrated around the end of the devolatilization regime[203]. It is plausible that, despite the slow devolatilization


rates established for the inner core of the particles, theoutward flux rates of volatile products are even slower,owing to the presence of a thick char layer, creating in thisway the conditions for the pressure to increase. On the otherhand, as overpressures attain a maximum at near completeconversion, it can be postulated that for a large part of theconversion process the particles are not highly affected bystructural failure. On the contrary, the effects of primaryfragmentation are important for the subsequent oxidationof char as reported for both coal [203] and wood [204,205].

3.3. External heat transfer coefficients

The boundary conditions, at the particle surface, can beused to describe the effects of external heating ratesestablished in different reactor configurations. However,in several cases, the transport models are applied tosimulate conversion of particles exposed to hot gasesand/or radiative heating, where external conditions areassigned so as to describe bench-scale systems withoutreference to any specific conversion unit. More precisely,the majority of the models [16,85,165–167,169,170,172,173,175,176,178,179,181] examines the conventional pyr-olysis of particles or logs exposed to thermal radiation. Toevaluate the Biot number, a radiative external heat transfercoefficient is introduced [178,179,181] as hrad ¼ fs(Ts+TN)(Ts

2+TN

2), where the surface temperature TN isa function of time. However, evaluations can be made forsteady conditions (char heating) assuming, for simplicity,that Ts and TN are coincident. This leads to maximum heattransfer coefficients between 80 and 230W/m2 for tem-peratures in the range 700–1000K. Convective heattransfer is comparatively much smaller and described bycoefficients of the order of 5W/m2 or using the Ranz–Mar-shall correlation [206]. In a few cases [85,173,183,184], themeasured temperatures are used as boundary conditions atthe solid surface.

Several applications deal with the fast pyrolysis ofcellulose or wood, that is ablative pyrolysis of cellulosepellets subjected to a high-pressure contact against a hotspinning disk [168] or to concentrated radiation [193], andconversion in fluidized-bed reactors of cellulose pellets[174] and wood particles [177,180]. The conditions typicallyestablished in pulverized coal/biomass burners are investi-gated in [192], where millimeter-sized biomass particles areexposed to rapid heating and high temperatures. This lastfeature introduces a significant difference with fastpyrolysis systems.

As the external heat transfer coefficient is the mostimportant parameter for the design of pyrolysis reactors,especially for fast pyrolysis processes, specific investiga-tions are available for ablative pyrolysis [207,208], thePyrovac process [209], the rotating cone reactor [210], notto mention the numerous studies for fluidized-bed reactors(for instance, [206,211]).

For contact (ablative) pyrolysis the global external heattransfer coefficient is found to be proportional to the

applied pressure [207,208], with values in the range500–1675W/m2K as simulated in [168] by the transportmodel for cellulose pellets pressed against a hot spinningdisk.The Pyrovac process is based on a moving and stirred

bed reactor using an eutectic mixture of high-temperaturemolten salts as heat carrier and a sophisticated agitationdevice to enhance heat transfer between the feedstock andthe heating plate. During pyrolysis, the feedstock is heatedunder vacuum to a temperature of about 770K. The rangeof estimated values [209] for the heat transfer coefficient is100–200W/m2K (versus 5–30W/m2K of static bed reac-tors). Agitation speed, properties of the bed and particlemovement are the chief parameters affecting the heattransfer process. This model has not yet been coupled withthe description of transport phenomena and chemicalreaction of vacuum pyrolysis of biomass.The external heat transfer coefficient for particles

flowing along the surface of a rotating cone reactor isevaluated in [210]. For biomass particles (without sand) thevalues are in the range 100–1000W/m2K and an improve-ment up to 1500W/m2K is reported when sand is alsosupplied. The rotating cone frequency (variations in thecontributions of gas-phase convection and wall heattransfer rate) and size of the particle (variations in theparticle flow pattern over the conical surface) are the keyvariables.Fluidized-bed reactors allow for very high heat transfer

rates between the gas and the solid, as a result of the highsurface area of the particle phase. It is widely recognizedthat, for large particles, convective heat transfer betweenthe two phases may become controlling [212] and that thelow biomass thermal conductivity introduces significantinternal heat transfer limitations even for very smallparticle sizes [174,180]. Therefore, isothermal conversionat the temperature of the surrounding environment andchemical reaction control are not established in practicalsituations. Numerous correlations are proposed for theheat transfer coefficient between fluidizing gas and beds ofuniform particles [206] and for fixed tubes of diametermuch larger than the bed particles [206,212]. These are ofinterest for thermochemical conversion processes, giventhat the active particles are larger (and with differentdensity) than the inert bed particles. Also, correlations forheat transfer to large mobile particles in fluidized beds arereported especially in relation to coal conversion (reviewsare reported in [211,212]), based on the assumption thatthey reside only in the emulsion phase. In reality, the bed-to-surface heat transfer is an unsteady process andinstantaneous measurements show sharply varying values,suggesting that the object surface is being bathed alter-nately by gas bubbles (low values) and emulsion packets(high values) [206]. The unsteadiness of the heating processis enhanced for active particles as they are not fixed. Activeparticles show a circulation pattern which causes heattransfer with the emulsion phase and at a certain extentwith the bubble phase. In addition, the average contact


time with the emulsion phase (and the heat transfer rate)during ascent is different in comparison with that duringdescent.

The model for the average heat transfer coefficientdeveloped by Agarwal [211] takes into account all thefeatures listed above and is used to simulate the fast pyrolysisof cellulose and wood in a fluidized bed [174,180]. Itexpresses the overall heat transfer coefficient, h, as the sumof three components, hpc, the particle convective componentduring ascent (a) and descent (d), the gas convectivecomponent, hgc, and the bubble component hbub, averagedaccording to the probabilities of the particle to reside in theemulsion phase during one circulation or to be rising duringone circulation, p and p0, respectively. The assumptions andthe resulting equations, based the two-phase flow theory, arethe same as those in the original paper [211]. The modelpredictions [211] compare well with experimental data for theaverage heat transfer coefficient, although improvementscould be probably obtained by taking into account thevariation of the bubble parameters within the bed and with amore accurate description of the movement of the particle. Ingeneral, the gas convective component is of importance onlyfor beds of large group B and group D materials [212], whilefor group A and small group B powder the particleconvective contribution dominates.

In addition to the Agarwal model [211], two furthermodels are considered to describe cellulose or woodpyrolysis in a fluidized-bed reactor [174,180]: the Ranz–-Marshall correlation which is representative of theconvective heating of a single particle, and the whole bedcoefficient, corresponding to a bed of particles heated by ahot gas stream [206]. For the wood particle characteristicstypical of fuidized-bed reactors (particle half thicknessbetween 0.1 and 5mm and bed temperature of 800K), thefollowing range of values for the global heat transfercoefficient is evaluated [180]: 650–22W/m2K (Ranz–Mar-shall correlation), 1.5–4.5W/m2K (whole-bed correlation),1880–440W/m2K (the Agarwal model). Janse et al. [177]use a global heat transfer coefficient equal to 500W/m2K.Furthermore, the assumption of an infinitely fast externalheat transfer rate is also examined for comparisonpurposes [177,189]. Finally, effects of the efflux of volatilesgenerated as a consequence of the degradation process onthe heat transfer to the particle are taken into accountthrough the introduction of a correction factor.

In Bharadwaj et al. [192], the particle surface exchangesheat with the external environment through convection andradiation. A global mass transfer coefficient is also used forthe volatile species equations. Empirical correlations areapplied for such parameters suitable for small-sizedparticles and the thermal conditions of commercial coal-fired boilers.

3.4. Extra-particle processes

Extra-particle processes are not taken into accountexcept for the simplified treatments proposed for an

updraft gasifier [185], a fluidized-bed reactor [174,180]and an entrained-flow reactor [192]. Also, Miller andBellan [118,167] describe extra-particle processes to simu-late conversion in an infinite domain.In the simplified model for the pyrolysis of a moist wood

particle in an updraft gasifier [185], the effects of thereaction environment are taken into account by means ofthe boundary conditions at the particle surface whereheating occurs essentially by convection. The externaltemperature is obtained from the energy equation takinginto account the heat transported by the gas flow and theheat exchanged between the gas and the particle surface.The temperature of the gas at the inlet section of thedrying/pyrolysis zone of the gasifier should be assigned.The effects of particle shrinkage are incorporated only inthe calculation of the height of the reaction zone.Extra-particle processes for fluidized bed reactors

[174,180] are described according to the following assump-tions: (1) all the particles experience the same thermal (andconversion) history, (2) the organic fraction of liquidsproduced undergo extra-particle cracking according to anapparent residence time as defined by Scott and Piskorz[18,19] and Scott et al. [20,21], or to a residence timeevaluated with reference to the expanded bed height andthe bubble velocity, (3) secondary reactions occur at thebed temperature.The model of Ref. [192], for conversion in an entrained

flow, considers a particle traveling through a one-dimen-sional plug flow reactor, where the assigned temperatureand the velocity profiles are used to evaluate the externalheat and mass transfer coefficients.For the spherical geometry considered in [118,167], a

fictitious sphere is introduced whose radius is from 5 to 10times larger than that of the particle. These are a measureof the distance from the sample at which the computationalboundary must be placed in order to correctly simulateparticle pyrolysis in an infinite domain. The conservationequations are valid for the entire integration domain whichtakes into account the spatial variation in the mediumproperties. However, the schematization of a particleundergoing pyrolysis in an infinite domain does notreproduce the experimental conditions established eitherat laboratory scale or in practical chemical reactors.

3.5. Unreacted-core-shrinking models

Another approach used in wood pyrolysis modeling isbased on the use of the unreacted-core shrinking approx-imation usually associated with the assumptions of nomoisture content and one-dimensional system [213–220].Degradation is described by a one-step reaction, where theratio between the yields of char and volatiles (compositionresulting from the activity of both primary and secondaryreactions) is constant. The reaction takes place at aninfinitely thin surface which propagates from the surface ofthe particle toward the center. It is the moving boundarybetween the completely charred and the virgin solid


(unreacted-core-shrinking approximation), where the solidproperties (density, porosity, thermal conductivity, etc.)vary from the initial virgin wood values to the final charvalues. Both infinite-rate kinetics [214–217], that is, aconstant pyrolysis temperature, and finite-rate kinetics[213,218–220], based on the usual Arrhenius law and alinear dependence on the solid density and the surface area,are considered.

The thickness of the reaction zone is proportional tolDTqc [213] (l is the thermal conductivity, DT acharacteristic temperature difference across the reactionfront and qc the heat flux reaching the reaction front). As land DT are determined by the nature of the fuel (l is a fuelproperty and DT is of the order of 200K, given woodpyrolysis temperatures between 550 and 750K [99]), highapplied external heat fluxes appear to be a conditionnecessary for the validity of this treatment.

In the dynamic evolution of the process, different stagesexist, which should be properly modeled. The sample,initially at ambient conditions, is exposed to radiative/convective heating. Hence, a pre-heating stage occurs, withnegligible activity of the degradation reactions and anextension of the unreacted core (and integration domain)coincident with the sample thickness. This is followed bythe reacting stage, where the unreacted shrinking core issurrounded by a char layer. The passage from the pre-heating to the reacting stage is assumed to occur, forinstance [218] when the thickness of the char layer equals asmall assigned value. From here on, the integration domainis divided into two zones, the char layer and the unreactedcore.

The special techniques needed to track infinitely thinfronts [221] may become computationally complicated andexpensive. On the contrary, a uniform temperature profilefor the unreacted core associated with a quasi-steady charzone [213] or the use of integral solution methods for theheat conduction problem [214–220] are particularly attrac-tive because they simplify the mathematical model frompartial to ordinary differential equations. In the integralmethod, the temperature profile is assumed to be a knownfunction of the spatial coordinate, chosen so as to satisfythe boundary conditions. It is then substituted into theenthalpy equation which, upon integration with respect tothe space variable, reduces to an ordinary differentialequation. Different treatments, including polynomial andexponential profiles, are reported in the literature. How-ever, a quadratic profile is observed to describe well notonly several moving boundary problems but also experi-mental data obtained for standard fire resistance testconditions and wood particle conversion [218]. In this case,a parabolic profile is used for the one-dimensionalunsteady enthalpy equation written for either the charlayer or the unreacted core.

Some of the unreacted-shrinking-core models are pro-posed for fire safety engineering, thus there are severalspecific features and limitations which do not allow theirstraightforward application in the biomass thermochemical

conversion sector. These include: the geometry of the solid(a slab [215], generally with infinite thickness [214,216,217]), the completely absent [213,214] or very limited[215] experimental verification, the lack of sensitivityanalysis on model assumptions and parameters. Incontrast, the model presented in [218,220] is specificallydeveloped for biomass thermochemical conversion andthus takes into account the effects of finite sample size.Aside from a more accurate description of pyrolysisprocesses (convective, conductive and radiative heattransfer, finite-rate kinetics), it uses realistic input dataand is experimentally validated in relation to mass losscharacteristics and conversion times.The unreacted-shrinking-core model, originally devel-

oped for dry wood [218,220] is also modified to include theeffects of moisture evaporation [219]. This process isdescribed assuming that (1) evaporation takes place at aninfinitely thin, constant-temperature front, which separatesthe moist from the dry region; (2) the evaporationtemperature at the drying front coincides with the normalboiling point of water; (3) the moist core of the particle is atambient (initial) temperature; (4) the heat flux at the dryingfront is applied exclusively for raising the surfacetemperature, from the initial to the evaporation value,and sustaining the endothermic evaporation process.

3.6. Simulation results

Detailed particle models are extensively applied tosimulate the effects of sample properties and heatingconditions on the pyrolysis characteristics. More specifi-cally, parametric analysis is focused on the role of modelassumptions [77,85,165,175,192], physical properties, suchas particle size, initial moisture content, shrinkage, trans-port parameters, and operating conditions [164,166–181].Simplified models, based on either volumetric reactionrates or a reaction located at an infinitely thin surface, arealso extensively used for numerical simulation. In the lattercase, attention is mainly focused [218,219] on mass losscharacteristics, as these are the key variables of interestwhen considering the coupling of particle and reactormodels. On the other hand, it cannot be expected that thissimplification can provide quantitatively accurate tempera-ture and concentration profiles. The main results arediscussed below for both conventional and fast pyrolysis.The dynamics of wood particle/log conversion are

examined by all the transport models cited above. Thequalitative features for one-dimensional systems remain thesame as already discussed in a previous review [17].Numerical simulation of two-dimensional particle dy-namics [171,183,184] shows that the propagation ofpressure and reaction fronts from the heat-exposed surfacetoward the inner core of the particle is highly affected bythe anisotropic structure of wood. These features clearlyappear from the example of process dynamics shown inFigs. 14A–D (temperature color maps), Fig. 15A–D(pressure color maps) and Fig. 16A–D (vector velocity

ARTICLE IN PRESS

Fig. 14. Color maps of temperature for a particle (half thickness equal to 5mm) exposed to an external temperature of 900K as simulated in [171] for

times of 31 s (A), 63 s (B), 93 s (C) and 125 s (D).


field) as simulated in [171] for the cross section of a particleexposed to a high-temperature environment. In this case, acomparison is also made [171] between the total heattransferred to the virgin solid (conduction minus convec-tion) along and across the grain. Despite the lower thermalconductivities, owing the concomitant slower convectivetransport (lower gas permeabilities), the largest contribu-tion is that across the solid grain. The role played byconvective heat transport becomes successively less im-portant as the particle size is increased. A comparisonbetween the two-dimensional and the one-dimensionalsimulations shows that the multi-dimensional structure ofthe reaction zone affects not only the details of sampleconversion dynamics, but also global parameters, such asconversion time and product distribution. On the average,the process is faster and the volatiles yields larger for thetwo-dimensional configuration. Further results of thecomparison between the two-dimensional model [171]and a one-dimensional pure heat conduction model arepresented in [222,223].

Conversion regimes are defined for particles exposed toradiative heating, based on either limit values for the totalsolid residue [77] or the Biot number [181]: the pure kineticregime (chemical time much longer than heat transfertimes), the thermally thin regime (internal heat transferrates much faster than external heat transfer rates), the

thermally thick regime (comparable values for the internaland external rates of heat transfer) and thermal waveregime (internal heat transfer rates much slower thanexternal heat transfer rates). In the presence of moisture[181], drying and pyrolysis occur in series (thermally thinregime), slightly overlap (thermally thick regime) or occursimultaneously through a large part of the conversionprocess (thermal wave regime). In general, the overlapbetween the two processes is enhanced as successivelyhigher initial moisture contents are considered [181,187].Given that the separation between char and virgin solid, onone side, and between drying and pyrolysis, on the other, isabsent or scarce, the initial moisture content and shrinkageessentially affect only the conversion time for the first tworegimes. The effects are comparatively much higher for thethermal wave regime, where the impact on the productyields is also important.These results are also confirmed by the simulations

obtained with an unreacted-shrinking-core model [219], asshown by Fig. 17A, where the thickness of the dry woodlayer is plotted as a function of time for several particlesizes and two initial moisture contents. In all cases, itinitially increases to attain a maximum, as consequence ofthe faster propagation speed of the evaporation front withrespect to the decomposition front. Then, as moistureevaporation terminates and the decomposition front moves

ARTICLE IN PRESS

Fig. 15. Color maps of gas pressure (atm) for a particle pyrolysis as simulated in [171] for the same conditions and times of Fig. 14.


toward the particle center, it rapidly goes to zero. The drywood layer also becomes successively thinner as the particlesize or the initial moisture content are increased, that is,moisture evaporation becomes more strongly coupled withwood decomposition. For the strongest coupling (initialmoisture contents of 50% and particle radius of 100mm),the dry wood layer only varies between about 5 and 20% ofthe initial particle radius (R0). This finding supports theassumption of a single infinitely thin front where bothmoisture evaporation and wood decomposition take place.This simplification is clearly not applicable for thinparticles (for instance, the case of R0 ¼ 20mm), wherethe maximum thickness of the dry wood layer may attainvalues of about 50% of the initial particle radius. For thickparticles (R0 ¼ 100mm), the assumption of a singleevaporation/decomposition front becomes progressivelyless accurate as the heating conditions are made less severe,as shown in Fig. 17B (the dry wood layer as a function oftime for several heat fluxes and two moisture levels).

Model results can also be used to construct maps[77,181], where the main features (regime) of the pyrolysisprocess can be read, for instance, on dependence of samplesize and final temperature. However, it should be notedthat they are valid only for the specific material properties,mode of external heating and reaction kinetics used in thenumerical simulations.

Extensive simulations are also used to evaluate theeffects of the initial moisture contents on the conventionalpyrolysis of wood based on descriptions of the evaporationprocess more accurate [16,167,176,183,184,197] than thefirst-order Arrhenius law or the infinitely thin evaporationfront at constant temperature examined above. In all casesit is found that moisture evaporation occurs at tempera-tures near to the normal boiling point of water. Watervapor is driven toward the cooler zones and condenses.This is a local process which takes place over a distance ofa few millimiters in the zones with temperatures around373K. For the conditions of interest in thermochemicalconversion (external temperatures well above the boilingpoint of water) it is found [197] that liquid phase processesare not controlling. Moreover, the thickness of theevaporation zone is relatively thin, gas overpressures inthe wet region are significant and gas phase convectivetransport is important. These results and the considerationthat the parameters for the drying process are determinedfor conditions quite different from those of interest inpractical systems [178] support the application of simplifiedmodels of moisture evaporation during wood pyrolysis.The role of some model assumptions is discussed for

conditions of internal heat transfer control using detailedtransport models. It is shown that, for permeability valuestypical of cellulosic materials, the assumption of constant

ARTICLE IN PRESS

Fig. 16. Constant contour levels of the decomposition rate (kg/m3s) from 1 and then with step 1 and vector velocity field for a particle pyrolysis as

simulated in [171] for the same conditions and times of Fig. 14. Maximum velocity (m/s): 0.126 (A), 0.095 (B), 0.08 (C) and 0.09 (D).


pressure does not affect significantly process predictions[165]. This finding is also confirmed by the negligiblesensitivity of model outputs to variations in the biomassand char permeabilities [169,176]. The assumption ofconstant properties does not modify the qualitative trendsof the conversion process but quantitative variations maybe high, especially for density and thermal conductivity[165,169]. The important role played by the last parameteris also confirmed by the analysis reported in [16,176] wheredifferent correlations are compared. The quasi-steadyassumption is not applicable [165] for the gas-phase speciesowing to secondary reaction activity, whereas it can bemade for the energy conservation equation.

Analyses carried out by different authors indicate that animportant role on the conversion time is played byconvective transport [85,174,177,180,189], although struc-tural failure and cracks may reduce the cooling effects as aresult of a lack of local thermal equilibrium between thesolid and the vapor phase. The role played by differentmodel assumptions is also examined for the unreacted-core-shrinking models. The model presented in [218]provides a careful analysis about the influences on thepredicted process characteristics of three assumptions oftenmade in this class of simplified models of wood pyrolysis:

(1) steady formulation of the enthalpy equation for thechar layer, (2) no convective heat transport across the charlayer, (3) uniform temperature of the unreacted core. Theremoval of assumption (1) does not result in any significantvariation in the output variables but the computationaltime may increase up to factors of three. The computa-tional time is practically unaffected by the assumption (2)when the quasi-steady formulation of the equations isadopted, though the process is made faster up to maximumfactors of about 20–40%. The assumption (3), whileweakly affecting the computational time and the qualita-tive trends of the solution, introduces highly quantitativedifferences which hinder its application for the predictionof real conversion systems. A comparison is also madebetween finite- and infinite-rate kinetics of the pyrolysisreaction [220]. Both models predict particle dynamicsqualitatively similar. Extensive simulations, carried outby varying the parameters of the kinetic models, theexternal heating conditions and the particle size, indicatethat the unknown parameter Tp (pyrolysis temperature),though comprised in the range of experimental values,should not be chosen coincident with the pyrolysistemperatures predicted by the finite-rate model. However,a range of Tp values can be determined which produces

ARTICLE IN PRESS

0 5000 10000 15000 20000 25000 30000

0.0

0.1

0.2

0.3

0.4

0.5

Qf=49 kW/m2

U=25%

U=50%

10080

60

40

R0=20mm

(Rp-R

u)/

R0

(Rp-R

u)/

R0

t [s]

0 5000 10000 15000 20000 25000 30000

0.0

0.1

0.2

0.3

0.4

R0=100mm

Qf=40 kW/m2

49

69

80 U=25%

U=50%

t [s]

Fig. 17. A–B predictions [219] of the dry wood layer, expressed as a

fraction of the initial radius, R0, of cylindrical particles (Rp and Ru are the

positions of the pyrolysis and drying fronts, respectively), as a function of

time for different radii (A, external heat flux, Qf, equal to 49 kW/m2) and

different external heat fluxes (B, particle radius, 100mm) for two initial

moisture contents (dry wood basis), U.


chief process characteristics, such as maximum devolatili-zation rate and conversion time, very close to those of thefinite-rate model. In this way, acceptable agreement is alsoobtained between predicted and measured integral anddifferential mass loss curves of thick wood exposed toradiative heating.

Physical properties influence both the dynamics and theglobal characteristics of the thermal degradation [165,169],especially when internal heat transfer rates are controlling.The conversion time becomes successively longer as thedensity increases and/or the thermal conductivities de-crease, but primary products change only slightly. On thecontrary, the activity of secondary reactions is highlyaffected by physical properties, through variations in thevolatile residence times, resulting from variations in thevelocity profiles. As expected, for thin samples the influenceof physical properties is completely negligible on the finalproduct yields, though the conversion time is still affected.The highest sensitivities, in relation to conversion time and

char yields, are computed for the solid density. Therefore,for primary degradation, this is the most importantvariable. Large variations in the conversion time are alsoassociated with the char thermal conductivity withsignificant changes in the yields of gas and tar, that is, onthe activity of secondary reactions.An extensive sensitivity analysis is also presented for the

unreacted-shrinking core models (kinetic constants, physi-cal property values and model parameters) for dry wood[218]. The most important variables are the activationenergy of the pyrolysis reaction, the thermal conductivitiesof wood and char and the thermal capacity of wood. Thesefindings are also confirmed in the presence of moisture[219]. As for the parameters specific to moist wood, thestrongest effects are caused by the initial moisture contentand the enthalpy of water evaporation on all the outputvariables, except for the temperature at the pyrolysis front,whose variations are comparatively smaller. The highsensitivity of the simulated process to the endothermicityof moisture evaporation also suggests that, in some cases,the energetics of the pyrolysis reactions may becomeimportant. In reality, they have not been describedaccurately, most likely as a consequence of the widelyvariable reaction enthalpies reported in literature and thedifficulty in separating the thermal effects of primary andsecondary decomposition. For instance, the high exother-micity associated with char formation at high pressures[80], has not yet been described by a transport model.Numerical simulations of the ablative pyrolysis of

cellulose pellets [168] investigate the process on dependenceof two key parameters, the plate temperature and theexternal heat transfer coefficient. The experimentallyobserved behavior is well predicted, that is, rather sharp,thin, thermal and reactive waves propagate through thecold, unreacted solid at comparable rates. The simulationof the detailed reaction zone shows the existence of tworeaction fronts, the first associated with the melting ofcellulose and the second with the vaporization of themolten intermediate. Thus, from a rigorous point of view,solid conversion in the ablation regime cannot beassimilated to a fusion-like process, though at the highexternal heat transfer rates, a tendency of the two fronts tocoincide is found. Furthermore, other important experi-mental findings are confirmed such as those related to thelow char yields, the linear dependence of the ablation rateon the plate temperature and the constant value of theproduct between the thickness of the molten layer and theablation rate.The main result of the experimental and modeling study

by Boutin et al. [193] is a confirmation of the existence of aliquid-phase intermediate product in the fast pyrolysis ofcellulose. Given the difficulties in carrying out measure-ments, the model is used only to evaluate the characteristictemperatures of the process. These correspond to theablation and the phase change (solid-phase cellulose andliquid-phase intermediate product) fronts and are in therange 810–1100K and 705–739K, respectively. The time

ARTICLE IN PRESS

700 800 900 1000 1100

625

650

675

700

725

750

0.25

0.25

Y=0.8

0.5

Tc [K

]

Tr [K]

0

30

60

90

120

150

180

hr

Tc

hr [K

/s]

Fig. 19. Predicted temperature, Tc, and heating rate, hr, at the particle

center when the total mass fraction is equal to 0.8 (solid lines), 0.5 (dashed

lines) and 0.25 (dashed-dotted lines) as functions of the reactor

temperature, Tr, for a particle half thickness of 1mm [180].


needed to achieve quasi-steady conditions, that is ablationwith a constant rate, is also predicted but quantitativecomparison with the experiments is not discussed. In fact,given the absence of char ablation, steady conditions couldnot be experimentally investigated.

Simulation results reported by Janse et al. [177] for theexperimental conditions typical of fluidized-bed reactorsindicate that intra-particle transport phenomena do notinfluence product yields from fast pyrolysis of wood.However, conversion times are significantly affected byparticle size and convective cooling due to the outflow ofvolatile products. These results are also confirmed by otherstudies [174,180,192]. It is also observed [192] that, whenvolatiles and water vapor are assumed to leave instanta-neously the particle, the qualitative features of thepredictions are preserved but the quantitative agreementwith the measured conversion time and shape of the weightloss curve is lost. However, because of uncertainty aboutthe applicability of the global devolatilization kinetics(representative of cellulose chemistry) to wood/biomassand the rough estimates of the physical properties used inthis model, this is not a conclusive remark.

In [174,180], an analysis is carried out of the actualparticle heating rate and conversion temperatures duringfluidized-bed pyrolysis, as several model and processparameters are varied. As expected, the heating ratebecomes successively slower as the particle size is increased,and/or successively higher conversion levels are consideredbecause of enhanced internal heat transfer resistance andconvective cooling caused by volatile release. For condi-tions of interest in fast pyrolysis, actual heating ratesexperienced by wood particles are roughly comprisedbetween 450 and 5K/s and are significantly lower thanusually assumed. The actual reaction temperatures arebetween 770 and 640K and again lower than the bedtemperature. Examples of the simulated values are reportedin Figs. 18 and 19.

0.0 1.5 3.0 4.5 6.0

575

625

675

725

775

τ[mm]

0.2

5

0.5

0.8

hr

Tc

0.5

0.25

Y=0.8

hr [K

/s]

Tc [

K]

0

130

260

390

520

650

Fig. 18. Predicted temperature, Tc, and heating rate, hr, at the wood

particle center when the total mass fraction is equal to 0.8 (solid lines), 0.5

(dashed lines) and 0.25 (dashed-dotted lines) as functions of the half-

thickness of the particle, t, for a reactor temperature of 800K [180].

The role played by the external heat transfer coefficienton the predictions of the pyrolysis characteristics isexamined [180] for: (a) infinitely fast external heat transferrates, (b) the model by Agarwal [211], (c) the Ranz–Marshall correlation [206] and (d) the whole bed correla-tion [206] (actual coefficient values for cases (b)–(d) arelisted in Section 3.3). From the physical point of view, asidefrom different conversion units, the actual external heattransfer rates experienced by the wood particle may bedifferent, owing to the flow conditions, the mixing degree,the particle properties (highly variable in the course of thereaction process). For instance, segregation may cause thatparticle conversion takes place above the sand bed, so thatexternal heating rates can be more adequately described bythe Ranz–Marshall correlation (convective heating of asingle particle), or by a whole bed coefficient (a bed ofparticles heated by a hot gas stream), instead of the moreaccurate Agarwal [211] model. As expected, qualitativetrends are the same in all cases, as shown in Fig. 20 for thefinal char yields and the conversion time. Predictions ofcase (a) are very close to those obtained for the case (b).Predictions for the models (b), (c) and (d) are highlydifferent. Assuming sizes of 1–2mm as representative offast pyrolysis conditions, the Agarwal model [211], which isa realistic approximation of the actual external heattransfer rate between a particle and a fluid bed underconditions of good mixing, provides conversion times of10–22 s and char yields of 11–13%. Departures from theseoptimal operating conditions may result in conversiontimes of 18–42 s and char yields of 13–16% (Ranz–Mar-shall correlation) or even of 34–62 s and char yields of15–17%. Hence, variations in the external heat transferrates, caused by poor solid mixing and/or segregation, arequantitatively important and affect both conversion timesand product yields. Miller and Bellan [167] also show thatsimplified descriptions of extra-particle processes introduceapproximations in the predictions of both pyrolysis rateand yields of liquid products.

ARTICLE IN PRESS

0.0 1.5 3.0 4.5 6.0

5

10

15

20

25

30

t c [

s]

YC [

wt

%]

0

320

640

960

1280

1600

τ[mm]

tc

YC

a

b

d

c

d

c

ba

Fig. 20. Char yields and conversion times as functions of the half-

thickness of the particle, t, as predicted [180] for a reactor temperature of

800K and different external heat transfer models: (a) infinitely fast

external heat transfer rate; (b) model by Agarwal [211]; (c) Ranz–Marshall

correlation [206]; (d) whole-bed correlation [206].


It is also found [180] that the effects of particle shrinkageand shape are scarcely important for small sized particlesand mainly appear as variations in the conversion times.On the other hand, it is observed [185] that mass loss curvesof particles of different shapes undergoing pyrolysis fallclose to each other if each test is made for the same particlevolume to surface area ratio. It is worth noting thatshrinkage is very important for the dynamics of thickparticles [166].

3.7. Experimental validation

Extensive experimental validation of both detailed andsimplified models is carried out only in a few cases. Forconventional pyrolysis, the comparison between modelpredictions and experimental measurements concerns theconversion time, weight loss characteristics and the thermalprofiles as, given external temperatures sufficiently high,the yields of products are only weakly affected by theconversion conditions. On the contrary, the focus is onproduct yields for fast pyrolysis processes.

Experiments carried out under well-controlled condi-tions (temperature profiles and yields of products) are usedto validate the model proposed in [16,176] for thick woodparticles. Measured and predicted temperature and con-version profiles are compared of spherical pine woodparticles (diameters between 2 and 5.6 cm) in a furnaceheated at 2 and 12K/min in [85] and 19-mm-thick slabsexposed to thermal radiation in [189]. Acceptable agree-ment is obtained for both cases and it is suggested thatstructural failure, possible mass transfer resistance andactivity of secondary reactions, not taken into account inthe mathematical descriptions, are among the causes of thedisagreement for thick spheres or high heating rates.

A detailed comparison between the measured andpredicted profiles of temperature, pressure and density isgiven in [183,184], limited to a single experimental test (awood block exposed on one side to an assigned radiative

heat flux). Quantitative agreement is good except for thepressure profiles, which are only qualitative, presumably asa consequence of structural changes not accounted for bythe model. In reality the one-dimensional version of themodel is used for an extensive analysis to evaluate the fireresistance defined as the time when the unexposed surfacetemperatures exceed 413K. Comparison with experimentsis then limited to this parameter.The validation considers temperature profiles and mass

loss curves in [166], using the experiments [224] for paralleland perpendicular grain heating of wood logs exposed tohigh radiative heat fluxes. In particular, it is observed thatheat transfer and related properties play a controlling roleand, to get quantitative agreement, shrinkage should betaken into account. In [178,179,181], only the averagecharring rate is considered, providing a comparisonbetween the model predictions and the measurements[225] made in a heat release rate calorimeter for externalheat fluxes between 15 and 55 kW/m2.Experimental validation of the model presented in [118]

makes use of the measured char yields for beech and poplarwood and olive husks [119]. It is interesting to observe that,in qualitative agreement with experimental observation, theuse of a three-component mechanism allows the tempera-ture overshoot to be predicted resulting from a largeproduction of char. The weight loss characteristicsmeasured in [62,85] are also considered. However, thoughthe predictions are qualitatively correct, quantitativeagreement is barely acceptable.In some cases, when simplified models are proposed, the

main objective of the simulation results is not to improvethe understanding of the conversion process. On thecontrary, the models are considered essentially as toolsfor engineering applications and the focus is on theexperimental validation, which is, however, limited to arestrict range of experimental conditions [185–188]. More-over, the simplification introduced in the description ofboth chemical and physical processes and the use ofuncertain kinetics and approximate values of the physicalproperties do not lead to a final conclusion about thegeneral validity of such models.Extensive experimental validation is carried out for the

unreacted-core-shrinking models developed in [218–220] inrelation to integral and differential data of weight loss fromthick cylinders of beech wood exposed to thermal radiation[27,28]. The models predict correctly both the qualitativeand the quantitative trends observed in the experiments.For dry particles (Figs. 21 and 22) [218], the agreement isgood for the integral data and, for the higher heat fluxes,also for the differential data. The fair agreement at lowheat fluxes obtained in the latter case can be probablyattributed to the wide extension of the reaction zone, whichis poorly described by the unreacted-core-shrinkingapproximation. For moist particles [219] (Figs. 23 and24), the model is again capable of predicting the mainqualitative and quantitative features of the weight losscurves.

ARTICLE IN PRESS

0 200 400 600 800 1000

0.0

0.2

0.4

0.6

0.8

1.0

404969

Qf=80 kW/m2

Y

t [s]

Fig. 21. Solid mass fractions, Y, (solid lines) as functions of time as

predicted by the unreacted-core-shrinking model [218] for several external

heat fluxes, Qf. Experimental measurements (symbols) derived from [28]

are also reported.

0 200 400 600 800 10000

1

2

3

4

40

49

69

Qf=80 kW/m2

-dY

/dt

x 1

03 [

s-1

]

t [s]

Fig. 22. Devolatilization rates, �dY/dt, (solid lines) as functions of time

as predicted by the unreacted-core-shrinking model [218] for several

external heat fluxes, Qf. Experimental measurements (symbols) derived

from [28] are also reported.

0 300 600 900 1200 1500 18000.0

0.2

0.4

0.6

0.8

1.0

U=0%

Qf=49 kW/m2

47352711

Y

t [s]

Fig. 23. Solid mass fractions, Y, (solid lines) as functions of time as

predicted by the unreacted-core-shrinking model [219] for several initial

moisture contents (dry wood basis), U, and an external heat flux, Qf, of

49 kW/m2. Experimental measurements (symbols) derived from [27] are

also reported.

0 300 600 900 1200 1500 1800

0.0

0.5

1.0

1.5

2.0

47

27

U=0%

Qf=49 kW/m2

-dY

/dt

x 1

03 [

s-1

]

t [s]

Fig. 24. Devolatilization rates, �dY/dt, (solid lines) as functions of time

as predicted by the unreacted-core-shrinking model [219] for several initial

moisture contents (dry wood basis), U, and an external heat flux, Qf, of

49 kW/m2. Experimental measurements (symbols) derived from [27] are

also reported.


For fast pyrolysis, extensive validation is made inrelation to the product yields for both cellulose [77,174]and wood [180]. The models of particles conversion in afluidized bed, incorporating the kinetics of primary (Brad-bury et al. [91] for cellulose and by Di Blasi and Branca [99]for wood) and secondary (essentially Liden et al. [127])reactions, predict with acceptable accuracy the productyields on dependence of the bed temperature and gas flowrate, with reference to experimental data obtained forcellulose and hardwood species [18,19,21,26]. Examples areshown for wood in Figs. 25–28. For reactor temperaturesbelow 800K and apparent residence times of volatilestypically achieved in fast pyrolysis devices (0.5 s), productyields are determined by single-particle behavior. Hence,given the small sizes, intra-particle degradation of tarryspecies is negligible. A critical parameter is the conversion

time which should be at least equal to the residence time ofparticles within the hot reactor environment. Indeed,before all the pyrolysis products are swept from thereactors to cyclones, where a large part of char is separatedfrom gas and vapors, it is highly desirable to attain thehighest conversion of wood to volatiles. It is understoodthat this may be problematic at very low temperatures(below 700K) which, however, are not of interest inpractical applications. For reactor temperatures above800K and the small-sized particles of fast pyrolysistechnologies, the product yields are essentially determinedby the extra-particle activity of tar degradation reactionseven for the short residence times of fast pyrolysis units.Estimations of chemical kinetics derived from literaturecorrectly predict the magnitude of the reaction rates to be

ARTICLE IN PRESS

600 700 800 900 1000 1100

0

10

20

30

40

50

Ref.

[18]

[19]

[21]

[26]

YC,

[wt%

]

Tr [K]

Fig. 26. Char yields, as predicted [180] for wood pyrolysis in a fluidized

bed reactor, as functions of the reactor temperature, for complete

conversion (solid line) and a particle residence time of 60 s (dotted line).

Experimental data (symbols) are also included for comparison purposes.

600 700 800 900 1000 1100

0

15

30

45

60

75

90

Ref.

d

cb

a[18]

[19]

[21]

[26]

YL [

wt%

]

Tr [K]

Fig. 27. Liquid yields (lines), as predicted [180] for wood pyrolysis (6mm

thick particles) in a fluidized bed reactor, as functions of the reactor

temperature, for different volatile residence times, tr, and tar cracking

kinetics: (a) tr ¼ 0.44 s and [127], (b) tr ¼ 0.154 s and [127], (c) tr ¼ 0.44 s

and [128], (d) tr ¼ 0 and [127]. Experimental data (symbols) are also

included for comparison purposes.

0 100 200 300 400

0

20

40

60

80

100Ref.Ref.

YC

YG

YL

[128] [126]

[127] [120]

YC, Y

G, Y

L [w

t%]

tr [ms]

Fig. 25. Gas, liquid and char yields (lines) as functions of the (extra-

particle) residence time of volatiles as predicted [180] for a particle half

thickness of 0.1mm and a bed temperature of 973K (kinetics of tar

cracking from [120,126–128]). Experimental data (symbols) from [21]

(maple wood, entrained-bed reactor) are also included for comparison

purposes.


used for optimization and scaling purposes. However, asdiscussed in [222,223], none of the mechanisms of biomasspyrolysis currently available can predict the quality of theliquid products. Moreover, poor predictions are alsoshown of the yields of products for a laboratory-scalefluidized bed reactor batch-fed with different biomass fuels,which put into evidence the problematic aspects of thekinetic mechanisms already illustrated in Section 2.2.

3.8. Empirical correlations and apparent kinetics

The most simplified formulation of wood pyrolysis(devolatilization) models consists of empirical correlationsfor the conversion time [190,204,205,226–228], similar to

those proposed for coal pyrolysis [203,229,230]: tv ¼ Acdn,

where Ac and n are empirical parameters and d acharacteristic particle size (the diameter for sphericalshapes). The values of n are comprised in the range 1–2with variations caused by the different fuel, technique ofmeasurement and definition of devolatilization time. It isknown that the use of simple thermal theories [231] appliedto combustible spheres exposed in a hot gas environment,leads to devolatilization times proportional to either d ord2, depending on external or internal heat transfer control.The dependence of tv on other parameters, such as bedtemperature, coal rank, oxygen concentration, etc., isusually incorporated in the parameter Ac. In particular, atreatment of the devolatilization times by regressionanalysis, assuming a power-law function of the operatingvariables, results in an exponent for the bed temperaturevarying from �1.8 to �3 for different coal varieties[203,229,230]. Also, an Arrhenius temperature dependenceis postulated, as it fits equally well the experimental datawith activation temperatures varying in the range1122–3396K: tv ¼ aeb=T rdn.The thermal history undergone by pre-dried cylindrical

beech wood particles, injected in a sand bed fluidized bynitrogen, for conditions typical of fast pyrolysis (particlediameter d ¼ 2–10mm and bed temperature Tr ¼ 712–1107K) is reported in [228]. The analysis allows the particleheating rates, reaction temperatures and conversion timesto be evaluated. In qualitative agreement with coal particleanalyses, the latter is well predicted by an empirical power-law relation: tv ¼ 0.8e1525/Trd1.2s, over the entire range ofexperimental conditions examined, as shown in Fig. 29.The analysis of char particles also reveals significantshrinkage and structural failure.A power-law correlation between devolatilization time

and particle size is also proposed by de Diego et al.

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600 700 800 900 1000 11000

15

30

45

60

75

90

Ref.

d

cb

a

[18]

[19]

[21]

[26]

YG

[w

t%]

Tr [K]

Fig. 28. Gas yields (lines) as functions of the reactor temperature as

predicted [180] for wood pyrolysis (6mm thick particles) in a fluidized bed

reactor, as functions of the reactor temperature, for different volatile

residence times, tr, and tar cracking kinetics: (a) tr ¼ 0.44 s and [127], (b)

tr ¼ 0.154 s and [127], (c) tr ¼ 0.44 s and [128], (d) tr ¼ 0 and [127].

Experimental data (symbols) are also included for comparison purposes.

1 3 5 7 9 110

20

40

60

80

100

807

Tr=1107K

t v [s]

d [mm]

625 725 825 925 1025 1125

Tr [K]

4

d=8mm

Fig. 29. Devolatilization times, tv, as functions of the particle diameter

(reactor temperatures of 807 and 1107K) and the bed temperature

(particle diameters of 4 and 8mm) as measured (symbols) and predicted

(solid lines) by an empirical correlation for beech wood particles injected

in a hot fluidized bed [228].


[204,226,227] for higher temperatures (923–1223K), in theview of applications in gasification and combustionprocesses. Experiments are carried out for pine woodparticles of different shapes and initial moisture contentsup to 50%. Particle characteristics, in a fluidized bedreactor, are evaluated using continuous measurements ofCO2 and O2 concentrations, obtained after the completecombustion of volatiles (the presence of oxygen is notinfluent on the devolatilization times) and visual observa-tion of volatile species burning. The correlations are

extended to include the effects of particle shape, byreplacing the particle diameter with the equivalent particlediameter multiplied by the shape factor, and moistureevaporation: tv ¼ a(deqF)

n, where deq is the diameter of asphere having the same volume as the particle and F theratio between the surface area of the equivalent sphere andthe initial surface area of the particle. The parameters a andn are reported to be functions of temperature (n comprisedbetween 1.5 and 1.7 for deq between 7–39mm and Fbetween 0.4 and 0.75). In this way, non-spherical woodparticles can be modeled as spherical particles by means ofthe equivalent diameter and the shape-factor, whichextends the applicability of models developed for sphericalparticles. A further correlation is also developed describinga linear dependence of the particle devolatilization time onthe initial moisture content.In some cases, highly simplified descriptions of the

transport phenomena are coupled with a one-step reaction,with the aim of evaluating the kinetic parameters by curvefitting of selected variables for a set of experimentalconditions, such as in [194,205,226]. As pointed in [226],the important role played by physical processes (intra-particle processes, external heat transfer rate, etc.) andtheir highly simplified description produce parametervalues representative of apparent kinetics, whose applic-ability is limited to empirical design rules.Mass loss curves of different amounts (1–800mg) of

birch or pine wood particles obtained for reactor tempera-tures in the range 588–1029K are presented in [194]. Theyare interpreted by a simple one-step devolatilizationreaction assuming isothermal conditions, although theauthors note that the actual reaction temperatures maybe significantly different from the external (reactor)temperatures. The Arrhenius plot evidences two differentreaction zones separated by a temperature of 643K. Thefirst (low temperature) zone corresponds to A ¼ 1.19�1012 s�1 and E ¼ 177 kJ/mol, whereas the second zone isdescribed by A ¼ 3 s�1 and E ¼ 31 kJ/mol, a clear indica-tion of internal/external heat transfer control.De Diego et al. [226] propose an unsteady and one-

dimensional equation of heat conduction coupled with aDAE model to describe the devolatilization of woodparticles in a fluidized-bed reactor (temperatures of923–1223K and particle of different shapes with maximumsizes of about 15–20mm). Moisture evaporation isassumed to occur at an infinitely thin front at a constanttemperature. Also, the particle volume is assumed to beproportional to the amount of volatiles released. Theexternal heat transfer coefficient takes into account bothconvective and radiative heat transfer according tocorrelations specifically developed for fluidized-bed reac-tors. Possible numerical complications associated with thetreatment of an evaporation front are not discussed. Theparameters of the kinetic equation are obtained by curvefitting of the release rate curves of combustible species(A ¼ 1013 s�1 and E ¼ 20076 kJ/mol and s ¼ 2573 kJ).The authors do not exclude possible compensation effects


and point out that the set of values determined are typicalof fluidized bed combustion.

Apparent kinetics for a global reaction are alsodetermined in [205] for the pyrolysis of beech wood spheres(diameters between 5 and 20mm) in a fluidized-bedreactors (temperatures in the range 833–913K). Yields ofchar and gaseous species are determined and the on-lineanalysis of gaseous species is used to evaluate thedevolatilization time. The model consists of a one-dimensional and unsteady heat conduction equation withboundary conditions accounting for convective and radia-tive heating. An additional term describes convectivecooling assuming that volatiles have to be heated fromthe average particle temperature to the bed temperature.Physical properties are assumed to remain constant. Solidconversion obeys a first-order law where the kineticconstant is obtained by volume averaging local valuesalong the particle radius and incorporating the usualArrhenius law dependence on temperature (negligiblereaction energetics). The pre-exponential factor is assumedto present a power-law dependence on the initial particlediameter. Moreover, the particle shrinks linearly withconversion (up to 50% of the initial volume). The modelparameters are estimated using a two-step procedure.Experimental measurements for thin particles give theparameters A and E, whereas results obtained for thickparticles give the exponent. The validity of the estimatedset of values (A ¼ 1.5 s�1, E ¼ 20.576 kJ/mol and n ¼ 0.7)is then checked against measurements carried out forintermediate particle sizes. The very low activation energyconfirms that apparent kinetics are determined.


Comprehensive models of single-particle dynamics areavailable where the description of physical processes iscoupled with primary and secondary reaction kinetics.Heat, mass and momentum transport are taken intoaccount across a medium where volume and other physicalproperties vary with the conversion degree and/or tem-perature. In a few cases, multi-dimensional equations areconsidered to model anisotropy effects. External heattransfer is approximated in terms of global coefficients,specifically determined for different reactor configurations.Extra-particle processes are either disregarded or subjectedto high simplifications. Numerical simulations are used tounderstand the role played by model assumptions, sampleproperties and operating conditions on the pyrolysischaracteristics and to clarify the interactions betweenphysical and chemical processes. In particular, the actualtemperatures and heating rates experienced by particles areevaluated, when exposed to conditions typical of practicalconversion systems. Among the physical properties, densityand thermal conductivity cause the highest sensitivity ofthe model predictions. The effects of particle shrinkage andshape become successively more important as the particlesize become larger and mainly appear as variations in the

conversion times. For fluidized-bed conditions, variationsin the external heat transfer rates, caused by poor solidmixing and/or segregation, may become quantitativelyhigh and affect both conversion times and product yields.Structural failure and primary fragmentation have not yetbeen described in detail. In general, the limits of detailedmodels lie in the use of questionable kinetics or arbitraryvalues of physical properties and, in numerous cases, in thescarce experimental validation. In other words, only abetter knowledge about decomposition kinetics and phy-sical properties of the biomass fuels can improve thepredictive capabilities of transport models.Numerical simulations of particle dynamics confirm that

only a few of the one-stage mechanisms can predict thecorrect trends of the yields of char, liquid and gas on theoperating conditions. For conditions of interest in flui-dized-bed reactors, experimental verification of the modelsis focused only on product distribution on dependence ofthe reaction conditions, whereas scarce attention is given toconversion times and global decomposition rate. This maybe due to the lack of measurements on single-particlecharacteristics. Systematic measurements of conversiontimes of single wood and biomass particles in fluidized-bed reactors could be useful for improving the operatingconditions of industrial plants and the validation oftransport models currently available. Conversely, for theconditions of interest in wood log boilers, essentially theconversion times and the global mass loss characteristicsare considered in a few cases. Hence, extensive modelvalidation in relation to both product yields and conver-sion times is required.Detailed modeling of single-particle dynamics can still

contribute to improve the understanding of pyrolysisfundamentals, by developing additional submodels in rela-tion to (1) absence of thermal equilibrium between thedegrading solid and the volatile products, (2) mediumanisotropy, (3) structural changes, (4) dependence of physicalproperties on conversion conditions and biomass species, (5)reaction energetics, (6) more accurate kinetics for the globalcharacteristics (conversion time and yields of products) ofbiomass pyrolysis, (7) detailed mechanisms of primary andsecondary degradation. Furthermore, although severaltreatments are available for moisture evaporation in wood,they are scarcely used. In reality, even small moisturecontents exert a significant impact on pyrolysis. Simplifiedmodels can be applied as for the high temperatures of interestin thermochemical conversion of biomass, transport phe-nomena of the liquid phase do not play a controlling role.It should be pointed out that significant effort, in both

theoretical and experimental research activities, is stillrequired to formulate and validate truly comprehensivemodels. Indeed, not only additional processes should bemathematically described but new experimental systemsand extensive measurements are needed to provide thebasis for process understanding and model formulationand to produce data to be used as inputs for numericalsimulation and comparison terms for model validation. It


is likely that such an objective cannot be accomplished overa short time. Therefore, simplified models are stillappealing and worth additional efforts for improvement.Furthermore, though the current detailed models ofbiomass particle dynamics have greatly contributed toprocess understanding, due to the significant computa-tional requirements, they are unsuitable for describingsingle-particle effects in reactor modeling. Indeed, toreduce the mathematical complexity of the problem, soliddevolatilization is usually described assuming infinitely fastreaction rates or external heat transfer control, whichintroduce severe inaccuracies in the predictions of im-portant design parameters. Hence, simplified descriptionsof single-particle pyrolysis accounting for spatial gradientsof temperature, once incorporated in reactor models, couldproduce a significant advancement with respect to thecurrent state of the art.

For this purpose, unreacted-core-shrinking models areavailable, where the char region is separated from the virginwood by an infinitely thin (surface) front, where pyrolysistakes place. It is shown that a quasi-steady char zonecombined with the use of integral solution methods for theheat conduction problem across the unreacted core areparticularly attractive because they transform from partial toordinary the differential equations of the model. Suchformulations include one-step finite-rate kinetics of woodpyrolysis, heat convection and conduction, different proper-ties for the unreacted solid and the char, moisture evapora-tion and the use of realistic input data. However, extensiveexperimental verification is made only for the conditionstypical of conventional pyrolysis, assuming that the yields ofthe three classes of pyrolysis products are assigned. Thus,before application for fast pyrolysis, the unreacted shrinkingcore models should be extended to include competitivereaction rates for primary product formation and secondaryreactions of tar vapors. The applicability should also bechecked of other simplified models, which still retain theassumption of volumetric reaction rates for the decomposi-tion process. Indeed, the few examples currently available areused only in the view of determining the apparent kinetics ofa global reaction, which are of small scientific relevance andlimited practical applicability.

Empirical correlations, which give the conversion time as afunction of the external temperature and the properties (size,moisture content, shape) of the biomass particle, can also beconsidered as simplified models. The work currently doneconcerns different types of woods. Further developmentsshould consider the enlargement of the range of experimentalconditions investigated, the type of measurements carried out(temperature profiles, rate of mass loss, product yields andcomposition) and the influence of the biomass type.

4. Models of pyrolysis reactors

Modeling chemical reactors, applied for biomass pyr-olysis, is the subject of only a very few papers. A rough

classification of the models currently available can be madebased on conventional or fast pyrolysis processes.

4.1. Fixed-bed reactors

The particle heating rates established in fixed-bed reactorsare usually slow, because of indirect heating. Therefore, thisconfiguration is suitable to carry out conventional pyrolysis ofbiomass. Models are proposed in [232–236]. In [234], apacked-bed of wood particles undergoing drying and pyrolysisis modeled as an ensemble of finite number of particles withspecific properties, but the assumption that the bed is acontinuum porous solid is generally made. In [232], anunsteady one-dimensional (along the radial co-ordinate)model is presented for a cylindrical fixed-bed, batch pyrolyzer,where convective transport is neglected. Decomposition ofcoconut shells is described by a deactivation model for theseparate formation of char and volatiles. The one-dimensionaland steady model of maize pyrolysis in a bench-scale rotatorykiln in semi-continuous operation, proposed in [233], takesinto account the difference between the temperatures of thetwo phases. Kinetics are described by a five-step reactionscheme, where the formal parameters are determined by theanalytical solution of the mass balance equations forexperiments carried out with both the rotatory kiln and athermogravimetric balance. The pyrolysis of sawdust in apacked bed of annular shape is modeled in [235] with a pureheat conduction equation (unsteady state and radial and axialcoordinates) combined with a first order global reaction andan empirical formula for the effective thermal conductivity.The use of empirical parameters in these models [232–236]highly limit their general validity. Thus, they are in realityempirical models capable of simulating only the specificmeasurements upon which they are based. The assumptionsmade highly simplify the solution procedure, but a realisticdescription of heat and mass transfer effects is not retained forreliable process design and development.The unsteady, two-dimensional model of a continuous

fixed-bed reactor (a horizontal cylindrical reactor continu-ously fed from the end by straw, while hot gas entersthrough holes distributed along the lateral surface),developed in [236], represents a significant advancementin the field of fixed-bed pyrolyzer modeling. The modelincludes the unsteady, two-dimensional conservation equa-tions of heat and mass transfer for the solid and the gasphase, the generalized Darcy law and a multi-componentmechanism for straw pyrolysis, previously determined[116]. The influences of gas temperature and gas to strawratio are investigated. It is found that the key parameter forhigh conversion of straw to volatile products and char isthe solid residence time. Predicted and measured conver-sion efficiencies, at a pilot scale, compare well.

4.2. Fast pyrolysis reactors

Among the fast pyrolysis technologies applied forwood/biomass conversion into liquid fuels, the bubbling


fluidized-bed reactor is extensively applied at both labora-tory and industrial scale. A major effort in modelingfluidized bed reactors arises from the description of thehydrodynamic behavior of the bubbling bed. Though, inprinciple, the motion of the fluid can be described by theNavier–Stokes equations and the motion of particle by theNewtonian equations, the huge number of particles doesnot make this approach computationally feasible. Theintroduction of a continuum mathematical description ofthe fluid-solid flow processes, based on spatial averagingtechniques, highly reduces the number of partial differ-ential equations. That is, the point variables, referred to theparticle size scale, are replaced by averaged variablesrepresentative of processes on a scale large than the particlesize but small compared to the reactor size. Usually, themathematical models are based on the conservationequations of mass, momentum and energy for the solidand the gas fluid which may foresee several components.Conservation equations are completed by appropriateinteraction terms representing the coupling between thetwo fluids. This approach provides detailed simulations ofthe fluid dynamics of the fluidized bed through the timeand space distribution of process variables. Hence, it isespecially suitable for the description of the influences ofthe reactor geometry (design) on the hydrodynamicbehavior. Applications in the field of biomass pyrolysisare reported in [237–240].

In [237], a single-particle model [118] is coupled with adetailed hydrodynamic model for the gas/particle mixturein a fluidized bed reactor. The latter model is based on athree-fluid description (sand, biomass, gas) where macro-scopic transport equations are derived from the kinetictheory of granular flows using inelastic sphere models, inthis way accounting for collisional transfer in high-densityregions. Separate transport equations are formulated fordifferent particle classes, allowing for the independentacceleration of particles in each class and the interactionbetween the size classes and the processes followingmomentum and energy exchange between the particlephases and the gas. The reactor model is used only for aparametric analysis about process variables in relation toefficiency in bio-oil production and potential for scale-up.

The platform provided by the software CFX is used atAston University [238–240] to model the hydro-dynamicbehavior of fluidized bed pyrolysis reactors. The bulk ofthe particles in the fluidized bed is modeled using thestandard Eulerian-Eulerian two-phase model. According tothis approach the particles are not single entities but alocally averaged second phase. With reference to biomasspyrolysis, fuel, char and any solid phase reaction inter-mediate, such as active cellulose, are implemented asscalars within the solid phase. Heat transfer and particleentrainment are also taken into account. The analysis isfocused on the validation of the bed hydrodynamics forseveral geometric configurations using cold flow experi-ments with reactor diameters up to 0.5m. Interactionsbetween bed hydrodynamics and chemical reactions are

significant. In particular, the location of vapor release andthe flow field highly affect the vapor residence time and, inthis way, the bio-oil yield.Miller and Bellan [241] propose a model for the vortex

pyrolysis reactor developed at NREL (Colorado), whichuses the concept of ablative (contact) pyrolysis betweenparticle and hot reactor walls. The mathematical descrip-tion is based on submodels for the single-particle pyrolysis,a turbulent reactor flow and the test particles trajectories.The intra-particle processes are described using a modelpreviously developed [118] and modified to take intoaccount fragmentation (mechanical break-up of the charlayer formed during ablative pyrolysis and absence of amolten phase intermediate product class). The individualparticle trajectories are modeled assuming a prolatespheroid particle drag coefficient and contact friction withthe reactor wall. Numerical simulations indicate thatsingle-particle dynamics are de-coupled from the temporaldisturbances of the temperature and pressure of theexternal flow. Thus, time-averaged values of the flowproperties are used for the particle boundary conditions.The reactor flow is described by a compressible form of thefull Reynolds stress transport model for swirling axisym-metric flow. Despite the detailed modeling and the quitehigh number of simulations presented, the results are stillonly qualitative because, owing to the lack of experimentaldata, the model is not experimentally validated.Engineering models, based on highly simplified descrip-

tion of the fluid dynamics, are also proposed for some fastpyrolysis units, such as rotating cone reactor [242] andcirculating fluidized-bed reactor [243,244]. It should benoted that, although the specific applications of engineer-ing models for fluidized-bed reactors for biomass pyrolysisare practically inexistent, a huge literature on such a topicis available, starting from a classical textbook [206] andnumerous references for gasification and combustion ofcoal (for instance, among the most recent publications, see[245,246]).The rotating cone reactor model [242] includes the

description of the particle flow behavior, the particleconversion and the gas-phase cracking of tar vapors. Theparticle flow model, based on the Newton second law ofmotion takes into account bouncing flow (similar to aseries of particles-on-wall collisions and free flights of theparticle through the gas phase), sliding flow (particles slideover the cone wall) and sticking particles (particles stickingto the cone walls). The particle model disregards thepresence of spatial gradients and secondary reactionactivity. Primary decomposition is described by theShafizadeh and Chin mechanism [88] with the kineticconstants estimated in [97] and an assigned yield of charalways corresponding to 10% of the initial wood dry mass.The external heat transfer rate is expressed by correlationsdepending on the type of particle flow. The reactor volumeand the dead volume of the surrounding oven, which servesas a collection chamber for ash, char and unconvertedwood, are the sites for the secondary reactions. They are


modeled as continuous stirred tank reactors under steadystate conditions, where the inlet volumetric flow rate isproduced from primary decomposition. Simulations, madeupon selection of the particle flow type and using smallsized particles, indicate that the gas-phase kinetics and theresidence time of volatile pyrolysis products (the latter isdetermined by the reactor volume and the wood fed rate)are the key parameters of the process.

A single-particle model, previously developed [244], ismodified to express variable boundary conditions repre-sentative of a circulating fluidized bed and is combinedwith additional equations for the particle motion and thegas flow in the model proposed in [243]. Simple steady andone-dimensional (along the reactor axis) mass balances useempirical correlations for the drag coefficient, the apparentparticle density and the bed voidage. Variables of relevantimportance (profiles of conversion, solid and gas velocity,etc.) are predicted, but experimental validation is againlacking.


Contrary to single-particle processes, models of pyrolysisreactors are only a few but both fixed-bed reactors, appliedfor conventional pyrolysis, and fast pyrolysis units(fluidized-bed and rotating cone reactors) have beenconsidered. The fixed-bed models are usually based onthe assumption that the bed is a continuum porous solid,where the flow velocity is constant or, in the most advancedformulation, obtained from the generalized Darcy law.With the exception of [236], where a novel reactor for theconversion of straw and herbaceous biomass is modeled,these are highly simplified models presumably not validoutside the narrow range of experimental conditions usedfor the determination of the input and validation data.Therefore, design and operation of fixed-bed reactors arestill, for a large part, empirical. Only the development andvalidation of more advanced models can provide a reliablebasis for technology optimization and scale-up. Moreaccurate models should be developed for the particle flowand gas, which are affected by the non-isotropic andcontinuously changing properties of the bed. Indeed,correlations/models are also needed for the properties ofthe bed, such as porosity, density, effective thermalconductivity, solid-gas heat transfer coefficients, etc., andtheir dependence on the operating conditions and/orconversion degree. As already observed for particleprocesses, detailed experimental data should be producedor made public for the experimental validation of modelsfor the different reactor categories. In particular, informa-tion should be produced not simply on the yields of thethree classes of pyrolysis products but also on conversiontime, temperatures of solid and volatile species, composi-tion of the gas and tar vapors and pressure, and reactorcharacteristics and operating conditions.

Models for entrained- and fluidized-bed reactors havebeen proposed using the conservation equations of mass,

momentum and energy for the solid and the gas fluid,based on either proprietary or commercial CFD codes. Asthis approach is especially suitable for the description ofthe influences of the geometry (design) on the reactorhydrodynamics, the validation of the bed hydrodynamicsfor several geometric configurations using cold flowexperiments has been made. However, simulation resultsof biomass pyrolysis produced until now are again onlyqualitative. Indeed, further improvements should be madein the mathematical formulation of the problem to couplethe detailed description of the flow field with the chemistryand transport phenomena of the solid phase, also usingsimplified models of single-particle processes. Theseimprovements are not likely to be achieved shortly and,in any case, adequate experimental validation will berequired before applications for process scaling andoptimization. For this reason, the development of engi-neering models is still of great importance. However, thesemodels, usually based on simplified description of the fluiddynamics, have been given scarce consideration.In particular, consideration should be given to the two-

phase theory of fluidization for fluidized-bed reactors. Thistreatment, widely used in chemical reaction engineering,assumes that the fluidized bed consists of a solid-freebubble phase and a solids-laden emulsion (dense) phase.Different treatments for the bubble (well mixed, in plugflow or in dispersed plug flow) and the emulsion (wellmixed or with a dispersion term in the mass balance) phaseand also more complex three-phase or bubble-assemblagemodels are available, where the fluid-dynamic processes areapproximated by means of empirical correlations. Thissimplification permit accurate descriptions of particleprocesses and their interaction with the bed, as shown bythe numerous publications dealing with coal conversion,and can be used as an intermediate research stage betweensingle particle and CFD reactor models.

Acknowledgments

This review is part of the activities carried out in theframework of the European network PyNe (PyrolysisNetwork), the partial support of which is gratefullyacknowledged. CDB also thanks all the PyNe members,for the numerous and fruitful discussions on biomasspyrolysis, and M.J. Antal and two other anonymousreviewers for their constructive comments on this work.

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Documents

Modeling chemical and physical processes of wood and biomass