biomass pyrolysis kinetics. a comparative critical review with relevant

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Journal of Analytical and Applied Pyrolysis 91 (2011) 1–33

Contents lists available at ScienceDirect

Journal of Analytical and Applied Pyrolysis

journa l homepage: www.e lsev ier .com/ locate / jaap

eview

iomass pyrolysis kinetics: A comparative critical review with relevantgricultural residue case studies

ohn E. Whitea,∗, W. James Catallob,1, Benjamin L. Legendrea

Audubon Sugar Institute, Louisiana State University AgCenter, 3845 Hwy 75, St. Gabriel, LA 70776, USALaboratory for Ecological Chemistry, Comparative Biomedical Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803, USA

r t i c l e i n f o

rticle history:eceived 21 March 2009ccepted 8 January 2011vailable online 14 January 2011

eywords:gricultural residuesiomassinetic models

a b s t r a c t

Biomass pyrolysis is a fundamental thermochemical conversion process that is of both industrial and eco-logical importance. From designing and operating industrial biomass conversion systems to modeling thespread of wildfires, an understanding of solid state pyrolysis kinetics is imperative. A critical review ofkinetic models and mathematical approximations currently employed in solid state thermal analysis isprovided. Isoconversional and model-fitting methods for estimating kinetic parameters are compara-tively evaluated. The thermal decomposition of biomass proceeds via a very complex set of competitiveand concurrent reactions and thus the exact mechanism for biomass pyrolysis remains a mystery. Thepernicious persistence of substantial variations in kinetic rate data for solids irrespective of the kinetic

inetic tripletutshellsyrolysis kineticsugarcane bagassehermal decomposition

model employed has exposed serious divisions within the thermal analysis community and also causedthe broader scientific and industrial community to question the relevancy and applicability of all kineticdata obtained from heterogeneous reactions. Many factors can influence the kinetic parameters, includ-ing process conditions, heat and mass transfer limitations, physical and chemical heterogeneity of thesample, and systematic errors. An analysis of thermal decomposition data obtained from two agriculturalresidues, nutshells and sugarcane bagasse, reveals the inherent difficulty and risks involved in modeling
heterogeneous reaction systems.
© 2011 Published by Elsevier B.V.

ontents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22. Fundamentals of thermal analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1. Concise history of thermal analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2. Experimental kinetic analysis techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.3. Arrhenius rate expression and the significance of the kinetic parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

3. Biomass pyrolysis kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.1. Kinetic expressions for biomass thermal decomposition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.2. Biomass pyrolysis kinetic models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.3. Multiple-step models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73.4. Isoconversional techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73.5. Comparative evaluation of integral and differential isoconversional techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.6. Other kinetic models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4. Analysis of kinetic data obtained from various nutshells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
5. Biomass thermal decomposition mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6. Influence of experimental conditions on biomass reaction kinetics . . . . . . .
6.1. Heat and mass transport models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6.2. Heating rate and particle size effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

∗ Corresponding author. Present address: USDA, ARS, Pacific Basin Agricultural Researcax: +1 808 959 5470.

E-mail address: [email protected] (J.E. White).1 Deceased.

165-2370/$ – see front matter. © 2011 Published by Elsevier B.V.oi:10.1016/j.jaap.2011.01.004

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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

h Center, 64 Nowelo St., Hilo, HI 96720, USA. Tel.: +1 808 932 2177;

dx.doi.org/10.1016/j.jaap.2011.01.004

http://www.sciencedirect.com/science/journal/01652370

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mailto:[email protected]

dx.doi.org/10.1016/j.jaap.2011.01.004

2 J.E. White et al. / Journal of Analytical and Applied Pyrolysis 91 (2011) 1–33

6.3. Significance of surrounding atmosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166.4. Catalytic effect of inorganic material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

7. Variations in kinetic data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177.1. Systematic errors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177.2. Temperature gradients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177.3. Temperature lag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177.4. Kinetic compensation effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

8. Sugarcane bagasse case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188.1. Sugarcane bagasse – background and properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188.2. Review of sugarcane bagasse pyrolysis studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208.3. Analysis of published kinetic data for sugarcane bagasse pyrolysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208.4. Suggestions for mitigating inconsistencies in kinetic triplet data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238.5. Evaluation of kinetic compensation effect for sugarcane bagasse data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

9. Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2510. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

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References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. Introduction

Increased volatility in traditional fossil fuel markets has revivednterest in the production of alternative fuels from biomass. Renew-ble energy derived from biomass reduces reliance on fossil fuelsnd it does not add new carbon dioxide to the atmosphere [1].yrolysis is a fundamental thermochemical conversion process thatan be used to transform biomass directly into gaseous and liquiduels. Pyrolysis is also an important step in combustion and gasifica-ion processes. In this regard, a thorough understanding of pyrolysisinetics is vital to the assessment of items including the feasibility,esign, and scaling of industrial biomass conversion applications2,3]. An awareness of pyrolysis kinetics can also be useful in mod-ling the propagation of wildfires [4], which ravage 550 million haorldwide annually [5].

Vegetative biomass, also known as phytomass, is comprisedrimarily of cellulose, hemicellulose, and lignin along with lessermounts of extractives (e.g., terpenes, tannins, fatty acids, oils, andesins), moisture, and mineral matter [6]. Cellulose is the mostbundant organic compound in nature, comprising up to 50 wt%f dry biomass [7,8]. It is a linear polysaccharide formed fromepetitive �-(1,4)-glycosidic linkage of d-glucopyranose units. Cel-ulose from different biomass types is chemically indistinguishablexcept for its degree of polymerization (DP), which can rangerom 500 to 10,000 depending on the type of biomass [9]. Strongydrogen bonding between the straight chains imparts a crys-alline structure to the cellulose, making it highly imperviouso dissolution and hydrolysis using common chemical reagents9,10]. Unlike cellulose, the composition of hemicelluloses andignin is heterogeneous and can vary greatly even within a giveniomass species. Hemicelluloses have an amorphous structurend display branching in their polymer chains. Several sugaronomers are contained in hemicellulose, including xylose, man-

ose, galactose, and arabinose. Lignin accounts for almost 30%f terrestrial organic carbon and provides the rigidity and struc-ural framework for plants [11]. The lignin biopolymer consistsf a complex network of cross-linked aromatic molecules, whicherves to inhibit the absorption of water through cell walls. Thetructure and chemical composition of lignin are determined byhe type and age of the plant from which the lignin is isolated12]. Studies addressing the transformation kinetics of biomass

ust account for the intrinsically heterogeneous nature of the sub-trate. In this regard, the frequent practice of typifying the overall
inetic behavior of a particular biomass substrate based on theinetic results from just a single benchmark component is trou-lesome.
Pyrolysis of solid state materials, such as biomass, can be classi-ed as a heterogeneous chemical reaction. The reaction dynamics

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

and chemical kinetics of heterogeneous processes can be affectedby three key elements [13], i.e., the breakage and redistribution ofchemical bonds, changing reaction geometry, and the interfacialdiffusion of reactants and products. Unlike homogeneous reac-tions, concentration is an inconsequential parameter that cannotbe used to monitor the progress of heterogeneous reaction kinet-ics because it can vary spatially [13–16]. Heterogeneous reactionsusually involve a superposition of several elementary processessuch as nucleation, adsorption, desorption, interfacial reaction, andsurface/bulk diffusion, each of which may become rate-limitingdepending on the experimental conditions. The initiation step insolid state decomposition reactions frequently involves a “randomwalk” of defects and vacancies within the crystal lattice which givesrise to nucleation growth [17]. Equally significant is the conceptof a “reaction interface”, which is defined as the boundary sur-face between the reactant and the product. This representation hasbeen used extensively to model the kinetics of solid state reactions[18].

The only extant review of sugarcane pyrolysis was publishedmore than thirty years ago [19]. Solid state kinetic theory was ina state of considerable disarray during this era and decompositionmechanisms for cellulose pyrolysis were in their formative stages.Understanding of the reaction dynamics involved in pyrolytic pro-cesses has evolved substantially since then, and the correspondingkinetic schemes have been refined to encompass the entire ligno-cellulosic substrate. In light of this, the original intent of this paperwas to provide a succinct overview of modern biomass pyrolysiskinetics supported by an analytical survey of rate data obtainedfrom a particular biomass species (i.e., sugarcane bagasse). How-ever, considering the uncertainty and flux that continue to envelopthe field of thermal analysis, it was decided that an experimentalcase study isolated from a contextual discourse on the current stateof affairs in heterogeneous kinetics might only add to the existingturmoil. Therefore, the objective of this critical review is to not onlyexpose the nature and origin of the rampant inconsistencies in pub-lished biomass kinetic data but also emphasize the urgent need todispense with the “. . .hundreds of cute and clever mathematicalmanipulations [that] were performed on variations of three (highlystylized) equations” [i.e., the degree of conversion rate equation(Eq. (2)), the Arrhenius expression (Eq. (1)), and the temperatureintegral (Eq. (11))], and instead focus on the reexamination of fun-damental solid state reaction kinetic theory as it applies to biomasspyrolysis. After a précis of experimental kinetic techniques and fun-
damental rate equations, various biomass degradation models andprocess parameters that impact rates of biomass degradation areexamined. This treatment is then followed by an analytical evalu-ation of experimental studies on the kinetics of sugarcane bagassepyrolysis.

J.E. White et al. / Journal of Analytical and

Nomenclature

A frequency factor (s−1)a,b correlation parameters in the linear compensation

effect relationC constant of integrationEa apparent activation energy (kJ mol−1)f(˛) reaction model (function expressing the depen-

dence of the reaction rate on the conversion)g(˛) integrated reaction modelI(Ea,T˛) equivalent function for p(x)k reaction rate constant (s−1)k(T) temperature-dependent rate constant (s−1)n reaction orderp(x) temperature integralr reaction initiation parameterR universal gas constant (8.3144 × 10−3 kJ mol−1 K−1)t time (s)T absolute temperature (K)Vi cumulative mass of released volatiles corresponding

to fraction i through time tVi* effective volatile content for fraction iv volatile mass at time tw substrate mass at time tx equivalent to Ea/RTy unreacted fraction of substratez activity of solid

Greek letters˛ extent of reaction (degree of conversion)ˇ heating rate (◦C s−1)� minimization function� deactivation rate constant

Superscriptsc, d, e adjustable reaction exponents in the SB equationn reaction orderq number of experimentss adjustable nucleation parameter used in the modi-

fied Prout–Tompkins model

Subscripts0 initiala apparentf finaliso isokinetici volatile fractionj ordinal number of experiment

2

2

meciar1l

k ordinal number of experimentm maximum

. Fundamentals of thermal analysis

.1. Concise history of thermal analysis

The storied field of thermal analysis is no stranger to disagree-ent and uncertainty. Thus it should come as no surprise that

ven the origins of modern thermal analysis remain blurred inontroversy. Although Le Chatelier is frequently credited with hav-
ng initiated thermal analysis in 1887 [20–23], Jakob Rudberg hadlready employed a crude form of thermal analysis in 1829 to obtainate data for various metals and their alloys [22], and as early as780, Bryan Higgins had observed the effect of heating chalk and
imestone at various temperatures [24]. Likewise, dissent has pre-

Applied Pyrolysis 91 (2011) 1–33 3

vented the adoption of a mutually acceptable definition for thermalanalysis methods. Thermal analysis has been formally defined bythe International Confederation for Thermal Analysis and Calorime-try (ICTAC) as “a group of techniques in which a property of thesample is monitored against time or temperature while the tem-perature of the sample, in a specified atmosphere, is programmed”[25]. The ICTAC definition has been criticized [26] for being tooconstrictive (i.e., “monitoring” does not adequately reflect the ele-ments of evaluation and experimental investigation that comprise“thermal analysis”) or immaterial (i.e., a “specified atmosphere” isa unique, local operational factor that is inappropriate for a globaldefinition). It has been proposed that the essence of thermal analy-sis can be summarized “as the measurement of a change in a sampleproperty, which is the result of an imposed temperature alteration”[26].

2.2. Experimental kinetic analysis techniques

Kinetic data from solid state pyrolysis reactions has tradi-tionally been obtained using discrete isothermal methods ofanalysis. Isothermal kinetic data usually is acquired by perform-ing several experiments under isothermal conditions at differenttemperatures. Additionally, isothermal experiments still possess anelement of non-isothermal behavior during the initial heating rampto the desired temperature. Interest in isothermal methods, how-ever, has gradually waned because they are considered toilsome[27]. Conversely, dynamic methods, which are performed undernon-isothermal conditions, have attracted much appeal given theirability to investigate a range of temperatures expeditiously [27,28].Non-isothermal analytical techniques use modern thermobalancesthat subject samples to a programmed continuous temperaturerise, which ensures that no temperature regions are omitted, ascan occur during a sequence of discrete isothermal measurements.Despite their touted convenience [29,30], non-isothermal tech-niques have received pointed criticism [31–35] and, sometimes,outright rejection [36] because of their perceived inability to reli-ably assess kinetic parameters, besides their increased sensitivityto experimental noise as compared to isothermal methods [37,38].Benoit et al. [39] advised against the use of non-isothermal tech-niques for solid state decomposition processes where there is achange in the reaction kinetics over the temperature range ordegree of conversion. Studies have shown that there are wide dis-parities among values obtained from dynamic techniques that useonly a single heating rate. A consensus emerged that the accuracyof these methods could be improved using multiple sets of ther-mal data collected by performing experiments at multiple heatingrates [33,40]; it is a perspective shared by participants in a recentkinetics project commissioned by ICTAC [41–45]. Paradoxically, theinherent efficiency with which dynamic methods collect kineticdata is partially negated in that reasonably resolved data typicallyis obtained using low heating rates [46].

Thermogravimetric analysis (TGA) is the most commonlyapplied thermoanalytical technique in solid-phase thermal degra-dation studies [47], and it has gained widespread currency inthermal studies of biomass pyrolysis [48–54]. TGA measures thedecrease in substrate mass caused by the release of volatiles, ordevolatilization, during thermal decomposition [55]. In TGA, themass of a substrate being heated or cooled at a specific rate ismonitored as a function of temperature or time. Taking the firstderivative of such thermogravimetric curves (i.e., −dm/dt) curves,known as derivative thermogravimetry (DTG), provides the maxi-
mum reaction rate [56]. The development of a system in 1899 by SirWilliam Roberts-Austen [57] that uses thermocouples to measurethe temperature difference between a sample and an adjacent inertreference material subjected to an identical temperature alterationwas the naissance of differential thermal analysis (DTA) [58]. By


Table 1Classification scheme of thermoanalytical techniques.

Property Technique Parameter measured Abbreviation

Mass Thermogravimetric analysis Sample mass TGADerivative thermogravimetry First derivative of mass DTG

Temperature Differential thermal analysis Temperature difference between sample and inert reference material DTADerivative differential thermal analysis First derivative of DTA curve

Heat Differential scanning calorimetry Heat supplied to sample or reference DSCPressure Thermomanometry PressureDimensions Thermodilatometry Coefficient of linear or volumetric expansionMechanical properties Thermomechanical analysis TMA

trical

ustic w

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k

wcsad

Electrical properties Thermoelectrical analysis ElecMagnetic properties Thermomagnetic analysisAcoustic properties Thermoacoustic analysis AcoOptical properties Thermoptical analysis

lotting the time (t) versus temperature difference (�T) a DTAurve can be generated from which the reaction rate can be cal-ulated in terms of the slope (d�T/dt) and height (�T) of the curvet any temperature [59]. Another common method of thermal anal-sis is differential scanning calorimetry (DSC). In DSC, heat flux intor out of a sample is compared against an inert reference material,sually alumina, as the two specimens are simultaneously heatedr cooled at a constant rate. The integral (or area) of the DSC peak isirectly proportional to the heat of transition for a particular reac-ion and the change in heat capacity can readily be correlated tohe enthalpy change of the reaction. DTA is similar to DSC, excepthat the conditions in DTA are adiabatic causing a temperature dif-erence between the sample and the reference material. Table 1rovides a listing of thermoanalytical techniques classified accord-

ng to the physical properties that are measured.Thermal analysis provides an excellent tool that may provide

nsight regarding the kinetic workings of heterogeneous reactions.owever, it cannot be overstressed that the kinetic data obtained

rom a single thermoanalytical technique, in and of itself, does notrovide the necessary and sufficient evidence to draw mechanisticonclusions about a solid state decomposition process [60]. Theinetic behavior of a given heterogeneous reaction system mayhange during the process and so it is possible that the completeeaction mechanism cannot be represented suitably by a single spe-ific kinetic model [61]. Various other analytical techniques (e.g.,lectrical, nuclear, optical, and X-ray) must be employed to detectnd analyze changes that occur in the chemical composition and/ortructure of the sample. One such specialized method, evolved gasnalysis (EGA), involves a qualitative and quantitative assessmentf the gases that are evolved during thermal analysis. EGA cane performed using a variety of analytical tools, including Fourierransform infrared spectroscopy (FTIR), gas chromatography (GC),igh performance liquid chromatography (HPLC), mass spectrom-try (MS), and GC–MS. The use of these species-specific techniquesn consort with thermal analysis can help facilitate the elucidationf an appropriate kinetic scheme and, hopefully, bring investigatorsne step closer to understanding the actual reaction mechanism.

.3. Arrhenius rate expression and the significance of the kineticarameters

Virtually every kinetic model proposed employs a rate law thatbeys the fundamental Arrhenius rate expression:

(T) = A exp(−Ea

RT

)(1)

here T is the absolute temperature in K, R is the universal gasonstant, k(T) is the temperature-dependent reaction rate con-tant, A is the frequency factor, or pre-exponential, and Ea is thectivation energy of the reaction. The main temperature depen-ence in the Arrhenius equation arises from the exponential term,

resistance TEA

aves TAATOA

although the frequency factor, A, does exhibit a slight temperaturedependency [17,62]. For homogeneous reactions involving gases,the physical significance of the Arrhenius parameters (i.e., Ea andA) can be interpreted in terms of molecular collision theory. Theactivation energy, Ea, can be regarded as the energy threshold thatmust be overcome before molecules can get close enough to reactand form products. Only those molecules with adequate kineticenergy to surmount this energy barrier will react. Alternatively,transition state theory describes the activation energy as the differ-ence between the average energy of molecules undergoing reactionand average energy of all reactant molecules [63]. The frequencyfactor provides a measure of the frequency at which all molecularcollisions occur regardless of their energy level [64]. The exponen-tial term in Eq. (1) can be thought of as the fraction of collisionshaving sufficient kinetic energy to induce a reaction [65]. Thus,the rate constant, k(T), being the product of A and the exponen-tial term, exp−Ea/RT, yields the frequency of successful collisions[65].

Vociferous debate continues to swirl about the relevancy ofkinetic parameters obtained from solid state reactions. The cruxof the controversy stems from the indiscriminate adoption ofhomogeneous reaction kinetic theory to describe heterogeneousprocesses [66–68]. Indeed, it is plausible that much of the incon-sistency arising in biomass kinetic data is ascribable to the use ofkinetic expressions that are merely adaptations of those used inhomogeneous reactions and that do not incorporate terms thatdepend upon the solid state nature of biomass. Over thirty yearsago, Garn [69] contended that the discrepancies observed in cal-culated activation energies for solid phase decomposition are areminder that the concept of a symmetric distribution of energystates as implied by the Arrhenius equation does not apply to solids.The fact that the most commonly occurring and minimum pos-sible energy state in solids is that of the perfect crystal obviatesthe use of a statistical treatment for solids [70]. Garn advised [69]that if the calculated “activation energy varies with experimentalconditions then it is necessarily true that: (1) there is no uniquelydescribable activated state and consequently the Arrhenius equa-tion has no application to solid reactions; or (2) the assumptionthat the rate is a function only of temperature and the [mass] frac-tion remaining is incorrect; or (3) both”. Consequently, the physicalconnotation of the Arrhenius parameters in heterogeneous kineticsis opaque and “. . .they do not characterize the chemical reactionitself, but only the whole complexity of processes occurring dur-ing the pyrolysis under the given experimental conditions” [71].Hence, experimentally determined kinetic parameters from ther-mally activated, solid state transformations can only be expected
to provide a rough approximation for the overall rate of a com-plex process that typically entails numerous steps, each havingdistinct activation energies [40,72]. Garn [66] also raised salientconcerns about other weaknesses associated with the transfer ofhomogeneous kinetic principles to heterogeneous processes.


Table 2Unconventional phenomena represented by the Arrhenius rate law.

Temperature-dependent phenomenon(applicable temperature range)

Ea (kJ mol−1)

Rate of counting 100.4Rate of forgetfulness 100.4Frequency of the heart beat of a terrapin

(18–34 ◦C)76.6

Creeping velocity of the millipede (Parajuluspennsylvanicus) (6–30 ◦C)

51.2

Creeping velocity of the ant (Liometopumapiculatum) (16–38.5 ◦C)

51.0

Frequency of flashing of fireflies 51.0Rate of chirping of common tree crickets

(Oecanthus)51.0

Velocity of amoeboid progression in humanneutrophilic leucocytes (27–40 ◦C)

45.2

Creeping velocity of the spotted leopard slug(Limax maximus) (11–28 ◦C)

44.8

Rate of filament movements in the blue-green 38.7

btdtbtaitmtsktmM[otnsenaBcctaihewak

3

3

o

component initially present in the unreacted solid substrate. The

algae (Oscillatoria) (6–36 ◦C)Human alpha brain-wave rhythm 29.3

Although alternative expressions (e.g., linear relationshipsetween ln k and T, and between ln k and ln T) do exist for describinghe influence of temperature on the rates of chemical reactions, Lai-ler [73] emphasized that none of these other relationships enjoyshe universal acceptance bestowed upon the Arrhenius equationecause of their “theoretical sterility”. The additional parametershat are included in these surrogate rate expressions presum-bly would permit better fitting of experimental data, but theres no theoretical rationale for their existence, thereby, deprivinghem of any physico-chemical significance [62]. Were the ther-

al analysis community to approve an alternative expression forhe temperature dependence of reaction rates, it would neces-itate the recalculation of all previous Ea and A values so thatinetic parameters dating back to 1899 could be compared againsthose generated by the new rate law [62]. An undertaking of this

agnitude would be incredibly laborious and seems improbable.oreover, rejection of the Arrhenius expression would, as Sesták

74] said, “certainly deny the fifty [i.e., now eighty] years’ workf famous scientists in the field of heterogeneous kinetics”. For allhe barbed accusations that have been hurled against the Arrhe-ius rate law, it remains the only such kinetic expression that canatisfactorily account for the temperature-dependent behavior ofven the most unconventional processes, as shown in Table 2 andoted originally in a series of review papers by Crozier et al. [75–77],nd subsequently expanded by Laidler [78] and then tabulated byrown [68]. Laidler’s purpose for revisiting these intriguing pro-esses was to underscore that relatively complex reaction systemsan be represented by the Arrhenius law and also that above a cer-ain energy threshold (i.e., about 21 kJ mol−1) many phenomenare likely to proceed via chemical reactions rather than by phys-cal processes. The prominent role of the Arrhenius expression ineterogeneous reaction systems is undeniable and was acknowl-dged by Agrawal [28], who stated, “. . .it is perhaps the mostidely used equation and is satisfactory in explaining the temper-

ture dependence of the rate constant in solid-state decompositioninetics”.

. Biomass pyrolysis kinetics

.1. Kinetic expressions for biomass thermal decomposition

The kinetics of biomass decomposition are routinely predicatedn a single reaction [79,80] and can be expressed under isothermal


conditions by the following canonical equation:

d˛

dt= k(T)f (˛) = A exp

(−Ea

RT

)f (˛) (2)

where t denotes time, ˛ signifies the degree of conversion, or extentof reaction, d˛/dt is the rate of the isothermal process, and f(˛) isa conversion function that represents the reaction model used anddepends on the controlling mechanism. The extent of reaction, ˛,can be defined either as the mass fraction of biomass substrate thathas decomposed or as the mass fraction of volatiles evolved andcan be expressed as shown below:

˛ = w0 − w

w0 − wf= v

vf(3)

where w is the mass of substrate present at any time t, w0 is theinitial substrate mass, wf is the final mass of solids (i.e., residue andunreacted substrate) remaining after the reaction, v is the mass ofvolatiles present at any time t, and vf is the total mass of volatilesevolved during the reaction. The combination of A, Ea, and f(˛) isoften designated as the kinetic triplet, which is used to characterizebiomass pyrolysis reactions [81,82]. Non-isothermal rate expres-sions, which represent reaction rates as a function of temperatureat a linear heating rate, ˇ, can be expressed through an ostensiblysuperficial transformation [81,83] of Eq. (2):

d˛

dT= d˛

dt

dt

dT(4)

where dt/dT describes the inverse of the heating rate, 1/ˇ, d˛/dtrepresents the isothermal reaction rate, and d˛/dT denotes the non-isothermal reaction rate. An expression of the rate law for non-isothermal conditions can be obtained by substituting Eq. (2) intoEq. (4):

d˛

dT= k(T)

ˇf (˛) = A

ˇexp

(−Ea

RT

)f (˛) (5)

The use of reaction-order models is ubiquitous in the thermalanalysis of biomass because of their simplicity and propinquity torelations used in homogeneous kinetics [28,83]. In these order-based models, the reaction rate is proportional to the fraction ofunreacted substrate raised to a specific exponent, known as thereaction order:

d˛

dT= k(T)(1 − ˛)n (6)

where (1 − ˛) is the remaining fraction of volatile material in thesample and n represents the reaction order. The devolatilizationdynamics of biomass pyrolysis are frequently expressed as a firstorder decomposition process that results in the formation of dis-crete volatile fractions [49,84–91]:

dVi

dt= ki(T)(V∗

i − Vi) (7)

where ki(T) is the rate constant for an evolved volatile fractioni, Vi is the cumulative mass of released volatiles correspond-ing to fraction i through time t, and Vi* is the effective volatilecontent for fraction i. In most devolatilization schemes, theseparate volatilized fractions are classified in terms of three prin-cipal biomass pseudo-components (i.e., hemicellulose, cellulose,and lignin) and, sometimes, moisture [49,88,89,92,93]. The totaldevolatilization rate for a particular system is given by linear sum-mation of the individual volatilization rates for each fraction, whichare weighted according to the percentage of respective pseudo-

release of biomass volatiles has also been hypothesized to involveseveral independent concurrent reactions that produce a set oflumped volatile products [94,95]. This alternative kinetic represen-tation uses Eq. (7) as a template but the rate of devolatilization is


Table 3Expressions for the most common reaction mechanisms in solid state reactions.

Reaction model f(˛) = (1/k)(d˛/dt) g(˛) = kt

Reaction orderZero order (1 − ˛)n ˛First order (1 − ˛)n −ln(1 − ˛)nth order (1 − ˛)n (n − 1)−1 (1 − ˛)(1−n)

NucleationPower law n(˛)(1−1/n); n = 2/3, 1, 2, 3, 4 ˛n; n = 3/2, 1, 1/2, 1/3, 1/4Exponential law ln ˛ ˛Avrami–Erofeev (AE) n(1 − ˛) [−ln(1 − ˛)](1−1/n); n = 1, 2, 3, 4 [−ln(1 − ˛)]1/n; n = 1, 2, 3, 4Prout–Tompkins (PT) ˛(1 − ˛) ln[˛(1 − ˛)−1] + Ca

Diffusional1-D 1/2˛ ˛2

2-D [−ln(1 − ˛)]−1 (1 − ˛)ln(1 − ˛) + ˛3-D (Jander) 3/2(1 − ˛)2/3[1 − (1 − ˛)1/3]−1 [1 − (1 − ˛)1/3]2

3-D (Ginstling–Brounshtein) 3/2[(1 − ˛)−1/3 − 1]−1 1 − 2/3˛ − (1 − ˛)2/3

Contracting geometry= 2= 3

mfpa[eetoariitariCrttc[

mavtsAwircocpsmhtpmtpt

Contracting area (1 − ˛)(1−1/n); nContracting volume (1 − ˛)(1−1/n); n

a Integration constant.

easured with respect to individual reactions rather than volatileractions. Integration of the preceding kinetic equations is oftenerformed using a fourth order Runge–Kutta method [39,96,97]nd the method of least squares using nonlinear regression analysis39,98–100] is regularly employed to fit the experimental data andvaluate the Arrhenius parameters as predicted by the kinetic mod-ls. Some of the more important rate equations used to describehe kinetic behavior of solid state reactions are listed in Table 3,r simply “The Table”. Other than for didactic purposes or reviews,uthors should assume that their audience is acquainted with theelevant background information and refrain from the repetitivenclusion of “The Table” each time a new thermal analysis papers published. Furthermore, the argument that reference texts con-aining a comprehensive listing of reaction models are not readilyvailable is no longer valid. Elsevier Science Publishers [101] hasecently republished Vol. 22 of the Comprehensive Chemical Kinet-cs series entitled: Reactions in the Solid State by C.H. Bamford and.F.H. Tipper, Eds. [18], which includes a complete set of solid stateeaction models. Another fine thermal analysis reference book con-aining “The Table” that is accessible at most academic libraries ishe Handbook of Thermal Analysis and Calorimetry, Vol. 1: Prin-iples and Practice by M.E. Brown, Ed. (P.K. Gallagher, Series Ed.)13].

It should be noted that the application of first order reactionodels in biomass pyrolysis kinetics has become almost formulaic

nd their indiscriminate acceptance has occurred without rigorouserification or sufficient awareness of their fundamental limita-ions [82,102]. The imposition of an order-based model on a solidtate reaction system can cause a substantial divergence in therrhenius parameters (i.e., A and Ea) [82]. This discrepancy ariseshen an inappropriate reaction order is affixed to the last term

n Eq. (6). The strongly correlated Arrhenius parameters in theate constant, k(T), are then forcibly adjusted to accommodate thehosen reaction order. Accordingly, any reaction model, not onlyrder-based models, can suitably fit kinetic data because of theorresponding “kinetic compensation effect” among the Arrheniusarameters [103]. The manifestation of this compensation relation-hip is common to both isothermal and non-isothermal kineticodels, yet the increasing popularity of non-isothermal single

eating rate techniques in preceding decades necessarily gave riseo a surge of unreliable and erratic results [28,104,105]. Much sus-
icion was cast upon the validity of non-isothermal model-fittingethods, although isothermal methods are just as culpable in that
hey are also susceptible to a similar vacillation in the Arrheniusarameters [106]. To quote Ninan [47], “As far as the values ofhe kinetic parameters are concerned, there is no significant differ-

1 − (1 − ˛)1/n; n = 21 − (1 − ˛)1/n; n = 3

ence between isothermal and non-isothermal methods or betweenmechanistic and non-mechanistic approaches, in the sense thatthey show the same degree of fluctuation or trend, as the case maybe”.

Garn [66] underscored several critical assumptions included inthe generalized rate expression (Eq. (2)), which is often used todescribe solid state decomposition kinetics. A violation of any ofthese assumptions in a particular system will invalidate the use ofthe rate equation. The use of the mathematical terms, f(˛) and k(T),explicitly affirms that the reaction rate is exclusively a functionof the degree of conversion, ˛, and the temperature, T. Changes inother process parameters (e.g., heating rate, residence time, particlesize, sample quantity, reaction interface, atmosphere, and pres-sure) theoretically should have no effect on the reaction rate. Ifchanges in reaction rate are found to result from variation in theseother parameters, the conventional rate equation has failed. Inother words, the rate of reaction may be influenced by parametersbesides the concentration that are not incorporated in the gener-alized “reaction statement”. A logical explanation for this can bededuced by recognizing that the rate constant for a given reactionis clearly an intensive property [34], like temperature or density,because it is “measured from changes in an extensive propertyof the system such as mass, enthalpy, and volume” [17]. Hence,the rate constant has important merit because it is specific to aparticular substance and process and it can potentially be used todiscriminate amongst various reaction systems.

3.2. Biomass pyrolysis kinetic models

A comprehensive review of the myriad models available foranalyzing the kinetics of biomass pyrolysis reactions is beyondthe scope of this communication. Instead pertinent kinetic mod-els used in biomass pyrolysis studies will be presented alongwith selected additional models that are noteworthy for theirinnovative efforts to achieve improved predictive success by bet-ter reflecting the heterogeneous character of biomass thermaldecomposition. The numerous pyrolysis models can be dividedinto three principal categories: single-step global reaction mod-els, multiple-step models, and semi-global models [107–110]. Theprocesses comprising pyrolysis frequently are described as pro-ceeding along (a) concurrent (i.e., competitive and independent
parallel) routes [6,53,91,107–110], (b) consecutive (or sequential)routes [111–115], or (c) combinations thereof [116–121]. Singlereaction global schemes describe the overall rate of devolatilizationfrom the biomass substrate. Single-step global models have pro-vided reasonable agreement with experimentally observed kinetic

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J.E. White et al. / Journal of Analytic

ehavior [84,122–124]. One frequently cited study [125] revealedhat the pyrolysis of many different cellulosic substrates can bedequately described by an irreversible, single-step endothermiceaction that follows a first order rate law with a global apparentctivation energy of ca. 238 kJ mol−1. The usefulness of single-steplobal models, however, is limited by the assumption of a fixedass ratio between pyrolysis products (i.e., volatiles and chars),hich prevents the forecasting of product yields based on pro-

ess conditions [126]. Furthermore, in most pyrolysis systems theinetic pathways are simply too complex to yield a meaningfullobal apparent activation energy [127].

Much related work has examined the use of semi-global mod-ls, all of which assume that biomass pyrolysis products cane aggregated into three distinct fractions: volatiles, tars, andhar. Semi-global models are able to facilitate a simpler ‘lumped’inetic analysis [53,89,107,126,128,129]. This analysis is usedidely because its depiction of biomass devolatilization in terms

f three concurrent first order reactions is intuitive [90]. Thisechnique is a suitable tool for correlating and evaluating kineticata from different biomass types under similar reaction condi-ions, but it is ill-suited for comparisons of thermal decompositionata obtained from dissimilar reaction conditions [84]. Semi-globalodels also allow coupling of transport phenomena parametersith the secondary devolatilization reactions. This procedure has

een demonstrated to correctly predict trends in product yield asfunction of volatiles residence time [130].

.3. Multiple-step models

The inability to predict the kinetic behavior of biomass underifferent process conditions has vexed researchers and encour-ged the development of complex multiple-step models. A rigorousinetic treatment of pyrolysis data must account for the forma-ion rates of all the individual product species [88,108], alongith any potential heat and mass transfer limitations. Alves and

igueiredo [113] concluded that the pyrolysis of cellulose coulde successfully modeled using three consecutive first order reac-ions. The first reaction represents approximately 30% of the totalevolatilization, while the third reaction releases the remaining0% of the volatile matter [131]. The second reaction released noolatile matter and is theorized to involve rearrangement of theolid. Alternative reaction schemes, while possible, were deemedmpractical because they would require either more than threeeactions or three reactions of order other than unity to describehe complex devolatilization process. A study by Diebold [132]rovided an elegant seven-step global kinetic model for celluloseyrolysis that achieved accurate predictions using published rateonstants for both fast and slow pyrolysis. The model accounted fornteractions between heating rate, residence time, pressure, andemperature. It was demonstrated by Vargas and Perlmutter [112]hat the reaction kinetics of coal subjected to non-isothermal pyrol-sis can be understood to proceed via a series of ten consecutivesothermal steps, each associated with the degradation of a specificseudo-component of the coal. Not to be outdone, Mangut et al.133] revealed that kinetic data obtained from the pyrolysis of foodndustry wastes related to tomato juice production (i.e., peels andeeds) could be satisfactorily modeled using twelve consecutiveyrolytic reactions that were identified from DTG curves. Althoughseful in some applications, multi-step reaction models are limitedy their incorporation of several interdependent serial reactions,herein subtle inaccuracies in the kinetic parameters obtained

or the first rate equation can be greatly magnified in successiveeactions [134]. Except for a few extremely simple cases, compre-ensive kinetic approaches are intractable because of the sheerumber of reactions that would need to be considered. Further-ore, the identification of constituents in pyrogenic tar mixtures


remains incomplete and the intermediate pyrogenic species havescarcely been characterized. Consequently, these ‘elegant’ modelscan sometimes be of limited practical use.

3.4. Isoconversional techniques

Historically, model-fitting methods were thought to satisfac-torily predict reaction kinetics in solid state processes. Arrheniusparameters obtained from model-fitted isothermal data are oftennearly independent of the kinetic models employed [40]. Iterativeapproaches to model-fitting empirical endpoints from isothermaldata may provide consistent values for the Arrhenius parameters,but only a single global kinetic triplet is obtained for each set ofdata. As stated previously, solid state processes, such as biomasspyrolysis, frequently proceed via a complex suite of concurrent andconsecutive reactions. Each step likely has its own unique apparentactivation energy, and thus the use of an average, global apparentactivation energy to describe the kinetics of such processes couldbe construed as an inadequate oversimplification at best [135] and,more alarmingly, the DTG curves from these models may concealthe true multistage character of pyrolytic reactions under a sin-gle peak [136]. Conversely, force fitting models to non-isothermaldata obtained from a single heating rate can generate very incon-sistent Arrhenius parameters that display a strong dependence onthe selected kinetic model [40]. Non-isothermal methods that usemultiple heating rates can provide more reliable estimates of thekinetic parameters as mentioned earlier, but various decomposi-tion processes can exhibit different dependencies on heating rate,which may lead to overlapping reactions in the DTG curves that aredifficult to separate [137].

The consternation in the scientific community [68,138] overthe wide variation in Arrhenius parameters for similar reactionconditions and biomass species using different reaction modelsserved as a lightning rod that precipitated additional research anddevelopment [29,40,139–143]. Innovative methods for determin-ing Arrhenius parameters based on a single parameter began toemerge in the 1960s. These so-called “model-free” methods arefounded on an isoconversional basis, wherein the degree of con-version, ˛, for a reaction is assumed to be constant and thereforethe reaction rate, k, depends exclusively on the reaction temper-ature, T. By allowing Ea to be calculated a priori, isoconversionalapproaches eliminate the need to initially hypothesize a form andrate order for the kinetic equation. Hence, isoconversional meth-ods do not require previous knowledge of the reaction mechanismfor biomass thermal degradation. Another advantage of isoconver-sional approaches is that the systematic error resulting from thekinetic analysis during the estimation of the Arrhenius parametersis eliminated [41].

Isoconversional models can follow either a differential or anintegral approach to the treatment of TGA data. The Friedmanmethod [144] is a differential isoconversional technique that canbe expressed in general terms as written below:

d˛

dt= ˇ

(d˛

dT

)= A exp

(−Ea

RT

)f (˛) (8)

Taking natural logarithms of each side from Eq. (8) yields:

ln(

d˛

dt

)= ln

[ˇ

(d˛

dT

)]= ln[Af (˛)] − Ea

RT(9)

It is assumed that the conversion function f(˛) remains constant,which implies that biomass degradation is independent of temper-
ature and depends only on the rate of mass loss. A plot of ln[d˛/dt]versus 1/T yields a straight line, the slope of which corresponds to−Ea/R.
The Flynn–Wall–Ozawa (FWO) method [62,145–150] is anintegral isoconversional technique that assumes the apparent acti-

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ation energy remains constant throughout the duration of theeaction (i.e., from t = 0 to t˛, where t˛ is the time at conversion). Integrating Eq. (9) with respect to variables ˛ and T:

(˛) =∫ ˛

0

d˛

f (˛)= A

ˇ

∫ T˛

0

exp(−Ea

RT

)dT (10)

here T˛ is equal to the temperature at conversion ˛. If we define≡ Ea/RT, Eq. (10) becomes:

(˛) = AEa

ˇR

∫ ∞

˛

exp−x

x2= AEa

ˇRp(x) (11)

here p(x) representing the rightmost integrand in Eq. (10) isnown as the temperature integral. The temperature integral doesot have an exact analytical solution in closed form [29] but can bepproximated via an empirical interpolation formula proposed byoyle [62,149,151,152]:

og p(x) ∼= −2.315 − 0.4567x, for 20 ≤ x ≤ 60 (12)

sing Doyle’s approximation for the temperature integral and tak-ng logarithms of both sides of Eq. (11) one obtains:

og ˇ = log(

AEa

Rg(˛)

)− 2.315 − 0.4567

Ea

RT(13)

n the FWO method, plots of log ˇ versus 1/T for different heatingates produce parallel lines for a fixed degree of conversion. Thelope (−0.4567Ea/R) of these lines is proportional to the apparentctivation energy. The value of log A is given by the intercept of thisine with the y-axis, log ˇ.

Another widely utilized integral isoconversional methods known as the Kissinger–Akahira–Sunose (KAS) method56,104,105,153,154]. The KAS method employs another empiricalpproximation derived by Doyle [62,149,151,152]:

og p(x) ∼= exp−x

x2, for 20 ≤ x ≤ 50 (14)

ubstitution of Eq. (14) into Eq. (11) and taking the ln of bothides leads to the expression for the KAS integral isoconversionalethod:

n

(ˇ

T2m

)= −Ea

R

(1

Tm

)− ln

[(Ea

AR

)∫ ˛

0

d∂

f (˛)

](15)

here Tm is the temperature at the maximum reaction rate. Assum-ng ˛ has a fixed value, Ea can be determined from the slope of thetraight line obtained by plotting ln(ˇ/Tm

2) versus 1/Tm.The integral method based on the Coats and Redfern (CR) equa-

ion [155,156] is a popular non-isothermal model-fitting methodhat requires an assumption be made regarding the value of theeaction order for g(˛). The method approximates p(x) in Eq. (11)sing a Taylor series expansion to yield the following expression:

n(−ln(1 − ˛)

T2

)= ln

[AR

ˇEa

(1 − 2RT

Ea

)]− Ea

RT(16)

q. (16) can be simplified by recognizing that for customary valuesf Ea (e.g., 80–260 kJ mol−1), the term 2RT/Ea � 1:

n(

g(˛)T2

)= ln

(AR

ˇEa

)− Ea

RT(17)

straight line can be obtained from single heating rate data bylotting ln[g(˛)/T2] versus T−1. From the slope of the line, −Ea/R,nd its intercept ln(AR/ˇEa), Ea and A can be derived. The attrac-iveness of the CR method resides in its ability to directly furnish

and Ea for single heating rate. The criticism of the CR approachollows the same general arguments presented against all of the

odel-fitting methods, namely, that the kinetic triplet resultingrom evaluation of a single DTG curve may be non-unique, or indis-inguishable, because of the high degree of correlation between �

Applied Pyrolysis 91 (2011) 1–33

and d˛/dt (or dT/d˛) [28,157–160]. A multi-heating rate applicationof the original Coats and Redfern equation, known as the modifiedCoats–Redfern (CR*) method [41,161], has been advanced that pro-vides an integral isoconversional technique equivalent to those ofFWO and KAS. The CR* method rearranges terms in Eq. (16) to yield:

ln

[ˇ

T2(1 − 2RT/Ea)

]= − Ea

RT+ ln

(AR

g(˛)Ea

)(18)

Given a fixed degree of conversion, the left-hand term is plottedversus T−1 for each heating rate, generating a set of straight lines,each having slope −Ea/R. The frequency factor, A, is calculated byinserting −Ea/R into the intercept. Because the left-hand side of Eq.(18) is weakly dependent on Ea, an iterative process must be usedby assuming an initial value for Ea and then re-evaluating the left-hand side until the desired level of convergence [161]. It should benoted as a point of clarity that there are other so-called “modifiedCoats–Redfern” methods in the literature, but they cannot be con-sidered isoconversional because they still require the selection ofa reaction order. These alternative “modified Coats–Redfern” for-mulations often involve a regression analysis of one or more of thekinetic triplet parameters [162,163]. One such “modified” method[163] reported errors for Ea estimates that are an order of magni-tude lower than those obtained from isoconversional techniques.

3.5. Comparative evaluation of integral and differentialisoconversional techniques

The advantages of the integral isoconversional methods aretempered by several weaknesses not present in the differentialmethods [164], viz.,

(1) Picard iteration [165] of the temperature integral is needed.(2) Integral methods are prone to error accretion during such suc-

cessive approximations.(3) The temperature integral requires boundary conditions which

are frequently ill-defined.

Flynn [62] remarked that use of “. . .the mathematicallyintractable temperature integral has often become a necessary evilin the analysis of thermal analysis kinetics”. To circumvent thehazards posed by these oversimplified approximations, Vyazovkinand Dollimore [166] introduced a non-linear isoconversional tech-nique, known as the Vyazovkin (V) method, which uses a revisedexpression for the temperature integral, p(x):

I(Ea, T˛) =∫ T˛

0

exp(−Ea

RT

)dT = p(x) (19)

The V method evaluates Ea for a set of q experiments conductedat different heating rates, ˇj and ˇk, where the subscripts j and kdenote the ordinal number of the experiment:

q∑j=1

q∑k /= j

ˇkI(Ea, T˛,j)ˇjI(Ea, T˛,k)

= � (Ea) (20)

where I(Ea,T˛,j) and I(Ea,T˛,k) represent the temperature integralp(x) corresponding to the heating rates ˇj and ˇk, respectively.The apparent activation energy is given by the value that mini-mizes � . Values of I(Ea,T˛) can be determined via either numericalintegration or the Senum–Yang [167] approximation:(

exp−x)(

(x3 + 18x2 + 88x + 96))

p(x) =x (x4 + 20x3 + 120x2 + 240x + 120)

(21)

Unfortunately, the constraints imposed by the mathematical con-structs used in the standard integral isoconversional methods(CR, FWO, and KAS) prevent a straightforward determination of

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acsttctthtaftNrsmAo


he remaining kinetic parameters, A and f(˛) [40,105]. The fre-uency factor obtained from standard isoconversional techniques

s tainted by association with the reaction model that must bessumed to permit its calculation [40]. Flynn [164] developed aeneral differential isoconversional method that allows A and f(˛)o be disconnected and evaluated independently. Another proce-ure to unambiguously evaluate A was proposed by Vyazovkin andesnikovich [168], wherein a linear relation that exists between therrhenius parameters is used to extract the frequency factor for aiven isoconversional value of Ea:

n A = aEa + b (22)

here a and b are correlation parameters that are evaluated usinginear regression. The use of this procedure, however, is not entirelyaultless because the linear correlation, known as the apparentompensation effect, has been the recipient of rigorous criticisms noted later in this paper.

All of the integral isoconversional methods (viz., CR, FWO,AS, and V) assume that the values of Ea and A remain constant

hroughout the reaction until the desired level of conversion, ˛,s reached, making these techniques somewhat analogous to thenflexible global one-step models, which also assume an unchang-ng Ea for pyrolysis processes [105]. The supposition of constanta and A values is only possible when the Arrhenius parametersre independent of the extent of reaction [140]. When Ea dependsn ˛, however, it was found that the use of integral isoconver-ional methods can lead to systematic errors [139,140,169,170].i et al. [139,171] found that values of Ea are consistently over-stimated using integral isoconversional methods versus thosevaluated using Friedman’s differential isoconversional methodecause of error introduced by the truncation of the additionaligher-order terms in Doyle’s approximations, given by Eqs. (11)nd (13). Data provided by Budrugeac et al. [169] for the dehy-ration of calcium oxalate indicates that Ea values obtained from

ntegral methods can deviate by up to 21% from values determinedy differential methods. In response, Vyazovkin [103] providedmodification for the V isoconversional method that accounts

or the variation in apparent activation energy with increasing ˛.nstead of evaluating the temperature integral over the completeoundary conditions (i.e., 0–t˛), the integration is now performedumerically over small time increments using the trapezoidal rule,hich requires considerable more computational effort than the

enum–Yang approximation [168]. In a rebuttal, Budrugeac andegal [172] remarked that the modification proposed by Vyazovkin103] using “low ranges of variables” is an artifact that in realityonceals the true differential character of the method.

Differential isoconversional methods are not encumbered withtemperature integral and thus kinetic parameters can be directly

alculated. Numerical differentiation of experimental data is highlyusceptive to data noise [43,173] and can result in significant scat-er in the resulting derivative curves. Widespread use of differentialechniques has also been inhibited because of the daunting cal-ulations involved [164]. The advent of powerful computationalools [164,174–176] coupled with the development of sophis-icated smoothing and fitting functions [137,173,177–180] haselped to curtail some of these objections, although some resis-ance yet remains among those who insist that integral methodsre a “safer alternative” [43] because differential methods still “suf-er from excessive random errors” [139], especially in the vicinity ofhe reaction onset and endpoint, where d˛/dt is often small [170].onetheless, Burnham and Dinh [105] recently indicated that if the

ate of data collection is sufficiently high then the raw data can bemoothed appreciably such that the vulnerabilities of the Fried-an method to experimental noise can be “effectively mitigated”.n examination by Burnham et al. of the predictive performancef several isoconversional and model-fitting techniques applied on


data sets from the ICTAC kinetics project and other lifetime projectsrevealed that the Friedman differential method was the most reli-able and accurate method in all cases.

There are also some disadvantages that are common to all“model-free” techniques. The use of the descriptor, “model-free”, isdeceptive [181] because it insinuates that awareness of the kineticmodel and, in particular, the conversion function f(˛), is superflu-ous information not needed in the kinetic analysis. An accuratedescription of kinetic behavior is not possible when members of thekinetic triplet are interpreted independently of one another [182].“Model-free” methods simply “postpone” the consideration of anappropriate conversion function until an estimate of the kineticparameters (i.e., Ea and A) is calculated [181]. Furthermore, iso-conversional methods are unsuitable for those reaction schemescontaining competing reactions, where the net rate of reactiondepends on changes in temperature, or concurrent reactions thatswitch which reaction is rate-limiting over the experimental tem-perature range [105].

It has also been cautioned that the selection of kinetic expres-sions wherein “f(˛) is assumed to be a function of mass can be avery poor choice” because these models presume that the activ-ity of every reactant particle is identical regardless of its locationin the substrate matrix (i.e., in the bulk or on the surface) [17].In heterogeneous reactions this is seldom the case because sub-strate reactivity can vary depending on the location of active surfacesites, the partial pressure of the surrounding atmosphere, andphysical changes in the specimen that are temperature-dependentphenomena (e.g., sintering, melting, and vitrification) [17,70,138].According to Flynn [17], it is possible in certain solid state reactionsthat the “. . .crucial, rate-controlling event may be the occurrence ofthe temperature-dependent physical transformation which is notmass dependent”. Sesták and Berggren [15] succinctly conveyedthese concerns regarding proper selection of ˛ when he stated,“[DTA] is still of questionable validity, because a representativevalue which would unambiguously define the change in the systemfrom the initial or from the final state is not yet available. . .”.

3.6. Other kinetic models

Kinetic models other than traditional reaction order modelshave been proposed that ostensibly afford improved predictionsfor biomass pyrolysis data. For example, an interesting deactiva-tion theory was proposed by Balci et al. [183] that is based onkinetic models typically applied toward catalyst deactivation. In thebiomass deactivation model (DM), the first order rate constant wasassumed to vary with the degree of decomposition due to changesthat occur in the chemical composition and physical structure of thesubstrates during the pyrolysis process. Individual biomass com-ponents degrade at different temperatures, demonstrating that thecomposition of the reactive portion of the substrate is modified dur-ing the reaction. A combination of altered solid geometry, shrinkingvolume, and changing pore structure during pyrolysis results in adepletion of the active surface area. The deactivation of the solidduring pyrolysis by the aforementioned changes influences theapparent rate constant as shown below:

kapp = zk = z(�)(

Ai exp(

− Ei

RT

))(23)

where z is the activity of the solid substrate expressed as a func-
tion of a deactivation rate constant, � , and kapp is the apparent rateconstant.
Reynolds et al. [161,184,185] developed a generalizednucleation-growth model, which is essentially a modificationof the Prout–Tompkins rate equation [186], first used to describe

1 al and

t[

wri(rmvc

s[eptfisDtM(aplatdmltt

nakn“cmtaspbSs

wmoasmcVtiws

0 J.E. White et al. / Journal of Analytic

he thermal decomposition kinetics of potassium permanganate187]:

d˛

dt= kyn(1 − ry)s (24)

here y designates the remaining fraction of substrate, n is still theeaction order, r is an initiation parameter frequently set to 0.99, ss used as an adjustable nucleation parameter that can reduce Eq.24) to a first order reaction, and the quantity in parentheses (1 − ry)eplaces (1 − y) to prevent the initial rate from being zero [188]. Thisodel demonstrated a better fit with experimental data than con-

entional first order models, yielding a much tighter degradationurve [184].

The distributed activation energy model (DAEM) has beenuccessfully applied to both plant [91,117,189–196] and fossil95,190,197–202] biomass pyrolysis. The DAEM assumes that sev-ral irreversible first order parallel reactions having unique kineticarameters take place concurrently [202]. A continuous distribu-ion function, f(Ea), is used to represent the activation energiesrom the various reactions. The distribution function is approx-mated by a Gaussian distribution that yields a mean value andtandard deviation of Ea. Várhegyi et al. [189] have asserted that theAEM is the best method available for mathematically representing

he physical and chemical heterogeneity of substances. Miura andaki [203] proposed a revised distributed activation energy model

DAEM) that provides a method for estimating the frequency factornd f(Ea) without requiring a priori assumptions of either kineticarameter. This method was used to successfully predict weight

oss curves from the pyrolysis of coal at different heating rates. Caind Liu [204] advocated the use of a Weibull distribution modelo fit non-isothermal kinetic data. Under this approach, the kineticegradation for each biomass component is represented by one orore Weibull distribution functions. This procedure allows over-

apping processes in the TGA curve to be deconvoluted. The use ofhis model requires estimation of the scale and shape parametershat are unique to the Weibull distribution function.

The cacophonous debate over the relative merits of isothermal,on-isothermal, and isoconversional methods can sometimes over-rch the common thread among all these methods: the use of ainetic model that has been preordained by the scientific commu-ity. A significant liability can be incurred by simply consulting theTable” for the “best model” and expecting that it indeed is theorrect model. Galwey and Brown [13] commented that “the for-al models in the accepted set [i.e., the “Table”] are far too simple

o account for all the features of real processes”. Using a “gener-lized description” of the kinetics involved in solid state reactionystems offers the freedom and flexibility to choose the most appro-riate elements from the set of existing formal models in order toest characterize the various aspects of the true process [13]. The

ˇesták–Berggren (SB) equation [15], as shown below, was the firstuch “generalized description”:

d˛

dt= k˛c(1 − ˛)d(−ln(1 − ˛))e (25)

here c, d, and e are adjustable exponent factors that can be used toodel the different aspects of solid state reactions. The SB approach

ffers two distinct theoretical advantages [205]: (1) no implicitssumptions are made concerning the mechanism governing theolid state reaction and (2) no approximations or heavy-handedathematical intricacies are involved as the values of c, d, and e

an be calculated directly using a matrix system of linear equations.
yazovkin and Lesnikovich [206] acknowledged the importance of
he generalized description afforded by the SB equation, remark-ng that “. . .a comprehensive comparison of the [SB] approach

ith other methods based on model discrimination has demon-trated its preferability”. Other functions (e.g., polynomials, splines,


fractals, etc.) can also be used to provide a generalized phenomeno-logical description of the reaction, though incorporation of toomany adjustable parameters can be rather unwieldy and cause theparameters to lose their physical connotation and become strictlyprocedural factors [13,80].

4. Analysis of kinetic data obtained from various nutshells

The validity of kinetic parameters derived from thermo-gravimetric data has become a topic fraught with controversy.The substantial variation in apparent activation energies (i.e.,11.2–262 kJ mol−1) among different nutshells listed in Table 4is representative of the differences found across the entirebiomass spectrum. Even narrowing the type of biomass to aspecific species does not necessarily correlate to a satisfactorycontraction in the range of Ea values, as demonstrated by the val-ues of Ea for hazelnut shells (e.g., 40.3–144.9 kJ mol−1), almondshells (e.g., 11.2–254.4 kJ mol−1), and cashew nut shells (e.g.,130.2–293.5 kJ mol−1) in Table 4. Accordingly, Wilson et al. [207]aptly note in their recent publication about the thermal character-ization of tropical biomass feedstocks that the marked variabilityobserved in the kinetic parameters of cashew nut shells is simplya consequence of the geographical origin and “specific nature” ofgiven biomass materials. Besides the lack of parity in the kineticresults, few trends are evident from Table 4 regarding the heat-ing rate, the sample mass, or the kinetic model used. However, acomparative plot of Ea values for nutshells using various first order,single-step kinetic models, as shown in Fig. 1, does reveal that useof the DM model generally results in lower apparent activationenergies than those obtained using the corresponding standardArrhenius kinetic model (SM). Specifically the DM model yieldsvalues of Ea that are approximately 56% lower than those of theSM model, with respect to almond [183,208] and hazelnut shells[183,209,210], and about 31% lower than those given by the firstorder Friedman method in the case of peanut shells [52,183,211].The Ea values (78.9–131.1 kJ mol−1) obtained by Bonelli et al. [211]for hazelnut shells using the DM would appear to contradict theprevious findings, yet the Ea values reported by Demirbas’s group[209,210] for hazelnut shells may be uncharacteristically low as aresult of the probable heat and mass transfer limitations incurredby the use of large sample sizes (250–1000 mg), which has beenobserved to correspond with pronounced decreases in apparentactivation energy [212].

A conspicuous feature that is exposed by Table 4 concerns thelower Ea values obtained under isothermal, or static, conditionsfor both almond and coconut shells. Closer examination of theisothermal experiment [208] that recorded an overall Ea value of99.7 kJ mol−1 for almond shells reveals that the reaction model usedin the kinetic analysis was a first order, single-step SM. The Ea

value derived from this static experiment is 27% lower than theaverage minimum Ea value computed for almond shells whosenon-isothermal, or dynamic, reactions were modeled using an nthorder, parallel reaction SM [213,214], but this difference decreasesto just 6% when the latter group is replaced with almond shellswhose dynamic reactions were modeled using a first order, paral-lel reaction SM [216], which compares well with the 7% differenceobtained between the static and dynamic almond shell pyrolysisexperiments that were both evaluated using a first order, single-step SM [183,208]. In the case of coconut shells, there is a 67%decrease in the Ea values calculated for a non-isothermal study and
those for an isothermal study. Both studies were modeled usingparallel reactions with the salient exception that the dynamic testemployed the CR method, while the static test used the standardSM method. Although some of the differences within the activationenergies reported for both almond and coconut shells in Table 4

J.E.White

etal./JournalofA

nalyticalandA

ppliedPyrolysis

91(2011)

1–3311

Table 4Kinetic parameters for thermal decomposition of various nutshell types.

Nutshell type Heating profile, rate (◦C min−1) Temp. range (◦C) Sample mass (mg) Reaction scheme,order and model

Ea (kJ mol−1) Equipment Refs.

Almond shell Dynamic, 5–100 RT–850 NAa Single step1st order DM

42.4 Netzch STA 429 [183]

Almond shell Dynamic, 5–100 RT–850 NAa Single step1st order SM

92.9 Netzch STA 429 [183]

Almond shell Dynamic, 5–25 100–550b 5 2 parallel reactionsnth order SM

123.6–199.6 Perkin-Elmer TGA7 [213]

Almond shell Dynamic, 2 100–700 3–4 2 parallel reactionsnth order SM






Almond shell Dynamic, 2–25 100–700 3–4 2 parallel reactionsnth order SM


Almond shell Dynamic, 2–25 100–700 3–4 3 parallel reactionsnth order SM


Almond shell Static, 1.2 × 106 RT–900b 0.7–1 Serial dual step1st order SM

11.2–70.1 Pyroprobe 100 [215]

Almond shell Static, 1.2 × 106 RT–900b 0.7–1 Single step1st order SM

99.7 Pyroprobe 100 [208]

Almond shell Dynamic, 5–45 100–800 <2b 2 parallel reactions1st order SM

112.0–242.1 Mettler TG50 [216]


107.8–243.3 Mettler TG50 [216]


106.2–225.3 Mettler TG50 [216]


108.3–229.1 Mettler TG50 [216]


104.5–215.3 Mettler TG50 [216]


100.3–203.6 Mettler TG50 [216]

Brazil nut shell Dynamic, 10–100 RT–900 10 Single step1st order DM

47.2–82.0 Netzch STA 409 [217]

Cashew shell Dynamic, 5–50c RT–110110–900

<15 2 parallel reactions1st order CR

130.2–174.4 Setaram 92 [110]

Cashew shell Dynamic, 10 RT–1200 NAa Single step1st order CR

293.5 Netzch STA 409PC Luxx

[207]

Coconut shell Static, 13 250–750 1000 2 parallel reactions1st order SM

58.9–114.8 Tube furnace [218]

Coconut shell Dynamic, 5–50c RT–110110–900

<15 2 parallel reactions1st order CR

179.6–216.0 Setaram 92 [110]

12J.E.W

hiteet

al./JournalofAnalyticaland

Applied

Pyrolysis91

(2011)1–33

Table 4 (Continued)

Nutshell type Heating profile, rate (◦C min−1) Temp. range (◦C) Sample mass (mg) Reaction scheme,order and model

Ea (kJ mol−1) Equipment Refs.

Hazelnut shell Dynamic, 20 RT–800 NAa Single step1st order DM

40.3 Netzch STA 429 [183]

Hazelnut shell Dynamic, 20 RT–800 NAa Single step1st order SM

92.4 Netzch STA 429 [183]

Hazelnut shell Dynamic, 15 RT–900 10 Single step1st order DM

78.9–131.1 Netzch STA 409 [211]

Hazelnut shell Dynamic, 10 RT–500b 1000 Single step1st order SM

77.6–123.3 Netzch 429/409 [209]

Hazelnut shell Dynamic, 120 150–625 250 Single step1st order SM

89.8–128.6 Netzch 429/409 [210]

Hazelnut shell Dynamic, NAa RT–475b 1000 Single step1st order SM

97.1–144.9 Tube furnace [209]

Peanut shell Dynamic, 5–100 RT–400b NAa Single step1st order Friedman

84.5 Seiko TG-DTA6200 [52]

Peanut shell Dynamic, 15 RT–900 10 Single step1st order DM

44.3–71.5 Netzch STA 409 [211]

Peanut shell Dynamic, 10 RT–550 NAa Complex multi-stepnth order DAEM

150.0–183.3 NAa [219]

Pistachio shell Dynamic, 5–20 RT–800 20 Serial dual step1st order CR

124–149 Shimadzu TGA-50 [8]

Pistachio shell Dynamic, 5–20 RT–800 20 Serial dual step1st order FWO


Pistachio shell Dynamic, 5–20 RT–800 20 Serial dual step3/2 order CR


Pistachio shell Dynamic, 5–20 RT–800 20 Serial dual step3/2 order FWO


Walnut shell Dynamic, 5–40 RT–550 2 Serial dual step1st order SM

120.2–154.4 Perkin-Elmer Pyris 1 [220]

a NA, data not available.b Estimated or inferred value.c 10 ◦C min−1 to 110 ◦C, isothermal hold 110 ◦C for 10 min; non-isothermal to 900 ◦C, isothermal hold 900 ◦C for 10 min.

J.E. White et al. / Journal of Analytical and Applied Pyrolysis 91 (2011) 1–33 13

F arious 1st order, single-step kinetic models, including the biomass deactivation model(

mtwkicctaitiis

sdd4dthti[

Fh

ig. 1. Comparison of apparent activation energies for nutshells evaluated using vDM), the standard kinetic model (SM), and the Friedman model.

ay be attributable to dissimilarities in the thermal characteris-ics of the experiments themselves (i.e., static versus dynamic), itould appear from the preceding analysis that the nature of the

inetic approach used to model the reactions also has a substantivempact on the activation energy and should, therefore, not be dis-ounted. This latter observation is further borne out if the results forashew nut shells in Table 4 are evaluated [110,207]. In both cases,he experiments were conducted under non-isothermal conditionsnd modeled using first order CR kinetics. The only major differences that one lab group used a parallel reaction scheme [110], whereashe other scientific team used a single-step format [207]. Accord-ngly, the minimum Ea value obtained using the single-step methods 56% lower than the Ea value realized using the parallel reactioncheme.

The effect of heating rate on Ea for non-isothermal almondhell pyrolysis [214,216] modeled using two concurrent reactions isepicted in Fig. 2. The maximum Ea value for the first order reactionseclines 16% when the heating rate is increased from 5 ◦C min−1 to0 ◦C min−1, while the minimum Ea value for first order reactionsecreases 10% over the same heating rate increase. Interestingly,he minimum E value for nth order reactions rises 21% when the
a
eating rate is increased from 2 ◦C min−1 to 25 ◦C min−1. A his-ogram illustrating the effects of particle size on Ea is presentedn Fig. 3. A reduction in the particle size range of pistachio shells8] from 0.250–0.600 mm to 0.071–0.125 mm decreases the aver-

ig. 2. Comparison of apparent activation energies for almond shells at differenteating rates using parallel reaction models of either 1st order or nth order.

Fig. 3. Comparison of apparent activation energy values obtained for pistachio shellsfor various particle sizes using two-step sequential CR and FWO models.

age value of Ea by 10% and 20% for the CR and the FWO models,respectively. This result serves to reaffirm the theory that largerparticles require a higher level of energy to react because they aremore prone to transport limitations. Fig. 3 also depicts that the CRmodel consistently returns higher Ea values than the FWO model.Taken collectively, the data from Figs. 1–3 indicate that there isa strong correlation between the kinetic model that is chosen toevaluate Ea and the resulting value. Another probable source ofvariance in the presented nutshell data might be a result of changesin major reaction mechanisms occurring at different temperatures.The differences in lignocellulosic composition of the various nut-shell types, as shown in Table 5, are also a possible factor behindthe inconsistent Ea values. In the case of heterogeneous thermalreactions, the measured kinetic data are “. . .primarily influencedby the experimental conditions and not the reaction itself. There-fore, a change in experimental factors makes the interpretation ofthe estimated parameters impossible” [221]. In light of this, theunpredictability of the results provided in Table 4 illustrates thefrustrating inability to use kinetic parameters for anything otherthan providing local comparisons of the thermal stability of iden-tical processes.

5. Biomass thermal decomposition mechanisms

In addition to the large assortment of kinetic models availablefor biomass pyrolysis, the literature contains a diverse set of possi-ble decomposition pathways. It is generally accepted that biomass


Table 5Lignocellulosic composition of various nutshell types (dry wt% basis).

Nutshell type Cellulose Hemicelluloses Lignin

Almond shella [214] 31.1 38.0 27.7Almond shella [222] 37.4 31.2 27.5Almond shella [223] 50.7 28.9 20.4Almond shellb [224] 24.7 27.0 27.2Brazil nut shella [217] 48.5c – 59.4Coconut shell [225] 35.0 29.0 28.0Coconut shellb [224] 24.2 24.7 34.9Hazelnut shella [209] 25.9 28.7 44.4Hazelnut shella [223] 26.8 30.4 42.9Macadamia nut shellb [224] 26.9 17.8 40.1Peanut shell [226] 36.6 19.4 33.4Peanut shell [52] 35.7 18.7 30.2Pecan shellb [224] 5.6 3.8 70.0Pistachio shell [227] 60.6 NA 20.8Walnut shella [223] 25.6 22.1 52.3Walnut shellb [224] 21.0 18.8 32.7

icellulose = 0.9 (% galactose + % mannose) + 0.88 (% xylose + % arabinose).raction of plant material left after removal of lignin.

ptopvcpo4iPprrparapi

tptvoiclroigecwtchd

iBma

Cellulose “Active” Cellulose

CA

+vols. CB

+vols. CC

+vols.

Volatile tars

ki

kv

kcA kcB kcC

cellulose typically has a value of around 2500, whereas the DP for“active” cellulose is generally below 200 [251,252]. According tothe BS model the initiation step requires a high apparent activa-tion energy (242.7 kJ mol−1), yet only a 3–6% mass loss is observedduring this period [251,252]. It has been shown that the rate of cel-

a Dry ash free basis.b Calculated using the following formulas: % cellulose = 0.9 (% glucose) and % hemc Value reported is for holocellulose which is the term used to indicate the total f

yrolysis proceeds via the following primary transformations: ini-ially free moisture in the solid evaporates, followed by degradationf the more unstable polymers, and, finally, with increasing tem-erature the more refractory components begin to decompose andolatiles are released from the substrate matrix [228,229]. Solidhar residue that is formed during the primary decompositionhase, i.e., 200–400 ◦C, slowly undergoes aromatization in a sec-ndary pyrolysis stage that takes place at temperatures in excess of00 ◦C [229]. Apart from the broad scheme presented above, there

s little consensus on the mechanisms behind the pyrolysis process.erhaps this is in no small part because there has been “little realrogress towards understanding the chemistry of these solid stateeactions” [230]. Incidentally, it is appropriate to comment hereegarding the flagrant misuse of the term ‘mechanism’ in biomassyrolysis literature. Frequently, ‘mechanism’ is used interchange-bly with “model” to denote the characterization of the kineticate equation for a given decomposition reaction [230]. It would bedvisable to reserve the use of ‘mechanism’ for its traditional pur-ose of describing the detailed sequence of physicochemical steps

nvolved in the process of transforming reactants into products.Cellulosic decomposition is believed to proceed primarily by

wo separate routes that are dependent on the reaction tem-erature [9,231,232]. The first route predominates at loweremperatures (<280 ◦C) and involves reactions that lower the DPia bond scission, dehydration, free radical formation, creationf oxygenated moieties (e.g., carbonyls, carboxyls, and perox-des), evolution of CO and CO2, and ultimately the production ofarbonaceous residues. At higher temperatures (280–500 ◦C) cellu-ose degradation follows a different pathway. In this temperatureegion, depolymerization reactions associated with the cleavagef glycosidic bonds prevail and yield a tarry pyrolyzate contain-ng levoglucosan, other anhydrosugars, oligosaccharides, and somelucose decomposition products [9,233]. A possible third routemploying flash pyrolysis at even higher temperatures (>500 ◦C)ould involve the direct conversion of cellulose to low moleculareight gases and volatiles via fission, disproportionation, dehydra-

ion, and decarboxylation reactions [9]. The DP, crystallinity, andrystallite orientation of cellulose fibers in lignocellulosic materialsave been proposed as fundamental factors that regulate thermalecomposition behavior [234,235].

The seminal predictive mechanism for cellulose pyrolysis kinet-cs, which was developed during the mid 1960s to mid 1970s byroido and his colleagues [231,236–238], involved a competitive,ulti-step reaction sequence, as shown in Scheme 1. In Scheme 1,stable form of cellulose is converted to a more reactive cellulose

Scheme 1. Broido mechanism, where CA, CB, and CC denote successive fractions ofchars A, B, and C, respectively, that are produced along with accompanying volatilesformation.

(i.e., labeled “active” cellulose) at elevated temperatures, with rateconstant ki. The “active” cellulose can then degrade thermally bytwo parallel routes, forming either volatiles with no char, or pro-ceeding via a sequence of serial reactions to form chars CA, CB, andCC and accompanying volatiles.

In 1979, Shafizadeh [239] modified the Broido model slightly byneglecting the secondary reactions in the char and gas product. Thisproposed model, now known as the Broido–Shafizadeh (BS) model(Scheme 2), has become widely cited in biomass pyrolysis and gasi-fication literature [92,134,174,240–246]. Although the validity ofthis model has also been frequently assailed [6,92,241,247–249],there appears to be consensus that the main features of the BSmodel are serviceable. Specifically, it is widely acknowledged thatpyrolysis consists of primary initiation and fragmentation reactionsfollowed by secondary cracking reactions of volatiles [250]. Con-versely, the chief criticism regards the inclusion of the zero-orderinitiation step at low temperatures (<300 ◦C) to convert cellulosefrom an “inactive” to an “active” stage. Once cellulose is convertedfrom an “inactive” to an “active” state, pyrolysis is then able toproceed at higher temperatures. It is likely that this initiation stepwas included because several cellulose pyrolysis studies producedresults suggesting the initial stage of pyrolysis did not follow a firstorder reaction law. The initiation step is sometimes described asa depolymerization process because the DP of the starting, native

Scheme 2. Broido–Shafizadeh mechanism.

al and

lis

qcTtpttoloowcpadtit

sha(lattttsttcdiUaobtrcohgwpohfdLtnAcTmfca


ulose pyrolysis can be influenced by several structural elements,ncluding the DP, crystallinity, orientation, and accessibility of theample [234,251,253].

The thermal decomposition behavior of plant biomass fre-uently is assumed to be approximated by the sum of theontributions of the respective components [2,6,92,119,254–258].hermogravimetric (TG) curves for biomass pyrolysis data confirmhat the pyrolysis rate is related to the biomass composition. Theyrolysis curves of biomass species closely trace the decomposi-ion curves of their dominant lignocellulosic constituents; hence,he curves of primarily cellulosic biomass share resemble thosef pure cellulose, while degradation curves for biomass with highignin contents are similar to those of lignin standards [118]. Therder of decomposition of the biomass components is a functionf their intrinsic reactivity [259]; hence, the typical sequence inhich biomass degrades is given here: extractives, hemicellulose,

ellulose, and finally, ash. Notably, lignin was excluded from thereceding sequence because lignin begins to decompose beginningt temperatures that are equivalent to those seen for hemicelluloseegradation and continues to degrade slowly over a very broademperature range [260]. Typically, the rate of biomass pyrolysiss controlled by the rate of cellulose degradation which is subjecto autocatalytic effects [19].

The composition of lignin varies intrinsically according to itsource and the manner in which it is extracted [261]. The complexydrogen-bonding network present within lignin [262] serves asrigid structural lattice that is resistant to thermal decomposition

i.e., tends to char more than less stable cellulose or hemicellu-ose) [263–265]. Although there typically is no discernible peakssignable to lignin degradation because of its slow decomposi-ion over a broad temperature span [241,257], the wide, obliqueailing that follows the cellulose peak in DTG diagrams is sugges-ive of lignin degradation [49,266]. It has been noted that this broadailing baseline appears to be a prolongation of the first peak corre-ponding to hemicellulose degradation [214], suggesting that thehermal decomposition of lignin may occur simultaneously withhat of hemicellulose. Some researchers have been able to over-ome the challenges of delineating the boundaries of this poorlyefined lignin degradation zone, or fourth “lump”, by deconvolut-

ng the TG curve through second-order differentiation techniques.sing real-time molecular-beam, mass spectrometry (MS) Evansnd Milne [267] were able to monitor the chronological evolutionf primary pyrolysis oils from different biomass substrates underoth slow and rapid heating settings. Primary pyrolysis oils arehose that have not been subjected to temperatures (>600 ◦C) andesidence times (>1 s) that would promote secondary gas-phaseracking reactions. Mass spectra revealed that primary pyrolysisil composition was not significantly affected by changes in theeating rate of the wood substrate. The mass spectra from sweetum revealed that products containing hardwood lignin monomersere generated early and in high abundance. The earliest pyrolysisroduct to form was coniferyl alcohol at a mass to charge (m/z) peakf 180 amu. This was followed by a derivative of hemicellulose (3-ydroxy-2-penteno-1,5-lactone) at m/z 114 amu. A species derived

rom cellulose (CH3O+) evolved next at m/z 43 amu and a lignin-erived product (methylguaiacol) at m/z 138 amu eluted last [266].ignin peaks were observed to evolve sequentially over the dura-ion of the pyrolysis suggesting that lignin decomposition coincidesot only with the degradation of hemicellulose, but also cellulose.separate study [268] has found substantial interactions between

ellulose and lignin during pyrolysis at high temperatures (800 ◦C).
he presence of cellulose promoted the formation of guaiacol, 4-ethylguaiacol, and 4-vinylguaiacol but curtailed char production
rom secondary cracking reactions. The presence of lignin was asso-iated with increased production of levoglucosan, glycoaldehyde,nd hydroxyacetone from cellulose and reduced char formation.


These findings would appear to contradict the earlier postulate[2,6,92,119,254–258] that suggests the pyrolysis of lignocellulosicmaterials consists of three independent decomposition reactions,each involving a major pseudo-component: cellulose, hemicellu-lose, and lignin.

The temperature regime giving the most rapid decompositionrates is aptly designated the active pyrolysis zone, or sometimes,primary pyrolysis region [269]. The active pyrolysis zone can varydepending upon the heating rate applied in the thermal analy-sis and the type of biomass being investigated. Though there isdisagreement on the exact temperature boundaries of the activepyrolysis zone, it is generally accepted to be in the range of200–400 ◦C for lignocellulosic biomass substrates [229]: 95% of theweight loss from devolatilization occurs in this temperature band.Lignocellulosic biomass is thought to be stable until 200 ◦C, withminor mass losses associated with the removal of moisture and thehydrolysis of some extractives [54]. TGA data has revealed that thedegradation of the principal lignocellulosic components can be cat-egorized into discrete temperature ranges [191,254]. This indicatesthat a key step in the reaction mechanism of the primary biomasscomponents occurs at some critical transition temperature, Tc, dur-ing thermal decomposition.

6. Influence of experimental conditions on biomassreaction kinetics

Seemingly slight differences in certain process variables alongwith heat and mass transport limitations can have significantimpacts on the nature and rates of lignocellulosic decomposi-tion reactions [259]. Experimentally derived kinetic parametersare affected by reaction conditions, including temperature, heat-ing rate, residence time (i.e., for solids and volatiles), particlesize, pressure, gaseous atmosphere, and the presence of inorganicmineral content within the biomass material [85,229]. From theobservation that the amount of char produced in cellulose pyroly-sis varies proportionally with sample size and reaction pressure,it was inferred that the residence time of the volatiles in thebiomass matrix during pyrolysis is instrumental in determiningthe extent of char formation [239,270]. Extended residence timesfor the volatile components can promote secondary reactions (e.g.,cracking, cross-linking, and repolymerization) that lead to morechar formation. Conversely, the yield of volatiles can be adverselyimpacted if the residence time of various autocatalytic volatilespecies in the biomass substrate is too brief. Lewellen et al. [270]demonstrated that char formation can be nearly eliminated at veryshort residence times (i.e., 0.2–30 s) given appropriate selection ofthe operational temperature and heating rate. Cognizance of theprominent role played by the residence time of volatiles within thepyrolyzing biomass matrix foreshadowed the importance of dif-fusional constraints upon biomass kinetics because the residencetime of volatile vapors in the biomass matrix depends on the natureof heat and mass transfer through the substrate.

6.1. Heat and mass transport models

Internal and external heat and mass transport limitations oftenplay a pivotal role in influencing biomass pyrolysis kinetics andyields. Bamford et al. [271] developed the first kinetic model toaccount for heat conduction and generation in pyrolytic reactions.Kung [272] explored the dependence of weight loss rates on the
thermal conductivity of char. Through the use of dimensionlessgroups, Pyle and Zaror [273] were able to validate whether pyroly-sis reactions are controlled by kinetic processes or heat transfer (i.e.,either external or internal). Chan et al. [130] extended the function-ality of heat and mass transport models by considering a lumped

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cheme and also by avoiding the necessity of having to assume finalhar values. Alves and Figueiredo [274] provided a useful mathe-atical model for the pyrolysis of wet wood. These earlier models

rovided satisfactory assessments of the heat and mass transferimitations in pyrolytic reactions and the hallmark common to all

as their pragmatic approach, which lends itself well for possiblemplementation in an industrial environment. Since then, manytudies [275–289] have developed sophisticated kinetic models foriomass pyrolysis that incorporate various elements of transporthenomena. Generally these increasingly complex kinetics mod-ls are used to describe the pyrolysis of a single biomass particlend are contingent upon several assumptions. Although some ofhese transport models have been “validated” using simulated ormpirical data, it is unlikely that such complicated models will bef practical use in industrial applications [290]. The environment inctual pyrolytic systems is far from any normative standard usedn such models and the conditions “experienced” by one particle

ay be wildly different than those “experienced” by an adjacentarticle, let alone the substrate bulk. Furthermore, in real ther-al decomposition processes there are numerous factors that may

nfluence the rate of reaction that are often omitted from such mod-ls (i.e., lattice defects, impurities, melting, sintering, weak bonds,echanical strain, catalytic effects from metal reaction vessels,

nd ambient or evolved gases that may interact with the reactantr product) [13,291]. The validity of these models can also suf-er from the erroneous assumption that the particles in the bulkre entirely uniform and thus neglect the influence of particle-sizeffects [13]. This is impracticable when dealing with lignocellu-osic matter, whose constituent particles can have not only a rangef sizes and shapes but also different chemical compositions andeactivities. A conclusion drawn by Garn [70] is apropos here, “Lim-ting the diffusion models to collections with uniform geometry andize is not productive: it divorces the computation from reality.imple or uniform geometries are seldom encountered in prac-ice, and should not be accepted even as approximations withoutxperimental evidence”.

.2. Heating rate and particle size effects

The dependence of biomass pyrolysis kinetics on heating rate istill unresolved, with some evidence supporting the notion that these of different heating rates during biomass pyrolysis has minimal

mpact on the frequency factor [191], and other data indicating thatiomass conversion reactions are kinetically slower at higher heat-

ng rates [134,241,292]. Suuberg et al. [293] hypothesized that massransport limitations become increasingly influential as the heat-ng rate increases during rapid cellulose pyrolysis. The evaporativescape of tars from the substrate matrix via diffusive processes oronvective flow was proposed as the primary weight loss route.his result has been corroborated by recent research at Philiporris that employed EGA-FTIR [233,294]. Milosavljevic and Suu-

erg [292] observed that a shift in the mechanism of celluloseyrolysis occurs at 327 ◦C when heating rates above 10 ◦C min−1

re used, such that a relatively low apparent activation energies140–155 kJ mol−1) are obtained above this temperature. Belowhis temperature threshold at lower heating rates, Milosavljevict al. reported that the pyrolytic weight loss of cellulose was char-cterized by a high apparent activation energy (218 kJ mol−1).

It has been established that high heating rates significantlyower char yields when compared with slower heating rates108,295]. A study involving rapeseed revealed that the total quan-
ity of substrate that was decomposed decreased when the heatingate was increased, but the loss was more pronounced when theeating rate was changed from 25 to 50 ◦C min−1 (4.8 wt%) than itas changed from 50 to 100 ◦C min−1 (1.9 wt%). It was speculated
hat the increased heating rate allowed ample time for the comple-


tion of thermal degradation reactions. Grønli et al. [296] observedthat the apparent activation energy of cellulose (242 kJ mol−1 at5 ◦C min−1) decreased with increased heating rate (222 kJ mol−1 at40 ◦C min−1). It has been suggested that inter-particle diffusion lim-itations are accentuated at increased heating rates, thereby leadingto reduced kinetic rates [293]. Studies of cellulose pyrolyzed at15 ◦C min−1 and 60 ◦C min−1 yielded an apparent activation energyof 140 kJ mol−1, a value which is similar to the latent heat ofvaporization of fresh cellulose tar (141 kJ mol−1) [293]. Pyrolysis ofmustard straw and stalk under a nitrogen atmosphere at differentheating rates gave further evidence that the heating rate can influ-ence reaction kinetics [297]. The reaction order was observed tobe higher at lower heating rates, which may imply the occurrenceof complex, concurrent reactions. Nassar [298] noticed the exis-tence of a transition temperature corresponding to 360 ◦C, basedon changes measured in the apparent activation energy of sugar-cane bagasse pyrolyzed in air. Bagasse in the slow decompositionregime below this temperature had an Ea value of 139.7 kJ mol−1,while in the exothermic zone above this temperature bagasse hadan Ea value of 76.6 kJ mol−1.

In general, the solid-state kinetic theory involves the assump-tion that solid materials are at uniform temperatures duringpyrolytic decomposition. However, the poor thermal conductiv-ity exhibited by lignocellulosic substances impedes heat transferwithin biomass particles and can result in a particle tempera-ture gradient. As heating rates increase, the temperature gradientwithin the biomass particle increases, elevating the minimum tem-perature by which the pyrolysis process may progress [52]. Thekinetic rate of biomass decomposition eventually surpasses theassociated heat transfer rate as the reaction temperature rises.At some point the biomass degradation kinetics will be restrictedby heat transfer limitations and kinetic analysis then requires atransport model for the system [299]. The regime in which thiscrossover occurs (i.e., between kinetically driven rates and heattransfer regulated rates) is dependent upon the biomass particledimension, making the use of relatively small particles absolutelyimperative for the validity of the aforesaid uniform biomass tem-perature assumption. It has been reported [299] that the thermaldecomposition of biomass materials with particle thicknesses upto 0.2 mm may be kinetically evaluated up to about 450–500 ◦Cwithout accounting for internal heat transport restrictions. Lowertemperature thresholds, however, apply if the rate of external heattransfer to the particle surface is sufficiently slow. Coincidentally,most of the biomass pyrolysis conversion is completed in thistemperature range, which implies that conclusions derived fromprevious kinetic studies conducted at or below this temperaturezone are not affected by transport limitation inaccuracies. Detailedmathematical kinetic models have been developed to describelarger biomass particles up to 2 cm in dimension [279,300,301].

6.3. Significance of surrounding atmosphere

The ambient gas atmosphere in the reaction system can havea substantial impact on the behavior of biomass thermal decom-position. It has long been known that the thermal degradation ofwood is greater in the presence of air than in a vacuum [302].Thermal degradation in air has been shown to lower the activepyrolysis temperature and boost the combustion of chars at highertemperatures [4]. Roque-Diaz et al. [303] noticed that the ther-mal decomposition of sugarcane bagasse was more active in anoxidative environment than in an inert atmosphere. In this study,
the activation energy of bagasse in air between 170 and 250 ◦Cwas 1429% higher than it was in helium for the correspondingtemperature region (see Tables 8 and 9). The greater produc-tion of char in the presence of air supports the observation ofMamleev et al. [244] that oxygen interacts vigorously with the

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roducts derived from the thermal depolymerization of cellulose.educed tar production from biomass pyrolyzed in air can bexplained by returning to the reaction interface of the biomassubstrate. It is conceivable that the cellulose structure, which ishemically resilient to the penetration of even the most aggressivehemical agents (e.g., H2SO4), is equally inaccessible for oxygeniffusion; hence, only the surface of the cellulose is available forxidation.

.4. Catalytic effect of inorganic material

The inorganic mineral matter present in biomass has previouslyeen found to catalytically promote char-forming secondary tar-racking reactions while concomitantly suppressing additional tarormation [304]. Müller-Hagedorn et al. [305] found that alkaline

etal chlorides can substantially lower the pyrolysis temperaturef biomass. Anion type was also observed to affect the pyroly-is temperature, as given in order of decreasing influence by theollowing list: chlorides > sulfates > bicarbonates. The presence ofven trace levels (e.g., 0.1 wt% NaCl) of mineral matter in biomassan alter pyrolysis behavior appreciably [92,306]. The pyrolysisate, tar yield, and initial degradation temperature are all observedo increase with decreasing mineral content [306]. For instance,ood (e.g., 1 wt% [average] ash) undergoes more rapid thermalegradation than bagasse (e.g., 4 wt% ash) because of the lowerineral content in wood [307]. Broido [308] discovered that cel-

ulose pyrolysis was affected by the addition of as little as 0.15 wt%otassium carbonate. Tang [309] detected that the reaction rateor wood pyrolysis jumped by two orders of magnitude whenwt% monobasic ammonium phosphate was added. Exceptions

o this rule include species that have high lignin contents cou-led with high potassium levels (e.g., rice husks, ground nutshells,nd coir pith). Lignin is known to be intractable in pyrolyticrocesses [309,310] and potassium strongly promotes char gasi-cation [306]. Nassar [298] concluded that the presence of alkalinealts in biomass (i.e., rice straw and bagasse), whether addedr innate, acts to lower the apparent activation energy of ther-al reactions and promote the formation of char. Várhegyi et al.

311] treated sugarcane bagasse samples with dilute inorganicalt solutions (i.e., MgCl2, NaCl, FeSO4, and ZnCl2). Treated andntreated samples were then thermally decomposed and the evo-

ution of low molecular weight products was evaluated using MS.he treated samples had higher char yields than the untreatedamples, except in the case of MgCl2 for which there was no sig-ificant difference. The increased char production was attributedo the alteration of reaction pathways by the salts. The MS inten-ities of all the catalytically treated samples were lower thanhose of the untreated samples, suggesting that the presence ofnorganic additives suppresses the secondary cracking of high

olecular weight primary products. It was speculated that inor-anic salts cause the fibrous structure of the bagasse to expand,hereby assisting the release of vapors from the solid matrix. Wash-ng experimental samples with water before has been shown toliminate much of the mineral salt content present in the nativeiomass [305]. Removal of the catalytically active mineral mat-er via washing has been linked with a corresponding increase inhe apparent activation energy of biomass. Teng and Wei [312]ompared the kinetic data from pyrolysis experiments that uti-ized both water-washed rice hulls (i.e., 80 ◦C water for 2 h) andnwashed rice hulls. The main lignocellulosic components in theashed rice hulls displayed higher peak pyrolysis temperatures

nd activation energies than the untreated rice hulls. Further-ore, the washed rice hulls also had higher volatile and lower

har yields, which were ascribed to the loss of hydrocarbon moi-ties capable of promoting cross-linking reactions that foster charroduction.


7. Variations in kinetic data

7.1. Systematic errors

Systematic errors are presumed responsible for much of thescatter present in published values of the kinetic triplet. The pres-ence of unrecognized secondary reactions coupled with the highlydisparate chemical composition of biomass materials immediatelydraws attention to mechanistic inadequacies, which are usually thechief source of systematic errors [63]. Lack of a standard procedurethat establishes rigid criteria for evaluating the endpoint of pyrol-ysis reactions has introduced further discrepancy into the derivedkinetic parameters. Some laboratories take the final substrate mass,wf, to be the remaining ash content after the entire reaction, whileother laboratories deem the final substrate mass to be the massremaining after the rapid pyrolysis zone. Inconsistencies in thedefinition of wf have doubtlessly introduced further scatter in thepublished kinetic data. There is also a fundamental flaw inherent tothe differential isoconversional methods that have been customar-ily used to evaluate kinetic data collected by non-isothermal TGA.Temperature values for given degrees of conversion are necessar-ily obtained by nonlinear interpolation of the conversion data andconversion rates must be extracted via numerical differentiation ofthe experimental results. Both of these techniques are extremelysensitive to experimental noise and slight systematic inaccuraciesin this data can be grossly amplified in the corresponding differen-tial conversion rates [173]. One approach to solve the systematicerrors related to noise in the data is to apply generalized functionsthat will provide a better fit to the experimental conversion datathan the traditional Arrhenius rate expression.

7.2. Temperature gradients

In thermal kinetic measurements, systematic errors may arisenot only from methodological errors or mechanistic inaccuraciesbut also from fundamental instrumental shortcomings. Flynn [17]commented that “temperature imprecision is probably the greatestsource of error in thermal analysis experiments”. It has been positedthat the reduction in apparent activation energy and frequency fac-tor values that occurs during rapid pyrolysis may be the result ofunfulfilled heat requirements [313]. During the highly endother-mic cellulosic devolatilization, the demand for heat by the chemicalreaction and the endothermic pyrolysis reaction overwhelms thefinite heat supply which results in a phenomenon wherein the pro-cess temperature remains almost constant throughout the reaction.Consequently, thermal equilibrium between the biomass substrateand the heating apparatus may not be realized at all experimen-tal conditions, especially if heat transfer characteristics betweenthem are poor, in which case there will be a large temperaturegradient between the sample and the thermobalance. In turn, thisthermal lag can cause substantial errors if the researcher simplyassumes that the sample realized the same temperature as thethermobalance furnace. Indeed, Sharp [27] remarked that “tem-perature gradients of 5 ◦C are unavoidable, 10 ◦C are common, and20 ◦C, or even more, not unknown”. Samples that have a variabletemperature distribution will not react uniformly and the kineticdata generated from such processes “may not only be meaninglessbut also can be misleading” [27].

7.3. Temperature lag

Because the size of the sample had long ago been implicated asa crucial factor in determining the magnitude of the temperaturegradient, it was recommended that sample size be kept as smallas possible [212]. The use of small sample sizes in thermoanalyt-ical studies, however, prevents the placement of thermocouples

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n direct contact with samples, effectively requiring thermocoupleips to be positioned proximally to the sample in order to estimateample temperature [313]. This inability to accurately measure theample temperature results in conventional thermocouple thermalag, which is the difference between the true sample tempera-ure and an externally measured sample temperature. Thermal lagas identified by Antal et al. [125] as an insidious agent persis-

ently lurking within thermogravimetric studies [86]. Antal et al.314] discovered as much as a 25 ◦C temperature difference whenhermocouple position in the thermal analyzer was switched fromn upstream to a downstream position relative to the sample.ariations in the temperature measurements among the different

hermobalances utilized by laboratory researchers were isolated inround-robin study as a likely source of the significant variation iniomass kinetic data [296]. The threat of instrumental error arisingrom thermal lag is so acute that the architect of the round-robintudy advised that the resulting paper [296] was the single “mostmportant paper ever written on thermogravimetry as applied toiomass” and that it is due to these instrumental limitations thatesearchers now “favor ‘model-free’ approaches” [315].

Although it is common for small samples to be employed inGA, their use can give rise to the aforementioned thermal lagffect and its deleterious consequences. Milligram-size samplesre commonly used in thermogravimetry to combat the influencef transport phenomena, yet experimental results have shownhat even very small samples (e.g., less than 1 mg) still experi-nce diffusional effects [316]. Additionally, the surface to bulkatio increases with decreasing sample size so that the impor-ance of surface reactions in small sample sizes is often magnifiedt the expense of underlying rate-controlling processes. Thus, theinetic data obtained from thermal studies involving small sam-le sizes often provides an unsatisfactory correlation with large

ndustrial processes [316]. The needs of both the scientific andndustrial community would be better served if the thermogravi-

etric characterization of biomass substrates were performedcross a continuum of sample sizes, ranging from a single-crystalayer to large gram-size samples.

.4. Kinetic compensation effect

In biomass pyrolysis the apparent activation energy has fre-uently been observed to increase with the frequency factor. Asarly as 1980, Chornet and Roy [317] commented that a kineticompensation effect (KCE) exists in the pyrolysis of various biomassaterials such that there is a definite linear correlation between

he variables ln A and Ea. According to the (KCE), any alteration inxperimental conditions that impels Ea to change will also promptcomplementary compensating response in A. A group of reac-

ions that demonstrates a linear fit of ln A and Ea values is known asompensation set. It is claimed that reactions within a given com-ensation set exhibit unique properties, including shared chemicalharacteristics and the existence of an isokinetic temperature, Ti,t which all reactions advance at the same rate, ki [318]. The linearelation between ln A and Ea is derived from the Arrhenius equationnd is provided below:

n A = ln kiso + Ea

RTiso(26)

Although several theories have been expounded that impartither a mathematical or physicochemical explanation for theppearance of such compensating behavior [319–323], the valid-
ty and physical relevance of the KCE are a source of contentiousebate amongst the scientific community [318,324]. Much of thekepticism regarding the KCE has arisen because a satisfactoryechanistic interpretation of such compensation behavior has not
et been established [325,326]. Indeed, it has been asserted that the


presence of a KCE when studying “identical specimens under thesame conditions must be a false effect and either the result of scat-ter of the experimental data, misapplication of kinetics equations,or errors in the experimental procedures” [17].

A possible explanation for the KCE arises from the inevitablescatter of ln A and Ea/R values, which occurs when thermoanalyticaldata is collected over narrow bands of rate and temperature [17].Agrawal [28] concluded that the “compensation behavior for thepyrolysis of cellulosic materials reported by Chornet and Roy [317]is primarily due to inaccurate temperature measurement and largetemperature gradients within the sample”. Further experimentalwork by Narayan and Antal [313] revealed that values of Ea andlog A monotonically decrease with increasing thermal lag in such afashion that the ratio Ea/log A remains nearly unchanged. Besidesthe existence of experimental inaccuracies, computational errorsand inappropriate conversion function selection are also commonlycited as important causal factors behind the KCE [327].

Garn [328] submitted another viable justification for the occur-rence of the KCE in solid state reactions explaining that the KCEis mathematically inevitable because of the reciprocal relation-ship between A and exp(−Ea/RT) in the Arrhenius expression. Anychange in one of these calculated quantities necessarily demandsa compensatory change in the other. Given that the temperaturerange over which most reactions are studied is so narrow that Tmay be considered essentially constant and that measured rate con-stants generally remain within two to three orders of magnitude incontrast to calculated pre-exponential terms which vary by twentyor more orders of magnitude, Garn contends that the ensuing lin-ear relationship between ln A and Ea is but a foregone mathematicalconclusion.

Reports abound in thermal analysis literature regarding obser-vations of an experimental KCE, whose existence is oftensubstantiated solely by correlating ln A with Ea. Unfortunately, theveracity of an experimental KCE is rarely transparent from plots ofln A versus Ea because, as Agrawal [329] remarked, “the occurrenceof a linear relation between ln A and Ea does not imply the occur-rence of a true compensation effect”. Agrawal declared that thethermal analysis community would be better served if the validityof potential compensation effects were confirmed using Arrhe-nius plots of ln k versus the inverse temperature. Reaction systemswhose Arrhenius plots do not contain a single point of concurrenceare devoid of a KCE. A compensation set that behaves linearly in aplot of ln A versus Ea but does not display a unique isokinetic pointin the Arrhenius plot may be described as having a pseudo KCE.Although some researchers [330,331] questioned Agrawal’s pro-cedures to distinguish between a true and a pseudo KCE, Agrawal[332,333] rejoined that these criticisms were unfounded and theArrhenius plot has since become the “critical test” for validatingthe KCE [318,324].

8. Sugarcane bagasse case study

8.1. Sugarcane bagasse – background and properties

Agricultural residues and food processing wastes from agro-industry represent an important source of biomass havingwidespread availability. Sugarcane is an important agriculturalcommodity that is cultivated in over 100 countries with an annualworldwide production in 2008 of 1.74 billion metric tons [334].It is grown commercially in a broad swath that extends roughly
from 13.5◦ latitude north of the Tropic of Cancer (i.e., Salobrena,Spain) [335,336] to 8◦ latitude south of the Tropic of Capricorn(Salto, Uruguay) [336]. Sugarcane is a perennial C4 grass whosephotosynthetic efficiency is virtually nonpareil in the plant king-dom [337]; only the giant sequoia tree (Sequoia gigantea) is capable

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f producing more biomass [338]. Despite the current excite-ent surrounding the efficacy of microalgal carbon dioxide fixation

339–345], sugarcane still appears superior to microalgae at con-erting incident solar radiation into carbohydrates (maximumecorded solar energy capture efficiency of 5.0% for sugarcanen Hawaii [346] and 5.1% for sugarcane in S. Africa [347] versus.9% for green algae in Thailand [348]) [346–351]. Interestingly,he debate regarding whether microalgae or sugarcane is the bet-er synthesizer of sunlight is not new and dates back at least0 years, when Ledón and González [352] determined that sug-rcane had a higher photosynthesis conversion efficiency (3.4%)han the microalgae Chlorella pyrenoidsa. It is germane to point outhat microalgae grown outdoors under full sunlight experiences aubstantially lower rate of photosynthesis than microalgae that isultivated in a precisely controlled laboratory environment becausef the “light saturation effect”. Productivity of microalgae grownnder full sunlight is at least 75% lower than that of microalgaerown under low level lighting [353]. Alexander [354] deter-ined that the average annual energy output for a first-generation

nergy cane grown in Puerto Rico was 1138 GJ ha−1 year−1, whileuber et al. [355] reported a maximum annual energy produc-

ivity of 1460 GJ ha−1 year−1 for sugarcane. These values are 23%nd 57% greater than the net annual energy yield for microalgae928 GJ ha−1 year−1), respectively, as calculated by Christi [356].

The predominant components in sugarcane are water, solubleolids, of which sucrose is foremost, and lignocellulosic fiber, ofhich cellulose is the main constituent. The composition of sug-

rcane is influenced by numerous environmental determinantsnd cultural practices, including climatic factors, weather haz-rds, topography, soil type, sugarcane variety, planting practices,rainage, irrigation, diseases, pests, fertilization, and harvestingethods [336–359]. Contemporary harvesting of sugarcane is per-

ormed with mechanical combines that cut whole cane stalk intoections called billets. The billeted sugarcane is then processedn a sugar mill where it is macerated and shredded using swing-ammer crushers. After this stage, the crushed cane is conveyed totrain of multiple-roller mills to be pressed. During this step, imbi-
ition water is introduced to the system so as to increase the juicextraction efficiency at each successive mill. The shredded fibrousesidue that exits the last mill is called bagasse.
Given its provenance from sugarcane, it is natural that bagasselso exhibits great compositional and morphological heterogene-

able 6omposition of whole bagasse from various origins (dry wt% basis).

Origin Cellulose Hemicellulose

Australia [367] 41.3 30.3China [368] 43.6 33.5Egypt [369] 41.8 27.5Guadeloupe [307] 41.7 28.0Mauritius [361] 26.6 29.7Mexico [361] 34.9 31.8Mexico [361] 37.6 31.1Mexico [370] 40.0 32.0Philippines [364] 34.9 31.8South Africa [371] 38.5 31.4Hawaii [361] 38.1 23.7Hawaii [372] 36.5d 25.0d

Louisiana [361] 36.8 29.4Louisiana [373] 36.3 28.2Louisiana [374] 50.4 28.5Louisiana [375] 36.7f 24.7f

Puerto Rico [364] 30.1 29.6

a Alcohol, toluene extractives; represents wax fraction.b NA, data not available.c Hot water extractives.d Calculated using the following formulas: % cellulose = 0.9 (% glucose) and % hemicellue Alcohol extractives.f An amount equivalent to the detected level of arabinose (2.4 wt%) was deducted from


ity. On average, fresh bagasse consists of 44–56 wt% moisture,43–52 wt% lignocellulosic fiber, and 2–6 wt% soluble solids, and1–5 wt% inorganic matter [360–362]. The amount of ash in bagasseis largely dependent on the amount of dirt brought in from thefields with the sugarcane [307]. The stem structure of sugarcaneis akin to that of other monocotyledons (grasses) with the excep-tion that the sugarcane stalk is not hollow as are most grass stems[363]. Sugarcane bagasse contains four major structural compo-nents [361,362,364], viz.,

(1) Long, hard-walled cylindrical cells that compose the rind aredesignated as the true fiber.

(2) Fibrous vascular bundles, also called sclerenchyma bundles,comprised of large exterior xylem vessels and separate group-ings of small phloem vessels and thick-walled, lignifiedsclerenchyma cells, respectively, in the interior.

(3) Soft, thin-walled parenchyma cells from the inner stalk that areknown as pith.

(4) A dense, non-fibrous epidermis commonly referred to as wax.

Dry bagasse typically contains about 50 wt% true fiber, 15 wt%fibrovascular bundles, 30 wt% pith, and 5 wt% wax [338,361,362].The proportion of the major components in bagasse depends largelyon the aforementioned environmental factors that influence sug-arcane, the variety of cane, its maturity at harvest, harvestingpractices, and the milling efficiency [361]. Table 6 provides acompositional analysis of bagasse cultivated in various countries.Multiple listings for a single country indicate that the analyzedbagasse came from samples collected at different locations withinthe country, in different years, or possibly both. An indication of thecompositional variation that arises because of varietal differencesin sugarcane is given in Table 7. The danger of falsely assumingthat bagasse samples collected from a sugar mill pile are uniformlyhomogeneous is clearly illustrated in Table 7 by examining thecompositional differences that occur between “average” samples1 and 2. The chemical composition of bagasse varies between 27and 50% cellulose, 20 and 35% hemicellulose, 10 and 25% lignins,
and 1 and 6% ash on a dry weight basis. A nominal composition of40% cellulose, 32% hemicellulose, 20% lignin, 6% extractives, and 2%ash for dry bagasse is sometimes reported [365,366].
The calorific values of most biomass materials and fossil fuelsare commonly reported in terms of the gross calorific value (GCV)

Lignin Ash Extractives

10.0 6.1 12.318.1 2.3 0.8a

17.9 2.0 NAb

21.8 3.5 4.014.3 2.4 NAb

22.3 2.3 2.8c

19.4 3.2 2.2c

20.0 2.0 6.022.3 2.3 NAb

22.2 3.1 NAb

20.5 2.4 2.5c

25.5 3.7 1.8e

21.3 2.9 4.0c

20.2 2.3 12.814.9 2.0 4.224.5 4.4 NAb

18.1 3.9 NAb

lose = 0.9 (% galactose + % mannose) + 0.88 (% xylose + % arabinose + % uronic acids).

the total glucan content (39.1 wt%) and attributed to the hemicellulose complex.


Table 7Composition of whole bagasse from different sugarcane varieties (dry wt% basis).

Origin Variety Cellulose Hemicellulose Lignin Ash Extractives

Australia [376] Badilla 28.2 22.2 24.4 4.1 3.0a

Australia [376] 1900 30.6 23.9 24.4 2.6 1.9a

Australia [376] Mixed sample 1b 32.5 24.3 21.7 2.5 3.2a

Australia [376] Mixed sample 2b 28.0 21.8 21.7 2.5 4.4a

Cuba [361] Mecladas P. Noriega 46.6 25.2 20.7 2.6 4.1c

Mauritius [361] M 134.32 40.6 28.4 19.6 6.3 3.1c

South Africa [361] PO3 2878 45.3 24.1 22.1 1.6 4.7c

Florida [361] CL-41-233 30.6 26.6 18.2 1.0 15.1c

Hawaii [361] 44-3098 38.7 27.1 21.6 4.6 2.6c

Hawaii [361] 37-1933 38.3 27.3 19.4 1.3 2.2c

Hawaiid [377] H65-7052 36.2e 22.5e 24.2 4.0 4.4f

a Alcohol, benzene extractives.b Samples were collected from a bagasse pile containing different varieties and thus represent an “average” variety.

icellu

arcvGtaoG9etf1

absstplamUttves

8

bdc[aayamdoda

c Hot water extractives.d NIST reference material 8491.e Calculated using the following formulas: % cellulose = 0.9 (% glucose) and % hemf Alcohol extractives.

nd the net calorific value (NCV). The GCV is the amount of heateleased from a specific quantity of fuel (initially at 20 ◦C) after it isombusted and the products cool back to 20 ◦C. The latent heat ofaporization of water is included in the GCV. The NCV is equal to theCV less the latent heat of the vapor, and it is often used to denote

he true calorific value of moist biomass. Hugot [378] reported theverage GCV of dry bagasse to be 19.25 MJ kg−1 and the average NCVf dry bagasse to be 17.78 MJ kg−1. Hugot also reported the averageCV and average NCV of wet bagasse (i.e., 50 wt% moisture) to be.62 MJ kg−1 and 7.64 MJ kg−1, respectively. Behne [379] analyzedleven varieties of dry, ash-free bagasse and found the average GCVo be 19.52 MJ kg−1. Nicolai [380] disclosed that the GCV of dry, ash-ree bagasse obtained from sugarcane in six countries ranged from9.13 to 23.97 MJ kg−1 with a mean value of 20.42 MJ kg−1.

There are nearly 1200 sugar mills in 80 nations that processlmost 1.2 Gt of sugarcane annually [381]. About 280 kg of wetagasse (i.e., 50 wt% moisture) is generated per metric ton of milledugarcane. Up to 90% of this quantity is combusted in furnaces toupply the heat and steam requirements for the sugar mill, whilehe remainder is simply discarded by burning, composting, stock-iling, or landfilling it [382]. Bagasse is often intentionally burned at

ow efficiencies to avoid the preceding disposal issues. The extrav-gant intake of raw bagasse as a principal fuel source at sugarills could be deemed “wasteful”, considering its low NCV [383].pgrades to aging sugar mill boiler units and ancillary infrastruc-

ure could decrease overall sugar mill energy demand to 50% ofhe bagasse generated [384]. Naturally, the thermochemical con-ersion of sugarcane bagasse into a gaseous or liquid fuel wouldnhance the overall energy value of this residue and solve a sub-tantial biomass disposal dilemma.

.2. Review of sugarcane bagasse pyrolysis studies

As expected, thermoanalytical investigations of sugarcaneagasse pyrolysis have revealed that there are essentially threeistinct zones of degradation, corresponding with the main ligno-ellulosic fractions in bagasse (hemicellulose, cellulose, and lignin)366,385,386]. Although bagasse pyrolysis has been detected as lows 150 ◦C [386], it is generally agreed that active pyrolysis occursbove 200 ◦C [135,385,386] and below 450 ◦C [387]. Bagasse pyrol-sis experiences its maximum decomposition rate between 250nd 400 ◦C [135,307,386–388]. These results are in good agree-
ent with a Cuban study [389] on the kinetics of the thermal
ecomposition of sugarcane bagasse, which indicated that volatilerganics were evolved beginning at 205 ◦C, while the maximumegradation rate of hemicellulose and cellulose occurred at 305nd 350 ◦C, respectively. Antal’s group [366] pyrolyzed bagasse

lose = 0.9 (% galactose + % mannose) + 0.88 (% xylose + % arabinose + % uronic acids).

and obtained two peaks for hemicellulose degradation: a small,poorly defined peak at 240 ◦C and a larger peak at 310 ◦C; thelargest peak observed was at 370 ◦C which was attributed to cel-lulose decomposition. Nassar [385] obtained a robust, bifurcatedexothermic peak between 280 and 520 ◦C. An endothermic peakattributed to the vaporization of volatile products interposed itselfin the exothermic peak at about 420 ◦C. The first exothermic spike at350 ◦C was credited to oxidation of the products, while the secondexothermic spike at 460 ◦C was reasoned to denote the oxidation ofchar. Researchers have observed inflection points in the TG curvesfor bagasse at 325–350 ◦C indicating a transition in the pyrolysisdecomposition mechanism [303,385,390]. At temperatures above325–350 ◦C bagasse pyrolysis is primarily a result of lignin andcellulose devolatilization, while below 325–350 ◦C lignin and hemi-cellulose degradation control the rate of bagasse decomposition[307,385].

Similar to most other biomass types, pyrolysis of sugarcanebagasse under an oxidative environment influences the reactiondynamics by lowering the pyrolytic reaction temperature and sub-stantially increasing the rate of bagasse volatilization [307,385]. Forinstance, it was found that 5 wt% of bagasse is vaporized at 262 ◦C inN2, 240 ◦C in dry air, and 228 ◦C in O2 [307]. A recent investigation ofbagasse pyrolysis by Munir et al. [387] found that peak devolatiliza-tion under an oxidative (air) environment occurred between 304and 312 ◦C, while rate of weight loss under inert (N2) conditionsreached its apex between 346 and 355 ◦C. The average devolatiliza-tion rate for oxidative degradation was calculated to be twice thatfor devolatilization in an inert atmosphere. Besides lowering thepeak pyrolysis temperature and active pyrolysis zone, the presenceof oxygen was associated with an increase in overall apparent acti-vation energy. It has also been observed that the elevated levels ofmoisture present in raw bagasse can retard the onset of primarypyrolysis by requiring additional time for drying, thereby affect-ing the overall pyrolysis rate and product yields [388,391]. Theapparent activation energies of bagasse are in the vicinity of thosereported for hardwoods [385], which is presumably because thechemical constitutions of sugarcane bagasse (i.e., 32% hemicellu-lose, 40% cellulose, and 20% lignin) [365] and hardwoods (i.e., 35%hemicellulose, 39% cellulose, and 19.5% lignin) [9] are similar.

8.3. Analysis of published kinetic data for sugarcane bagassepyrolysis

Extensive data on the thermal decomposition of sugarcanebagasse under different reaction environments is provided inTables 8–10. Parenthetical number ranges following certain Ea

values demarcate the temperature regions in which the given

al and

aasica1[ed2av1[afalsaEs

abnwassfbtnpcaapdoaatnaEvvkus1p2vi

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ctivation energy values are valid. Despite the good agreementmong intra-laboratory results, there is a frustrating lack of con-istency when results are compared amongst groups. As illustratedn Table 8, the sheer breadth of Ea values, irrespective of pseudo-omponent fractions, obtained from slow bagasse pyrolysis undern inert atmosphere (N2, He, or Ar) is simply egregious (i.e.,4 kJ mol−1 [assumed hemicellulose fraction] to 460.6 kJ mol−1

assumed cellulose fraction]). A sizeable gulf in Ea values remainsven after the comparison is restricted to just hemicelluloseegradation (e.g., 14 kJ mol−1 [assumed hemicellulose fraction] to50 kJ mol−1 [hemicellulose fraction]). A comparison of apparentctivation energy values for rapid pyrolysis under nitrogen pro-ides more consistent results, as shown in Table 10, wherein a6% difference was observed between two independent studies383,391] conducted in furnaces at a heating rate of 60,000 ◦C min−1

nd with a residence time of 30 s. The choice of kinetic model sur-aced as a parameter that had a crucial impact on the evaluatedpparent activation energy. An average total Ea value was calcu-ated for every pyrolytic process in Table 8 that incorporated aequential reaction model (N.B., all of the experiments employingsequential model used unwashed bagasse). The mean value of

a for the five sets of sequential processes is 72.3 kJ mol−1, with atandard deviation of 13.1 kJ mol−1.

In Table 8, fourteen sets of kinetic data evaluated using a par-llel reaction model are presented, of which three utilized washedagasse and the remainder used unwashed bagasse. It should beoted that the four sets of data for the unwashed bagasse thatere obtained by Garcia-Perez et al. [382] at various heating rates

ll have identical Ea values resulting from the use of a compen-atory shift in the log A values and can, thus, be considered as aingle set of unique Ea values. The apparent activation energiesor each of the resulting eight unique sets of data for unwashedagasse pyrolysis were separated into three fractions accordingo the respective contributions from each lignocellulosic compo-ent. An overall average Ea value was obtained by normalizing theseudo-component fractions according to a nominal average ligno-ellulosic composition of sugarcane bagasse [361] taken on a drysh- and extractive-free basis (i.e., 24% lignin, 32% hemicellulose,nd 44% cellulose). The mean value of Ea obtained for the eightarallel processes was determined to be 159.6 kJ mol−1, with a stan-ard deviation of 40.5 kJ mol−1. The 121% average increase in Ea thatccurs when the kinetic model is amended from a consecutive toconcurrent reaction scheme is a stark reminder that inappropri-

te model selection can have dire implications on the validity ofhe generated kinetic parameters. The four isoconversional tech-iques (Friedman, CR, FWO, and KAS) used by Yao et al. [245] tonalyze bagasse pyrolyzed under inert conditions (N2) had a meana value of 167.0 kJ mol−1, which is only 5% higher than the meanalue for the concurrent model but also 131% higher than the meanalue for the consecutive model. Miranda et al. [370] evaluatedinetic data from bagasse pyrolyzed under a nitrogen atmospheresing the Friedman differential isoconversional method, along witherial and parallel methods, and obtained an overall Ea value of54.4 kJ mol−1, which was calculated based on their reported com-osition of bagasse (i.e., 40 wt% cellulose, 32 wt% hemicellulose, and0 wt% lignin). This value compares reasonably well with the Ea

alue of 168.5 kJ mol−1 reported by Yao et al. using the Friedmansoconversional method.

The impact of an oxidative environment versus that of an inerttmosphere upon bagasse pyrolysis was investigated by severalesearch groups [303,387,392,393]. In each case, there was an
ncrease in apparent activation energy when the inert (N2) atmo-phere was replaced with an oxidative environment. Excludinghe extraordinary 1429% increase in E given by Roque-Diaz et al.303], the average increase in Ea on the basis of five studies byhese four groups was 47% with a standard deviation of 17%. Nassar

[298,385,392] conducted his bagasse pyrolysis experiments undertwo different types of inert atmosphere (N2 and He). The Ea val-ues recorded for pyrolysis under nitrogen were 87.9 kJ mol−1 and46.7 kJ mol−1 for the low and high temperature regions, respec-tively, while the corresponding Ea values obtained for pyrolysisunder helium were 118.1 kJ mol−1 and 69.1 kJ mol−1, respectively.These results suggest that the type of inert atmosphere also hasan impact on the apparent activation energy of sugarcane bagasse.This is consistent with findings in literature that report a shift in theDTA and DTG peaks toward higher temperatures as the molecularweight of the inert gas increases [138,398].

The last set of kinetic data given for oxidative pyrolysis inTable 9 was obtained from non-isothermal thermogravimetricexperiments run at different heating rates using unwashed bagasseand then estimated as a function of temperature using the V iso-conversional technique [135]. The first Ea value (76.1 kJ mol−1)reported occurs in the region of 2–5% bagasse conversion (i.e.,T < 200 ◦C) and corresponds with the dehydration of the bagassesample. The highest Ea value (333.3 kJ mol−1) is associated withthe primary pyrolytic combustion zone (i.e., 200 ◦C ≤ T ≤ 350 ◦C),where there is 15–60% bagasse conversion. The final step involvesthe secondary combustion of the initial pyrolysis products (i.e.,400 ◦C ≤ T ≤ 600 ◦C); this stage attains 70–95% bagasse conversionand has an Ea value of 220.1 kJ mol−1. Interestingly, the aforemen-tioned highest Ea value (333.3 kJ mol−1) that was obtained usingthe V isoconversional approach in Table 9 is still 27.6% lowerthan the maximum Ea value (460.6 kJ mol−1) [citation here] pro-vided in Table 8 for milled bagasse pyrolyzed under nitrogen,yet it is 35% greater than the next highest value (246.5 kJ mol−1

[cellulose fraction]) [97] given in Table 8 for unwashed bagassepyrolyzed in nitrogen, 47% greater than the value (226 kJ mol−1

[cellulose fraction]) [241] reported in Table 8 for unwashed bagassepyrolyzed in nitrogen that had previously been the highest andmost oft-cited for bagasse [97], and 56% greater than the next high-est value (214 kJ mol−1 [likely hemicellulose fraction]) [303,396]for unwashed bagasse pyrolyzed in an oxidative environment, asshown in Table 9. It is also observed that inter-laboratory valuesof Ea obtained via isoconversional techniques do not correlate wellwith each other. The Ea value of 333.3 kJ mol−1 that was obtainedby Ramajo-Escalera et al. [135] (Table 9) for the bagasse conver-sion range of ˛ = 0.15–0.6 was compared against the global Ea value,169.5 kJ mol−1, recorded by Yao et al. [245] (Table 8) over the rangeof ˛ = 0.1–0.6, using a similar integral isoconversional approach(FWO). Although Ramajo-Escalera et al. performed the bagassepyrolysis under an oxidative (O2) environment and Yao et al. uti-lized an inert (N2) atmosphere, it is dubious that the 97% increasein the value of Ea in the presence of oxygen can be justified onthe mere basis of converting from anoxic to oxidative conditions,given that the average increase in Ea by switching to an oxidativeatmosphere is 39%, as mentioned earlier.

An estimation of a theoretical rate constant at 800 K usingthe reported kinetic parameters for ultrafast bagasse pyrolysis(Table 10) with a heated screen assembly at a heating rate ofup to 600,000 ◦C min−1 (Ea = 59.5 kJ mol−1, A = 1.10 × 104 s−1) [391]and for a slow pyrolysis (Table 8) at 10 ◦C min−1 (Ea = 215 kJ mol−1,A = 2.51 × 1015 s−1) [125] returns values of 1.4 s−1 and 23 s−1,respectively, which is a factor of almost 16. Although it couldordinarily be surmised from the above result that rapid pyroly-sis processes have much lower rate constants than slow pyrolysisreactions, the capriciousness of the data indicates otherwise. Theabove conjecture is proven to be incorrect when the theoretical
rate constant at 800 K calculated for ultrafast bagasse pyroly-sis (Table 10) at heating rates between 60,000 and 600,000 ◦Cmin−1 (Ea = 54.0 kJ mol−1, A = 3.31 × 103 s−1) [391] and that for amoderately slow pyrolysis (Table 8) conducted at 50 ◦C min−1
(Ea = 52 kJ mol−1, A = 5.50 × 102 s−1) [386] are compared, providing

22J.E.W

hiteet


Applied

Pyrolysis91

(2011)1–33

Table 8Kinetic parameters for slow pyrolysis of sugarcane bagasse under an inert atmosphere.

ˇ(◦C min−1)

Sample mass(mg)

Particle size(mm)

Temp. range(◦C)

Reactionmodel

n Ea(kJ mol−1)

log A(s−1)

Apparatus Region Refs.

Nitrogen atmosphere5 18 0.841–1.00 RT–800 Sequential

(dual-step)1 87.9 (225–350)a

46.7 (380–560)b4.60a,c

−0.22b,cCST Stona PremcoModel 202 DTAModel 1050 TGA

Egypt [392]

5 10 0.25–1.2 RT–900 Parallel(3 reactions)

113

194.0 hemicellulose243.3 cellulose53.6 lignin

15.718.01.9

Cahn TG-151Tucumán,Argentina [97]

5d 10 0.25–1.2 RT–900 Parallel(3 reactions)

113


15.718.02.3

Cahn TG-151Tucumán,Argentina [97]

10 – – RT–1500 Single-step 1 460.6 2.58 Netzsch STA 409PC Luxx

Tanzania [207]

10e 2 <0.2 RT–500 Initial rate(single-step)

1 63 (220–260) 2.70c Mettler-ToledoTGA/SDTA 851e

Hawaii [393]

10/20f 5–7 <0.3 RT–105–950 Sequential(triple-step)

0.5 58 (216–445) – Shimadzu TGA-50 C Punjab,Pakistan

[387]

10/20f 5–7 <0.3 RT–105–950 Sequential(triple-step)

0.5 71 (214–424) – Shimadzu TGA-50 S Punjab,Pakistan

[387]


113


15.6718.002.58

Cahn TG-151 Tucumán,Argentina

[394]

20d 10 0.25–1.0 RT–900 Parallel(3 reactions)

113


15.4318.092.28

Cahn TG-151Tucumán,Argentina

[394]

50 10 0.037–0.044 25–450 Single-step 1 49g 2.75 Netzsch STA 409 Tucuman,Argentina

[395]

50 10 0.037–0.044 25–900 Single–step 1 52h 2.74 Netzsch STA 409 Tucuman,Argentina

[395]

2–15i 8–10 0.595–0.841 25–800 Model-freej – 168.5 Friedman169.5 FWO168.7 CR*161.1 KAS

–TA InstrumentTGA Q50

Louisiana [245]

1–40k 10 ≤0.450 20–1000 Differentiall

Parallel/Serial(3 reactions)

1 250 hemicellulose125 cellulose60 lignin

–TA InstrumentTG/DTG Q500

Tamaulipas,Mexico

[370]

5–50m 4.3–7.5 0.064–0.076 RT–800 Single-step 1 93.2 (195–395) 5.64 DuPont 951 TGA/Dupont Series 99Thermal Analyzer

Queensland,Australia

[390]



17.717.43−0.78

Seiko SSC/5200TG/DTA 220

Clewiston,Florida

[382]



17.627.52−0.42


Clewiston,Florida

[382]



17.487.58−0.18


Clewiston,Florida

[382]



17.507.67−0.08


Clewiston,Florida

[382]

Helium atmosphere5 18 0.841–1.00 RT–800 Sequential

(dual-step)1 118.0 (RT–350)

69.0 (350–800)– CST Stona Premco

Model 202 DTAModel 1050 TGA

Egypt [298]

10 – – RT–800 Sequential(multi-step)

0.110.41

21.0 (110–170)14.0 (170–250)64.0 (250–310)188.0 (310–380)

–DuPont 1090Thermal Analyzer/MOM OD-130

Cuba[303][396]

Argon atmosphere10 1–2 – RT–450n Parallel

(3 reactions)(1)o 215p 15.4 Perkin Elmer TGS-2 Hawaii [241]


10q

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values of 0.99 s−1 and 0.22 s−1, respectively. Not only are the rateconstants much closer but this comparison would lead to the spu-rious conclusion that rate constants obtained from fast pyrolysisare larger than those from slow pyrolysis; exactly contrary to theearlier hypothesis.

8.4. Suggestions for mitigating inconsistencies in kinetic tripletdata

Irrespective of the multiple causes, the incongruence among thekinetic parameters clearly reflects the need for a more uniformapproach toward the kinetic analysis of biomass pyrolysis, espe-cially one that minimizes the substantial impact that experimentalconditions can have upon the process chemistry, physical proper-ties of the biomass substrate, and systematic experimental errors.Várhegyi [80] has recently proffered a list of suggestions to cir-cumvent the evaluative quandary posed by changing experimentalconditions, viz.,

(1) The experiments can be evaluated simultaneously by themethod of least squares and using exactly the same kineticparameters [49,87,189,214,257].

(2) Additional terms can be included in the kinetic model (i.e., sim-ilar to the general description approach used in the SB model)to describe the systematic experimental errors [51]. Again, theinclusion of too many terms may lead to strong interdepen-dencies among the kinetic parameters that can obscure theirphysical significance and also complicate the numerical solu-tion of the model.

(3) A few parameters can be allowed to “float”, while the remain-ing parameter(s) is/are held constant [249,312]. This techniquecan help assess model validity over a specified range of exper-imental conditions.

(4) Each experiment can be evaluated individually so that com-parisons can be made among the resulting kinetic parameters[214,248,249,399]. This procedure requires a comprehensiveexperimental design that will permit collection of sufficientdata to determine the unknown parameters.

Aiman and Stubington [390] emphasized that the derivedkinetic parameters are highly sensitive to the value of wf that is usedcalculate the degree of conversion. Drummond and Drummond[383] concluded that the use of different heating rates can affect thekinetic parameters obtained for the pyrolysis of sugarcane bagasse.These conclusions might now be amended to accurately reflectthe kinetic triplet’s dependence on differences in the chemical andphysical properties of the pyrolyzed bagasse (e.g., moisture, particlesize, sugarcane variety, and lignocellulosic composition), differentoperating conditions (e.g., heating rate, temperature range, processatmosphere, sample size, and isothermal or non-isothermal oper-ational mode), experimental systematic errors (e.g., thermocoupleand reaction thermal lag), the kinetic model selected, the mathe-matical approximations employed in these models, and the criteriaused to evaluate the endpoint (i.e., wf) of pyrolytic reactions.

8.5. Evaluation of kinetic compensation effect for sugarcanebagasse data

A comprehensive survey of the published kinetic data for sugar-cane bagasse pyrolysis would be incomplete without ascertaining
the existence of a KCE between the variables ln A and E. Thedata used to construct the KCE plot in Fig. 4 was obtained fromTables 8–10 using only those investigations whose activation ener-gies and frequency factors were evaluated using first order models.A few important caveats are specified forthwith regarding the

24J.E.W

hiteet


Applied

Pyrolysis91

(2011)1–33

Table 9Kinetic parameters for slow pyrolysis of sugarcane bagasse under an oxidative atmosphere.

ˇ(◦C min−1)

Sample mass(mg)

Particle size(mm)

Temp. range(◦C)

Reactionmodel

n Ea

(kJ mol−1)log A(s−1)

Apparatus Region Refs.

Air atmosphere5 18 0.841–1.00 RT–800 Sequential

(dual-step)1 139.7 (RT–360)

76.6 (360–800)– CST Stona Premco

Model 202 DTAModel 1050 TGA

Egypt [298]

5a – 0.250–0.420 RT–480b Sequential(multi-step)

1 53.6 (212–380) 2.90 Netzch 348472c Egypt [397]

5c – 0.250–0.420 RT–480b Sequential(multi-step)

1 38.5 (220–430) 1.54 Netzch 348472c Egypt [397]

10 – – RT–800 Sequential(multi-step)

0.840.360.901.000.62

34.0 (20–110)46.5 (110–170)214.0 (170–245)74.8 (245–380)33.2 (380–600)

– DuPont 1090Thermal Analyzer/MOM OD-130

Cuba [303][396]

10/20d 5–7 < 0.3 RT–105–950 Sequential(triple-step)

0.5 75 (226–350) – Shimadzu TGA-50 C Punjab,Pakistan

[387]

10/20d 5–7 < 0.3 RT–105–950 Sequential(triple-step)

0.5 116 (247–357) – Shimadzu TGA-50 S Punjab,Pakistan

[387]

Oxygen atmosphere10e 2 <0.200 RT–500 Initial rate

(single-step)1 78 (220–260) 2.95f Mettler-Toledo

TGA/SDTA 851eHawaii [393]

5–20 1–2 – 25–1000 Model-freeg – 76.1 (25–100)333.3 (200–350)220.1 (400–600)

– Mettler-ToledoTGA/SDTA 851eDSC 822e

Olimpia,SP, Brazil

[135]

a Values reported are for bagasse holocellulose.b Estimated from DTG curve plots.c Values reported are for bagasse hemicellulose.d 10 ◦C min−1 ramp from RT to 105 ◦C followed by 10 min hold; 20 ◦C min−1 ramp from 105 ◦C to 950 ◦C followed by 40 min hold.e Isothermal conditions (heating rate to desired temperature).f Calculated from available rate constant data.g Isoconversional kinetic analysis; ˛ = 0.02–0.05, 0.15–0.60, 0.70–0.95, in order of increasing temperature ranges.

J.E. White et al. / Journal of Analytical and Applied Pyrolysis 91 (2011) 1–33 25

Tab

le10

Kin

etic

par

amet

ers

for

rap

idp

yrol

ysis

ofsu

garc

ane

baga

sse

un

der

anin

ert

atm

osp

her

e.

ˇ(◦ C

min

−1)

Res

tim

e(s

)Sa

mp

lem

ass

(mg)

Part

icle

size

(mm

)Te

mp

.ran

ge(◦ C

)R

eact

ion

mod

eln

E a(k

Jmol

−1)

log

A(s

−1)

Ap

par

atu

sR

egio

nR

efs.

Nit

roge

nat

mos

pher

e1.

2–60

×10

40–

120

–35

0.06

4–0.

422

300–

1000

Sin

gle-

step

159

.54.

04D

Cgr

idfu

rnac

eQ

uee

nsl

and

,Au

stra

lia

[391

]60

,000

1020

–35

0.06

4–0.

076

300–

1000

Sin

gle-

step

160

.33.

74D

Cgr

idfu

rnac

eQ

uee

nsl

and

,Au

stra

lia

[391

]60

,000

3020

–35

0.06

4–0.

076

300–

1000

Sin

gle-

step

177

.94.

78D

Cgr

idfu

rnac

eQ

uee

nsl

and

,Au

stra

lia

[391

]6.

0–60

×10

41

20–3

50.

064–

0.07

630

0–10

00Si

ngl

e-st

ep1

54.0

3.52

DC

grid

furn

ace

Qu

een

slan

d,A

ust

rali

a[3

91]

12,0

00a

120

–35

0.06

4–0.

076

300–

1000

Sin

gle-

step

166

.14.

42D

Cgr

idfu

rnac

eQ

uee

nsl

and

,Au

stra

lia

[391

]60

,000

307

0.10

–0.1

530

0–90

0Si

ngl

e-st

ep1

92.6

6.33

Wir

em

esh

reac

tor

Pern

ambu

co,B

razi

l[3

83]

aPr

imar

yta

rp

rod

uct

ion

kin

etic

par

amet

ers.

Fig. 4. Compensation plot for Arrhenius parameters obtained from sugarcanebagasse pyrolysis data listed in Tables 7–9.

treatment of the data. A single set of averaged ln A values wasused for the data supplied by Garcia-Perez et al. [382]; the datumpoint from Wilson et al. [207] was rejected from the analysisbecause of its anomalously high Ea value (460.6 kJ mol−1) at arelatively low ln A value (5.94 s−1). The remarkably linear relation-ship between ln A and E in Fig. 4 (i.e., coefficient of determinationequal to 0.972) would seem to imply the existence of a KCE. How-ever, the plot in Fig. 4 contains several important assumptionsregarding the data used therein. Namely, it is assumed that avalid kinetic conversion function was chosen and that the datais free of computational, experimental, and instrumental errors.If none of these assumptions is violated, then the only possi-ble conclusion that can be drawn from Fig. 4 is that there is anapparent KCE.

However, an Arrrhenius plot, as shown in Fig. 5, is required toestablish whether the necessary criterion met by the data in Fig. 4is indeed sufficient to confirm an actual KCE in the pyrolysis ofsugarcane bagasse. The Arrhenius plot in Fig. 5 consists of a sub-set of data from Fig. 4 because valid temperature ranges were notavailable for all of the calculated activation energies. The lack of acommon isokinetic point in the Arrhenius plot indicates that thelinear relation between ln A and Ea in Fig. 4 is spurious and repre-sentative of a pseudo KCE. This reviewer does not find the precedingresult entirely unexpected given the tremendously diverse testingconditions employed in the sugarcane pyrolysis reactions surveyedin this paper.

9. Recommendations

The gross disparities evidenced in the kinetic data from the nut-shells and sugarcane bagasse are representative of the ambiguouskinetic results that have paralyzed the broader biomass pyrolysiscommunity. In the rush to identify the culprits behind this “shifty”data, another skulking variable is frequently forgotten: the het-erogeneity of the biomass itself. Such inconsistencies demonstratethe need to reassess the fundamental principles and phenomenaunderlying biomass thermal degradation. In particular, the identi-fication and improved control of all possible experimental factors(seen and unseen) that may regulate the behavior of solid statereactions is imperative. Moreover, elucidation of reaction mecha-
nisms for solid state thermal processes cannot occur unless thermalanalysis is used in tandem with an ancillary analytical tool thatcan evaluate the chemical composition and structure of evolvedproducts, such as FTIR [12,233,400,401], GC [84,108,402,403], HPLC


F a truel aining[ es cona ., [36

[m

dpswcisbpvntmdkm

kbodtsciaociitra

Kk

ig. 5. Arrhenius plot for a subset of data shown in Fig. 4 illustrating the absence ofines refer to the references cited in Tables 7–9. Lines from reference sources cont399-2] refers to the second set of data from Ref. [399]). Lines from reference sourcre also listed in order of appearance of the respective reactions or components (e.g

12], MS [311,401,404,405], GC–MS [305], and scanning electronicroscopy (SEM) [53,211].Extravagant mathematical manipulations of kinetic data will

o nothing to further the understanding of the fundamentalhysicochemical mechanisms that govern the thermal decompo-ition of biomass. Elaborate models that integrate reaction kineticsith transport phenomena are often developed in a theoreti-

al vacuum that fails to properly account for the myriad factorsnvolved in actual pyrolysis reactions. Application of these intrin-ically incomplete models to industrial processes is constrainedy idealized assumptions that are not valid under a bona fideyrolytic environment. Furthermore, the use of numerous inputariables that cannot be measured accurately presents an engi-eering nightmare. Antal and Várhegyi [241] commented thathe best approach to modeling the kinetic behavior of single,

acroscopic biomass particles may involve statistical methodseveloped by Krieger-Brockett’s laboratory [406,407] that correlateinetic data from judiciously designed experiments using empiricalethods.It has been suggested that the existing body of heterogeneous

inetic data is so hopelessly flawed that much of it should simplye relegated to a circular file [182]. The current authors strenu-usly oppose the notion that previously collected data should beismissed as rubbish. Although it is possible that the analyticalreatment of such data was unsound, the data itself should be pre-erved. Discarding old empirical data to make room in the “kineticsupboard” is not a viable solution, and it disregards the possibil-ty of future advances in heterogeneous kinetics theory that mayfford the opportunity to accurately interpret the kinetic behaviorf re-examined data. Nevertheless, the current authors can appre-iate the paucity of reliable kinetic data in the current literature;t is true that kinetic parameters drawn from the raw data mayndeed be unsalvageable. Still, it would be premature to discardhese “flawed” kinetic triplets before agreement can be achieved
egarding which mathematical methods are truly inappropriatend, ergo, which kinetic results are also incorrect.
Although integral isoconversional techniques (i.e., CR*, FWO,AS, and V) appear to provide reasonably consistent results for theinetic triplet in certain controlled situations, it remains unclear

compensation effect for sugarcane bagasse pyrolysis. The labels for the individualmore than one data pair are labeled in order of appearance from Tables 7–9 (e.g.,taining Arrhenius parameters for multiple reactions or lignocellulosic components6-2c] refers to the third component from the second set of data for Ref. [366]).

whether these isoconversional methods can be used reliably tocompare kinetic data obtained from identical biomass speciestested under similar, yet not identical, conditions. Regrettably,there appears to be a perception that the current concepts usedto describe biomass pyrolysis kinetics are satisfactory. Perhaps thefield of solid state kinetics has become somewhat jaded after all theyears of acrimonious and incisive debate regarding the “competi-tion” between isothermal and non-isothermal kinetic techniques.Nevertheless, there is also a growing undercurrent of exasperationin the biofuels community regarding the failure of modern kinetictheory to accurately predict the pyrolytic behavior of biomass.A literature survey [38] of the apparent activation energies forwood and cellulose pyrolysis reactions reveals an Ea range of15–217 kJ mol−1 for wood and 109–251 kJ mol−1 for cellulose; a sit-uation which is described as “very unsatisfactory” and that “needsto be clarified”. This annoyance is further compounded by theinability to use the resulting kinetic data for comparative evalu-ations between different biomass feedstocks under similar processconditions or identical biomass species under different operatingconditions. Maciejewski and Reller [106] recognized that interestin the course of solid state thermal decomposition processes isspurred in part by the desire to obtain “. . .kinetic and mechanisticdata [that] could be of great help in accurate process control. . .” Asubsequent paper by Maciejewski [138], however, concludes thatif “. . .for whatever reason, the quantitative characterization of theprocess is required, it is necessary to treat the kinetic parame-ters as mathematical numbers only, which describe the course ofthe reaction under particular conditions, but which do not haveparticular significance and are not intrinsic to the investigatedcompound”. Obviously, this paradoxical disconnect between theneeds of industry and the exclusivity of the kinetic data obtainedfrom solid state reactions is problematic. Thermochemical biomassconversion facilities often operate using variable feedstocks underdifferent operating conditions. It is naive to assume that such indus-
trial systems can be optimized without the use of generalizedcorrelations to predict the kinetic behavior of different biomassmaterials under various processing environments. Font et al. [408]recognized the industrial importance of being able to comparekinetic rate constants and devised a convenient, yet seldom used

al and

[a

tcitttofiim

1

bina[roIeCsahmaSfaK

R


409], comparison factor to relate rate constants having similarctivation energy and reaction order.

Underlying principles in solid state reaction theory need to behoroughly re-evaluated and those that are unsound should be dis-arded. The venerability and prior adequacy of certain constructs,ncluding the Arrhenius rate law, should not be used as justifica-ion for their continued presence in kinetic expressions. At the sameime, it may be appropriate to revisit generalized kinetic equationshat permit additional process factors to be introduced into the the-retical model. In addition, the use of novel kinetic approaches thatt data according to semi-empirical and logistic models may help

dentify phenomenological regularities and patterns present in theeasurements [173,400,410].

0. Conclusion

The chaos in solid state reaction kinetics has spilled over into theiomass pyrolysis community and continuation of the status quo

s utterly unacceptable. Ultimately, the thermal analysis commu-ity may have to further probe troublesome reaction systems onn individual basis to develop rate equations specific to each one102]. It was the long-suffering work of Bodenstein on the gaseouseaction between bromine and hydrogen that led to his discoveryf the unique rate equation for hydrogen bromide formation [411].n conclusion, a few memorable quotations found in Churchill’sngrossing book, The Interpretation and Use of Rate Data: The Rateoncept [412] are befitting of the quandaries confronting the field ofolid state reaction kinetics: ‘It is a condition that confronts us – nottheory’ President Grover Cleveland; ‘No satisfactory justificationas ever been given for connecting in any way the consequences ofathematical reasoning with the physical world’ Bell; ‘Life is the

rt of drawing sufficient conclusions from insufficient premises’amuel Butler; ‘Close to the western summit there is the dried androzen carcass of a leopard. No one has explained what the leop-rd was seeking at that altitude’ Ernest Hemingway in The Snows ofilimanjaro.

eferences

[1] P. McKendry, Energy production from biomass (part 1): overview of biomass,Bioresource Technology 83 (2002) 37–46.

[2] K. Raveendran, A. Ganesh, K.C. Khilar, Pyrolysis characteristics of biomass andbiomass components, Fuel 75 (1996) 987–998.

[3] S. Sensöz, M. Can, Pyrolysis of pine (Pinus Brutia Ten.) chips: 1. Effect of pyrol-ysis temperatures and heating rate on the product yields, Energy Sources, PartA: Recovery, Utilization, and Environmental Effects 24 (2002) 347–355.

[4] H.X. Chen, N.A. Liu, W.C. Fan, Two-step consecutive reaction model and kineticparameters relevant to the decomposition of Chinese forest fuels, Journal ofApplied Polymer Science 102 (2006) 571–576.

[5] J.A. González-Pérez, F.J. González-Vila, G. Almendros, H. Knicker, The effectof fire on soil organic matter—a review, Environment International 30 (2004)855–870.

[6] J.J.M. Órfão, F.J.A. Antunes, J.L. Figueiredo, Pyrolysis kinetics of lignocellulosicmaterials—three independent reactions model, Fuel 78 (1999) 349–358.

[7] D.L. Klass, G.H. Emert (Eds.), Fuels from Biomass and Wastes, Ann Arbor Sci-ence Publishers, Ann Arbor, MI, 1981.

[8] Y. Tonbul, Pyrolysis of pistachio shell as a biomass, Journal of Thermal Analysisand Calorimetry 91 (2008) 641–647.

[9] F. Shafizadeh, Introduction to pyrolysis of biomass, Journal of Analytical andApplied Pyrolysis 3 (1982) 283–305.

[10] Cellulose, in: FiberSource, American Fiber Manufacturers Associa-tion/Fiber Economics Bureau, Arlington, VA. Available from: http://www.fibersource.com/F-TUTOR/cellulose.htm (accessed 02.03.09).

[11] J. Ralph, G. Brunow, W. Boerjan, Lignins, Encyclopedia of Life Sci-ences, John Wiley & Sons, Ltd., 2007, http://dx.doi.org/10.1002/9780470015902.a0020104.

[12] R. Singh, S. Singh, K.D. Trimukhe, K.V. Pandare, K.B. Bastawade, D.V. Gokhale,A.J. Varma, Lignin–carbohydrate complexes from sugarcane bagasse: prepa-
ration, purification, and characterization, Carbohydrate Polymers 62 (2005)57–66.
[13] A.K. Galwey, M.E. Brown, Kinetic background to thermal analysis andcalorimetry, in: M.E. Brown (Ed.), Handbook of Thermal Analysis andCalorimetry, Vol. 1: Principles and Practice, 1st ed., Elsevier Science, Ams-terdam, The Netherlands, 1998, pp. 147–224.


[14] W. Gomes, Definition of rate constant and activation energy in solid statereactions, Nature 192 (1961) 865–866.

[15] J. Sesták, G. Berggren, Study of the kinetics of the mechanism of solid-statereactions at increasing temperatures, Thermochimica Acta 3 (1971) 1–12.

[16] L. Helsen, E. van den Bulck, Kinetics of the low-temperature pyrolysis ofchromated copper arsenate-treated wood, Journal of Analytical and AppliedPyrolysis 53 (2000) 51–79.

[17] J.H. Flynn, Temperature dependence of the rate of reaction in thermal analy-sis: the Arrhenius equation in condensed phase kinetics, Journal of ThermalAnalysis 36 (1990) 1579–1593.

[18] M.E. Brown, D. Dollimore, A.K. Galwey (Eds.), Reactions in the Solid State,Elsevier Scientific Publishing, Amsterdam, The Netherlands, 1980.

[19] B.W. Lamb, R.W. Bilger, Combustion of bagasse: literature reviews, SugarTechnology Reviews 4 (1976/1977) 89–130.

[20] H.L. Le Chatelier, De l’Action de la chaleur sur les argiles, Bulletin de la SocieteFrancaise de Mineralogie et de Cristallographie 10 (1887) 204–207.

[21] M.J. Vold, Differential thermal analysis, Analytical Chemistry 21 (1949)683–688.

[22] R.C. MacKenzie, De calore: prelude to thermal analysis, Thermochimica Acta73 (1984) 251–306.

[23] J. Tamminen, Thermal analysis for investigation of solidification mechanismin metals and alloys, Ph.D. Dissertation, Department of Structural Chemistry,Stockholm University, Stockholm, Sweden, 1988.

[24] C.J. Keattch, D. Dollimore, Studies in the history and development of ther-mogravimetry. I. Early development, Journal of Thermal Analysis 37 (1991)2089–2102.

[25] J.O. Hill (Ed.), For Better Thermal Analysis and Calorimetry, 3rd ed., Interna-tional Confederation for Thermal Analysis and Calorimetry (ICTAC), London,UK, 1991.

[26] W. Hemminger, S.M. Sarge, Definitions, nomenclature, terms and literature,in: M.E. Brown (Ed.), Handbook of Thermal Analysis and Calorimetry, Vol. 1:Principles and Practice, 1st ed., Elsevier Science, Amsterdam, The Netherlands,1998, pp. 1–73.

[27] J.H. Sharp, Reaction kinetics, in: R.C. MacKenzie (Ed.), Differential Ther-mal Analysis, Vol 2: Applications, Academic Press, New York, NY, 1972,pp. 47–77.

[28] R.K. Agrawal, Analysis of non-isothermal reaction kinetics: Part 1. Simplereactions, Thermochimica Acta 203 (1992) 93–110.

[29] T.B. Tang, M.M. Chaudhri, Analysis of dynamic kinetic data from solid-statereactions, Journal of Thermal Analysis 18 (1980) 247–261.

[30] S. Gaur, T.B. Reed, Prediction of cellulose decomposition rates from thermo-gravimetric data, Biomass & Bioenergy 7 (1994) 61–67.

[31] R.M. Felder, E.P. Stahel, Nonisothermal chemical kinetics, Nature 228 (1970)1085–1086.

[32] J. Norwisz, The kinetic equation under linear temperature increase conditions,Thermochimica Acta 25 (1978) 123–125.

[33] J.D. Cooney, M. Day, D.M. Wiles, Thermal degradation of poly(ethylene tereph-thalate): a kinetic analysis of thermogravimetric data, Journal of AppliedPolymer Science 28 (1983) 2887–2902.

[34] P.D. Garn, Introduction and critique of non-isothermal kinetics, Thermochim-ica Acta 110 (1987) 141–144.

[35] B.A. Howell, J.A. Ray, Comparison of isothermal and dynamic methods for thedetermination of activation energy by thermogravimetry, Journal of ThermalAnalysis and Calorimetry 83 (2006) 63–66.

[36] E.V. Boldyreva, Problems of the reliability of kinetic data evaluated by thermalanalysis, Thermochimica Acta 110 (1987) 107–109.

[37] J.M. Criado, J. Málek, J. Sesták, 40 years of the Van Krevelen, Van Heerden, andHutjens non-isothermal kinetic evaluation method, Thermochimica Acta 175(1991) 299–303.

[38] T. Willner, G. Brunner, Pyrolysis kinetics of wood and wood components,Chemical Engineering & Technology 28 (2005) 1212–1225.

[39] P.M.D. Benoit, R.G. Ferrillo, A.H. Granzow, Kinetic applications of thermal anal-ysis: a comparison of dynamic and isothermal methods, Journal of ThermalAnalysis 30 (1985) 869–877.

[40] S. Vyazovkin, C.A. Wight, Isothermal and non-isothermal kinetics of thermallystimulated reactions of solids, International Reviews in Physical Chemistry 17(1998) 407–433.

[41] M.E. Brown, M. Maciejewski, S. Vyazovkin, R. Nomen, J. Sempere, A. Burnham,J. Opfermann, R. Strey, H.L. Anderson, A. Kemmler, R. Keuleers, J. Janssens, H.O.Desseyn, C.-R. Li, T.B. Tang, B. Roduit, J. Malek, T. Mitsuhashi, Computationalaspects of kinetic analysis. Part A: the ICTAC kinetics project—data, methods,and results, Thermochimica Acta 355 (2000) 125–143.

[42] M. Maciejewski, Computational aspects of kinetic analysis. Part B: the ICTACkinetics project—the decomposition kinetics of calcium carbonate revisited,or some tips on survival in the kinetic minefield, Thermochimica Acta 355(2000) 145–154.

[43] S. Vyazovkin, Computational aspects of kinetic analysis. Part C. The ICTACkinetics project—the light at the end of the tunnel? Thermochimica Acta 355(2000) 155–163.

[44] A.K. Burnham, Computational aspects of kinetic analysis. Part D: the
ICTAC kinetics project—multi-thermal-history model-fitting methods andtheir relation to isoconversional methods, Thermochimica Acta 355 (2000)165–170.
[45] B. Roduit, Computational aspects of kinetic analysis. Part E: the ICTAC kineticsproject—numerical techniques and kinetics of solid state processes, Ther-mochimica Acta 355 (2000) 171–180.

http://www.fibersource.com/F-TUTOR/cellulose.htm

http://www.fibersource.com/F-TUTOR/cellulose.htm

2 al and
[46] I.M. Salin, J.C. Seferis, Kinetic analysis of high-resolution TGA variable heatingrate data, Journal of Applied Polymer Science 47 (1993) 847–856.

[47] K.N. Ninan, Kinetics of solid-state thermal decomposition reactions, Journalof Thermal Analysis 35 (1989) 1267–1278.

[48] M. Momoh, A.N. Eboatu, E.G. Kolawole, A.R. Horrocks, Thermogravimetricstudies of the pyrolytic behaviour in air of selected tropical timbers, Fire andMaterials 20 (1996) 173–181.

[49] H. Teng, H.-C. Lin, J.-A. Ho, Thermogravimetric analysis on global mass losskinetics of rice hull pyrolysis, Industrial & Engineering Chemistry Research36 (1997) 3974–3977.

[50] M. Stenseng, A. Jensen, K. Dam-Johansen, Investigation of biomass pyrolysisby thermogravimetric analysis and differential scanning calorimetry, Journalof Analytical and Applied Pyrolysis 58–59 (2001) 765–780.

[51] E. Mészáros, G. Várhegyi, E. Jakab, B. Marosvölgyi, Thermogravimetric andreaction kinetic analysis of biomass samples from an energy plantation,Energy & Fuels 18 (2004) 497–507.

[52] X. Zhang, M. Xu, R. Sun, L. Sun, Study on biomass pyrolysis kinetics, Journalof Engineering for Gas Turbines and Power—Transactions of the ASME 128(2006) 493–496.

[53] C. Branca, A. Iannace, C. Di Blasi, Devolatilization and combustion kinetics ofQuercus cerris bark, Energy & Fuels 21 (2007) 1078–1084.

[54] A.L.F.S. d’Almeida, D.W. Barreto, V. Calado, J.R.M. d’Almeida, Thermal anal-ysis of less common lignocellulosic fibers, Journal of Thermal Analysis andCalorimetry 91 (2008) 405–408.

[55] P.L. Waters, Fractional thermogravimetric analysis, Analytical Chemistry 32(1960) 852–858.

[56] H.E. Kissinger, Reaction kinetics in differential thermal analysis, AnalyticalChemistry 29 (1957) 1702–1706.

[57] R.C. MacKenzie, Origin and development of differential thermal analysis,Thermochimica Acta 73 (1984) 307–367.

[58] B.D. Mitchell, A.H. Knight, The application of differential thermal analysis toplant materials, Journal of Experimental Botany 16 (1965) 1–15.

[59] H.J. Borchardt, F. Daniels, The application of differential thermal analysis tothe study of reaction kinetics, Journal of the American Chemical Society 79(1957) 41–46.

[60] B.A. Howell, Utility of kinetic analysis in the determination of reaction mech-anism, Journal of Thermal Analysis and Calorimetry 85 (2006) 165–167.

[61] M.E. Brown, A.K. Galwey, The distinguishability of selected kinetic models forisothermal solid-state reactions, Thermochimica Acta 29 (1979) 129–146.

[62] J.H. Flynn, The ‘temperature integral’—its use and abuse, Thermochimica Acta300 (1997) 83–92.

[63] J.I. Steinfeld, J.S. Francisco, W.L. Hase, Chemical Kinetics and Dynamics, 2nded., Prentice-Hall, Upper Saddle River, NJ, 1999.

[64] A.K. Galwey, M.E. Brown, Application of the Arrhenius equation to solid statekinetics: can this be justified? Thermochimica Acta 386 (2002) 91–98.

[65] P.W. Atkins, Physical Chemistry, 5th ed., W.H. Freeman, New York, NY, 1994.[66] P.D. Garn, Kinetics of decomposition of the solid state: is there really a

dichotomy? Thermochimica Acta 135 (1988) 71–77.[67] A.K. Galwey, M.E. Brown, A theoretical justification for the application of the

Arrhenius equation to kinetics of solid state reactions (mainly ionic crystals),Proceedings of the Royal Society of London A 450 (1995) 501–512.

[68] M.E. Brown, Steps in a minefield: some kinetic aspects of thermal analysis,Journal of Thermal Analysis 49 (1997) 17–32.

[69] P.D. Garn, Kinetic parameters, Journal of Thermal Analysis 13 (1978) 581–593.[70] P.D. Garn, Kinetics of thermal decomposition of the solid state: II. Delimiting

the homogeneous-reaction model, Thermochimica Acta 160 (1990) 135–145.[71] J. Zsakó, Kinetic analyses of thermogravimetric data. XXIX. Remarks on the

‘many curves’ methods, Journal of Thermal Analysis 46 (1996) 1845–1864.[72] S. Vyazovkin, C.A. Wight, Isothermal and nonisothermal reaction kinetics in

solids: in search of ways toward consensus, Journal of Physical Chemistry A101 (1997) 8279–8284.

[73] K.J. Laidler, The development of the Arrhenius equation, Journal of ChemicalEducation 61 (1984) 494–498.

[74] J. Sesták, Philosophy of non-isothermal kinetics, Journal of Thermal Analysis16 (1979) 503–520.

[75] W.J. Crozier, G.F. Pilz, The locomotion of Limax: I. Temperature coefficient ofpedal activity, Journal of General Physiology 6 (1924) 711–721.

[76] W.J. Crozier, On the critical thermal increment for the locomotion of a diplo-pod, Journal of General Physiology 7 (1924) 123–136.

[77] W.J. Crozier, H. Federighi, Critical thermal increment for the movement ofOscillatoria, Journal of General Physiology 7 (1924) 137–150.

[78] K.J. Laidler, Unconventional applications of the Arrhenius law, Journal ofChemical Education 49 (1972) 343–344.

[79] R. Capart, L. Khezami, A.K. Burnham, Assessment of various kinetic models forthe pyrolysis of a microgranular cellulose, Thermochimica Acta 417 (2004)79–89.

[80] G. Várhegyi, Aims and methods in non-isothermal reaction kinetics, Journalof Analytical and Applied Pyrolysis 79 (2007) 278–288.

[81] S. Vyazovkin, C.A. Wight, Model-free and model-fitting approaches to kineticanalysis of isothermal and nonisothermal data, Thermochimica Acta 340–341
(1999) 53–68.
[82] S. Kim, Y. Eom, Estimation of kinetic triplet of cellulose pyrolysis reactionfrom isothermal kinetic results, Korean Journal of Chemical Engineering 23(2006) 409–414.

[83] A. Khawam, D.R. Flanagan, Solid-state kinetic models: basics and mathemat-ical fundamentals, Journal of Physical Chemistry B 110 (2006) 17315–17328.


[84] T.R. Nunn, J.B. Howard, J.P. Longwell, W.A. Peters, Product compositions andkinetics in the rapid pyrolysis of sweet gum hardwood, Industrial & Engineer-ing Chemistry Process Design and Development 24 (1985) 836–844.

[85] R. Bilbao, J. Arauzo, M.L. Salvador, Kinetics and modeling of gas formation inthe thermal decomposition of powdery cellulose and pine sawdust, Industrial& Engineering Chemistry Research 34 (1995) 786–793.

[86] J.L. Valverde, C. Curbelo, O. Mayo, C.B. Molina, Pyrolysis kinetics of tobaccodust, Transactions of the Institution of Chemical Engineers Part A 78 (2000)921–924.

[87] M.G. Grønli, G. Várhegyi, C. Di Blasi, Thermogravimetric analysis anddevolatilization kinetics of wood, Industrial & Engineering ChemistryResearch 41 (2002) 4201–4208.

[88] C. Branca, A. Albano, C. Di Blasi, Critical evaluation of global mechanisms ofwood devolatilization, Thermochimica Acta 429 (2005) 133–141.

[89] R. Radmanesh, Y. Courbariaux, J. Chaouki, C. Guy, A unified lumped approachin kinetic modeling of biomass pyrolysis, Fuel 85 (2006) 1211–1220.

[90] M. Lapuerta, J.J. Hernández, J. Rodríguez, Comparison between the kineticsof devolatilisation of forestry and agricultural wastes from the middle-southregions of Spain, Biomass & Bioenergy 31 (2007) 13–19.

[91] A.J. Tsamba, W. Yang, W. Blasiak, M.A. Wójtowicz, Cashew nut shells pyrol-ysis: individual gas evolution rates and yields, Energy & Fuels 21 (2007)2357–2362.

[92] G. Várhegyi, M.J. Antal Jr., E. Jakab, P. Szabó, Kinetic modeling of biomasspyrolysis, Journal of Analytical and Applied Pyrolysis 42 (1997) 73–87.

[93] C. Branca, C. Di Blasi, Kinetics of the isothermal degradation of wood in thetemperature range 528–708 K, Journal of Analytical and Applied Pyrolysis 76(2003) 207–219.

[94] D.B. Anthony, J.B. Howard, H.C. Hottel, H.P. Meissner, Rapid devolatilizationand hydrogasification of bituminous coal, Fuel 55 (1976) 121–128.

[95] D.B. Anthony, J.B. Howard, Coal devolatilization and hydrogasification, AIChEJournal 22 (1976) 625–656.

[96] J.A. Conesa, J.A. Caballero, A. Marcilla, R. Font, Analysis of different kineticmodels in the dynamic pyrolysis of cellulose, Thermochimica Acta 254 (1995)175–192.

[97] J.J. Manyà, E. Velo, L. Puigjaner, Kinetics of biomass pyrolysis: a reformulatedthree-parallel-reactions model, Industrial & Engineering Chemistry Research42 (2003) 434–441.

[98] J. Málek, A computer program for kinetic analysis of non-isothermal thermo-analytical data, Thermochimica Acta 138 (1989) 337–346.

[99] O. Senneca, Kinetics of pyrolysis, combustion and gasification of threebiomass fuels, Fuel Processing Technology 88 (2007) 87–97.

[100] S. Hu, A. Jess, M. Xu, Kinetic study of Chinese biomass slow pyrolysis: com-parison of different kinetic models, Fuel 86 (2007) 2778–2788.

[101] Science: Technology e-book Store, Elsevier Science, Amsterdam,The Netherlands. Available from: http://elsevier.insidethecover.com/searchbook.jsp?isbn=9780444418074 (accessed 16.03.09).

[102] J.H. Flynn, Thermal analysis kinetics—past, present, and future, Thermochim-ica Acta 203 (1992) 519–526.

[103] S. Vyazovkin, Modification of the integral isoconversional method to accountfor variation in the activation energy, Journal of Computational Chemistry 22(2001) 178–183.

[104] M.J. Starink, The determination of activation energy from linear heating rateexperiments: a comparison of the accuracy of isoconversion methods, Ther-mochimica Acta 404 (2003) 163–176.

[105] A.K. Burnham, L.N. Dinh, A comparison of isoconversional and model-fittingapproaches to kinetic parameter estimation and application predictions, Jour-nal of Thermal Analysis and Calorimetry 89 (2007) 479–490.

[106] M. Maciejewski, A. Reller, How (un)reliable are kinetic data of reversible solid-state decomposition processes? Thermochimica Acta 110 (1987) 145–152.

[107] F. Thurner, U. Mann, Kinetic investigation of wood pyrolysis, Industrial &Engineering Chemistry Process Design and Development 20 (1981) 482–488.

[108] M.R. Hajaligol, J.B. Howard, J.P. Longwell, W.A. Peters, Product compositionsand kinetics for rapid pyrolysis of cellulose, Industrial & Engineering Chem-istry Process Design and Development 21 (1982) 457–465.

[109] R.K. Agrawal, Kinetics of reactions involved in pyrolysis of cellulose I. Thethree reaction model, Canadian Journal of Chemical Engineering 66 (1988)403–412.

[110] A.J. Tsamba, W. Yang, W. Blasiak, Pyrolysis characteristics and global kinet-ics of coconut and cashew nut shells, Fuel Processing Technology 87 (2006)523–530.

[111] S.M. Ward, J. Braslaw, Experimental weight loss kinetics of wood pyrolysisunder vacuum, Combustion and Flame 61 (1985) 261–269.

[112] J.M. Vargas, D.D. Perlmutter, Interpretation of coal pyrolysis kinetics, Indus-trial & Engineering Chemistry Process Design and Development 25 (1986)49–54.

[113] S.S. Alves, J.L. Figueiredo, Kinetics of cellulose pyrolysis modelled by threeconsecutive first-order reactions, Journal of Analytical and Applied Pyrolysis17 (1989) 37–46.

[114] M. Lanzetta, C. Di Blasi, Pyrolysis kinetics of wheat and corn straw, Journal ofAnalytical and Applied Pyrolysis 44 (1998) 181–192.

[115] A.N. García, R. Font, Thermogravimetric kinetic model of the pyrolysis andcombustion of an ethylene-vinyl acetate copolymer refuse, Fuel 83 (2004)1165–1173.

[116] R.K. Agrawal, Kinetics of reactions involved in pyrolysis of cellulose. Part II.The modified Kilzer–Broido model, Canadian Journal of Chemical Engineering66 (1988) 413–418.

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117] M.L. Boroson, J.B. Howard, J.P. Longwell, W.A. Peters, Product yields and kinet-ics from the vapor phase cracking of wood pyrolysis tars, AIChE Journal 35(1989) 120–128.

118] C.A. Koufopanos, G. Maschio, A. Lucchesi, Kinetic modelling of the pyrolysis ofbiomass and biomass components, Canadian Journal of Chemical Engineering67 (1989) 75–84.

119] R.S. Miller, J. Bellan, A generalized biomass pyrolysis model based on super-imposed cellulose, hemicellulose and lignin kinetics, Combustion Science andTechnology 126 (1997) 97–137.

120] P. Lv, J. Chang, T. Wang, C. Wu, N. Tsubaki, A kinetic study on biomass fastcatalytic pyrolysis, Energy & Fuels 18 (2004) 1865–1869.

121] O. Senneca, R. Chirone, P. Salatino, L. Nappi, Patterns and kinetics of pyrolysisof tobacco under inert and oxidative conditions, Journal of Analytical andApplied Pyrolysis 79 (2007) 227–233.

122] T.R. Nunn, J.B. Howard, J.P. Longwell, W.A. Peters, Product compositions andkinetics in the rapid pyrolysis of milled wood lignin, Industrial & EngineeringChemistry Process Design and Development 24 (1985) 844–852.

123] J. Villermaux, B. Antoine, J. Lede, F. Soulignac, A new model for thermalvolatilization of solid particles undergoing fast pyrolysis, Chemical Engineer-ing Science 41 (1986) 151–157.

124] T. Cordero, F. García, J.J. Rodríguez, A kinetic study of holm oak wood pyroly-sis from dynamic and isothermal TG experiments, Thermochimica Acta 149(1989) 225–237.

125] M.J. Antal Jr., G. Varhegyi, E. Jakab, Cellulose pyrolysis kinetics: revisited,Industrial & Engineering Chemistry Research 37 (1998) 1267–1275.

126] C. Di Blasi, Comparison of semi-global mechanisms for primary pyrolysis oflignocellulosic fuels, Journal of Analytical and Applied Pyrolysis 47 (1998)43–64.

127] J.H. Flynn, Thermal analysis kinetics—problems, pitfalls and how to deal withthem, Journal of Thermal Analysis 34 (1988) 367–381.

128] F. Shafizadeh, P.P.S. Chin, Thermal deterioration of wood, in: I.S. Goldstein(Ed.), Wood Technology: Chemical Aspects, vol. 43, American Chemical Soci-ety, Washington, DC, 1977, pp. 57–81.

129] C. Di Blasi, Kinetic and heat transfer control in the slow and flash pyrolysis ofsolids, Industrial & Engineering Chemistry Research 35 (1996) 37–46.

130] W.-C.R. Chan, M. Kelbon, B.B. Krieger, Modelling and experimental verifica-tion of physical and chemical processes during pyrolysis of a large biomassparticle, Fuel 64 (1985) 1505–1513.

131] S.S. Alves, J.L. Figueiredo, Interpreting isothermal thermogravimetric data ofcomplex reactions: application to cellulose pyrolysis at low temperatures,Journal of Analytical and Applied Pyrolysis 15 (1989) 347–355.

132] J.P. Diebold, A unified, global model for the pyrolysis of cellulose, Biomass &Bioenergy 7 (1994) 75–85.

133] V. Mangut, E. Sabio, J. Ganán, J.F. González, A. Ramiro, C.M. González, S.Román, A. Al-Kassir, Thermogravimetric study of the pyrolysis of biomassresidues from tomato processing industry, Fuel Processing Technology 87(2006) 109–115.

134] A.L. Brown, D.C. Dayton, J.W. Daily, A study of cellulose pyrolysis chemistryand global kinetics at high heating rates, Energy & Fuels 15 (2001) 1286–1294.

135] B. Ramajo-Escalera, A. Espina, J.R. García, J.H. Sosa-Arnao, S.A. Nebra, Model-free kinetics applied to sugarcane bagasse combustion, Thermochimica Acta448 (2006) 111–116.

136] V. Mamleev, S. Bourbigot, Calculation of activation energies using the sinu-soidally modulated temperature, Journal of Thermal Analysis and Calorimetry70 (2002) 565–579.

137] R. Cao, S. Naya, R. Artiaga, A. García, A. Varela, Logistic approach to polymerdegradation in dynamic TGA, Polymer Degradation and Stability 85 (2004)667–674.

138] M. Maciejewski, Somewhere between fiction and reality: the usefulness ofkinetic data of solid-state reactions, Journal of Thermal Analysis 38 (1992)51–70.

139] C.-R. Li, T.B. Tang, Isoconversion method for kinetic analysis of solid-statereactions from dynamic thermoanalytical data, Journal of Materials Science34 (1999) 3467–3470.

140] P. Budrugeac, Differential non-linear isoconversional procedure for evalu-ating the activation energy of non-isothermal reactions, Journal of ThermalAnalysis and Calorimetry 68 (2002) 131–139.

141] T. Wanjun, C. Donghua, An integral method to determine variation in acti-vation energy with extent of conversion, Thermochimica Acta 433 (2005)72–76.

142] A. Ortega, A simple and precise linear integral method for isoconversionaldata, Thermochimica Acta 474 (2008) 81–86.

143] Y. Han, H. Chen, N. Liu, New incremental isoconversional method for kineticanalysis of solid thermal decomposition, Journal of Thermal Analysis andCalorimetry, http://dx.doi.org/10.1007/s10973-010-1029-9, in press.

144] H.L. Friedman, Kinetics of thermal degradation of char-forming plastics fromthermogravimetry. Application to a phenolic plastic, Journal of Polymer Sci-ence Part C: Polymer Symposia 6 (1964) 183–195.

145] T. Ozawa, A new method of analyzing thermogravimetric data, Bulletin of theChemical Society of Japan 38 (1965) 1881–1886.

146] J.H. Flynn, L.A. Wall, A quick, direct method for the determination of activa-tion energy from thermogravimetric data, Journal of Polymer Science Part B:Polymer Letters 4 (1966) 323–328.

147] J.H. Flynn, L.A. Wall, General treatment of the thermogravimetry of poly-mers, Journal of Research of the National Bureau of Standards—A. Physicsand Chemistry 70A (1966) 487–523.


[148] T. Ozawa, Kinetic analysis of derivative curves in thermal analysis, Journal ofThermal Analysis 2 (1970) 301–324.

[149] J.H. Flynn, The isoconversional method for determination of energy of acti-vation at constant heating rates. Corrections for the Doyle approximation,Journal of Thermal Analysis 27 (1983) 95–102.

[150] T. Ozawa, Estimation of activation energy by isoconversion methods, Ther-mochimica Acta 203 (1992) 159–165.

[151] C.D. Doyle, Kinetic analysis of thermogravimetric data, Journal of AppliedPolymer Science 5 (1961) 285–292.

[152] C.D. Doyle, Estimating isothermal life from thermogravimetric data, Journalof Applied Polymer Science 6 (1962) 639–642.

[153] T. Akahira, T. Sunose, Method of determining activation deterioration con-stant of electrical insulating materials, Research Report of Chiba Institute ofTechnology 16 (1971) 22–31.

[154] G. Chunxiu, S. Yufang, C. Donghua, Comparative method to evaluate reli-able kinetic triplets of thermal decomposition reactions, Journal of ThermalAnalysis and Calorimetry 76 (2004) 203–216.

[155] A.W. Coats, J.P. Redfern, Kinetic parameters from thermogravimetric data,Nature 201 (1964) 68–69.

[156] A.W. Coats, J.P. Redfern, Kinetic parameters from thermogravimetric data. II,Journal of Polymer Science Part B: Polymer Letters 3 (1965) 917–920.

[157] J.M. Criado, A. Ortega, F. Gotor, Correlation between the shape of controlled-rate thermal analysis curves and the kinetics of solid-state reactions,Thermochimica Acta 157 (1990) 171–179.

[158] J. Málek, The kinetic analysis of non-isothermal data, Thermochimica Acta200 (1992) 257–269.

[159] F.J. Gotor, J.M. Criado, J. Málek, N. Koga, Kinetic analysis of solid-statereactions: the universality of master plots for analyzing isothermal andnonisothermal experiments, Journal of Physical Chemistry A 104 (2000)10777–10782.

[160] A.K. Galwey, Perennial problems and promising prospects in the kinetic anal-ysis of nonisothermal rate data, Thermochimica Acta 407 (2003) 93–103.

[161] A.K. Burnham, R.L. Braun, Global kinetic analysis of complex materials, Energy& Fuels 13 (1999) 1–22.

[162] P.E. Fischer, C.S. Jou, S.S. Gokalgandhi, Obtaining the kinetic param-eters from thermogravimetry using a modified coats and redferntechnique, Industrial & Engineering Chemistry Research 26 (1987)1037–1040.

[163] E. Sima-Ella, T.J. Mays, Analysis of the oxidation reactivity of carbonaceousmaterials using thermogravimetric analysis, Journal of Thermal Analysis andCalorimetry 80 (2005) 109–113.

[164] J.H. Flynn, A general differential technique for the determination of parame-ters for d(˛)/dt = f(˛)A exp(−E/RT): energy of activation, preexponential factorand order of reaction (when applicable), Journal of Thermal Analysis 37 (1991)293–305.

[165] R.J. Lopez, Advanced Engineering Mathematics, Addison-Wesley, Boston, MA,2001.

[166] S. Vyazovkin, D. Dollimore, Linear and nonlinear procedures in isoconver-sional computations of the activation energy of nonisothermal reactions insolids, Journal of Chemical Information and Computer Sciences 36 (1996)42–45.

[167] G.I. Senum, R.T. Yang, Rational approximations of the integral of the Arrheniusfunction, Journal of Thermal Analysis 11 (1977) 445–447.

[168] S.V. Vyazovkin, A.I. Lesnikovich, Estimation of the pre-exponential factor inthe isoconversional calculation of effective kinetic parameters, Thermochim-ica Acta 128 (1988) 297–300.

[169] P. Budrugeac, A.L. Petre, E. Segal, Some problems concerning the eval-uation of non-isothermal kinetic parameters: solid–gas decompositionsfrom thermogravimetric data, Journal of Thermal Analysis 47 (1996)123–134.

[170] C.-R. Li, T. Tang, Dynamic thermal analysis of solid-state reactions: the ulti-mate method for data analysis? Journal of Thermal Analysis and Calorimetry49 (1997) 1243–1248.

[171] C.-R. Li, T.B. Tang, A new method for analysing non-isothermal thermoanalyt-ical data from solid-state reactions, Thermochimica Acta 325 (1999) 43–46.

[172] P. Budrugeac, E. Segal, Where is the confusion? ICTAC News 36 (2003) 63–64.[173] J. Cai, R. Liu, C. Sun, Logistic regression model for isoconversional kinetic

analysis of cellulose pyrolysis, Energy & Fuels 22 (2008) 867–870.[174] G. Várhegyi, M.J. Antal Jr., P. Szabó, E. Jakab, F. Till, Application of complex

reaction kinetic models in thermal analysis—the least squares evaluation ofseries of experiments, Journal of Thermal Analysis 47 (1996) 535–542.

[175] G.A. Carrillo Le Roux, I. Bergault, H. Delmas, X. Joulia, Development of a com-putational tool for the transient kinetics of complex chemical heterogeneousreaction systems, Studies in Surface Science and Catalysis Volume 109 (1997)571–576.

[176] F. Aluffi-Pentini, V. De Fonzo, V. Parisi, A novel algorithm for the numericalintegration of systems of ordinary differential equations arising in chemicalproblems, Journal of Mathematical Chemistry 33 (2003) 1–15.

[177] R.N. Whittem, W.I. Stuart, J.H. Levy, Smoothing and differentiation of thermo-gravimetric data by digital filters, Thermochimica Acta 57 (1982) 235–239.

[178] G. Várhegyi, F. Till, Computer processing of thermogravimetric-mass spec-trometric and high pressure thermogravimetric data. Part 1. Smoothing anddifferentiation, Thermochimica Acta 329 (1999) 141–145.

[179] S. Naya, R. Cao, R. Artiaga, Local polynomial estimation of TGA derivativesusing logistic regression for pilot bandwidth selection, Thermochimica Acta406 (2003) 177–183.

3 al and
[180] N. Liu, H. Chen, L. Shu, R. Zong, B. Yao, M. Statheropoulos, Gaussiansmoothing strategy of thermogravimetric data of biomass materials in anair atmosphere, Industrial & Engineering Chemistry Research 43 (2004)4087–4096.

[181] J.D. Sewry, M.E. Brown, “Model-free” kinetic analysis? Thermochimica Acta390 (2002) 217–225.

[182] M.E. Brown, Stocktaking in the kinetics cupboard, Journal of Thermal Analysisand Calorimetry 82 (2005) 665–669.

[183] S. Balci, T. Dogu, H. Yucel, Pyrolysis kinetics of lignocellulosic materials, Indus-trial & Engineering Chemistry Research 32 (1993) 2573–2579.

[184] J.G. Reynolds, A.K. Burnham, Pyrolysis decomposition kinetics of cellulose-based materials by constant heating rate micropyrolysis, Energy & Fuels 11(1997) 88–97.

[185] J.G. Reynolds, A.K. Burnham, P.H. Wallman, Reactivity of paper residues pro-duced by a hydrothermal pretreatment process for municipal solid wastes,Energy & Fuels 11 (1997) 98–106.

[186] E.G. Prout, F.C. Tompkins, The thermal decomposition of potassium perman-ganate, Transactions of the Faraday Society 40 (1944) 488–498.

[187] M.E. Brown, The Prout–Tompkins rate equation in solid-state kinetics, Ther-mochimica Acta 300 (1997) 93–106.

[188] A.K. Burnham, R.L. Braun, T.T. Coburn, E.I. Sandvik, D.J. Curry, B.J. Schmidt,R.A. Noble, An appropriate kinetic model for well-preserved algal kerogens,Energy & Fuels 10 (1996) 49–59.

[189] G. Várhegyi, P. Szabó, M.J. Antal Jr., Kinetics of charcoal devolatilization,Energy & Fuels 16 (2002) 724–731.

[190] E. Biagini, F. Lippi, L. Petarca, L. Tognotti, Devolatilization rate of biomassesand coal-biomass blends: an experimental investigation, Fuel 81 (2002)1041–1050.

[191] D. Ferdous, A.K. Dalai, S.K. Bej, R.W. Thring, Pyrolysis of lignins: experimentaland kinetic studies, Energy & Fuels 16 (2002) 1405–1412.

[192] M. Becidan, G. Várhegyi, J.E. Hustad, Ø. Skreiberg, Thermal decomposition ofbiomass wastes. A kinetic study, Industrial & Engineering Chemistry Research46 (2007) 2428–2437.

[193] T. Sonobe, N. Worasuwannarak, Kinetic analyses of biomass pyrolysis usingthe distributed activation energy model, Fuel 87 (2008) 414–421.

[194] G. Wang, W. Li, B. Li, H. Chen, TG study on pyrolysis of biomass and its threecomponents under syngas, Fuel 87 (2008) 552–558.

[195] G. Várhegyi, Z. Czégény, E. Jakab, K. McAdam, C. Liu, Tobacco pyrolysis. kineticevaluation of thermogravimetric-mass spectrometric experiments, Journal ofAnalytical and Applied Pyrolysis 86 (2009) 310–322.

[196] G. Várhegyi, H. Chen, S. Godoy, Thermal decomposition of wheat, oat, bar-ley, and Brassica carinata straws. A kinetic study, Energy & Fuels 23 (2009)646–652.

[197] D.B. Anthony, J.B. Howard, H.C. Hottel, H.P. Meissner, Rapid devolatiliza-tion of coal, Proceedings of the Combustion Institute 15 (1975)1303–1317.

[198] J.B. Howard, Fundamentals of coal pyrolysis and hydropyrolysis, in: M.A. Elliot(Ed.), Chemistry of Coal Utilization: Second Supplementary Volume, WileyInterscience, New York, NY, 1981, pp. 665–784.

[199] P.K. Agarwal, Distributed kinetic parameters for methane evolution duringcoal pyrolysis, Fuel 64 (1985) 870–872.

[200] R.L. Braun, A.K. Burnham, Analysis of chemical reaction kinetics using a dis-tribution of activation energies and simpler models, Energy & Fuels 1 (1987)153–161.

[201] P.K. Agarwal, J.B. Agnew, N. Ravindran, R. Weimann, Distributed kineticparameters for the evolution of gas species in the pyrolysis of coal, Fuel 66(1987) 1097–1106.

[202] G.J. Pitt, The kinetics of the evolution of volatile products from coal, Fuel 41(1962) 267–274.

[203] K. Miura, T. Maki, A simple method for estimating f(E) and k0(E) in the dis-tributed activation energy model, Energy & Fuels 12 (1998) 864–869.

[204] J. Cai, R. Liu, Weibull mixture model for modeling nonisothermal kineticsof thermally stimulated solid-state reactions: application to simulated andreal kinetic conversion data, Journal of Physical Chemistry B 111 (2007)10681–10686.

[205] C.M. Wyandt, D.R. Flanagan, A critical evaluation of three non-isothermalkinetic techniques, Thermochimica Acta 197 (1992) 239–248.

[206] S.V. Vyazovkin, A.I. Lesnikovich, On the method of solving the inverse problemof solid-phase reaction kinetics. II. Methods based on generalized descrip-tions, Journal of Thermal Analysis 36 (1990) 599–615.

[207] L. Wilson, W. Yang, W. Blasiak, G.R. John, C.F. Mhilu, Thermal characteriza-tion of tropical biomass feedstocks, Energy Conversion and Management,http://dx.doi.org/10.1016/j.enconman.2010.06.058, in press.

[208] R. Font, A. Marcilla, A.N. García, J.A. Caballero, J.A. Conesa, Kinetic models forthe thermal degradation of heterogeneous materials, Journal of Analytical andApplied Pyrolysis 32 (1995) 29–39.

[209] A. Demirbas, F. Akdeniz, Y. Erdogan, V. Pamuk, Kinetics for fast pyrolysis ofhazelnut shell, Fuel Science and Technology International 14 (1996) 405–415.

[210] A. Demirbas, Kinetics for non-isothermal flash pyrolysis of hazelnut shell,Bioresource Technology 66 (1998) 247–252.

[211] P.R. Bonelli, E.G. Cerrella, A.L. Cukierman, Slow pyrolysis of nutshells: char-acterization of derived chars and of process kinetics, Energy Sources, Part A:Recovery, Utilization, and Environmental Effects 25 (2003) 767–778.

[212] P.K. Gallagher, D.W. Johnson Jr., The effects of small sample size and heatingrate on the kinetics of the thermal decomposition of CaCO3, ThermochimicaActa 6 (1973) 67–83.


[213] J.A. Caballero, R. Font, A. Marcilla, Comparative study of the pyrolysis ofalmond shells and their fractions, holocellulose and lignin. Product yields andkinetics, Thermochimica Acta 276 (1996) 57–77.

[214] J.A. Caballero, J.A. Conesa, R. Font, A. Marcilla, Pyrolysis kinetics of almondshells and olive stones considering their organic fractions, Journal of Analyt-ical and Applied Pyrolysis 42 (1997) 159–175.

[215] R. Font, A. Marcilla, J. Devesa, E. Verdú, Kinetic study of the flash pyrolysisof almond shells in a fluidized bed reactor at high temperatures, Journal ofAnalytical and Applied Pyrolysis 27 (1993) 245–273.

[216] R. Font, A. Marcilla, E. Verdú, J. Devesa, Thermogravimetric kineticstudy of the pyrolysis of almond shells and almond shells impreg-nated with CoCl2, Journal of Analytical and Applied Pyrolysis 21 (1991)249–264.

[217] P.R. Bonelli, P.A. Della Rocca, E.G. Cerrella, A.L. Cukierman, Effect of pyroly-sis temperature on composition, surface properties and thermal degradationrates of Brazil nut shells, Bioresource Technology 76 (2001) 15–22.

[218] S. Bandyopadhyay, R. Chowdhury, G.K. Biswas, Thermal deactivation stud-ies of coconut shell pyrolysis, Canadian Journal of Chemical Engineering 77(1999) 1028–1036.

[219] J. Cai, L. Ji, Pattern search method for determination of DAEM kinetic param-eters from nonisothermal TGA data of biomass, Journal of MathematicalChemistry 42 (2007) 547–553.

[220] H.R. Yuan, R.H. Liu, Study on pyrolysis kinetics of walnut shell, Journal ofThermal Analysis and Calorimetry 89 (2007) 983–986.

[221] M. Arnold, G.E. Veress, J. Paulik, F. Paulik, A critical reflection upon the appli-cation of the Arrhenius model to non-isothermal thermogravimetric curves,Thermochimica Acta 52 (1982) 67–81.

[222] J.F. González, A. Ramiro, C.M. González-García, J. Ganán, J.M. Encinar, E. Sabio,J. Rubiales, Pyrolysis of almond shells. Energy applications of fractions, Indus-trial & Engineering Chemistry Research 44 (2005) 3003–3012.

[223] A. Demirbas, Fuel characteristics of olive husk and walnut, hazelnut, sun-flower, and almond shells, Energy Sources, Part A: Recovery, Utilization, andEnvironmental Effects 24 (2002) 215–221.

[224] M.J. Antal Jr., S.G. Allen, X. Dai, B. Shimizu, M.S. Tam, M. Grønli, Attainmentof the theoretical yield of carbon from biomass, Industrial & EngineeringChemistry Research 39 (2000) 4024–4031.

[225] B. Bhushan, Agricultural residues and their utilization in some coun-tries of south and south East Asia, in: FAO/UNEP Seminar on ResidueUtilization—Management of Agricultural and Agro-Industrial Wastes, Rome,Italy, 18 January, 1977.

[226] R.S. Govil, Chemical pulp from groundnut shells, Tappi 43 (1960) 215A–216A.[227] E. Apaydin-Varol, E. Pütün, A.E. Pütün, Slow pyrolysis of pistachio shell, Fuel

86 (2007) 1892–1899.[228] D.F. Arseneau, Competitive reactions in the thermal decomposition of cellu-

lose, Canadian Journal of Chemistry 49 (1971) 632–638.[229] T. Fisher, M. Hajaligol, B. Waymack, D. Kellogg, Pyrolysis behavior and kinetics

of biomass derived materials, Journal of Analytical and Applied Pyrolysis 62(2002) 331–349.

[230] A.K. Galwey, Is the science of thermal analysis kinetics based on solid foun-dations?: a literature appraisal, Thermochimica Acta 413 (2004) 139–183.

[231] F.J. Kilzer, A. Broido, Speculation on the nature of cellulose pyrolysis, Pyrody-namics 2 (1965) 151–163.

[232] P.T. Williams, S. Besler, The influence of temperature and heating rate on theslow pyrolysis of biomass, Renewable Energy 7 (1996) 223–250.

[233] J.L. Banyasz, S. Li, J.L. Lyons-Hart, K.H. Shafer, Cellulose pyrolysis: the kineticsof hydroxyacetaldehyde evolution, Journal of Analytical and Applied Pyrolysis57 (2001) 223–248.

[234] A. Basch, M. Lewin, The influence of fine structure on the pyrolysis of cellulose.I. Vacuum pyrolysis, Journal of Polymer Science: Polymer Chemistry Edition11 (1973) 3071–3093.

[235] K.E. Cabradilla, S.H. Zeronian, Influence of crystallinity on the thermal proper-ties of cellulose, in: F. Shafizadeh, K.V. Sarkanen, D.A. Tillman (Eds.), ThermalUses and Properties of Carbohydrates and Lignin, Academic Press, New York,NY, 1976, pp. 73–96.

[236] A. Broido, M. Weinstein, Kinetics of solid-phase cellulose pyrolysis, in: Pro-ceedings of the Third International Conference on Thermal Analysis, Davos,Switzerland, August 1971, Birkhauser Verlag, Basel, Switzerland, 1972, pp.285–296.

[237] A. Broido, M.A. Nelson, Char yield on pyrolysis of cellulose, Combustion andFlame 24 (1975) 263–268.

[238] A. Broido, Kinetics of solid-phase cellulose pyrolysis, in: F. Shafizadeh, K.Sarkanen, D.A. Tillman (Eds.), Thermal Uses and Properties of Carbohydratesand Lignins, Academic Press, New York, NY, 1976, pp. 19–36.

[239] A.G.W. Bradbury, Y. Sakai, F. Shafizadeh, A kinetic model for pyrolysis ofcellulose, Journal of Applied Polymer Science 23 (1979) 3271–3280.

[240] J. Piskorz, D. Radlein, D.S. Scott, On the mechanism of the rapid pyrol-ysis of cellulose, Journal of Analytical and Applied Pyrolysis 9 (1986)121–137.

[241] M.J. Antal Jr., G. Várhegyi, Cellulose pyrolysis kinetics: the current state ofknowledge, Industrial & Engineering Chemistry Research 34 (1995) 703–717.

[242] S. Völker, T. Rieckmann, Thermokinetic investigation of cellulosepyrolysis—impact of initial and final mass on kinetic results, Journal ofAnalytical and Applied Pyrolysis 62 (2002) 165–177.

[243] V. Mamleev, S. Bourbigot, J. Yvon, Kinetic analysis of the thermal decomposi-tion of cellulose: the change of the rate limitation, Journal of Analytical andApplied Pyrolysis 80 (2007) 141–150.

al and

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[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[


244] V. Mamleev, S. Bourbigot, J. Yvon, Kinetic analysis of the thermal decomposi-tion of cellulose: the main step of mass loss, Journal of Analytical and AppliedPyrolysis 80 (2007) 151–165.

245] F. Yao, Q. Wu, Y. Lei, W. Guo, Y. Xu, Thermal decomposition kinetics of naturalfibers: activation energy with dynamic thermogravimetric analysis, PolymerDegradation and Stability 93 (2008) 90–98.

246] A. Aho, N. Kumar, K. Eränen, B. Holmbom, M. Hupa, T. Salmi, D.Y. Murzin,Pyrolysis of softwood carbohydrates in a fluidized bed reactor, InternationalJournal of Molecular Sciences 9 (2008) 1665–1675.

247] J. Piskorz, D.S.A.G. Radlein, D.S. Scott, S. Czernik, Pretreatment of wood andcellulose for production of sugars by fast pyrolysis, Journal of Analytical andApplied Pyrolysis 16 (1989) 127–142.

248] G. Várhegyi, P. Szabó, W.S.-L. Mok, M.J. Antal Jr., Kinetics of the thermaldecomposition of cellulose in sealed vessels at elevated pressures. Effects ofthe presence of water on the reaction mechanism, Journal of Analytical andApplied Pyrolysis 26 (1993) 159–174.

249] G. Várhegyi, E. Jakab, M.J. Antal Jr., Is the Broido–Shafizadeh model for cellu-lose pyrolysis true? Energy & Fuels 8 (1994) 1345–1352.

250] R.G. Graham, M.A. Bergougnou, B.A. Freel, Kinetics of vapour-phase cellulosefast pyrolysis reactions, Biomass & Bioenergy 7 (1994) 33–47.

251] A. Basch, M. Lewin, The influence of fine structure on the pyrolysis of cellulose.II. Pyrolysis in air, Journal of Polymer Science: Polymer Chemistry Edition 11(1973) 3095–3101.

252] C. Fairbridge, R.A. Ross, S.P. Sood, A kinetic and surface study of the ther-mal decomposition of cellulose powder in inert and oxidizing atmospheres,Journal of Applied Polymer Science 22 (1978) 497–510.

253] A. Basch, M. Lewin, Influence of fine structure on the pyrolysis of cellulose. III.The influence of orientation, Journal of Polymer Science: Polymer ChemistryEdition 12 (1974) 2053–2063.

254] T.R. Rao, A. Sharma, Pyrolysis rates of biomass materials, Energy 23 (1998)973–978.

255] C.J. Gómez, J.J. Manyà, E. Velo, L. Puigjaner, Further applications of a revisitedsummative model for kinetics of biomass pyrolysis, Industrial & EngineeringChemistry Research 43 (2004) 901–906.

256] J.M. Heikkinen, J.C. Hordijk, W. de Jong, H. Spliethoff, Thermogravimetry as atool to classify waste components to be used for energy generation, Journalof Analytical and Applied Pyrolysis 71 (2004) 883–900.

257] C.J. Gómez, G. Várhegyi, L. Puigjaner, Slow pyrolysis of woody residues and anherbaceous biomass crop: a kinetic study, Industrial & Engineering ChemistryResearch 44 (2005) 6650–6660.

258] R. Miranda, C. Sosa-Blanco, D. Bustos-Martínez, C. Vasile, Pyrolysis of textilewastes. I. Kinetics and yields, Journal of Analytical and Applied Pyrolysis 80(2007) 489–495.

259] A.F. Roberts, A review of kinetics data for the pyrolysis of wood and relatedsubstances, Combustion and Flame 14 (1970) 261–272.

260] F.C. Beall, H.W. Eickner, Thermal degradation of wood components: a reviewof the literature, in: USDA Forest Service Research Paper FPL 130, Forest Prod-ucts Laboratory, U.S. Forest Service, U.S. Department of Agriculture, Madison,WI, 1970.

261] D. Montané, V. Torné-Fernández, V. Fierro, Activated carbons from lignin:kinetic modeling of the pyrolysis of kraft lignin activated with phosphoricacid, Chemical Engineering Journal 106 (2005) 1–12.

262] S. Kubo, J.F. Kadla, Hydrogen bonding in lignin: a Fourier transform infraredmodel compound study, Biomacromolecules 6 (2005) 2815–2821.

263] M.J. Antal Jr., Biomass pyrolysis: a review of the literature. Part1—carbohydrate pyrolysis, in: K.W. Böer, J.A. Duffie (Eds.), Advances inSolar Energy, vol. 1, American Solar Energy Society, Newark, DE, 1982,pp. 61–111.

264] M.J. Antal Jr., Biomass pyrolysis. a review of the literature. Part2—Lignocellulose pyrolysis, in: K.W. Böer, J.A. Duffie (Eds.), Advances in SolarEnergy, vol. 2, Plenum Press, New York, NY, 1985, pp. 175–255.

265] F. Shafizadeh, Pyrolytic reactions and products of biomass, in: R.P. Overend,T.A. Milne, L.K. Mudge (Eds.), Fundamentals of Thermochemical Conver-sion of Biomass, Elsevier Applied Science Publishers, London, UK, 1985,pp. 183–217.

266] R.J. Evans, T.A. Milne, Molecular characterization of the pyrolysis of biomass.1. Fundamentals, Energy & Fuels 1 (1987) 123–137.

267] R.J. Evans, T.A. Milne, Molecular characterization of the pyrolysis of biomass.2. Applications, Energy & Fuels 1 (1987) 311–319.

268] T. Hosoya, H. Kawamoto, S. Saka, Cellulose–hemicellulose andcellulose–lignin interactions in wood pyrolysis at gasification temperature,Journal of Analytical and Applied Pyrolysis 80 (2007) 118–125.

269] F. Karaosmanoglu, B.D. Cift, A. Isigigür-Ergüdenler, Determination of reac-tion kinetics of straw and stalk of rapeseed using thermogravimetric analysis,Energy Sources 23 (2001) 767–774.

270] P.C. Lewellen, W.A. Peters, J.B. Howard, Cellulose pyrolysis kinetics and charformation mechanism, Symposium (International) on Combustion 16 (1977)1471–1480.

271] C.H. Bamford, J. Crank, D.H. Malan, The combustion of wood, Part I Mathemat-ical Proceedings of the Cambridge Philosophical Society 42 (1946) 166–182.

272] H.-C. Kung, A mathematical model of wood pyrolysis, Combustion and Flame18 (1972) 185–195.

273] D.L. Pyle, C.A. Zaror, Heat transfer and kinetics in the low temperature pyrol-ysis of solids, Chemical Engineering Science 39 (1984) 147–158.

274] S.S. Alves, J.L. Figueiredo, A model for pyrolysis of wet wood, Chemical Engi-neering Science 44 (1989) 2861–2869.


[275] C.A. Koufopanos, N. Papayannakos, G. Maschio, A. Lucchesi, Modelling of thepyrolysis of biomass particles. Studies on kinetics, thermal and heat transfereffects, Canadian Journal of Chemical Engineering 69 (1991) 907–915.

[276] J. Saastamoinen, J.-R. Richard, Simultaneous drying and pyrolysis of solid fuelparticles, Combustion and Flame 106 (1996) 288–300.

[277] A.M.C. Janse, R.W.J. Westerhout, W. Prins, Modelling of flash pyrolysis of a sin-gle wood particle, Chemical Engineering and Processing 39 (2000) 239–252.

[278] J. Larfeldt, B. Leckner, M.C. Melaaen, Modelling and measurements of thepyrolysis of large wood particles, Fuel 79 (2000) 1637–1643.

[279] M.J. Hagge, K.M. Bryden, Modeling the impact of shrinkage on the pyrolysisof dry biomass, Chemical Engineering Science 57 (2002) 2811–2823.

[280] K.M. Bryden, M.J. Hagge, Modeling the combined impact of moisture and charshrinkage on the pyrolysis of a biomass particle, Fuel 82 (2003) 1633–1644.

[281] B. Peters, C. Bruch, Drying and pyrolysis of wood particles: experiments andsimulation, Journal of Analytical and Applied Pyrolysis 70 (2003) 233–250.

[282] B.V. Babu, A.S. Chaurasia, Dominant design variables in pyrolysis of biomassparticles of different geometries in thermally thick regime, Chemical Engi-neering Science 59 (2004) 611–622.

[283] B.V. Babu, A.S. Chaurasia, Heat transfer and kinetics in the pyrolysis of shrink-ing biomass particle, Chemical Engineering Science 59 (2004) 1999–2012.

[284] B.V. Babu, A.S. Chaurasia, Pyrolysis of biomass: improved models for simul-taneous kinetics and transport of heat, mass and momentum, EnergyConversion and Management 45 (2004) 1297–1327.

[285] A. Galgano, C. Di Blasi, Modelling the propagation of drying and decomposi-tion fronts in wood, Combustion and Flame 139 (2004) 16–27.

[286] N. Jand, P.U. Foscolo, Decomposition of wood particles in fluidized beds,Industrial & Engineering Chemistry Research 44 (2005) 5079–5089.

[287] A.S. Chaurasia, B.D. Kulkarni, Most sensitive parameters in pyrolysis of shrink-ing biomass particle, Energy Conversion and Management 48 (2007) 836–849.

[288] B. Benkoussas, J.-L. Consalvi, B. Porterie, N. Sardoy, J.-C. Loraud, Modellingthermal degradation of woody fuel particles, International Journal of ThermalSciences 46 (2007) 319–327.

[289] S.R. Wasan, P. Rauwoens, J. Vierendeels, B. Merci, Numerical simulations ofpyrolysis of wet charring materials using a one-dimensional enthalpy basedmodel, in: 4th European Combustion Meeting, Vienna, Austria, 2009.

[290] A.K. Sadhukhan, P. Gupta, R.K. Saha, Modelling and experimental studies onpyrolysis of biomass particles, Journal of Analytical and Applied Pyrolysis 81(2008) 183–192.

[291] G. Várhegyi, Reaction kinetics in thermal analysis: a brief survey of funda-mental research problems, Thermochimica Acta 110 (1987) 95–99.

[292] I. Milosavljevic, E.M. Suuberg, Cellulose thermal decomposition kinetics:global mass loss kinetics, Industrial & Engineering Chemistry Research 34(1995) 1081–1091.

[293] E.M. Suuberg, I. Milosavljevic, V. Oja, Two-regime global kinetics of cellulosepyrolysis: the role of tar evaporation, Symposium (International) on Combus-tion 26 (1996) 1515–1521.

[294] J.L. Banyasz, S. Li, J.L. Lyons-Hart, K.H. Shafer, Gas evolution and the mecha-nism of cellulose pyrolysis, Fuel 80 (2001) 1757–1763.

[295] R. Zanzi, K. Sjöström, E. Björnbom, Rapid pyrolysis of agricultural residues athigh temperature, Biomass & Bioenergy 23 (2002) 357–366.

[296] M.G. Grønli, M.J. Antal Jr., G. Várhegyi, A round-robin study of cellulosepyrolysis kinetics by thermogravimetry, Industrial & Engineering ChemistryResearch 38 (1999) 2238–2244.

[297] S. Maiti, S. Purakayastha, B. Ghosh, Thermal characterization of mustardstraw and stalk in nitrogen at different heating rates, Fuel 86 (2007)1513–1518.

[298] M.M. Nassar, Thermal analysis kinetics of bagasse and rice straw, EnergySources, Part A: Recovery, Utilization, and Environmental Effects 20 (1998)831–837.

[299] G.M. Simmons, M. Gentry, Particle size limitations due to heat transfer indetermining pyrolysis kinetics of biomass, Journal of Analytical and AppliedPyrolysis 10 (1986) 117–127.

[300] K.M. Bryden, K.W. Ragland, C.J. Rutland, Modeling thermally thick pyrolysisof wood, Biomass & Bioenergy 22 (2002) 41–53.

[301] J. Porteiro, E. Granada, J. Collazo, D. Patino, J.C. Morán, A model for the combus-tion of large particles of densified wood, Energy & Fuels 21 (2007) 3151–3159.

[302] A.J. Stamm, Thermal degradation of wood and cellulose, Industrial & Engi-neering Chemistry 48 (1956) 413–417.

[303] P. Roque-Diaz, V.Z. Shemet, V.A. Lavrenko, V.A. Khristich, Studies on thermaldecomposition and combustion mechanism of bagasse under non-isothermalconditions, Thermochimica Acta 93 (1985) 349–352.

[304] F. Shafizadeh, Pyrolysis and combustion of cellulosic materials, Advances inCarbohydrate Chemistry 23 (1968) 419–474.

[305] M. Müller-Hagedorn, H. Bockhorn, L. Krebs, U. Müller, A comparative kineticstudy on the pyrolysis of three different wood species, Journal of Analyticaland Applied Pyrolysis 68–69 (2003) 231–249.

[306] K. Raveendran, A. Ganesh, K.C. Khilar, Influence of mineral matter on biomasspyrolysis characteristics, Fuel 74 (1995) 1812–1822.

[307] A. Ouensanga, C. Picard, Thermal degradation of sugar cane bagasse, Ther-mochimica Acta 125 (1988) 89–97.

[308] A. Broido, Thermogravimetric and differential thermal analysis of potassiumbicarbonate contaminated cellulose, Pyrodynamics 4 (1966) 243–251.

[309] W.K. Tang, Effect of inorganic salts on pyrolysis of wood, alpha-cellulose,and lignin: determined by dynamic thermogravimetry, in: U.S. Forest Ser-vice Research Paper FPL 71, Forest Products Laboratory, U.S. Forest Service,U.S. Department of Agriculture, Madison, WI, 1967.

3 al and
[310] M.M. Nassar, G.D.M. MacKay, Mechanism of Thermal Decomposition of Lignin,Wood and Fiber Science 16 (1984) 441–453.

[311] G. Várhegyi, M.J. Antal Jr., T. Szekely, F. Till, E. Jakab, P. Szabó, Simultaneousthermogravimetric-mass spectrometric studies of the thermal decomposi-tion of biopolymers. 2. Sugar cane bagasse in the presence and absence ofcatalysts, Energy & Fuels 2 (1988) 273–277.

[312] H. Teng, Y.-C. Wei, Thermogravimetric studies on the kinetics of rice hullpyrolysis and the influence of water treatment, Industrial & EngineeringChemistry Research 37 (1998) 3806–3811.

[313] R. Narayan, M.J. Antal Jr., Thermal lag, fusion, and the compensation effectduring biomass pyrolysis, Industrial & Engineering Chemistry Research 35(1996) 1711–1721.

[314] M.J. Antal Jr., H.L. Friedman, F.E. Rogers, Kinetics of cellulose pyrolysis innitrogen and steam, Combustion Science and Technology 21 (1980) 141–152.

[315] M.J. Antal Jr., Personal communication, 2008.[316] J. Czarnecki, J. Sesták, Practical thermogravimetry, Journal of Thermal Analysis

and Calorimetry 60 (2000) 759–778.[317] E. Chornet, C. Roy, Compensation effect in the thermal decomposition of cel-

lulosic materials, Thermochimica Acta 35 (1980) 389–393.[318] A.K. Galwey, M.E. Brown, Arrhenius parameters and compensation behaviour

in solid-state decompositions, Thermochimica Acta 300 (1997) 107–115.[319] W.C. Conner Jr., A general explanation for the compensation effect: the rela-

tionship between �S‡ and activation energy, Journal of Catalysis 78 (1982)238–246.

[320] A. Lesnikovich, S. Levchik, Isoparametric kinetic relations for chemicaltransformations in condensed substances (analytical survey). I. Theoreti-cal fundamentals, Journal of Thermal Analysis and Calorimetry 30 (1985)237–262.

[321] A.I. Lesnikovich, S.V. Levchik, Isoparametric kinetic relations for chemicaltransformations in condensed substances (analytical survey). II. Reactionsinvolving the participation of solid substances, Journal of Thermal Analysis30 (1985) 677–702.

[322] J. Pysiak, B. Sabalski, Compensation effect and isokinetic temperature in ther-mal dissociation reactions of the type Asolid �Bsolid + Cgas, Journal of ThermalAnalysis 17 (1979) 287–303.

[323] N. Koga, J. Sesták, Kinetic compensation effect as a mathematical consequenceof the exponential rate constant, Thermochimica Acta 182 (1991) 201–208.

[324] M. Suárez, A. Palermo, C. Aldao, The compensation effect revisited, Journal ofThermal Analysis 41 (1994) 807–816.

[325] A.K. Galwey, Compensation effect in heterogenous catalysis, Advances inCatalysis 26 (1977) 247–322.

[326] M.E. Brown, A.K. Galwey, The significance of “compensation effects” appear-ing in data published in “computational aspects of kinetic analysis”: ICTACproject, 2000, Thermochimica Acta 387 (2002) 173–183.

[327] N. Koga, H. Tanaka, A kinetic compensation effect established for thethermal decomposition of a solid, Journal of Thermal Analysis 37 (1991)347–363.

[328] P.D. Garn, The kinetic compensation effect, Journal of Thermal Analysis 10(1976) 99–102.

[329] R.K. Agrawal, On the compensation effect, Journal of Thermal Analysis 31(1986) 73–86.

[330] J. Sesták, Remark on the kinetic compensation effect, Journal of Thermal Anal-ysis and Calorimetry 32 (1987) 325–327.

[331] J. Zsakó, K.N. Somasekharan, Critical remarks on “on the compensation effect”,Journal of Thermal Analysis 32 (1987) 1277–1281.

[332] R.K. Agrawal, The Compensation Effect: A Reply to Zsakó and Somasekharan’sremarks, Journal of Thermal Analysis 34 (1988) 1141–1149.

[333] R.K. Agrawal, The compensation effect: a fact or a fiction, Journal of ThermalAnalysis 35 (1989) 909–917.

[334] FAOSTAT, Food and Agriculture Organization of the United Nations,Rome, Italy. Available from: http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancor (accessed 18.10.10).

[335] Azucarera del Guadalfeo—Cane Sugar Azucarera del Guadalfeo S.A., LaCaleta, Spain. Available from: http://www.az-guadalfeo.com/en/sugar.html(accessed 28.02.09).

[336] H. Blume, Geography of Sugar Cane, Verlag Dr. Albert Bartens, Berlin,Germany, 1985.

[337] G.O. Burr, C.E. Hartt, H.W. Brodie, T. Tanimoto, H.P. Kortschak, D. Takahashi,F.M. Ashton, R.E. Coleman, The sugarcane plant, Annual Review of Plant Phys-iology 8 (1957) 275–308.

[338] G. Hunsigi, Production of Sugarcane: Theory and Practice, Springer-Verlag,Berlin, Germany, 1993.

[339] K. Miyamoto (Ed.), Renewable Biological Systems for Alternative SustainableEnergy Production, FAO Agricultural Services Bulletin No. 128, FAO, Rome,Italy, 1997.

[340] S.R. Chae, E.J. Hwang, H.S. Shin, Single cell protein production of Euglenagracilis and carbon dioxide fixation in an innovative photo-bioreactor, Biore-source Technology 97 (2006) 322–329.

[341] Y. Chisti, Biodiesel from microalgae beats bioethanol, Trends in Biotechnology26 (2008) 126–131.

[342] M. Gross, Algal biofuel hopes, Current Biology 18 (2008) R46–R47.[343] G.C. Dismukes, D. Carrieri, N. Bennette, G.M. Ananyev, M.C. Posewitz, Aquatic

phototrophs: efficient alternatives to land-based crops for biofuels, CurrentOpinion in Biotechnology 19 (2008) 235–240.

[344] Y. Li, M. Horsman, N. Wu, C.Q. Lan, N. Dubois-Calero, Biofuels from microalgae,Biotechnology Progress 24 (2008) 815–820.


[345] A. Kumar, S. Ergas, X. Yuan, A. Sahu, Q. Zhang, J. Dewulf, F.X. Malcata, H.van Langenhove, Enhanced CO2 fixation and biofuel production via microal-gae: recent developments and future directions, Trends in Biotechnology 28(2010) 371–380.

[346] G.D. Thompson, Production of biomass by sugarcane, Proceedings of the Aus-tralian Society of Sugar Cane Technologists 52 (1978) 180–187.

[347] G.D. Thompson, Comparisons of the growth of plant and first ratoon crops atPongola, Proceedings of the Australian Society of Sugar Cane Technologists62 (1988) 180–184.

[348] Biomass for renewable energy and fuels, in: D.L. Klass, C. Cleveland (Eds.),Encyclopedia of Energy, vol. 1, Elsevier Science Publishers, Amsterdam, TheNetherlands, 2004, pp. 193–212.

[349] J.A. Polack, M. West, Bioconversions of sugar cane products, in: U.S.-Republicof China Symposium, Philadelphia, PA, 1978.

[350] L. Reijnders, Microalgal and terrestrial transport biofuels to displace fossilfuels, Energies 2 (2009) 48–56.

[351] H.R. Bungay, Energy, The Biomass Options, Wiley Interscience, New York, NY,1981.

[352] A.C. Ledón, F.A.Z. González, Industrializacion de la Fotosintesis Mediantela Cana de Azucar, Asociación de Técnicos Azucareros de Cuba 24 (1950)581–590.

[353] J.B. van Beilen, Why microalgal biofuels won’t save the internal combustionengine, Biofuels, Bioproducts and Biorefining 4 (2009) 41–52.

[354] A.G. Alexander, The Energy Cane Alternative, Elsevier Science Publishers,Amsterdam, The Netherlands, 1985.

[355] G.W. Huber, S. Iborra, A. Corma, Synthesis of transportation fuels frombiomass: chemistry, catalysts, and engineering, Chemical Reviews 106 (2006)4044–4098.

[356] Y. Chisti, Response to reijnders: do biofuels from microalgae beat biofuelsfrom terrestrial plants? Trends in Biotechnology 26 (2008) 351–352.

[357] B.C. Biswas, Agroclimatology of the Sugar-Cane Crop, Technical Note No. 193,WMO No. 703, World Meterological Organization, Geneva, Switzerland, 1988.

[358] C.N. Babu, Sugarcane, 2nd ed., Allied Publishers, New Delhi, India, 1990.[359] G.L. James, Sugarcane, 2nd ed., Blackwell Science, Oxford, UK, 2004.[360] T.F. Dixon, Spontaneous combustion in bagasse stockpiles, Proceedings of the

Australian Society of Sugar Cane Technologists 10 (1988) 53–61.[361] J.M. Paturau (Ed.), By-Products of the Cane Sugar Industry: An Introduction

to Their Industrial Utilization, 3rd, completely revised ed., Elsevier SciencePublishers, Amsterdam, The Netherlands, 1989.

[362] P.W. van der Poel, H. Schiweck, T.K. Schwartz, Sugar Technology: Beet andCane Sugar Manufacture, Dr. Albert Bartens KG Verlag, Berlin, Germany, 1998.

[363] J. Lacey, The microbiology of the bagasse of sugarcane, Proceedings of theISSCT 3 (1980) 2442–2461.

[364] P.J.M. Rao, Industrial Utilization of Sugar Cane and its Co-Products, ISPCKPublishers, New Delhi, India, 1997.

[365] E.S. Lipinsky, Perspectives on preparation of cellulose for hydrolysis, in: R.D.Brown Jr., L. Jarasek (Eds.), Hydrolysis of Cellulose: Mechanisms of Enzymaticand Acid Catalysis, vol. 181, American Chemical Society, Washington, DC,1979, pp. 1–23.

[366] G. Várhegyi, M.J. Antal Jr., T. Szekely, P. Szabó, Kinetics of the thermal decom-position of cellulose, hemicellulose, and sugar cane bagasse, Energy & Fuels3 (1989) 329–335.

[367] M.J. Playne, Increased digestibility of bagasses by pretreatment with alka-lis and steam explosion, Biotechnology and Bioengineering 26 (1984)426–433.

[368] J.-X. Sun, F. Xu, X.-F. Sun, R.-C. Sun, S.-B. Wu, Comparative study of lignins fromultrasonic irradiated sugar-cane bagasse, Polymer International 53 (2004)1711–1721.

[369] A.E. El-Ashmawy, S. El-Kalyoubi, Y. Fahmy, Hemicelluloses of bagasse and ricestraw, Egyptian Journal of Chemistry 18 (1975) 149–156.

[370] R. Miranda, C.S. Blanco, F. Tristán, E. Ramírez, Pyrolysis of sugarcane bagasse:kinetics studies, in: 98th Air & Waste Management Association Annual Con-ference and Exhibition, Minneapolis, MN, 21–24 June, 2005.

[371] R.C. Trickett, F.G. Neytzell-de Wilde, Dilute acid hydrolysis of bagasse hemi-cellulose, CHEMSA 8 (1982) 11–15.

[372] K.L. Kadam, Environmental benefits on a life cycle basis of using bagasse-derived ethanol as a gasoline oxygenate in India, Energy Policy 30 (2002)371–384.

[373] J.E. Irvine, G.T.A. Benda, Genetic potential and restraints in saccharum asenergy source, in: Symposium on Alternative Uses of Sugarcane for Develop-ment in Puerto Rico, Caribe Hilton Hotel, San Juan, Puerto Rico, 26–27 March,1979.

[374] Y.W. Han, E.A. Catalano, A. Ciegler, Chemical and physical properties ofsugarcane bagasse irradiated with � rays, Journal of Agricultural and FoodChemistry 31 (1983) 34–38.

[375] M. Saska, E. Ozer, Aqueous extraction of sugarcane bagasse hemicelluloseand production of xylose syrup, Biotechnology and Bioengineering 45 (1995)517–523.

[376] R.W.G. Hessey, Bagasse—a chemurgic raw material, South African Sugar Jour-nal 26 (1942) 329–337.

[377] J. Rumble Jr., Reference Materials 8491, 8492, 8493, and 8494, National Insti-tute of Standards and Technology, Gaithersburg, MD, 2001.

[378] E. Hugot, Handbook of Cane Sugar Engineering, 3rd ed., Elsevier Science Pub-lishers, Amsterdam, The Netherlands, 1986.

[379] E.R. Behne, Thermal value of bagasse, Proceedings of the Queensland Societyof Sugar Cane Technologists 5 (1934) 175–177.

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380] A.L. Nicolai, Combustion calculations by graphical methods—wood andbagasse, Combustion 14 (1942) 40–44.

381] M. Morris, L. Waldheim, F.A.B. Linero, H.M. Lamônica, Increased power gen-eration from sugar cane biomass—the results of a technical and economicevaluation of the benefits of using advanced gasification technology in a typ-ical Brazilian sugar mill, International Sugar Journal 104 (2002), 243–244,247–248, 267.

382] M. Garcia-Perez, A. Chaala, J. Yang, C. Roy, Co-pyrolysis of sugarcane bagassewith petroleum residue. Part I: thermogravimetric analysis, Fuel 80 (2001)1245–1258.

383] A.-R.F. Drummond, I.W. Drummond, Pyrolysis of sugar cane bagasse in awire-mesh reactor, Industrial & Engineering Chemistry Research 35 (1996)1263–1268.

384] B. Edwards, Energy use in cane and beet factories, Proceedings of AustralianSociety of Sugar Cane Technologists 13 (1991) 227–229.

385] M.M. Nassar, Kinetic studies on thermal degradation of nonwood plants,Wood and Fiber Science 17 (1985) 266–273.

386] P.R. Bonelli, E.L. Buonomo, A.L. Cukierman, Pyrolysis of sugarcane bagasse andco-pyrolysis with an argentinean subbituminous coal, Energy Sources, Part A:Recovery, Utilization, and Environmental Effects 29 (2007) 731–740.

387] S. Munir, S.S. Daood, W. Nimmo, A.M. Cunliffe, B.M. Gibbs, Thermal analy-sis and devolatilization kinetics of cotton stalk, sugar cane bagasse and sheameal under nitrogen and air atmospheres, Bioresource Technology 100 (2009)1413–1418.

388] K.S. Shanmukharadhya, K.G. Sudhakar, Investigations of effect of bagassepyrolysis kinetics on combustion and boiler performance, Journal of theEnergy Institute 80 (2007) 40–45.

389] E.E.S. Lora, P.B. Soler, Determination of kinetic parameters governing the pro-cess of bagasse thermal decomposition and combustion, Ingeniera Energetica9 (1988) 146–148.

390] S. Aiman, J.F. Stubington, The pyrolysis kinetics of bagasse at low heating rates,Biomass & Bioenergy 5 (1993) 113–120.

391] J.F. Stubington, S. Aiman, Pyrolysis kinetics of bagasse at high heating rates,Energy & Fuels 8 (1994) 194–203.

392] M.M. Nassar, E.A. Ashour, S.S. Wahid, Thermal characteristics of bagasse, Jour-nal of Applied Polymer Science 61 (1996) 885–890.

393] I. Simkovic, K. Csomorová, Thermogravimetric analysis of agriculturalresidues: oxygen effect and environmental effect, Journal of Applied PolymerScience 100 (2006) 1318–1322.

394] J.J. Manyà, J. Arauzo, An alternative kinetic approach to describe the isother-
mal pyrolysis of micro-particles of sugar cane bagasse, Chemical EngineeringJournal 139 (2008) 549–561.
395] P.R. Bonelli, E.L. Buonomo, Pyrolysis of sugarcane bagasse and co-pyrolysiswith an Argentinean subbituminous coal, Energy Sources 29 (2007) 731–740.

396] A.D. Rodriguez, P. Roquet, V.A. Khristich, Combustion of pulverized biomass(bagasse) in a new rotary burner, Heat Transfer Research 25 (1993) 73–81.


[397] M.Z. Sefain, S.F. El-Kalyoubi, N. Shukry, Thermal behavior of holo- and hemi-cellulose obtained from rice straw and bagasse, Journal of Polymer Science:Polymer Chemistry Edition 23 (1985) 1569–1577.

[398] J.M. Criado, J.M. Trillo, Effect of diluent and atmosphere on DTA peaks ofdecomposition reactions, Journal of Thermal Analysis 9 (1976) 3–7.

[399] G. Várhegyi, P. Szabó, E. Jakab, F. Till, Least squares criteria for the kineticevaluation of thermoanalytical experiments. Examples from a char reactivitystudy, Journal of Analytical and Applied Pyrolysis 57 (2001) 203–222.

[400] A.E.S. Green, J. Feng, Systematics of corn stover pyrolysis yields and com-parisons of analytical and kinetic representations, Journal of Analytical andApplied Pyrolysis 76 (2006) 60–69.

[401] F.A. López, A.L.R. Mercê, F.J. Alguacil, A. López-Delgado, A kinetic study on thethermal behaviour of chitosan, Journal of Thermal Analysis and Calorimetry91 (2008) 633–639.

[402] H.E. Jegers, M.T. Klein, Primary and secondary lignin pyrolysis reaction path-ways, Industrial & Engineering Chemistry Process Design & Development 24(1985) 173–183.

[403] J.M. Encinar, F.J. Beltrán, A. Bernalte, A. Ramiro, J.F. González, Pyrolysis of twoagricultural residues: olive and grape bagasse. Influence of particle size andtemperature, Biomass & Bioenergy 11 (1996) 397–409.

[404] G. Várhegyi, E. Jakab, F. Till, T. Szekely, Thermogravimetric-mass spectromet-ric characterization of the thermal decomposition of sunflower stem, Energy& Fuels 3 (1989) 755–760.

[405] E. Bonnet, R.L. White, Series-specific isoconversion effective activation ener-gies derived by thermogravimetry-mass spectrometry, Thermochimica Acta311 (1998) 81–86.

[406] W.-C.R. Chan, M. Kelbon, B. Krieger-Brockett, Single-particle biomass pyrol-ysis: correlations of reaction products with process conditions, Industrial &Engineering Chemistry Research 27 (1988) 2261–2275.

[407] W.-C. Lai, B. Krieger-Brockett, Single particle refuse-derived fuel devolatiliza-tion: experimental measurements of reaction products, Industrial &Engineering Chemistry Research 32 (1993) 2915–2929.

[408] R. Font, I. Martín-Gullón, M. Esperanza, A. Fullana, Kinetic law forsolids decomposition. Application to thermal degradation of heteroge-neous materials, Journal of Analytical and Applied Pyrolysis 58–59 (2001)703–731.

[409] M.A. Olivella, F.X.C. de las Heras, Kinetic analysis in the maximum tempera-ture of oil generation by thermogravimetry in spanish fossil fuels, Energy &Fuels 16 (2002) 1444–1449.

[410] A.E.S. Green, M.A. Zanardi, J.P. Mullin, Phenomenological models of cellulose
pyrolysis, Biomass & Bioenergy 13 (1997) 15–24.
[411] M. Bodenstein, S.C. Lind, Geschwindigkeit der Bildung des Bromwasserstoffsaus seinen Elementen, Zeitschrift für Physikalische Chemie-Leipzig 57 (1906)168–192.

[412] S.W. Churchill, The Interpretation and Use of Rate Data: The Rate Concept,Revised ed., Hemisphere Publishing Corporation, Washington, DC, 1979.

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