## Ciriaf.it

Third International Conference on Applied Energy - 16-18 May 2011 - Perugia, Italy
K. Slopiecka, P. Bartocci, F. Fantozzi
Thermogravimetric analysis and Kinetic study of poplar wood pyrolysis

**Thermogravimetric analysis and Kinetic study of poplar wood **
**pyrolysis **
**Katarzyna Slopiecka, Pietro Bartocci, Francesco Fantozzi **
University of Perugia, CRB-Biomass Research Centre
Via G.Duranti-Strada S.Lucia Canetola s.n., 06125 Perugia, Italy

**Abstract **

A kinetic study of the pyrolysis process of poplar wood (populus L.) was investigated using a

thermogravimetric analyzer. The weight loss was measured in nitrogen atmosphere. The

samples were heated over a range of temperature from 298 K to 973 K with four different

heating rates of 2, 5, 10, 15 K min-1. The results obtained from thermal decomposition process

indicate that there are three main stages such as dehydration, active and passive pyrolysis. In

the DTG thermograms the temperature peaks at maximum weight loss rate changed with

increasing heating rate. The kinetic parameters such as activation energy and pre-exponential

factor were obtained by model free methods proposed by FWO, KAS and Kissinger. The

activation energy and pre-exponential factor obtained by Kissinger method are 153.92 kJ/mol

and 2.14 x 1012 min-1, while, the same average parameters calculated from FWO and KAS

methods are 158.58 and 157.27 kJ mol-1 and 7.96 x 1013 and 1.69 x 1013min-1, respectively. The

results obtained from the first method represented actual values of kinetic parameters which are

the same for the whole pyrolysis process, while the second method presented apparent values

of kinetic parameters, because they are the sum of the parameters of the physical processes and

chemical reaction that occur simultaneously during pyrolysis. Experimental results showed that

values of kinetic parameters from both method are in good agreement and can be successfully

used to understand the degradation mechanism of solid-state reaction. It is planned on using the

results from TGA in computer softwares for simulating pyrolysis to achieve a better

understanding of the devolatilization process of different type of biomass.

**Keywords **
TGA, kinetics, biomass, model-free methods

**1 Introduction **

Biomass is a renewable source of energy which is programmable through storage. Pyrolysis in

particular converts biomass into high energy content biofuels provided that the adequate

temperature and heating rate are reached and may be used to fuel internal combustion engines

and gas turbines after an intermediate process that converts the feedstock into a liquid or

gaseous biofuel [1,2]. Pyrolysis is one of the first step of all thermochemical processes

occurring in an inert atmosphere [3]. It is a thermal decomposition process based on a series of

complex reaction that are influenced by many factors, such as heating rate, temperature,

pressure, residence time, moisture, composition of biomass material and size of particles.

Adequate models to forecast pyrolysis products are also necessary for power plant optimisation

and to better understand the behaviour of engines fuelled by pyrolysis products [4,5]. For a

Corresponding author: Francesco Fantozzi,

[email protected]
Third International Conference on Applied Energy - 16-18 May 2011 - Perugia, Italy
better understanding of pyrolysis process, many researchers studied thermal decomposition of biomass by TGA. Thermogravimetric analysis (TGA) is the most common technique used for kinetic analysis of devolatilization process. In the Literature numerous works describe TGA analysis and behaviours of different materials such as plastic [6], rubber-derivative [7], water evaporation [8], natural fibers [9], various types of biomass [10,11] during thermal degradation. Usually biomass devolatilization is referred to in terms of its three main components namely lignin, cellulose and hemicelluloses [12]. Gasparovic et al. [13] examined the pyrolysis of wood and main wood compounds by thermogravimetry and revealed that thermal decomposition of wood by TGA proceeds in three stages: water evaporation, active and passive pyrolysis, and that the decomposition process of wood depends on the composition and concentration of the main components. The decomposition of hemicelluloses and cellulose take place in active pyrolysis in the temperature from 473-653 K and 523-653 K, respectively. Whereas lignin is decomposed in both stages: active and passive pyrolysis in range from 453-1173 K without characteristic peaks, shown in Figure 1. Kumar et al. [14] investigated the thermal decomposition of corn stoves by TGA in nitrogen and air atmospheres and concluded that there are three distinct stages of weight loss in both condition and that kinetic parameters were similar only at slow heating rates. At higher heating rates, the second stage occurred very rapidly and activation energy was higher than activation energy in nitrogen atmosphere. Also Lv et al. [15] studied the thermal decomposition characteristics of hemicelluloses extracted from corn stalk and examined several different sugar units by C13NMR spectra to show the presence of species of hemicellulose. Gronli et al. [16] compared the thermogravimetric curves of several hardwoods and softwoods. A comparison between both type of wood shows that the decomposition of softwood starts at lower temperatures and the hemicellulose and cellulose zone are wider. There are many methods for analyzing non-isothermal solid-state kinetic data from TGA [17, 18]. These methods can be divided into two types: model-fitting and model-free (isoconversional) methods, both presented in Table 1 [19]. Model fitting methods consist in fitting different models to the data so that a model is chosen when it gives the best statistical fit as the model from which the kinetic parameters are calculated. The second isoconversional methods require several kinetic curves to perform the analysis. Calculations from several curves at different heating rates are performed on the same value of conversion, which allows to calculate the activation energy for each conversion point.

**Figure 1: Pyrolytic decomposition at the heating rate 5 K min-1: a) hemicelluloses (H) **
**cellulose (C) and lignin (L); b) wood chips (solid line) and main compounds: **
**hemicelluloses, cellulose, lignin (dashed line) [13]. **
Historically, model-fitting methods were widely used for solid-state reaction because of their ability to directly determine the kinetic parameters from a single TGA measurement. However, these methods suffer from several problems among which is their inability to uniquely determine the reaction model [20], especially for non-isothermal data; several models can be
Third International Conference on Applied Energy - 16-18 May 2011 - Perugia, Italy
found as statistically equivalent, whereas the fitted kinetic parameters may differ by an order of magnitude and therefore selection of an appropriate model can be difficult. Application of model-fitting methods for non-isothermal data gives higher values for kinetic parameters [19]. This method has recently declined in favor of isoconversional methods. The advantage of the model-free analysis is founded on its simplicity and on the avoidance of errors connected with the choice of a kinetic model [21]. These methods allow estimates of the activation energy,

*E*a, at specific extent of conversion, α

*i*, for an independent model. Repeating this procedure at different conversion values, we obtain a profile of the activation energy as a function of α

*i*. The
underlying assumption is that the reaction model, f(α), is identical at a given α

*i* for a given reaction under different conditions [22]. Disadvantage of these methods are a series of measurements at different heating rate which must be made for the same samples mass and the same volume flow of inert gas and their fluctuation can cause of errors. Not all model-free methods are isoconversional: the Kissinger method is one of these exceptions because it does not calculate

*Ea* values at progressive

*α* values but assumes a constant activation energy [19]. The purpose of this work is to investigate the kinetics of thermal decomposition of poplar wood (populus L.) The pyrolysis process was performed by TGA and the thermal analysis curves were recorded at several linear heating rates. The three model-free non-isothermal methods were used to calculate activation energy (

*Ea*) and pre-exponential factor (

*A).* The effect of heating rate on decomposition was also studied.
-Conventional -Differential
-Freeman-Carroll -Friedman
-Flynn-Wall and Ozawa
-Vyazovkin and AIC -Kissinger-Akahira-Sonuse

**Table 1: Methods for studying solid-state kinetics [19]. **
**2 Materials and methodology **

2.1 Experimental

The experiments were performed using thermogravimetric analyzer Leco TGA-701 at the

Biomass Research Center of the University of Perugia [23]. To maintain pyrolysis conditions,

high purity nitrogen was used as the carrier gas. The volume flow of N2 was 3.5 l min-1.

Thermogravimetric analysis for dehydration step had an heating rate of 10 K min-1 for all

analysis, while devolatilization step were performed at four different heating rates: 2, 5, 10 and

15 K min-1. For experimental tests we used fine powder size (about 0.20 mm) of density 0.2872

g/cm3 of poplar wood (populus L.) obtained from a cutting mill Retsch SM 2000. Weight of the

wood samples was 1.5 g. The composition based on proximate and ultimate analyses are shown

in Table 2. The sample was put in a ceramic crucible each time and first dried from laboratory

temperature to 378 K and then heated from 378 K to 973 K. During the heating, the mass of the

wood sample and furnace temperature were recorded.

Proximate analysis (wt % )
Ultimate analysis (wt %)
*calculate by difference
Third International Conference on Applied Energy - 16-18 May 2011 - Perugia, Italy

**Table 2: Characteristic of poplar wood. **
2.2 Kinetic theory The kinetics of reactions in solid-state are described by the following equation:
Conversion, α, is normalized form of weight loss data of decomposed sample and is defined as follows:
where mi is the initial mass of the sample, ma is the actual mass and mf is the mass after pyrolysis. According to Arrhenius equation, the temperature dependence of the rate constant

* k* is given by:
where Ea is the activation energy (kJ mol-1), T is the absolute temperature (K), R is the gas
constant (8,314 J K-1 mol-1) and A is the pre-exponential factor (min-1). Combination of the two equations (1) and (3) gives the fundamental expression (4) of analytical methods to calculate kinetic parameters, on the basis of TGA results.
! ∝ = ! ∙ ! ∝ ∙ !!!! !" (4)
The expression of the function !(!) and its derivative !! ! = −1 are used for describing solid-state first order reaction, hence many authors restrict the mathematical function !(!) to the following expression:
where

*n* is the reaction order. Substituting expression (5) into equation (4) gives the expression of reaction rate in the form:
! ∝ = ! ∙ (1 − !)! ∙ !!!! !" (6)
For non-isothermal TGA experiments at linear heating rate ! = !"/!", equation (6) can be written as:
= ∙ (1 − !)! ∙ !!!! !" (7)
This equation expresses the fraction of material consumed in the time. In this work the activation energy was obtained from non-isothermal TGA. The methods used to calculate kinetic parameters are called model-free non-isothermal methods and require a set of experimental tests at different heating rates. 2.2 Model-free methods 2.2.1.Kissinger method These methods allow to obtain the kinetic parameters of a solid-state reaction without knowing the reaction mechanism. Kissinger [24] developed a model-free non isothermal method where is no need to calculate Ea for each conversion value in order to evaluate kinetic parameters. This method allows to obtain the value of activation energy from a plot of ln(β/T2m) against
Third International Conference on Applied Energy - 16-18 May 2011 - Perugia, Italy
1000/Tm for a series of experiments at different heating rates (β), where Tm is the temperature peak of the DTG curve (shown in Figure 4). The equation is the following:
The activation energy

*Ea* can be calculated from the slope of the plot, which is equal to –Ea/R. 2.2.2. Flynn-Wall-Ozawa method
The FWO method [25, 26] allows to obtain apparent activation energy (Eaα) from a plot of natural logarithm of heating rates, lnβi, versus 1000/Tαi,

* *which represents the linear relation
with a given value of conversion at different heating rates.
− 5.331 − 1.052
9

* *
where g(α) is constant at a given value of conversion. The subscripts i and α denotes given value of heating rate and given value of conversion, respectively. The activation energy Eα is calculated from the slope -1.052Eα/R. 2.2.3. Kissinger-Akahira-Sunose The KAS method [24, 27] is based on the following expression:
The apparent activation energy can be obtained from a plot of ln(β
i/Tαi ) versus 1000/Tαi for a
given value of conversion, α, where the slope is equal –Eα/R.

**3 Results and discussion **
3.1 Thermogravimetric analysis Differential mass loss (DTG) thermograms of thermal decomposition of poplar wood pyrolysis, at four heating rates 2, 5, 10, 15 K min-1 under nitrogen atmosphere, are shown in Figure 2. As expected three regions are evident which correspond to water evaporation, active and passive pyrolysis. The first region from 330 K to 380 K is related to the extraction of moisture and adsorbed water in the wood sample. The main pyrolysis process proceeds in a range from approximately 450 K to 650 K for low heating rate and 740 K for high heating rate. In this region (active pyrolysis), there are two peaks which the Literature [13, 28] shows to be related to hemicellulose and cellulose decomposition, while lignin is decomposed in both regions of active and passive pyrolysis without characteristic peaks. The weight loss curve (TG) in Figure 3 shows the loss of mass with temperature at different heating rates for poplar wood. As can be seen from the plot, the devolatilization process begins at about 450 K and proceeds rapidly with increasing temperature until about 660 K and then the weight loss decreases slowly to the final temperature. The solid residue yields are about 28 % for poplar wood. The effect of heating rate is shown in Figures 2, 3 and 4. Heating rate affects TG curve positions, maximum decomposition rate and location of maximum Tm peaks. Figure 4 shows
DTG curves of poplar wood (populus L.). When heating rate increases, starting and final temperature of active and passive pyrolysis region (Figure 2 and 4) also increase. The water evaporation region does not vary because the heating rate used for dehydration was identical for each analysis (Figure 2). Maximum points of TG and minimum points of DTG curves are shifted towards higher temperature. This data are coherent with the Literature [16] for woods with similar cellulose and hemicelluloses content to poplar such as beech wood. This
Third International Conference on Applied Energy - 16-18 May 2011 - Perugia, Italy
phenomena can be explained on the basis of heat transfer limitation. During the analysis, at low heating rate, a larger instantaneous thermal energy is provided in the system and a longer time may be required for the purge gas to reach equilibrium with the temperature of the furnace or the sample. While at the same time and in the same temperature region, higher heating rate has a short reaction time and the temperature needed for the sample to decompose is also higher. This causes the maximum rate curve to shift to the right [29]. To verify if the same heating rate causes a shift in the curves, we performed experiment to compare of time derivatives curves at 5K min-1 for 1.5 g of poplar and beech wood samples. This test concluded does not any shift for the same heating rate, as shown in Figure 5.

**Figure 2: DTG of a poplar wood recorded in nitrogen at different heating rates. There are **
**three stages: dehydration, active and passive pyrolysis. **
ha c 40 eight 20

**Figure 3: TG of weight loss curves of poplar wood recorded in nitrogen at four heating **
Temperature [K]

* *
**Figure 4: DTG curves of poplar wood recorded in nitrogen at different heating rates. **
Third International Conference on Applied Energy - 16-18 May 2011 - Perugia, Italy

**Figure 5: Comparison of DTG curves of the mass fraction as functions of temperature for **
**beech and poplar wood recorded in nitrogen at heating rates of 5 K min-1. **
3.2 Kinetic analysis
The results obtained from termogravimetric analysis were elaborated according to model-free methods to calculate the kinetic parameters. The activation energy (Ea) and pre-exponential
factor (A) were obtained using Kissinger, KAS and FWO methods. In the first method the activation energy and pre-exponential factor were calculated from Eq. (8), where Tm is a
temperature which corresponds to the maximum weight loss peaks. The peak temperatures were obtained from Figure 4. Kissinger plot of ln(β/T 2
m ) versus 1000/T K-1 of decomposition
process for poplar wood is shown in Figure 6. The regression equations and the square of the correlation coefficient (R2) is also presented. The activation energies (Ea) and pre-exponential
factor (A) were derived from the slope and intercept of plotting regression line, respectively. The results obtained from Kissinger methods are 153.92 kJ/mol and 2.14 x 1012 min-1 for activation energy and pre-exponential factor, respectively.
y = -18,513x + 18,566
β/Tln( -‐11,5

**Figure 6: Kissinger plot of poplar wood. **
The kinetic parameters obtained by FWO and KAS methods were calculated according to eq. (9) and (10), respectively, for a given value of conversion, α. Figure 7 shows the change of the conversion with temperature of the poplar wood samples at any moment at different heating rate. To determine the kinetic parameters, we chose the same value of α from range 0.05 to 0.7 for all curves at different heating rate and we found the corresponding temperature. The FWO plots of ln(βi) versus 1000/Tαi K-1 for different values of conversion are shown in Figure 8. The KAS plots of ln(β
i/Tαi ) versus 1000/Tαi K-1 for different values of conversion are shown in
Figure 9.The apparent activation energies were obtained from the slope and pre-exponential factors from the intercept of regression line and are given in Table 3. The calculated squares of the correlation coefficients, R2, correspond to linear fittings in Figure 8 and 9, were higher for all cases and were in range from 0.975 to 0.996.
Third International Conference on Applied Energy - 16-18 May 2011 - Perugia, Italy

**Figure 7: Extent of conversion curves for the devolatilization process of poplar wood at **
**different heating rate. **
**Figure 8: FWO plot of poplar wood for different values of conversion. **
**Figure 9: KAS plot of poplar wood for different values of conversion. **
Third International Conference on Applied Energy - 16-18 May 2011 - Perugia, Italy
2.14 x 1012 [min-1]

**Table 3: The results of Ea and A for a poplar wood obtained by FWO, KAS and Kissinger **
**methods. R2 corresponding to linear fittings in Figure 8 and 9. **
**Figure 9: The activation energy as a function of conversion. **
In Figure 9, we can observe that apparent activation energy for the pyrolysis of poplar wood was not similar for all conversion indicates the existence of a complex multistep mechanism that occurs in the solid state. The apparent value of activation energy is about 107.86 - 209.49 kJ mol-1 and 104.95 - 209.90 kJ/mol for FWO and KAS, respectively. This means that the reaction mechanism is not the same in the whole decomposition process and that activation energy is dependent on conversion. The model-free methods allow to estimate activation energy as a function of conversion without previous assumption on the reaction model and allows nearly unmistakably detecting multi-step kinetics as a dependence of activation energy on conversion in contradistinction to Kissinger method which producing a single value of the Ea for the whole process and complexity may not be revealed [30]. The values of activation energy obtained from the Kissinger method are consistent with the range of values obtained by the FWO and KAS methods and is very near to their average values, which are equal 158.58 and 157.27 kJ mol-1; the value of the pre-exponential factor is also contained in a region of average value. The Arrhenius parameters for poplar wood found in the Literature was calculated from different methods and at different conditions. Vecchio et al. [11] examined thermal degradation of poplar wood by DSC-TG in air and applied the Kissinger method to calculate the kinetic parameters and obtained activation energies for two peaks in active pyrolysis region: 146 kJ mol-1 for the first peak and 188 kJ mol-1 for the second peak. As described in Kumar et. al [14], the activation energy in air is higher than Ea in nitrogen atmosphere. Biagini et al [31] applied
traditional isoconversional method (Friedman) within TGA in nitrogen atmosphere and obtained activation energy and pre-exponential factor for poplar wood 168.9 kJ mol-1 and 28.1 (lnA). The differences in kinetic parameters can be attribute to complicated nature of wood constituted by a mixture of cellulose, hemicelluloses, lignin and extractives, with proportion, reactivity and chemistry affected by variety. Moreover, use the different experimental conditions and various procedures for calculations causes that the derived kinetic parameters (even if it were calculated with the same method) may differ for the same type of biomass.
Third International Conference on Applied Energy - 16-18 May 2011 - Perugia, Italy

**4 Conclusion **

In this work, an experimental kinetic study of poplar wood pyrolysis is presented.

Thermogravimetric analysis was investigated under nitrogen atmosphere at different heating

rates of 2, 5, 10 and 15 K mol-1. Thermal decomposition of poplar wood proceeds in three

steps: water evaporation, passive and active pyrolysis. It was found that the mainly pyrolysis

process occurred at about 450 to 740 K. Effect of heating rate on TG and DTG curves was also

presented. Activation energy and pre-exponential factor were obtained by the model-free

methods. The kinetic parameters calculated by Kissinger method were the same for the whole

pyrolysis process, whereas in the FWO and KAS methods apparent activation energy and

apparent preexponential factor vary with conversion and revealed the complex mechanism of

reaction that occur during the pyrolysis process. The values of activation energy obtained from

the Kissinger method are consistent with the range of values obtained by the FWO and KAS

methods and is very near to their average values which are equal 158.58 and 157.27 kJ mol-1;

the value of the pre-exponential factor is also contained in a region of average value.

Experimental results showed that values of kinetic parameters are in good agreement and that

the model-free methods satisfactorily described the complexity of devolatilization process, but

to better understand this phenomena, to learn which chemical compounds are formed and

which chemical and physical process occur during pyrolysis, further analysis is needed. It has

been planned on using the results from TGA in computer softwares for simulating pyrolysis to

achieve a better understanding of the devolatilization process.

**Nomenclature **

TGA: Thermogravimetry analysis

DTG: Differential thermogravimetry

Ea: Activation energy [kJ mol-1]

A: Preexponential factor [s-1]

α: conversion [1]

mi: initial mass [kg]

ma: actual mass [kg] mf: final mass [kg]
k: reaction rate constant [s-1] n: reaction order [1] R: gas constant [J mol-1 K-1] R2: correlation coefficient T: Temperature [K] Tm: Maximum temperature peak [K]
β: Heating rate [K min-1]

**References **

[1] Goyal H., Seal D., Saxena R., 2008, "Bio-fuels from thermochemical conversion of

renewable resources: A review", Renewable and Sustainable Energy Reviews, Vol 12, No 2,

pp. 504-517.

[2] Fantozzi F., D'Alessandro B., Bidini G., 2003, "IPRP - Integrated pyrolysis regenerated

plant – gastuirbine and externally heated rotary-kiln pyrolysis as a biomass waste energy

conversion system. Influence of thermodynamic parameters", Proceedings of the Institution of

Mechanical Engineers, Part A: Journal of Power and Energy, Vol 217, No 5, pp. 519-527.

[3] Laranci P., Fantozzi F., Bidini G., 2010, "CFD simulation of biomass pyrolysis syngas vs.

natural gas in a microturbine annular combustor", Proceedings of ASME Turbo Expo 2010:

Power for Land, Sea and Air, June 14-18, Glasgow, UK.

Third International Conference on Applied Energy - 16-18 May 2011 - Perugia, Italy
[4] Colantoni S., Della Gatta S., De Prosperis R., Russo A., Fantozzi F., Desideri U., 2010, "Gas turbines fired with biomass pyrolysis syngas: analysis of the overheating of hot gas path components", Journal of Engineering Gas Turbines

* *Power, Vol.132, No 6. [5] Zhang L., Xu C., Champagne P., 2010, "Overview of recent advances in thermo-chemical conversion of biomass", Energy Conversion and Management, Vol.51, No 5, pp. 969-982. [6] Shakya B., 2007, "Pyrolysis of waste plastics to generate useful fuel containing hydrogen using a solar thermochemical process", Sydney University Master of Engineering, March 2007. [7] Senneca O., Chirone R., Masi S., Salatino P., 2002, "A thermogravimetric study of nonfossil solid fuels 1. Inert pyrolysis", Energy and Fuel, Vol.16, No 3, pp. 653-660. [8] Cai J., Liu R., 2007, "Research on water evaporation in the process of biomass pyrolysis", Energy and Fuel, Vol.21, No 6, pp. 3695-3697. [9] Yao F., Wu Q., Lei Y., Guo W., Xu Y., 2008, "Thermal decomposition kinetics of natural fibers: Activation energy with dynamic thermogravimetric analysis, Polymer Degradation and Stability, Vol.93, No 1, pp. 90-98. [10] Muller-Hagedorn M., Bockhorn H., Krebs L., Muller U., 2003, "A comparative kinetic study on the pyrolysis of three different wood species", Journal of Analytical and Applied Pyrolysis, Vol.68-69, pp. 231-249. [11] Vecchio S., Luciano G., Franceschi E., 2006, "Explorative kinetic study on the thermal degradation of five wood species for applications in the archeological filed", Annali di chimica, Vol.96, No 11-12, pp. 715-725. [12] Poletto M., Dettenborn J., Pistor V., Zeni M., Zattera A., 2010, "Materials produced from plant biomass. Part I: evaluation of thermal stability and pyrolysis of wood", Materials Research, Vol.13, No 3, pp. 375-379. [13] Gasparovic L., Korenova Z., Jelemensky L., 2010, "Kinetic study of wood chips decomposition by TGA", Chemical Papers, Vol.64, No 2,pp. 174-181. [14] Kumar A., Wang L., Dzenis Y., Jones D., Hanna M., 2008, "Thermogravimetric characterization of corn stover as gasification and pyrolysis feedstock", Biomas and Bioenergy, Vol.32, No 5, pp. 460-467. [15] Lv G., Wu S., Lou R., 2010, "Kinetic study of thermal decomposition of hemicelluloses isolated from corn stalk", BioResources, Vol.5, No 2, pp. 1281-1291. [16] Gronli M., Varhegyi G., Di Blasi C., 2002, "Thermogravimetric analysis and devolatilization kinetics of wood", Industrial and Engineering Chemistry Research, Vol.41, No 17, pp. 4201-4208. [17] Simon P., 2004, "Isoconversional methods: fundamental, meaning and application", Journal of Thermal Analysis and Calorimetry, Vol.76, No 1, pp. 123-132. [18] Sbirrazzuoli N., Vincent L., Mija A., Guio N., 2009, "Integral, differential and advanced isoconversional methods: Complex mechanisms and isothermal predicted conversion–time curves", Chemometrics and Intelligent Laboratory Systems, Vol.96, No 2, pp. 219-226. [19] Khawam A., 2007, "Application of solid state-kinetics to desolvation reactions", Iowa University Doctorial Thesis, September 2007. [20] Khawam A., Flanagan D.R., 2005, "Complementary use of model-free and modelistic methods in the analysis of solid-state kinetics", The Journal of Physical Chemistry B, Vol.109, No 20, pp. 10073-10080. [21] Opfermann J.R., Kaisersberger E., Flammersheim H.J., 2002, "Model-free analysis of thermoanalytical data-advantages and limitations", Thermochimica Acta, Vol.391, No 1-2, pp. 119-127. [22] Zhou D., Schmitt E., Zhang G., Law D., Vyazovkin S., Wight C., Grant D., 2003, "Crystallization kinetics of amorphous nifedipine studied by model-fitting and model-free approaches", Journal of Pharmaceutical Sciences, Vol.92, No 9, pp.1779-1792. [23] Buratti C., Costarelli I., Cotana F., Crisostomi L., Fantozzi F., 2005, "The biomass research centre laboratory for biomass characterization", 14th European Biomass Conference
Third International Conference on Applied Energy - 16-18 May 2011 - Perugia, Italy

and Exhibition. Biomass for Energy, Industry and Climate Protection, 17-21 Octobre, 2005,

Parigi, France.

[24] Kissinger H., 1956, "Variation of peak temperature with heating rate in differential

thermal analysis", Journal of Research of the National Bureau of Standards, Vol.57, No 4, pp.

217-221.

[25] Flynn J., Wall L., 1966, " A quick, direct method for the determination of activation

energy from thermogravimetric data", Journal of Polymer Science Part B:Polymer Letters, Vol.

4, No 5, pp. 323-328.

[26] Ozawa T., 1965, " A new method of analyzing thermogravimetric data ", Bulletin of the

Chemical Society of Japan, Vol.38, pp. 1881-1886.

[27] Akahira T., Sunose T., 1971, "Joint convention of four electrical institutes", Science

Technology Vol.16, pp. 22–31.

[28] Strezov V., Moghtaderi B., Lucas J., 2003, "Thermal study of decomposition of selected

biomass samples", Journal of Thermal Analysis and Calorimetry, Vol.72, No 3, pp. 1041-1048.

[29] Quan C., Li A., Gao N., 2009, "Thermogravimetric analysis and kinetic study on large

particles of printed circuit board wastes", Waste Management, Vol.29, No 8, pp. 2353-2360.

** **

[30] Vyazovkin S., Wight C.A., 1999, "Model-free and model-fitting approaches to kinetic

analysis of isothermal and nonisothermal data", Thermochimica acta, Vol.340-341, pp. 53-68.

[31] Biagini E.,Guerrini L., Nicolella C., 2009, "Development of a variable energy model for

biomass devolatilization", Energy Fuel, Vol.23, No 6, pp. 3300-3306.

**Acknowledgements **

The authors gratefully acknowledge the support of Regione Umbria through EU POR-FSE

2007-2013, OB. 2 measure.

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VESTIBULAR MEIO DE ANO 2008 PROVA DE CONHECIMENTOS GERAIS CADERNO DE QUESTÕES 1. Conferir seu nome, número de inscrição e número da carteira na capa deste 2. Esta prova contém 84 questões e terá duração de 4 horas. 3. Para cada questão, existe somente uma alternativa correta. Anotar na tabela ao lado a alternativa que julgar certa. 4. Depois de assinaladas todas as respostas, transcrevê-las para a folha defi nitiva de

© Med Sci Monit, 2005; 11(12): SR27-31 The growth of a lie and the end of "conventional" Accepted: 2005.09.28Published: 2005.12.01 Domenico Mastrangelo1, Cosimo Loré2 1 Department of Ophthalmology, University of Siena, Italy2 Department of Forensic Medicine, University of Siena, Italy Source of support: Self fi nancing Throughout its over 200-year history, homeopathy has been proven effective in treating diseases for which conventional medicine has little to offer. However, given its low cost, homeopathy has always represented a serious challenge and a constant threat to the profi ts of drug companies. Moreover, since drug companies represent the most relevant source of funding for biomedical re-search worldwide, they are in a privileged position to fi nance detractive campaigns against home-opathy by manipulating the media as well as academic institutions and the medical establishment. The basic argument against homeopathy is that in some controlled clinical trials (CCTs), compari-son with conventional treatments shows that its effects are not superior to those of placebo. Against this thesis we argue that a) CCT methodology cannot be applied to homeopathy, b) misconduct and fraud are common in CCTs, c) adverse drug reactions and side effects show that CCT meth-odology is deeply fl awed, d) an accurate testing of homeopathic remedies requires more sophis-ticated techniques, e) the placebo effect is no more "plausible" than homeopathy, and its real na-ture is still unexplained, and f) the placebo effect is nevertheless a "cure" and, as such, worthy of further investigation and analysis. It is concluded that no arguments presently exist against home-opathy and that the recurrent campaigns against it represent the specifi c interests of the pharma-ceutical industry which, in this way, strives to protect its profi ts from the "threat" of a safer, more effective, and much less expensive treatment modality.