Pyrolysis of Pine Bark, Wheat Straw and Rice Husk:
Thermogravimetric Analysis and Kinetic Study
Ana Isabel Marques Ferreiro
Thesis to obtain the Master of Science Degree in
Mechanical Engineering
Supervisors: Prof. Mário Manuel Gonçalves da Costa,
MSc Miriam Estefânia Rodrigues Fernandes Rabaçal
Examination Committee
Chairperson: Prof. Viriato Sérgio de Almeida Semião
Members of the Committee: Prof. Luís António da Cruz Tarelho
MSc Miriam Estefânia Rodrigues Fernandes Rabaçal
July 2015
ii
ACKNOWLEDGEMENT
Agradeço ao Professor Catedrático Mário Costa e à Mestre Miriam Rabaçal por todo o apoio,
motivação e amizade prestados ao longo de todo o meu percurso sob as suas orientações.
Em modo particular, quero dizer ao Professor Doutor Mário Costa que tudo o que me ensinou
a todos os níveis me ajudou a crescer como pessoa e que lhe estou muito grata por isso. Agradeço
também todas as críticas, porque sempre me ajudaram a melhorar e a fazer um trabalho melhor.
Quero agradecer ainda, em modo particular, à Mestre Miriam Rabaçal por me ter ajudado a
alcançar muitas metas, que melhor que ninguém sabe quais foram. Agradeço tudo o que aprendi
contigo, que foi muito, e espero ter oportunidade de continuar a aprender. A admiração que tenho por
ti é mesmo muito grande.
Agradeço aos meus colegas e amigos David Nascimento, Duarte Magalhães, Nuno Barbas,
José Branco, Vera Branco, Gonçalo Guedes, Filipa Ferreira e João Ribau pelo apoio, convívio e bons
momentos. Vocês tornaram tudo mais fácil.
Agradeço também a amizade da Rita Maia, Mª do Céu Miranda e Manuel Pratas. São pessoas
fantásticas e a quem tenho um enorme carinho.
Por último quero agradecer à minha família, em especial à minha mãe, ao meu pai e ao meu
irmão pelo incansável apoio, ajuda e conselhos. Agradeço-vos por estarem sempre comigo e nunca
me deixarem sentir sozinha.
Por último quero agradecer ao meu namorado Filipe Pinto por todo o amor, carinho, apoio e
por ter escolhido partilhar a sua vida comigo. Fazes-me muito feliz.
iii
RESUMO
Este estudo foca-se na pirólise da casca de pinheiro, palha de trigo e casca de arroz. Foram
obtidas curvas termogravimétricas para os três tipos de biomassas, usando taxas de aquecimento de
5, 10 e 15 K/min numa atmosfera de Árgon, para investigar o efeito do tipo de combustível no processo
de pirólise sob diferentes taxas de aquecimento. Foram obtidos diferentes perfis termogravimétricos e
taxas de perda de massa, dada a composição distinta das biomassas consideradas, mas a influência
das taxas de aquecimento foi marginal. Para melhor perceber qual o impacto da composição da
biomassa no processo de pirólise, os componentes principais (hemicelulose, celulose e lenhina) de
cada tipo de biomassa foram estimados. Adicionalmente, foi utilizado um algoritmo de dois passos para
estimar os parâmetros cinéticos de um modelo de reação única (SFOM) e de um modelo de três
reações paralelas (3PM). As energias de ativação obtidas através do ajuste de cada modelo aos
resultados experimentais estão de acordo com os valores da literatura. As energias de ativação que se
obtiveram utilizando o SFOM foram 55.5, 79.6 e 87 kJ/mol para a casca de pinheiro, palha de trigo e
casca de arroz, respetivamente. Para a celulose, hemicelulose e lenhina, as energias de ativação que
se obtiveram utilizando o 3PM foram, respetivamente, 152.5, 95.7 e 44.3 kJ/mol para casca de pinheiro,
143.3, 83.6 e 37 kJ/mol para palha de trigo, e 163.8, 107.3 e 37.2 kJ/mol para casca de arroz. Para
cada biomassa, todas as taxas de aquecimento foram ajustadas com erros da ordem de 5 – 7% para o
SFOM e de ~ 2% para o 3PM. Os resultados provaram que a ferramenta cinética implementada neste
estudo é capaz de reproduzir o processo de pirólise com boa precisão e mostraram que o nível de
complexidade do 3PM é suficiente.
Keywords:
Biomassa, pirólise, termogravimetria, estudo cinético, otimização
iv
ABSTRACT
The present work focuses on the pyrolysis of pine bark, wheat straw and rice husk.
Thermogravimetric curves were obtained for the three biomass fuels for heating rates of 5, 10 and 15
K/min in an inert atmosphere of Argon to investigate the impact of the type of biomass in the pyrolysis
behavior under different heating conditions. Distinctive thermogravimetric and differential
thermogravimetric curves were obtained owing to the different composition of the biomass fuels, but the
impact of the heating rates was marginal. In order to better understand the impact of the biomass
composition in the pyrolysis, their main components were estimated (hemicellulose, cellulose and
lignin). Additionally, a two-step optimization algorithm was used to estimate the global kinetic parameters
of a single reaction model (SFOM) and of a three parallel reaction model (3PM). The activation energies
obtained by fitting each model to the experimental data are within the values reported in the literature.
The activation energies obtained using the SFOM were 55.5, 79.6 and 87 kJ/mol for pine bark, wheat
straw and rice husk, respectively. For cellulose, hemicellulose and lignin the activation energies
obtained, using 3PM, were respectively, 152.5, 95.7 and 44.3 kJ/mol for pine bark, 143.3, 83.6 and 37
kJ/mol for wheat straw, and 163.8, 107.3 and 37.2 kJ/mol for rice husk. For each biomass fuel, all heating
rates were globally fitted, with errors of the order of 5%-7% for the SFOM and of ~ 2% for the 3PM. The
results proved that the kinetic tool implemented in this work was capable of reproducing the pyrolysis
behavior with good accuracy and showed that the degree of complexity of 3PM suffices.
Keywords:
Biomass, pyrolysis, thermogravimetry, kinetic study, optimization
v
TABLE OF CONTENTS
1. Introduction ...................................................................................................................................... 1
1.1. Motivation ................................................................................................................................ 1
1.2. Previous studies ...................................................................................................................... 3
1.3. Objectives ................................................................................................................................ 9
2. Theoretical foundations ................................................................................................................. 10
2.1. Biomass: definition and characterization ............................................................................... 10
2.2. Thermogravimetry ................................................................................................................. 12
2.3. Pyrolysis kinetics ................................................................................................................... 13
2.4. Optimization methods ............................................................................................................ 17
3. Materials and methods .................................................................................................................. 19
3.1. Biomass characterization ...................................................................................................... 19
3.2. Thermogravimetric study ....................................................................................................... 21
4. Kinetic analysis .............................................................................................................................. 22
4.1. Arrhenius plot method ........................................................................................................... 22
4.2. Optimization methods ............................................................................................................ 24
4.3. Comparison between methods .............................................................................................. 28
5. Results ........................................................................................................................................... 29
5.1. Experimental results .......................................................................................................... 29
5.2. Kinetic analysis results ...................................................................................................... 31
6. Conclusions ................................................................................................................................... 37
7. Future perspectives ....................................................................................................................... 38
8. References .................................................................................................................................... 39
vi
LIST OF FIGURES
Figure 1.1: Portuguese energetic balance related to the final energy consumption in 2013 (source:
http://www.apren.pt (APREN – Associação de Energias Renováveis). .................................................. 1
Figure 1.2. Biomass conversion technologies and the correspondent primary energy products (source:
www.e-education.psu.edu). ..................................................................................................................... 2
Figure 1.3. Typical TG and DTG curves [13]........................................................................................... 4
Figure 2.1. Scheme of the TGA furnace [33]......................................................................................... 13
Figure 2.2. General scheme of the decomposition of a component. .................................................... 14
Figure 2.3. General scheme for secondary reactions of the vapor phase of tars. ................................ 15
Figure 2.4. Chemical structure of all the components (source: Cellulose [12, 39]; hemicellulose [12,40];
lignin [38]). ............................................................................................................................................. 15
Figure 2.5. Multistep mechanism of cellulose pyrolysis [38]. ................................................................ 16
Figure 2.6. Multistep mechanism of hemicellulose pyrolysis [38]. ........................................................ 16
Figure 2.7. Multistep mechanism of the pyrolysis of LIG-H (top), LIG-O (mid) and LIG-C (bottom) [38].
............................................................................................................................................................... 16
Figure 2.8. Genetic Algorithm: scheme of the three types of children [42]. .......................................... 17
Figure 3.1. Triangulation method test for the biomass samples studied. .............................................. 20
Figure 3.2. Contents of cellulose, hemicellulose and lignin. ................................................................. 20
Figure 3.3. Temperature profiles for the three heating rates tested. ..................................................... 21
Figure 4.1. Representative example of the FWO method [49]. ............................................................. 23
Figure 4.2. Pyrolysis algorithm. ............................................................................................................. 25
Figure 4.3. Kinetic optimization procedure. ........................................................................................... 26
Figure 5.1. Pyrolysis yields for pine bark (PB), wheat straw (WS) and rice husk (RH) for 5, 10 and 15
K/min. ..................................................................................................................................................... 29
Figure 5.2. DTG curves as a function of temperature for pine bark (PB), wheat straw (WS) and rice husk
(RH) for 5, 10 and 15 K/min................................................................................................................... 30
Figure 5.3. Arrhenius plot method applied to experimental TG curves of pine bark (PB), wheat straw
(WS) and rice husk (RH), respectively. ................................................................................................. 31
Figure 5.4. TG (top), DTG (bottom) and predicted curves for pine bark (PB). ...................................... 34
Figure 5.5. TG (top), DTG (bottom) and predicted curves for wheat straw (WS). ................................ 34
Figure 5.6. TG (top), DTG (bottom) and predicted curves for rice husk (RH). ...................................... 35
Figure 5.7. DTG and predicted curves using the 3PM for pine bark (PB), wheat straw (WS) and rice
husk (RH) at 5 K/min. ............................................................................................................................ 35
vii
LIST OF TABLES
Table 1.1. Summary of the most relevant studies (continues). ............................................................... 6
Table 2.1. Chemical characteristics and structural components composition of various biomass groups
and sub-groups [24]. .............................................................................................................................. 11
Table 3.1. Proximate and ultimate analysis of the biomass fuels studied. ............................................ 19
Table 3.2. Composition range of the correlation method [45]. .............................................................. 20
Table 3.3. Comparison between the estimated composition of the three main components and literature
values [12,21,46]. .................................................................................................................................. 21
Table 4.1. Typical range of activation energies for the SFOM [13, 22, 47, 53]. .................................... 27
Table 4.2. Typical kinetic parameters for the 3PM and fraction of char produced by each component [12,
14, 20]. ................................................................................................................................................... 27
Table 4.3. Average simulation times and specific standard deviations ................................................. 27
Table 4.4. Work computer specifications. ............................................................................................. 27
Table 5.1. Characteristics of the first region of decomposition ............................................................. 30
Table 5.2. Kinetic parameters estimated with the Arrhenius plot method. ............................................ 31
Table 5.3. Kinetic parameters obtained by fitting the SFOM to the experimental data for the three
biomass fuels. ........................................................................................................................................ 32
Table 5.4. Activation energies and char fractions obtained by fitting the 3PM to the experimental data
for the three biomass fuels. ................................................................................................................... 32
Table 5.5. Pre-exponential factors and model constants obtained by fitting the 3PM to the experimental
data for the three biomass fuels. ........................................................................................................... 33
Table 5.6. Comparison between the Arrhenius plot method and the fitting procedure. ........................ 36
viii
NOMENCLATURE
Symbols
A Pre-exponential factor
C Carbon
C𝑖 Fraction of char produced by the ith component
E Activation energy
f(∝) Conversion function
f(E) Funtion of the distribution of the activation energy
g(∝) Integral function of conversion
H Hydrogen
k(T) Reaction rate
𝑁 Number of iterations
O Oxygen
R Ideal gas constant
T Temperature
𝑈𝑒𝑥𝑝 Experimental results
𝑈𝑝𝑟𝑒𝑑 Predictive results
VM Volatile matter
Vg Total amount of volatile gases released from particle
Greek Letters
α Conversion degree
β Heating rate
𝛿 Fitting error
ε Activation energy threshold
σDEV Standard deviation
γ Model constant
λ Shape parameter
η Width parameter
Acronyms
3PM Three parallel model
CELL Cellulose
daf dry, ash free
db dry basis
DSC Differential scanning calorimetry
DTG Differential thermogravimetric
GA Genetic algorithm
ix
HAB Herbaceous and agricultural biomass
HAR Herbaceous and agricultural straws
HCE Hemicellulose
LIG Lignin
LSQ Least squares
PB Pine bark
RH Rice husk
SFOM Single first order reaction model
TG Thermogravimetric
TGA Thermogravimetric analysis
WS Wheat straw
WWB Wood and woody biomass
1
1. INTRODUCTION
1.1. MOTIVATION
For the past few decades, fossil fuels have been chosen to fulfill world energy demands,
but since these resources are limited and generally have a negative impact on the environment,
recent dramatic developments in renewable energy production occurred. These are supported by
policies aiming to change the energy mix, especially for electricity production [1]. Biomass has a
major advantage over other renewable energy sources, as it can be stored and used on demand
to give controllable energy. It is therefore free from the weather conditions intermittency, a
problem for all other forms of renewable energy [2-4]. Bioenergy source is any fuel derived from
biomass – recently living organisms or their metabolic byproducts. Being highly available and
diverse, biomass is becoming a promising renewable source due to its capability to be linked with
many economic sectors like agriculture, forestry, food processing, paper and pulp and, of course,
the energy sector [5]. In order to support the growth of bioenergy, biomass supply has to grow as
well, but not all the available biomass from forests and fields can be removed. Agricultural and
forest residues and energy crops planted on idle or released cropland are an attractive alternative
[4, 6]. Additionally, biomass can be upgraded through conversion processes, like pyrolysis, to
increase its value as a fuel.
Figure 1.1 shows the Portuguese energetic balance related to the final energy
consumption in 2013 and biomass only contributes with 6.6%. The energy policies in Portugal
have been targeting mainly the development of solar and wind power [5], which may explain the
low share of biomass in the energy mix. Much work needs to be done in the Portuguese bioenergy
sector, however, an important limitation can be the investment costs and in the specific case of
Portugal, there is still lack of governmental support and incentives to ensure the interest from
private investors in bioenergy technologies.
Figure 1.1: Portuguese energetic balance related to the final energy consumption in 2013
(source: http://www.apren.pt (APREN – Associação de Energias Renováveis).
2
Figure 1.2 shows the biomass conversion technologies. The final products of the
conversion technologies are used mainly for the production of heat, power, fuels and chemicals,
where the unconverted residues can be used for soil amendment. Pyrolysis can be found in the
group of thermochemical conversion, along with gasification.
Figure 1.2. Biomass conversion technologies
(Source: http://www.extension.org/pages/26517/woody-biomass-properties#.VaDi9PlVhBc).
Pyrolysis is a form of thermochemical treatment that decomposes organic materials into
liquid, solid and gaseous forms in the absence of oxygen. Due to its versatility, pyrolysis is
becoming a more relevant process since all the three-output fractions have potential as fuels for
transports, power generation and combined heat and power [7]. Fast pyrolysis, in particular, is a
relatively mature technology in the verge of commercialization [8], mostly located in the North of
Europe [9-11]. Portugal however, despite showing great potential to adopt this technology with
an estimated dry biomass production in the order of 5630 thousand tons, has yet to start
developing this industry [27]. The most common reactors used in industrial facilities for the
pyrolysis process are the fluidized bed reactors and the rotating cone reactors for the production
of bio-oil. In fluidized bed reactors the biomass feedstock needs a pre-treatment that involves
drying (< 10% moisture), milling and/or sieving (particle size between 1 and 2 mm). This feedstock
is then fed into the reactor, generally with the aid of a screw, heated to approximately 500 ºC in
the absence of oxygen and decomposed into gaseous vapors and char particles, which are
separated in the cyclone chamber. At this point, charcoal is collected while the gases move on
into the condensers turning them into bio-oil. The non-condensable gases are recirculated into
the main reactor chamber to be re-used for pre-heating. In rotating cone reactors, the pyrolysis
3
process occurs in the rotating cone while mixes biomass particles and hot sand, in the absence
of oxygen. The charcoal and the sand resultant from the previous stage are recycled into the
combustion chamber where charcoal is burned to reheat the sand. The gaseous vapors are led
to the condenser yielding the bio-oil. The non-condensable gases and the extra heat coming from
the chamber can be used to generate steam for power generation or for drying the biomass. Some
of the industrial facilities adopted these technologies to produce electricity, bio-oil and district
heating. For instance, Fortum, founded in Joensuu (Finland) in 2013 [9], is a combined heat and
power production plant (CHP) that produces electricity, district heating and also aims to produce,
in the future, 50,000 tons of bio-oil per year from the conversion of lignocellulosic biomass in a
fluidized bed reactor. Dynamotive Energy Systems Corporation [10] in Canada that is a leader in
the bio-oil production also via a fluidized bed reactor, while BTG – Biomass Technology Group
[11] in Netherlands is using rotating cone reactor to produce bio-oil.
As most biomass upgrading processes, pyrolysis plants are typically optimized to woody
biomass [12]; however, other types of biomass sources need to be considered as discussed
above. But the use of “difficult” biomass fuels can bring complications in the operating system of
these reactors, mostly operational problems caused by high ash content typically found in
agricultural biomass, and yield/composition of the obtained products [8]. In this context, there is
a need to better understand the pyrolysis process of non-woody biomass through fundamental
research. Thermogravimetric studies and kinetic analysis can provide a better understanding of
the governing processes of the pyrolysis of alternative biomass.
1.2. PREVIOUS STUDIES
Di Blasi [13] made an extensive review of a significant number of studies focused on
modeling of the biomass pyrolysis process based on thermogravimetric studies. In this review the
author describes one component and multiple component mechanisms. The assumption of a
single component behavior, inevitably introduces imprecisions in the decomposition rates (and
conversion time), since it considers only one zone of decomposition. Multi-component
mechanisms of biomass pyrolysis have been developed to describe different decomposition
zones, based on the pseudo-components hemicellulose, cellulose and lignin that compose
biomass. Thermogravimetry provides one single curve of mass loss (and rate) over the residence
time and the different components specific decomposition will overlap in this single curve. Figure
1.3 shows a typical thermogravimetric (TG) and differential thermogravimetric (DTG) curves.
Hemicellulose and cellulose are associated with the shoulder and the peak of the DTG curves,
respectively, meaning that the rate curves of these two pseudo components overlap each other
during their decomposition process. On the other hand, lignin decomposes slowly over a very
broad range of temperatures. The accuracy in the predictions of weight loss characteristics is
improved as the number of model parameters is increased. However, simplicity is always desired
for the global reaction mechanisms. Di Blasi [13] pointed out that the application of these kinetic
models to the study of pyrolysis/devolatilization processes has been mostly concentrated on wood
species and, in a small number of cases, on agricultural residues. Therefore, it is necessary to
4
pursue the development of general reaction schemes that can be applied for other biomass fuels
and broaden the current application. Given the high variety in chemical composition among the
different species, this matter is of particular practical importance.
Figure 1.3. Typical TG and DTG curves [13].
In the remainder of this section, a number of relevant studies will be reviewed. Focus was
given to studies that in their kinetic analysis implemented a single first order reaction (SFOM),
considering only one stage of decomposition, a three-parallel model (3PM) that describes the
global decomposition of cellulose, hemicellulose and lignin and a five parallel model (5PM) that
adds extractives. Table 1.1 summarizes previous relevant studies, where the type of biomass,
heating rates (β), mechanisms used, effects evaluated and main conclusions are listed. A
discussion of the estimation methods and mechanisms used, as well as the effect of the main
controlling parameters on the pyrolysis behavior is presented. Several studies [12, 14-16], among
others, estimated activation energies (Ea) using different methods and the estimated values are
discussed in section 4.2.
Estimation methods and mechanisms used
The Arrhenius plot method is a linear regression method able to estimate the kinetic
parameters (activation energy and pre-exponential factor) of one or multiple conversion stages,
of one single component [15-17]. The non-linear squares procedure is also an estimation method
that appeared as an alternative of the Arrhenius plot to estimate the kinetic parameters with better
accuracy [12, 14, 18-21], since it considers all the information obtained from the experimental
data.
SFOM mechanisms are usually applied to describe the decomposition of one single
component assuming one or multiple stage conversion [15-17]. Additionally, some authors have
included the prediction yields (gas, tar and char) when products were measured experimentally
5
[15, 16]. Slightly more complex mechanisms are used for a more detailed conversion description
by considering the thermal decomposition of multiple components such as 3PM and 5PM, used
by Grønli [14], Vamvuka et al. [20], Li et al. [19], Damartzis et al. [18] and Burhenne et al. [12].
These mechanisms are frequently referred to as multi-parallel reactions models, since it is
assumed that each reaction occurs independently of others. However, with the exception of Grønli
et al. [14], Vamvuka et al. [20], Li et al.[19], Damartzis et al. [18] and Burhenne et al. [12] measured
products yields, but this aspect was not considered in the mechanism.
Heating rate effects
Most of the referred authors studied the effects of the heating rates on the pyrolysis
behavior. Sharma et al. [22], Li et al. [19], Seo et al. [15], Mani et al. [21] and Damartzis et al. [18]
concluded that transition temperatures slightly increase with increasing heating rate, shifting the
DTG profiles. These constituents of biomass have characteristic individual decomposition peaks
in certain temperature ranges and it has been showed that an increase of the heating rate tends
to delay thermal decomposition processes towards higher temperatures [12, 14, 18, 19, 21].
Furthermore, at higher heating rates the distinct peaks associated with the different constituents
may not appear because some of them can be thermally decomposed simultaneously,
overlapping each other in DTG profiles. This behavior was observed by Grønli et al. [14],
Vamvuka et al. [20], Li et al. [19], Seo et al. [15], Mani et al. [21], Burhenne et al. [12] and Guerrero
et al. [16].
Seo et al. [15], Damartzis et al. [18] and Guerrero et al. [16] concluded that lower heating
rates lead to higher volatile matter production due to the fact that when heating rate decreases,
the required time to reach a certain temperature value increases, enabling other chemical
reactions to occur (for example, dehydration). As a consequence, the amount of devolatilized
matter is increased.
Sharma et al. [22] and Damartzis et al. [18] studied the effect of the heating rate in the
activation energy. The former authors concluded that the increasing of the heating rate leads
generally to the increase of the activation energy. The latter authors concluded that a higher
heating rate increased the activation energy values for hemicellulose and cellulose
decomposition, whereas decreased the activation energy values for lignin decomposition.
6
Table 1.1. Summary of the most relevant studies (continues).
Reference Sample β (K/min) Mechanism Studied effects Conclusions
Sharma et al. [22] Rice husk 5 – 100 Arrhenius plot method Multi stage conversion SFOM
Heating rates Size particle Reaction order
Ea varies with stage and heating rate Similar results independently of the particle size Variable reaction order gave the best results
Grønli et al. [14] Alder beech Birch oak Douglas fir Pine A Pine B Redwood Spruce
30 Least squares 5PM
Fuel type Overlapping of cellulose, hemicellulose and extractives
3PM suffices for hardwoods Softwoods need 5PM due to extractives Fixing Ea for all the components predicted good
results
Vamvuka et al. [20] Olive Kernel Straw
10 Least squares 3PM
Particle size Fuel type
Particle size had marginal influence on volatile and char yields (particle size between 250 µm and 1000 µm).
Overlapping of cellulose, hemicellulose
Li et al. [19] Corn straw 20 – 100 Least squares 3PM
Heating rate Reaction order
Different DTG profiles when heating rate changed Overlapping of cellulose and hemicellulose Maximum pyrolysis rate and temperature peak
increased with increasing heating rate Reaction order different of one predicted overall
better results Similar Ea for each pseudo-components regardless
the heating rates Ci and A parameters varied with the heating rate
variation.
7
Table 1.1. Summary of the most relevant studies (continued).
Reference Sample β (ºC/min) Mechanism Studied effects Conclusions
Seo et al. [15] Sawdust 5 – 30 Arrhenius plot method Multi stage conversion SFOM
Heating rates Overlapping of cellulose and hemicellulose Smaller heating rates allow higher volatile matter
production
Mani et al. [21] Wheat straw 5 – 20 Least squares 3PM
Heating rates Particle size
Maximum pyrolysis rate and temperature peak increased with increasing heating rate
Overlapping of cellulose and hemicellulose Lignin decomposed in a wide range of temperatures The char yield increased as the particle size and
heating rate of the pyrolysis process increased
Damartzis et al. [18] Cardoon 5 – 30 Arrhenius plot method Least squares 3PM
Heating rates Estimation methods
Higher heating rates delay thermal decomposition processes
Thermal decomposition rates increased with the heating rate
Hemicellulose and cellulose react at low temperatures
Lignin decomposed in a wide range of temperatures Higher heating rate increases Ea of hemicellulose
and cellulose Higher heating rate decreases Ea of lignin
Burhenne et al. [12] Wheat straw Rape straw Spruce + bark
20 Least squares 3PM
Fuel type Lignin is the main controlling factor of pyrolysis process
Woody biomass needs more energy to decompose than agricultural biomass
Guerrero et al. [16] Aple pomace 5 – 20 Arrhenius plot method SFOM
Heating rates Particle size
Overlapping of cellulose and hemicellulose Thermal decomposition rates increased with the
heating rate Smaller particles allow higher volatile matter
production
8
Particle size
Sharma et al. [22], Vamvuka et al. [20], Mani et al. [21] and Guerrero et al. [16] studied the
effects of the particle size. Sharma et al. [22] and Vamvuka et al. [20] observed that particle size,
between powder and grains and in the range of 250 μm to 1000 μm, had practically no influence on the
pyrolysis process. Mani et al. [21] studied particles within the range of 150 to 1350 μm; similar results
were obtained except for the smaller particles (150 and 250 μm) that showed a different pattern in the
TG profile at high temperatures. On the other hand, Guerrero et al. [16] used particles between 150 and
425 μm and concluded that the smaller particles are easy to degrade so they allow the generation of a
greater amount of volatile matter. However, the variation of the volatile matter with particle size was
marginal.
Fuel type
Studies that do not include the explicit study of the component composition [15-17, 21] can only
focus on differences in TG curves relatively to the product yields (gas, tars and char) and only one set
of kinetic parameters is estimated to describe the entire pyrolysis process. Overall, the activation energy
varies with the type of fuel, as well as the relative amounts of products.
Grønli et al. [9], Vamvuka et al. [20] and Burhenne et al. [12] studied the effetcs of the
composition of the biomass fuel and conclude that different amounts of each component lead to
distinctive pyrolysis peaks and width. Additionaly, Grønli et al. [9] and Burhenne et al. [12] showed that
the lignin content of any biomass feedstock is the main controlling factor in pyrolysis, since it shows a
slower decomposition. Furthermore, cellulose and hemicellulose are typically fully consumed and the
residual char comes from lignin. Grønli et al. [9] and Burhenne et al. [12] opted to fix the activation
energies of each pseudo-component for all the biomass fuel and obtained consistent predictions of the
DTG curves. Di Blasi [13] underlines that this is a valid procedure when heating rates do not vary
significantly. When moving to much higher heating rates a new set of activation energies needs to be
estimated. Grønli et al. [9] observed that when amount of extractives is high the transition of 3PM to
5PM is required.
Reaction order
Only Sharma et al. [22] and Li et al. [19] studied the effect of the reaction order. In both studies
it was concluded that variable reaction orders produce better results. For instances, the former authors
found better results below 350°C when considering a reaction order of 1.5, and above this temperature
a value of 2.0. The latter authors obtained variations between 1.3 and 3.7. However it is common
practice to assume a reaction order of one and typically this assumption leads to good predictions of the
pyrolysis behavior, as pointed out by Di Blasi [13] and seen in most of the previous studies [12, 14, 18,
20].
9
1.3. OBJECTIVES
The general objective of this work is to study experimentally and kinetically the pyrolysis of pine
bark, wheat straw and rice husk. The specific objectives are as follows:
Perform thermogravimetric experiments in a TGA to isolate the chemical kinetics from the
transport phenomenon;
Investigate the impact of the type of biomass in the pyrolysis behavior under different heating
conditions.
Estimate the activation energy of the pyrolysis reaction using the Arrhenius plot method.
Develop an optimization tool capable of fitting pyrolysis model predictions to experimental
curves in order to better estimate the kinetic parameters.
Test how complex should the kinetic model be to describe the pyrolysis process of a biomass
sample, by comparing SFOM and 3PM predictions to experimental curves.
This study contributes to filling a gap still existing in the kinetic modeling research of non-woody
biomass. Furthermore, the tool has the capability to be applied to a variety of biomass types, making it
a useful pre-processing approach that can be used to estimate the kinetic parameters of sub-models
used in complex numerical simulations of pyrolysis reactors operating under similar heating conditions.
10
2. THEORETICAL FOUNDATIONS
2.1. BIOMASS: DEFINITION AND CHARACTERIZATION
Biomass is biological material derived from living, or recently living organisms. It includes plants,
leftovers from agricultural materials and forestry processes, as well as organic industrial, animal and
human wastes [23]. Biomass has highly variable properties, especially with respect to moisture,
structural components and inorganic constituents. This is directly related to the conditions of biomass
growth, i.e., the properties of the soil that are associated with the location site, weather conditions and,
especially, with use of chemicals (pesticides) and fertilizers [4]. It is composed mostly by Carbon (C),
Hydrogen (H), Oxygen (O), and other minor elements like Nitrogen (N), Sulphur (S), Calcium (Ca),
Potassium (K), Silicon (Si), Magnesium (Mg), Aluminum (Al), Iron (Fe), Phosphorus (P), Chlorine (Cl),
Sodium (Na), Manganese (Mn), Titanium (Ti) that are generally present in the form of oxides [24]. The
structural organic components are the hemicellulose, cellulose and lignin, from where the designation
of lignocellulosic biomass was originated, and additionally extractives that may comprise organic and
inorganic matter in their composition.
Hemicellulose is described as a complex mixture of various polysaccharides (xylose, mannose,
glucose, galactose, some acids, etc.), or a macromolecular substance of different sugars. Hemicellulose
appears to have an irregular and amorphous structure, rich in branches that are very easy to degrade
to volatiles (CO, CO2, some hydrocarbons, etc.) at low temperatures [24-26]. Cellulose forms long
glucose polymeric chains bounded to each other by hydrogen bounds, with no branches, making its
structure very strong with high thermal stability. Lignin is a non-sugar polymer mainly composed of
aromatic rings, alcohols and some acids. Lignin has a highly branched and irregular structure and its
very chemically active, which turns its decomposition process very difficult within a wide range of
temperatures (100-900 ºC) [14, 24, 26]. Extractives are defined as those compounds that are not an
integral part of the biomass structure and their composition includes various saccharides and other
carbohydrates, proteins, hydrocarbons, oils, aromatics, lipids, fats, starches, phenols, waxes and
inorganic materials, which can be extracted using different solvents (water, ethanol, benzene, etc.) [25].
There are different groups of biomass which can be classified in (1) wood and woody biomass
(WWB), (2) herbaceous and agricultural biomass (HAB) including straws (HAS) and residues (HAR), (3)
aquatic biomass, (4) animal and human biomass wastes, (5) contaminated biomass and industrial
biomass wastes (semi-biomass) and (6) biomass mixtures (blends from the previous varieties). Table
2.1 shows the chemical characteristics and the structural components composition of various biomass
groups and sub-groups of 47 species.
11
Table 2.1. Chemical characteristics and structural components composition of various biomass groups
and sub-groups [24].
Biomass WWB mean HAB mean HAS mean
Proximate analysis (wt.%, ar)
Moisture 4.7 – 62.9 19.3 4.7 – 62.9 19.3 7.4 – 16.8 10.2
Volatile Matter 30.4 – 79.7 62.9 41.5 – 76.6 66.0 58.0 – 73.9 66.7
Fixed Carbon 6.5 – 24.1 15.1 9.1 – 35.3 16.9 12.5 – 17.8 15.3
Ash 0.1 – 8.4 2.7 0.1 – 8.4 2.7 4.3 – 18.6 7.8
Ultimate analysis (wt.%, daf)
C 48.7 – 57.0 52.1 42.2 – 58.4 49.9 48.5 – 50.6 49.4
O 32.0 – 45.3 41.2 34.2 – 49.0 42.6 40.1 – 44.6 43.2
H 5.4 – 10.2 6.2 3.2 – 9.2 6.2 5.6 – 6.4 6.1
N 0.1 – 0.7 0.4 0.1 – 3.4 1.2 0.5 – 2.8 1.2
S 0.01 – 0.42 0.08 0.01 – 0.60 0.15 0.08 – 0.28 0.15
Structural components (wt.%, daf)
Cellulose 12.4 – 65.5 39.5 23.7 – 87.5 46.1 18 – 54.8 45.4
Hemicellulose 6.7 – 65.6 34.5 12.3 – 54.5 30.2 18 – 39 31.5
Lignin 10.2 – 44.5 26.0 0.0 – 54.3 23.7 14.9 – 35.3 23.1
Extractives (wt.%, daf) 1.0 – 9.9 3.1 1.2 – 86.8 13.7 3.8 – 21.7 13.6
Ash analysis (wt.%, db)
Cl 0.01 – 0.05 0.02 0.04 – 0.83 0.21 0.03 – 0.64 0.41
SiO2 1.86 – 68.18 22.22 8.73 – 84.92 46.18 7.87 – 77.2 43.94
CaO 5.79 – 83.46 43.03 2.98 – 44.32 11.23 2.46 – 30.68 14.13
K2O 2.19 – 31.99 10.75 2.93 – 53.38 24.59 12.59 – 38.14 24.49
P2O5 0.66 – 13.01 3.48 3.14 – 20.33 6.62 0.98 – 10.38 4.13
Al2O3 0.12 – 15.12 5.09 0.67 – 2.59 1.39 0.1 – 5.57 2.71
MgO 1.1 – 14.57 6.07 1.42 – 8.64 4.02 1.67 – 14.1 4.66
Fe2O3 0.37 – 9.54 3.44 0.58 – 1.73 0.98 0.41 – 2.82 1.42
SO3 0.36 – 11.66 2.78 0.83 – 9.89 3.66 1.18 – 4.93 3.01
Na2O 0.22 – 29.82 2.85 0.09 – 6.2 1.25 0.16 – 3.52 1.35
TiO2 0.06 – 1.2 0.29 0.01 – 0.28 0.08 0.02 – 0.33 0.16
Mn (ppm) 775 – 35740 13160 – 3100 155 – 2790 865
* am – as measured, daf – dry ash free basis
A detailed analysis of Table 2.1 reveals that, relatively to the weight percentage within the
referred groups, the decreasing order is as follows:
Moisture and volatile matter is WWB > HAB > HAS
Carbon is WWB > HAB > HAS
Oxygen is HAS > HAB > WWB
Hydrogen is (WWB, HAB) > HAS
12
Cl and SiO2 is HAS > HAB > WWB
CaO, Al2O3, MgO, Fe2O3, Na2O, TiO2 and Mn is WWB > HAB > HAS
K2O, P2O5 and SO3 is HAB > HAS > WWB
As for the structural components and extractives all the groups mentioned show, relatively to
the weight percentage within the referred groups, the following decreasing order:
Cellulose is HAB > HAS > WWB
Hemicellulose is WWB > HAS > HAB
Lignin is WWB > HAB > HAS
Extractives is HAS > HAB > WWB.
HAS presents the higher amount of ash, which can reach to 18.6%. This brings up the matter
of the disposability of biomass ash. The complex character of this parameter is the reason for such a
problem because ash originates simultaneously from inorganic, organic and fluid matter during biomass
conversion [24]. Table 2.1 shows that the ash composition strongly varies with the group or sub-group
where biomass is inserted. Nitrogen, Sulphur and Chlorine, despite contributing with small amounts for
the biomass composition, are the main responsible for the pollutants emissions [27]. For instance, when
the ratio between Chlorine and Sulfur contents is superior to one indicates a highly corrosive potential,
by active oxidation mechanism [28]. This indicator presents higher values in the HAB group. Nitrogen,
which is an essential element for the biomass growth, is responsible for the emission of NO and NO2,
also a characteristic more evident in the HAB group [29]. The K2O content is also higher in the HAR
sub-group, when compared with other biomass, which is attributed to the use of fertilizers [30]. The
presence of high ash content usually gives rise to higher particle emissions and fouling and deposits on
the surfaces where heat transfer occurs [31].
2.2. THERMOGRAVIMETRY
Thermogravimetry is a technique that can measure both heat flows and weight changes that
occur in a material as a function of temperature and time in a controlled atmosphere [15, 16, 32, 33].
This technique is usually referred as differential scanning calorimetry – thermogravimetry (DSC-TGA).
This combination allows identifying endothermic and exothermic events that can be associated to weight
losses, like melting or decomposition. In particular, thermogravimetric analysis is a very useful technique
for determining (1) composition of multicomponent systems, (2) atmosphere effects on materials, (3)
reaction kinetics and (4) ash, moisture and volatile contents of materials, being for this reason a very
powerful tool in the study of biomass thermal conversion processes, like pyrolysis.
Figure 2.1 shows a schematic diagram of a TGA furnace. The procedure is simple: A small
amount of the sample (~ 5 mg) is placed on a crucible supported by a precision balance inside a high
temperature furnace. Specifically, for the pyrolysis process, the atmosphere in the furnace must be inert,
where generally it is used Nitrogen or Argon. The temperature is measured with a thermocouple placed
near the crucible (see Figure 2.1). The information regarding to temperature and mass variations is send
to a computer unit to process the data in the form of thermogravimetric (TG) and differential
thermogravimetric (DTG) curves. Due to the considered low amount of the sample, this process should
be repeated several times to obtain a representative pyrolysis behavior. These curves represent the
13
mass variation and the rate of mass loss, respectively, with temperature and allow to identify the main
reactions involved in the pyrolysis process and to estimate its correspondent kinetic parameters. This
process is limited since the number of reactions occurring simultaneously during a simple pyrolysis
process is greater than the ones that can be identified, thus, pyrolysis is typically studied using models
in which the overall pyrolysis behavior is considered as the combination of each individual component
[16].
Figure 2.1. Scheme of the TGA furnace [33].
2.3. PYROLYSIS KINETICS
The pyrolysis kinetics is a mean of analyzing how the thermal conversion evolves through the
study of reaction rates, order of reaction and other parameters that can influence those rates [34]. The
heating rate can affect the distribution of the pyrolysis products and therefore the values of activation
energy may also differ. Consequently, the reaction rate can vary as well. However, many authors have
obtained good results with first order [12, 14-16]. Di Blasi [13] noticed that activation energies are higher
when it is assumed first-order reactions. The conversion can be described by one single process or
multiple, depending on the species taken into consideration. Empirical and predictive models have
already been developed to study the process of pyrolysis. Empirical models are based in apparent
kinetics, using estimation methods to find global kinetic parameters, while predictive models are
developed to be able to describe in detail the decomposition process using multiple species without any
fitting procedure.
There are several empirical pyrolysis kinetic models with distinctive levels of complexity already
discussed in the section 1.2, with distinctive levels of complexity available in the literature. The simplest
one is the single first order reaction model (SFOM) [13, 15, 16, 22, 35] that considers only one stage of
decomposition. Another one is the three parallel reactions model (3PM) [12-14, 18] that describes the
global decomposition of cellulose, hemicellulose and lignin. The decomposition of extractives can be
added, as in the work of Grønli et al. [9], who considered five parallel reactions (5PM). The reaction rate
constant of each component, is described by the Arrhenius law:
ki(Tp) = AiTpγ
exp (−Ei
RTp
) (2.1)
14
where ki(Tp) is the reaction rate constant of the ith component, Ai the pre-exponential factor (s-1), Ei the
activation energy (kJ/mol) and R is the ideal gas constant (J.K-1mol-1) [35].
A slightly more complex model is the distributed activation energy model (DAEM) [14,36,37].
This approach can be applied to the total amount of volatiles released or simply to the volatiles released
from a single component. Like the previous models, DAEM considers the Arrhenius law, but allows
expressing the distribution of the activation energy in a Gaussian form as:
𝑓𝑖(𝐸) =1
𝜎𝑖2𝜋1 2⁄exp (
−(𝐸 − 𝐸𝑖,0)2
2𝜎𝑖2 ) (2.2)
where 𝐸 is the activation energy (J/mol), 𝐸𝑖,0 the mean activation energy (J/mol) and σ the standard
deviation (J/mol) [37]. But this distribution is symmetric and, since the asymmetry of reactivity
distributions have to come into consideration, the Weibull distribution is used in the form:
𝑓(𝐸) =𝜆
𝜂(
𝐸 − 휀
𝜂)
𝜆−1
exp [(𝐸 − 휀
𝜂)
2
] (2.3)
where λ is the shape parameter, η is the width parameter, and ε is the activation energy threshold
(E ≥ ε) [36].
There are also some multi-component mechanisms that are able to predict products yields of
the three main components of biomass (hemicellulose, cellulose and lignin). Di Blasi [13] proposed this
model based on an extensive examination of literature data. Figure 2.2 shows a general scheme of the
decomposition of a component. νc is the volatile matter.
Figure 2.2. General scheme of the decomposition of a component.
The first step of the decomposition (depolymerization) does not lead to chemical composition
changes, but to modify the physical properties, as porosity. This scheme was originally developed for
cellulose, but also can be applied to hemicellulose and lignin. Extractives and ash contents are
integrated in the hemicellulose component. This mechanism can also consider secondary reactions of
the vapor phase of tars (see Figure 2.3) that account the complete decomposition of tars.
15
Figure 2.3. General scheme for secondary reactions of the vapor phase of tars.
The Bio-PoliMi mechanism is a predictive multistep devolatilization model that considers 43
species and 14 chemical reactions to describe, in detail, the devolatilization process of cellulose,
hemicellulose and lignin, including product speciation. Due to the complexity of lignin structure, were
differentiated three structures identified as LIG-C, LIG-O and LIG-H, being respectively rich in carbon,
oxygen and hydrogen [38]. Figure 2.4 shows the chemical structure of all the components.
Figure 2.4. Chemical structure of all the components (source: Cellulose [12, 39]; hemicellulose [12,40];
lignin [38]).
Figure 2.5 shows the multistep mechanism of cellulose pyrolysis. The devolatilization of
cellulose involves multiple reactions that lead to the formation of the levoglucosan, decomposition
products, char and water. The volatile products include CO, CO2, CH4, CH3CHO, C3H6O, among others.
16
Figure 2.5. Multistep mechanism of cellulose pyrolysis [38].
Figure 2.6 shows the multistep mechanism of hemicellulose pyrolysis. The devolatilization of
hemicellulose involves multiple reactions that lead to the formation of xylose, char and other
decomposition products. The volatile products include CO, CO2, CH4, CH2O, C2H5OH, CH2OH, among
others.
Figure 2.6. Multistep mechanism of hemicellulose pyrolysis [38].
Figure 2.7 shows the multistep mechanism of the pyrolysis of the three lignin reference
components. The devolatilization of hemicellulose involves multiple reactions that lead to the formation
of intermediate lignin species, char and other decomposition products. The volatile products include CO,
CO2, H2, CH4, CH2O, phenols and some acids, among others.
Figure 2.7. Multistep mechanism of the pyrolysis of LIG-H (top), LIG-O (mid) and LIG-C (bottom) [38].
17
2.4. OPTIMIZATION METHODS
The purpose of this work was not to implement new optimization techniques or improving
existing ones but to use and combine already existing techniques to perform kinetic analysis. For this
reason, the various optimization methodologies will not be extensively reviewed here. For more
information, the reader is referred to [41, 42]. In this work, the genetic algorithm (GA) and the non-linear
curve fitting (LSQ) MATLAB solvers were used.
A genetic algorithm (GA) is a method for solving optimization problems based on a randomly
selection process that mimics the process of natural selection. A typical genetic algorithm requires: (1)
a genetic representation of the solution domain and (2) a fitness function to evaluate the solution domain.
The GA has the objective to find global minima for nonlinear problems through an iterative process. In
each iteration, individuals (in this case kinetic parameters) are selected from the current population and
uses them as parents to produce the children for the next generation [40]. Elite children are the
individuals in the current generation with the best fitness values. So they go automatically to the next
generation. The other children’s genome are modified (through crossover and randomly mutation, see
Figure 2.8) to form a new generation. The algorithm terminates when it reaches the stopping criteria
defined by the user (maximum number of generations, stall generation number, etc.) or if it reaches the
optimal solution, i.e., convergence of the error to the minimum value [41, 42].
Figure 2.8. Genetic Algorithm: scheme of the three types of children [42].
GA methods have a broad range of applicability. Authier et al. [43] applied a genetic algorithm
to model kinetically the devolatilization of coal and described it as an efficient optimization procedure.
Cai et al. [44] applied a genetic algorithm to estimate the optimal kinetic parameters associated with
peanut shells decomposition and compared with the obtained from thermogravimetric curves.
The least square fitting is an optimization method that solves nonlinear problems by minimizing
the error (𝛿) between a fitting function (𝑈𝑝𝑟𝑒𝑑 (𝑖,𝑗)) and reference data (𝑈𝑒𝑥𝑝(𝑖,𝑗)
) according to:
𝛿 = √1
𝑁𝛽∑ ∑ (𝑈𝑒𝑥𝑝(𝑖,𝑗)
− 𝑈𝑝𝑟𝑒𝑑(𝑖,𝑗))
2𝑁
𝑗=1
𝛽
𝑖=1 (2.5)
18
where β is the number of heating rates considered for the optimization, N is the number of iterations.
This method is based on an initial guess, and the function coefficients (in this case kinetic parameters)
can be evaluated in a deterministic way or, recurring to optimization solvers, through a specific number
of function evaluations. Matlab has a number of algorithms available for the function evaluation. During
the course of the tool development, it was observed that the default set up produced fast, stable and
very satisfactory results.
The combination of these two optimization methods has been used by Authier et al. [43],
although for coal devolatilization studies. The author proved that this two-step optimization procedure
leads to more accurate and reliable results. This is due to the fact that the genetic algorithm does not
need an initial guess and rapidly converges to an optimal solution. The GA output is then used has an
initial guess for the non-linear least squares fitting procedure to provide an adequate start. The addition
of this last procedure gives more precise results since it does not consider random effects.
19
3. MATERIALS AND METHODS
3.1. BIOMASS CHARACTERIZATION
Table 3.1 lists the proximate and ultimate analysis for the three biomass fuels (pine bark, wheat
straw and rice husk) used in this work.
Table 3.1. Proximate and ultimate analysis of the biomass fuels studied.
Parameter Pine bark Wheat straw Rice husk
Proximate analysis (wt.%, ar)
Volatile matter (VM) 63.7 64.9 65.5
Fixed carbon (FC) 21.2 11.5 14.6
Ash 2.6 14.7 10.5
Moisture 12.5 8.9 9.4
Ultimate analysis (wt.%, daf)
C 52.6 51.6 52.1
H 7.4 6.8 7.2
N 1.0 0.6 0.6
S < 0.02 < 0.02 < 0.02
Oa 39.0 41.0 40.1
a Calculated by difference, ar – as received, daf – dry-ash-free
Comparing the chemical analysis of the three samples, the main characteristic that stands out
is the ash content. Pine bark has significantly lower content of ash than wheat straw and rice husk. On
the other hand pine bark has the higher quantity of moisture, while wheat straw and rice husk have lower
but similar content. Other than that, the remaining parameters are quite similar and the results discussed
here are in agreement with the survey data listed in Table 2.1. It is not possible to use this method due
to the samples containing a higher quantity of hydrogen than the triangulation method reference
biomass. Figure 3.1 shows that the samples fall outside the PoliMi triangle.
The correlation method [45] was used to estimate the contents of cellulose (CELL),
hemicellulose (HCE) and lignin (LIG). The mass fractions of cellulose and hemicellulose were calculated
as follows:
CELL = −1019.07 + 293.810(O C⁄ ) − 187.639(O C⁄ )2 + 65.1426(H C⁄ ) − 19.3025(H C⁄ )2
+ 21.7448(VM) − 0.132123(VM)2 (3.1)
HCE = 612.099 + 195.366(O C⁄ ) − 156.535(O C⁄ )2 + 511.357 (H
C) − 177.025(H C⁄ )2
− 24.3224(VM) + 0.1453063(VM)2
(3.2)
20
Figure 3.1. Triangulation method test for the biomass samples studied.
where O/C and H/C are the molar fractions and VM is the volatile matter in wt.%, dry-ash-free (daf). The
mass fraction of lignin was calculated by difference. Table 3.2 lists the composition range of the
correlation method. The precision of the correlation for cellulose is 90% and for hemicellulose is 81%
[45].
Table 3.2. Composition range of the correlation method [45].
O/C (molar ratio) H/C (molar ratio) VM (wt. %)
Range 0.56 - 0.83 1.26 - 1.69 73 - 90
Figure 3.2 shows the contents of cellulose, hemicellulose and lignin of the three biomass fuels
estimated through Eqs. (3.1) and (3.2). The figure reveals that the pine bark is richer in hemicellulose,
while both the wheat straw and rice husk are richer in cellulose.
Figure 3.2. Contents of cellulose, hemicellulose and lignin.
Table 3.3 shows the comparison between the estimated composition of the three main
components and values reported in the literature. The estimated composition of pine bark and rice husk
is consistent with the literature data, but the lignin fraction of the wheat straw and rice husk are slightly
higher than that reported by other authors.
21
Table 3.3. Comparison between the estimated composition of the three main
components and literature values [12,21,46].
Biomass Correlation Literature
CELL HCE LIG CELL HCE LIG
Pine bark (wt.%, db) 28.2 24.2 45.1 20 - 45 19 - 50 22 - 46
Wheat straw (wt.%, db) 34.1 15.1 41.4 29 - 43 18 - 39 19 - 30
Rice husk (wt.%, db) 33.0 18.5 35.7 18 - 44 18 - 35 18 - 30
3.2. THERMOGRAVIMETRIC STUDY
The thermogravimetric tests were performed in a SDT 2960 simultaneous DSC-TGA (TA
Instruments), under an inert atmosphere of Argon, with a constant flow of 100 mL/min. The samples
were grinded to less than 1 mm and placed on a measuring crucible with an initial weight of 5 (± 1) mg.
Due to the simplicity of the used method to detect and record mass loss variations, the precision of the
measurements is only influenced by the TGA balance sensitivity, which is 0.1 µg.
Figure 3.3 shows the temperature profiles for the three heating rates tested. The drying stage
consisted in heating each biomass from 300 K to 383 K over 10 min, followed by a plateau of 30 min.
When this stage was completed, the sample was heated to 1173 K followed by a plateau of 30 min,
using heating rates of 5, 10 and 15 K/min. Since the objective of this work does not consider the study
of the drying process, the results that will be presented in the following sections will be normalized to
dry basis. Also, it will only be considered the interval of the pyrolysis process until 1073 K. At this
temperature, the pyrolysis process has approach completion as typified by the DTG curves that will be
presented in the results section.
Figure 3.3. Temperature profiles for the three heating rates tested.
22
4. KINETIC ANALYSIS
In this chapter it is discussed in detail the different methods (Arrhenius plot and optimization
methods) considered to estimate the kinetic parameters for the pyrolysis process, using the kinetic
models SFOM and 3PM. At first kinetic analysis the FWO Arrhenius plot method was used to estimate
the kinetic parameters to compute the mass loss curves. But this method showed to be time consuming
and limited regarding the quantity of experimental information that can be considered. To fight this
fragility a combination of existing optimization tools in MATLAB (genetic algorithm and least squares
procedure) was implemented to guarantee that the kinetic parameters will always be determine faster,
systematically and above all, with precision.
4.1. ARRHENIUS PLOT METHOD
The Arrhenius plot method is typically used to estimate 𝐸𝑎 and 𝐴 kinetic parameters of non-
isothermal reactions where the heating rate is constant in time, as in thermogravimetric analysis. Within
this context, the most commonly used methods are the integral iso-conversion methods Flynne-Walle-
Ozawa (FWO) [16, 47, 48] and Kissinger-Akahira-Sunose (KAS) [15, 16, 47, 48]. Both methods were
tested in this work, but only FWO will be further described since it gave better results. The FWO method
relies on the following steps [16]:
1. Establishing the equation of the decomposition rate
𝑑𝛼
𝑑𝑡= 𝐴 exp [
−𝐸𝑎
𝑅𝑇] 𝑓(𝛼) (4.1)
where t is time (s), α the fraction of reacted sample or, in other words, the conversion degree and 𝑓(𝛼)
is the conversion function that represents the reaction model. The unknown parameters are Ea, A and
𝑓(𝛼).
2. Definition of the conversion degree (α)
𝛼 =𝑚𝑜 − 𝑚
𝑚𝑜 − 𝑚𝑓
(4.2)
where mo is the initial mass, mf the final mass and m the mass at a specific time and temperature.
3. Adaptation of Equation 4.1 for non-isothermal reactions
𝑑𝛼
𝑑𝑡= 𝛽
𝑑𝛼
𝑑𝑇= 𝐴. exp [
−𝐸𝑎
𝑅𝑇] 𝑓(𝛼) (4.3)
which can be integrated with respect to α and T resulting in
23
ln 𝛽 ≅ log [𝐴𝐸𝑎
𝑅𝑔(𝛼)] − 2.315 − 0.4567 [
𝐸𝑎
𝑅𝑇] (4.4)
where β is the heating rate (K/s) and g(α) is the integral function of conversion. For first order reactions,
typically used to describe the devolatilization process, the integral function reads as [32]
𝑔(𝛼) = −ln (1 − 𝛼) (4.5)
4. Assuming the conversion degree (∝) to be a constant value [32], the kinetic parameters can be
determined from the slope of a series of lines, each correspondent to a specific heating rate, when
plotting ln(β) vs 1/T. Figure 4.1 shows a representative example of the FWO method [16], where it
were tested five heating rates.
Figure 4.1. Representative example of the FWO method [49].
.
It is very important to notice that if the apparent estimated activation energy increases with the
conversion degree, we are dealing with a complex reaction mechanism, otherwise, if there is not a
significant change it is assumed that we are dealing with a single step reaction [16]. There are a few
examples of studies in the literature that apply the Arrhenius plot to describe the devolatilization using
[50,51], as reviewed in section 1.2. However, the Arrhenius plot method is time consuming and prone
to the introduction of errors during the process when dealing with multiple data sets.
24
4.2. OPTIMIZATION METHODS
Given the impossibility of measuring and quantifying the amount of extractives present in the
biomass samples, this work focuses only on 3PM and SFOM to test the degree of complexity of the
model required to describe the pyrolysis process. Species were not considered in the model because
they were not measured.
The total weight loss of each component i (in the case of the SFOM it is just one component) is
governed by a single reaction as function of time, temperature and mass loss history as follows [35]:
�̇�𝑝𝑖𝑟,𝑖 =dmp,i
dt= ki(T)(VMi − Vg,i) (4.6)
where dmp,i/dt is the mass loss in weight percent of the ith component, VMi is the maximum volatile
matter that can be loss and Vg,i is the total amount of volatile gases that have left the particle, both in
wt.%. For the 3PM, the amount of VM is corrected using the fraction of char produced by the i th
component, Ci, as follows [14]:
VMi = VM(1 − Ci) (4.7)
The rate constant ki (T) is expressed by the Arrhenius equation as follows
ki = AiTpγ
exp (−Ei
RTp
) (2.1)
where R is the ideal gas constant (J.K-1mol-1), Ai is the pre-exponential factor (s-1), Ei is the activation
energy (J.mol-1) and γ is the temperature power coefficient [5]. The mass balance is integrated over the
time considering the experimental duration using an explicit Euler method with a small enough time step
to ensure sufficient accuracy. This can be easily confirmed graphically by observing if the resultant mass
loss curves present smooth gradients (small enough time step) or sharp corners (too large time step).
The mass balance discretized equation reads as follows
𝑚𝑝,𝑖𝑡+∆𝑡= 𝑚𝑝,𝑖𝑡
+ �̇�𝑝𝑖𝑟,𝑖∆𝑡 (4.8)
The experimental heating rates are imposed to the mass balance. Figure 4.2 shows the process flow of
the pyrolysis algorithm.
25
Figure 4.2. Pyrolysis algorithm.
The kinetic optimization procedure was programmed using an object-oriented structure with two
steps: the genetic algorithm (GA) from MATLAB’s global optimization toolbox and the MATLAB’s least
squares fitness function (LSQ). The error function to minimize, in other words, the objective function
reads as [52]:
𝛿 = √1
𝛽𝑁∑ ∑ (𝑈𝑒𝑥𝑝(𝑖,𝑗)
− 𝑈𝑝𝑟𝑒𝑑(𝑖,𝑗))
2𝑁
𝑗=1
𝛽
𝑖=1 (2.5)
where β is the number of heating rates considered for the optimization, N is the number of time
integrations at each rate, and Uexp and Upred are the experimental and predicted yields at each time step
and rate, respectively. The objective function can have a number of local minima when multiple heating
rates are considered. The GA was used to provide an initial guess of the global minimum, which was
then fed to the LSQ to minimize further the error. Figure 4.3 shows the process flow of the kinetic
optimization procedure.
26
Figure 4.3. Kinetic optimization procedure.
For both kinetic models considered, initial bounds were imposed in the GA following typical
ranges from the literature (see below). The maximum number of generations was set to 100, with a limit
of stall generation number of 50. The population size for each generation was set to 30. Finally, it was
allowed 80% crossover between generations and 5% of mutation within an individual to ensure variety
and new chromosomes in the next generation. Regarding the least squares function, the same bounds
of GA were set for the 3PM, while for the SFOM no bounds were imposed. The maximum function
evaluations and the maximum iterations number were both set to 1×105. Both methods converge when
the average change in the fitness value is less than 1×10-12.
The kinetic parameters are E, A and γ in the case of the SFOM, and Ei, Ai, γi and Ci in the case
of the 3PM. Table 4.1 shows the typical range of activation energies reported in the literature for the
SFOM. In the case of woody biomass and wheat straw, the wide range of activation energies
encountered can be a consequence of the different heating conditions used in the experiments and/or
of the different biomass characteristics (particle size and composition) [13]. Table 4.2 shows the typical
kinetic parameters for the 3PM and the fraction of char produced by each component. These parameters
are typically used for woody biomass.
27
Table 4.1. Typical range of activation
energies for the SFOM [13, 22, 47, 53].
Biomass E (kJ/mol)
Woody biomass 89 - 175
Wheat straw 55 - 130
Rice husk 72 - 82
Table 4.2. Typical kinetic parameters for the 3PM and fraction of char produced by
each component [12, 14, 20].
ECELL EHCE ELIG ACELL AHCE ALIG CCELL CHCE CLIG
175 - 240 80 - 129 18 - 87 1013 - 1020 106 - 1011 1 - 105 0.14 - 0.38 0.15 - 0.43 0.1 - 0.47
To ensure consistency in the predicted results with the 3PM, it was estimated only one activation
energy and one fraction of char produced by each component (cellulose, hemicellulose and lignin) for
each biomass, regardless of the heating rate. These tests showed good agreement with the literature
data for the SFOM, but for the 3PM it was necessary a larger universe for the activation energy of the
cellulose, for the pre-exponential factor of the hemicellulose and for the char fraction produced by the
three main biomass components. Regarding the activation energy of the cellulose the lower bound was
set to 80 kJ/mol, as for the pre-exponential factor of hemicellulose the upper bound was set to 1011 and
for CCELL, CHCE and CLIG the new ranges were reset to 0 - 0.60, 0 – 0.60 and 0.1 - 0.60, respectively. It
is important to notice that since the Arrhenius plot method does not allow fitting the temperature
exponents, the limits were imposed empirically from −10 to +10. Table 4.3 shows the average
simulation times and specific standard deviations relative to the chosen model and Table 4.4 lists the
work computer specifications.
Table 4.3. Average simulation times and specific standard deviations
relative to the chosen model.
Model Average time (min) σDEV (min)
SFOM 9.6 1.8
3PM 29.3 7.4
Table 4.4. Work computer specifications.
CPU Intel® core™ i7-4770 CPU @ 3.40GHz
RAM 8.00 GB DDR3 1333 MHz
Hard Drive 1TB 7200 RPM 64MB Cache
28
4.3. COMPARISON BETWEEN METHODS
The most evident difference between the methods is that more constants of the Arrhenius
equation can be estimated using the optimization method, in particular the gamma coefficient. A
common characteristic shared is the fact of both can consider multiple reactions, but in the case of the
Arrhenius plot method the number of reactions is limited, up to five [13]. Beyond that, the application of
the two-step fitting procedure is more accurate and robust than the Arrhenius plot method. The fitting
procedure delivers results much faster, is less prone to errors introduced by the user and uses all the
experimental data available – there is no loss of information. In the Arrhenius plot method, due to the
relatively small finite number of points typically considered, information is lost. Nevertheless,
implementation errors may be introduced during the development stage of the fitting procedure. In order
to validate the implementation, the fitting procedure was tested using the results published by Burhenne
et al. [12] and Grønli et al. [14] and an excellent agreement was obtained.
29
5. RESULTS
5.1. EXPERIMENTAL RESULTS
Figure 5.1 shows the pyrolysis yields for pine bark, wheat straw and rice husk for the heating
rates of 5, 10 and 15 K/min. At the end of all stages of decomposition, each biomass reaches a similar
value typical of the mass solid residue, regardless of the heating rate, as seen in other studies [16].
Independently of the heating rate, pine bark finishes decomposition at higher temperatures after rice
husk and wheat straw that finish decomposition earlier, hence at lower temperatures. These results are
consistent with the estimated composition. Pine bark has the highest amount of lignin after rice husk
and wheat straw (cf. Figure 3.2), and it is the lignin content that controls the pyrolysis process - the
higher the amount of lignin, the slowest is the decomposition of the biomass.
Figure 5.2 shows the DTG curves as a function of the temperature for pine bark, wheat straw
and rice husk for the heating rates of 5, 10 and 15 K/min. The results show that pine bark behaves
differently than wheat straw and rice husk, which have similar weight loss curves. The first region of
decomposition, referred to as such due to the overlap of the first and second stages of decomposition
[12–14,18–20,54], corresponds to the hemicellulose and cellulose, respectively. The DTG curves also
show that the hemicellulose decomposition (first stage) usually appears as a more or less pronounced
“shoulder” instead of a distinct peak, as it happens for cellulose [12–14,18,19]. The second region (third
stage) corresponds to the decomposition of the lignin that occurs in a wide range of temperatures until
it reaches temperatures of 973 K [12–14,18,54]. In summary, Figure 5.2 reveals that the distinct peaks
associated with the different constituents are not so noticeable, indicating that the components thermal
decompose simultaneously, overlapping each other in DTG profiles, as seen in other studies [12,14–
16,19,20].
Figure 5.1. Pyrolysis yields for pine bark (PB), wheat straw (WS) and rice husk (RH) for 5, 10 and 15
K/min.
Table 5.1 shows the characteristics of the first region of decomposition for the three biomass
fuels. In the table Ti is the temperature where the decomposition of the first region begins, THCE is the
temperature in which hemicellulose maximum pyrolysis rate occurs, TCELL is the temperature in which
30
cellulose maximum pyrolysis rate occurs and Tf is the temperature where the decomposition of the first
region ends.
Figure 5.2. DTG curves as a function of temperature for pine bark (PB), wheat straw (WS) and rice
husk (RH) for 5, 10 and 15 K/min.
Table 5.1. Characteristics of the first region of decomposition
for the three biomass fuels.
Biomass β Ti (K) THCE (K) TCELL (K) Tf (K) Tf −Ti (K)
Pine bark
5 505 543 609 649 141
10 510 547 616 658 148
15 513 552 617 666 153
Wheat straw
5 520 555 587 620 100
10 524 565 599 630 106
15 530 573 605 638 108
Rice husk
5 525 568 592 627 102
10 528 569 603 634 106
15 536 573 614 648 112
The decomposition characteristics are consistent with other studies [13,14,18,19,55]. The
pyrolysis maxima for the hemicellulose occur in between 543 K and 573 K and for the cellulose occur in
between 587 K and 617 K. Also, the cellulose decomposition resulted in a much higher decomposition
maximum compared to the hemicellulose and lignin decomposition [18,19,54]. Figure 5.1 and Table 5.1
reveal that as the heating rate increases, the transition temperatures slightly increase and the DTG
curves tend to be wider. This is due to the fact each biomass constituents have individual decomposition
peaks in specific temperature ranges and the increase of the heating rate tends to delay thermal
decomposition processes towards higher temperatures [18,19]. In the case of pine bark Tf −Ti is larger
than the corresponding values for rice husk and wheat straw, underlining that the higher amount of lignin
leads to the slowest decomposition [12,13,18,54].
31
5.2. KINETIC ANALYSIS RESULTS
Figure 5.3 shows the Arrhenius plot method applied to experimental TG and DTG curves of pine
bark (PB), wheat straw (WS) and rice husk (RH), respectively, presented in the previous section (see
figures 5.1 and 5.2), and Table 5.2 shows the kinetic parameters, Ea and A, estimated with the Arrhenius
plot method.
Figure 5.3. Arrhenius plot method applied to experimental TG curves of pine bark (PB), wheat straw
(WS) and rice husk (RH), respectively.
Table 5.2. Kinetic parameters estimated with the Arrhenius plot method.
All the samples show similar activation energy, specifically 161, 152 and 166 kJ/mol for pine
bark, wheat straw and rice husk, respectively. This method was successfully applied from a conversion
factor of 0.2 to 0.6, correspondent to a range of temperatures within the interval between T i and Tf.
Conversion factors superior to 0.7 due to the impossibility of establishing a correlation, since the
correspondent temperature is approximately 800 K, near to the pyrolysis end. From α = 0.2 to α = 0.6
the values of the apparent Ea are similar, which is an indicator of the presence of a single step reaction
(reinforced by the correlation factor (R2) being close to the unit value), as Guerrero et al. concluded [16].
Table 5.3 shows the kinetic parameters obtained by fitting the SFOM to the experimental data
for the three biomass fuels. The obtained results are consistent with the literature data except for pine
bark [22,47,53,55]. This may be because most of the literature results are relative to various types of
pine wood, excluding generally the pine bark.
Pine Bark Wheat Straw Rice Husk
α Ea (kJ/mol) A (s-1) R2 Ea (kJ/mol) A (s-1) R2 Ea (kJ/mol) A (s-1) R2
0.2 167 1.14×1012 0.999 145 4.76×1012 0.999 165 2.16×1014 0.995
0.3 151 4.20×1012 0.996 142 2.03×1012 0.999 169 3.19×1014 0.997
0.4 161 1.87×1013 0.993 174 1.03×1015 0.994 166 1.18×1014 0.999
0.5 164 1.46×1013 0.992 151 7.67×1012 0.999 166 8.64×1013 0.998
0.6 169 4.31×1012 0.992 149 3.97×1012 0.998 166 6.70×1013 0.998
Average 161 9.64×1012 0.994 152 2.11×1014 0.998 166 1.61×1014 0.997
32
Table 5.3. Kinetic parameters obtained by fitting the SFOM to the experimental data for the three
biomass fuels.
Biomass E
(kJ/mol)
A (s-1) ×1011 γ δ (%)
5 10 15 5 10 15
Pine bark 55.3 27.5 47.3 71.5 -3.842 -0.982 2.450 7.1
Wheat straw 77.5 1.86 93.8 2.77 -2.532 3.801 8.992 5.2
Rice husk 84.6 97.3 23.7 38.9 -2.981 0.440 -1.692 4.8
Table 5.4 shows the activation energies and char fractions and Table 5.5 shows the pre-
exponential factors and model constants, all obtained by fitting the 3PM to the experimental data for the
three biomass fuels. Given the optimization procedure, through which the kinetic parameters were
optimized specifically for each biomass, some variations were obtained for the activation energies of the
cellulose, hemicellulose and lignin. Nevertheless, the values are very close when comparing all the
biomass fuels, as verified also by [56,57]. Also, activation energies of hemicellulose and cellulose are
higher than lignin, this is due to the reactivity of the components [12,18,20]. It is expected that the
cellulose and the hemicellulose decomposed almost entirely due to its weaker structure making lignin
the main contributor to the formation of char [12,58], as typified by CLIG in Table 5.4. Li et al. [19], studied
the variation of the parameters of A and Ci with the heating rate concluding that both parameters change
with a significant increase of heating rate. Due to the fact that heating rates did not varied significantly
in this study, the variation of Ci with the heating rate was not considered.
Table 5.4. Activation energies and char fractions obtained by fitting the 3PM to
the experimental data for the three biomass fuels.
Biomass ECELL
(kJ/mol)
EHCE
(kJ/mol)
ELIG
(kJ/mol)
CCELL
(wt.%)
CHCE
(wt.%)
CLIG
(wt.%)
δ
(%)
Pine bark 152.5 95.7 44.3 0.080 0.038 0.574 2.2
Wheat straw 143.3 83.6 37.0 ≈ 0 ≈ 0 0.441 1.9
Rice husk 163.8 107.3 37.2 ≈ 0 ≈ 0 0.567 2.2
* All the values ≈ 0 are smaller than 1×10-5.
33
Table 5.5. Pre-exponential factors and model constants obtained by fitting the 3PM to the
experimental data for the three biomass fuels.
Biomass β (K/min) ACELL (s-1) AHCE (s-1) ALIG (s-1) γCELL (-) γHCE (-) γLIG (-)
Pine
bark
5 9.14×1018 1.03×1017 337.8 -5.322 0.315 1.369
10 2.09×1018 7.57×1016 827.7 -5.773 -2.470 7.212
15 9.42×1018 9.98×1016 175.3 -7.945 -6.917 -2.815
Wheat
straw
5 4.68×1017 2.46×107 1000 -2.587 -0.784 -1.096
10 5.92×1018 6.24×107 101 -4.644 -0.703 -5.204
15 8.33×1017 4.35×107 692 -8.543 -1.197 6.324
Rice
husk
5 1.67×1018 7.47×107 3.8 -2.263 -0.089 -0.237
10 2.06×1016 2.15×107 360 0.350 4.926 -7.904
15 3.41×1018 3.41×107 412 -2.616 1.166 0.624
Figures 5.4 to 5.6 show the TG, DTG and predicted curves for pine bark, wheat straw and rice
husk, respectively. The SFOM captures the pyrolysis behavior in a satisfactory way but only the 3PM
reproduces it rather well, since it considers three stages of decomposition. In the case of wheat straw,
nearly in the end of the decomposition process (~920 K), DTG curves show a small pyrolysis peak that
indicates the occurrence of inorganic reactions [59].
When comparing the fitting errors obtained for the SFOM and for the 3PM (cf. Tables 5.4 and
5.5), it becomes obvious that the latter describes better the pyrolysis processes. Nevertheless, the fitting
error of the SFOM is very satisfactory, evidencing the appropriateness of the optimization method. Only
the 3PM is capable to predict correctly the maximum pyrolysis rate for all biomass fuels, but in the case
of pine bark there is a slight shift of +20 K.
34
Figure 5.4. TG (top), DTG (bottom) and predicted curves for pine bark (PB).
Figure 5.5. TG (top), DTG (bottom) and predicted curves for wheat straw (WS).
35
Figure 5.6. TG (top), DTG (bottom) and predicted curves for rice husk (RH).
Figure 5.7 shows DTG and predicted curves using the 3PM for pine bark, wheat straw and rice
husk, where the contributions of the cellulose, hemicellulose and lignin are plotted. Since the results are
similar for all heating rates, only the DTG curves for 5 K/min are included in the Figure 5.7. For the cases
of the wheat straw and rice husk the predicted pyrolysis maxima of the cellulose and hemicellulose
components show excellent agreement with the experimental curves. In the case of pine bark the
predicted pyrolysis maxima do not overlap. This may be due to an over estimation of the cellulose
component as discussed previously.
Figure 5.7. DTG and predicted curves using the 3PM for pine bark (PB), wheat straw (WS) and rice
husk (RH) at 5 K/min.
The results of the activation energies obtained through the Arrhenius plot method and the fitting
procedure cannot be directly compared because (1) in the latter, the temperature power coefficient, γ,
36
was considered and (2) the estimation procedure is different since the former estimates a single value
of Ea and A for all the heating rates tested and the latter can extend the estimation procedure to multiple
values of Ea, A and γ, according to the number of heating rates tested. Giving that the range of variation
of the heating rates in this study is not significant, it is reasonable to consider the activation energy
constant [14].
Nevertheless, TG curves were also predicted with the parameters estimated with the former and
compared to the experimental results. Table 5.6 shows the error and execution time for the Arrhenius
plot method and the fitting procedure for the results obtained with SFOM. The value of the error
associated with the overall process and the execution time of the Arrhenius plot method are significantly
higher than those of the fitting procedure.
Table 5.6. Comparison between the Arrhenius plot method and
the fitting procedure.
Method Arrhenius Fitting
Error (%) (PB; WS; RH) 22; 32; 21 2.2; 1.9; 2.2
Execution time Several hours ≈ 9.6 min
37
6. CONCLUSIONS
The proposed objectives were successfully achieved. The pyrolysis behavior of pine bark, wheat
straw and rice husk was studied by thermogravimetry using heating rates of 5, 10 and 15 K/min. A fitting
tool was developed to estimate kinetic parameter based in optimization methods. The kinetic parameters
were obtained by fitting the SFOM and 3PM to the experimental curves using a two-stage optimization
procedure. The main conclusions of this work are as follows:
The results reveal that for each biomass the variation of the heating rates had a small impact in
the pyrolysis process, particularly in the total mass loss; the biomass decomposition, however,
started earlier in time but at a slightly higher temperature for the highest heating rate.
The activation energies obtained in this work using the SFOM were 55.5, 79.6 and 87 kJ/mol
for pine bark, wheat straw and rice husk, respectively.
The activation energies for cellulose, hemicellulose and lignin obtained in this work using the
3PM were, respectively, 152.5, 95.7 and 44.3 kJ/mol for pine bark, 143.3, 83.6 and 37 kJ/mol
for wheat straw, and 163.8, 107.3 and 37.2 kJ/mol for rice husk.
Overall, the optimized parameters for the SFOM resulted in a very satisfactory fitting error of
7.1%, 5.2% and 4.8% for pine bark, wheat straw and rice husk, respectively. The optimized
parameters for the 3PM resulted in a good fitting error (≈ 2%) and were generally within typical
values.
The obtained results proved that the kinetic tool developed in this work was capable of
reproducing the pyrolysis behavior with good accuracy and showed that the degree of complexity of
3PM suffices. This tool showed the advantage of being fast, able to estimate multiple kinetic parameters
and to test any type of lignocellulosic biomass in multiple heating rates.
38
7. FUTURE PERSPECTIVES
A few aspects of the fitting procedure for 3PM can be improved. Namely, the activation energies
for each component should be fixed for all types of biomass. Furthermore, a sensitivity analysis varying
significantly the relative amount of each component would show how sensitive is the procedure to major
composition variations.
Finally, the tool developed is a base for future developments, specifically the inclusion of
transport effects (energy and momentum balance for other application such as drop tubes) and also to
include kinetic models for the thermal conversion of biomass under different atmospheres (gasification
and steam pyrolysis).
39
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