Review
Biomass pyrolysis: a state-ofthe-art review
B. V. Babu, Birla Institute of Technology and Science, Pilani, India
Received February 20, 2008; revised version received June 1, 2008; accepted June 19, 2008
Published online August 4, 2008 in Wiley InterScience (www.interscience.wiley.com); DOI: 10.1002/bbb.92;
Biofuels, Bioprod. Bioref. 2:393–414 (2008)
Abstract: Biomass pyrolysis is a process by which a biomass feedstock is thermally degraded in the absence of
air/oxygen. It is used for the production of solid (charcoal), liquid (tar and other organics) and gaseous products.
These products are of interest as they are possible alternate sources of energy. The study of pyrolysis is gaining
increasing importance, as it is not only an independent process, it is also a first step in the gasification or combustion process, and has many advantages over other renewable and conventional energy sources. Studies have
been conducted on pyrolysis of biomass and other substances by several researchers. The actual reaction scheme
of pyrolysis of biomass is extremely complex because of the formation of over a hundred intermediate products.
Modeling of pyrolysis includes chemical kinetics model, heat transfer model and mass transfer model. Various
kinetic models, heat and mass transfer models reported in the literature and our previous study are reported in the
present review with experimental validations to provide the current status of the study. Plasma pyrolysis provides
high temperature and high energy for reaction as the reaction sample is rapidly heated up to a high temperature.
This review also covers the experimental and modeling study status of plasma-assisted pyrolysis. © 2008 Society
of Chemical Industry and John Wiley & Sons, Ltd
Keywords: pyrolysis; renewable energy; biomass; gasification; modeling; heat transfer; mass transfer; kinetics;
plasma pyrolysis
Introduction
nergy around us can be stored, converted and amplified in many different ways. Energy resources may be
categorized as either finite (e.g. minerals) or perpetual,
such as the so-called renewable resources (solar, wind, tidal,
etc.). In the case of finite resources, reserves denote the
amount within the designated resource that is recoverable
under specified criteria. Whilst each major energy source has
its own characteristics, applications, advantages and disadvantages, the fundamental distinction is between those that
E
are finite and those that are, on any human scale, effectively
perpetual or everlasting. The finite resources comprise a
number of organically based substances – coal, crude oil, oil
shale, natural bitumen and extra-heavy oil, and natural gas,
together with the metallic elements uranium and thorium.
The principal perpetual resources are solar energy, wind power
and bioenergy, all of which are ultimately dependent on an
extra-terrestrial source, namely the sun. Other perpetual
resources are various forms of marine energy – tidal energy,
wave power and ocean thermal energy conversion (OTEC).
There are also two types of energy resource – peat and
Correspondence to: B. V. Babu, Professor of Chemical Engineering & Dean, Educational Hardware Division, Birla Institute of Technology
and Science (BITS) PILANI – 333 031 (Rajasthan) India. E-mail: [email protected]; [email protected]
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd
393
BV Babu
Review: Biomass pyrolysis
geothermal energy – which are to some extent intermediate
in nature, with both finite and perpetual elements in their
make-up. Bioenergy is arguably the one truly renewable
energy resource, in that each new crop or harvest represents
a partial renewal of its resource base, which is itself subject to
constant depletion through its use as a fuel or feedstock. The
other perpetual energy resources are available on a continuing, albeit varying, basis, are not depleted by the utilization of their energy content, and are therefore not subject to
renewal.1 Table 1 shows the comparison of various energy
sources in terms of advantages, disadvantages and their
natural reserves. Information related to the natural reserves
given in Table 1 is collected from the executive summary of
the committee of survey of energy sources.1
The world primary energy consumption is about 400 EJ/
year, mostly provided by fossil fuels (80%). The effects on
global and environmental air quality of pollutants released
into the atmosphere from fossil fuels provide strong arguments for the development of renewable energy resources.
Clean, domestic, renewable energy is commonly accepted as
the key for future life. The renewables collectively provide
14% of the primary energy, in the form of traditional
biomass (10%), large (>10 MW) hydropower stations (2%),
and the ‘new renewables’ (2%). Nuclear energy provides
6%. The World Energy Council expects the world primary
energy consumption to have grown by 50–275% in 2050,
depending on different scenarios. The renewable energy
sources are expected to provide 20–40% of the primary
energy in 2050 and 30–80% in 2100. The technical potential of the renewables is estimated at 7600 EJ/year, and thus
certainly sufficiently large to meet future world energy
requirements. Of the total electricity production from
renewables of 2826 TWh in 1998, 92% came from hydropower, 5.5% from biomass, 1.6% from geothermal and 0.6%
from wind. Solar electricity contributed 0.05% and tidal
0.02%. The electricity cost is 2–10 USc⁄/kWh for geothermal
and hydro, 5–13 USc⁄/kWh for wind, 5–15 USc⁄/kWh for
biomass, 25–125 USc⁄/kWh for solar photovoltaic and 12–18
US¢/kWh for solar thermal electricity. Biomass constitutes
93% of the total direct heat production from renewables,
geothermal 5%, and solar heating 2%. Heat production from
renewables is commercially competitive with conventional
energy sources. Direct heat from biomass costs 1–5 USc⁄/kWh,
394
while that from geothermal costs 0.5–5 USc⁄/kWh, and from
solar heating it costs 3–20 USc⁄/kWh.2
Biomass is the term used to describe all biologically
produced matter and it is the name given to all Earth’s
living matter. It is a general term for material derived from
growing plants or from animal manure (which is effectively
a processed form of plant material). Solar energy drives the
photosynthesis process in all the plant matter. The chemical
energy contained in the biomass is derived from solar energy
using the process of photosynthesis. (Photo means to do with
light and synthesis is the putting together.) This is the process
by which plants take in carbon dioxide and water from their
surroundings and, using energy from sunlight, convert them
into sugars, starches, cellulose, lignin etc., which make up
vegetable matter, loosely termed carbohydrates (and shown
for simplicity as [CH2O]). Oxygen is produced and emitted.
CO2 + 2H2O → [CH2O] + H2O + O2
(1)
Biomass energy is derived from plant and animal material,
such as wood from natural forests, waste from agricultural
and forestry processes, and industrial, human or animal
wastes. The stored energy in the plants and animals (that
eat the plants and other animals), or the waste that they
produce is called biomass energy. It is a natural process that
all biomass ultimately decomposes to its molecules with the
release of heat. And the combustion of biomass imitates the
natural process. So the energy obtained from biomass is a
form of renewable energy and it does not add carbon dioxide
to the environment in contrast to the fossil fuels.3 Of all the
renewable energy sources, biomass is unique in that it is,
effectively, stored solar energy. Furthermore, it is the only
renewable energy source of carbon and is able to convert
into convenient solid, liquid and gaseous fuels.4
Bioenergy is essentially renewable or carbon neutral.
Carbon dioxide released during the energy conversion
of biomass (such as combustion, gasification, pyrolysis,
anaerobic digestion or fermentation) circulates through the
biosphere, and is reabsorbed in equivalent stores of biomass
through photosynthesis. Figure 1 shows the combustion of
wood and thereby CO2 generation. It also depicts that net
CO2 generation is zero as new biomass is developed photosynthetically. Biomass for energy is a unique form of renewable,
solar energy. Of the massive 178,000 × 1012 Watts of solar
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
Review: Biomass pyrolysis
BV Babu
Table 1. Comparison of energy sources.
Energy
source
Main features
Disadvantages
Reserves
Coal
• Finite energy source
• Burning of fossil fuel produces
dust, smoke and oxides of impurities, which may lead to environmental pollution.
• 850 billion tonnes of coal as
currently recoverable.
• The most rapidly growing fuel on a
global basis
Oil
• Finite energy source.
• Several different categories of oil,
each having different costs, characteristics and, above all, depletion
profiles.
• In terms of global consumption,
crude oil remains the most important
primary fuel, accounting for 36.4% of
the world’s primary energy consumption (without biomass).
Nuclear Power
• Generated using uranium, which is
a metal mined in various parts of the
world.
• Nuclear fission of Uranium produces
heat.
• Neutrons smash into the nucleus
of the uranium atoms, which split
roughly in half and release energy in
the form of heat.
• Burning any fossil fuel produces
carbon dioxide, which contributes
to the ‘greenhouse effect’, warming
the Earth.
• Burning fossil fuels also produces
photochemical pollution from
nitrous oxides, and acid rain from
sulfur dioxide.
• Waste produced is highly
dangerous.
• Waste must be sealed up and
buried for many years to allow the
radioactivity to die away.
• Available in more than 70
countries worldwide.
• Expected resources of 82 billion
tones.
• 47% of the total reserves of
conventional oil discovered so far
have been consumed.
• Cumulative crude oil production
until the end of 2005 reached
143 billion tonnes – half of it was
produced within the last 23 years.
• Not renewable; once all the
Earth’s uranium dugged up and
used it, there is not any more.
• Lot of investment on safety – if it
does go wrong, a nuclear accident
can be a major disaster.
• No smoke or carbon dioxide production.
• Huge amounts of energy from small
amounts of fuel with small amounts
of waste.
Geothermal
Energy
• The natural heat of the Earth.
–
• Not a clear-cut example of a
perpetual source of energy as is
solar, wind and marine energy.
Hydro-electric
• Currently the largest of the
perpetual or so-called renewable
energy resources.
–
• Total world hydro capacity to
nearly 778 GW.
Solar Energy
• The Sun is the most abundant
permanent source of energy.
• Large investment cost for solar
photovoltaic collectors.
–
• The annual solar radiation reaching the earth is over 7500 times
the world’s annual primary energy
consumption of 450 exajoules.
Wind Energy
• Winds are generated by complex
mechanisms involving the rotation of
the earth, heat energy from the sun,
the cooling effects of the oceans and
polar ice caps, temperature gradients between land and sea and the
physical effects of mountains and
other obstacles.
• Large investment cost for
windmills.
• Some of the windiest regions are
to be found in the coastal regions
of the Americas, Europe, Asia and
Australasia.
• The world’s wind resources are
vast: it has been estimated that
if only 1% of the land area were
utilized, and allowance made for
wind’s relatively low capacity factor, wind-power potential would
roughly equate to the current level
of worldwide generating capacity.
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
395
BV Babu
Review: Biomass pyrolysis
fossil fuels, because it is renewable, and with soft energies,
like solar and wind, on account of its energy-storage capability. It is being used in the domestic (for cooking and water
heating), commercial (water heating), and industrial (for
water heating and process heat) sectors and also in rural
industries, like brick kilns, potteries etc.6
Figure 1. Lifecycle of forest biomass (Source: http://www.paisatge.
net/SapreRenovables/ENG/eSproj.htm).
energy that falls on the Earth’s surface, some 0.02% or
40 × 1012 Watts is captured by plants via photosynthesis and
bound into biomass energy. This translates into the production of some 220 billion ‘dry’ tonnes of biomass per year
which, as an energy source, represents some ten times the
world’s total current energy use. Currently some 15% of the
planet’s energy requirements are met from biomass, mainly
for cooking and heating in developing countries, but also
increasingly for fuelling a growing number of large-scale,
modern biomass energy plants in industrialized countries.
By comparison, the world population consumes around
10EJ/year of energy in the form of food, which of course is a
biomass energy resource in itself.5
Conventionally biomass was used in a similar way to fossil
fuels, by burning it at a constant rate in boiler furnace to
heat water and produce steam. Biomass-generated steam
passes through the multiple blades of a turbine, spinning
the shaft. The turbine shaft drives an electricity generator
which produces an alternating current for local use or to
supply the national grid. Wood is still a predominant fuel in
many non-OPEC, tropical, developing countries and it will
continue to be used for many years. It competes well with
396
In nature, biomass is not concentrated, and so, the use of
naturally occurring biomass requires transportation, which
increases the cost and reduces the net energy production.
Biomass is having a low bulk density, which makes transportation and handling more difficult and costly. Apart
from transportation, incomplete combustion of biomass
generates a concern among the environmentalists, as it may
produce organic particulate matter, carbon monoxide and
other organic gases. If high-temperature combustion is used,
oxides of nitrogen would be produced. The health impact
of air pollution is a significant problem in developing countries, where fuel wood is burnt inefficiently in open fires for
domestic cooking and space heating.4
The conversion technologies for utilizing biomass can
be separated into four basic categories: direct-combustion
processes, thermochemical processes, biochemical processes and agrochemical processes. The evaluation of the
potential of thermochemical biomass conversion for production of power and energy requires extensive and quantitative analysis of the thermal and chemical behavior of the
different classes of feedstocks as operating conditions are
varied.7 Conversion characteristics can be grouped into
thermochemical (ash and volatile yields, reactivity of volatile
products etc.), intra-particle rate (thermal properties, moisture content, size, kinetics and energetics of chemical processes etc.) and extra-particle rate (heat transfer from reactor
to particle, residence time and mass transfer conditions,
dependent in turn on the type of conversion unit).8 There is
a further classification of intra-particle rate characteristics
into two main categories, that is, those related to feedstock
preparation, such as particle size and moisture content, and
the intrinsic physical and chemical properties. In relation to
the devolatilization (pyrolysis) stage, the effects of particle
size have been extensively examined by both experiment and
comprehensive theories.9,10
Physicochemical properties of biomass depend widely on
the type of feedstock. For example, density may vary from
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
Review: Biomass pyrolysis
BV Babu
100 kg/m3 (balsa, bagasse and straw) to 1200 kg/m3 (lignum
vitae).11 It is expected that effective physical properties of
char should reflect those of virgin biomass and, thus, show
significant variation with the feedstock.
There has been an increasing interest for thermochemical
conversion of biomass and urban wastes for upgrading the
energy in terms of more easily handled fuels, namely gases,
liquids, and charcoal, in the past decade. The thermochemical conversion of biomass (pyrolysis, gasification, combustion) is one of the promising routes amongst the renewable
energy options of future energy. It is a renewable form with
many ecological advantages. Thermochemical conversion
processes can be subdivided into gasification, pyrolysis, and
direct liquefaction. Gasification is a process of conversion
of solid carbonaceous fuel into combustible gas by partial
combustion. The resulting gas, known as producer gas, is a
mixture of carbon monoxide, hydrogen, methane, carbon
dioxide and nitrogen. The producer gas is more versatile
than the original solid biomass. It is burnt to produce
process heat and steam or used in gas turbines to produce
electricity.12 In pyrolysis and liquefaction both, feedstock
organic compounds are converted into liquid products. In
case of liquefaction, feedstock macromolecules compounds
are decomposed into fragments of light molecules in the
presence of a suitable catalyst. These unstable and highly
reactive fragments repolymerize into oily compounds
having appropriate molecular weights, whereas in pyrolysis,
catalysts are not used and light decomposed fragments are
converted to oily compounds through homogeneous reactions in the gas phase. The difference in operating conditions
for liquefaction and pyrolysis are shown in Table 2.4
There is a tremendous potential for obtaining renewable
energy from biomass and other forms of biowastes. Pyrolysis
(conventional and plasma-based) is the route through which
useful energy can be obtained from waste. Th is review is
a pointer in this direction for focused research in the near
future. The overall objectives of this review are: (i) to provide
the chronological development in the field of experimental
and theoretical (mathematical modeling and simulation)
aspects associated with pyrolysis; and (ii) to bring out clearly
the present status of the research in the field of conventional and plasma pyrolysis of various biomasses and the
existing research gaps, emphasizing the scope and potential.
The main areas covered in this review include: pyrolysis,
chemical kinetic modeling, heat mass and momentum
transfer modeling, simulation of model equations,
experimental validation, and plasma-assisted pyrolysis.
Pyrolysis
The pyrolysis of biomass is a promising route for the production of solid (charcoal), liquid (tar and other organics, such
as acetic acid, acetone and methanol) and gaseous products
(H2 , CO2 , CO). These products are of interest as they are
possible alternate sources of energy. Pyrolysis is a process
by which a biomass feedstock is thermally degraded in the
absence of oxygen/air. The study of pyrolysis is gaining
increasing importance, as it is not only an independent
process, but it is also a fi rst step in the gasification or
combustion process.13,14 The basic phenomena that take
place during pyrolysis are: (i) heat transfer from a heat
source, leading to an increase in temperature inside the fuel;
(ii) initiation of pyrolysis reactions due to this increased
temperature, leading to the release of volatiles and the
formation of char; (iii) outflow of volatiles, resulting in
heat transfer between the hot volatiles and cooler unpyrolysed fuel; (iv) condensation of some of the volatiles in the
cooler parts of the fuel to produce tar; and (v) autocatalytic
secondary pyrolysis reactions due to these interactions.7,5–17
Pyrolysis can be used as an independent process for the
production of useful energy (fuels) and/or chemicals. Most
biomass materials are chemically and physically heterogeneous, and their components have different reactivities and
yield different products. The overall process of pyrolysis
can be classified into primary and secondary stages. When
Table 2. Comparison of liquefaction and pyrolysis.
Process
Temperature
Pressure (MPa)
Liquefaction
525–600
5–20
Pyrolysis
650–800
0.1–0.5
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
Drying
Unnecessary
Necessary
397
BV Babu
Review: Biomass pyrolysis
a solid particle of biomass is heated in an inert atmosphere
the following phenomena occur. Heat is first transferred
to the particle surface by radiation and/or convection and
then to the inside of the particle. The temperature inside
the particle increases, causing (i) removal of moisture
that is present in the biomass particle; and (ii) the prepyrolysis and main pyrolysis reaction takes place. The heat
changes due to the chemical reactions, and phase changes
contribute to a temperature gradient as a function of time,
which is nonlinear. Volatiles and gaseous products flow
through the pores of the particle and participate in the heattransfer process. The pyrolysis reactions proceed with a rate
depending upon the local temperature. During the pyrolysis
process, the pores of the solid are enlarged, and the solid
particle merely becomes more porous because the biomass
converts into gases as discussed by Curtis and Miller.18
According to Anthony and Howard,19 the enlarged pores of
the pyrolyzing solid offer many reaction sites to the volatile
and gaseous products of pyrolysis and favor their interaction with the hot solid. Inside the pyrolyzing particle, heat
is transmitted by the following mechanisms: (i) conduction
inside the solid particle; (ii) convection inside the particle
pores; and (iii) convection and radiation from the surface of
the pellet.
Depending upon the operating conditions, the pyrolysis
process can be divided into three subclasses: conventional
pyrolysis (carbonization), fast pyrolysis, and flash pyrolysis.
The ranges of the main operating parameters for pyrolysis
processes are given in Table 3. Conventional pyrolysis is
defined as the pyrolysis that occurs under a slow heating
rate. This condition permits the production of solid, liquid,
and gaseous pyrolysis products in significant portions. The
first stage of biomass decomposition occurs between 395
and 475 K and is called as pre-pyrolysis. During this stage,
some internal rearrangement, such as water elimination,
bond breakage, appearance of free radicals, and formation of
carbonyl, carboxyl, and hydroperoxide groups, takes place.20
The second stage of the solid decomposition corresponds
to the main pyrolysis process. It proceeds with a high rate
and leads to the formation of the pyrolysis products. During
the third stage, the char decomposes at a very slow rate and
carbon-rich residual solid forms. If the aim is the production
of mainly liquid and/or gaseous products, a fast pyrolysis
is recommended. The achievement of fast heating rates
requires high operating temperatures, very short contact
times, and very fi ne particles. Flash pyrolysis differs strongly
from that of conventional pyrolysis performed slowly with
massive pieces of wood. Flash pyrolysis gives mostly gaseous
products due to the high heating rate and very small particle
size.21 Hydropyrolysis (pyrolysis in a hydrogen atmosphere)
is also considered to have a potential application in the
conversion of biomass to liquids enriched in hydrocarbons.22
Biomass is mainly composed of three constituents which
are hemicelluloses, cellulose, and lignin. There are minor
amounts of extractives also present. Each component
of biomass pyrolyzes at different rates and by different
mechanisms and pathways. It is believed that as the reaction progresses, the carbon becomes less reactive and forms
stable chemical structures, and consequently the activation
energy increases as the conversion level of biomass increases.
Cellulose and hemicellulose decomposes over a very narrow
temperature range as compared to lignin. The rate and
extent of degradation of each of these components depends
on the process parameters of reactor type, temperature, and
particle size heating rates and pressure.23 Thermal degradation properties of hemicelluloses, cellulose, and lignin can
be summarized as follows:
Thermal degradation of hemicelluloses > of cellulose > of
lignin. The hemicelluloses break down first, at temperatures
of 470 to 530 K, and cellulose follows in the temperature
range 510 to 620 K, with lignin being the last component to
pyrolyze at temperatures of 550 to 770 K.24
Table 3. Range of main operating parameters for pyrolysis processes.
Conventional pyrolysis
Fast pyrolysis
Flash pyrolysis
Pyrolysis temperature (K)
Parameters
550–950
850–1250
1050–1300
Heating rate (K/s)
0.1–1.0
10–200
< 1000
Particle size (mm)
Solid residence time (s)
398
5–50
<1
< 0.2
450–550
0.5–10
< 0.5
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
Review: Biomass pyrolysis
BV Babu
Kinetics of pyrolysis
Degradation kinetics of lignocellulosic fuels was studied in
either dynamic or static conditions. Static conditions are
achieved by maintaining the selected constant temperatures
in the pyrolyzing chamber. During dynamic conditions,
biomass particles submitted in pyrolyzing chamber experience an increase in temperature with time according to an
assigned heating rate. Heating rates highly affect the reaction
process. Limitations are encountered in both techniques.
It is difficult to maintain higher heating rates in laboratory
conditions, those usually achieved in gasification or pyrolysis
reactors. On the other hand, in the static analysis, tests are
carried out according to two different methodologies to
attain the isothermal stage. In the first methodology, the
small dynamic stage consists of very slow heating rates to
avoid spatial gradients of temperature. In the second methodology, very fast, external, heat-transfer rates to keep short
the first dynamic stage are used. However, in the first case,
the weight loss is not negligible during heating and the subsequent interpretation of the data may be lacking an important
part of the whole process. In the second case, the results may
be seriously affected by heat transfer limitations; unless an
accurate control of the sample temperature is accomplished.
Different classes of mechanisms were proposed for the
pyrolysis of wood and other cellulosic materials.25,26 The
models are classified into three categories: one-step global
models; one-stage multireaction models; and two-stage
semiglobal models. The first category of models considers
pyrolysis as a single-step first order reaction.
Parallel reactions:
Virgin biomass
Virgin biomass
(Volatiles + Gases)1
(Char)1
(2)
(3)
As shown in Table 4, pyrolysis process is described by
means of a one-step global reaction for degradation or
devolatilization by many research groups.27–33 Thurner and
Mann27 conducted experiments on oak wood of 650 μm in
Table 4. Single-step mechanisms for isothermal pyrolysis of wood.27–33
Feedstock
(variety, size
and mass)
Experimental
system
Thurner and
Mann (1981)
Oak, 650 µm
Tube furnace
573–673
dY
= − kY
dt
⎛ 106.5 ⎞
k = 2.47 × 106 exp ⎜ −
⎝ RT ⎟⎠
Gorton and
Night (1984)
Hardwood
Entrained flow
reactor
677–822
dY
= − kY
dt
⎛ 89.52 ⎞
k = 1.483 × 106 exp ⎜ −
⎝ RT ⎟⎠
Ward and
Braslaw (1985)
Wild cherry
Tube furnace
538–539
dY
= − kV (Y − YC∞)
dt
YC∞ = 0.25 − 0.3
⎛ 173.7 ⎞
kV = 11.9 × 1011 exp ⎜ −
⎝ RT ⎟⎠
Wagenear et al.
(1994)
Pine 100 m–125
µm
TGA
553–673
Drop tube
773–873
dY
= − kY
dt
⎛ 150 ⎞
k = 1.4 × 1010 exp ⎜ −
⎝ RT ⎟⎠
Di Blasi and
Branca (2001)
Beech < 80 µm, 9 g
Tube furnace
573–708
dY
= − kY
dt
⎛ 141⎞
k = 3.6 × 108 exp ⎜ −
⎝ RT ⎟⎠
Samolada and
Vasalos (1991)
Fir wood
Batch fluid-bed
reactor
673–773
dYV
= − kV (YV∞ − YV )
dt
YV∞ = 0.557
⎛ 56.48 ⎞
kV = 1.36 × 102 exp ⎜ −
⎝ RT ⎟⎠
Reina et al.
(1998)
Forest waste,
<1000 µm, 25 mg
TGA
498–598
dY
= − kV (Y − YC∞)
dt
YC∞ ≅ 0.25
⎛ 124.87 ⎞
kV = 7.68 × 107 exp ⎜ −
⎟
⎝
RT ⎠
973–1173
dY
= − kV (Y − YC∞)
dt
YC∞ ≤ 0.15
⎛ 91.53 ⎞
kV = 6.33 × 102 exp ⎜ −
⎝ RT ⎟⎠
Reference
300–350 µm
300–425 µm, 2 g
Tr (K)
Reaction
mechanism
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
Kinetic constants
E (kJ/mol), A(s−1)
399
BV Babu
Review: Biomass pyrolysis
Table 5. Multistep mechanisms for isothermal pyrolysis of wood.36–37
Reference
Feedstock
(variety, size
and mass)
Experimental
system
Barooah and
Long (1976)
Beech, 400–700 µm,
40 g
Batch fluid-bed
reactor
Tr (K)
523–673
Reaction
mechanism
Kinetic constants
E (kJ/mol), A(s−1)
T < 603 K
dY
= − kIY
dt
⎛ Y − YC∞⎞
dY
= − k II ⎜
dt
⎝ 1− YC∞ ⎟⎠
n
YC∞ = 0.48 − 0.8
⎛ 18 ⎞
k I = 5.3 × 10−2 exp ⎜ −
⎝ RT ⎟⎠
⎛ 17 ⎞
k II = 10.67 exp ⎜ −
,
⎝ RT ⎟⎠
n=2
T > 603 K
⎛ 84 ⎞
k I = 2.33 × 104 exp ⎜ −
⎝ RT ⎠⎟
⎛ 115 ⎞
k II = 4 × 109 exp ⎜ −
,
⎝ RT ⎟⎠
n=2
Alves and
Figueredo
(1988)
Pine, 180–220 µm,
10 mg
TGA
525–860
k
V1
YV 1∞ = 0.19, SV 1 ⎯⎯⎯
→G
k
V2
YV 2∞ = 0.5, SV 2 ⎯⎯⎯
→G
kV 3 = 4.3 × 103 exp ( −77 RT )
k
kV 4 = 2.9 × 10 exp ( −146 RT )
k
kV 5 = 5.1 × 106 exp ( −139 RT )
k
kV 6 = 3.2 × 105 exp ( −130 RT )
V4
YV 4∞ = 0.03, SV 4 ⎯⎯⎯
→G
V5
YV 5∞ = 0.02, SV 5 ⎯⎯⎯
→G
V6
YV 6∞ = 0.02, SV 6 ⎯⎯⎯
→G
Secondary interactions:
(Volatiles + Gases)1 + (Char)1
(Volatiles + Gases)2
+ (Char)2
(4)
The second category of models discuss those mechanisms,
which consider simultaneous and competing first order
reactions in which virgin wood decomposes into different
400
kV 2 = 2 × 109 exp ( −146 RT )
k
V3
YV 3∞ = 0.02, SV 3 ⎯⎯⎯
→G
tube furnace reactor for the temperature range of 573–673 K.
Gorton and Night28 performed experiments on hardwood of
the size 300–350 μm in entrained flow reactor for the temperature range of 677–822 K. Ward and Braslaw,29 Wagener
et al.30 Di Blasi and Branca,31 Samolada and Vasalos,32 and
Reina et al.33 performed thermal degradation studies on
various biomasses and of size less than 1 mm. The value of
activation energy is least (56.48 kJ/mol) for fir wood reported
by Samolada and Vasalos32 and maximum for wild cherry
(173.7 kJ/mol) reported by Ward and Braslaw.29
kV 1 = 7 × 104 exp ( −83 RT )
constitutes of pyrolysis products, namely, tar, char, and gases
(Reactions (2) & (3)). The third class of models considers
pyrolysis to be a two-stage reaction, in which the products of
the first stage break up further in the presence of each other
to produce secondary pyrolysis products. Such models are
presented for wood by Koufopanos et al.34,35 (Reactions (2),
(3) & (4)). Koufopanos et al.34,35 carried out isothermal mass
change determination of beech wood, olive husks, hazelnut
shells and corn cobs to obtain the kinetic data for the
proposed scheme. It is shown that the rate of pyrolysis of one
biomass type could be represented by the sum of the corresponding rates of the main biomass components (cellulose,
hemicellulose and lignin). It is also reported that pyrolysis
of lingocellulosic materials of size less than 1 mm is kinetically controlled and, for larger particles, kinetic equations
are required to be coupled with equations describing the
transport phenomena.34,35 Barooah and Long36 performed
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
Review: Biomass pyrolysis
the pyrolysis of beech wood in a batch fluid-bed reactor for a
particle size of 400–700 μm (Table 5). A kinetic model based
on a two-stage process in the temperature range 523–673 K is
proposed and validated. Two sets of kinetic parameters
(activation energies of 17–115 kJ mol−1) are needed,
depending on a limit temperature of 603 K.36 Alves and
Figueredo37 conducted TGA experiments on pinewood of
size 220 μm covering the temperature range of 525–860
K. Six parallel reactions corresponding to different volatile
fractions are proposed by Alves and Figueredo.37 The first
two, quantitatively more important, were associated with
hemicellulose and cellulose, respectively (activation energies
of 83 and 146 kJ mol−1). The other components were assumed
to correspond mainly to parts of the lignin macromolecule
(activation energies between 60 and 130 kJ mol−1). The
highly different reaction mechanisms and kinetic constants
proposed indicate that further analysis is needed about the
multistep degradation kinetics of wood under isothermal
conditions.
The scheme of reactions of Koufopanos’ models is adapted
by Jalan and Srivastava 38–40 and Babu and Chaurasia.13
Chan41 proposed a mechanism where volatiles and tar
formed by primary pyrolysis undergo secondary pyrolysis.
The other primary reactions of formation of char and gas
from wood are parallel and competing with the tar formation reaction. Babu and Chaurasia7,12–17 did extensive
modeling and simulation on pyrolysis. The kinetic scheme
proposed by Koufopanos et al.34 for the pyrolysis of biomass
based on the two-stage model is accepted and validated.
The model proposed by Babu and Chaurasia15 includes
kinetic and heat-transfer effects for the shrinking biomass
particle. Th is model was again improved to incorporate
simultaneous effects of kinetics and transport of heat, mass
and momentum.16 Di Blasi42 proposed a mathematical
model, which includes transport phenomena and chemical
reactions for the pyrolysis of thermoplastic polymers
(polyethylene). It was found that depolymerization and
melting are followed by devolatilization. Surface regression, property variation, heat convection and conduction
were considered in the proposed model. Simulations were
carried out for external heating conditions corresponding
to fi xed-bed reactors, hot-plate contact and fi re-level
radiation exposures.
BV Babu
Chao-Hsiung et al.43 performed the thermogravimetric
studies of paper mixtures in municipal solid waste (MSW).
The pyrolysis kinetics of a mixture of the four principal
papers (uncoated and coated printing/writing papers,
newsprint, and tissue paper) in MSW was investigated.
The experiments were carried out in a nitrogen environment over the temperature range of 450 to 900 K at various
constant heating rates of 1, 2, and 5 Kmin–1. The pyrolysis
of a paper mixture is described by a two reaction model as
described below:
Papers
Intermediates
Intermediates + Volatiles
Solid residues + Volatiles
(5)
(6)
The experimental results were satisfactorily fitted by the
proposed chemical reaction kinetic equations.
Balci et al.44 performed the thermogravimetric experiments
for the hazelnut shell and other lignocellulosic biomasses,
and proposed several kinetic models. In these kinetic models,
an exponential decrease of solid reactivity with respect to
conversion level is proposed and the rate expression based
on first-order decomposition of the reactive solid is defined
in terms of fractional conversion. Arrhenius relation of rate
constant is replaced with an expression, where rate constant
is expressed as a function of extent of reaction. Demirbas45
performed the thermogravimetric experimental runs and
presented the weight-loss data for different particle sizes
of ground hazelnut shell and for various heating rates.
An experimental technique comprising a simplified, fast
pyrolysis device for obtaining the pyrolysis products and
kinetic parameters is presented. The effects of heating rate,
particle size, reaction temperature and catalyst are studied
by performing the experiments. Kinetic analysis has also
been carried out but the expression for the kinetic constants
with respect to temperature is not developed, and the experimental data validation with theoretical models for these
experiments are not reported in the literature. Experimental
and modeling studies have been conducted on pyrolysis
by many researchers.7,12–17,21,34,35,46–49 Kinetic modeling
of lignocellulosic biomass by method of least squares is
performed by Varhegyi et al.49 by using thermogravimetry
data. Experiments are performed on large wood particles
and a mathematical model is presented for the packed-bed
pyrolysis.50 Thermogravimetric data has been generated to
study the kinetics of isothermal degradation of wood.51
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
401
BV Babu
Review: Biomass pyrolysis
The kinetic model developed by Balci et al.44 is modified
and used for the hazelnut-shell biomass of 0.180 mm by
Sheth and Babu.52 Instead of apparent decomposition rate
expression, the kinetic scheme proposed by Koufopanos
et al.34,35 and validated by many researchers for various
biomasses, is applied. Model simulation results are validated
with the data reported in the literature.45 The proposed
model of the study includes the rate of change of activity
with respect to solid reactant conversion in pyrolysis of
hazelnut-shell biomass. Reaction rate constant is expressed
as a function of extent of reaction, which has replaced the
Arrhenius relation of rate constant with temperature. To
find kinetic parameters of the modified model, an objective
function based on least square error between experimental
data and simulated results has to be minimized. A population based search algorithm, differential evolution (DE),
which is simple and robust and has proven successful record,
is employed for optimization in the present case.
Equations in Table 6 show the four different models developed by Sheth and Babu.52 Model-1 assumes the Arrhenius
Table 6. Mathematical model for kinetic parameter estimation.
Koufopanos et al. (1991) mechanism
Virgin biomass B ( n1 order decay)
Reaction 1
Reaction 2
Reaction 3
( Volatile + Gases)1 + (Char)1
(Volatile + Gases)2 + (Char)2
( n3 order decay)
( n2 order decay)
Residual weight fraction ( W) calculation (n1 = n2 = n3 =1)
W = B + C1
(7)
dB
= − ( k1 + k 2 ) B
dt
(8)
dC1
= k1B
dt
(9)
dW
= − k1B
dt
(10)
T = (HR)t + T0
(11)
Residual weight fraction (W) variation with temperature ( T )
dW
1
= − k1B
dT
HR
(12)
dB
1
= − ( k1 + k 2 ) B
dT
HR
(13)
Initial Conditions (at time t=0)
T0 = 325 K;
B = 1.0;
C1 = 0.0;
G1 = 0.0
(14)
Arrhenius relation of kinetic rate constants with temperature ( Model 1)
⎛ −E ⎞
k1 = A1 exp ⎜ 1 ⎟
⎝ RT ⎠
(15)
⎛ −E ⎞
k 2 = A2 exp ⎜ 2 ⎟
⎝ RT ⎠
(16)
Objective function ( Model 1)
(
)
n
(
F A1, E1, A2 , E2 = ∑ Wexp, j - Wcal, j
402
j =1
)
2
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
(17)
Review: Biomass pyrolysis
BV Babu
Table 6. Mathematical model for kinetic parameter estimation. (continued.)
Model 2 ( decrease of frequency factor of pyrolysis rate constants with conversion)
⎛ −E ⎞
k1 = A1 1− z n+1 exp ⎜ 1 ⎟
⎝ RT ⎠
(18)
⎛ −E ⎞
k 2 = A2 1− z n+1 exp ⎜ 2 ⎟
⎝ RT ⎠
(19)
)
(
)
(
Model 3 ( increase of activation energy of pyrolysis with conversion)
⎡
⎛ 1+ β ' Tz ⎞ ⎤
k1 = A1 exp ⎢ − E1 ⎜
⎝ RT ⎟⎠ ⎥⎦
⎣
(20)
⎡
⎛ 1+ β ' Tz ⎞ ⎤
k 2 = A2 exp ⎢ − E2 ⎜
⎝ RT ⎟⎠ ⎥⎦
⎣
where β ' =
(21)
βR
E
(22)
Model 4 ( increase of activation energy of pyrolysis with conversion)
⎡
⎛ 1+ β ' Tz n+1 ⎞ ⎤
k1 = A1 exp ⎢ − E1 ⎜
⎟⎠ ⎥
RT
⎝
⎣
⎦
(23)
⎡
⎛ 1+ β ' Tz n+1 ⎞ ⎤
k 2 = A2 exp ⎢ − E2 ⎜
⎟⎠ ⎥
RT
⎝
⎣
⎦
(24)
where
⎛ β ⎞R
β' = ⎜
⎝ n + 1⎠⎟ E
(25)
Objective function ( Models 2 to 4)
n
(
F ( A1, E1, A2 , E2 , β, n) = ∑ Wexp, j − Wcal, j
j =1
)
relation of kinetic rate constant variation with temperature
(Eqn (7) to Eqn (16)). Equation (16) denotes the objective
function that is the square of error between the experimental data reported by Demirbas 45 and model predicted
values. Models 2 to 4 are based on the fact that the activity
of solid reactant is expected to decrease with the extent of
reaction due to the changes in chemical and pore structure
of solid.
Using DE algorithm, 53,54 the objective function (Eqn (17)
or (26)) is minimized and the global optimum set of kinetic
parameters is found out. To fi nd the theoretical value of
residual weight fraction (W), forward fi nite difference technique is applied to Eqns (12)–(16) for Model 1. For Model 2,
the theoretical value of residual weight fraction (W) is found
(26)
by applying forward finite difference technique to Eqns
(12)–(14) and Eqns (18) and (19). Equations (20) and (21)
are used to fi nd the residual weight fraction of the biomass
for Model 3 along with Eqns (12)–(14). For values of theoretical value of W in Model 4, Eqns (23) and (24) are used in
combination with Eqns (12)–(14). Initial conditions used to
solve the above-mentioned fi rst-order differential equations
are given by Eqn (14) in Table 6. Simulations are performed
to fi nd the kinetic parameters of reaction 1 and reaction 2
(A1, E1, A2 , E2 , n, β ) for Model 1 to Model 4 for heating rates
of 10, 25 and 40 K/s for the ground-hazelnut-shell biomass
sample of 0.180 mm size. Figure 2 shows that the Model 3
fits better and gives minimum objective function value for
the heating rate of 40 K/s.
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
403
BV Babu
Review: Biomass pyrolysis
1.1
Expt Data
Model 1 Predicted data
Model 2 Predicted data
Model 3 Predicted data
Model 4 Predicted data
1.0
REsidual Weight Fraction
0.9
0.8
0.7
0.6
0.5
0.4
0.3
300
400
500
600
700
800
900
1000
Temperature (K)
Figure 2. Experimental and theoretical residual weight fraction for
various models (heating rate = 40 K/s).
Modeling of biomass pyrolysis
Several researchers have developed the mathematical models
for biomass pyrolysis. Fan et al.55 developed a model for
pyrolysis process, which includes heat and mass transfer
effects in the particle. The reaction, however, is considered
to be first order with respect to the initial particle concentration. The concentration of products cannot be analyzed
from the above model, as the secondary reactions are not
considered. Miyanami et al.56 incorporated the effect of
heat of reaction in the above model. In the Bamford et al.57
model, the equation for heat conduction in a pyrolyzing
solid is combined with that for heat generation, assuming
first order kinetics. However, the effect of density as a function of time has not been considered in the above model.
This model is modified by Kung58 in order to incorporate the
effects of internal convection and variable transport properties. However, no specific kinetic mechanism is suggested to
predict the concentration of various components produced
during the pyrolysis process. Kansa et al.59 included the
momentum equation for the motion of pyrolysis gases
within the solid. But, a suitable kinetic mechanism has
not been utilized by them (secondary reactions are not
considered), and the solution to the heat and momentum
balance equation is based on arbitrary boundary conditions. Pyle and Zaror60 used the Bamford et al. 57 model and
404
dimensionless groups to define the relative importance of
the internal and external heat transfer and of the intrinsic
pyrolysis kinetics. They utilized the first order kinetic model
based on the density of initial biomass. In the model of
Koufopanos et al.35 the effect of density as a function of time
is not considered while solving the heat transfer equation.
Jalan and Srivastava38 have solved the heat transfer equation by neglecting the effect of specific heat and thermal
conductivity of char, which are the functions of temperature
as reported by Koufopanos et al.34,35 The convective heat
transfer coefficient is a function of the Reynolds and Prandtl
numbers and is given by h = 0.322 ( k l ) Pr1/3 Re0.5 W/m 2 K,
as reported by Koufopanos et al.35 Jalan and Srivastava, 38
however, have considered that the value of h is constant at
0.322 W/m 2 K, neglecting the effect of other parameters.
Di Blasi25,61,62 pointed out that a detailed transport model
incorporating the kinetics, heat, and mass transfer effects
are necessary to predict the effects of the widely variable
physical properties (density, thermal conductivity, specific
heat capacity) in the pyrolysis of biomass. However, she
used a different kinetic scheme wherein the active cellulose
is considered to be formed as an intermediate product. But
as pointed out by Koufopanos et al.35 it is very difficult to
define physically the components or composition of the
intermediates, and consequently it is not possible to measure
their concentration experimentally in order to test the model
rigorously.
Babu and Chaurasia13 carried out the pyrolysis study on
single solid particle for a wide range of temperature from
303–2700 K and of particle diameter from 0.0005–0.026 m. It
is found that as the particle size increases, the time required
for completion of pyrolysis at a certain pyrolysis temperature
and the effect of secondary reactions increase. It is observed
that, for particle sizes below 1 mm, the process is controlled
by the primary pyrolysis reactions and, possibly, by the
external heat transfer. For particles greater than 1 mm, heat
transfer, primary pyrolysis and secondary pyrolysis control
the pyrolysis process. Babu and Chaurasia13 estimated the
optimum parameters in the pyrolysis of biomass for both
nonisothermal and isothermal conditions. The modeling
equations are solved numerically using the fourth-order
Runge–Kutta method over a wide range of heating rates
(25–360 K/s) and temperatures (773–1773K). The simulated
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
Review: Biomass pyrolysis
results are compared with those reported in the literature
and found to be in good agreement qualitatively in the range
of operating conditions covered. The final pyrolysis time
first decreases at lower values of net heating rate or temperature and then increases as net heating rate or temperature
is further increased, providing an optimum value of net
heating rate or temperature at which fi nal pyrolysis time
is minimum.13 The mathematical model to describe the
pyrolysis of a single solid particle of biomass is modified by
incorporating the heat transfer equation with the chemical
kinetics equations in the further study carried out by Babu
and Chaurasia.14 The dependence of convective heat transfer
coefficient on Reynolds number and Prandtl number is
incorporated in the model. A fi nite difference method using
a pure implicit scheme is used for solving the heat transfer
equation and the Runge–Kutta fourth-order method for
the chemical kinetics equations. The model equation is
solved for cylindrical pellets, spheres and slab geometries
of equivalent radius ranging from 0.00025 to 0.013 m and
temperature ranging from 303 to 1000K. The simulated
results obtained using this modified model, are in excellent
agreement with the experimental data, much better than
the agreement with the earlier models reported by Pyle and
Zaror.60 Babu and Chaurasia15 studied the effect of convective heat transfer and orders of reactions on pyrolysis of
biomass particle using the model developed in their previous
studies.13 The wide ranges of temperature (303–2700 K) and
pellet diameters (0.0005–0.026 m) are considered. It is found
that the pyrolysis is faster for zeroth order as compared
to first order of reaction 1, as the rates are independent of
initial biomass concentration for zeroth order. The effects
of the parameters such as thermal conductivity, reactor
temperature and particle size on product concentrations are
analyzed.48
To describe the chemical process of the pyrolysis
completely, an unsteady state, one-dimensional, variable
property model of transport phenomena, including heat
convection, conduction and radiation, volatiles and gas
transport diff usion and convection and momentum transfer
is required. This generalized reference model is developed
by Babu and Chaurasia16 in their subsequent study of
pyrolysis. A finite difference pure implicit scheme utilizing
a Tri-Diagonal Matrix Algorithm (TDMA) is employed for
BV Babu
solving the heat transfer and mass transfer model equations. A Runge–Kutta fourth-order method is used for
the chemical kinetics model equations. Simulations are
performed considering different geometries of equivalent
radius ranging from 0.0001 to 0.017 m and temperatures
ranging from 303 to 2800 K. The results obtained using
these improved models are in excellent agreement with the
experimental data of Pyle and Zaror,60 much better than
the agreement with earlier models reported in the literature.38,57 The effects of the heat of reaction on the biomass
conversion, concentration, and temperature profi le in the
particle have been analyzed based on the improved model.
The variation in temperature profi le and concentration
profi le for exothermic and endothermic pyrolysis reaction
is explained by using heat of reaction number. Effects of the
most important thermal and thermodynamic properties
(thermal conductivity, heat transfer coefficient, emissivity
and heat of reaction number) of the feedstock on the convective-radiant pyrolysis of biomass fuels are also carried out
by Babu and Chaurasia.7 Sensitivity analysis is conducted
to find the most dominant properties affecting the pyrolysis
and found that the highest sensitivity is associated with the
emissivity and thermal conductivity of the biomass.7 Simulations are carried out for different geometries considering
the equivalent radius ranging from 0:0000125 m to 0:011 m,
and the temperature ranging from 303 K to 2100 K.15 Effects
of heating conditions, density of biomass, product yields
and conversion on pyrolysis of biomass fuels is studied and
found that the yield of (char)1 increases while the yield of
(volatile and gases)1 decreases as the particle thickness is
increased. There is no effect of density of biomass on both
the primary and secondary reaction products. The conversion time does not depend on the density of biomass and is
nearly constant for complete conversion. Complete conversion of pyrolysis occurs at successively shorter times as the
heating rate is increased. The time required for complete
conversion of pyrolysis is the highest for the slab and lowest
for the sphere.47
The impact of shrinkage on pyrolysis of biomass particles
is studied employing a kinetic model coupled with a heat
transfer model using a practically significant kinetic scheme
consisting of physically measurable parameters.17 The
numerical model is used to examine the impact of shrinkage
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
405
BV Babu
Review: Biomass pyrolysis
on particle size, pyrolysis time, product yields, specific heat
capacity and Biot number considering cylindrical geometry.
Finite difference pure implicit scheme utilizing TDMA is
employed for solving heat transfer model equation. Runge–
Kutta fourth-order method is used for chemical kinetics
model equations. Simulations are carried out for radius
ranging from 0.0000125 to 0:05 m, temperature ranging
from 303 to 900 K and shrinkage factors ranging from 0.0 to
1.0.17 It is found that the temperature profi le of the particle
changes due to increased density and decreased distance
across the pyrolysis region. The magnitude of the temperature gradient is more for shrinking particles as compared to
non-shrinking particles. Shrinkage affects the pyrolysis time
in thermally thick regime. The pyrolysis conversion process
is fast for shrinking particles as compared to nonshrinking
particles. The modeling and simulation of the above process
is coupled with the optimization of a nonlinear function
using DE to find the optimal time of pyrolysis and heating
rate under the restriction on concentration of biomass. It
serves as the input to the coupled ordinary differential equations to find the optimum values of volatiles and char using
Runge-Kutta fourth-order method.
In the present study, therefore, the model developed and
modified by Babu and Chaurasia7,12–17 using physically
measurable parameters and a practically explainable kinetic
scheme, incorporating the convective and diff usion effects is
presented.
Model description of biomass pyrolysis
Babu and Chaurasia17 proposed three models for the pyrolysis
of biomass under two categories namely, (i) generalized reference model (Model I), and (ii) Simplified models (Model II
and Model III). The three models are presented here briefly
for clarity and continuity.
Generalized reference model (Model I)
The generalized model incorporated all the possible effects
of kinetics, heat transfer, mass transfer, and momentum
transfer. The assumptions made in developing this model are
as follows:
(1) The thermal and transport properties (porosity, thermal
conductivity, specific heat, mass diff usivity) vary with
the conversion level.
406
(2) Heat transfer occurs by all the three modes (i.e. conduction, convection and radiation).
(3) Gas-phase processes occur under unsteady-state conditions.
(4) Transport of mass takes place by convection and diff usion of volatile species.
(5) Pressure and velocity vary along the porous sample.
(6) Local thermal equilibrium exists between solid matrix
and the flowing gases.
(7) The system is one-dimensional.
(8) No moisture content and no particle shrinkage.
Utilizing the kinetic scheme as described by Koufopanos
et al.35 and with the assumptions as stated above, the generalized model (Model I) is obtained and reported in Table 7.
The equations shown in Table 7 are written in dimensionless
forms with the help of dimensionless groups given in Babu
and Chaurasia.17 This generalized model, consisting of Eqns
(38)–(44), which is the most comprehensive one, is named as
Model I (generalized reference model).
Simplified models
These simplified models include additional assumptions
other than those in the generalized reference model
(Model I). Many times, in practical situations, the generalized models may not give good predictions. In such cases,
there is a need to relax some of the assumptions made in the
generalized models. Starting from the generalized model,
the following two simplified models are proposed by Babu
and Chaurasia17 for specific cases.
First simplified model (Model II)
The first simplified model (Model II) is proposed by making
an additional assumption that there is no bulk motion
contribution (i.e. convective transport is neglected) to the
temperature profile and the product-yield predictions. In this
treatment, a conservation equation for the mass concentration
of (gases and volatiles)1 (Eqn (39)) is modified by neglecting
the second term on left-hand side. Hence, the first simplified
model (Model II) consists of Eqns (38)–(49) and (52).
Second simplified model (Model III)
The second simplified model (Model III) is proposed on
the following two assumptions concerning the practical
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
Review: Biomass pyrolysis
BV Babu
Table 7. Mathematical model.
Koufopanos et al. (1991) mechanism
Virgin biomass B ( n1 order decay)
Reaction 1
Reaction 2
( Volatile + Gases)1 + (Char)1
( n2 order decay)
Reaction 3
(Volatile + Gases)2 + (Char)2
( n3 order decay)
Particle model
Mass conservation for biomass, (gases and volatiles)1 (char)1, (gases and volatiles)2 and (char)2:
∂ CB
n
n
= − k1CB1 − k 2 CB1
∂t
(27)
⎛ b − 1 ∂ CG1 ∂ 2 CG1 ⎞
∂ (CG1ε ′′ ) ∂ (CG1u )
n
n
n
+ k1CB1 − ε ′′ k 3 CG21CC31
+
= DeG1 ⎜
+
∂t
∂r
∂r
∂ r 2 ⎠⎟
⎝ r
(28)
∂ CC1
n
n
n
= k 2 CB1 − k 3 CG21CC31
∂t
(29)
∂ CG2
n
n
= k 3 CG21CC31
∂t
(30)
∂ CC 2
n
n
= k 3 CG21CC31
∂t
(31)
Enthalpy:
⎛ b − 1 ∂ T ∂ 2T ⎞ ⎛
∂C ⎞
⎛ ∂ρ ⎞
∂
∂T
+ ( − ⌬H ) ⎜ − ⎟
+
− ⎜ DeG1 G1 ⎟ CpG1
Cp ρT = k ⎜
⎝ ∂t⎠
∂r ⎠
∂t
∂r
⎝ r ∂ r ∂ r 2 ⎟⎠ ⎝
(
)
(32)
Initial conditions:
(33)
t = 0; CB = CB0 , CG1 = CC1 = CG2 = CC 2 = 0, T(r ,0) = T0
Particle boundary conditions:
t > 0; r = 0,
∂ CG1
= 0,
∂r
t > 0; r = R,
⎛∂C ⎞
DeG1 ⎜ G1 ⎟ = k mG1 ( CG10 − CG1 )
⎝ ∂r ⎠
t > 0; r = R,
⎛ ∂T ⎞
= h (Tf − T ) + σε Tf4 − T 4
k⎜
⎝ ∂ r ⎟⎠ r = R
⎛ ∂T ⎞
=0
⎝⎜ ∂ r ⎠⎟ r =0
(
(34)–(35)
(36)
)
(37)
Dimensionless forms of equations (1)–(11):
∂ CB
n
n
= − k1C B1 − k 2 C B1
∂t
(38)
n
ε ′′
n
n
2
3
1
∂ CG1 uR ∂ CG1 DG1 ⎛ b − 1 ∂ CG1 ∂ 2 CG1 ⎞ R 2 k1C B ε ′′R 2 k 3 CG1CC1
+
=
+
+
−
⎜
⎟
α
α
Le ⎝ x ∂ x
∂τ
α ∂x
∂ x2 ⎠
(39)
∂ CC1
n
n
n
= k 2 C B1 − k 3 CG21CC31
∂t
(40)
∂ CG 2
n
n
= k 3 CG21CC31
∂t
(41)
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
407
BV Babu
Review: Biomass pyrolysis
Table 7. Mathematical model. (continued.)
∂ CC 2
n
n
= k 3 CG21CC31
∂t
(42)
∂θ b − 1 ∂θ ∂ 2θ Q ′′ R 2 k1 1 ⎛
∂ CG1 ⎞
∂θ
=
+
+
+
DG1
C pG1C B0
x ∂ x ∂ x2
Le ⎜⎝
∂τ
α
∂ x ⎟⎠
∂x
(43)
τ = 0; C B = 1, CG1 = CC1 = CG2 = CC 2 = 0, θ (x,0) = 1
(44)
τ > 0; x = 0,
∂ CG1
= 0,
∂x
∂θ
=0
∂x
τ > 0; x = 1,
⎛ ∂ CG1 ⎞
DG1 ⎜
= Sh( CG10 − CG1 )
⎝ ∂ x ⎟⎠
(47)
τ > 0; x = 1,
∂θ
= −θ BiM
∂x
(48)
(45)–(46)
Koufopanos et al. (1991) correlation:
h = 0.322 ( k l ) Pr 1/ 3 Re0.5
(49)
Darcy law and state equation:
φ ∂p
µ ∂x
(50)
p = CG1Rc T Wm
(51)
u=−
Other relations:
ε ′′ = ε 0 ′′ + γ (1− C B ), φ = ηφB + (1− η )φC1, η = CB CB0
(52)–(54)
Conversion of biomass:
C B0 − ⎡⎢⎛⎜ ∑ CB ⎞⎟
M
X=
⎣⎝ i = 1
⎠
⎤
( M +1) ⎥
⎦
(55)
C B0
applications: (i) The basic mode of transfer inside the solid
particle in the process of pyrolysis is by conduction heat
transfer only; and (ii) the effect of porosity of the solid
particle is negligible. Based on these assumptions, the
conservation equation for the mass concentration of (gases
and volatiles)1 (Eqn (39)) and heat transfer model (Eqn (43))
become:
∂ C G1
n
n
n
= k1 C B1 − k3 C G21 C C31
∂t
(56)
∂θ b − 1 ∂θ ∂ 2θ Q n R 2 k1
=
+
+
∂τ
α
x ∂x ∂x2
(57)
Thus, the second simplified model (Model III) consists
of Eqns (38), (56), (40)–(42), (57), (44), (46), (48), and (49).
408
Interestingly, this is similar to the model proposed by the
Babu and Chaurasia in their earlier study,13 which means
that the generalized reference model reduced to the model
proposed by Babu and Chaurasia13 under specific conditions.
Numerical solution and simulation
Equations (39) and (43) along with the initial and boundary
conditions given by Eqns (44)–(50) were solved numerically
by finite difference method using pure implicit scheme.17 The
pure implicit scheme is an unconditionally stable scheme,
i.e. there is no restriction on the time-step in sharp contrast
with the Euler and Crank-Nicholson method as discussed by
Ghoshdastidar.63 The Eqns (38)–(43) are solved simultaneously. Equations (38), (40), (41), and (42) are solved by the
Runge-Kutta fourth-order method with both the fi xed-step
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
Review: Biomass pyrolysis
BV Babu
size and the variable-step size. It is found that the RungeKutta fourth-order variable-step-size method (RKVS) is
faster than the Runge-Kutta fourth-order method with
fi xed-step size (RKFS), as discussed by Babu and Angira.64
The RKVS method, however, does not give the solution for a
particular and fi xed interval of time. The discretized form of
Eqns (39) and (43) were solved by TDMA, also known as the
Thomas Algorithm.
Figure 3 shows the conversion profi le as a function of time
with the cylindrical pellet of radius 0.00915 m and fi nal
temperature of 679 K. The model developed is in better
agreement for experimental data of Alves and Figueiredo65
also when compared with the model of Liliedahl and
Sjöström.66 It overpredicts the conversion at higher values
of the pyrolysis time. This may be due to the fact that
while developing the model, Liliedahl and Sjöström66 did
not consider the variation of thermal conductivity and the
specific heat capacity of biomass with temperature.
Measurements of product distribution for different sized,
single, cellulose particles are not available to make quantitative comparisons with the model predictions. However,
cellulose pyrolysis in the fluid-bed and entrained-flow reactors67 and in low-temperature vacuum-tube furnace 68 is
considered for comparison and some conclusions are drawn.
To this end, the experimental data have been replotted
together with the simulation results, obtained for a particle
half-thickness of 0.0000125 m (thermally thin regime) as
sizes of about 100 μm by Scott et al.66 are used, it can be said
that the intraparticle resistance to heat transfer is negligible.
As shown, the match between the model and the experimental data is very good.
Babu and Chaurasia17 carried out simulations for the
temperature ranging between 303 K and 2100 K and the
equivalent particle radius ranging between 0.0000125 m
and 0.011 m. The pyrolysis rate was obtained by considering
two parallel primary reactions and a third, secondary reaction between the volatile and gaseous products and char.
It is found that the secondary reactions are responsible
for carbon enrichment of the fi nal residual. The effects of
the parameters, such as heat of reaction number, thermal
conductivity, heat transfer coefficient, emissivity, and reactor
temperature, were analyzed. The results obtained from the
model13 were in good agreement with many experimental
results60,65,67,68 as compared to the model developed by
the earlier researchers.38,59 It is found that the production
of (char)1 is favoured by the endothermic reactions while
the production of (volatile and gases)1 is favoured by the
exothermic reactions. It is also found that the conversion
time becomes successively longer as the thermal conductivity of biomass increases and/or emissivity decreases, thus
affecting the reactor throughput. It is observed that the
feedstocks with lower thermal conductivity produce a gas of
LEGEND
100
100
Alves & Figueiredo (1989) (Experimental)
Babu & Chaurasia (2003a)
Liliedahl & Sjostrom (1998)
90
80
90
80
70
70
60
60
Yield (%)
Conversion (%)
shown in Fig. 4. Since in both the experiments, very thin
particles (powder) by Shafi zadeh et al.68 and the particle
50
40
50
40
30
30
20
20
10
10
0
0
0
100
200
300
400
500
600
Time (s)
Babu & Chaurasia (2003a)
Shafizadeh et al. (1979a) (Experimental)
Scott et al. (1988) (Experimental)
CHAR
600
650
700
750
800
850
900
Temperature (K)
Figure 3. Conversion profile as a function of time with cylindrical
Figure 4. Average char yield as a function of temperature for particle
pellet (R = 0.00915 m, T0 = 303 K, Tf = 679 K).
half-thickness of 0.0000125 m.
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
409
BV Babu
Review: Biomass pyrolysis
better quality for a fi xed particle size. Babu and Chaurasia17
also performed sensitivity analysis for most of the variables
and it is found that convective heat transfer coefficient
is the least sensitive parameter and sensitivity for all the
parameters is highest for the slab geometry and is lowest for
the spherical geometry. The results obtained by Babu and
Chaurasia17 have a lot of practical importance and physical
significance in industrial pyrolysis applications. The results
are also important and useful for design of biomass gasifiers,
reactors, etc.
Thermal plasma pyrolysis
Thermal plasma pyrolysis can be described as the process
of reacting a carbonaceous solid with limited amounts of
oxygen at very high temperature to produce gas and solid
products. In the highly reactive plasma zone, there is a large
fraction of electrons, ions and excited molecules together
with the high-energy radiation. When carbonaceous particles are injected into plasma, they are heated very rapidly by
the plasma; and the volatile matter is released and cracked
giving rise to hydrogen and light hydrocarbons such as
methane and acetylene.69 There are four stages that can be
distinguished in the thermal plasma pyrolysis process:
Stage 1: A very fast heating of the particles as a result of
their heat exchange with the plasma jet.
Stage 2: An explosive liberation of volatile matter from the
particles.
Stage 3: A very quick gasification of the homogeneous phase
and rapid heat and mass exchange.
Stage 4: Further gasification of char particles with various
gaseous components.
A reaction scheme proposed by Huang et al.70 for rubber
pyrolysis in a dc arc plasma can be divided in four stages as
described below:
In Stage 1, primary pyrolysis reactions take place and the
volatile matter is released including heavy hydrocarbons
(tar), light hydrocarbons, and other gaseous components,
leaving behind solid char.
Rubber → char + heavy hydrocarbons + light hydrocarbons
+ gas (H2 ,CO,CH4 ,C 2H2 ,C 2H4 , etc.)
(58)
410
In Stage 2, tar gets cracked and light hydrocarbon may also
decompose.
heavy hydrocarbons → light hydrocarbons +
gas (H2 ,CO,CH4 ,C 2H2 ,C 2H4 , etc.)
(59)
light hydrocarbons → H2 +CH4 +C 2 H2 +C 2 H4 +C n Hm
(60)
In Stage 3, light hydrocarbons may further decompose.
This stage could be replaced by quench technology in order
to achieve certain technical purposes such as monomer
recovery.
In Stage 4, addition of water/steam could be effectively
used to promote syngas (H2 and CO) production.
char + H2O → CO + H2 + solid residue
(61)
This could be one of the reasons for the observation that
H2 and CO concentrations were increased, and solid yield
decreased with steam injection. The solid residue includes
the inorganic tire component, carbon black fi ller, and
carbonaceous deposit.70
Nema and Ganesh Prasad71 proposed following reactions for the plasma pyrolysis of simulated medical waste
(containing cellulose and polyethylene).
C6H10O5 + heat → CH4 + 2CO + 3H2O + 3C
(62)
[–CH2 – CH2 –]n + H2O + heat → xCH4 + yH2 + zCO
(63)
High temperature combined with the high heating rate
of the plasma results in the destruction of organic waste,
giving rise to a gas and a solid residue with varied properties depending on the feed characteristics and operating
conditions.72
Reaction mechanisms of plasma-assisted
pyrolysis
The pyrolysis mechanisms of polymer molecules, which
may comprise tens of thousands of atoms, are very complex.
Pyroly tic reactions have been broadly classified into four
groups: random main-chain scission, depolymerization, carbonization, and side-group reactions. Random
main-chain scission is defined as the breaking of the
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
Review: Biomass pyrolysis
main chain to produce smaller molecules of random sizes.
Depolymerization is defi ned as the successive removal of
monomer units from the chain and leads to the formation of
free radicals and chain reactions. Carbonization and sidegroup reactions include those reactions which lead to crosslinking, straight-chain-polymer formation by elimination of
side chains, cyclization, and aromatization by dehydrogenation. According to free-radical chain-reaction theory, both
chain scission and depolymerization mechanisms involve
initiation, propagation, chain transfer, and termination
reactions.
Based on the standard Gibbs free-energy changes for the
reactions, it is found that energy requirement for carbon–
carbon bond cleavage is less than that for dehydrogenation.
At a given temperature, chain scission of C–C bonds at the
ends of molecules is more probable than at the center of
the molecule. Removal of hydrogen by breaking C-H bond
from CH3, C2H5, n-C3H7 requires 435.2 kJ/mol, 410 kJ/mol
and 408.4 kJ/mol of energy respectively, whereas to remove
hydrogen from CH2=CHCH2 requires only 363.2 kJ/mol of
energy. Removal of CH3 by breaking C-C bond from CH3,
C2H5, n-C3H7 requires 369.9 kJ/mol, 353.6 kJ/mol and 355.2
kJ/mol of energy respectively, whereas to remove CH3 from
CH2=CHCH2 requires only 307.9 kJ/mol of energy.73 Based
on the bond dissociation energies of C–C and C–H bonds, it
is found that the C–C and C–H bonds to the allylic carbon
are weaker than the corresponding bonds in a pure saturated
chain. These bonds are β-bonds with respect to the double
bond and their scission is known as β-scission. All chain
ends of heavy molecular weight polyethylene have β-bond.
Collisions between the polymer molecules and electrons
and ions from the plasma initiate the β-scission process
in plasma reactor. Propagation occurs through a series of
reactions which convert the polymer fragments into reactants and, subsequently, to final products through radical
decomposition, radical isomerization, hydrogen transfer,
and/or radical addition. Termination reactions occur when
two radicals combine or are disproportionate to form stable
products.73 The range of product compositions obtained
will depend on both the relative sensitivity of secondary
versus primary reactions to changes in temperature and the
residence time of the material within the high-temperature
plasma region.
BV Babu
Kinetic modeling of plasma pyrolysis
Experimental studies on plasma pyrolysis have been
conducted by many researchers using a variety of raw materials, such as agricultural residue, waste tyre, municipal
solid waste etc. 69–74 Understanding the physical phenomena
of plasma-assisted pyrolysis and representing them with an
appropriate mathematical model is essential in the design of
reactors.
Shuangning et al.75 developed a plasma heated laminar
entrained flow reactor (PHLEFR) in order to study the volatilization characteristics of biomass particles at flash heating
rates. A simple kinetic model is proposed in order to predict
the reaction rate for a wide range of operating conditions
and various biomasses. The conversion process is mathematically expressed by a following equation.
E
−
dα
= Ae RT (1 − α )
dt
(64)
where α is the fraction of reactant decomposed at residence time t. A and E are the apparent frequency factor (s–1)
and apparent activation energy (kJ/mol), respectively; R is
the universal gas constant, 8.3145 (J/mol); T is the absolute
temperature (K) of the pyrolytic process. The fractional
reactant α is defined as the ratio of W to W ∞, where W is the
volatile mass fraction at time t; W ∞ is the maximum volatile
mass fraction (in wt%). The kinetic model presented here was
fitted to the experimental data and kinetic parameters are
found (Table 8). Shuangning et al.75 studied the volatilization characteristics of biomass particles at flash heating rates
and developed and validated the kinetic model and found
the kinetic parameters for the various agricultural residues.
However this analysis is not reported in the literature for
other carbonaceous wastes and also the model developed by
Shuangning et al.75 is not improved to incorporate the effects
Table 8. Kinetic parameters of biomass pyrolysis
reaction determined with PHLEFR.
A (s−1) (103)
E (kJ/mol)
Wheat straw
1.05
31.63
Coconut shell
6.84
48.73
Rice husk
1.19
39.30
Cotton stalk
2.44
40.84
Raw materials
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
411
BV Babu
Review: Biomass pyrolysis
of various parameters, such as feed rate, size of the particles, heating rate, thermal and thermodynamic properties
(thermal conductivity, heat transfer coefficient, emissivity
and heat of reaction number) of the feedstock on the convective-radiant pyrolysis of biomass fuels.
There is a lot of scope for improving the existing models
on plasma pyrolysis by incorporating simultaneous effects
of heat, mass and momentum in combination with reaction
kinetics. There is also a need to make use of CFD (computational fluid dynamics) simulations to have a better understanding of the physical phenomena occurring in plasma
pyrolysis at microlevel.
6. Demirbas A, Biomass resources for energy and chemical industry.
Energy Education and Science Technology 5:21–45 (2000).
7. Babu BV and Chaurasia AS, Parametric study of thermal and
thermodynamic properties on pyrolysis of biomass in thermally thick
regime. Energ Convers Manage 45:53–72 (2004a).
8. Kanury AM, Combustion Characteristics of Biomass Fuels. Combust Sci
Technol 97:469–491 (1994).
9. Chan WR, Kelbon M, Krieger-Brockett B, Single-particle biomass
pyrolysis: correlations of reaction products with process conditions, Ind
Eng Chem Res 27:2261 (1988).
10. Di Blasi C, Kinetic and heat transfer control in the slow and flash pyrolysis
of solids. Ind Eng Chem Res 35:37–47 (1996).
11. Kanury AM, Blackshear PL, Some considerations pertaining the problem
of wood-burning. Combust Sci Technol 1:339–355 (1970).
12. Babu BV and Sheth PN, Modeling and simulation of reduction zone
of downdraft biomass gasifier: effect of char reactivity factor. Energ
Conclusions
•
•
•
•
The pyrolysis of biomass is a promising route for the
production of solid (charcoal), liquid (tar and other
organics) and gaseous products (H2 , CO2 , CO).
Modeling and simulations are required to describe the
pyrolysis mathematically. It would be useful to predict
the product-gas concentration for various operating
conditions and for a variety of feed mixtures. To design
a suitable pyrolysis reactor, modeling and simulation of
pyrolysis are very important.
There is a tremendous scope for applying plasma-assisted
pyrolysis of assorted non-nuclear waste, such as biomass,
printed circuit boards, organic waste, medical waste etc.,
for obtaining useful energy.
There is a need to improve upon the existing simple
models on plasma pyrolysis by incorporating simultaneous effects of heat, mass and momentum in combination with reaction kinetics. CFD simulations would
enhance the understanding of physical phenomena
occurring in plasma pyrolysis.
Convers Manage 47:2602–2611 (2006).
13. Babu BV and Chaurasia AS, Modeling, simulation, and estimation of
optimum parameters in pyrolysis of biomass. Energ Convers Manage
44:2135–2158 (2003a).
14. Babu BV and Chaurasia AS, Modeling for pyrolysis of solid particle:
kinetics and heat transfer effects. Energ Convers Manage 44:2251–2275
(2003b).
15. Babu BV and Chaurasia AS, Dominant design variables in pyrolysis of
biomass particles of different geometries in thermally thick regime. Chem
Eng Sci 59:611–622 (2004b).
16. Babu BV and Chaurasia AS, Pyrolysis of biomass: improved models for
simultaneous kinetics & transport of heat, mass, and momentum Energ
Convers Manage 45:1297–1327 (2004c).
17. Babu BV and Chaurasia AS, Heat transfer and kinetics in the pyrolysis of
shrinking biomass particle. Chem Eng Sci 59:1999–2012 (2004d).
18. Curtis LJ and Miller DJ, Transport model with radiative heat transfer for
rapid cellulose pyrolysis. Ind Eng Chem Res 27:1775–1783 (1988).
19. Anthony DB and Howard JB, Coal devolatilization and hydrogastification,
AIChE Journal 22:625–656 (1976).
20. Shafizadeh F, Introduction to pyrolysis of biomass. J Anal Appl Pyrolysis
3:283–305 (1982).
21. Demirbas A and Arin G, An overview of biomass pyrolysis. Energ Sources
24:471–482 (2002).
22. Barth T, Similarities and differences in hydrous pyrolysis and source
rocks. Organic Geochem 30:1495–1507 (1999).
23. Bridgwater AV, Meier, D. and Radlein, D. An overview of fast pyrolysis of
References
1. Caillé A, Survey of Energy Resources. World Energy Council (2007).
2. Fridleifsson IB, Status of geothermal energy amongst the world’s energy
sources. Geothermics 32:379–388 (2003).
3. Twidell J, Biomass energy. Renew Energy World 3:38–39 (1998).
4. Demirbas A, Bioresource facilities and biomass conversion processing
for fuels and chemicals. Energ Conver Manage 42:1357–1378 (2001).
5. Stucley CR, Schuck SM, Sims REH, Larsen PL, Turvey ND and Marino
BE, Biomass energy production in Australia, A report for the Joint
Venture Agroforestry Program (in conjunction with the Australian
Greenhouse Office) (2004).
412
biomass. Organic Geochem 30:1479–1493 (1999).
24. Demirbas A and Küçük MM, Delignication of Ailanthus altissima and
spruce orientalis with glycerol or alkaline glycerol at atmospheric
pressure. Cellulose Chem Technol 27:679–686, (1999).
25. Di Blasi C, Modeling and simulation of combustion processes of
charring and non-charring solid fuels. Prog Energ Combust 19:71–104
(1993).
26. Di Blasi C, Modeling chemical and physical processes of wood and
biomass pyrolysis. Prog Energ Combust 34:47–90 (2008).
27. Thurner F and Mann U, Kinetic investigation of wood pyrolysis, Ind. Eng.
Chem. Proc. Des. Dev. 20:482 (1981).
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
Review: Biomass pyrolysis
28. Gorton WC and Knight JA, Oil from biomass by entrained-flow pyrolysis,
Biotechnol Bioeng Symp 14:15 (1984).
29. Ward SW and Braslaw J, Experimental weight loss kinetics of wood
pyrolysis under vacuum, Combust Flame 61:261 (1985).
30. Wagenear BM, Prins W and van Swaaij WPM, Flash pyrolysis kinetics of
pine wood, Fuel Proc Techn 36: 291 (1994).
31. Di Blasi C and Branca C, Kinetics of primary product formation from
wood pyrolysis. Ind Eng Chem Res 40:5547 (2001).
32. Samolada MC and Vasalos IA, A kinetic approach to the flash pyrolysis of
biomass in a fluidized bed reactor, Fuel 70:883 (1991).
33. Reina J, Velo E and Puigjaner L, Kinetic study of the pyrolysis of waste
wood, Ind Eng Chem Res 37:4290 (1998).
34. Koufopanos CA, Maschio G and Lucchesi A, Kinetic modeling of the
pyrolysis of biomass and biomass components. Can J Chem Eng
67:75–83 (1989).
35. Koufopanos CA, Papayannakos N, Maschio G and Lucchesi A, Modeling
of the pyrolysis of biomass particles. Studies on kinetics, thermal and
heat transfer effects, Can J Chem Eng 69:907–915 (1991).
36. Barooah JN and Long VD, Rates of thermal decomposition of some
carbonaceous materials in a fluidized bed, Fuel 55:116 (1976).
37. Alves SS and Figueiredo JL, Pyrolysis kinetics of lignocellulosic materials
by multistage isothermal thermogravimetry, J Anal Appl Pyrol 13:123
(1988).
38. Jalan RK and Srivastava VK, Studies on pyrolysis of a single biomass
cylindrical pellet-kinetic and heat transfer effects. Energ Convers Manage
40:467–494 (1999).
39. Srivastava VK and Jalan RK, Predictions of concentration in the pyrolysis
of biomass materials-I. Energ Convers Manage 35:1031–1040 (1994).
40. Srivastava VK, Sushil and Jalan RK, Predictions of concentration in the
pyrolysis of biomass material-II. Energ Convers Manage 37:473–483
(1996).
41. Chan WC, Kelbon M and Krieger B, Pyrolysis of a large biomass particle.
Fuel 64:1505–1513 (1985).
42. Di Blasi C, Transition between regimes in the degradation of
thermoplastic polymers. Polymer Degrad Stabil 64:359–367 (1999).
43. Chao-Hsiung W, Ching-Yuan C, and Jyh-Ping L, Pyrolysis kinetics of
paper mixtures in municipal solid waste. J Chem Technol Biotechnol
68:65–74 (1997).
44. Balci S, Timur D, and Yucel H, Pyrolysis kinetics of lignocellulosic
materials, Ind Eng Chem Res 32:2573–2579 (1993).
45. Demirbas A, Kinetics for non-isothermal flash pyrolysis of hazelnut shell.
Biores Technol 66:247–252 (1998).
46. Babu BV and Sheth PN, Modeling and simulation of reduction zone of
downdraft biomass gasifier: effect of air to fuel ratio. J Eng Technol
2:35–40 (2007).
47. Chaurasia AS and Babu BV, Influence of product yields, density, heating
conditions, and conversion on pyrolysis of biomass. J Arid Land Studies
14:159–162 (2004).
48. Chaurasia AS and Babu BV, Kaur A and Thiruchitrambalam V, Convective
and radiative heat transfer in pyrolysis of a biomass particle. Indian Chem
Eng 47:75–80 (2005).
49. Varhegyi G, Antal MJ, Jakab E and Szabo P, Kinetic modeling of biomass
pyrolysis. J Anal Appl Pyrol 42:73–87 (1997).
BV Babu
50. Peters B and Bruch C, Drying and pyrolysis of wood particles:
experiments and simulation. J Anal Appl Pyrol 70:233–250 (2003).
51. Branca C and Di Blasi C, Kinetics of the isothermal degradation of
wood in the temperature range 528–708 K. J Anal Appl Pyrol 67:207–219
(2003).
52. Sheth PN and Babu BV, Optimization of kinetic parameters in pyrolysis
of biomass using differential evolution (DE). Proceedings of International
Conference on Neural Network and Genetic Algorithm in Materials
Science and Engineering (NGMS-2008) Kolkata, January 9–11, (2008).
53. Babu BV and Munawar SA, Differential evolution strategies for optimal
design of shell-and-tube heat exchangers. Chem Eng Sci 62:3720–3739
(2007).
54. Babu BV and Sastry KKN, Estimation of heat transfer parameters in a
trickle bed reactor using differential evolution and orthogonal collocation.
Comput Chem Eng 23:327–339 (1999).
55. Fan LT, Fan LS, Miyanami K, Chen TY and Walawender WP,
A mathematical model for pyrolysis of a solid particle - effects of the
Lewis number. Can J Chem Eng 55:47–53 (1977).
56. Miyanami K, Fan LS, Fan LT, and Walawender WP, A mathematical model
for pyrolysis of a solid particle - effects of the heat of reaction. Can J
Chem Eng 55:317–325 (1977).
57. Bamford CH, Crank J and Malan DH, The combustion of wood. Part I,
Proceedings of the Cambridge Philosophical Society 42:166–182 (1946).
58. Kung HC, A mathematical model of wood pyrolysis. Combust Flame
18:185–195 (1972).
59. Kansa EJ, Perlee HE and Chaiken RF, Mathematical model of wood
pyrolysis including internal forced convection. Combust Flame
29:311–324 (1977).
60. Pyle DL and Zaror CA, Heat transfer and kinetics in the low temperature
pyrolysis of solids. Chem Eng Sci 39:147–158 (1984).
61. Di Blasi C, Influences of model assumptions on the predictions of
cellulose pyrolysis in the heat transfer controlled regime. Fuel 75: 58–66
(1996).
62. Di Blasi C, Influences of physical properties on biomass devolatilization
characteristics. Fuel 76:957–964 (1997).
63. Ghoshdastidar PS, Computer simulation of flow and heat transfer. Tata
McGraw-Hill Publishing Company Limited, New Delhi (1998).
64. Babu BV and Angira R, Optimal design of an auto-thermal ammonia
synthesis reactor. Comput Chem Eng 29:1041–1045 (2005).
65. Alves SS and Figueiredo JL, A model for pyrolysis of wet wood, Chem
Eng Sci 44: 2861–2869 (1989).
66. Liliedahl T and Sjöström K, Heat transfer controlled pyrolysis kinetics of a
biomass slab, rod or sphere, Biomass Bioenerg 15:503–509 (1998).
67. Scott DS, Piskorz J, Bergougnou MA, Graham R and Overend RP, The
role of temperature in the fast pyrolysis of cellulose and wood. Ind Eng
Chem Res 27:8–15 (1988).
68. Shafizadeh F, Furneaux RH, Cochran TG, Scholl JP and Sakai Y,
Production of levoglucosan and glucose from pyrolysis of cellulosic
materials. J Appl Polymer Sci 23:3525–3539 (1979).
69. Babu BV, Chemical kinetics and dynamics of plasma assisted pyrolysis
of assorted, non nuclear waste, Proceedings of one-day discussion
meeting on ‘New Research directions in the use of power beams for
environmental applications (RPDM-PBEA-2007)’, Laser & Plasma
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb
413
BV Babu
Review: Biomass pyrolysis
Technology Division, Bhabha Atomic Research Centre (BARC), Mumbai,
September 18 (2007).
70. Huang H, Tang L and Wu CZ, Characterization of gaseous and solid
75. Shuangning X, Weiming Y and Baoming L, Flash pyrolysis of agricultural
residues using a plasma heated laminar entrained flow reactor. Biomass
Bioenerg 29:135–141 (2005).
product from thermal plasma pyrolysis of waste Rubber. Environ Sci
Technol 37:4463–4467 (2003).
71. Nema S and Ganesh Prasad KS, Plasma pyrolysis of medical waste. Curr
Sci India 83:271–278 (2002).
72. Huang H and Tang L, Treatment of organic waste using thermal plasma
pyrolysis technology. Energ Convers Manage 48:1331–1337 (2007).
73. Guddeti RR, Knight R and Grossmann ED, Depolymerization of
Polyethylene Using Induction-Coupled Plasma Technology. Plasma
Chem Plasma P 20:37–64 (2000).
74. Hung-Lung C, Kuo-Hsiung L, Mei-Hsiu L, Ting-Chien C and Sen-Yi M,
Pyrolysis characteristics of integrated circuit boards at various particle
B. V. Babu
Dr B. V. Babu is Professor of Chemical Engineering
and Dean of Educational Hardware Division at BITSPilani, India. He has 23 years of teaching, research,
consultancy, and administrative experience. He
has around 150 research publications to his credit in the areas of
energy and environmental engineering, evolutionary computation,
and modeling and simulation. He has published five books, and
has written several chapters.
sizes and temperatures, J Haz Mat (In press 2007).
414
© 2008 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 2:393–414 (2008); DOI: 10.1002/bbb