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5/24/2018 Recent Advances in Bioenergy Research Volume I
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i
Recent Advances in Bioenergy Research
Volume I
Edited by
SACHIN KUMAR, ANIL K. SARMA
Sardar Swaran Singh National Institute of Renewable Energy, Kapurthala, India
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ISBN 978-81-927097-0-3
Sardar Swaran Singh National Institute of Renewable Energy, Kapurthala-2013
Electronic version published by SSS-NIRE
ALL RIGHTS RESERVED
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CONTENTS
Preface ix
Contributors xi
Part-I: Biomass Assessment and Management for Energy Purpose 1
1 Characteristics of Biomass 2
A.K. Jain
1.1 Introduction 2
1.2 Physical Properties 3
1.3 Thermal Characteristics 6
1.4 Chemical Analysis 14
1.5 Correlation Models 17
1.6 Conclusions 19
References 19
2 Global warming: A new paradigm for Bio-Energy Research 21
S.K. Sharma
2.1 Introduction 21
2.2 New Research opportunities in Bio Energy 22
2.3 Conclusions 26
References 26
3 Biomass Assessment for Growth of Bioenergy: 28
A Case Study in Assam, India
D.C. Baruah, Moonmoon Hiloidhari
Abstract 28
3.1 Introduction 28
3.2 Materials and methods 31
3.3 Results and discussions 36
3.4 Conclusion 41
References 42
4 Bio Mass Fuel Generation- An Ultimate Energy Resource 44
Ajeet Kumar Upadhyay
Abstract 44
4.1 Introduction 44
4.2 Bio fuels from biomass 46
4.3 Bio ethanol from biomass 48
4.4 Biodiesel from biomass 48
4.5 Methane generation from microbial action 49
4.6 Hydrogen from biomass 50
4.7 Conclusions 51
References 51
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Part-II: Thermo-chemical Conversion 52
5 Modeling of Biomass Gasification Processes in Downdraft Gasifiers: 53
A Review
Anjireddy Bhavanam, R.C. Sastry
Abstract 53
5.1 Introduction 53
5.2 Downdraft Gasifiers 55
5.3 Gasification Models 57
5.4 Model Validation 63
5.5 Conclusions 63
References 65
6 Prospect of Bioenergy Substitution in Tea Industries of 67
North East India
B.J. Dutta, D. Baruah, M. Saikia, R. Bhowmik, D.C. Baruah
Abstract 67
6.1 Introduction 67
6.2 Materials and Method 69
6.3 Results and Discussion 73
6.4 Conclusions 76
References 77
7 Drying Of Biomass Fuel Used For Gasifier Using Waste Heat 79
R. Soni, A.K. Jain, B.S. Panesar, P.K. Gupta
7.1 Introduction 79
7.2 Methodology 80
7.3 Results and Discussion 82
7.4 Conclusions 86
References 87
8 Improved Woodstove Tehtana Experience 89
Usha Bajpai, Suresh C. Bajpai
Abstract 89
8.1 Introduction 90
8.2 Energy, Health and Global Warming 90
8.3 The Indian National Programme on Improved Chulhas 96
8.4 Improved Woodstove at Tehtana 97
8.5 Conclusions 102
References 103
9 Development of a Briquetting Machine for Jatropha Seed Cake 105
H. Raheman, B. Singh, T. Alam, D. Padhee
Abstract 105
9.1 Introduction 105
9.2 Materials and Method 107
9.3 Results and Discussion 108
9.4 Conclusions 113
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References 113
10 Charcoal Activation at Low Temperature 115
A.P. Singh Chouhan, S.P. Singh
Abstract 115
10.1 Introduction 115
10.2 Materials and Method 117
10.3 Results and Discussion 118
10.4 Conclusions 125
References 126
Part-III: Biogas & Biohydrogen 128
11 MNRE Policy on Biogas Programme 129
M.L. Bamboriya
11.1 Introduction 12911.2 Biogas Programme 129
12 Biogas Plant a Check for Environment Pollution 143
and Global Warming
Sarbjit Singh Sooch
Abstract 143
12.1 Introduction 143
12.2 Materials and Method 144
12.3 Results and Discussion 147
12.4 Conclusions 149References 149
13 Todays Waste Tomarrows Fuel: 151
Hyderabad to Get 50MW from Garbage (MSW)
K.K. Jain, J. Praveen
Abstract 151
13.1 Introduction 151
13.2 RDF Fuel Conversion from MSW 153
(Segregated high CV fraction of MSW)
13.3 Testing Results of RDF 153
13.4 Monitoring Report of 6.6 MW Plant 153
13.5 Emission Characteristics of RDF 154
13.6 Details of 6.6 MW Power Plant 154
13.7 RDF from processed MSW 154
13.8 Case Study of Biogas from Slaughter House Waste to Energy 155
13.9 Conclusions 156
References 157
14 Municipal Solid Waste to Energy: 158
Experimental Studies on Biogas Plant
Usha Bajpai, Puja Singh
Abstract 158
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14.1 Introduction 159
14.2 Materials and Methods of Experimental Studies 164
14.3 Results and Discussion 168
References 169
15 Poultry Litter as an Alternate Feed Stock to Cattle Dung for 170
Biogas Production and Power GenerationSarabjit Singh Sooch, Urmila Gupta, Anand Gautam
Abstract 170
15.1 Introduction 170
15.2 Methodology 172
15.3 Results and Discussion 172
15.4 Conclusion 173
16 CFD Modelling of an UASB Reactor for Biogas Production from 176
Industrial Waste/Domestic Sewage
Partha Kundu, I.M. Mishra
Abstract 176
16.1 Introduction 176
16.2 Methods 179
16.3 Results and Discussion 184
16.4 Conclusions 190
References 191
17 AlgalBio-Hydrogen- Prospects and Challenges 194
Shailendra Kumar Singh, M.K. Jha, Ajay Bansal, Apurba dey
Abstract 19417.1 Introduction 195
17.2 Physiology of H2 production in green algae 196
17.3 Challenges and prospects 197
17.4 Design and cost of photobioreactors 202
17.5 Conclusions 203
References 204
Part-IV: Production Aspects of Biodiesel 207
18 Jatropha (Jatropha Curcas) L. Plantations and Climate Change 208
Avtar Singh
Abstract 208
18.1 Introduction 209
18.2 Status of jatropha plantations in the world 209
and future potential for expansion
18.3 Soil for Jatropha cultivation 209
18.4 Genetic improvement inJatropha 210
18.5 Tissue culture inJatropha curcas 210
18.6 Seedling production in nursery 21118.7 Plantation establishment 212
18.8 Plant protection 215
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18.9 Plant responses to climate change 215
18.10Effect ofJatrophaon climate change 217
References 218
19 Biodiesel Production from Algal Species Grown on Dairy Wastewater 221
Richa Kothari, Vinayak V. Pathak, D.P. Singh
Abstract 22119.1 Introduction 221
19.2 Materials and Methods 222
19.3 Results and Discussion 224
19.4 Conclusions 227
References 227
20 Green Technology for Biodiesel Production using 230
Waste Material Based Heterogeneous Catalyst
Anil Kumar Sarma, Ashish P. Singh Chouhan
Abstract 23020.1 Introduction 231
20.2 Materials 232
20.3 Results and Discussion 234
20.4 Conclusions 238
References 238
21 Production and Studied of Fuel Properties of 241
Sunflower Ethyl Ester and its Blends
R. Kumar, A.K. Dixit, S. K. Singh, G.S. Manes, R. Khurana
Abstract 24121.1 Introduction 241
21.2 Materials and Methods 242
21.3 Results and Discussion 244
21.4 Conclusions 246
References 246
Part-V: Lignocellulosic Ethanol Production 248
22 Thermophiles: smart bugs for ethanol production from 249
agricultural residuesSachin Kumar, Pratibha Dheeran, Dilip K Adhikari
Abstract 249
22.1 Introduction 249
22.2 Materials and methods 251
22.3 Results and Discussion 252
22.4 Conclusions 255
References 256
23 Study of Bioethanol Production from Brewers Spent Grain 258
usingFusarium oxysporum
Abhay Dinker, Arvind Kumar, Madhu Agarwal
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Abstract 258
23.1 Introduction 258
23.2 Materials and Methods 260
23.3 Results 262
23.4 Conclusions 262
References 263
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Preface
Sachin Kumar, A.K. Sarma
Bio-energy research has received tremendous attention all over the world due tosteep hike in petroleum prices and environmental concerns. At the current electricity
generating capacity and other available energy sources, a huge gap exists between the
demand and supply (above 15%) and the Conventional Energy resources of the country
are meagre. Agricultural crop residues production in the country is about 550 Mt/year
and is likely to increase in the coming years. Majority of the crop residues are either
processed in uneconomic way or get destroyed as such.
Apart from the crop residues, other biomass such as animal excreta, forestwastes and agro-industrial wastes are also available in abundance and can play a major
role in supplementing the energy resources of the country. Waste biomass materials
include various natural and derived materials, such as woody and herbaceous species,
bagasse, agricultural waste, waste from paper, municipal solid waste, industrial waste,
sawdust, grass, food processing waste, waste oil, non-edible oil or shell of oil-bearing
seed, aquatic plants and algae, etc., which could be potentially used for production of
useful fuels and chemicals. The average majority of biomass energy is produced from
wood and wood wastes (64%), followed by municipal solid waste (24%), agricultural
waste (5%) and landfill gases (5%). Waste and degraded lands are generally used for
energy plantation and biomass production.
There is no debate on the issue that renewable energy is the only sustainable
energy in nature. Biomass energy in particular is one of the cleanest form of energy
gifted by nature. This is also the waste to wealth making weapons for the farmers.
Because, all forms of derived agricultural waste can be converted to useful energy that
directly contribute to the income of farmers and nation as well. Moreover, they are
highly beneficial from the viewpoint of environmental pollution control and an asset for
carbon credit.
Keeping in view the need and importance of bioenergy research in our country,
we express pleasure to introduce the first edition of Recent Advances in Bioenergy
Research- Volume-I in the form of a book. The book is divided in five parts viz. Part-I:
Biomass Assessment and Management for Energy Purpose; Part-II: Thermo-chemicalConversion; Part-III: Biogas & Biohydrogen; Part-IV: Production Aspects of Biodiesel;
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Part-V: Lignocellulosic Ethanol Production. Each section includes respective chapters
from Eminent Academician, Scientists and Researchers in the field. We are really
grateful for their commendable contribution for this book.
Emphasis is given such that current trends of research and investigation in the
bioenergy sector can be easily worked out from the in-depth study of this book. Our
efforts will be successful if the readers dig up the expected gain out of these articles.
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Contributors
Adhikari Dilip Kumar,Biotechnology Area, Indian Institute of Petroleum, Dehradun
Agarwal Madhu,Department of Chemical Engineering, MNIT, Jaipur
Alam T.,Agricultural & Food Engineering Department, Indian Institute of Technology,
Kharagpur
Bajpai Suresh C.,BSIP, 53, University Road, Lucknow
Bajpai Usha, Renewable Energy Research Laboratory, Department of Physics,
University of Lucknow, Lucknow
Bamboriya M.L.,MNRE, New Delhi
Bansal Ajay, Department of Chemical Engineering, Dr. B. R. Ambedkar National
Institute of Technology, Jalandhar
Baruah D.,Department of Energy, Tezpur University, Napaam, Assam
Baruah D.C.,Department of Energy, Tezpur University, Napaam, Assam
Bhavanam Anjireddy,Department of Chemical Engineering, NIT, Warangal
Bhowmik R.,Department of Energy, Tezpur University, Napaam, Assam
Chouhan Ashish P. Singh, Sardar Swaran Singh National Institute of Renewable
Energy, Kapurthala
Dey Apurba, Department of Biotechnology, National Institute of Technology,
Durgapur
Dheeran Pratibha,Biotechnology Area, Indian Institute of Petroleum, Dehradun
Dinker Abhay,Department of Chemical Engineering, MNIT, Jaipur
Dixit A.K., Department of Farm Machinery and Power Engineering, Punjab
Agricultural University, Ludhiana
Dutta B.J.,Department of Energy, Tezpur University, Napaam, Assam
Gautam Anand, School of Energy Studies for Agriculture, College of Agricultural
Engineering and Technology, Punjab Agricultural University, Ludhiana
Gupta P.K., School of Energy Studies for Agriculture, College of Agricultural
Engineering and Technology, Punjab Agricultural University, Ludhiana
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Gupta Urmila, School of Energy Studies for Agriculture, College of Agricultural
Engineering and Technology, Punjab Agricultural University, Ludhiana
Hiloidhari Moonmoon,Department of Energy, Tezpur University, Napaam, Assam
Jain A.K.,Sardar Swaran Singh National Institute of Renewable Energy, Kapurthala
Jain K.K.,Ellenki Engineering College, Hyderabad
Jha M.K.,Department of Chemical Engineering, Dr. B. R. Ambedkar National Institute
of Technology, Jalandhar
Khurana R., Department of Farm Machinery and Power Engineering, Punjab
Agricultural University, Ludhiana
Kothari Richa, School for Environmental Sciences, Babasaheb Bhimrao Ambedkar
University, Lucknow
Kumar Arvind,Department of Chemical Engineering, MNIT, Jaipur
Kumar R., Department of Farm Machinery and Power Engineering, Punjab Agricultural
University, Ludhiana
Kumar Sachin, Sardar Swaran Singh National Institute of Renewable Energy,
Kapurthala
Kundu Partha,Department of Chemical Engineering, Indian Institute of Technology
Roorkee, Roorkee
Manes G.S., Department of Farm Machinery and Power Engineering, Punjab
Agricultural University, Ludhiana
Mishra I.M., Department of Chemical Engineering, Indian Institute of Technology
Roorkee, Roorkee
Padhee D., Agricultural & Food Engineering Department, Indian Institute of
Technology, Kharagpur
Panesar B.S., School of Energy Studies for Agriculture, College of Agricultural
Engineering and Technology, Punjab Agricultural University, Ludhiana
Pathak Vinayak V., School for Environmental Sciences, Babasaheb Bhimrao
Ambedkar University, Lucknow
Praveen J., Mall Reddy Engg. College, Hyderabad
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Raheman H., Agricultural & Food Engineering Department, Indian Institute of
Technology, Kharagpur
Saikia M.,Department of Energy, Tezpur University, Napaam, Assam
Sarma Anil Kumar, Sardar Swaran Singh National Institute of Renewable Energy,
Kapurthala
Sastry R.C.,Department of Chemical Engineering, NIT, Warangal
Sharma S.K.,Energy Research Centre, Panjab University, Chandigarh
Singh Avtar, Department of Forestry and N.R., Punjab Agricultural University,
Ludhiana
Singh B.,Agricultural & Food Engineering Department, Indian Institute of Technology,
Kharagpur
Singh D.P., School for Environmental Sciences, Babasaheb Bhimrao Ambedkar
University, Lucknow
Singh Puja, GCRG Group of Institutions, Bakshi Ka Talab, Lucknow
Singh S.K., Department of Farm Machinery and Power Engineering, Punjab
Agricultural University, Ludhiana
Singh S.K., School of Energy Studies for Agriculture, College of Agricultural
Engineering and Technology, Punjab Agricultural University, Ludhiana
Singh S.P., School of Energy and Environmental Studies, Devi Ahilya
Vishwavidyalaya, Takshila Campus, Khandwa Road, Indore
Singh Shailendra Kumar,Department of Chemical Engineering, Dr. B. R. Ambedkar
National Institute of Technology, Jalandhar
Soni R.,School of Energy Studies for Agriculture, College of Agricultural Engineering
and Technology, Punjab Agricultural University, Ludhiana
Sooch Sarbjit Singh,School of Energy Studies for Agriculture, College of Agricultural
Engineering and Technology, Punjab Agricultural University, Ludhiana
Upadhyay Ajeet Kumar, Department of Chemical Engineering, IITT College of
Engineering, Pojewal
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Part I
Biomass Assessment and Management
for Energy Purpose
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CHAPTER 1
CHARACTERISTICS OF BIOMASS
A.K. Jain
1.1 Introduction
All the plant materials produced through photosynthesis via carbon dioxide fixation is
biomass. This includes agricultural products and residues, fuel wood trees and agro-
industrial waste materials. Major agricultural products such as grains, fruits, vegetables
etc. are used for human consumption where as crop residues and forestry residues and fuel
woods are very important from energy point of view.
The word biomass in this text would further refer to agricultural crop residues and
fuel woods. Biomass can be used as energy source directly through combustion or can be
converted to gaseous liquid and solid fuels which are more convenient to use and efficient,
through thermochemical (combustion, gasification and pyrolysis) and biochemical
(anaerobic digestion and fermentation) conversion processes.
All agricultural crop residues, agro-industrial wastes and fuel trees are ligno-cellulosic materials but their individual characteristics vary over a wide range. In the
present scenario of biomass conversion to useful energy products, selection of the biomass
suitable for a specific use or application is extremely important which is possible with
sufficient property data. Therefore, importance of adequate characterization data has been
realized world wide for designing of any thermo-chemical or biochemical conversion
device.
During the last two decades several publications have appeared containing data on
thermodynamic properties of biomass materials. The characteristics of biomass reported in
the literature differ to a large extent. The difference may be attributed to many factors such
as agro-climatic conditions (type of soil and mineral content), variety of crop grown,
sampling technique etc. While conducting laboratory experiment on determination of
characteristics, the author observed the different characteristics of sample from main trunk,
primary and secondary branches of the same tree. The sample from the main trunk had
high ash, density, low calorific value and higher cellulose content compared to primary
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and second limbs. However the difference was of the order of 1 to 3% in most of the
cases. Another observation is that if a biomass ground sample is put to sieve analysis,
different biomass fractions obtained after the sieve analysis do not exhibit similar
characteristics. It is, therefore essential that the biomass sample should be carefully
selected and should be a true representative sample for reliable results.
Fuel characteristics important to the design and analysis of biomass conversion
processes are; Physical properties, i.e. density, angle of repose and moisture content;
thermal properties i.e. calorific value and proximate analysis and chemical properties
elemental analysis and chemical composition. The physical properties vary considerably
with environment and handling procedures whereas the remaining are intrinsic properties.
These properties are extremely useful in the design of biomass conversion device and
processes analysis.
1.2 Physical Properties
The important physical characteristics of biomass are density, moisture content and angle
of repose.
1.2.1 Density
One of the most important physical characteristics of biomass fuel is its density. It is
usually classified as bulk density and true density.
True density is the weight per unit volume of a single biomass piece. It is
determined using the Archemedies` principle (Pathak and Jain, 1985). It is also referred as
specific density in the literature. It depends on biomass moisture and has a constant value
on dry weight basis. The true density of several species of fast growing fuel wood trees
such asAcacia, Albizia, Eucalyptus, Derris indica, Leucaena Lecocephala, Arjunaetc, are
reported by Jain, 1997. The true density values for these woods vary from 600 to 820
kg/m3
The bulk is the weight of bulk biomass material divided by the volume occupied.
The weight of the biomass depends on the size, shape and level of its compaction or
densification. It determines the storage capacity of fuel charging hopper and the size of any
furnace, gasifier or other biomass conversion device. It is useful in the evaluation of
transportation cost and storage space for biomass fuel.
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Bulk density of a fuel is different from that of true or specific density of the single
fuel. For example, the true density of eucalyptus is 700 kg/m3, whereas the bulk density of
2-3.5 cm3 cube pieces of eucalyptus is around 250-300 kg/m3. Bulk densities of certain
fuels are given in Table 1.1.
Table 1.1. Bulk density and true density of certain fuel materials
Fuel Density (kg/m3)
Coal anthracite 830-900
Coal Bituminous 770-930
Wood hard 20-40mm3 330
soft 250
Charcoal 130-150
Saw dust 175
Paddy husk 105
Straws 50-80
Bagasse 70
Acacia nilotica 820*
Dalbergia sissoo 710*
Eucalyptus 770*
* true densities
Source: Kaupp and Goss, 1984; Jain, 1997
1.2.2 Angle of Repose
The angle of repose is the angle made by the biomass from the horizontal to the sides of
pile under free falling conditions. It is expressed in degrees. It is a flow property of the
material. It is generally determined by filling a large open ended tube with oven dry
biomass, keeping the tube with its one end on the ground and then lifting the tube in such a
manner that the biomass forms a pile on the ground. The angle made by the pile with the
horizontal base is the angle of repose. The values of angle of repose depend on the size
and moisture content of the biomass.
Angle of repose is useful in the determination of the angle of fuel hopper, fuel
transportation lines to the furnaces or gasifier. During the thermochemical conversion
process the angle of repose changes due to change in shape and size of the fuel particle. If
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the angle of repose approaches 90 degrees or more it indicates tendency of the fuel
towards bridging. Lower angle of repose is an indication of free flow behavior of biomass
material. As an example the angle of repose for oven dry paddy husk is 58 degrees.
1.2.3 Moisture Content
Most of the biomass are hygroscopic in nature and absorb moisture from the atmosphere.
Moisture in biomass is fundamentally subdivided into inherent, surface and decompo-
sition moisture. Inherent moisture is the moisture a fuel can hold in the capillary openings
of the biomass when in equilibrium with the atmosphere. Surface moisture occurs on the
surface of the biomass and is in excess of inherent moisture. The moisture content of
biomass cited in the literature usually refers to inherent plus surface moisture.
The percent moisture content (MC) of the biomass can be determined by drying
the sample at 110 oC in hot air oven till a constant weight is obtained. The method is
known as standard oven method. The following expression may be used for computing
percent moisture:
Moisture content of a biomass is usually reported on wet weight basis as indicated
by above equation. Since the moisture content of biomass varies from day to day due to
variation in atmospheric relative humidity and temperature It is, therefore, preferable to
report the biomass characteristic data on dry weight basis. At a relative humidity of 90 to
95%, the moisture content of most biomass ranges from 25 to 35%, which reduce to
around 10% at a relative humidity of 30 to 40%.
The moisture content on wet weight basis can be converted to dry weight basis
using the expression given below. In the following equation Mwand Mdare the percent
moisture content on wet and dry weight basis respectively.
During storage, the exposure of biomass to high relative humidity should be
avoided so that the high moisture in biomass due to high relative humidity do not
exceed too much, because higher moisture in biomass lead to its faster decay. The
MCWet weight dry weight
Wet weightx=
100
100100
xM
MM
w
wd
=
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chemical reactions during the biological decay of biomass are exothermic and can even
cause burning of the biomass.
It is desirable to use fuel with low moisture content, because considerable part of
the heat is used to evaporate the moisture which is never recovered in any practical
situations and the effective hating value of the biomass gets reduced. It may be noted
that this heat loss represents only the heat of evaporation of inherent and surface
moisture and not the heat loss caused by decomposition moisture.
The net heating value and the moisture content of a biomass can be correlated by
the following expression. In the expression below , Mf, CVwand CVdare latent heat
of vaporization, moisture fraction of biomass, heating value of wet and dry biomass
respectively.
CVw= (1-Mf) x CVdMf
The theoretical limit of moisture for cellulose at which the combustion is no
longer self sustaining is 88%, however, in practice, the moisture content at which the
biomass combustion can be sustained is much lower i.e. 70%. For gasifier, the optimum
moisture content of the biomass is 15%, and higher moisture in biomass leads to poor
gasifier performance. Also high moisture lowers the effective heating value of the
biomass and should be avoided while using as fuel in furnaces.
Decomposition moisture is the moisture formed from organic compounds of
biomass during thermal decomposition reactions. It is estimated stoichiometrically that
every kilo gram of biomass yields 450 to 600 gram of water during thermal
decomposition reactions depending on its composition.
1.3 Thermal Characteristics
The Important thermal characteristics are calorific value and proximate analysis.
1.3.1 Heating Value
Heating value or calorific value is the heat released by the fuel under ideal combustion
conditions. It is usually classified as higher heating value (HHV) and lower heating
value (LHV)
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1.3.1.1 Higher Heating Value
It is the amount of heat liberated when a known quantity of biomass is burned under
ideal combustion condition at constant volume and the decomposition moisture is
condensed i.e. its latent heat of vapourization is taken into account. It is determined
using standard bomb calorimeter, where known weight of biomass material is burnt in a
constant volume bomb in presence of oxygen. The heat liberated is absorbed by known
weight of water. It is a measure of heating value when combustion is taking place at
constant volume and the water formed during combustion or present as moisture in the
biomass is condensed. The latent heat of vaporization of water is also taken into account
and this heating value is usually referred as the higher heating value (HCV). In almost
all the thermochemical conversion devices, operation occurs at constant pressure and
vapors leave with flue gases without getting condensed. The heating value under these
conditions is called lower heating value (LCV). It is therefore, suggested that the LCV
should be used in preference to HCV for the energy and mass balance, and other design
and performance evaluation calculations for a thermo-chemical conversion device.
1.3.1.2 Lower Heating Value
Knowing the elemental analysis and higher heating value of the biomass, the lower
heating value can be determined. It is usually 10 to 15% lower as compared to the higher
heating value. The lower heating value can be linked with the higher heating value by
the following expression. and Wf are the latent heat of vaporization of water and
weight fraction of water formed during combustion process. The lower heating values of
selected biomass species are given in Table 1.2.
HCV = LCV + Wf+ expansion work
The heating value of a biomass per unit weight is a function of the moisture content of
the biomass. For a wet biomass available heat per unit weight of biomass is reduced and
also a part of heat is required to vaporize the water present in the biomass as moisture.
1.3.2 Proximate Analysis
Proximate analysis provides information on the combustion characteristics of biomass. It
is a measure of fixed carbon (FC), volatile matter (VM), Ash (A) and Moisture (M) in the
biomass material and expressed as percent. The term volatile matter and fixed carbon
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does not have clear definitions. The volatile matter of any substance in a broad sense is
the fraction that is driven off by heating the sample to a specific time and temperature.
The total amount of volatile matter and its composition is the function of heating rate as
well as the final temperature. The volatile matter is an important parameter because it
characterises the expected contamination of the raw gas with condensable vapours in
any gasifier or pyrolysis equipment.
Table 1.2. Ultimate analysis of selected fuels
Fuel MaterialCV
HCV LCV
Carbon 32.7
Anthracite 30.9
Bituminous 25.1
Charcoal 24.7
Lignite 23.9
Acacia Nilotica 19.2 17.8
Eucalyptus 19.4 18.0
Leucaena Leucocephala 19.4 18.2
Dalbergia sissoo 18.7 17.3
Bagasse 20.0 18.6
Paddy straw 15.0 13.9
Paddy husk 15.5 14.4
Wheat straw 17.2 16.0
Cotton sticks 17.4 16.3
Source: Pathak and Jain, 1984; Reed and Das, 1988.
There are no standard techniques for the proximate analysis of biomass as yet,
however, the most commonly adopted procedure for proximate analysis of coal outlined in
BS 1016 Part 3&4, 1973 are in use for the proximate analysis of biomass as well. The
biomass is placed in a muffle furnace at 915 oC for 7 minutes in a covered platinum
crucible. The moisture and VM and are driven off and the residue left after 7 minutes is
the fixed carbon and ash.
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Ash and moisture can be determined separately. Moisture content can be
determined using standard oven method as discussed earlier.
Ash is the mineral content in the fuel that remains in oxidized form after
combustion. The ash content and its composition have a major impact on the operation of
a gasifier or a furnace. Higher ash content lowers the energy available and more space
must be provided where the ash can be discharged, the composition of the ash determines
the slagging temperature of the ash. If the temperature in the combustion zone rises to the
ash melting point, the ash will melt and the molten mass will form clinkers, clinging to the
internal surface, tuyers and grate. It will severely affect the fuel flow and may result in
failure of the complete system.
The most common constituents of ash are SiO2, Al2O3, Fe2O3, TiO2, CaO, MgO,
Na2O, K2O and SO3as these minerals amounts to at least 95% of all minerals in the ash.
It has been found that the most troublesome components of the ash are SiO2 and oxides
of alkali metals Na2O and K2O. In most of the biomass SiO2content amounts to above
50% and can reach to extreme value of 97% in case of rice husk. These components
lower the ash melting temperature and the most dangerous is their tendency to vaporize
at temperatures usually obtained in gasifiers or combustion furnace. The problem
becomes even more severe when the biomass has sulfur and chlorine and the alkali
metals react to form chlorides and sulfide and sulfates. The melting temperature of these
compounds is much lower and they also form eutectic mixtures having much lower
melting temperature. The melting point of SiO2is fairly high i.e. around 2350oC but in
most of the cases it melts at much lower temperatures.
The vapors of molten ash reach the engine in extremely fine form (
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1. Low temperature operation that keeps the temperature well below the melting point
of ash.
2. High temperature operations that keep the temperature above the melting point of
ash and in addition flux are added to lower the ash melting temperature even more.
Some of the fluxes that lower the melting point of ash are iron ore, feldspar, salt
cake, limestone and dolomite.
For the determination of ash, biomass is heated in a tarred silica crucible in a
muffle furnace at a temperature of 600 oC for 2 to 3 hours till a constant residual weight is
obtained. The constant weight residue is taken as ash in the biomass. The percent ash
content can be determined using the following expression.
Knowing moisture content and combination of moisture and volatile matter, the
volatile matter of the biomass can be estimated. Also if ash and combination of ash and
FC are determined, fixed carbon content of the fuel can be estimated. The proximate
analysis is represented by the following expression.
Proximate analysis of certain fuel materials is given in Table 1.3. The volatile
matter of the biomass starts distilling off at moderate temperatures of 250-350 C in any
thermochemical conversion process. The vapors thus formed consist of water, oils, tar and
gases. It is, therefore, obvious that biomass fuels having high volatile matter have tendency
to form higher tar during pyrolysis or gasification. Most of the biomass materials have
volatile matter content around 75-80% on dry and ash free basis. Thus biomass tends to
release high tar as compared to materials having low volatile matter such as charcoalduring gasification.
When all the volatile matter is driven off from the biomass, the residue left is fixed
carbon and ash. In a gasification process, the fixed carbon provides favorable
environments where reduction reactions take place to form carbon monoxide, methane and
hydrogen. Thus biomass materials with higher fixed carbon are considered as better feed
Ash weight of ashweight of wet biomass
x=
100
[ ]FC VM Ash MC + + + = 100
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stock for gasifiers. In furnaces, the oxidation of fixed carbon component of biomass
releases heat energy and is fully utilized.
Table 1.3. Proximate analysis for selected fuels
Fuel VM FC Ash
Acacia nilotica 5
Bituminous 20-40 40-55 10-15
Lignite 40 46 14
Charcoal 10-30 50-65 5-15
Fuel wood 70-80 15-20 1-10
Crop residues 65-80 12-18 5-20
Paddy husk 71.0 12.5 16.5
Bagasse 15.9 79.2 4.9
Acacia nilotica 16.8 80.8 2.3
Dalbergia sissoo 15.7 80.4 3.9
Eucalyptus 16.6 82.2 0.9
Source: Pathak and Jain, 1984; Reed and Das, 1988
Ash is the inorganic matter in the biomass left after the volatiles, fixed carbon and
moisture are driven off. It contains varying quantities of oxides of silica, sodium,
potassium, phosphorus, magnesium, iron etc. Higher ash content in the biomass is coupled
with the ash handling problems in a thermochemical conversion device. Ash from some
biomass material fuses at temperatures i.e. 800-1200 oC which are usually attained in
gasifiers or furnaces and tends to fuse and form large hard clinker of ash. The fused ash
from certain biomass gets vaporized at these temperatures which condenses on relatively
low temperature surfaces such as boiler tubes and tends to plug the gas/air flow channels
in gasifiers and furnaces. Knowledge of slagging behavior of the biomass ash is, therefore,
essential before it is used as a feed stock for gasifier or furnaces. Ash slagging
temperatures of some selected biomass materials are given in Table 1.4.
1.3.3 Thermogravimetric Analysis
In thermogravimetric analysis, the biomass is heated under controlled conditions of
temperature and environment or reaction atmosphere. Thermogravimetric analysis
(TGA) provides information on weight change as a function of temperature and time
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whereas differential thermogravimetric analysis (DTG) i.e. rate of weight change with
respect to time. It also gives information on differential thermal analysis (DTA), the type
of reaction prevailing at a specific temperature i.e. weather the reaction was exothermic
or endothermic. The weight loss and temperature/time data can be used to work out
quantities of volatile matter, char and ash in the biomass. The data can further be used to
compute the thermal degradation reaction kinetic parameters such as activation energy,
order of reaction and pre-exponential factor.
Table 1.4. Softening and melting temperature of ash from biomass
BiomassSoftening
Temperature (C)
Melting
Temperature (C)
Almond shell 860 1350
Cotton gin trash 1010 1380
Maize cobs 900 1020
Maize stalk 820 1091
Rice straw 823 1190
Rice husk 1440 1650
Tree prunings 770 1550
Wood Chips 1050 1190
Mustard stalk 1030 -
Source: Kaupp, 1984.
Thermogravimetric analysis is carried under non-isothermal and isothermal
conditions. When the temperature increase is under a pre-set, programmed or at linear
heating rate, the analysis is non-isothermal. The size of material is normally very small
(20 to 50mg) and only finely ground samples are used. This is the most commonly used
technique for TGA, due to convenience, accuracy and reproducibility. The
instrumentation for this type of analysis is well developed and the operating conditions
can be closely monitored on thermo gravimetric analysis equipment. Also most of the
reported work on thermal analysis is on non-isothermal degradation.
Isothermal degradation is characterised by large samples, bigger size and is
carried out in specially designed thermo balances. The conditions prevailing in such
equipment resemble with the actual conditions during combustion or gasification where
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large samples are used, to some extent. The kinetic parameters determined using this
technique depend on the type of thermo-balance and differ to a large extent.
The non-isothermal TGA data on residual weigh and temperature/ time can be
used to evaluate the kinetic parameters using the following models (Jain et al., 1997):
lnln
ln -(1- )
T =
AR
qE 1 -
RT
E -
E
RT2
2
The above model is valid under the assumption that the first order reaction
mechanism is followed during thermal degradation. For n 1, the following equation is
used.
( )
( )ln ln
1 1
11 2
1
2
=
n
T n
AR
qE
RT
E
E
RT
In the above equations:
=(W - W)
(W - W )
o
o f
k = Rate constantWo= Initial weight of sample, mg
W = Time dependent weight of
sample, mg
Wf= Final weight of the sample, mg
= Fraction of A decomposed at any
time t
A = Pre-exponential factor, s-1
R = Universal gas constant
q = Linear heating rate C min-1
E = Energy of activation, kJ mole-1
T = Absolute temperature, K
n = Order of reaction
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The first term of right hand side of both the above equations tend to be
reasonably constant. Thus, a plot of left hand side against 1/T allows the activation
energy to be determined from the slop E/R. The pre-exponential factor A can be
determined with E known from the above equations. In all the determinations a prior
knowledge of the value of the order of reaction is to be assumed.
Jain et al (1996) reported the thermo gravimetric analysis of paddy husk, cellulose,
and lignin under oxidative, intermediate (O2 5:N2 95%) and inert atmospheres at
different linear heating rates (1 to 100 oC/min). The following observations are reported.
1. Activation energy for thermal degradation of cellulose was the highest followed by
paddy husk and lignin under similar conditions of environment and linear heating
rates.
2. Under oxidative environment, the activation energy was the highest followed by
intermediate and inert reaction environments under similar conditions of linear
heating rates and the biomass materials.
3. With the increasing linear heating rates the activation energy in general decreased.
4. Order of reaction was found to be a function of linear heating rate. At lower heating
rates the thermal degradation reactions followed the first order reaction mechanism
whereas at higher heating rates the appropriate order of reaction was 1.5 or 2.
1.4 Chemical Analysis
Chemical analysis gives information about the chemical composition (cellulose, hemi-
cellulose, pentosan lignin and alcohol benzene extractives) and elemental analysis (carbon,
hydrogen, nitrogen, oxygen, sulfur, silica, sodium, potassium etc.) of biomass.
1.4.1 Ultimate Analysis
Ultimate analysis gives information regarding the elemental composition of carbon,
hydrogen, oxygen and nitrogen content of a biomass fuel. Equipment for the analyses of
carbon, hydrogen and nitrogen (CHN analyzer) are now available commercially. Oxygen
is generally determined by the difference.
The ultimate analysis does not reveal the suitability of biomass for gasification,
combustion or any other process but is the main tool for the determination of
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stoichiometric formula, stoichiometric air requirement and air fuel ratio, gas composition,
temperature limits, gas production rate etc. through a mass and energy balance over the
thermochemical conversion processes. It is also used to predict the lower heating value of
the biomass Ultimate analysis and heating value of some selected fuel is given in Table
1.5. The data in the table is reproduced from Reed and Das (1987) and Jain (1997).
Table 1.5. Ultimate analysis of selected fuels
Fuel Material C H O
Carbon 100 0 0
Anthracite 95.0 1.50 3.50
Bituminous 87.0 5.00 13.00
Charcoal 75.0 5.00 15.00
Lignite 71.0 5.00 24.00
Acacia Nilotica 48.1 6.14 45.76
Eucalyptus 50.3 6.26 43.44
Leucaena Leucocephala 54.1 5.15 40.75
Dalbergia sissoo 48.6 6.2 0.33
Bagasse 48.2 6.1 0.2
Paddy straw 45.5 6.19 48.31
Paddy husk 45.2 5.99 48.81
Wheat straw 47.8 5.89 46.30
Cotton sticks 52. 7 5.07 42.23
Source: Pathak and Jain, 1984; Reed and Das, 1988.
The total carbon in the biomass is different from fixed carbon as determined by
proximate analysis. In order to avoid confusion, the total carbon may be split into basecarbon and volatile carbon. Base carbon represents the carbon that remains after
devolatilization, whereas volatile carbon is defined as the carbon estimated from the
difference between total carbon and base carbon. Base carbon does not equal the fixed
carbon as given by proximate analysis because the fixed carbon includes some other
organic components also which have not been evolved during the process of
devolatilization.
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1.4.2 Chemical Composition
Cellulose, lignin, hemi-cellulose and pentosan are the major chemical constituents of the
biomass. Cellulose is a linear polymer of anhydroglucose units; hemicellulose is a mixture
of polymer of 5 and 6 carbon anhydrosugars and lignin is an irregular polymer of phenyl
propane units. Pentosan is five carbon anhydrosugars. Composition of cellulose,
hemicellulose and lignin can be determined using the standard techniques described in the
text books for wood chemistry. Chemical analysis of certain biomass species is given in
Table 1.6.
Table 1.6. Chemical analysis of certain biomass
Biomass Cellulose Lignin Pentosan
Acacia nilotica 33.38 38.97 10.27
Eucalyptus 34.20 39.20 12.00
Leucaena leucocephala 44.87 22.36 17.74
Bagasse 40.00 14.80 22.60
Paddy husk 44.00 17.20 17.80
Maize cobs 36.80 11.20 27.80
Paddy straw 41.40 12.10 20.40
Cotton sticks 41.90 27.20 19.00
Source: Jain, 1997.
The chemical analysis gives very useful information regarding the use of biomass
for thermochemical, biochemical conversion processes or for industrial uses such as for
paper and furfural production. Hydrolysis of pentosan yields furfural which is a very useful
intermediate for resin industries and also used as solvent. Thus biomass rich in pentosan
e.g. rice husk, cotton stalks, maize cobs etc are excellent feed stock for furfural production.Some units for furfural production based on rice husk are commercially operating in India
and other parts of the world. Materials having high cellulose and hemicellulose are good
for biological conversion processes i.e. anaerobic digestion and alcoholic fermentation.
High cellulose also favors the use of biomass for paper and board production. Since lignin
is a irregular polymer of phenyl propane unit, it tend to yield high tar proportion during
thermal decomposition reactions. Thus the biomass rich in lignin are known to generate
producer gas with high tar during thermal gasification.
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1.5 Correlation Models
Correlation models for predicting the heating value, stoichiometric formula and air fuel
ratio are discussed in the following sections.
1.5.1 Heating Value
Using the ultimate analysis, ash and heating value data, three correlation models were
developed (Jain, 1997). The models are given below.
Model 1 : LCV = 17.89 - 0.21 x A
Model 2 : HCV = 19.24 - 0.22 x A
Model 3 : LCV = 0.19 x C + 0.38365 x
H + 0.217 x O - 3.4363
The first two model correlates lower and higher heating values and ash content
of biomass. It is assumed that the heating value of ash free biomass is constant and is a
linear function of ash content. The LCV or HCV obtained by these models is fairly in
agreement with the experimental values with a variation of 2-3%.
The third model predicts lower heating value knowing carbon hydrogen and
oxygen content of the biomass. In the models LCV and HCV, are lower and higher
heating values (MJ kg-1) whereas A, C, O and H are the percent ash, carbon, oxygen and
hydrogen of biomass on dry weight basis respectively. For biomass, which is not fully
characterized, these models can effectively be used to get first hand information of the
characteristics of biomass.
1.5.2 Stoichiometric Formula
Stoichiometric formula gives the atomic composition of carbon, hydrogen and oxygen in a
biomass. Knowing the elemental analysis of a biomass, its stoichiometric formula can bedetermined. For any biomass if the stoichiometric formula is represented by C HxOy, where
x and y are atomic ratios of hydrogen and oxygen, x and y can be determined using the
following expressions.
x = H / (C/12)
y = (O/16)/(C/12)
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The typical atomic ratios for biomass is CH1.4O0.6and for coal CH0.9O0.1. Once we
know the stoichiometric formula, the molecular weight and the stoichiometric air fuel ratio
can be determined. The Stoichiometric formula/values of x & y for certain biomass
materials is given in Table 1.7.
Table 1.7. Stoichiometric formula and air fuel ratio of certain biomass
Biomass X Y A/F (Nm3/kg)
Acacia nilotica 1.532 0.713 4.29
Arhar stalk 1.055 0.560 4.68
Eucalyptus 1.491 0.646 4.66
Leucaena leucocephala 1.142 0.564 4.78
Bagasse 1.519 0.635 4.55
Paddy husk 1.587 0.807 3.34
Maize cobs 1.273 0.765 3.06
Paddy straw 1.630 0.795 3.30
Cotton sticks 1.153 0.600 4.48
Source: Jain, 1997.
1.5.2 Stoichiometric Air Fuel Ratio
Stoichiometric air fuel ratio is the theoretical air required for complete oxidation for a unit
weight of the biomass. The stoichiometric air fuel ratio is useful for the determination of
air quantity requirement for furnaces or gasifiers and subsequently for designing air and
gas handling system. Using stoichiometric formula of biomass, the following procedure
may be used to determine the stoichiometric air fuel ratio. Combustion of a biomass
material can be represented by the following reaction.
CHxOy+ n(0.21O2+ 0.79N2) CO2+ x/2 H2O + 0.79 (n) N2
In the above equation air is assumed to have a molar composition as O2:N2::21:79.
The moles of air in the reaction are represented by n. Writing an oxygen balance over the
above reaction:
y + 0.21 (2n) = 2 + x/2
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If we substitute the values for x and y in the above equation number of moles of
air required for complete oxidation of biomass material can be determined. The
stoichiometric air fuel ratio for biomass materials varies to a large extent i.e. 3.34 m3/kg
for paddy husk to 5.1 m3/kg for acacia auriculiformis (Jain, 1996, 1997). The air fuel
ratio for certain biomass materials is given in Table 6.
1.6 Conclusions
On the basis of information on characteristics of biomass some general classification
regarding their suitability for different applications can be worked out. Fuels with low ash
content, high calorific value and density are suitable for gasification and fuel for furnaces.
Biomass with low ash slagging temperature are trouble some fuels. High ash biomass
coupled with poor flow properties such as paddy husk are not suitable for gasification in
down draft gasifier with throat, however, it is good fuel for throat less and updraft gasifiers
and furnaces. Fuels with high volatile matter have tendency to generate considerable tar
and are less suitable for updraft gasification. High moisture in the fuel is not suitable
regarding the application of fuel in gasifiers as well as furnaces. Biomass materials with
high cellulose content are suitable as feed stock for paper industry. High cellulose
materials are appropriate for alcoholic fermentation and anaerobic digestion as well. High
pentosan content in a biomass supports its use for furfural production. High silica biomass
such as paddy straw and paddy husk can be used to produce amorphous and precipitated
silica.
Biomass materials have certain limitations such as less density, high volatile
matter, high ash content, hygroscopic nature etc. But inspite of that there is no doubt that it
has tremendous potential for various energy related applications. It can be converted to
better quality fuels such as producer gas, biogas, methanol, ethanol, tar, charcoal etc. via
thermochemical and biochemical conversion route. It can be directly used as fuel for
industrial boilers and domestic kitchens for thermal application. Biomass also has
potential as feedstock for proper and board, furfural, activated carbon, silica industries.
References
1. Jain A.K. (1996) Mid term review report of the AICRP project on RES Producer
gas component. School of Energy Studies for Agriculture, PAU Ludhiana.
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2. Jain A.K. (1997) Correlation models for predicting heating value through biomass
characteristics. Journal of Agricultural Engineering 34 (3):12-25.
3. Jain A.K., Sharma S.K. and Singh D. (1999) Reaction Kinetics of paddy husk
thermal decomposition. Journal of Solar Energy Engineering ASME, USA,
121:25-30.
4. Kaupp A. (1984) Gasification of rice hulls-theory and practice. Published by
GATE/GTZ, Germany.
5. Kaupp A. and Goss J.R. (1984) Small scale gas producer engine system. Published
by GATE/GTZ, Germany.
6. Pathak B.S. and Jain A.K. (1985) Biomass Characteristics, Final report of the
project Energy in Agriculture and first report of the School of Energy Studies for
Agriculture. PAU Ludhiana, 49-64.
7. Reed T.B. and Das A. (1988) Handbook of biomass down draft gasifier engine
systems. SERI/SP-271-3022 DE88001135, UC Category: 245, USA.
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CHAPTER 2
GLOBAL WARMING: A NEW PARADIGM FOR BIO-
ENERGY RESEARCH
S.K. Sharma
2.1 Introduction
Global warming has pushed the use of biomass for bio energy and bio fuels to the center
stage for reducing green house gas emissions in transport and industrial sectors. Bio-fuel
production through first generation bio technologies in 2009 was 750 million liters of
gasoline equivalent. It has been projected by IEA that total biomass use in 2050 will be
3500 MT, which will account for 20% of the total consumption and is analogous to the
current global annual consumption of oil.
Bio-energy is considered renewable due to its origin from and end in carbon
dioxide, as a result of closed carbon cycle. However, a large number of first generation
technologies fail to meet the test of sustainability based on the criteria of ratio of
renewable energy output to fossil energy input; as considerable amount ofprimary/secondary energy is needed in biomass process chain during cultivation,
harvesting, transportation, conversion processes, supply chain, use of the products and
disposal. During production process, energy inputs are required for ploughing, sowing,
fertiliser and pesticide production. During production process, energy is required for
pre-treatment, processing, purification of products. As per sustainability criteria, it is not
only the amount of energy but also the source of energy used for processing, which is
important. If sustainability criteria are not applied to biomass it will result inbiodiversity loss from land use change, food insecurity, overuse of water, and
mismanagement of soil. Global warming concerns are becoming an overriding factor all
over the world, resulting in a paradigm shift in the development of bio energy
technologies. This has created a new window of opportunity for the researchers for
developing new technologies and modifying the old technologies. Life cycle analysis for
carbon and water footprints should be used to analyse the global warming impact of the
product.
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Keeping in view the competition between food and fuel, selection of raw
material for bio- energy should take in to consideration the fact that the first priority of
biomaterials is for food, then animal feed and bio-energy is the last claim. Food enjoys
higher commercial value than bio- energy. Hence, bio-energy has to be subsidised if it is
to compete with food. Best option for the raw material for bio-energy is waste material
from agriculture, animals, human, and non -edible oils etc. It is estimated that more
than 90 million tons of municipal solid waste is generated each year in India. 40% to 60
% of this waste is compostable matter. MNRE has estimated a potential of 2500 MW
under Energy to waste programme.
2.2 New Research opportunities in Bio Energy
2.2.1 Bio fuels
Alkali and acid (homogeneous and heterogeneous) catalysed esterification processes
have been extensively used for the production of bio-Diesel. Esterification Processes for
oils containing high free fatty acid (non edible oils, animal fat, waste oil) are energy
intensive. Use of homogeneous and heterogeneous catalysed processes for
transesterification suffer from heat transfer and Mass transfer limitations, as oil and
alcohol are not completely miscible (Canakei and Van Gerpen, 2001; Freedman et al.,
1986; Vicente et al., 2004). Use of Process intensification technologies such as
ultrasonic and microwaves can overcome these problems. It has been estimated that the
use of microwave for transesterification of commercial seed oils with methanol in the
presence of various catalysts gives yields greater than 97% with a reaction times of less
than 2 minutes and are more energy efficient (Balat et al., 2008). Other intensification
technologies such as static mixers, micro-channel oscillatory flow and cavitation are
also very promising. These can reduce molar ratio of alcohol to oil as well as energy
inputs due to increase in heat and mass transfer rates.
Use of enzymatic transesterification of triglycerides is environmentally more
attractive as compared to conventional physiochemical methods (alkali and acid
esterification) (Noureddini et al., 2005; Singh and Singh, 2010). High cost of enzyme is
one of the limitations of this process. This can be partially offset by immobilisation of
the enzymes, which helps in the stability, recovery and reuse of lipases. Different
methods of immobilisation include; physical adsorption on solid support, Covalent
bonding to a solid support and Physical entrapment within a polymer matrix
(Noureddini et al., 2005). However, use of polymer matrix for physical entrapment of
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lipase by sol- gel method appears to be better option due to ease of preparation and
greater stability and activity of lipase over longer period. A number of studies on
different lipases such as Mucor michi, Candida antartica, Pseudomonas cepacia,
Porcinepancreatic for different triglycerides and alcohols to optimise reaction
parameters such as molar ratios of reactants, kinetics, temperature, enzyme loading,
stability and reusability. Use of multiple enzymes in sequence for varied substrates has
given encouraging results.
However, biggest bottle neck in the use of enzymes for production of biodiesel
lies in their high initial and replacement cost. Research should focus on reducing
enzyme cost and increasing enzyme activity for large scale economically viable
industrial applications.
2.2.2 Bio-oils from Micro Algae
Microalgae have emerged as a potential source of bio oil due to its high oil productivity
as compared to other crops (Nigam and Singh, 2011) as shown in Table 2.2.
There are three main categories of micro algae namely: Diatoms, Green algae
and Golden algae. Each category has thousands of species.
The diatoms (Bacillariophyceae) not only dominate the phytoplankton of the
oceans, but are also found in fresh and brackish water. Approximately 100,000 species
are known to exist. Due to its ability to grow in saline, there is a great potential for them
in the area with brackish water, where it is not possible to grow normal oil crops.
The Golden algae have nearly 1000 species and are also quite similar to diatoms,
with a more complex pigmentation system. The golden algae produce natural oils and
carbohydrates as storage compounds.
The green algae (Chlorophyceae) grow quite abundantly, especially infreshwater. The main storage compound for green algae is starch, although it is possible
to produced oil under certain conditions
There are number of critical areas which require in depth studies for large scale
exploitation of this energy source. These include; studies on algal biology and
physiology, strain isolation, siting, resource management, regulation and policy,
cultivation, harvesting, dewatering.oil extraction, conversion to fuels, co product
production etc.
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Table 2.1. Conversion efficiencies of enzymatic esterification process used for different
oils (Singh and Singh, 2010).
S.N. Oil Alcohol LipaseConversion
(%)Solvent
1. Rapeseed 2-Ethyl-1-hexanol C. rugosa 97 None
2.Mowrah, Mango,
Kernel, Sal,C,-C, alcohols
M. miehei(Lipozyme IM-
20)86.8-99.2 None
3. Sunflower Ethanol M. miehei(Lipozyme) 83 None
4. Fish Ethanol C. antarctica 100 None
5.Recycled
restaurant GreaseEthanol
J. cepacia(Lipase PS-30)
+ C.antarctica(Lipase
SP435)
85.4 None
6.
Tallow,
Soyabean,
Rapeseed
Primary alcohols: methanol,
ethanol, propanol, butanol, and
isobutanol; Secondary alcohols:
isopropanol and 2-butanol
M. miehei(Lipozyme IM-
60) C. antarctica(SP435)
M. miehei(Lipozyme
IM60)
94.8-98.5;
61.2-83.8;
19.4-65.5
Hexane; Hexane;
None
7. Sunflower Methanol; Ethanol P. juorescens 3; 79; 82
None;
Petroleum ether;
none
8. Palm kernel; Oil Methanol; Ethanol L. cepucin(Lipase PS-30) 15; 72 None : None
9. Soyabean oil Methganol
Rhizomucor miehei
(Lipozyme IM-77)
enzyme amount 0.9
BAUN
92.2 Molar ratio 3:4:1
10. Soyabean oil Methanol C. antarcticalipase 93.8
> molar
equivalent
MeOH
11. Sunflower oil Methanol Pseudomonas fluorescens(Amano AK)
(>90) Oil : methanol(1: 4.5)
12. Palm oil Methanol Rhizopus oryzae 55 (w/w) Water
2.2.3 Bio- Ethanol
Bio Ethanol production through fermentation is an age old process. Fermentation is
carried out with yeast strains such as Saccharomyees cerevisiae, S. uvarum,
Schizosaccharomyces pombe, Kluyveromycessp. etc.
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Table 2.2. The oil productivity of different crops
Oil crops Productivity (gallons per acre per year)
Corn 18
Soybeans 48
Safflower 83
Sunflower 102
Rapeseed 17
Oil palm 635
Microalgae 500015,000
India is second largest producer of bio ethanol from sugar based substrates in the
world after Brazil. 5% alcohol is blended in the petrol sold in the country. With the rise
in the price of oil in the international market, the cost of production has become
favourable and no subsidy is required, in contrast to subsidised bio-ethanol produced in
USA and Europe, where main feed stock is costly grain. However, due to limited
cultivation area available for cane sugar in view of food security issues, it is important
to diversify the feedstock to agricultural residues.
There is a need for the development of genetically modified stable yeast strains
suitable for different feed stocks. Stability of the yeast strain is essential for ensuring a
prolonged continuous process, In order to improve stability of the yeast, new strains
should have better pH, ethanol, osmo and temperature tolerance. High osmo and ethanol
tolerance will allow greater recycling rates of the stillage, thus reducing energy
consumption. This will also result in prolonged stable fermentation process increasing
the overall productivity.
Studies show that bacteria such as Zymomonas mobilis, Clostridium
thermosaccharolyticum, Thermoanaerobacter ethanolicus) can also be used for ethanol
fermentation (Nigam and Singh, 2011). Thermophilic bacterial fermentations would
increase energy efficiency in distillation. Many bacteria also have the capability of
fermenting pentose sugars, thus increasing conversion efficiencies. As studies are at
bench scale, sustained efforts are need for the development of full-scale bacterial
ethanol fermentation process.
In addition residual stillage management is essential to further improve the
energy efficiency and economics of the fermentation process. It has been estimated that
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nearly 75% to 100% of the overall process heat demand could be met from biogass
produced by anaerobic digestion of stillage.
2.2.4 Bio Gas Slurry Management
Management of biogas slurry from large plants is the major bottle neck in large scale
propagation of this technology for power generation. There is a chronic shortage of
chemical fertiliser in the country. A huge subsidy is given to the farmer so as keep the
input costs low. Large foreign exchange is spent on the import of raw material and
fertiliser. There is an urgent need to develop techniques for upgrading organic fertiliser,
which could replace urea based chemical fertilisers. Chemical fertilisers in general
destroy soil flora and fauna, which keep the soil alive. Organic fertiliser adds value to
the crop and open export avenues. In addition, it will reduce crippling subsidy burden of
the government. Value addition of the slurry will make biogas based power units
economically more viable, resulting in achieving the targets of MNRE
2.3 Conclusions
Discussion given above shows that this is a unique period in the history of bioenergy
research. There is huge number of opportunities to develop clean and green bioenergy
technologies with low carbon foot print. There is an urgent need to create teams of
scientists in the diverse areas of biotechnology, chemistry, chemical and mechanical
engineering, microbiology etc to undertake focused and time bound programme for
developing new cost effective bio-energy technologies. NIRE can play a very import
role in this direction. This will help in achieving energy security for the country,
especially in the rural areas. At present nearly 70% of the rural population does not have
access to commercial energy. This is the main reason for deprivation and
underdevelopment of the rural areas. New bio-energy technologies can transform the
face of rural India.
References
1. Balat M., Balat H. and Oz C. (2008) Progress in bioethanol processing. Progress
in Energy and Combustion Science, 34:551-573.
2. Canakei M. and Van Gerpen J. (2001) Transactions of the ASAE, 44:1429-1436.
3. Freedman B., Butterfield R.O. and Pryde E.H. (1986) JACCS. 63(10).
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4. Nigam P.S. and Singh A. (2011) Progress in Energy and Combustion Science,
37.
5. Noureddini H., Gao X. and Philkana R.S. (2005) Bioresource Technology,
96:769-777.
6. Singh S.P. and Singh D. (2010) Renewable and Sustainable Energy Reviews,
14:200-216.
7. Vicente G., Martinez M. and Aracil J. (2004) Bioresource Technology, 92:297-
305.
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CHAPTER 3
BIOMASS ASSESSMENT FOR GROWTH OF
BIOENERGY: A CASE STUDY IN ASSAM, INDIA
D.C. Baruah, Moonmoon Hiloidhari
Abstract
Agricultural residue biomass could be a prospective source for decentralized electricity
generation in agriculturally dominant countries like India. A large amount of agricultural
residue is distributed over the rural farm areas of India. Precise assessment of its
availability is important for successful rural biomass energy planning. Application of
Remote sensing and GIS could increase the preciseness of assessment and hence aids in
successful renewable energy planning.
The present study is conducted in a representative district of Assam to assess the
potential agricultural residue biomass production for decentralized electricity
generation. Appropriately validated satellite images and other ancillary data are used in
GIS environment for mapping the potential energy generation. It is observed that, rice
crop residues share maximum portion of electricity generation potential followed by
sugarcane and rapeseed & mustard in the study area. The village level estimated
electricity would be sufficient to fulfill the domestic electricity demand in most of the
rural areas of the region. In view of the existing electricity consumption pattern and
shortage of grid connected electricity supply in rural areas of India, decentralized
biomass based electricity generation could be an attractive option in rural areas of
Assam.
Keywords: Biomass energy, Agricultural residue, Decentralized electricity, GIS
3.1 Introduction
The energy demand has increased many folds in the recent decades. Population growth
and improved living standard, urbanization, industrialization etc. are the obvious factors
contributing to the increased demand for energy. In global scale the increase is at
exponential rate. The similar rates of increase in demand are also common in many
countries including India. The alarming fact is that the uncertainty to fulfill the increased
demand is also increasing. The declining reserve of the fossil fuel has been serious
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concern. Reserves of our prime energy sources i.e., conventional fossil fuels (oil, coal,
natural gas) are declining at an alarming rate. Climate change catastrophe linked with
fossil fuel consumption has been another issue deepening the energy uncertainty.
Sustainable alternative energy sources have been considered as one of the
solutions to reduce such uncertainty. Renewable energy sources are getting worldwide
attention. There has been national commitment to increase the share of renewable
energy in total energy mix. India is on a fast track path of economic development
through growth and progress in crucial sectors of its economy. It is projected that the
current rate economic development at 8-9% would sustain for next couple of decades.
To achieve the development target, India requires more energy input. However, our
indigenous fossil fuel reserve is not adequate to meet the demand and therefore, a large
quantity of oil is imported from foreign countries. A substantial part of the GDP is
invested for oil import which otherwise could be used for other requirement if we had a
sufficient indigenous oil reserve. To meet the ever increasing energy demand, to fulfill
the international commitments for cleaner development and most importantly to attain
energy security, India has taken serious steps to harness its renewable energy resources.
Due to tropical location, India receives abundant solar radiation most of the year. Many
windy locations in coastal and hilly areas are favorable sites for trapping wind energy.
Numerous rivers and its tributaries are potential sites for harnessing hydro energy. Rich
forests, agricultural diversity opens up opportunities in biomass energy generation.
Ocean and geothermal prospective in the country are also under research consideration.
Thus, all these favorable conditions project India as a highly rich country in the world in
terms of renewable energy development. However, the success of applications of
renewable energy and hence stimulation of its growth to all corners of the territory
requires proper planning. There are several factors which influence the successful
applications and hence growth of renewable energy. The availability of resources,
soundness of conversion technology, prevailing economic competitiveness etc are some
of such factors governing success of renewable energy application. Amongst these
factors, adequacy of resources are considered major factor.
A strategic plan for renewable energy applications in a region could be prepared
and implemented accordingly, if precise assessment on resource availability could be
known. The availability of renewable energy resources is spatial and temporally varyingin nature. Therefore, their assessment should also be made considering spatial and
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temporal factors. Spatial and temporal assessments help regional planning of renewable
energy programme. Such assessment is also expected to be useful for the state like
Assam, where growth of renewable energy has not been impressive till now. The state of
Assam is one of the richest regions considering its fertile land and adequate rainfall.
However, economically it is one of the backward states of the country. Assessment of its
renewable natural resources is expected to assist growth of renewable energy
applications in this region. Remote sensing and GIS could play a significant role in
assessing the status of renewable energy resources of a region and also for planning
cost-effective exploitation of such resources. The major advantages of remote sensing
and GIS over traditional methods (survey, secondary data collection etc.) are (i) local to
global coverage, (ii) precise and timely information, and (iii) data retrieve and reiterative
capacity at user convenience.
Agricultural residue such as rice straw has been recognized as a potential
biomass energy feedstock. Energy generation from crop residue has been reported from
many parts of the world including Denmark (Nikolaisen et al., 1998). Utilization of rice
residue for heat and electricity generation (Suramaythangkoor and Gheewala, 2010),
bioethanol (Binod et al., 2010), and biogas production (Lei et al., 2010) are reported as
some attractive options. Agricultural residue biomass could be considered as potential
alternative fuel for power generation in rural areas of Assam. Thus, in this context, the
present study is carried out in a representative district in Assam to (i) map spatial
distribution of agricultural residue biomass, and (ii) estimate potential decentralized
electricity generation using agricultural residue biomass.
The present study is conducted in Udalguri District of Assam, India. Udalguri
district is one of the twenty seven districts of Assam. This district is bounded by Bhutan
and Arunachal Pradesh in the north, Sonitpur district in the east, Darrang district in thesouth and Baksa district in the west. It covers an area of 1852 sq.km. However, majority
of the areas are rural dominant. Geographically the district is located in 26046 to
27077N and 92008 to 95015E. Geographical location of the district is also shown in
Fig. 3.1. There are 11 development blocks and 802 villages in the district. As per 2011
Census, total population of the district is about 0.83 million. Rice based agriculture is
the major livelihoods for the people of this region. Winter rice cropping during the
month of June to December is widely followed in the district. In some cases summerrice is also cultivated.
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Fig. 3.1. Geographical location of the study area (The district is shown as seen in FCC
bands of LISS III satellite image. In the Assam map, undivided Udalguri and Darrang
districts are shown together)
3.2 Materials and methods
3.2.1 Data
Assessment of crop residue biomass has been done using remote sensing data,
geographical data and crop production statistics concerning the study area. Details about
the data are described below.
3.2.1.1 Remote sensing data
IRS-P6 (Indian Remote sensing Satellite) LISS III (Linear Imaging and Self Scanning
Sensor) multi-spectral satellite images (spatial resolution 23.5 m) pertaining to the study
area are collected from National Remote Sensing Centre (NRSC, Government of India).
The study area falls across multiple satellite scenes; hence the scenes are subsetted and
then mosaicked to make a single raster layer covering the entire area.
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3.2.1.2 Geographical data
Following geographical data are also required for the assessment of crop residue
biomass.
a) Survey of India (SOI) 1:50000 topographical maps are used as reference maps
for georeferencing the satellite images.
b) District administrative boundary maps, development block (DB) and village
maps are collected from the district administration of Udalguri district. These
maps are collected in hard copy or soft copy format. These maps are then
processed to make it useful for GIS analysis.
3.2.1.3 Agricultural crop data
The satellite imagery provides the information on area coverage by a crop.
Quantification of the residue requires the productivity data of crop. The spatially varying
productivity data could not be accounted from the satellite imagery. Crop yield data of
the concerned locations reported by recognized agency (Govt. recognized) has been used
for the present study. Based on the information collected during the study, 13 crops are
identified as potential to contribute crop residue biomass (Table 3.1). For mapping of
agricultural residue biomass potential, these 13 crops and their residues are considered
as given in Table 3.1.
3.2.1.4 Processing of satellite image data
Prior to interpreting and mapping the features present in a satellite image, accurate
geometric rectification is an important aspect. The satellite imagery is geometrically
rectified into a Universal Transverse Mercator (UTM) projection using ground control
points (GCP) taken from SOI topographic maps of 1:50000 scale. While georeferencing,
GCPs are chosen in such a way that they can be easily identified both in topographic
map and satellite image (e.g. road and railway line crossings). The image registration is
also verified with the GCPs collected during field verification. A false colour composite
(FCC) of the bands 2 (green), 3 (red) and 4 (near IR) displayed to blue, green and red
colour, respectively, is then created. Similarly, brightness, contrast values of the images
are also adjusted for accurate identification of features present in an image.
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3.2.2 Mapping of crop residue biomass
Spatial mapping for crop residue biomass is done using information of spatial
distribution of crop residue biomasses from all the crops under consideration on annual
basis. In general, rice based farming system prevails in Assam. Therefore, available
satellite image concerning the growing period of winter rice (June-December), which is
a major crop of this region, is considered to map the cropland. The details of the
mapping procedure are given below.
Table 3.1. Types of crop and their residues
Sl. No Crop Type of residue biomass
1 Rice husk straw
2 Wheat - straw
3 Maize cobs stalk
4 Gram - straw
5 Pigeon pea - stalk
6 Lentil - straw
7 Green gram - straw
8 Black gram - straw
9 Peas and beans - straw
10 Sesame - straw
11 Rapeseed and Mustard - straw
12 Linseed - straw
13 Sugarcane leaves and tops bagasse
3.2.2.1 Mapping of rice cropland
Mapping is carried out using GIS software ArcGIS 9.2. While interpreting and
delineating the rice fields, guidelines for IRS-P6 LISS III image interpretation provided
by the National Remote sensing Centre (NRSC), India are followed. After mapping the
rice fields, village wise availability of rice crop area is estimated by overlaying the rice
field vector layer with the village vector layer using Overlay analysis function of ArcGIS
9.2.
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3.2.2.2 Mapping of cropland for crops other than rice
The district level production statistics of all other crops (except rice) are used to map
their respective cropland. The village level spatial maps of these crops are generated
with an assumption that crops other than rice are grown in proportion to rice area.
3.2.3 Estimation of crop residue biomass
After mapping the cropland, the spatial availability of crop residue biomass (CRB) is
estimated using the following expression (Hiloidhari and Baruah, 2011a,b):
=
=
n
i
jiAjiYjiRjTCRB1
),(),(),()( (3.1)
where, TCRB(j) is the theoretical crop residue biomass availability at jth location from
all crops, tonne; R(i,j) is the residue production ratio of ithcrop at jth location; Y(i,j) is
the yield of ithcrop atjth location, tonne ha-1andA(i,j)is area of ithcrop atjth location,
ha.
Spatial variations ofR(i,j)and Y(i,j), attributed mainly by crop variety, soil type,
agricultural practice etc, are not considered in the present study. The value of R(i,j)for
the crops considered in the study have taken from available literature
(http://lab.cgpl.iisc.ernet.in/Atlas/) and given in Table 3.2. Further, five year averageyield of crops grown in the district during 2003 to 2007 as reported by Ministry of
Agriculture, Govt. of India is used (http://agricoop.nic.in/Agristatistics.htm).
Eq. 3.1 is used to estimate the theoretically available CRB. However, the
practical availability of CRB is limited by its competitive uses, harvesting and threshing
practices, and methods of collection of leftover portion. Traditional uses of crop residue,
particularly rice straw as feeds for livestock and as fuel are common for farmers in
Assam. However, in some cases, it is also used to support soil fertility and in
papermaking. More are the competitive uses, lesser is the availability. The harvesting
and threshing practices have remarkable influences on practical availability of CRB.
With manual methods of harvesting, there are wide variations of height of cut and
accordingly its availability. To incorporate such uncertainties, practically available CRB
is estimated using an availability factor as given below:
),(),(),(),()( 1 jiFjiAjiYjiRjPCRB
n
i
=
= (3.2)
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where, PCRB(J) is the practically available crop residue biomass at jth location, tonne;
and F(i,j)is the residue availability factor of ithcrop atjthlocation. In the present study,
the crop wise as well as spatial variations of F(i,j)is not considered. The value of F(i,j)
for rice straw and other remaining crop residues is taken as 50% and 80%, respectively.
Singh et al. (2008) reported surplus rice straw availability in Punjab as 83.5%. For rice
husk and other crop residues, a similar availability factor of 75% also reported by
Purohit (2007).
Table 3.2. Residue production ratio (RPR) of different crop residues
Crop residue RPR
Rice straw 1.50
Rice husk 0.20
Wheat straw 1.50
Maize cobs, stalk 0.30, 2.00
Gram straw 1.10
Pigeon pea stalk 2.50
Lentil straw 1.80
Green gram straw 1.10
Black gram straw 1.10Peas and beans straw 0.50
Sesame straw 1.47
Rapeseed and mustard straw 1.80
Linseed straw 1.47
Sugarcane leaves and tops, bagasse 0.05, 0.33
3.2.4 Estimation of crop residue biomass power potential
Conversion of biomass to energy is undertaken using two main process technologies
viz., thermo-chemical, and bio-chemical. Combustion, pyrolysis, gasification and
liquefaction are distinguishable thermo-chemical conversion processes. Similarly, bio-
chemical conversion encompasses digestion (biogas) and fermentation (ethanol).
Among the thermo-chemical conversion technologies, combustion is a matured
technology specifically suitable for loose biomass. The combustion process convert
chemical energy stored in biomass into heat, mechanical power and electricity using
various equipments, e.g. furnaces, boilers, steam turbines and generators. It is possible
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to burn any type of biomass with a moisture content of less than 50%. Literatures are
available citing typical size of combustion based biomass power plant from a few kW
up to hundreds of MW with net conversion efficiency between 20% and 40%
(Demirbas, 2001; Nussbaumer, 2003).
The Lower heating value (LHV) is an important parameter that is used to
estimate energy potential of CRB. Using the LHV, the energy potential is estimated as
follows:
),(),(),(),(),()(1
jiCjiFjiAjiYjiRjCRBEn
i
==
(3.3)
where CRBE(j)