recent advances in bioenergy research volume i

5/24/2018 Recent Advances in Bioenergy Research Volume I

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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


7.4 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



9.4 Conclusions 113


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References 113

10 Charcoal Activation at Low Temperature 115

A.P. Singh Chouhan, S.P. Singh

Abstract 115




10.4 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




12.4 Conclusions 149References 149

13 Todays Waste Tomarrows Fuel: 151

Hyderabad to Get 50MW from Garbage (MSW)

K.K. Jain, J. Praveen

Abstract 151


13.2 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


References 157

14 Municipal Solid Waste to Energy: 158

Experimental Studies on Biogas Plant

Usha Bajpai, Puja Singh

Abstract 158


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14.2 Materials and Methods of Experimental Studies 164


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


15.2 Methodology 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


16.2 Methods 179



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


References 204

Part-IV: Production Aspects of Biodiesel 207

18 Jatropha (Jatropha Curcas) L. Plantations and Climate Change 208

Avtar Singh

Abstract 208


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


19.2 Materials and Methods 222



References 227

20 Green Technology for Biodiesel Production using 230

Waste Material Based Heterogeneous Catalyst

Anil Kumar Sarma, Ashish P. Singh Chouhan


20.2 Materials 232



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





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


22.2 Materials and methods 251



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



23.3 Results 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



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Gupta Urmila, School of Energy Studies for Agriculture, College of Agricultural


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


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


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


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


Singh S.K., School of Energy Studies for Agriculture, College of Agricultural


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


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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12

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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13

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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14

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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16

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


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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17

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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18

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)


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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19

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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20

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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21

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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22

Keeping in view the competition between food and fuel, selection of raw

material for bioenergy 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 bioenergy. 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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23

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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26

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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27

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)

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