By Daniel A. Balcha A thesis submitted to the …graduate students: Amir Hossein Birjandi, Dave Gaden, Godwin Tay, Kwadjo Poku Owusu, James Arthur, Jonathan Mawuli Tsikata, Moftah

Numerical Modeling of Small-Scale Biomass Straw Gasifier

By

Daniel A. Balcha

A thesis submitted to the Faculty of Graduate Studies of

University of Manitoba

In partial fulfilment of the requirements for the degree of

Master of Science

Department of Mechanical and Manufacturing Engineering

University of Manitoba

Winnipeg, MB

Copyright © 2009


ii

Abstract

A 3-D numerical model of a two-stage 900-kWth gasifier built by Vidir Biomass, Manitoba

using the computational fluid dynamic code Fluent 6.2 was developed to predict the details of the

flow, gasification and thermal gradients within this small-scale straw gasifier. This gasifier is

unique in that it uses large round 1000 kg bales as the fuel and precipitates the silica in the

secondary chamber to avoid fouling of the convection section. The geometry and mesh of the

gasifier were generated using GAMBIT® 2.4, a 3-D solid modeling function provided with

Fluent. Boundary conditions during the operation of a two-stage gasifier were implemented in

the numerical model. The flow field is assumed to be a steady-state, turbulent, reacting

continuum field that could be described locally by general conservation equations. The

governing equations for gas-phase fluid momentum, heat transfer, thermal radiation, and

particle-phase transport were solved using the finite difference method implemented in Fluent.

All materials including gas species and solid biomass particles were assigned appropriate

properties. The properties of the gas species including density, viscosity, thermal conductivity,

and specific heat capacity vary with the local gas phase temperature. The ideal gas law for

density and the mass-weighted mixing law for viscosity, thermal conductivity and heat capacity

were used to model the local mixture properties. Gas-phase reactions were assumed to be limited

by mixing rates as opposed to chemical kinetic rates. Gaseous reactions were calculated

assuming local instantaneous equilibrium. The straw fuel bed was modeled as flow through a

porous media. Once the appropriate boundary conditions of the gasifier were developed and

applied to the model, the flow pattern, distribution of temperature and gas composition in the

gasifier was predicted throughout the primary and secondary chamber of the gasifier. A 1-D

equilibrium model was also used to model straw gasification with the biomass fuel represented

by the chemical formula, CHaOb. A steady state operation, thermodynamic equilibrium, and

complete conversion of the solid bio-fuel to gas were assumed in the equilibrium model. This

model was used to compare to the 3-D gasification model for validation. The 3-D base case was

also validated using the gasifier, including gas-phase measurements. A stoichiometric model

using the mass and energy balance was also developed to verify the syngas compositions

predicted by either the equilibrium model or the 3-D model to ensure mass and energy balance.


iii

Then 3-D numerical results were compared to the 1-D model, and experimental data obtained

using a 900-kWth gasifier indicated good agreement with the 1-D model and experimental data.

Process parameters such as moisture content, porosity, bed height, excess air ratio and

composition of biomass on the gasifier were then investigated to find an optimal controller. The

simulations have proved to be useful to designers who are using the model to optimise the air

system design. Of importance is to use the model results to develop an appropriate primary and

secondary air control to react to changes in fuel composition and moisture content. The results

show that maintaining an appropriate primary to secondary air ratio is critical to the operation of

the gasifier as the pressure drop through the porous bed varies as the fuel is being gasified.


iv

Acknowledgements

This piece of work would never be accomplished without our God Almighty with His blessings

and His power that work within me, and also without the people in my life specially my wife

Rebecca Melesse for inspiring, guiding and accompanying me through this work.

The thesis owes its existence to the help, support, and inspiration of many people. I am deeply

indebted to my advisor, Dr Eric Bibeau, whose motivation, enthusiasm, immense knowledge,

guidance, stimulating suggestions and encouragement helped me in every step of my thesis. I

owe special gratitude to him for his continuous and unconditional support and understanding of

all my undertakings, scholastic and otherwise.

I would like to express my sincere gratitude to Jeremy Langner for being an inexhaustible source

of modeling consultation during my work. The discussions and cooperation with all of my fellow

graduate students: Amir Hossein Birjandi, Dave Gaden, Godwin Tay, Kwadjo Poku Owusu,

James Arthur, Jonathan Mawuli Tsikata, Moftah Mohamed and Richard Lozowy contributed

substantially to this work and were able to cheer me up with their skill in spreading happiness on

those scientifically dark days. I also extend my appreciation to all Alternative Energy Group

members, for their assistance and support. I am very grateful for the technical support,

cooperative spirit and excellent working atmosphere provided by technical staff members Bruce

Ellis, Kim Majury and Paul Krueger from Mechanical and Manufacturing Engineering

Department at the University of Manitoba, whenever I needed it. I acknowledge with gratitude,

Biomass Best Inc., who provided the financial support, and MRAC who has funded Best Inc and

its partners, the University of Manitoba, and ManSEA during the pursuance of my thesis. I am

grateful to the staff of Biomass Best Inc., especially Anand Palanichamy, for their help and

support, interest and valuable hints.

Finally, I owe special gratitude to my family for their support throughout my seemingly endless

years in school and whose patient love enabled me to complete this work.


v

Nomenclature

ACFM Actual cubic feet per minute at stack conditions of temperature and pressure

AR Air ratio

Asp Cross-section area of a spherical particle

BW Moisture in flue gas (decimal fraction by volume)

CF Cubic feet

CFM Cubic feet per minute

CFD Computational Fluid Dynamics

CRF Char reactivity factor

CV Calorific value

DOM Discrete Ordinates Model

Dp Particle diameter

DSCF Dry standard cubic foot

FB Fluidized bed

FC Fixed carbon

FD Drag force

Gd Specific gravity of flue gas referred to that of air at flue gas temperature and pressure

GHG Greenhouses gases

hf Enthalpy of formation

ΔH Orifice draft gauge reading in equivalent inches of water

Kn Knudsen number

MC Moisture content

Md Molecular dry weight of stack gas (dry basis)


vi

Mha Mega hectare

mi Mass fraction of the ith species

Mi Molecular weight of the ith species

Ms Molecular weight of stack gas (wet basis)

Mt C Mega tone Carbon

MW(x) Molecular weight of gas x

MWth Mega thermal

ODT Over dried tones

Pb Barometric pressure in inches of mercury absolute

PDEs Partial differential equations

PDF Probability density function

Pe Peclet number

PISO Pressure-Implicit with Splitting of Operators

PPM Parts per million

Prt Turbulent Prandtl number

RANS Reynolds-averaged Navier-Stokes

Re Reynolds number

RNG Renormalization group

RTE Radiative transfer equation

Tm Temperature at meter in oF

Ts Flue gas temperature in oF

Vm Total volume of gas sampled as measured by meter in cubic feet

VM Volatile matter


vii

Vv Total volume of water in sample gas in cubic feet converted to meter conditions (vapor state)

Greek Symbols

δij Kronecker delta

ε Rate of dissipation

Φ Dependent variable in general discretised equation

ΓΦ Transport coefficient of the general variable ΓΦ

κ Turbulent kinetic energy

μ Molecular viscosity

μt Turbulent viscosity

ρ Density

σ Stefan Boltzmann constant (5.67 x 10 - 8 W / m 2 K 4)

∑ Summation

τω Wall shear stress

ν Kinetic viscosity


viii

Table of contents

Abstract ........................................................................................................................................... ii

Acknowledgements ....................................................................................................................... iv

Table of contents ........................................................................................................................ viii

List of figures ................................................................................................................................ xii

List of tables................................................................................................................................. xvi

Chapter 1. Introduction ................................................................................................................. 1

1.1 Biomass as renewable energy ........................................................................................... 2 1.2 Drivers for biomass ........................................................................................................... 3 1.3 Biomass in Manitoba ........................................................................................................ 6 1.4 Project motivation ............................................................................................................. 9 1.5 Project objectives ............................................................................................................ 11

Chapter 2. Biomass properties .................................................................................................... 13

2.1 Moisture content ............................................................................................................. 13 2.2 Calorific value ................................................................................................................. 14 2.3 Particle size and distribution ........................................................................................... 15 2.4 Bulk density .................................................................................................................... 15 2.5 Proportions of fixed carbon and volatiles ....................................................................... 15 2.6 Ash/ inorganic materials content .................................................................................... 16 2.7 Average Particle Diameter .............................................................................................. 18

2.7.1 Air ratio and excess air .................................................................................. 19 Chapter 3. Literature review ...................................................................................................... 20

3.1 Thermal conversion technologies ................................................................................... 20 3.2 Solid fuel gasification chemistry .................................................................................... 21

3.2.1 Drying ............................................................................................................ 24 3.2.2 Devolatization ................................................................................................ 27 3.2.3 Gasification .................................................................................................... 29 3.2.4 Combustion .................................................................................................... 30

3.3 Types of gasifiers ............................................................................................................ 32 3.3.1 Fixed bed gasifiers ......................................................................................... 32 3.3.1..1 Updraft gasifier .............................................................................................. 32 3.3.1..2 Downdraft gasifier ......................................................................................... 34 3.3.1..3 Crossflow gasifier .......................................................................................... 35


ix

3.3.2 Entrained-flow gasifiers ................................................................................. 36 3.3.3 Fluidized bed gasification–circulating fluidized bed/ bubbling bed .............. 37

3.4 Vidir Best gasifier ........................................................................................................... 38 3.5 Modeling gasification ..................................................................................................... 41 3.6 Ash deposition mechanism ............................................................................................. 46

3.6.1 Deposition mechanisms ................................................................................. 48 3.6.1..1.1 Eddy impaction .............................................................................................. 48 3.6.1..1.2 Thermophoresis ............................................................................................. 48 3.6.1..1.3 Condensation ................................................................................................. 48 3.6.1..1.4 Chemical reaction .......................................................................................... 49 3.6.1..1.5 Other mechanisms .......................................................................................... 49

Chapter 4. Numerical simulation methodology ........................................................................ 50

4.1 Basic governing equations .............................................................................................. 50 4.1.1 Conservation Equations ................................................................................. 50 4.1.2 General transport equation ............................................................................. 52

4.2 Turbulence models .......................................................................................................... 52 4.2.1 Time-averaged transport equations ................................................................ 54 4.2.2 The Reynolds stress model ............................................................................ 59

4.3 Near-wall treatments for turbulent flows ........................................................................ 59 4.4 Radiation modeling ......................................................................................................... 62

4.4.1 P-1 model ....................................................................................................... 62 4.4.2 Rosseland model ............................................................................................ 63 4.4.3 Discrete transfer radiation model ................................................................... 63 4.4.4 Discrete ordinates model................................................................................ 64

4.5 Species transport ............................................................................................................. 65 4.6 Gaseous turbulent combustion models ........................................................................... 66

4.6.1 The generalized finite rate reaction modeling ............................................... 66 4.6.2 The Arrhenius rate ......................................................................................... 68 4.6.3 The eddy-dissipation model ........................................................................... 68

4.7 Dispersed or discrete phase model .................................................................................. 69 4.7.1 Particle transport methods ............................................................................. 69

4.8 Particle motion in fluids .................................................................................................. 70 4.8.1 Drag force ...................................................................................................... 70 4.8.2 Pressure gradient force and unsteady forces .................................................. 72 4.8.3 Lift forces ....................................................................................................... 72 4.8.4 Gravity force .................................................................................................. 73 4.8.5 Thermophoretic force..................................................................................... 73 4.8.6 Brownian force............................................................................................... 74


x

4.9 Porous media model ........................................................................................................ 74 4.10 Discretization of the equations ....................................................................................... 75

4.10.1 Discretization schemes .................................................................................. 77 4.11 Discretization of the domain ........................................................................................... 79 4.12 Solution methods ............................................................................................................ 81

4.12.1 The SIMPLE and SIMPLEC algorithms ....................................................... 82 4.12.2 PISO algorithm .............................................................................................. 83

4.13 Residuals ......................................................................................................................... 84 4.14 Convergence criteria ....................................................................................................... 84 4.15 Under relaxation ............................................................................................................. 84

Chapter 5. Modeling Vidir Best gasifier .................................................................................... 86

5.1 Equilibrium model .......................................................................................................... 86 5.2 Simulation environment .................................................................................................. 92 5.3 Model set up .................................................................................................................... 92

5.3.1 Geometry/ mesh generation ........................................................................... 92 5.3.2 Boundary conditions ...................................................................................... 93 5.3.3 Porosity and bed height .................................................................................. 96 5.3.4 General description of model ......................................................................... 98

5.4 Base case results ........................................................................................................... 102 5.5 Validation ...................................................................................................................... 110 5.6 Design improvement for air control .............................................................................. 114

5.6.1 Moisture Content Variation ......................................................................... 116 5.6.2 Nozzle configuration .................................................................................... 122 5.6.3 Secondary to primary air ratio ..................................................................... 129 5.6.4 Straw bed height .......................................................................................... 135 5.6.5 Variation of biomass .................................................................................... 136

5.7 Impact on control strategy ............................................................................................ 141 Chapter 6. Conclusion and recommendations ........................................................................ 144

6.1 Conclusion .................................................................................................................... 144 6.2 Recommendations ......................................................................................................... 145


xi

References ................................................................................................................................... 147

Appendix A. Gasifier dimensions and FLUENT® model set up ............................................ 158

Appendix B. Sampling protocol for emission testing .............................................................. 178

Appendix C. Measurements of gas composition report .......................................................... 193

Appendix D. Measurement of particulate emission sampling and testing ............................ 196

Appendix E. Gas composition data sheets ............................................................................... 206

Appendix F. Particulate emission sampling and testing data sheets ..................................... 208

Appendix G. Derivation of equations for gas and emission testing ....................................... 214


xii

List of figures

Figure 1.1: Biomass availability in the world [9] ............................................................................ 3

Figure 1.2: Samples of biomass feed stocks [13] ............................................................................ 4

Figure 1.3: World biomass and use [11] .......................................................................................... 6

Figure 1.4: Classification of different soil zones of Canadian prairies [23] ................................... 8

Figure 1.5: Manitoban primary energy comparison [25] ................................................................. 9

Figure 2.1: Phase diagram for K2O-SiO2 [47] .............................................................................. 17

Figure 3.1: Typical moisture concentration as function of time during drying of a porous

particle: (a) water reduction, (b) rate of drying [68] ................................................. 25

Figure 3.2: Types of Gasifiers: clockwise from top left: (a) updraft (b) downdraft (c)

crossflow (d) fluidized [28] ....................................................................................... 34

Figure 3.3: Vidir Best gasifier, 3-D [87]........................................................................................ 40

Figure 3.4: Vidir Best gasifier schematic diagram [87] ................................................................ 40

Figure 3.5: Secondary chamber characterized by high temperature ............................................. 41

Figure 4.1: Universal log law [71] ................................................................................................. 60

Figure 4.2: Near wall grids [71] ..................................................................................................... 61

Figure 4.3: Drag coefficient for spherical particles versus Re [71] ............................................... 71

Figure 4.4: Simple 2-D domain showing the cell centres and faces (top), 1-D rectangular

simplification (bottom) [109] .................................................................................... 76

Figure 4.5: Elements used as computational grids [109] ............................................................... 79

Figure 4.6: Structured grids in 2-D and 3-D with I, J and K directions [109] ............................... 80

Figure 4.7: Unstructured grids using hexahedral or mixture elements [109] ............................... 81

Figure 4.8: SIMPLE algorithm chart [59]...................................................................................... 83

Figure 5.1: Gasifier grid ................................................................................................................. 93

Figure 5.2: Experimental schematic .............................................................................................. 97

Figure 5.3: Pressure drop as a function of air velocity for straw ................................................... 98


xiii

Figure 5.4: Boundaries of gasifier ................................................................................................. 99

Figure 5.5: Contours of mass fraction of straw volatiles at start of simulation ........................... 104

Figure 5.6: Contours of velocity magnitude once converged [m/s]............................................. 104

Figure 5.7: Velocity vectors colored by velocity magnitude at top of gasifier [m/s] .................. 105

Figure 5.8: Contours of velocity magnitude near secondary air inlet [m/s] ................................ 105

Figure 5.9: Contours of velocity magnitude near secondary air inlet [m/s]: (y = 0 plane) .......... 106

Figure 5.10: Contours of velocity magnitude near secondary chamber outlet [m/s] ................... 106

Figure 5.11: Fuel path lines colored by particle ID ..................................................................... 107

Figure 5.12: Contours of static temperature [K] .......................................................................... 107

Figure 5.13: Contours of mass fraction of O2 .............................................................................. 108

Figure 5.14: Contours of mass fraction of CO2 ........................................................................... 108

Figure 5.15: Contours of mass fraction of H2O ........................................................................... 109

Figure 5.16: Contours of mass fraction of H2 .............................................................................. 109

Figure 5.17: Contours of mass fraction of CO ............................................................................. 110

Figure 5.18: Vidir Best gasifier system for in-situ-experiments.................................................. 111

Figure 5.19: In-situ particulate emission sampling ...................................................................... 112

Figure 5.20: Comparison of outlet temperatures [K] in primary and secondary chamber

with in-situ experiments with the gasifier and the equilibrium model

predictions ............................................................................................................... 113

Figure 5.21: Comparison of mass composition of gases [%] with in-situ experiments with

the gasifier and the equilibrium model predictions ................................................. 114

Figure 5.22: Effect of MC on caloric value of producer gas in primary chamber ....................... 117

Figure 5.23: Effect of MC on the secondary temperature [K] ..................................................... 118

Figure 5.24: Effect of MC on the producer gas composition ...................................................... 118

Figure 5.25: Contours of velocity magnitude [m/s] with variation in MC: a) 14%, b) 20%

and c) 26% ............................................................................................................ 119

Figure 5.26: Contours of temperature [K] with variation in MC: a) 14, b) 20 and c) 26% ......... 120


xiv

Figure 5.27: Contours of mass fraction of O2 with variation in MC: a) 14%, b) 20% and c)

26% ......................................................................................................................... 121

Figure 5.28: An angled nozzle configuration: a) 90o, b) 45o and c) 30o .................................... 122

Figure 5.29: Effect of nozzle angle on velocity magnitude ......................................................... 124

Figure 5.30: Contours of velocity magnitude [m/s] with variation in secondary nozzle

angle: ....................................................................................................................... 125

Figure 5.31: Vector plot of velocity magnitude [m/s] with variation of nozzle angle: ............... 126

Figure 5.32: Pathlines colored by ID with variation of secondary air nozzle angle: a) 90o, b)

45o and c) 30o .......................................................................................................... 127

Figure 5.33: Vector plot of velocity magnitude [m/s] with variation of nozzle angle: ............... 128

Figure 5.34: Flow pattern tangential (a) versus perpendicular (b) to duct nozzle ...................... 129

Figure 5.35: Effect of primary air flow on pressure drop ............................................................ 130

Figure 5.36: Contours of velocity magnitude [m/s] with variation in primary air: a) 0.16

kg/s, b) 0.24 kg/s and c) 0.35 kg/s ......................................................................... 131

Figure 5.37: Effect of primary air flow rate on composition of gases at the secondary exit ....... 132

Figure 5.38: Contours of temperature [K] with variation in primary air flow rate ...................... 133

Figure 5.39: Contours of mass fraction of O2 with variations in primary air flow rate: a)

0.16 kg/s, b) 0.25 kg/s and c) 35 kg/s ...................................................................... 134

Figure 5.40: Bed height as ratio of primary chamber cylinder part ............................................. 135

Figure 5.42: Effect of bed height on mass composition of gases: a) 0.6, b) 0.7 and 0.8 times

cylinder part of primary chamber ............................................................................ 136

Figure 5.43: Effect of biomass variation on composition of gases at secondary outlet ............... 138

Figure 5.44: Effect of biomass type on outlet temperature in secondary chamber ...................... 138

Figure 5.45: Contours of temperature [K] with variation of biomass: a) wheat straw,

b) slough hay and c) wood chip .............................................................................. 139

Figure 5.46: Contours of velocity magnitude [m/s] with variation of biomass: a) wheat

straw, b) slough hay, and c) wood chips ................................................................. 140


xv

Figure A.1: Dimensions of 900-kWth Vidir proprietary gasifier modelled ................................ 158

Figure B.1: S-type pitot tube specifications and orientation ........................................................ 180

Figure B.2: Pitot tube and thermocouple placement .................................................................... 181

Figure B.3: Assembling pitot tube and sampling probe .............................................................. 181

Figure B.4: Probe with pitot tube and thermocouple ................................................................... 181

Figure B.5: a) Location of sample port and b) Distance away from duct wall ............................ 182

Figure B.6: Producer gas sample train ......................................................................................... 183

Figure B.7: Pitot tube-sampling nozzle ....................................................................................... 186

Figure B.8: Pitot tube-sampling nozzle configuration ................................................................. 187

Figure B.9: Impinger assembly .................................................................................................... 187

Figure B.10: Sampling train set up .............................................................................................. 188

Figure B.11: Leak free check ....................................................................................................... 192

Figure C.1: MODEL 375K / 375WP – Portable flue gas analyzer http://www.nova-gas.com ... 194

Figure D.1: Method 5 isokinetic sampling Train (http://www.cleanair.com) ............................. 199


xvi

List of tables

Table 1.1: Biomass from agricultural crop residues in Canada, 2001 [22] ..................................... 7

Table 3.1: Gasification reactions with reaction enthalpy [56] ....................................................... 21

Table 3.2: Summary of CFD modeling attempts ........................................................................... 45

Table 5.1: Ultimate (a) and proximate analyses (b) ....................................................................... 87

Table 5.2: The value hf (kJ/mol) and the coefficients of empirical equation for ΔgfT

(kJ/mol) ..................................................................................................................... 89

Table 5.3: Sample mass balance equilibrium model results .......................................................... 91

Table 5.4: Sample energy balance equilibrium model results ....................................................... 92

Table 5.5: Mesh density dependence for equilibrium gasifier outlet temperature [K] ................ 102

Table 5.6: Summary of parameters investigated .......................................................................... 115

Table 5.7: Ultimate analysis for slough hay and wood chips ...................................................... 137

Table 5.8: Proximate analysis results........................................................................................... 137

Table A.1: Solid straw and combusting straw particles properties .............................................. 159

Table A.2: Straw-volatiles and straw-vol-air properties .............................................................. 160

Table A.3: CH4 and CO properties ............................................................................................... 161

Table A.4: H2O and CO2 properties ............................................................................................. 162

Table A.5: H2, N2 and O2 properties ............................................................................................ 163

Table A.6: Fluent sub-models set up and inputs summary .......................................................... 163

Table A.7: Fluent® sub-models set up and inputs summary, (continued) ................................... 164

Table A.8: Fluent® sub-models set up and inputs summary, (continued) ................................... 164

Table A.9: Chemical reactions ..................................................................................................... 165

Table A.10: Chemical reactions, (continued) .............................................................................. 166

Table A.11: Operating conditions ............................................................................................... 166

Table A.12: Boundary conditions: zone, air ................................................................................ 167


xvii

Table A.13: Injection of particles ................................................................................................ 167

Table A.14: Boundary conditions: primary air inlet .................................................................... 168

Table A.15: Boundary conditions: secondary air inlet ................................................................ 169

Table A.16: Boundary conditions: fuel inlet................................................................................ 170

Table A.17: Boundary conditions: fuel bed ................................................................................. 171

Table A.18: Boundary conditions: outlet ..................................................................................... 172

Table A.19: Boundary conditions: default-interior ...................................................................... 172

Table A.20: Boundary conditions: walls ..................................................................................... 173

Table A.21: Solution controls ...................................................................................................... 174

Table A.22: Solution controls, (continued) .................................................................................. 175

Table A.23: Solution initialization ............................................................................................... 176

Table A.24: Residual controls...................................................................................................... 177

Table C.1: Applicable methods and references .......................................................................... 193

Table C.2: Summary of combustion gas concentration ............................................................... 195

Table D.1: Applicable methods and references ........................................................................... 197

Table D.2: Clean Air Express® Method 5 train ........................................................................... 200

Table D.3: Test validation chart ................................................................................................... 202

Table D.4: Operating conditions during the measurement (14% moisture) ................................ 203

Table D.5: Operating conditions during the measurement period (26% moisture). .................... 203

Table D.6: Operating conditions during the measurement (20% moisture) ................................ 204

Table D.7: Particulate emissions from Vidir Best gasifier exhaust ............................................. 205

Table E.1: Data recording sheet for wheat straw ......................................................................... 206

Table E.2: Gas analysis wheat straw of moisture content = 20% summary ................................ 207

Table E.3: Gas analysis for wheat straw of moisture content = 26% summary ......................... 207

Table F.1: Preliminary stack test data sheet (Run 1) .................................................................. 208

Table F.2: Particulate emission sampling data recording sheet-MC = 14% ................................ 209


xviii

Table F.3: Preliminary stack test data sheet (Run 2) ................................................................... 210


Table F.5: Preliminary stack test data sheet (Run 3) ................................................................... 212



1

Chapter 1. Introduction

Global energy consumption has increased steadily for much of the twentieth century, particularly

since 1950 [1]. Today, the world consumes approximately 320 billion kilowatt-hours a day and

the total energy consumption has increased 57 percent globally since 1980 [2]. The International

Energy Agency has predicted world energy demand will rise 1.6 percent per year on average

between 2006 and 2030 [3]. A number of national and global issues have encouraged Canada to

consider biomass resources for energy to address energy drivers. These include greenhouse gases

that lead to climate change, sustainability, energy price increase, and a need for rural

diversification and revitalization.

Various energy resources have been exploited and utilized and biomass is one of the energy

resources that is abundant and has been widely used in Canada [4]. Biomass gasification can be

an efficient and advanced technology for extracting energy from biomass and has received

increasing attention in the energy market due to its potential for reduced emissions. It is a

century old technology, which was used during the Second World War [5]. The technology

disappeared soon after the Second World War, when liquid fuel became easily available. Soon

after, interests in the gasification technology have undergone many ups and downs throughout

the century [6].

Today, because of increased fuel prices and environmental concern, there is renewed interest in

this century old technology. As a result, gasification has renewed interest as a technology to

reduce emissions by operating as a two-stage combustor and to possibly generate syngas for both

energy and chemical feedstock [4]. Although many references indicate that biomass gasification

is more effective for electricity production, this results has not been attained in demonstration

plants, both small and large, because many issues have yet to be resolved, including syngas

cleaning and lower energy requirements for fuel preparation.

With developing of modern science and technology, the challenge that people will face in the

21st century is how to develop and use, scientifically and reasonably, the biomass energy

resource [7].


2

The purpose of the research work was to apply a numerical tool to optimize biomass gasification

systems for various small-scale energy applications. In particular, the research focused on the

gasifier/combustion system that uses post harvest biomass, such as wheat straw in 1000 kg round

bales as a fuel source, and efficiently converts the fuel into heat energy. The system is

considered greenhouse gas neutral and is environmentally friendly. The system can also use a

variety of different, readily available biomass fuel types such as pellets, wood chips, flax straw,

corn Stover, cattail, and swamp grass. This project is part of research and development by

Vidir Biomass Inc., a manufacturer of custom-made agricultural and industrial machinery in

rural Manitoba, the University of Manitoba, Manitoba Sustainable Energy Association, and

Manitoba Hydro. The goal was to design an automated control system to allow unattended

gasifier operation.

1.1 Biomass as renewable energy

Biomass is recognized to be one of the major potential sources for renewable energy production

(Figure 1.1). Environmental concern is expressed over the release of CO2 from burning fossil

fuels [8]. Fossil fuel combustion needs to be substantially reduced for three main reasons: energy

security, environmental emissions and climate change mitigation [4]. When fossil fuels are burnt,

carbons from fuels react with oxygen from air and produce CO2. This is the reason for the steady

increasing CO2 content in the atmosphere. As a result carbon dioxide contributes over 50% of the

green house effect [8].

One of the remedies to limit the rising content of CO2 in the atmosphere is energetic use of

biomass fuel. Biomass is an organic material made up of mainly carbon and hydrogen, and

includes wood, crop residues, solid waste, animal wastes, sewage, and waste from food

processing [9].

There has been an increasing interest for thermo-chemical conversion of biomass and urban

wastes for upgrading energy in terms of more easily handled fuels, namely gases, liquids, and

charcoal in the past decade [10]. Biomass is a renewable source of energy and has many

advantages from an ecological point of view. Each of these products has commercial importance

depending upon the type of application [1]. The large scale deployment of efficient technology,


3

with interventions to enhance the sustainable supply of biomass fuels, can transform the energy

supply situation in rural areas [8], although transportation of feedstock remains a major hurdle

without densification.

Figure 1.1: Biomass availability in the world [9]

Biomass has the potential to become the growth engine for rural development in Canada. Small

scale gasifier/combustors applications may dominate rural applications and it is this avenue that

this thesis focuses on.

1.2 Drivers for biomass

The term biomass covers a large number of materials with different properties that can be used as

fuels. It is the term used for organic material originating from plants (including algae), trees and

crops and includes collecting and storing of the sun’s energy through photosynthesis [11]. These

materials can be classified in a few main categories, each of which can be divided into several

types [12].

• wood from forestry

Red = very high Yellow = high Green = medium Blue = low


4

• residues from wood and food industries

• agricultural residues

• energy crops

Figure 1.2: Samples of biomass feed stocks [13]

The fundamental process of biomass accumulation within the context of energy is based on

photosynthesis. This is the process by which plants convert solar energy into biomass, as the sun

is the source of most renewable forms of energy. The green plant is the only organism able to

absorb solar energy with the help of chlorophyll. It converts solar energy into chemical energy of

organic compounds with the aid of carbon dioxide and water [12].

The chemical composition of biomass varies among species, but plants consist of about 25%

lignin and 75% carbohydrates or sugars [14]. A typical biomass has an energy density of

approximately 18 to 20 MJ/ kg on a dry basis [13]. On a wet basis this value can be substantially

less and can even be less than zero indicating that the fuel is not capable of burning in a

sustainable manner while liberating energy [11]. On a dry basis, biomass has a calorific value

about half that of coal [1]. The low energy density, its low packing density, and its difficulty in

handling make the economics of transporting biomass large distances unfeasible [15]. Thus, the


5

utilization of biomass for small-to-medium scale distributed energy producing processes has

some synergy [1] and advantages.

The biomass for distributed generation would be sourced locally and probably within a 50 km

radius. Power may be generated and used to improve feeder lines [7]. In such a system, local

communities would use locally grown biomass and potentially make use of some volume of

waste currently being land filled to generate their own power or convert the material into fuels.

In effect, a community could become power and fuel self-sufficient while producing essentially

no or nominal greenhouse gas emissions [16].

As temperatures rise, ice caps melt and sea levels rise, or due to increased CO2 levels, biomass

gasification offers a carbon neutral technology and a true environmental performer concerning

GHG [17]. In 1992 at the Rio United Nations Conference on environment and development, the

renewable intensive global energy scenario (RIGES) suggested that, by 2050, approximately half

the world’s current primary energy consumption of about 400 EJ/yr, could be met by biomass

and that 60% of the world’s electricity market could be supplied by renewable means, of which

biomass is a significant component [18].

While world energy demand is increasing, fossil fuel usage is increasing and conventional oil

reserves are declining. Furthermore, natural gas prices are high and gas is likely to remain in

short supply. This trend is expected to continue as the world’s population grows at an

exponential rate [19]. The energy sector has become increasingly important as demand, cost, and

greenhouse gas emissions from fossil fuels rise. Change to the way we produce and use energy is

necessary to stabilize energy supply and demand, and improve quality of life on earth.

To help address these energy issues, renewable energy must become more widely used in all

sectors. Many technologies exist, but they are not yet well known or accepted by the public [20].

The agricultural sector is a good place to use distributed renewable bio-energy (Figure 1.3). The

approach developed in this sector can serve as a benchmark for other distributed energy sectors.

Agricultural by-products are a good source of bio-energy. For example, wood and crop residues

can be processed by thermal conversion to produce energy.


6

Figure 1.3: World biomass and use [11]

1.3 Biomass in Manitoba

Of the 998 mega hectares (Mha) of land in Canada, about 42% is forested, and about 25%,

(245 Mha) is considered timber productive forest [21]. A further 6.8% (67.5 Mha) of Canada is

agricultural land, of which 3.6% (36.4 Mha) is cropland [22]. The 245 Mha of timber productive

forest in Canada has a biomass carbon stock of about 15,835 Mt C [21]. This resource has an

energy content (566 EJ) that is equal to 69 years of Canada’s current energy demand that is met

by fossil fuels, 8.24 EJ/y [18]. Each year, the biomass harvest from Canada’s forestry and

agricultural sectors is about 143 Mt C, an amount of carbon that is similar to the atmospheric

emissions of carbon from fossil fuel use in Canada that was about 150 Mt C/yr in 1998 [22]. The

energy content of the annual biomass harvest in Canada (5.1 EJ/yr) is equal to 62% of the energy

derived from fossil fuel combustion [21]. A 25% increase in forestry and agricultural production

in Canada could provide about 1.25 EJ/yr in biomass energy, an amount equivalent to about 15%

of the energy that Canada now gets from fossil fuels [22]. The amount of residual or waste

biomass carbon streams associated with the existing agriculture and forestry is around 66 Mt

C/yr [23]. Of the 66 Mt C/yr in the residual or waste biomass carbon stream, about 60 Mt C/yr

may be considered theoretically available feedstock for a bio-based economy [22]. This


7

represents about 42% of the entire forestry and agricultural harvest with the energy content

ranging from 1.5 to 2.2 EJ/yr, equivalent to between 18% and 27% of Canada’s current energy

demand that is met by fossil fuels, 8.24 EJ/yr [21].

Agricultural activity in Canada produces millions of tons of biomass each year and can offer

feedstock for bio-energy (Table 1.1) and specific bio-products while improving the rural

economy [4]. Canada has about 36.4 Mha of crop lands available for agricultural

production [22]. Out of that, more than 85% or about 32 Mha are located on the Canadian

Prairies: Alberta, Saskatchewan, Manitoba and a small portion of northeast

British Columbia [11]. Seeded area is dominated by cereal crops, followed by oilseeds and pulse

crops. After grain harvesting, most crop residues [21] are left on the field. Some of these residues

have been used for livestock feeding, bedding, insulation, and mulching. In terms of feed quality,

wheat, barley and oat residues have relatively low crude protein and digestible dry mater content

as compared to sorghum and corn residues [11]. Alberta, Saskatchewan, and Manitoba

collectively produce more than 37 Mt of wheat, barley, oat, and flax grain [21]. The grain

production yielded approximately 37 Mt of straw (Alberta 13.6 Mt, Saskatchewan 18.7 Mt, and

Manitoba 5.0 Mt.) over the 10 year period from 1994 to 2003 [23]. Biomass, such as wheat straw

in Manitoba could play an important role in tackling one of the problems related to energy

supply: energy loss as power travels along the power line from the power plant to its destination.

Table 1.1: Biomass from agricultural crop residues in Canada, 2001 [22]

Total production Straw/ Stover Amount available Energy potential M ODT /yr M ODT /yr M ODT /yr EJ /yr Wheat 20.6 26.7 7.49 0.241 Barely 10.8 10.8 3.04 0.098 Oats 2.7 2.7 0.75 0.024 Grain corn 8.3 8.3 3.33 0.054 Canola 4.9 4.9 2.76 0.044 Soybeans 1.6 1.6 0.16 0.003 Flax seed 0.72 0.72 0.2 0.006 Rye 0.23 0.23 0.06 0.002 Fodder corn 5.2 0 0.26 0.009 Tame hay 23.1 0 1.16 0.041 Totals 78.27 56.09 17.79 0.523


8

Farms are also often located at the end of transmission lines, stressing the benefit of on-site

power generation [24]. Biomass has a high net energy yield for heat applications and is also

scalable with the potential for small scale to large scale energy systems [1].

Even though Manitoba has significant biomass resources and capability, as shown in Figure 1.4

(black and dark brown zones are the high yielding straw producing areas), 74% of the energy

consumed is imported and non-renewable, and used for transportation and heat as shown on

Figure 1.5.

A number of consumers are interested in replacing fossil fuels with bio-energy. In response to

this demand, several companies such as a W2E Technologies, Home Farms Technologies Inc.,

Vidir Machine Gasifier, Mesh Technologies, Heat Innovations Gasifier, and Modern Organics

are involved in the bio-energy sector in Manitoba. Among these conversion technologies,

gasification/combustion or two-stage combustion is one of the leading technologies.

Figure 1.4: Classification of different soil zones of Canadian prairies [23]


9

Figure 1.5: Manitoban primary energy comparison [25]

1.4 Project motivation

Manitoba and its micro-economies are at present heavily exposed to changes in both cost and

availability of their fossil fuel energy supplies. Therefore, it is important to concentrate on

gradually reducing a community’s use of, and reliance upon externally sourced fossil fuel energy

and switching to local renewable energy resources. In this context, biomass is seen as an

important part of a future, renewable energy mix, because unlike wind or solar energy, biomass-

based power generation can be operated on demand and can provide both heat and power [26].

Considering the case of agricultural residues in Manitoba such as wheat straw, large amounts of

these residues are burned in the fields. It is estimated that in Manitoba, province-wide, about five

percent of producers burn unwanted straw [22]. Crop residue burning has become a concern in

the Prairies, due to its adverse impact on human health, the environment, and soil quality.

Cen [26] conducted a survey in 2001 to investigate crop residue burning situations on farms in

four rural municipalities of Manitoba, Canada. Of the 84 eligible respondents, 47% practiced or

possibly practiced crop residue burning. The motivating factors included the timeliness of field


10

operations, such as fall tillage, fall fertilizer application and spring seeding; lower cost for

residue disposal; increased crop yield, and better control of weeds and crop diseases.

The consequences of this practice is that it increases the particulate matter in the air, which is

linked to increased respiratory illness and death, especially in those with heart or lung conditions,

children, and the elderly. The impact is not only air pollution but crop burning also wastes the

potential energy utilization. Agricultural residue should no more be considered as an

environmental burden and its rational use can help meet fuel substitution towards renewable and

away from fossil fuels. Thus, the conversion of biomass to the gaseous fuel through a

thermochemical process like gasification is found to be more convenient for biomass-to-energy

conversion.

Gasification can be a suitable technology for converting agricultural waste to energy. However,

biomass applied for heat and electricity production should be converted in processes with a high

efficiency, low operating costs and should achieve environmental compliance. Furthermore the

processes should be environmentally sustainable and they should provide a net reduction in CO2

emissions.

Operational conditions and performance of a biomass gasifier are strongly influenced by flow

conditions in the chambers. Compared to experimental data, computational fluid dynamics

(CFD) model results can predict qualitative information and in some cases accurate quantitative

information. A CFD model, compared to a physical experiment operation is cost saving, timely,

safe and easy to scale-up. The results offer flow analysis for optimizing of biomass gasifiers at

the design stage, and in retrofit situations.

Due to the high complexity of the heterogeneous gasification/combustion of moving biomass

fuel beds, only few research projects have so far dealt with introducing CFD as a tool in the

optimization of small-scale biomass gasifiers. The most of previous works either were able to

model part of the gasification process, or assumed all the four stages of gasification as one to

simplify the problem. Most previous studies were done for large-scale coal power plants and

biomass combustors, including black liquor. Clearly, coal gasifiers and biomass gasifiers are

different systems because coal char in gasification reactivity is significantly different from


11

biomass reactivity. In addition, there are limited 3-D models of a full scale gasifier. Therefore, it

will be advantageous to develop a 3-D CFD model of a working industrial scale gasifier that

accounts for drying, fast pyrolysis, combustion, gasification, and shift and reforming processes in

detail for biomass gasification. With such detailed modeling of a gasification process, gasifier

manufacturers in Manitoba will benefit by having a tool to develop air control systems for an

improved efficiency, enhanced quality combustion/gasification, reduced emissions and

eventually competitive technology.

1.5 Project objectives

This research has two parts. The first is a numerical component where numerical techniques and

the advanced CFD computing approach was used to provide proficient design solutions, and the

second part involves with the experimental approach where the emission issue is addressed and

the model is validated. Details of the outcomes from this project are as follows:

• A general-function equilibrium model based on the global Gibbs free energy

minimization at the equilibrium state in the system combined with energy balance and

elemental balances (e.g. C, H, O, N and S) was formulated to predict the maximum

achievable thermodynamic limits.

• To comprehensively understand the gasification process and provide the theoretical

basis for the optimized operation and scale-up/down designs a 3-D-model was

developed using commercial CFD software FLUENT®. The model considers drying,

fast pyrolysis, combustion, gasification, and shift and reforming processes in detail.

• Validation of the model was done using available experimental measurements and

1- D equilibrium models.

• A parametric analysis was performed to comprehensively investigate operating

parameters to help develop an air control mechanism.

• Gas and particulate matter sampling protocol was prepared and implemented.

• Emission testing was performed to check compliance to Manitoba emission standard.


12

• The findings were made available for developing of an air control system to optimize

unattended operation of a gasifier that complies with the provinces emission

standards.


13

Chapter 2. Biomass properties

Biomass is a complex mixture of organic compounds and polymers [9]. The major types of

compounds are lignin and carbohydrates that are cellulose and hemi cellulose whose ratios and

resulting properties are species dependent [27]. Lignin, the cementing agent for cellulose, is a

complex polymer of phenyl propane units [28]. Cellulose is a polymer formed from D-glucose;

the hemi cellulose polymer is based on hexose and pentose sugars [29]. Biomass such as wood

typically has low ash, nitrogen, and sulphur contents. However, some agricultural materials such

as straws and grasses have substantially higher amounts of ash. To estimate yields during

gasification, the complex material must be reduced to a simplified chemical formula such as

CH1.4

O0.6

[24]. Elements such as sulphur and nitrogen are considered to be present in small

amounts and are not considered in terms of overall chemistry throughout this discussion.

The main material properties of interest, during subsequent processing as an energy source,

relate to moisture content, calorific value, particle size and distribution, bulk density, proportions

of fixed carbon and volatiles, ash/residue content and alkali metal content [30]. For straw,

special attention to silica is required because silica can solidify onto heat transfer surfaces

severely impeding heat transfer rates.

2.1 Moisture content

The moisture content of biomass fuel depends on the type of fuel, its origin, and its treatment

before it is used for gasification. Moisture content (MC) of a fuel is usually referred to as

inherent moisture plus surface moisture [18].

Dry moisture content is defined as [31]

%100×−

=weightDry

weightDryweightWetMCdry (1)

Alternatively, the moisture content on a wet basis is defined as [18]


14

%100×−

=weightWet

weightDryweightWetMC wet (2)

Conversions from one to another can be obtained by

wet

wetwet CM

CMMC..100..100

+×

= (3)

MC below 15% by weight is desirable for trouble free and economical operation of a

gasifier [32] and for a gasifier/combustor. Higher moisture content reduces the thermal

efficiency of gasifiers, impedes gasification reaction to proceed, requires increasing supply air in

the primary chamber and results in low gas heating values [18]. Igniting the fuel with higher MC

becomes increasingly difficult, and the gas quality and the yield are also poor [33].

2.2 Calorific value

Combustion produces thermal heat energy. The quantity of heat generated by complete

combustion of a unit of specific fuel is constant and is termed the heating value, heat of

combustion, or caloric value of that fuel [34]. The heating value of a fuel can be determined by

measuring the heat evolved during combustion of a known quantity of the fuel in a calorimeter,

or it can be estimated from chemical analysis of the fuel and the heating values of the various

chemical elements in the fuel [35]. Fuel with higher energy content is always better for

gasification [36].

Higher heating value, gross heating value or total heating value includes the latent heat of

vaporization and is determined when water vapour in the fuel combustion products is

condensed [34]. Conversely, lower heating value or net heating value is obtained when the latent

heat of vaporization is not included. Heating values are usually expressed in MJ/m3 for gaseous

fuels, MJ/litre for liquid fuels, and kJ/kg for solid fuels. Heating values are always given in

relation to a certain reference temperature and pressure, usually 60°F, 68°F, or 77°F and

101.325 kPa depending on the particular industry practice [37]. The heating values are also

reported on moisture and ash basis.


15

2.3 Particle size and distribution

The fuel sizes affect the pressure drop across the gasifier and the power that must be supplied to

draw the air and gas through the gasifier [38]. Large pressure drops will reduce of the gas load in

the downdraft gasifier, resulting in low temperature and tar production. Excessively large sizes

of particles reduce reactivity of fuel, causing start-up problems and poor gas quality [39].

Acceptable fuel sizes depend to a certain extent on the design of the gasifier. In general, a wood

gasifier work well on wood blocks and wood chips ranging from 80 x 40 x 40 mm to

10 x 5 x 5 mm [40]. For charcoal gasifiers, charcoal ranging from 10 x 10 x 10 mm to

30 x 30 x 30 mm is quite suitable [1].

2.4 Bulk density

Bulk density is defined as the weight per unit volume of loosely tipped fuel [41]. Bulk density

varies significantly with moisture content and fuel particle size of fuel [38]. The volume

occupied by the stored fuel depends on not only the bulk density of fuel, but also on the manner

in which fuel is piled. It is also recognized that bulk density has considerable impact on gas

quality, because it influences the fuel residence time in the fire box, fuel velocity and gas flow

rate [39].

2.5 Proportions of fixed carbon and volatiles

Volatile matter and inherently bound water in the fuel are given up in the pyrolysis zone at

temperatures of 100oC to 150oC forming a vapour consisting of water, tar, oils and gases [42].

Fuel with high volatile matter content produces more tar, causing problems to internal

combustion engines. Volatile matters in the fuel determine the design of the gasifier for

removing tar. Compared to other biomass materials (crop residue (63%–80%), wood (72%–

78%), peat (70%), coal (up to 40%)), charcoal contains the least percentage of volatile matter

(3%–30%) [2].


16

2.6 Ash/ inorganic materials content

The mineral content of fuel is called ash [43]. In practice, ash also contains some unburned fuel.

The distribution of ash and specific inorganic components in herbaceous biomass may vary

significantly among different plant fractions. For example, Sommerfeld [44] determined total ash

and silica in different botanical fractions of rice straw including leaf, stem, node, and panicle,

and concluded that ash and silica content varied significantly among straw fractions: leaves

contained 18%–19% total ash of which 76% consisted of silica, whereas stems only contained

12% ash with 42% silica. Distribution of inorganic constituents among plant parts is often

specific and can have a direct impact on the application of the biomass type [45]. For instance

rice hulls, a by-product of rice grain processing and a high ash-high silica material, are generally

considered a good biomass fuel for combustion, whereas oat straw is considered a difficult fuel

due to the combination of high ash, high silica, and high alkali content (leading to ash

agglomeration) in the material [46].

Potassium and sodium, the alkali metals such as oxides, hydroxides, or in metallo-organic

compounds, will form low melting compounds with silicates [4]. Straws and grasses contain

alkali and silica in proportions that promote the formation of these organic mixtures that melt at

low temperatures. Silica alone melts at 2000 K (1700oC or 3100oF) [28]. The phase diagram in

Figure 2.1 shows the melting point of various mixtures of potassium oxides (K2O), with silica

(SiO2) which make up the bulk of ash in biofuels such as wheat straw.


17

Figure 2.1: Phase diagram for K2O-SiO2 [47]

Slag in the straw combustion is often associated with temperatures above 750oC, which is near

the eutectic point of 770oC for a mixture of 35% potassium oxide and silicon oxide as shown in

the phase diagram [47]. Above this temperature, one or both of the elements in the mixture may

be liquid. A mixture of 32% K2O and 68% SiO2 melts at 769oC. This ratio is close to the ratio of

25% to 35% alkali, (K2O + Na2O) to silica found in many biomass ashes [48].

Ash content and ash composition affect the smooth running of gasifiers. Melting and

agglomeration of ashes in reactor causes slagging and clinker formation [43]. If no measures are

taken, slagging or clinker formation leads to excessive tar formation or complete blocking of the

reactor. In general, no slagging occurs with fuel having ash content below 5% [15]. Ash content

varies fuel-to-fuel. Wood chips contain 0.1% ash, while wheat straw contains a high amount of

ash, from 16%–23% [46]. The wide variability in ash is in itself a potential bottleneck for

biomass conversion; however, ash content alone cannot predict the potential impact that ash in

herbaceous biomass types may have on thermo-chemical conversion [4].


18

2.7 Average Particle Diameter

The average fuel particle diameter is an important variable in any thermal conversion process.

Any sample of fuel particles, generated by a shredding process, presents a statistical distribution

of diameters [38]. Determining an average particle size is not a trivial matter because a proper

choice should consider the intended utilization. There are several possible definitions for the

average particle diameter. One may consider, for instance, defining an average based on the

following principles [24]:

• Simple average of particles and given by [4]

i

n

iPiavP dd ω∑

=

=1

, (4)

where wi mass fraction of particle with diameter dpi

n number of size levels used in the particle distribution analysis

This average is not useful because it does not consider properties related to the solid phase, e.g.,

volume and area.

• Average based on the area of the particles and given by [24]

21

ddn

1iiPi

2av,p

⎥⎥⎦

⎤

⎢⎢⎣

⎡= ∑

=ω (5)

Combustion or gasification processes involve gas-solid or heterogeneous reactions. These

reactions occur at the surface or at layer near the surface of a particle. Therefore, the area of a

particle should be important for the above processes.

• Average based on the volume of the particles given by [49]

31

ddn

1iiPi

3av,p

⎥⎥⎦

⎤

⎢⎢⎣

⎡= ∑

=

ω (6)


19

Because the density is assumed approximately the same for all particles of a given species, an

average based on volume would be equivalent to that based on mass.

It is important to decide which one among these averages should be adopted in cases of

combustion and gasification of particles. The solution to this dilemma is provided by a

compromise between the two last averages, which is called area-volume average or surface-

volume average and is given by [50]

∑

∑

ω

ω

= n

iii

2

n

iii

3

av,p

Pd

Pdd (7)

This average is widely [13] employed in the area of combustion and gasification.

2.7.1 Air ratio and excess air

Air ratio and excess air are among the most basic parameters that almost every technical decision

on combustors and gasifiers refers to. The air ratio is defined as [38]

air-tricstoichiome

airactualF

F −=ϖ (8)

where Factual-air mass flow of air actually injected into the combustion chamber

Fstoichiometric-air theoretical minimum mass flow that would be necessary for the

complete or stoichiometric combustion of the fuel.

The air excess, usually expressed as percentage, is defined as [45]

( )1100F

F100F

airricstoichimet

airactualair −ϖ=×= (9)

In simplified calculations, nitrogen is usually assumed as an inert or non-reacting

component [39]. In these cases, the air ratio is equal to the oxygen ratio. Of course, the molar or

mass ratio would give the same value for “air ratio.”


20

Chapter 3. Literature review

3.1 Thermal conversion technologies

Biomass is a material that is derived from living or recently living biological organisms. In the

energy context it is often used to refer to plant material; however, by-products and waste from

livestock farming, food processing and preparation, and domestic organic waste, can all form

sources of biomass [52]. With such a wide range of material potentially described as biomass,

the range of methods to process it must be equally broad.

There are a number of technological options available to use a wide variety of biomass types as a

renewable energy source. Conversion technologies may release the energy directly, in the form

of heat or electricity, or they may convert it to another form, such as liquid biofuel or

combustible biogas [53]. While for some classes of biomass resource there may be a number of

usage options; for others, there may be appropriate technology. Conversion of biomass to energy

can be undertaken using two main process technologies: thermo-chemical and bio-

chemical/biological. Mechanical extraction with esterification is the third technology for

producing energy from biomass, e.g. rapeseed methyl ester (RME) bio-diesel [24].

Within thermo-chemical conversion, four process options are available: combustion, pyrolysis,

gasification and liquefaction. Bio-chemical conversion encompasses two process options:

digestion (production of bio-gas, a mixture of mainly methane and carbon dioxide) and

fermentation which produces of ethanol [54].

The products from any thermo-chemical process are

• a solid residue, called char

• a gas product

• a tarry liquid of complex composition, known as “tar,” often present in vapour phase

at process temperature

As commented by Hallgren [28], the characteristics of the products: gas, liquids and solid depend

on a broad range of factors such as the chemical and physical characteristics of the feedstock, the

heating rate, the initial and final process temperature, pressure, and the type of reactor.


21

3.2 Solid fuel gasification chemistry

Biomass gasification, a century old technology, is viewed today as an alternative to conventional

fuel [55]. In gasification processes, wood, charcoal and other biomass materials are gasified to

produce so called producer gas for power or electricity generation [11]. A gasification system

basically consists of a gasifier unit, purification system, and energy converters–burner or

engine [11].

Gasification is a thermo-chemical process that converts biomass materials into a gaseous

component. The result of gasification is the producer gas, containing carbon monoxide,

hydrogen, methane and some other inert gases [16]. Mixed with air, the producer gas can be used

in gasoline or diesel engines with little modifications.

The complexity of the gasification process is due the number of reactions taking place, and the

considerable number of components in the biomass. The main reactions in the gasification

process are listed in Table 3.1.

Table 3.1: Gasification reactions with reaction enthalpy [56]

Reaction ΔH298, kJ mol-1

Volatile matter CH4 + C Mildly Exothermic

C + 0.5 O2 CO –111

CO + 0.5 O2 CO2 –254

H2 + 0.5 O2 H2O –242

C + H2O CO + H2 +131

C + CO2 2 CO +172

C + 2 H2 CH4 –75

CO + 3 H2 CH4 + H2O –206

CO + H2O CO2 + H2 –41

CO2 + 4 H2 CH4 + 2 H2O –165


22

According to the first law of thermodynamics it is justified to state that energy conversion

processes do not have energy losses, except for losses from the process system into the

environment [57]. However, the second law of thermodynamics should also be considered.

Energy conversion processes are accompanied by an irreversible increase in entropy, which leads

to a decrease in available energy [35]. Thus, even though the energy is conserved, the quality of

energy decreases because energy is converted into a different form of energy, from which less

work can be obtained.

For a fuel containing carbon, hydrogen and oxygen, at a fixed pressure, the temperature of the

combustion system is determined by the equivalence ratio (ER) which is the amount of air added

relative to the amount of air required for stoichiometric combustion [4]. Depending on ER, a

thermo-chemical fuel conversion process may be classified as pyrolysis (ER = 0), gasification

(ER = 0.25–0.50) or combustion (ER ≥ 1) [58].

Generally, thermal processes would depend on the physical and chemical properties of the fuel,

as well as conditions around the fuel, such as its temperature, pressure, and atmosphere

composition [28]. Additionally, the rate at which the heating is imposed in the solid particles

plays an important role in the characteristics of the fuel thermal decomposition, its main steps

being drying, pyrolysis, gasification and combustion [32].

Drying is when liquid water leaves the fuel particles in the form of steam [58]. Pyrolysis or

devolatilization is the process during which gases, such as H2, CH4, CO, CO2, H2O, etc., and as

tar, are released to the surroundings [59]. In addition, important reactions and transformations

take place inside the particle [60].

During gasification, the particle’s solid components react with gases in the surrounding

atmosphere [61]. If the atmosphere contains oxygen, the gasification process is usually called

combustion [36].

The substance of a solid fuel is composed of the elements carbon, hydrogen and oxygen.

Nitrogen and sulphur may also exist, but since these are present only in small quantities they will

be disregarded in the following discussion. In the types of gasifiers considered here, the solid


23

fuel is heated by combustion of a part of the fuel. The combustion gases are then reduced by

being passed through a bed of fuel at high temperature.

In complete combustion, carbon dioxide is obtained from the carbon, and water from the

hydrogen. Oxygen from the fuel will of course be incorporated in the combustion products

thereby decreasing the amount of combustion air needed [62].

Oxidation, or combustion, is described by the following chemical reaction formulae [24]

22 COOC =+ –401.9 kJ/mol

OHO21H 22 =+ –41.1 kJ/mol

These formulae mean that burning 1 gram atom, i.e. 12.00 g of carbon, to carbon dioxide, a heat

quantity of 401.9 kJ is released, and that a heat quantity of 241.1 kJ results from the oxidation of

1 gram molecule, i.e. 2.016 g of hydrogen to water vapour.

In all types of gasifiers, the carbon dioxide, CO2, and water vapour, H2O, are reduced as much as

possible to carbon monoxide, hydrogen and methane, which are the main combustible

components of producer gas [63]. The most important reactions that take place in the reduction

zone of a gasifier between the different gaseous and solid reactants are given below [64]. A

minus sign indicates that heat is generated in the reaction, a positive sign that the reaction

requires heat.

a) COCOC 22 ⇔+ + 164.9 kJ/kmol

b) 22 HCOOHC +⇔+ + 122.6 kJ/kmol

c) OHCOHC 22 +⇔+ + 42.3 kJ/kmol

d) 422 CHHC ⇔+ 0

e) OHCHHCO 2423 +⇔+ –205.9 kJ/kmol


24

Equations a and b above, which are the main reactions of reduction, show that reduction requires

heat. Therefore the gas temperature will decrease during reduction. Reaction c, describes the so-

called water-gas equilibrium. For each temperature, in theory, the ratio between the product of

the concentration of carbon monoxide (CO), and water vapour (H2O), and the product of the

concentrations of carbon dioxide (CO2) and hydrogen (H2) is fixed by the value of the water-gas

equilibrium constant, KWE, given by [65]

( ) ( )( ) ( )2

2we HCO

OHCOK××

= (10)

In practice, the equilibrium composition of the gas will only be reached in cases where the

reaction rate and the time for reaction are sufficient [33]. The reaction rate decreases with

decreasing temperature [66]. In the case of the water-gas equilibrium, the reaction rate becomes

so far below 700°C that the equilibrium is said to be "frozen". The gas composition then remains

unchanged [67].

3.2.1 Drying

Drying is the first process to take place when the heating a solid fuel. At atmospheric pressures,

it occurs in the temperature range from ambient to around 380 K [68]. Despite its seeming

simplicity, drying a solid particle is a complex combination of events involving three phases:

liquid water, vapour, and solid porous phase through which the liquid and vapour migrate [69].

In addition, it depends on ions of sodium, potassium, and others dissolved in the water inside the

particle pores and complex surface tension phenomena that are present during the drying

process [64]. To better illustrate the various drying characteristics of process consider a porous

solid particle suddenly exposed to an ambiance with constant temperature and concentration of

water below the respective saturation.


25

Figure 3.1: Typical moisture concentration as function of time during drying of a porous particle: (a) water reduction, (b) rate of drying [68]

In this situation, Figure 3.1(a) shows the typical evolution of moisture content in the particle

against time, while Figure 3.1(b) presents the respective drying rates concentration of liquid

water (or moisture) inside the solid particle. The following periods or regions can be

recognized [70]:

1. The period from A to B represents the heating of the solid particle. Two situations

might occur, depending on the temperature of the gas atmosphere around the

particle [70]:

a. If that temperature is equal to or above the boiling point for water at the pressure of

atmosphere around the particle, the liquid water at the particle surface tends to

saturation temperature.

b. If that temperature is below the boiling point of water at the pressure of atmosphere

around the particle, the liquid water at the surface tends to the wet-bulb temperature

computed for the gas mixture near the surface.

2. The period from B to C represents the constant-rate drying region. In a simplified view,

liquid water is stored in internal pores of the solid particle structure. When the solid is

wet, liquid water migrates by several mechanisms to the surface. This provides a surface


26

continuously covered by a thin film of liquid water. If the conditions allow, this water

evaporates to the gas phase around the particle. Therefore, the rate at which water leaves

the surface somewhat independent on the nature of the solid particle. Thus, as long as the

surface remains wet and the ambient conditions constant, the rate of drying will be

constant. This process is also known as the first drying period [69].

3. The period from C to D represents the decreasing drying region. Here, the free water is

no longer available at the particle surface and the wet boundary retracts to the particle

interior. Therefore, phase change from liquid to steam occurs in the interior of the

particle. To leave the particle, the steam has to travel through a layer of dried material

surrounding the wet core. If external conditions remain constant, the drying rate

decreases due to the increase in the thickness of the dried layer. Therefore, the resistance

for mass and heat transfer between the wet interface and the particle surface increases.

This process is called the second drying period [33].

The rate of mass transfer from the particle surface to the ambiance is mainly affected by [68]

• Temperature of the particle, especially of the water liquid-vapour interface

• Rate of heat transfer between ambiance and particle

• Water vapour concentration in the surrounding gas layer

If only pure water is present, i.e., no ions are involved, the partial pressure of the water vapour at

the liquid-vapour interface is equal to the steam saturation pressure at the temperature of that

interface [46]. This is established, no matter if in the first or second drying period [60]. The gas

layer just above the water liquid surface contains water vapour. To be transferred through to the

gas mixture, either outside or inside the particle, a concentration gradient of water should

exist [63]. Therefore, the lower the concentration of water in the gas mixture, the faster would be

the mass transfer. The process stops when the concentrations of water vapour in the gas mixture

reach the saturation value. However, the amount of water to provide saturation conditions in a

gas mixture is higher for higher temperatures. This shows how important the temperature of the

gas phase is for the rate of drying [67].


27

Another aspect is the time taken by each drying period. The relative time between them would

depend too much on the conditions of the surrounding atmosphere, and on the properties of the

porous particles [71]. Nevertheless, as the second period involves higher mass-transfer

resistances than the first period, it is reasonable to assume that usually the former would take

longer than the latter [38]. This is especially true for increasing temperatures because the rate of

diffusion process during the second period is not increased as much as the rate of evaporation of

free available liquid water at the surface [56].

As a wet particle is injected into a gasifier, the following sequence of events usually takes

place [72]:

1. Fast heating of the particle. Therefore, the region from A to B in Figure 3.1 will occur in a

short time. For modeling purposes, that process can be considered instantaneous or fast.

2. Constant-rate drying is also fast due to the usually relatively large differences in

temperature and water concentration between particle surface and involving gas.

Therefore, high heat and mass transfers take place. It is reasonable to assume a fast first

drying period.

3. Decreasing rate of drying takes place. It starts almost immediately after the injection of

particles into the furnace. Decrease in the time taken for the second drying period results

in an increase of temperature in the gas around the particle.

3.2.2 Devolatization

Devolatilization or pyrolysis is a complex process that involves several reactions, including heat

and mass transfer resulting in releasing of mixtures of organic and inorganic gases and liquids

from the particle into the surrounding atmosphere [2]. This release is provoked by the increase of

particle temperature. Usually, the devolatilization starts when the carbonaceous solid reaches

temperatures just above the drying or as low as 390 K [73]. The temperatures employed for coal

or biomass may reach 1300 K [73].

Early researchers verified that increases in the temperature and/or heating rate led to increases in

the yield of volatile products. For instance, Habibi and collaborators [53] studied the influence of


28

conditions on the total amount of volatile that could be extracted from the coal. They established

a simple correlation for the mass flow of volatile released given by:

( )LLWFF dafVdafIPV ′′−′−= ,,, (11)

where WV, daf mass fraction of volatiles (dry, ash-free basis) on the original coal

This value can be determined by standard proximate analysis. The index I indicates the entering

conditions of the particle into the equipment given by:

( )[ ]daf.vS 15.115.273Tln961.341.26exp01.0L ω+−−=′ (12)

( )0.109v.dafω20L −=′′ (13)

The rates at which the volatiles are released from the solid carbonaceous fuel are not uniform.

For instance, for several biomasses the temperature of maximum release occurs between 600 K

and 700 K [74]. However, some typical components of biomass may present peaks of release

100 K below that range [62].

To understand the processes involved during volatile release, several mechanisms have been

proposed. For biomass, Habibi and collaborators [53] propose the following temperature ranges

regarding devolatilization:

1. Zone I: < 373 K, mainly moisture evolution

2. Zone II: 373 – 523 K, extractives start decomposing

3. Zone III: 523 – 623 K, predominantly hemicelluloses decomposition

4. Zone IV: > 773 K, mainly lignin decomposition

During any thermal analysis, a heating rate is imposed. Therefore, it is almost impossible to

avoid the influence of heating rate on pyrolysis. Indeed, it is common to classify the pyrolysis as

slow, moderate, and fast. The first assumes a heating rate of the sample below 10 K/s, while fast

pyrolysis refers to rates above 103 K/s [56]. The main reason for this classification is related to

the process of carbonaceous solid utilization. Fast pyrolysis occurs in almost all combustions of

pulverized solid (or suspension combustion), where values reaching 105 or even 106 K/s can be

found [42]. Fluidized bed (bubbling and circulating) imposes lower values, or around


29

102–104 K/s [75]. Moderate to slow rates may happen in sections of moving or fixed bed

combustion or gasification.

3.2.3 Gasification

In general terms, the gasification process is the total or partial transformation of solid fuel

components into gases. This is usually accomplished by thermal treatments or chemical

reactions, or a combination of both. Therefore, devolatilization is part of the gasification process,

as is combustion or the reaction of carbonaceous fuel and oxygen. However, in the usual context

of thermal sciences, gasification reactions are the ones taking place between the char (or

devolatilized solid fuel) and gases excluding oxygen [76]. In addition, one should be aware that

in a real process, devolatilization, gasification, and combustion reactions might occur

simultaneously, at least during part of the time taken by these processes. Actually, is almost

impossible to have combustion without other reactions typical of gasification [2]. This is because

almost all fuels have hydrogen as a component, thus water forms during combustion, which in

turn reacts with carbon.

The combustible gas mixtures produced during gasification consisting primarily of carbon

monoxide (CO), hydrogen (H2), and methane (CH4). This conversion occurs at elevated

temperatures and pressures according to several competing reactions. These reactions are as

follows [50]:

C + O2 ⇔ CO2 (combustion reaction; highly exothermic at –111 MJ/kmol)

C + CO2 ⇔2CO (Boudouard reaction; endothermic at +172 MJ/kmol)

C + H2O ⇔ CO + H2 (carbon-steam reaction; endothermic at +131 MJ/kmol)

CO+H2O ⇔CO2 + H2 (water gas shift reaction; mildly exothermic at

–41 MJ/kmol)

Another reaction that can take place at temperatures less than 1093°C (2000°F) and at high

operating pressures is [77]:

C + 2 H2 ⇔ CH4 (carbon hydrogenation reaction; exothermic at

–75 MJ/kmol)


30

Whenever the carbonaceous feedstock can generate significant quantities of volatile matter,

methane can be formed by thermal cracking of the volatile matter according to the qualitative

reaction [78]:

CmHn ⇔ n/4CH4 + (m–n)/4C (thermal cracking reactions; endothermic)

Gasification of carbonaceous feedstock is typically done in the presence of an oxidant, air or O2

and steam/water vapour in order to conduct the gasification reactions shown above.

3.2.4 Combustion

Combustion is a chemical reaction in which an oxidant reacts rapidly with a fuel to liberate

stored energy as thermal energy, generally in the form of high-temperature gases [58]. Small

amounts of electromagnetic energy, light, electric energy, free ions and electrons, mechanical

energy, and noise are also produced during combustion [65]. Except in special applications, the

oxidant for combustion is oxygen in the air. Worldwide, biomass is the largest contributor to

renewable energy, of which most applications produce heat and power relies on combustion as

the conversation process.

Conventional hydrocarbon fuels contain primarily hydrogen and carbon, in elemental form or in

various compounds. Their complete combustion produces mainly carbon dioxide (CO2) and

water (H2O); however, small quantities of carbon monoxide (CO), and partially reacted flue gas

constituents consisting of gases and liquid or solid aerosols, may form [79]. Most conventional

fuels also contain small amounts of sulphur, which is oxidized to sulphur dioxide (SO2), or

sulphur trioxide (SO3) during combustion, and non-combustible sub-stances such as mineral

matter-ash, water, and inert gases [78].

Flue gas is the product of complete or incomplete combustion and includes excess air if present,

but not diluted air [80]. Fuel combustion rate depends on [72]:

• the rate of the chemical reaction of the combustible fuel constituents with oxygen

• the rate at which oxygen is supplied to the fuel or the mixing of air and fuel

• the temperature in the combustion region.


31

The reaction rate is fixed by fuel selection. Increasing the mixing rate or temperature increases

the combustion rate.

With complete combustion of hydrocarbon fuels, all hydrogen and carbon in the fuel are

oxidized to H2O and CO2. Generally, for complete combustion, excess oxygen or excess air must

be supplied beyond the amount theoretically required to oxidize the fuel [46]. Excess air is

usually expressed as a percentage of the air required to completely oxidize the fuel.

In stoichiometric combustion of a hydrocarbon, fuel is reacted with the exact amount of oxygen

required to oxidize all carbon, hydrogen, and sulphur in the fuel to CO2, H2O and SO2 [52].

Therefore, exhaust gas from stoichiometric combustion theoretically contains no incompletely

oxidized fuel constituents and no un-reacted oxygen i.e., no carbon monoxide and no excess air

or oxygen. The percentage of CO2 contained in products of stoichiometric combustion is the

maximum attainable and is referred to as the stoichiometric CO2, ultimate CO2, or maximum

theoretical percentage of CO2 [38].

Stoichiometric combustion is seldom realized in practice because of imperfect mixing and finite

reaction rates [73]. For economy and safety, most combustion equipment should operate with a

degree of excess air [36]. This ensures that fuel is not wasted and that combustion is complete

despite variations in fuel properties and in the supply rates of fuel and air. The amount of excess

air to be supplied to any combustion equipment depends on [35]

• expected variations in fuel properties and in fuel and air supply rates

• equipment application

• control requirements.

For maximum thermal efficiency, combustion at low excess air is desirable [82].

Incomplete combustion occurs when a fuel element is not completely oxidized during

combustion. For example, a hydrocarbon may not totally oxidize to carbon dioxide and water,

but may form partially oxidized compounds, such as carbon monoxide, aldehydes and

ketones [83].

Conditions that promote incomplete combustion include [67]


32

• insufficient air and fuel mixing causing local fuel-rich and fuel-lean zones

• insufficient air supply to the flame providing less than the required quantity of oxygen

• insufficient reactant residence time in the flame preventing completion of combustion

reactions

• flame impingement on a cold surface quenching combustion reactions, or

• flame temperature that is too low slowing combustion reactions.

Incomplete combustion uses fuel inefficiently, presents a hazard because of carbon monoxide

production, and contributes to air pollution.

For practical combustion calculations, dry air consists of 20.95% oxygen and 79.05% inert gases:

nitrogen, argon, and so forth by volume, or 23.15% oxygen and 76.85% inert gases by mass [62].

For calculation purposes, nitrogen is either assumed to pass through the combustion process

unchanged or a small quantity of nitrogen oxides is considered through the mechanisms of

thermal, prompt and fuel NOx [64].

3.3 Types of gasifiers

Gasifiers are devices used to convert a solid fuel such as biomass, to a combustible gaseous fuel

through a thermo-chemical process under controlled temperature, pressure and atmospheric

conditions using less air or oxygen [59]. The types of gasifier vary, and may be divided into

three main groups: fixed bed gasifier, entrained flow gasifier and fluidized bed gasifier either

bubbling or circulating. Fixed bed gasifiers are divided into three types: counter-current-updraft,

co-current-downdraft and cross-current moving bed [82]. The main differences are due to how

reactants and products are moved around in the reactor, and the resulting reaction conditions.

3.3.1 Fixed bed gasifiers

3.3.1..1 Updraft gasifier

The simplest type of gasifier is the fixed bed counter current gasifier where the principle is

shown on Figure 3.2.a. The biomass is fed from above the grate of the reactor and moves

downwards as a result of the conversion of the biomass and the removal of ashes. The air intake

is at the bottom and the gas leaves at the top. The biomass moves in counter current to the gas


33

flow, and passes through the drying zone, the pyrolisation zone, the reduction zone, and the

hearth zone.

In the drying zone the biomass is dried. In the pyrolisation zone the biomass is decomposed in

volatile gases and solid char [42]. The heat for pyrolisation and drying is mainly delivered by the

upwards flowing producer gas and partly by radiation from the hearth zone.

In the reduction zone, many reactions take place involving char, carbon dioxide and water

vapour in which carbon is converted and carbon monoxide and hydrogen are produced as the

main constituents of the producer gas [84]. In the hearth zone the remaining char is combusted

providing the heat, the carbon dioxide, and water vapour for the reactions involved in the

reduction zone.

The major advantages of this type of gasifier are its simplicity, high fix carbon burn-out and

internal heat exchange leading to low gas exit temperatures and high gasification efficiencies.

Because of the internal heat exchange, the fuel is dried in the top of the gasifier and therefore

fuels with relatively high moisture content can be used. Furthermore, this type of gasifier can

even process relatively small fuel particles and accepts some size variation in the fuel

feedstock [64].

Major drawbacks are the high amounts of tar and pyrolysis products, because the pyrolysis gas is

not combusted. This is of minor importance if the gas is used for direct heat applications, in

which the tar is simply burnt. If the gas is used for engines, extensive gas cleaning is

required [32] making small scale applications problematic due to the relatively high cost of

syngas clean up per unit energy.


34

Figure 3.2: Types of Gasifiers: clockwise from top left: (a) updraft (b) downdraft (c) crossflow (d) fluidized [28]

3.3.1..2 Downdraft gasifier

In a downdraft reactor, biomass is fed at the top and the air intake is also at the top or from the

sides of the reactor. The gas leaves at the bottom of the reactor, so the fuel and the gas move in

the same direction, see Figure 3.2 (b). The same zones can be distinguished as in the updraft

gasifier, although the order is somewhat different.

The biomass is dried and pyrolysed in the drying and distillation zone respectively. These zones

are mainly heated by radiation and partly convection heat from the hearth zone, where a part of

(a) (b)

(c) (d)


35

the char is burnt [80]. The pyrolysis gases pass also through this zone to be burnt as well. The

extent to which the pyrolysis gases are actually burnt depends on design, the biomass feedstock

and process control. After the oxidation zone, the remaining char and the combustion products

carbon dioxide and water vapour pass to the reduction zone where the reduction reactions take

place forming CO and H2 [72].

Hence, the main advantage of a downdraft gasifier is to produce gas with low tar content, which

is nearly suitable for engine applications. In practice however, a tar-free gas is seldom if ever

achieved over the whole operating range of the equipment [58], especially on a 24/7 basis. The

main reason seems to be that not all gases pass through the hottest zones and that their residence

time in the combustion zone might be too short. In each particular design other features are

included to realize a high conversion rate of the pyrolysis gases.

Drawbacks of the downdraft gasifier include [52]

• high amounts of ash and dust particles in the gas due to the fact that the gas has to

pass the oxidation zone collecting small ash particles

• relatively strict requirements on fuel which has to be uniformly sized in the range of

4–10 cm to realize regular flow, no blocking in the throat, enough "open space" for

the pyrolysis gases to flow downwards and to allow heat transport from the hearth

zone upwards; therefore pelletization or briquetting of the biomass is often necessary

• moisture content of the biomass must be less than 25% on a wet basis

• relative high temperature of the leaving flue gases resulting in lower gasification

efficiency

This type of gasifier is used in power production applications in a range from 80 to 500 kWe or

more [37].

3.3.1..3 Crossflow gasifier

Cross draft gasifiers are shown on Figure 3.2 (c). Although they have certain advantages over

updraft and downdraft gasifiers, they are not ideal. The disadvantages such as high exit gas

temperature, poor CO2 reduction, and high gas velocity are the consequences of the design [24].

Unlike downdraft and updraft gasifier, the ash bin, and the fire and reduction zone in crossdraft


36

gasifiers are separated [52]. These design characteristics limit the type of fuel for operation to

low ash fuels such as wood, charcoal and coke. The load-following ability of crossdraft gasifier

is quite good due to concentrated partial zones which operate at temperatures up to 2000°F [56].

Start up time, 5 – 10 minutes is much faster than that of downdraft and updraft units [57]. The

relatively higher temperature in crossdraft gas producers has an obvious effect on gas

composition such as high carbon monoxide, and low hydrogen and methane content when dry

fuel such as charcoal is used [62]. Crossdraft gasifiers operate well on dry air blast and dry fuel.

3.3.2 Entrained-flow gasifiers

In the entrained-flow gasifier, a dry pulverized solid, an atomized liquid fuel or a fuel slurry is

gasified with oxygen (much less frequent: air) in co-current flow [85]. The gasification reactions

take place in a dense cloud of fine particles. Most coals are suitable for this type of gasifier

because of the high operating temperatures and because the coal particles are well separated from

one another [60]. The high temperatures and pressures also mean that a higher throughput can be

achieved; however thermal efficiency is somewhat lower because the gas must be cooled before

it can be cleaned with existing technology [52]. The high temperatures also mean that tar and

methane are not present in the product gas; however the oxygen requirement is higher than for

the other types of gasifier [72]. There is also a cost associated with O2 separation.

Two types of entrained-flow gasifier can be distinguished: slagging and non-slagging [37]. In a

slagging gasifier, the ash forming components melt in the gasifier, flow down the walls of the

reactor and finally leave the reactor as a liquid slag. Generally, the slag mass flow should be at

least 6% of the fuel flow to ensure proper operation [32]. In a non-slagging gasifier, the walls are

kept free of slag. This type of gasifier is suitable for fuels with low ash content. The two-stage

gasifier/combustor used in this study uses slagging in the secondary combustor to address the

relatively high silica content of agricultural feedstocks.

Entrained-flow gasifiers rapidly convert pulverized fuel to synthesis gas in a short residence time

by partial oxidation typically with oxygen at high temperatures of 1370°C to 1925°C

(2500°F to 3500°F) [86]. Molten ash produced at these elevated operating temperatures is

continuously water-quenched and removed as glassy slag. Fuel feed to pressurized units is


37

accomplished either by pumping fuel–water slurry or injecting dry fuel in a dense phase of

transporting nitrogen [14]. The principal advantages of entrained-flow gasifier are in their

conceptually simple design, good tolerance of caking fuels, high throughput, high carbon

conversion efficiency, and thermal flexibility for increasing operating temperatures well beyond

the melting point of ash [60]. Disadvantages are the large amount of gas cooling and heat

recovery necessitated by the high exit gas temperature, the limited opportunities for in-gasifier

sulphur capture, the complex feeding systems required for pressure operation, and the necessity

for close control of oxygen feed rate for safe operation [34].

3.3.3 Fluidized bed gasification–circulating fluidized bed/ bubbling bed

Fluidized bed gasification was originally developed to overcome the operational problems with

fixed bed gasification of fuels with high ash content, but it is suitable for the larger capacities

(larger than 10 MWth) in general [57]. The features of fluidized bed gasification are comparable

with those of fluidized bed combustion. In the fluid bed gasifier, the fuel is fluidized in oxygen,

air, or steam [49]. The ash is removed dry or as heavy agglomerates that defluidize [61]. The

temperatures are relatively low in a dry ash gasifier, so the fuel must be highly reactive; low-

grade coals are particularly suitable. Compared to a fixed bed gasifier the gasification

temperature is relatively low: approximately 750°C–900°C [27]. In a fixed bed gasifier the

temperature in the hearth zone may be as high as 1200°C, and a charcoal gasifier even

1500°C [67]. The fuel is fed into a hot sand bed which is in a state of suspension as in a bubbling

fluidised bed or circulating as in a circulating fluidised bed [85]. The bed behaves more or less

like a fluid and is characterized by high turbulence. Fuel particles mix quickly with the bed

material, resulting in a fast pyrolysis and a relatively large amount of pyrolysis gases [57].

Because of the low temperatures, the tar conversion rates are not high [84].

Advantages of fluidized bed reactors (Figure 3.2 (d)) compared with fixed bed reactors are [52]

• Compact construction is possible because of high heat exchange and reaction rates

due to the intensive mixing in the bed.

• Flexible to changes in fuel characteristics are possible such as moisture and ash

content; in other words the fluidized bed reactor can ability to deal with fluffy and

fine grained materials with high ash contents and/or low bulk density.


38

• Relatively low ash melting points are allowed due to the low reaction temperatures

Drawbacks are [64]

• high tar and dust content of the produced gas

• high producer gas temperatures containing alkali metals in the vapour state

• incomplete carbon burn out

• complex operation because of the need to control the supply of both air supply and

solid fuel

• need for power consumption for the compression of the gas stream

The carbon burn out in circulating fluidized bed gasifiers may be considerably better than in

bubbling fluidised beds [1]. The flexibility in particle size is not that high compared to fluidized

bed combustion; where as for gasification, relative fine fuel particle sizes are preferred [57].

A schematic presentation of a fluidized bed gasifier is given in Figure 3.2 (d). In the Figure,

steam and pure oxygen are also mentioned as fluidizing and gasification agents instead of air. If

the gasification is done with pure oxygen the caloric value of the producer gas will be higher

because of the absence of nitrogen coming from the air in the producer gas [45]. However,

producing oxygen is expensive and therefore it is only feasible in large scale applications, for

example in the large scale coal gasification power plant in Europe. For the scale of biomass

applications however an oxygen factory is not expected to be economic.

3.4 Vidir Best gasifier

The gasifier system(Figure 3.3 and 3.4) used in this study and modelled using CFD is Vidir Best

built by Vidir Biomass Inc, St Adolph, Manitoba. The biomass gasification system is designed

for gasifying then combusting biomass material containing high silica content like straw. It is a

combination of a downdraft/updraft, atmospheric pressure heating system consisting of the

following system components [87]:

1. bale magazine to automatically supply gasifier with fuel using 1,000-kg round bales

2. fuel delivery and preparation: straw shredder and product conveyor system

3. primary combustion chamber: ash removal system, grate system and air distribution

system


39

4. secondary combustion chamber with tray for manual silica removal

5. hot water heat exchanger including automatic cleaning system and tray for clean-out

6. exhaust system including blowers, cyclones, and chimney stack to control air flow and

exhaust

7. main computerized control system with SmartFireTM technology

The primary combustion chamber is an enclosed area where drying, pyrolysing and oxidizing

occur. The rotating grate slowly agitates the fire bed and allows for under fire air to be blown up

through the fuel. The hot exhaust gases exit at the top of the primary combustion chamber and

pass through a refractory duct that includes an oxygen mixer, and go into the secondary

combustion chamber. As the gases are being transported from the primary to the secondary

chamber, the injection of oxygen ignites the syngas, allowing spontaneous gas combustion to

take place in the secondary chamber, as shown in Figure 3.5. A liquid stream of silica forms

along the wall of the secondary chamber, accumulating at the bottom of the secondary

combustion chamber when agricultural straw is being utilized as the primary bio-fuel.

The heat from the secondary chamber is transferred to the atmospheric pressure hot water heat

exchanger. The heat exchanger consists of a series of tubes through which the heated flue gases

pass transferring the heat to the water surrounding the tubes. Hot water is the medium being used

to transport the heat through insulated underground pipes to the desired location and supply heat

for public, commercial, residential or agricultural buildings. Fly ash, moved by combustion gas

flow, can deposit on the heat exchanger surfaces in the boiler. This ash must be regularly

removed to maintain good heat transfer performance. Scrubbers are used to automatically clean

the boiler tubes [46].

The complete feed and gasification process requires a control system to match heat delivery with

demand. Therefore, a computerized control system is incorporated and it is important for

efficient operation in response to energy demand. A key task of the control system is determining

the rate at which fuel and air are fed at various points in the sequence to ensure efficient

combustion. Therefore, fuel and air optimization needs to occur under high or low energy

demand. This aspect is made difficult because the porosity of the bed changes with time making

the resistance through the bed vary with time.


40

Figure 3.3: Vidir Best gasifier, 3-D [87]

Figure 3.4: Vidir Best gasifier schematic diagram [87]


41

Figure 3.5: Secondary chamber characterized by high temperature

3.5 Modeling gasification

Biomass gasification and pyrolysis are thermally degraded processes in the absence of air or

oxygen for producing solid charcoal, or liquid fuels like bio-oil and gaseous products for syngas

production [88]. CFD numerical models can be used to describe these processes because they

have become an important analysis and design tool to achieve the flow and temperature pattern,

the products’ concentration contour, and yields [20].

The understanding of the interaction between chemical and physical mechanisms during

gasification/combustion is of fundamental importance for the optimal design of a biomass

gasifier [89], to address control problems, scale up, and resolve issues. In view of the

considerable interest in the gasification process worldwide, it is necessary to model and predict

the performance of the gasifier to improve designs and solve operational problems. Modeling of

biomass gasification implies representing chemical and physical phenomena constituting fuel

drying, pyrolysis, gasification, combustion and char reduction in mathematical form.


42

There are several modeling studies on packed-bed biomass combustion. Lists of some studies are

tabulated in Table 3.2. Zhou et al. [54] numerically modelled the combustion of straw in a

bench-top stationary fixed bed with focus on NO formation and reduction. Higman et al. [72]

employed a two-dimensional model for straw combustion in a moving bed where the bed is

assumed to be at steady state, and the time elapsed since ignition is related to a horizontal

position on the grate by a simple linear function. The model was validated based on fixed-bed

experiments. Kaer [30, 33] carried out numerical modeling of a 33 MW straw-fired grate boiler

incorporating a stand-alone bed model and a commercial CFD code for gas-space computation.

He concluded that poor mixing in the furnace is a key issue leading to high emission levels and

relatively high amounts of unburnt carbon in the fly ash. The stand-alone bed model is based on

a one-dimensional “walking-column” approach and included the energy equations for both the

fuel and the gas accounting for heat transfer between the two phases.

The above models have the disadvantage of not taking the particle mixing effect into account

when applied to a moving-grate. Yang et al. [90] have indicated that solid-fuel mixing caused by

the grate movement can significantly enhance the reaction rates of biomass fuel in a packed-bed,

and hence can change the combustion characteristics of the furnace. The latter work overcame

the shortcomings of the previous modeling works on straw combustion by introducing a particle

mixing coefficient in the model. This method helped to further understand the operating

characteristics of large-scale, straw-burning plants.

Many researchers have developed models for biomass gasification [3, 18 and 78].

Zainal et al. [91] used the equilibrium model to predict the composition of producer gas.

Fletcher et al. [27] developed a detailed CFD model to simulate the flow and reaction in an

entrained-flow biomass gasifier. The model is based on the Computational Fluid DynamiX

(CFX) package and describes the phenomena of turbulent fluid flow, heat transfer, species

transport, devolatilization, particle combustion, and gas phase chemical reactions. Biomass

particulate is modelled via a Lagrangian approach because it entered the gasifier, released its

volatiles and finally underwent gasification. Transport equations were solved for the

concentration of CH4, H2, CO, CO2, H2O and O2 and heterogeneous reactions between fixed

carbon and O2, CO2 and H2O were modelled. The model provided detailed information on the


43

gas composition and temperature at the outlet and allowed different operating scenarios to be

examined in an efficient manner. The initial calculations suggested that simulations to examine

the effect of gasifier height and the steam flux in the upper inlets could be beneficial in process

optimization. The simulation of sawdust gasification in one case gave an exit composition on a

dry basis of 10% CO, 12% CO2, 20% H2 and 1.2% CH4; compared with 16% CO, 14% CO2,

10% H2, and 1% CH4 measured in the experiments, the hydrogen generation was too high. The

effects of initial moisture content in the wood and the temperature in the gasification zone on the

calorific value were investigated.

Mathieu and Dubuisson [82] developed a model based on minimization of Gibbs free energy

which was performed in the 1-D ASPEN PLUS process simulator. The effects of the oxygen

factor, the air temperature, operating pressure and the injection of steam were studied. Di

Blasi [7] developed a one-dimensional, unsteady-state model for biomass gasification in a

stratified concurrent downdraft reactor. Heat and mass transfer across the bed were coupled with

moisture evaporation, biomass pyrolysis, char combustion, and gasification, gas-phase

combustion and thermal cracking of tars. The model was used to simulate the structure of the

reaction fronts and the gasification behaviour of a laboratory-scale plant as the reactor

throughput and the air-to-wood or char weight ratio were varied.

Giltrap et al. [92] developed a model for the reduction zone of a downdraft biomass gasifier to

predict the composition of producer gas under steady-state operation. Factors affecting the gas

composition were the fraction of pyrolysed gas in the initial gas entering the reduction zone of

the biomass gasifier, air-to-fuel ratio, moisture content of biomass, bed temperature, and

reactivity of char. Molar balance, energy balance, pressure gradient equation, and Arrhenius type

of temperature dependence kinetic equation formed the set of first order differential equations,

which were solved by finite difference method. The accuracy of the model was limited by the

availability of data on the initial conditions at the top of the reduction zone. Moreover it was

assumed that char reactivity factor (CRF), which represented the reactivity of char and the key

variable in simulation, was constant throughout the reduction zone. Kaer et al. [93] developed a

model incorporating Milligan’s [84] flaming pyrolysis sub-model along with the gasification

zone sub-model. The model did not include the effect of packed char particles in a reduction


44

zone because it was limited by considering only a single particle. Babu and Sheth [56] modified

Giltrap’s model by incorporating the variation of CRF along the reduction zone of the downdraft

biomass gasifier. The model was simulated with the finite difference method to predict the

temperature and composition profiles in the reduction zone. A finite difference technique was

successfully applied to solve such type of partial differential equations in other studies [65, 82].

The CRF value is increased linearly and exponentially along the reduction bed length in the

model. The model predictions were compared with the experimental data reported by

Kaer et al. [93]. Babu and Sheth [50] studied the effects of pyrolysis fraction and bed

temperature on gas compositions.

The crucial combustion parameter, primary air flow rate, has been widely studied by researchers

such as Dixon et al. [11], Roos et al. [36], Moilanen, A. & Saviharju [49], Gil et al. [88],

Yang et al. [90]. Luo et al. [99] developed a two-dimensional steady model for straw combustion

in a cross-current moving bed and verified the model with experimental data for the effects of the

inlet air flow rate and air temperature on the combustion process. Their work was extended by

Zhou et al. [54] who developed a one-dimensional transient model of fixed-bed straw

combustion and validated the results with experimental measurements of temperature, gas

emissions, and ignition front propagation rate and flame temperature. Kaer [79] investigated a

straw-fired grate boiler using the computation fluid dynamics (CFD), in which the model

predictions were compared with measurements obtained from a full-scale boiler plant at

Masnedø (Denmark). Rapagnà et al. [94] investigated the physical and chemical transformation

of biomass chars (pine and switch grass) during combustion. A major problem associated with

the utilization of straw is the low ash melting temperature, producing cakes of sintered ash.

Olivares et al. [73] carried out the characterization of ashes from wood and straw.

Gerun et al. [95] developed a 2-D axisymmetric CFD model for the oxidation zone in a two-

stage, downdraft gasifier. The oxidation zone is crucial for tar cracking. The simulations fit

satisfactorily to the experimental data regarding temperature pattern and tar concentration. The

heat of reaction was released mainly close to the injector. It induced a hot zone in this area. The

gas path strongly depended on the initial departure point. The strong recirculation zone was


45

located above the air injection in the centre of the reactor. It played a major role in air-gas

mixing and thus enhanced the quality of the gasification.

Baxter [57] conducted a study on eleven biomass fuels representing a broad class of

commercially available fuels: straws and grasses, pits, shells, hulls and other lignocellulosic

biomass, woods and waste fuels of commercial interest. A systematic and reasonably detailed

analysis of fuel property, operating condition, and boiler design issues that dictate ash deposit

formation and property development was provided.

Table 3.2: Summary of CFD modeling attempts

Application Code Dim

Turb. Model

Extra Model Agreement with Exp.

Authors

Two-stage downdraft

gasifier

Fluent

2-D

RNG k-ε DOM Satisfactory Gerun, L. (2008)

Cone calorimeter

reactor

Code

1-D

N/A Porous N/A Giltrap, G.L. (2003)

Entrained flow gasifier

CFX

2-D

Std k-ε Langragian, DTRM

N/A Ma, et al. (2007)

Downdraft gasifier

Code

1-D

N/A Porous N/A Sharma, A.K.(2007)

Horizontal entrained flow

reactor

Fluent

1-D

N/A Langragian Reasonable Zhou, S.N. (2006)

Moving packed bed

Fluent

2-D

Std k-ε DOM N/A Kaer, S.K.

(2004)

Entrained flow gasifier

CFX4

2-D

Std k-ε RSM, Langragian

Acceptable Feltcher, D.F. (2000)

Herbaceous fuels contain silicon and potassium as principal ash forming constituents. They are

also high in chlorine and exhibit severe ash deposition problems due to the reaction of alkali with


46

silica to form alkali silicates that melt or soften at temperatures as low as 700oC and due to the

reaction of alkali with sulphur to form alkali sulphates on heat transfer surfaces. All biologically

active alkali, potassium in particular is traced to be the principal cause of most deposits. There

also exists the non-biological form of the alkali in soils and it exhibits much less reactivity. As

potassium is the cause of deposits in biomass, sodium does the same with coal.

Ma et al. [96] performed CFD application in a 1 MW industrial wood test furnace coupled with

the potassium release and NOx formation model. The potassium release during biomass

combustion is still a subject of current investigation. Ma et al. assumed that the biomass

potassium release during devolatilization rapidly forms KOH. Both the HCN and the NH3 route

have been considered for the NOx formation. The particle tracks and temperature distribution are

also studied in this work. Good agreement between the predicted and the measured furnace

temperature and concentrations of CO2 and NOx were achieved.

3.6 Ash deposition mechanism

Understanding ash deposition is critical to the proper functioning of a gasifier to ensure

convection section tubes stay unfouled. Biomass fuels contain considerable quantities of ash,

forming elements in addition to their main organic constituents C, H, O, and N [90]. The most

important of these elements are Si, Ca, Mg, K, Na, S, Cl as well as heavy metals such as Zn and

Pb [47, 89]. A typical composition of the most commonly used solid biomass fuels data reveals

extreme variations in ash content and ash composition between and within the different types of

biomass fuels. Wood has much lower ash content, for example, than bark, waste wood and

herbaceous fuels [95].

There are generally two sources for inorganic ash forming matter in biomass fuels [97]. Ash

forming elements originate from the plant itself, because they are part of the structure of the

fibers (e.g.: Si, Ca) or are macro or micro plant nutrients (e.g.: K, P, S, Zn) [98]. Alternatively,

inorganic matter in biomass fuels can also come from contamination with soil, sand or

stones [88]. Coatings, paints, glass pieces and metal parts are major sources of contamination in

waste wood [88].


47

Upon entering the combustion unit, the fuel is first dried, followed by devolatilization of the

volatile organic matter. Subsequently, the remaining fixed carbon is oxidized during

heterogeneous gas-solid reactions, which is called char combustion [55]. During these steps the

ash forming elements behave in two different ways according to their volatility [99]. Non-

volatile compounds such as Si, Ca and Mg are engaged in ash fusion as well as coagulation

processes [94]. Once the organic matter has been oxidized, these elements remain as coarse ash

structures. Easily volatile species such as K, Na, S, Cl, Zn and Pb generally behave

differently [97]. A considerable proportion of these elements are released to the gas phase due to

the high temperatures occurring during combustion [82]. There they undergo homogeneous gas

phase reactions and later on, due to super saturation in the gas phase, these ash forming vapours

start to nucleate, leading to the formation of submicron aerosol particles or condensation on

surfaces of existing particles [47]. The submicron particles are so-called aerosols and form one

important fraction of the fly ashes.

The processes that govern ash deposition can be divided into five categories: inertial impaction,

eddy impaction, thermophoresis, condensation and chemical reaction [55]. The net deposition

rate can be represented as the sum of these deposition mechanisms [99].

Inertial impaction is believed to be the dominant deposition mechanisms on chamber walls or at

any location where entrained ash particles are required to turn sharp corners at high velocity [60].

Inertial impaction occurs when a particle has sufficient momentum to impact an obstruction by

penetrating the flow field surrounding the obstruction [56]. Of the particles that do reach the wall

some fraction will tend to stick to the tube and become deposited there. The tendency of particles

to stick to the wall is thought to depend on the properties of the particle at the moment of impact

as well as those of the impacted surface, especially the previously accumulated ash layer [102].

While the physics that lead to particle impaction are fairly well understood, predicting what

fraction of impacting particles will stick is much more difficult [78]. As shown in Equation 14,

inertial deposition can be thought of as the product of impaction efficiency, η, and capture

efficiency, G, which together forms collection efficiency (ζ) [75]:


48

GAm

m

CrossAsh

Deposit .ηξ ==&

& (14)

3.6.1 Deposition mechanisms

3.6.1..1.1 Eddy impaction

Eddy impaction occurs when fine ash particles located near a solid surface are a blown by

turbulent eddies onto the surface where they become deposited [98]. In part, because of the

complexities of describing near-wall turbulent eddies, this mechanism is not well

understood [60].

3.6.1..1.2 Thermophoresis

Steep thermal gradients surrounding a particle can give rise to thermophoretic forces [85].

Thermophoresis, which is significant only for fine particles, typically transports particles towards

regions of lower temperature, which, in the case of a cooled section of a chamber, drives

particles toward the wall and can lead to deposition [103]. Ash deposited by thermophoresis is

generally more evenly distributed around a wall whereas inertial impaction occurs only on the

chamber’s upstream side [102]. Thermophoretic deposition also decreases as the insulating layer

of ash accumulates and the temperature difference between the gas and the deposit surface

temperature decreases [85].

3.6.1..1.3 Condensation

Condensation occurs when mass from the gas phase collects on a cool surface [38]. Relatively

low temperatures near a cooled chamber can cause certain gas-phase constituents to condense

and accumulate [47]. In addition to temperature, condensation also depends on the

concentrations of these various constituents in the gas phase [82]. Condensation is typically of

greater concern in biomass applications or wherever a large amount of inorganic material

particularly alkali salts, is present in the fuel [74]. Condensation may be the mechanism that

allows the silica to slag in the Vidir downcomer.


49

3.6.1..1.4 Chemical reaction

Chemical reactions between the solid and gas phases can also change the net rate of mass

deposition [46]. These reactions may also affect the properties of the ash deposit by changing the

temperatures at which sintering and melting occur, which can in turn affect the number of

particles captured during inertial impaction processes [101].

3.6.1..1.5 Other mechanisms

Several other mechanisms exist which may play a role in deposit formation. These other

mechanisms include electrostatic forces, photophoresis, and Brownian motion. Current

understanding however suggests that these mechanisms are not significant in the formation of

deposits [47].


50

Chapter 4. Numerical simulation methodology

Using the CFD commercial software, FLUENT 6.2®, a model of the gasifier was developed to

understand the combustion/gasification mechanism of biomass fuel, particularly straw, and

applied to the Vidir gasifier. A CFD model mainly consists of three main parts. The first part is

the physical models which are a set of conservation equations of mass, momentum, energy, state

equation, turbulent equations, chemical reaction and source term equations. The second part is a

series of solution approaches for solving these physical models and the third part is the pre-

processor of discretization of computational domain and the postprocessor of visualization of

numerical results.

As a CFD code, FLUENT® solves the fundamental conservation equations for microscopic

regions [105]. It solves the Navier-Stokes equations using a finite volume method on a grid,

which is generated directly integrated in FLUENT® 6.2 [93]. Boundary conditions and selecting

the right sub-model have critical importance in the model of a system. Discussions on the

available options to address major problems in achieving heat transfer, turbulence and species

evaluation leading to ways to set up an adequate gasifier model are emphasized. The sub-models

and solution methods available in FLUENT®, and those chosen in the model are also discussed

in detail in this chapter.

4.1 Basic governing equations

For a homogenous Newtonian fluid flow, the mathematical modeling was based on a set of

coupled conservation equations of mass, momentum, energy, and chemical species transport and

reactions, and the state equations of the fluid system [79]. Furthermore, since most practical

flows are turbulent, these conservation equations should be treated into the time-averaged or

spatial filtered forms, which needed to be closed by using additional turbulent models [57].

4.1.1 Conservation Equations

A numerical study was performed for obtaining the flow field characteristics of the Vidir

Gasifier. It involved solving the following equations [18]:


51

( ) iii

Suxt

=ρ∂∂

+∂ρ∂

(15)

where Si mass source in the system.

For a multi-component system, the mass balance can be expressed as [96]:

( ) ( )iiii

ii

iii SRJXX

mutm

′′′′′ ++

∂∂

−=∂

∂+

∂∂

,ρρ (16)

where mi’ local mass fraction of each species in the system

J i’,i diffusion flux of species i’, which arises due to concentration gradients

Ri’ mass rate of creation or depletion by chemical reaction

Si’ mass rate of any other sources

For laminar flows of dilute gas system, the diffusion flux meets the Fick’s law as [48]:

i

imiii x

mDJ∂∂

−= ′′′ ,, ρ (17)

where D i’, m diffusion coefficient for species i’ in the mixture

The conservation equations of momentum can be described as Navier-Stokes equations as [84]:

( ) ( )mi

j

ij

ij

jii SxX

pX

uutu

+∂

∂+

∂∂

−=∂

∂+

∂∂ τρρ (18)

where p static pressure

τij stress tensor

Smi momentum source in i direction

The stress tensor τij is given by [105]:

iji

i

i

j

j

iij x

uxu

xu

δμμτ∂∂

−⎟⎟⎠

⎞⎜⎜⎝

⎛

∂

∂+

∂∂

=32 (19)

where μ molecular viscosity.


52

The conservation equation of energy can be written as [95]

( ) ( ) hk

iikjj

jiiiii

i

Sxu

Jhxx

TKxpu

tphu

Xth

+∂∂

+∂∂

−⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

∂∂

+∂∂

=∂∂

+∂

∂′′

′

τρρ

where 'jj hmh = , ∫=

T

Tjpj

ref

dTCh ., (20)

The energy source due to chemical reactions can be expressed as [91]

jj

jpTT

j

of

reactionh RdTCMh

S ref

ret ′′

′′

∑ ∫+= ,, (21)

and the energy source due to radiation was calculated in radiation models.

4.1.2 General transport equation

For a general variable Φ of the fluid, such as mass or species, momentum, energy, the above

conservation equations can be summarized into a general transport equation of Φ as [48]

( ) ( )φφ

φρφρφ SxXXt ijj

j +⎥⎦

⎤⎢⎣

⎡∂∂

Γ∂∂

=∂

∂+

∂∂

(22)

where ΓΦ transport coefficient of the general variable Φ

For a perfect gas system, the state equations can be written as [79]:

i

iNi M

mRTp 1== ρ (23)

where mi and Mi are the mass fraction and the molecular weight of the ith species, respectively.

4.2 Turbulence models

The above basic governing equations for a homogenous Newtonian fluid flow form a closed set

of partial differential equations (PDE) [48]. This situation is only suitable to numerically solve a

laminar flow. For turbulence, it can be characterized as a three dimensional, time-dependent,


53

chaotic, random and dissipative flow [79]. In a turbulent flow, there exists an energy cascade

corresponding to a wide range of length scales from largest eddies (anisotropic integral length

scales) to smallest eddies (isotropic Kolmogorov length scales) [35]. Therefore, the direct

numerical simulation (DNS) of a turbulent flow with a high Reynolds number in a complicated

modern furnace or boiler is practically impossible even with today’s computer capacity [79].

For most engineering applications, the time-averaged or spatial filtered properties of the flow are

of interest, thus the time-averaged transport equations, such as the Reynolds averaged Navier-

Stokes (RANS) equations, were established [97]. However, time-averaged equations are not in a

closed form and thus additional closure methods corresponding to different turbulence models

had to be developed [91].

The most often used method is one that is arranged in order of the number of differential

equations solved for turbulence: zero-, one-, two- and stress equation models as presented by

Bentzen [55]. According to Bentzen

• Zero equation models are based mostly on the eddy viscosity concept and give the shear

stress. They need a field of mixing length, which can be difficult to obtain in 3-D

complex flows.

• One equation models solve the equation for turbulent kinetic energy. They need a field of

a turbulent length scale for calculating dissipation and effective viscosity. In rapid

transient flows, one equation models with the length scale set proportional to the grid size

may give more representative turbulence field than other models.

• Two equation models are most widely used in present day engineering calculations. The

length scale used here was found from an algebraic relation with the solution of a second

equation. The most popular two equation model is the k-ε model, presented in the next

section. There are also stress equation models that calculate the Reynolds stresses either

through an algebraic relation with k, as the algebraic stress models, or by replacing the

equation for k with equations for the Reynolds stresses.


54

4.2.1 Time-averaged transport equations

By using Reynolds decomposition approach, an instantaneous flow variable Φ can be

decomposed as the mean variable Φ and the fluctuation variable Φ’. For the sake of convenience,

let Φ denote the mean variable Φ for the time-averaged transport equations. Thus, the time-

averaged continuity equations can be written as [105]

( ) ( )i

i

i SX

ut

=∂

∂+

∂∂ ρρ (24)

where ρ, ui, and Si are mean variables.

Reynolds-averaged Navier-Stokes (RANS) equations can be written as [71]

( ) ( ) ( )i

j

ji

i

i

j

j

j

i

jj

jii Sx

uuxu

xu

xu

XXuu

tu

+∂

′′−∂−

⎥⎥⎦

⎤

⎢⎢⎣

⎡

∂∂

+⎥⎥⎦

⎤

⎢⎢⎣

⎡

∂

∂+

∂∂

∂∂

=∂

∂+

∂∂ ρ

μμρρ

32 (25)

where − uui ′′ρ are Reynolds stresses and can be modelled using Boussinesq hypothesis as [35]:

iji

it

j

j

j

itji x

uxu

xuuu δμρκμρ ⎥

⎦

⎤⎢⎣

⎡∂∂

+−⎥⎥⎦

⎤

⎢⎢⎣

⎡

∂

∂+

∂∂

=′′−32 (26)

where μt is the turbulent viscosity.

Time-averaged transport equations can be written as [48]

( ) ( ) ( )φφ

φρφρφρφ SX

uxXXt i

i

iij

j +∂

′′−∂+⎥

⎦

⎤⎢⎣

⎡∂∂

Γ∂∂

=∂

∂+

∂∂ (27)

where the turbulent term ρ ' Φ ' ui’ can be taken to be proportional to the gradient of the mean

variable Φ as [105]


55

( ) ⎥⎦

⎤⎢⎣

⎡∂∂

Γ∂∂

=′′−ii

i xXu φφρ φ

(28)

where ΓΦ is the turbulent transport coefficient of the mean variable Φ .

The time-averaged transport equations, including the turbulent transport coefficient, such as

turbulent viscosity μ t in RANS, needs to be modelled [105].

a. The Standard k-ε Model

Launder and Spalding [81], developed the standard k-ε mode to close the time-averaged systemic

PDEs. The k-ε model equations are derived from the RANS equations, and the various model

constants, and rely on phenomenological considerations and empiricism. By using dimensional

analysis, the turbulent viscosity μ t can be assumed as [48]

VLCt ρμ = (29)

where C dimensionless constant

V turbulent velocity scale (m/s)

L turbulent length scale (m)

According to dimensional analysis, the turbulent velocity scale, V, and the length scale, L, can be

defined by using two quantities of turbulent kinetic energy κ and its rate of dissipation ε as [79]:

εκκ

23

21

== LandV (30)

Thus the turbulent viscosity can be evaluated by k and ε as [65]:

εκμρμ

2

Ct = (31)

where C μ is a dimensionless constant.

In the standard k-ε model, the k and ε can be obtained from the following transport equations

as [91]


56

( ) ( )ρεκ

δμ

μκρρκ

κκ

−+−⎥⎦

⎤⎢⎣

⎡

∂∂

+⎥⎦

⎤⎢⎣

⎡+

∂∂

=∂

∂+

∂∂

bi

t

ii

i GGxXX

ut

(32)

( ) ( )κερ

κεε

δμ

μερρε

εεκεε

2

231 ))1(( CGCGCxXX

ut b

i

t

ii

i −−+−⎥⎥⎦

⎤

⎢⎢⎣

⎡

∂∂

+⎥⎦

⎤⎢⎣

⎡+

∂∂

=∂

∂+

∂∂ (33)

where Gk is the generation of k due to the turbulent stress as [71]

( )i

jij

i

jji x

uXu

uuG∂

∂=

∂

∂′′−= τρκ (34)

It can be evaluated by Boussinesq hypothesis as [93]

2SG tμκ = (35)

where S is the modulus of the mean strain rate and Sij expressed as [71]:

⎥⎥⎦

⎤

⎢⎢⎣

⎡

∂

∂+

∂∂

==i

j

j

iijijij x

uxu

SandSSS212 (36)

Gb is the generation of k due to the buoyancy as [105]

it

tib x

TG∂∂

=Prμ

β (37)

where Prt is the turbulent Prandtl number for temperature or enthalpy and β is the coefficient of

thermal expansion as [35]

pix ⎥⎦

⎤⎢⎣

⎡∂∂

−=ρ

ρβ 1 (38)

This method is popular and widely used in practical engineering turbulent flow problems [61].

For this reason the standard κ- ε model was used for the simulations in this thesis. The standard

k-ε model constants C1ε, C2ε, Cμ, σk, σε, values used are C1ε = 1.44, C2ε = 1.92, Cμ = 0.09,


57

σk = 1.0, σ ε = 1.3 [81]. This semi-empirical model is robust, economical, and reasonably

accurate.

b. The RNG k-ε model

The RNG model [44] was developed in response to the empirical nature of the standard k-ε

model. Rather than being based on observed fluid behaviour, it was derived using statistical

methods used in the field of renormalization group (RNG) theory [44]. The transport equations

for turbulent kinetic energy k and its dissipation rate ε in RNG k-ε model have the same forms as

in the standard k-ε model except of the additional quantities of the inverse effective Prandtl

numbers α k and α ε, and the R term in the ε equation [48].

( ) ( )ρεκ

δμ

μακρρκ

κκ

κ −++⎥⎦

⎤⎢⎣

⎡

∂∂

⎥⎦

⎤⎢⎣

⎡∂∂

=∂

∂+

∂∂

bi

teff

ii

i GGxXX

ut

)( (39)

( ) ( ) RCGCGCxXX

ut b

i

teff

ii

i −−−+−⎥⎥⎦

⎤

⎢⎢⎣

⎡

∂∂

⎥⎦

⎤⎢⎣

⎡∂∂

=∂

∂+

∂∂

κερ

κεε

δμμαερρε

εεκεε

ε

2

231 ))1(()( (40)

κε

βηηηρημ 2

30

3

1

1

+

⎟⎟⎠

⎞⎜⎜⎝

⎛−

=C

R (41)

where η0 = 4.38

β = 0.012

εη κS

= (42)

The effective viscosity can be solved by the following equations as [57]

μμ

ν

νν

νβεμκρ

ν

eff

i wheredC

d

=

+−=

⎥⎥⎦

⎤

⎢⎢⎣

⎡

)

))

)

172.1

3

2

(43)


58

In the RNG k-ε model, the effects of swirl can be accounted for by modifying the turbulent

viscosity as [27]

⎟⎠⎞

⎜⎝⎛ Ω=

εκαμμ ,,sefft f (44)

where α s swirl constant (0.05 at mildly swirling flows).

Ω characteristic swirl number.

The RNG k-ε model constants C1ε and C2ε have the following values:

C1ε = 1.42, C2ε = 1.68

In both k-ε model transport equations 33, 40, the model constant C3ε for buoyancy term can be

calculated as [48]

ενμε tanh3 =C (45)

where, v is the velocity component parallel to the gravitational vector and u is the velocity

component perpendicular to the gravitational vector.

c. Standard k–ω model

The last turbulence model that will be discussed is the κ–ω model. This model is based on the

Wilcox k–ω model [48]. It incorporates modifications for low-Reynolds number effects,

compressibility and shear flow spreading. The Wilcox model predicts free shear flow spreading

rates that closely agree with measurements for far wakes, mixing layers and different types of

jets. Therefore this model applies to both wall-bounded flows and free shear flows.

The k-ω model is a two-equation, semi-empirical turbulence model [48]. The transport equation

for the kinetic energy is comparable with the previous k–ε models. The equation for the

dissipation on the other hand, is different. Instead of the dissipation per unit of mass, the specific

dissipation rate ω is used. This quantity can be seen as the ratio of ε to κ [40].


59

4.2.2 The Reynolds stress model

In this model, using the Boussinesq relation, the Reynolds stresses (ρ u' i u' j) are linked to the

mean flow velocities, such as in k-ε models [27]. However, these kind of models of first moment

closure approach are quite limited in predicting the flows with complex strain fields or

significant body forces [57]. This drawback advocated Launder et al. [40] to have developed a

more complex second-moment closure approach, so called Reynolds stress model (RSM), in

which the Reynolds stresses are modelled using both the mean flow velocities and the first-

moment terms.

4.3 Near-wall treatments for turbulent flows

Turbulence models are largely valid for turbulent core flows, i.e., flows in the regions somewhat

far from walls [79]. When the flow to be calculated involves walls, turbulent flows in the regions

close to the walls are affected by the presence of the walls. First, the mean velocity field is

affected through the no-slip condition that has to be satisfied at the wall. Turbulence is also

changed by the presence of the wall. Close to the wall, turbulence is damped due to the presence

of walls. Toward the outer part of the near-wall region, turbulence is rapidly augmented by the

production of turbulent kinetic energy due to Reynolds stresses and the large gradient of the

mean velocity [50].

Experiments have shown that the near-wall region can be largely subdivided into three

layers [35]. In the innermost layer called the viscous sub-layer, the flow is almost laminar-like,

and the viscosity plays a dominant role in momentum and heat transfer [35]. In the outer layer,

called the fully-turbulent layer, turbulence plays a major role [79]. Finally, there is an interim

region between the viscous sub-layer and the fully-turbulent layer called the buffer layer where

the effects of viscosity and turbulence are equally important [61].

In the near-wall region, the velocity has a universal distribution [56]. Numerous measurements

of this distribution exist. According to these measurements, the viscous sub-layer and the fully

turbulent region can be represented as functions between the dimensionless wall distance y+ and

the dimensionless velocity u+ as [56]


60

For viscous sublayer: u+ = y+ 0 < y+ < 5

For full-turbulent region: u+ = 1/κ ln y+ + C+ 30 < y+

where τu

uu =+ , μ

ρ τ yuy =+ , ρ

τ ωτ =u (46)

and κ Karman constant, 0.42

C+ empirical constant, 5.0

τω Wall shear stress

Y normal distance to the wall

Figure 4.1: Universal log law [71]

There are two approaches for treating of the near-wall region [54]. The first one does not solve

the viscous affected region that is the buffer layer and viscous sub-layer [33]. Instead the first

method uses semi-empirical formulas to bridge the region between the wall and the fully

turbulent flow [71]. These formulas are called wall functions [71]. The second approach

modifies the turbulence models to enable the viscosity affected region to be resolved all the way


61

to the wall [95]. In this case the mesh must be fine enough. This method is called near-wall

modeling [50].

Figure 4.2: Near wall grids [71]

There are two wall function approaches offered in Fluent® [109]:

a. Standard wall functions: (standard in industrial applications and the default in Fluent).

The logarithmic law for mean velocity farther away from walls (turbulent region) and

linear closer is applied to solve for the momentum [61]. The energy and species transport

equations are solved using a linear law for a thermal conduction sublayer and a

logarithmic law for the turbulent region, whereas to solve for turbulence in both k-ε

models and in RSM, the k equation is solved in the whole domain including the wall-

adjacent cells [56].

b. Non-equilibrium wall functions: This approach uses the log-law for mean velocity

sensitized to pressure-gradient effects and a two-layer (viscous and fully turbulent layers)

base that allows for computing of turbulence kinetic energy in wall-adjacent cells [71].

Standard wall function has a low computational burden with limitations only to

applications where there is a pervasive low-Reynolds-number or near-wall effects (e.g.

flow through a small hole, or highly laminar flow), massive transpiration through the wall

(blowing/suction), large pressure gradients, strong body forces (e.g. flow near a rotating

body) or high three dimensionality near the walls (e.g. strongly skewed flows) [54]. Since


62

none of these characteristics were exhibited in the gasifier, which is the subject of

investigation, the standard wall functions was used.

4.4 Radiation modeling

The radiative transfer equation (RTE) for an absorbing, emitting, and scattering medium at

position r in the direction s can be written as follows [53]:

Ω ′′Φ′+=++ ∫ dsrsrITansrIads

srdI ss ),(),(

4),()(),( 4

0

42

π

πσ

πσσ (46)

In Fluent, there are four common radiation models and these are [109]:

• P-1 Radiation Model

• Rosseland Model

• Discrete Ordinates Model (DOM)

• Discrete Transfer Radiation Model (DTRM)

4.4.1 P-1 model

P-1 model is the simplest formulation of the more general P-N radiation model, which is based

on the expansion of the radiation intensity I into an orthogonal series of spherical

harmonics [98]. The method of spherical harmonics provides a means to obtain an approximate

solution of arbitrary high order (i.e. accuracy), by transforming the radiative transfer equation

into a set of simultaneous partial differential equations [53]. Using only four terms in the series

solution of the respective differential equation, the following relation is obtained for the radiation

flux [53]:

Gqs

r ∇+

−=)(3

1σα

(47)

where G is the incident radiation. The problem is then much simplified since it is only necessary

to find a solution for G rather than determine the direction dependent intensity [53]. Then the

following expression for qr can be directly substituted into the energy equation to account for

heat sources (or sinks) due to radiation as follows:


63

44 TaaGq r σ−=∇− (48)

4.4.2 Rosseland model

The Rosseland radiation model can be derived from the P-1 radiation model with some

approximations. The radiative heat flux vector in a gray medium is approximated by [53]:

Gq r ∇Γ−= (49)

The Rosseland radiation model differs from the P-1 model in that the Rosseland model assumes

the intensity is equal to the black-body intensity at the gas temperature. Thus,

424 TnG σ= (50)

while the P-1 model actually calculates the transport equation for G. Substituting this value for G

into equation (49) yields:

TTnq r ∇Γ−= 3216σ (51)

This model is also called “diffusion approximation” model, since the radiation problem reduces

to a simple conduction problem with strongly temperature dependent conductivity [96]. It is

important to keep in mind that the diffusion approximation is not valid near a boundary [54].

4.4.3 Discrete transfer radiation model

The main assumption of the Discrete Transfer Radiation Model (DTRM) is that the radiation

leaving the surface element in a certain range of solid angles can be approximated by a single

ray [53]. The equation for the change of radiant intensity, dI, along a path, ds, can be written

as [65]

πσ 4TaaI

dsdI

=+ (52)

Here, the refractive index is assumed to be unity. The DTRM integrates Equation 52 along a

series of rays emanating from boundary faces. If a is constant along the ray, then I (s) can be

estimated as [105]


64

aso

as eIeTsI −− +−= )1()(4

πσ

(53)

The “ray tracing” technique used in the DTRM can predict of radiative heat transfer between

surfaces without explicit view-factor calculations. The accuracy of the model is limited mainly

by the number of rays traced and the computational grid [55].

4.4.4 Discrete ordinates model

The Discrete Ordinates Model (DOM) solves the radiative transfer equation (RTE) for a finite

number of discrete solid angles, each associated with a vector direction s i (i = 1, 2,...n) fixed in

the global Cartesian system, and the integrals over these directions are replaced by numerical

quadratures [91]. The DOM considers the RTE in the direction si as a field equation, thus the

RTE is transformed into a transport equation for radiation intensity in the spatial

coordinates [79]:

( )( ) Ω ′′Φ′+=++•∇ ∫ dsrsrITansrIassrI ss ),(),(

4),()(,

4

0

42 rrrrrrrrr π

πσ

πσσ (54)

The standard form DOM suffers from a number of serious drawbacks, such as false scattering

and ray effects [95]. Perhaps the most serious drawback of the method is that it does not ensure

conservation of radiative energy. This is because the standard discrete ordinates method uses a

simple quadrature for angular discretization. Thus, it is a logical step in the evolution of the

method to move to a fully finite volume approach, in space and in direction. The finite volume

method uses an exact integration to evaluate solid angle integrals and the method is fully

conservative [56].

The optical thickness aL where L is an appropriate length scale is a good indicator of which

model to use. When aL >> l the P-1 and Rosseland models are suitable [53]. The P-1 model

should typically be used for optical thicknesses large than one [33]. The Rosseland model is

computationally cheaper and more efficient but should only be used for optical thicknesses larger

than three [65]. The DOM model works across the range of optical thicknesses, but is

substantially more computationally expensive than the Rosseland model [93]. The Rosseland


65

model does not take in to account for radiation exchange between gas and particulates.

Therefore, the DOM radiation model using the finite volume approach was considered for the

CFD model for the gasifier.

4.5 Species transport

The species equation accounts for the conservation of species. Multiple species equations can be

used to represent fluids in a mixture with different physical properties [104]. Solution of the

species equations can predict how different fluids mix, but not how they will separate [50].

Separation is the result of different body forces acting on the fluids, such as gravity acting on

fluids of different density [98]. To model the separation, separate momentum equations are

required for each of the fluids so that the body forces can act on the fluids independently [93].

Species transport is nevertheless a useful tool for predicting blending times or chemical

reaction [105]. For the species i’, the conservation equation is for the mass fraction of that

species, mi’, and has the following form [105]:

( ) ( ) iiiji

iii

i SRJX

mUXpm

t ′′′′′ ++∂∂

=∂∂

+∂∂

,ρρ (55)

In Equation 55, J i’,i is the i component of the diffusion flux of species i’ in the mixture. For

laminar flows, J i’,i is related to the diffusion coefficient for the species and local concentration

gradients. For turbulent flows, J i’,i also includes a turbulent diffusion term, which is a function

of the turbulent Schmidt number [71]. R i’ is the rate at which the species are either consumed or

produced in one or more reactions, and Si’ is a general source term for species. The general

source term can be used for non-reacting sources, such as the evaporated vapour from a heated

droplet, for example [48].

When two or more species are present, the sum of the mass fractions in each cell must add to 1.0.

For this reason, if there are n species involved in a simulation, only n-1 species equations need to

be solved. The mass fraction of the nth species can be computed from the required condition [65]:

1=∑ ′

n

iim (56)


66

A volumetric reaction with species transport can be handled in CFD using three different models

[98]:

1. laminar finite rate model: The effects of turbulent fluctuations are ignored, and reaction

rate is determined by Arrhenius expressions.

2. eddy-dissipation model: The reaction rates are assumed to be controlled by the

turbulence, so Arrhenius chemical kinetic calculations can be avoided.

3. eddy-dissipation concept model: A detailed Arrhenius chemical kinetics expression can

be incorporated into the turbulence flames.

It should be noted that detailed chemical kinetic calculations are computationally expensive in

the eddy-dissipation concept model. Eddy-dissipation model is robust and less demanding in

terms of inputs [56]. Therefore, eddy-dissipation is used for the gasifier simulations since it can

model reactions with relative ease without sacrificing accuracy.

4.6 Gaseous turbulent combustion models

The reaction rate of a gaseous reaction process is determined by mixing the reacting species, and

by the reaction kinetics, which is usually strongly depends on the reaction temperature in a

combustion chamber [98]. Actually, the combustion process, even only for simple fuel

combustion, concerns numerous intermediate reactions that are, in practice, impossible to

calculate in detail [79]. Therefore, some simplifications and assumptions have to be done to deal

with combustion reaction problems.

4.6.1 The generalized finite rate reaction modeling

The kth reaction taking place in a combustion system that contains N chemical species can be

described in general as [96]

)()(11

∑∑==

=n

pn

rk MM

κκκ

κκ νν (57)


67

where (Mk) denotes the concentration of species k in moles per unit volume, νrk are the

stoichiometric coefficients of the reactants, and νpk are the stoichiometric coefficients of the

products. When a species does not occur as a reactant or product, the corresponding coefficient is

zero [65].

The rate of disappearance of species k in an elementary reaction is governed by the law of mass

action, stating that it is proportional to the product of the concentrations of the reacting species,

where each species is raised to the power of the stoichiometric constant [88]. The law of mass

action can be verified experimentally and was first proposed by Obernberger and Joller [82]. The

reaction rate Rk is hence given by [56]

rj

n

jjMR νκκ )(

1∏=

= (58)

where k specific rate coefficient

Σνrk overall order of the reaction

νrj order of the reaction with respect to species k

The rate of change of the concentration of species k for a reaction with both forward and

reversible propagation becomes [102]

( )rj

rj n

j j

jbk

prn

j j

jfv

pr

wwM

dtd

ν

κ

ν

κκκ

ρθκν

ρθκνω ∏∏

== ⎥⎥⎦

⎤

⎢⎢⎣

⎡−

⎥⎥⎦

⎤

⎢⎢⎣

⎡==

11

& (59)

where: ( )rk

pk

pr ννν κ −=

Here Wk is the mean molecular weight

kfv and kbk forward and backward rate coefficients respectively


68

Reactions generally precede through the formation of reactive intermediate species and possibly

through different or parallel pathways. The overall reaction can however be represented as a

series of elementary reactions, all of the form above, collectively called the reaction

mechanism [34].

4.6.2 The Arrhenius rate

Arrhenius postulated that only molecules possessing energy exceeding a certain threshold, the

activation energy Ea, would react when colliding [92]. A temperature dependence of the specific

reaction rate in the form of:

⎟⎠⎞

⎜⎝⎛=

RTETAK k

f exp,κβ

κκ (60)

is therefore called the Arrhenius law where Ak is the pre-exponential factor, βk is the temperature

exponent. Ek is the activation energy for the reaction (J/kmol), and R is the universal gas

constant (J/kmolK).

The exponential term is the Boltzmann factor, which from kinetic theory can be seen to give the

fraction of all collisions that have energy greater than Eκ. The pre-exponential factor is the

collision frequency, and in general A (T) = AT b is used to account for a mild temperature

dependence [68].

4.6.3 The eddy-dissipation model

Magnussen and Hiertager [35] first considered the relation of the reaction rate to the dissipation

rate of the reactant and product containing eddies, and suggested that the reaction rate can be the

smaller of the two expressions below [31]:

pkRikii MV

mRAMR,

,, ′= ′′′ κ

ερνκ (61)

pkp

pikii MV

mABMR

,,, ′

= ′′′ κερνκ

(62)

where mp mass fraction of product species P


69

mR mass fraction of a particular reactant R

A and B empirical constants equal to 4.0 and 0.5, respectively

k/ε represents the time scale of the turbulent eddies.

The eddy dissipation model is based on the concept that chemical reaction is fast relative to the

transport processes in the flow [56]. When reactants mix at the molecular level, they

instantaneously form products. The model assumes that the reaction rate may be related directly

to the time required to mix reactants at the molecular level [82]. In turbulent flows, this mixing

time is dominated by the eddy properties, and therefore, the rate is proportional to a mixing time

defined by the turbulent kinetic energy, k, and dissipation, ε.

In the finite-rate/eddy-dissipation modeling, the smallest rate of those from the Arrhenius rate

expression or the eddy dissipation model is used as the reaction rate, which is used as the source

term in the species conservation and energy equations [78].

4.7 Dispersed or discrete phase model

The dispersed phase model uses the Navier-Stokes equations to describe a continuous fluid

phase, and a Lagrangian particle tracking method to describe a dispersed phase consisting of

particles, droplets, or bubbles [78]. Heat, mass, and momentum exchange is permitted between

the dispersed and fluid phases. Thus gas bubbles can rise in a liquid, sand particles can settle, and

water droplets can evaporate or boil, releasing steam to a background of warm gas [94]. The

model is widely used for coal and liquid fuel combustion, bubble columns, and gas spargers in

stirred tanks [47]. It is best when the dispersed phase does not exceed 10% by volume of the

mixture in any region [88].

4.7.1 Particle transport methods

There are two main families of methods to treat particle transport in fluid flows: Eulerean and

Lagrangian [74]. In the Eulerean or “two-fluid” approach, the particles are regarded as a

continuous phase for which the conservation equations (continuity, momentum and energy) are

solved in similar fashion to the carrier gas flow field [98]. The Eulerean approach is particularly

suitable for denser suspensions when particle-particle interactions are important and the particle

feedback on the flow is too large to neglect [97]. The main challenge facing Eulerean-type, two-


70

fluid approaches resides in accurately defining the inter-phase exchange rates and closure laws

that arise from the averaging procedures [93]. In addition, the strong coupling between the

phases renders the Eulerean approach quite delicate to handle, especially at boundaries where the

solid phase may be removed [103].

In the second approach, called Lagrangian, the particles are treated as a discrete phase made of

spherical particles dispersed in the continuous phase. The particle volume loading is usually

assumed negligible, so that particles have no feedback effect on the carrier gas and particle-

particle interactions are neglected [60]. In the Lagrangian framework, the controlling phenomena

for particle dispersion in the field are assessed using a rigorous treatment of the forces acting on

the particle. In general, the detailed flow field is computed first, then a representatively large

number of particles are injected into the field, and their trajectories determined by following

individual particles until they are removed from the gas stream or leave the computational

domain [85]. Particle motion is extracted from the time integration of Newton’s second law, in

which all the relevant forces can be incorporated (drag, gravity, lift, thermophoretic force, etc.).

The Lagrangian approach is computationally intensive, because it entails tracking a large number

of particles until stationary statistics are achieved [55]. On the other hand, the results of

Lagrangian particle tracking are physically easier to interpret. Therefore, in the following

investigation, the Lagrangian methodology was used, along with the assumption that the

dispersed phase was dilute enough not to affect the continuous flow field (one-way coupling).

4.8 Particle motion in fluids

4.8.1 Drag force

The steady state drag is the drag force that acts on the particle in a uniform pressure field when

there is no acceleration or deceleration of the relative motion between the particle and the

conveying fluid [96]. The drag force at various Reynolds numbers is based on introducing the

drag coefficient CD being defined as [79]:

spp

DD

Auu

FC2)(

21

−=

ρ (63)


71

where FD drag force in x-direction

Asp cross-section area of a spherical particle (0.25.π. Dp2)

Dp particle diameter

U particle velocity in x-direction

The Reynolds number for a spherical particle Rep is given by [50]

μ

ρ ppp

uuD −=Re (64)

The dependence of the drag coefficient of a spherical particle on Reynolds number is shown in

Figure 4.3 based on experimental investigations [52]. At low Reynolds numbers, the drag

coefficient varies inversely with Reynolds number. This is referred to as the Stokes flow and

under these conditions CD = 24/Rep [78]. When increasing Reynolds number, the drag coefficient

approaches a nearly constant value. At the critical Reynolds number, there is a sharp decrease in

the drag coefficient. The critical Reynolds number represents the transition from laminar to

turbulent flow past the particle [71].

Figure 4.3: Drag coefficient for spherical particles versus Re [71]

The turbulence level of the ambient flow reduces the critical Reynolds number. With increasing

turbulence intensity, the transition from a laminar to turbulent boundary layer is shifted towards


72

a smaller Reynolds number. Crowe et al. [106] showed that the critical Reynolds number reduces

by about two to three orders of magnitude by increasing the turbulence level. The particles

considered in this work are small. Therefore, the particles’ Reynolds numbers are small.

Consequently, the effect of the turbulence on the drag coefficient is not considered in this study.

The consideration of particle shape calculating particle motion is presented by Xiu et al. [59].

They described the shape of irregular particles by using a shape factor. The shape factor is

defined as the ratio of the surface area of a sphere, which has the same volume as the particle and

the surface area of the particle. They investigated the drag coefficient for various shape factors

and found that when the particle Reynolds number is larger than about 100, the effect of the

shape factor could not be ignored and the assumption of non-spherical particles could be used.

Therefore the calculations performed in this study are based on the assumption of non-spherical

particles with a shape factor.

4.8.2 Pressure gradient force and unsteady forces

The local pressure gradient in the flow gives an additional force in the direction of the pressure

gradient [82]. In addition, the acceleration or deceleration of the relative velocity between the

particle and the fluid produces forces that can be divided into two parts: the virtual mass effect

and the Basset force [93]. The virtual mass effect relates to the force required to accelerate or

decelerate the surrounding fluid [105]. The Basset term describes the force due to the lag of

boundary layer development with changing relative velocity [71]. Sommerfeld [44] presented an

analysis for the importance of these forces. The results indicated that these forces could be

neglected for large values of the ratio of particle material density to gas density.

4.8.3 Lift forces

Lift forces on a particle are caused by the velocity gradient in the fluid or by particle

rotation [92]. Particles moving in a shear layer experience a traverse lift force due to the non-

uniform relative velocity over the particle and the resulting non-uniform pressure

distribution [92]. If a particle leads the fluid motion, then the lift force is negative and the

particle moves down the velocity gradient towards the wall. Conversely, if the particle lags the


73

fluid, then the lift force is positive and it moves up the velocity gradient away from the wall.

Saffman [50] analysed this force and found the magnitude of the force, Saffman force (FS) to be

)(615.1 2p

rps uu

KDF −=

υμ (65)

where Kr local velocity gradient.

The other lift force is Magnus force, which is a lift developed due to rotation of the particle [99].

The lift is caused by the pressure difference between both sides of the particle resulting from the

velocity difference due to rotation. Kallio and Reeks [98] noted that in most regions of the flow

field, the Magnus force is not important and at least an order of magnitude smaller than the

Saffman force. As a consequence, it was ignored in this study.

4.8.4 Gravity force

Gravity force is simply the product of the particle mass and the vector representing the

acceleration due to gravity. Therefore, the gravity force was accounted for this study even if the

particles were small in order to hold the particles at the fuel bed before they started to fly.

4.8.5 Thermophoretic force

When a particle exists in a flow field with temperature gradient, another force arises on the

particle that is called the thermophoretic force [56]. This force is caused by the unequal

momentum exchange between the particle and the fluid. The higher molecular velocities on one

side of the particle due to the higher temperature give rise to more momentum exchange and a

resulting force in the direction of a decreasing temperature. An extensive review of

thermophoresis by Herguido et al. [42] indicated that the following equation for the

thermophoretic force FT provides the best fit with experimental data over a wide range of

Knudsen numbers:

( ) TT

KCKKKC

KCKK

CDFnt

p

fnm

ntp

f

spT∇

⎟⎠⎞

⎜⎝⎛ +++

⎟⎠⎞

⎜⎝⎛ +

−=22131

6πμυ (66)


74

where Kp particle thermal conductivity Kn Knudsen number ∇T temperature gradient Cs = 1.17 Ct = 2.18 Cm = 1.14

However, for particles considered here in this study, thermophoretic force could be neglected,

because the influence of other forces, such as the drag force, are much stronger for such small

particles of diameters less than 0.03 mm [107].

4.8.6 Brownian force

Brownian force is caused by random impact of the particle with agitated gas molecules [50]. For

submicron size particles, Brownian force could be quite important [79]. In particular, near solid

surfaces, where the intensity of turbulence becomes negligibly small, Brownian force could be

an important transport mechanism [98].

In previous work there was a general thinking that the Brownian force could dominate the

motion of submicron particles [48]. Recently, a clear model of the Brownian force and its effect

on particle trajectory was given by Ounis et al. [103]. They studied the Brownian dispersion of

submicron particles from a point source in a simulated turbulent channel flow field. The particle

equation of motion including the Brownian force was studied and ensembles of 8192 particle

trajectories were evaluated. The results are compared to those obtained from the exact solution of

the corresponding convective diffusion equation in the absence of turbulent fluctuations. It was

found in this work that the particles with diameters equal to or less than 0.03 μm were strongly

affected by turbulence, even those with a distance of one wall unit from the surface, and the

Brownian force could be neglected. Brownian force is not an important transport mechanism in

this region. Based on these results and since the particles considered in this study were larger

than 0.03 μm, the Brownian force was ignored.

4.9 Porous media model

The porous media assumption is generally used in the applications of biomass gasification in a

fixed bed [48]. The arrangement of biomass particles in the fixed bed forms void spaces. The


75

devolatilization volatiles and gases through the particle voids can be described as flow through a

porous media [84]. The particle position may change during the conversion process for the

devolatilization, combustion and shrinkage of biomass particles. In this process to mesh all

associated geometry with a complex unstructured or body fitted system was out of both

computational power and CFD algorithms a level [93].

Therefore, the simplified porous media assumption applies Darcy’s law to present the

relationship on pressure drop and volume averaged velocity caused by viscous drag [48]

υαμ

−=∇ p (67)

At high flow velocities, the modification of this law corrects for inertial losses in the porous

medium by the Darcy-Forchemier equation [48]

2υρυαμ

FCxp

+−=∂∂ (68)

Fluid flow, and heat and mass transfer are described in the sub-domain by the laws of

conservation of mass, momentum and energy in the terms of macroscopic variables and are

provided by the volume averaged Navier-Stocks equations in a version of Darcy’s law.

4.10 Discretization of the equations

Several methods have been employed over the years to solve the Navier-Stokes equations

numerically, including the finite difference, finite element, spectral element, and finite volume

methods [93]. The focus of the gasifier simulations is on the finite volume method, which is

described in detail below. Once this method and terminology are presented, the other methods

are briefly discussed.

To illustrate the discretization of a typical transport equation using the finite volume formulation,

a generalized scalar equation can be used with the rectangular control volume shown in

Figure 4.4. The scalar equation has the following form [56]:


76

( ) Sxx

Uxt ii

ii

′+⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

Γ∂∂

=∂∂

+∂

∂ φφρρφ )( (69)

The parameter Γ is used to represent the diffusion coefficient for the scalar Φ. If Φ is one of the

components of velocity, for example, Γ would represent the viscosity. All sources are collected

in the term S’. Again, if Φ is one of the components of velocity, S’ would be the sum of the

pressure gradient, the gravitational force, and any other additional forces that are present. The

control volume has a node, P, at its center where all problem variables are stored. The transport

equation describes the flow of the scalar Φ into and out of the cell through the cell faces [105].

To keep track of the inflow and outflow, the four faces are labelled with lower case letters

representing the east, west, north, and south borders. The neighbouring cells also have nodes at

their centres, and these are labelled with the capital letters E, W, N, and S. For the purpose of this

example, flow in the 1-D row of cells shown in Figure 4.4 was considered.

Figure 4.4: Simple 2-D domain showing the cell centres and faces (top), 1-D rectangular simplification (bottom) [109]

The first step in the discretization of the transport equation is integration over the control

volume. The volume integral can be converted to a surface integral by applying the divergence

theorem. Using a velocity in the positive x-direction, neglecting time-dependence, and assuming

that the faces e and w have area A, the integrated transport equation takes the following form

[61]:


77

SAdxd

dxdAUU

ww

eewwweee +⎟⎟

⎠

⎞⎜⎜⎝

⎛⎥⎦⎤

⎢⎣⎡Γ−⎥⎦

⎤⎢⎣⎡Γ=−

φφφρφρ )( (70)

where S is the volume integral of the source terms contained in S’. This expression contains four

terms that are evaluated at the cell faces. To obtain the face values of these terms as a function of

values that are stored at the cell centres, a discretization scheme is required.

4.10.1 Discretization schemes

Since all of the problem variables are stored at the cell centre, the face values (the derivatives,

for example) need to be expressed in terms of cell centre values [51]. To do this, consider a

steady-state conservation equation in one dimension without any source terms:

⎟⎠⎞

⎜⎝⎛

∂∂

Γ=xdx

ddx

Ud φφρ )( (71)

This equation can be solved exactly. On a linear domain that extends from x = 0 to x = L,

corresponding to the locations of two adjacent cell nodes, with Φ = Φ 0 at x = 0 and Φ = Φ L

at x = L, the solution for Φ at any intermediate location (such as the face) has the form [51]

( )1exp

1exp)(

−

⎟⎠⎞

⎜⎝⎛ −

−+=e

e

oL PLxP

φφφφ (72)

The Peclet number, Pe, appearing in this equation is the ratio of the influence of convection to

that of diffusion on the flow field and is given by [96]:

Γ=

ULPeρ (73)

Depending on the value of the Peclet number, different limiting behaviour exists for the variation

of Φ between x = 0 and x = L [48]. These limiting cases are discussed below, along with some

more rigorous discretization, or differencing schemes that are popular today.


78

a. Central differencing scheme

For Pe = 0, there is no convection, and the solution is purely diffusive [48]. This would

correspond to heat transfer due to pure conduction, for example. In this case, the variable Φ

varies linearly from cell centre to cell centre, so the value at the cell face can be found from

linear interpolation. When linear interpolation is used in general, i.e. when both convection and

diffusion are present, the discretization scheme is called central differencing [71]. When used in

this manner, as a general purpose discretization scheme, it can lead to errors and loss of accuracy

in the solution [109]. One way to reduce these errors is to use a refined grid, but the best way is

to use another differencing scheme. There is one exception to this rule. Central differencing is

the preferred discretization scheme when the large-eddy simulation (LES) turbulence model is

used [98].

b. Upwind differencing schemes

For Pe >> 1, convection dominates, and the value at the cell face can be assumed to be identical

to the upstream, or upwind value, i.e. Φ w = ΦW [98]. When the value at the upwind node is used

at the face, independent of the flow conditions, the process is called first order upwind

differencing [96]. A modified version of first order upwind differencing makes use of multi-

dimensional gradients in the upstream variable, based on the upwind neighbour and its

neighbours [84]. This scheme, which makes use of a Taylor series expansion to describe the

upwind gradients, is called second order upwind differencing [109]. It offers greater accuracy

than the first order upwind method, but requires additional computational effort [92].

c. Power law differencing scheme

For intermediate values of the Peclet number, 0 < Pe < 10, the face value can be computed as a

function of the local Peclet number, as shown in Equation (73) [98]. This expression can be

approximated by one that does not use exponentials, involving the Peclet number raised to an

integral power [96]. It is from this approximate form that the power law differencing scheme

draws its name. This first order scheme is identical to the first order upwind differencing scheme

in the limit of strong convection, but offers slightly improved accuracy for the range of Peclet

numbers mentioned above [90].


79

d. QUICK differencing scheme

The QUICK differencing scheme is similar to the second order upwind differencing scheme,

with modifications that restrict its use to quadrilateral or hexahedral meshes [105]. In addition to

the value of the variable at the upwind cell center, the value from the next upwind neighbour is

also used. Along with the value at the node P, a quadratic function is fitted to the variable at

these three points and used to compute the face value [84]. This scheme can offer improvements

over the second order upwind differencing scheme for some flows with high swirl [71].

4.11 Discretization of the domain

To break the domain into a set of discrete sub-domains, computational cells, or control volumes,

a grid is used [81]. Also called a mesh, the grid can contain elements of many shapes and sizes.

In 2-D domains, for example, the elements are usually either quadrilaterals or triangles. In 3-D

domains, they can be tetrahedral (with four sides), prisms (five sides), pyramids (five sides) or

hexahedral (six sides) (Figure 4.5).

Figure 4.5: Elements used as computational grids [109]

A series of line segments (2-D) or planar faces (3-D) connecting the boundaries of the domain

are used to generate the elements. Structured grids are always quadrilateral (2-D) or hexahedral

(3-D), and are such that every element has a unique address in I, J, K space, where I, J, and K are


80

indices used to number the elements in each of the three computational directions as shown in

Figure 4.6.

Figure 4.6: Structured grids in 2-D and 3-D with I, J and K directions [109]

The I, J, and K directions can, but need not be aligned with the coordinate directions x, y, and z.

Unstructured grids do not follow this addressing rule (Figure 4.7). Hybrid meshes are

unstructured meshes that make use of different types of elements (triangles and quadrilaterals,

for example). Block structured meshes use quadrilateral (2-D) or hexahedral (3-D) elements, and

have I, J, K structures in multi-cell blocks rather than across the entire domain.


81

Figure 4.7: Unstructured grids using hexahedral or mixture elements [109]

In general, the density of cells in a computational grid needs to be fine enough to capture the

flow details, but not so fine that the overall number of cells in the domain is excessively large,

since problems described by large numbers of cells require more time to solve [59]. Non-uniform

grids of any topology can be used to focus the grid density in regions where it is needed and

allow for expansion in other regions [99].

In laminar flows, the grid near boundaries should be refined to allow the solution to capture the

boundary layer flow detail. A boundary layer grid should contain quadrilateral elements in 2-D

and hexahedral or prism elements in 3-D, and should have at least five layers of cells [61]. For

turbulent flows, such as in the gasifier under investigation, it is customary to use a wall function

in the near-wall regions. Wall functions rely on the fact that the flow in a turbulent boundary

layer consists of a narrow viscous sub-layer and a broad, fully turbulent, or “log-law” layer in

which the behaviour is well documented [71]. In particular, the shear stress due to the wall can

be extracted from a linear relationship involving the log of the perpendicular distance to the wall.

This method will help to get the best possible predictions for pressure drop and heat transfer in

the simulation.

4.12 Solution methods

The result of the discretization process is a finite set of coupled algebraic equations that need to

be solved simultaneously in every cell in the solution domain. An iterative solution procedure

was required because the non-linearity of the equations that govern the fluid flow and related

processes [48]. Two methods are commonly used, namely the segregated and the coupled

approach [72]. A segregated solution approach is one where one variable at a time is solved

throughout the entire domain [71]. Thus the x-component of the velocity is solved on the entire

domain, and then the y-component is solved, and so on. An iteration of the solution is complete

only after each variable has been solved in this manner. A coupled solution approach, on the

other hand, is one where all variables, or at least, momentum and continuity, are solved

simultaneously in a single cell before the solver moves to the next cell, where the process is

repeated [98]. The segregated solution approach is popular for incompressible flows with


82

complex physics, typical of those found in mixing applications. For this reason, the segregated

approach will be implemented to solve the algebraic equations.

4.12.1 The SIMPLE and SIMPLEC algorithms

For 3-D simulations, the three equations of motion and the equation of continuity combine to

form four equations for the four unknowns that are the pressure and the three velocity

components [109]. Because there is no explicit equation for the pressure, special techniques have

been devised to extract it in an alternative manner. The most frequently used of these techniques

is the SIMPLE algorithm or Semi-Implicit Method for Pressure-Linked Equations [109]. Indeed,

a family of algorithm was derived from this basic one, which has a small modification that made

it well suited to one application or another called SIMPLEC [98].

The simulation has the following steps [51]:

1. Start the iterative process by guessing the pressure field. Denote the guessed pressure by

P'.

2. Use the values of p' to solve for u, v and w from the momentum equations. Since these

velocities are those associated with the values of p', denote these by u', v' and w'.

3. Since these values are obtained from guessed values of p', the values u', v', and w', when

substituted into the continuity equation, will not necessarily satisfy that equation.

Hence, using the continuity equation, a pressure correction p* is constructed, which when added

to p' brings the velocity field more into agreement with the continuity equation. That is the

corrected pressure p where p = p* + p'. Corresponding velocity corrections u', v', and w' can be

obtained from p' such that u = u* + u', v = v* + v' and w = w* + w'

4. For turbulent flows, solve turbulence model equations for k and e.

5. Designate the new value p on the left hand side in step 3 as the new value of p'.

Return to step 2, and repeat the process until a velocity field is found that satisfies the continuity

equation. When this is achieved, the correct flow field will be obtained.


83

SIMPLEC algorithms (see Figure 4.8 for the flow chart for the algorithm) are effective for

simple flows such as laminar flows where there are no additional submodels are activated.

Considering the complexity of the flow in the gasifier, the SIMPLE algorithm will give a better

chance for the solution to converge and avoid instability. Considering the complexity of the flow

in the gasifier, the SIMPLE algorithm will give a better chance for the solution to converge by

applying a lower under relaxation factor to stabilize the solution procedure [109].

Figure 4.8: SIMPLE algorithm chart [59]

4.12.2 PISO algorithm

The Pressure-Implicit with Splitting of Operators (PISO) pressure-velocity coupling scheme, part

of the SIMPLE family of algorithms, is based on the higher degree of the approximate relation

between the corrections for pressure and velocity [34].

One of the limitations of the SIMPLE and SIMPLEC algorithms is that new velocities and

corresponding fluxes do not satisfy the momentum balance after the pressure-correction equation

is solved [27]. As a result, the calculation must be repeated until the balance is satisfied. To


84

improve the efficiency of this calculation, the PISO algorithm performs two additional

corrections: neighbour correction and skewness correction. But for steady state problems, it does

not have a significant advantage over the SIMPLE or SIMPLEC algorithm [109].

4.13 Residuals

The total residual is the sum over all cells in the computational domain of the residuals in each

cell and is given by [34]

RRcellsP

p =∑,

(74)

Since the total residual, R, defined in this manner, depends on the magnitude of the variable

being solved, it is customary to either normalize or scale the total residual to gauge its changing

value during the solution process [34]. While normalization and scaling can be done in a number

of ways, it is the change in the normalized or scaled residuals that is important in evaluating the

rate and level of convergence of the solution [79].

4.14 Convergence criteria

The convergence criteria are preset conditions usually normalized or scaled for the residuals that

determine when an iterative solution is converged. One convergence criterion might be that the

total normalized residual for the pressure equation drop below 1 x 10-3 [96]. Another might be

that the total scaled residual for a species equation drop below 1 x 10-6 [79]. Alternatively, it

could be that the sum of all normalized residuals drop below 1 x 10-4 [96]. For any set of

convergence criteria, the assumption is that the solution is no longer changing when the

condition is reached, and that there is an overall mass balance throughout the domain. When

additional scalars are being solved (for example, heat and species), there should be overall

balances in these scalars as well.

4.15 Under relaxation

The solution of a single differential equation, solved iteratively, uses information from the

previous iteration. If Φn is the value of the variable from the previous iteration and Φn+1 is the


85

new value, then some small difference or change in the variable brings the variable from the old

value to the new one [98]. Rather than using the full computed change in the variable, ΔΦ it is

often necessary to use a fraction of the computed change when several coupled equations are

involved [59]. This process is called under relaxation, and under relaxation factors, f, typically

range from 0.1 to 1.0, depending on the complexity of the flow physics (laminar flow or

turbulent reacting flow, for example), the variable being solved (pressure or momentum), the

solution method being used, and the state of the solution (during the first few iterations or near

convergence) [93]. Under relaxation makes the convergence process stable, but slower.

Guidelines exist for the optimum choices for under relaxation factors for a variety of conditions.

As the solution converges, the under relaxation factors should be gradually raised to ensure

convergence that is both rapid and stable at all times. For the gasifier simulation, it was found

that relaxation factors around 0.3 were necessary to obtain a stable solution, of which the under

relaxation of the pressure equation was the most critical.


86

Chapter 5. Modeling Vidir Best gasifier

This section describes an analysis of the Vidir gasifier. At first, the simpler equilibrium model

with no spatial resolution was used to calculate gasification reactions. Once these results were

obtained, a fully resolved, spatial analysis was then performed for the gasifier using CFD.

5.1 Equilibrium model

The equilibrium model has been used by many researchers to analyse the gasification process.

Most models are based on minimizing Gibbs free energy [54, 101 and 105]. The understanding

of some mathematical theories is necessary for solving optimization and non-linear equation

problems. Another kind of equilibrium model is one based on an equilibrium constant.

Zainal et al. [91] used this type of equilibrium model to predict the composition of the producer

gas for different biomass materials. In this work, to develop the model, the chemical formula of

feedstock was defined as CHxOy. The global gasification reaction can be written as follows:

23.76mN4CHnO2Hn2CO

2COnnCO2H2Hn)23.76N2m(OO2ωHyOxCH +++++=−++ (75)

where x, y, z number of atoms of hydrogen, oxygen, and nitrogen per number

of atom of carbon in the feedstock

w amount of moisture per kmol of feedstock

m amount of oxygen per kmol of feedstock.

All inputs on the left side of Equation (75) are defined at 25oC. On the right side, ni are the

numbers of moles of the species i that are also unknowns.

Biomass fuels are characterized by the proximate and ultimate analyses [44]. They can be burned

directly for heat or to make steam for power. The proximate analysis gives moisture content,

volatile content usually when heated to 950oC, the free carbon remaining at that point, the ash

(mineral) in the sample, and the high heating value (HHV) based on the complete combustion of

the sample to carbon dioxide and liquid water [88]. The ultimate analysis gives the composition

of the biomass in wt% of carbon, hydrogen and oxygen that are the major components, and


87

sulfur and nitrogen if any [64]. The ultimate and proximate analyses of the biomass fuel were

used to obtain the fuel composition and the moisture content of the biomass. Table 5.1 shows the

ultimate and proximate analyses results table for wheat straw biomass used in the gasifier.

Table 5.1: Ultimate (a) and proximate analyses (b)

Sample Basis Moisture content

(%)

Ash (%)

Volatiles(%)

Fix Carbon

(%)

Caloric Value

(kJ/kg)

Wheat straw

As received

13.42 7.22 63.15 16.21 15632

Air dried 3.78 8.02 70.18 18.02 17373 Dry 0 8.34 72.94 18.73 18055

Sample Basis Moisture (%)

C (%)

H (%)

Ash (%)

N (%)

O (%)

Wheat straw

As received

13.42 40.81 4.48 7.22 0.94 33.01

Air dried 3.78 45.36 4.98 8.02 1.04 36.69 Dry 0 47.14 5.18 8.34 1.08 38.13

To find the five unknown species of the producer gas, five equations were required. Those

equations were generated using mass balance and equilibrium constant relationships.

Considering the global gasification reaction in Equation (75), the first three equations were

formulated by balancing each chemical element as shown in Equations (77) to (79).

Carbon balance

10421 −++== CHCOCO nnnf (77)

Hydrogen balance

wxnnnf CHOHH 242204222 −−++== (78)

Oxygen balance


88

ymwnnnf OHCOCO −−−++== 220223 (79)

Chemical equilibrium is usually explained either by minimization of Gibbs free energy or by

using an equilibrium constant. To minimize the Gibbs free energy, constrained optimization

methods are generally used [68]. The model in this study was based on thermodynamic

equilibrium and equilibrium constant was used instead of the Gibbs free energy to avoid

complicated mathematical theories that go with this approach [77]. The remaining two equations,

were obtained from the equilibrium constant of the reactions occurring in the gasification zone,

shown below:

Water-gas shift reaction

CO +H2O = CO2 + H2 (80)

Methane reaction:

C +2H2 = CH4 (81)

Higman and Van der Burgt [72] presented that Equation 80 can be created by combining the

Boudouard and water-gas reaction.

For the 1-D model in this study, the thermodynamic equilibrium is assumed for all chemical

reactions in the gasification zone. All gases were assumed to be ideal and all reactions took place

at a pressure of 1 atm. From Equations (80) and (81), the relationship between the equilibrium

constant, K, and mole of chemical species in each equation can be written as [91]

))(())((022214 HCOOHCO nnnnKf −== (82)

))(()(042

225 totalCHH nnnKf −== (83)

where K1 and K2 are equilibrium constant of the water-gas shift reaction and methane reaction,

respectively.

Equation (84) [91] is used for the equilibrium state of ideal gas mixture because of the

requirements of K1 and K2 values.


89

TRGK

oTΔ

−=ln , oiTf

ii

oT gG ,,Δ=Δ ∑υ (84)

R is the universal gas constant, 8.314 kJ/ (kmol⋅K), ΔGT is the standard Gibbs function of

reaction, and Δgfr represents the standard Gibb function of formation at a given temperature T of

the gas species i which can be expressed by the empirical equation below [84]:

TgfT

eKTdTcTbTTahg of

oTf ′+′+⎟

⎠⎞

⎜⎝⎛ ′

+⎟⎠⎞

⎜⎝⎛ ′

−⎟⎠⎞

⎜⎝⎛ ′

−′−′−=Δ232

)ln( 2432

, (85)

The values of coefficients a’ to g’ and the enthalpy of formation of the gases are presented in

Table 5.2 [84].

Table 5.2: The value hf (kJ/mol) and the coefficients of empirical equation for ΔgfT (kJ/mol)

Compound ofh

a’ b’ c’ d’ e’ f’ g’

CO -1.105

E+02

5.619

E-03

-1.190

E-05

6.383

E-09

-1.846

E+12

-4.891

E+02

8.684

E-01

-6.131

E+02

CO2 -3.935

E+02

-1.949

E-02

3.122

E-05

-2.448

E-08

6.946

E-12

-4.891

E+02

5.270

E+00

-1.207

E-01

H2O -2.418

E+02

-8.950

E-03

-3.672

E-06

5.209

E-09

-1.478

E-12

0.000

E+00

2.868

E+00

-1.722

E-02

CH4 -7.480

E+01

-4.620

E-02

1.130

E-05

1.319

E-08

-6.647

E+12

-4.891

E+02

1.411

E+01

-2.234

E+01

The temperature of the gasification zone needed to be calculated to calculate the equilibrium

constants (Equations 84 and 85). For this reason, the energy balance or enthalpy balance was

performed for the gasification process, which was assumed to be adiabatic as used

in reference [91]. When the temperature in the gasification zone is T and the temperature at the

inlet state is assumed to be 298 K (25oC), the enthalpy balance for this process can be written

as [76]


90

( )∑∑==

Δ+=prodi

iTofii

reactj

ofj hhnh ,

(86)

where hf enthalpy of formation in kJ/kmol and its value is zero for all

chemical elements at reference state (298 K, 1 atm)

ΔhT represents the enthalpy difference between any given state and at

reference state. It can be approximated by [97]:

32

298

)(,)( dTCTbTaTCdTTCTh p

T

pT +++==Δ ∫ (87)

where CP (T) is specific heat at constant pressure in kJ/kmol K and it is a

function of temperature.

a, b, c, and d are the specific gas species coefficients, which are shown in

Table 5.2.

Equation 86 can be rewritten as [91]:

⎥⎦

⎤⎢⎣

⎡+⎟

⎠

⎞⎜⎝

⎛+⎟

⎠

⎞⎜⎝

⎛+⎟

⎠

⎞⎜⎝

⎛+⎟

⎠

⎞⎜⎝

⎛+= ∑∑∑∑∑∑∑

== iii

iii

iii

iiiii

i

ofi

prodii

reactj

ofj knTdnTcnTbnTanhnh 432 (88)

where κ is a constant obtained from the integration. Sharma [48] suggested the relationship for

finding the enthalpy of formation for solid fuel in reactant that is:

( )( )kfkprodk

offuel hnLHVh 0∑

=

+= (89)

where hfk enthalpy of formation of product k under complete combustion of

the solid fuel

LHV lower heating value of the solid fuel in kJ/kmol.

The temperature in the gasification zone can now be calculated from Equation (88) using the

Newton-Raphson method. This relationship can predict the reaction temperature by knowing the


91

amount of oxygen. This makes the model a good tool to show the variation of reaction

temperature when a mole of oxygen is changed.

To solve the values of nH2, nCO, nCO2, nH2O and nCH4, an initial temperature was assumed and

substituted into Equations (84) and (85) to initially calculate K1 and K2. Then, both equilibrium

constants were substituted into Equations (82) and (83), respectively. Finally, all five equations

i.e. Equations 77, 78, 79, 82, and 83 were used and solved simultaneously by the Newton-

Raphson method [95]. For calculating the new value of temperature, Equation (88) was used.

The outlined procedure is repeated again until the temperature value converged.

To verify the equilibrium model, a stoichiometric calculation spread sheet developed by Langner

[100] that implements the equilibrium model was used for conditions in the Vidir gasifier. A

sample of results of the mass and energy balance is shown on Table 5.3 and 5.4, respectively. In

addition, the results from these calculations were used to make sure the predictions from the

3-D model were consistent overall with the equilibrium gasification model.

Table 5.3: Sample mass balance equilibrium model results

Mass in Mass out C 0.402 0.402 H 0.078 0.078 O 1.389 1.389 N2 2.999 2.999 Ash 0.042 0.042 Other 0.000 0.000 Total 4.910 4.910 Mass in Mass out Air 3.910 0.000 Fuel 1.000 0.000 Syngas 0.000 4.869 Ash + trace 0.000 0.042 Total 4.910 4.910


92

Table 5.4: Sample energy balance equilibrium model results

Energy in (kW) Energy out (kW) Air 0.00000 H2 317.37

Fuel -5522.77 CO -737.62 Moisture -1699.93 CO2 -6535.60

H2O(g) -5697.46 CH4 0.06945 N2 5430.54

Total -7222.71 Total -7222.71

5.2 Simulation environment

This Vidir gasifier simulation was performed using the computational fluid dynamics software

Fluent® 6.2 code which is licensed by Fluent® Inc. Fluent® 6.2 is a computer program for

modeling fluid flow and heat transfer in complex geometry [93]. It provides mesh flexibility, as

it solves fluid flow problems using structured and unstructured grid that can be generated for

complex geometry with relative ease. Gambit® 2.4 from Fluent Inc. was used for the geometry

set-up and mesh generation. Gambit® 2.4 is a pre-processor for geometry modeling, block-

structured mesh generation and unstructured triangular surface mesh generation in two and three

dimensions [109]. All simulations were performed on a PC computer equipped with two

processors AMD Opteron Processor 240, 1.40 GHz, and 2 GB main memory system located at

the Wind Tunnel Lab, University of Manitoba. The same Fluent 6.2 CFD commercial software is

used to specify physical models, boundary conditions, and fluid properties in the computational

domain. All the computations were performed assuming steady-state conditions.

5.3 Model set up

5.3.1 Geometry/ mesh generation

Fluent® 6 has a two-part package consisting of a pre-processor, Gambit®, and a main module,

Fluent® [109]. Gambit® 2.4 was used to create the geometry and generate structural grids, and the

triangular grids were developed to efficiently model the complex geometries of the gasifier (for

detailed dimensions of the gasifier in this study, refer to Appendix A). Special attention was paid


93

to developing meshes that represent known areas of known high gradients. Several different,

three dimensional grids were investigated involving the initial use of tetrahedral grids, but finally

converged to the use of hexahedral grids as much possible, despite the difficulty of adapting such

a grid to the highly complex configuration of the gasifier. Figure 5.1 shows the typical grid for

the cases studied.

5.3.2 Boundary conditions

The inlet air flow was assumed to be incompressible, vertically uniform in speed, and all the

computations were performed assuming steady-state conditions. The problem taken up in this

study had three types of boundaries: inlet, outlet, and the wall. These boundary conditions were

prescribed in the following ways.

Figure 5.1: Gasifier grid

a. Inlet boundary

The mass flow rate boundary condition was used to define the inflow with all relevant scalar

properties of the flow at the primary air, secondary air and fuel port flow inlets. The mass flow

rate and direction, and the velocity magnitude normal to the boundary were specified. The mass

flow rates used as a base case in the present study were 0.24, 0.65 and 0.05 kg/s to primary,


94

secondary air inlets and fuel port respectively. These values were obtained from the data log

gasification of wheat straw in the Vidir Best gasifier.

b. Outlet boundary

The outlet boundary condition was used to model the flow at exit where the details of the flow

velocity and pressure were not known prior to solving the problem. Because these variables were

not known for the case under study, the outlet boundary condition was applied to the secondary

chamber exit. When this condition was specified, the code extrapolated the required information

from the interior. In the present study, since there was only one outlet, the flow rate weighting

was set to one to indicate that the whole fraction of total flow rate takes place in this boundary.

c. Wall boundary

In any flow, Reynolds number of the flow becomes low and turbulent fluctuations are damped

considerably near the walls where the laminar viscosity starts to play a significant role. In the

present case, walls were assumed to be adiabatic with no-slip condition. The standard wall

functions were used to calculate the variables at the near wall cells and the corresponding

quantities on the wall.

The temperature gradients in axial and circumferential directions are much smaller than that in

radial direction [53]. It was therefore considered that heat was transferred radially. The

conduction heat transfer through the multi-layer wall of the cylindrical vessel can be calculated

by the following equation [48]:

∑−

=

ii

outWinWcond R

TTQ

,

,,

λ (90)

where TW,i inside wall temperature, K

TW,out outside wall temperature, K

Rl, heat resistance of i th layer, K/W


95

Lrr

Ri

iin

iout

i πλλ 2

ln,

,

, = (91)

where rout,I outside radius of ith layer, m

rin,i inside radius of ith layer, m

L height of the cylinder, m

λi thermal conductivity of ith layer, W/ (m K)

The heat transfer from the outside wall to ambience includes the heat convection and the heat

radiation as follows [53]:

)T(TL σσ2 ππ)T(TLh2 ππQ 4am

4outW,outoutamoutW,outoutout −+−= (92)

where rout outside radius of the cylinder, m

hout convection heat transfer coefficient outside cylinder, W/

(m2 K)

Tam ambient air temperature, K

Σ Stefan-Boltzmann constant, 5.67 x 10-8 W/ (m2 K4)

εout emissivity of outside wall

The heat transfer on the inside wall includes three parts: convection heat transfer between

combustion gas and the wall, radiation heat transfer from combustion gas to the wall, and

radiation heat transfer from the wall to combustion gas [48].

4,2 inWinininnet TLrQQ σεπ−=

(93)

where, Qnet net heat transfer to the wall refractory, W

Qin sum of convection and radiation heat transfer from

combustion gas to the wall, W

rin inside radius of the cylinder, m

εin emissivity of inside wall


96

5.3.3 Porosity and bed height

A packed bed is an assembly of individual particles and consists of a solid phase (the particles)

and the gas phase (gases flowing through the gaps between the particles). It is difficult to

measure airflow velocity within the porous media straw in the gasification chamber. The

common practice is to measure the static pressures in a cylinder to establish an airflow

distribution profile because the pressure differences at two points cause airflow [48]. Based on

this fact, since the straw had a non-homogenous structure, it would cause an uneven pressure

distribution in both horizontal and vertical direction, within itself. This type of pressure

distribution will cause non-uniform airflow in return. Thus, horizontal pressure distribution is

determined in a central plane of a wheat straw bed to study the uniformity of airflow. The

rationale for choosing the central plane was because the central plane was the plane separating

the straw into equal parts subjected to equal sensitivity to pressure changes at either part of the

bed.

The schematic of the experimental setup is shown in Figure 5.2. A cylindrical vessel was used to

place the straw. This cylinder represented the primary chamber of an updraft gasifier (the

primary chamber of the Vidir Best), in which the gasification occurs. The bottom of the vessel

was covered with a screen to hold the straw but allowed air to flow upward. Its height and

diameter were 100 and 50 cm, respectively. Four taps are arranged with the same height from the

bottom to measure the static pressure values in the same central plane.

Straw was loaded into the vessel by hand shovel and leveled by hand from time to time without

compressing. After the depth of the straw bed reached 80 cm, air was supplied to the bottom of

the vessel at the superficial velocity of 0.175 m3/s m2. The superficial velocity in a single phase

flow equals its mean velocity, while in multiphase flows is defined as the ratio of the velocity

and the volume fraction of the considered phase in a multiphase system [78]. An even pressure

was provided from the bottom and calibrated orifices were used to measure airflow. Static

pressure values in the central plane of the straw bed are measured by using probes made from

steel tube. The probe was connected to a digital manometer whose range and resolution were

± 249 Pa and ± 0.25 Pa, respectively. The probes were inserted through the pressure taps into the

can and duct tape is used to seal the space between the probe and the tap. The pressure readings


97

were taken through the four taps 80, 90 and 100 cm from the bottom of the can for different

angles and distances from the walls. In addition, the pressure drop across the packed bed was

measured by a differential U-tube manometer. Two tests were done using wheat straw with low

aspect ratio (the ratio of the largest to the smallest dimension of the particle) and cattail with

large cylindrical particles having the highest aspect ratio that is the ratio of width to height

biomass [29].

It was observed that the porosity of the wheat straw was 0.4333, while the cattails had the

highest porosity equal to 0.5409. The wheat straw particles were found to be in close surface-to-

surface contact among them, leading to a higher packing density, while cattail particles were

found to have more inter-particle branching, thus contributing to a higher observed porosity.

Figure 5.2: Experimental schematic

The results showed significant absolute pressure deviations, which indicated non-uniform

airflow distribution existed in the central plane of the straw bed (Figure 5.3). A pressure

deviation closer to the inner surface of the vessel was observed. The reason for this might have

been the presence of an edge effect. Edge effect is the occurrence of lower straw bed density


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closer to the inner surface of the reactor, since straw particles could not settle down near the

edges because of their long sizes [68]. Therefore, air flowed more easily near the edges of the

gasifier because of lower bed densities. Based on this observation one can conclude that a wide

straw size distribution resulted in a reduction of uneven airflow distribution. The angle effect on

the pressure deviation might have been caused by the uncontrollable factors associated with

straw loading by hand-shovel. The results of these experiments (Figure 5.3) were used to model

the straw bed.

Figure 5.3: Pressure drop as a function of air velocity for straw

5.3.4 General description of model

The gasifier operated in atmospheric condition and air was used as the oxidizing agent. Wheat

straw which was used as biomass was characterized using the main energetic parameters such as

higher heat value, chemical characteristics (carbon, hydrogen and nitrogen content), physical

parameters (moisture, ash and volatile compounds) based on the ultimate and proximate analysis

(Table 5.1). Finally, first order discretization method was used coupled with the k-ε model of

turbulence. A typical hexahedral three dimensional grid of the gasifier under investigation with

the corresponding boundary conditions are shown below in Figure 5.4.

The k-ε turbulence model was implemented with to an eddy-viscosity model in which the

Reynolds stresses were assumed to be proportional to the mean velocity gradients, with the

constant of proportionality being the turbulent eddy viscosity. The turbulent viscosity was


99

assumed to be proportional to the product of a turbulent velocity scale and length scale. In the κ-

ε model, these velocity and length scales were obtained from two parameters: the turbulent

kinetic energy (k), and the dissipation rate (ε).

The closure coefficients and auxiliary relations used for the κ-ε model were obtained from

Launder et al. [40], where the empirical turbulence coefficients within the dissipation rate term

are defined as Ce1 = 1.44 and Ce2 = 1.92.

The governing equations in the transformed coordinates were discretised using the finite volume

technique. Finite volume discretization is a numerical method approach of solving partial

differential equations instead of using an analytical method. The domain over which the

dependent variable was to be evaluated was broken into a finite number of discrete volumes and

algebraic equations were written for each volume. A numerical solution of a differential equation

consists of a set of numbers from which the distribution of the dependent variable is then

constructed. The pressure velocity coupling was numerically implemented using the SIMPLE

algorithm which is basically an iterative approach, where some innovative physical reasoning is

used to construct the next iteration from the results of the previous iteration.

Figure 5.4: Boundaries of gasifier


100

Close to solid walls, there are boundary layer regions where the local Reynolds number is so

small that viscous effects predominated over turbulent effects [71]. To account for this effect and

for the large gradients of variables near the wall, the wall function method of Launder and

Spalding [40] was used in the CFD model.

Thermal boundary conditions were defined at all the fluid inlets and at all the wall/fluid

interfaces in the computational domain. At the fluid inlet, the air temperature, mass flow rate,

direction, atmospheric pressure, gravitational acceleration, turbulence intensity, and hydraulic

diameter were specified. The thermal conditions of density, specific heat, viscosity, and thermal

conductivity were also specified for the fluid inlets. For the walls, several thermal boundary

conditions are specified such as surface temperature, emissivity of the wall, and conductive heat

transfer coefficient.

The arrangement of biomass straw in the fixed bed of the gasifier formed void spaces through

which the primary air flowed through. Therefore, devolatilization volatiles and gases through the

bed could be approximated to be described and modelled as flow through a porous media. Fluid

flow, and heat and mass transfer are described in the sub-domain by the laws of conservation of

mass, momentum and energy in the terms of macroscopic variables provided by the volume

averaged Navier-Stocks equations in a version of Darcy’s law [48]. The bottom of the primary

chamber where the straw rests was defined as porous media zone. One of the parameters when

dealing with a packed bed is the porosity. Observation of the actual gasifier operating indicated

the partial presence of more molten-like biomass at the centre of the bed. The biomass had lost

the packed wheat straw appearance, which resulted in a variation of porosity as a function of

radial distance. Molten biomass resulted in more resistance to the air flow, and hence exhibited

low porosity. Therefore, the porosity of the bed was expected to be high going outward from the

centre. To account for this variation a User Defined Function (UDF) was implemented relating

the porosity as a function of radial distance in Fluent. The fact that the distribution was linear has

also confirmed by doing an experiment and the correlation obtained from the experiment was

used in developing the UDF function for the porosity (see Section 5.3.3).

In addition, a better understanding of the conditions in the bed was required to allow prediction

of the gas composition to be predicted at the bed/freeboard interface. The fact that the bed was a


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pile of straw with elongated particles stacked on each other makes it difficult to accurately

model. To tackle this short coming, the method of holding straw elongated particles stationary on

the fuel bed became important. UDF developed by Langner [100] that keep the straw particle on

the bed until they lose their weight by evaporation and thermal conversion was used. These UDF

set the drag coefficient to a minimum value and applied a body force to counteract the gravity

force. Once they were held stationary, evaporation, devolatization and combustion allowed them

to lose weight. A 4.5% of initial mass loss of the particles was set as a threshold limit for the

particles before they could be entrained by the flow (Stoke flow), as suggested by Langner [100].

The flow in the biomass gasifier was a typical type of gas-solid flow with chemical reactions

found in many processes. Thus, hydrodynamics of the gas-solid flow was performed based on

the Eulerean–Lagrangian concept [111]. The discrete phase method was applied to the particle

flow since the particle phase could be considered to be sufficiently dilute that the particle-particle

interactions and the effects of the particle volume fraction on the gas phase could be

neglected [91]. The coupling of the continuous phase and the discrete phase was important and it

was solved by tracking the exchange of mass, momentum and energy.

Species transport model was implemented to simulate the transport and chemical reactions of

eight species namely O2, H2, H2O, CO, CO2, CH4, Straw-volatile and N2. A total of eight mixture

species with five reactions are modelled (detail in Appendix A). The properties of the individual

species are applied accordingly. Specifying dependencies of the properties of the medium on

temperature was also taken into account. When simulating fluid flows with heat transfer, as

source data, laws of variation of physical properties of the medium (density, viscosity, specific

heat conductivity, heat capacity) on the temperature needed to be specified properly. These

properties were specified using polynomial smooth functions. Thus, for computing properties at

certain temperature, piecewise-linear approximation is used.

A detailed summary of the boundary conditions, materials and their properties and sub-models

used are given in Appendix A.


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5.4 Base case results

Proper set-up of the parameters of the experiment impacted the accuracy of the calculation as

compared to the real gasifier. The main groups of parameters were the dimensional parameters

defined by the grid, the material properties defined in the entries and the boundaries conditions

defined both by the grid and the boundary entries. Three meshes were evaluated from 500,000 to

1,000,000 elements for grid independence. The limits required for these calculations were to

obtain residuals less than 10-5. This required equilibrium between the computational time and the

accuracy of the parameters. Even when the residuals went below the set levels it was not

guaranteed that the solution had converged. Therefore, in addition to the convergence criteria set

up, a check to know whether the solution had converged was done by monitoring the mass flow

rate at the outlets. When the mass flow rate at outlet did not vary significantly with the iterations

and the residuals were less than 10-5, the solution was taken as converged. The grid used for this

case was 1,000,457 elements. This choice allowed for reaching the convergence of the results

after 120 to 130 hours and 18,000 to 19,000 iterations for the 3-D gasifier geometry shown in

Table 5.5.

Table 5.5: Mesh density dependence for equilibrium gasifier outlet temperature [K]

Mesh density Temperature (K)

Temperature from Fluent

(K)

Time to converge (Hours)

Percentage error (%)

500,000 1,165 689 24 40.85 600,000 1,165 785 35 32.61 700,000 1,165 846 47 27.38 800,000 1,165 923 59 20.77 900,000 1,165 1109 83 4.80 1,000,000 1,165 1123 128 3.60 1,200,000 1,165 1125 147 3.43 1,500,000 1,165 1126 195 3.33

The gasifier used as a base case had a secondary air inlet nozzle configuration of 90 degree (see

Section 5.3.1 for geometry and Section 5.6.2 for alternative configurations). The primary and

secondary air inlet mass flow rates were 0.24 kg/s and 0.65 kg/s, respectively (see Section 5.3.2


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where the geometry of inlet air is described). The total air was set to produce an excess air ratio

of 40%, consistent with gasifier control operations for the base case. The wheat straw used for

the gasification model had moisture content of 14% as measured.

At the beginning of the simulation the fuel and air was set to a uniform temperature as an initial

guess and the volatiles were released from the straw and began to combust releasing heat as

shown in Figure 5.5. The moisture in the fuel started to leave once the boiling temperature was

reached, which resulted in a rapid increase of the molar fraction of water vapour and a

corresponding decrease of the molar fraction of oxygen and nitrogen, because of the dilution

caused by the released water vapour.

An immediate combustion took place at the bottom of the bed where the levels of available

oxygen were highest. Due to exothermic combustion reaction, the temperature of the bed rapidly

increases. At this stage, the O2 level was dropped and a sharp increase in CO mass fraction was

predicted. The local gas compositions remained at high and stable levels for a relatively long

period of time. During this stage, the mass fractions of CO, H2 and CH4 reached their maximum

level. Later, as the simulation progressed and approached convergence, a sharp decrease in all

volatile fuel species occurred.

Figure 5.6 shows the velocity magnitude contours in the vertical plane at Z = 0 (midsection of

the gasifier) when the simulation has converged. It was observed that the velocity magnitude

inside the gasifier was generally low. In the upper region of the gasifier, where syngas left the

primary chamber, the gas velocity was higher compared with the region inside the chamber due

to the convergent section. The velocity vector plot in the vertical plane at Z = 0 is shown in

Figure 5.7. The gas velocity increased when gas passed through the connecting duct between the

primary (Figure 5.9) and secondary chamber, and when it left the secondary chamber through the

outlet tube (Figure 5.10).

The formation of recirculation flow regions at several locations in the gasifier can be seen in

Figures 5.8 to 5.10; the path of fuel particles is shown in Figure 5.11. The recirculation flow

zone increased the particle residence time, and the local intensity of gas turbulence and thus

contributes to the particle deposition rate. The presence of the recirculation flows could also lead


104

to the gradual buildup of ash in these regions. The temperature contour profiles are shown in

Figure 5.12. The maximum temperature was 1900 K located in the area next to the secondary air

inlet, which is consistent with gasifier operation and controls. The reason for the high

temperature spot is the result of the excess oxygen and reaction of CO. At the bottom of the

primary chamber, a low temperature zone was observed because this zone is the zone of

gasification. The mass fractions of gases are also shown in Figures 5.13 to 5.17.

Figure 5.5: Contours of mass fraction of straw volatiles at start of simulation

Figure 5.6: Contours of velocity magnitude once converged [m/s]

Bed Height


105

Figure 5.7: Velocity vectors colored by velocity magnitude at top of gasifier [m/s]

Figure 5.8: Contours of velocity magnitude near secondary air inlet [m/s]


106

Figure 5.9: Contours of velocity magnitude near secondary air inlet [m/s]: (x = 0 plane)

Figure 5.10: Contours of velocity magnitude near secondary chamber outlet [m/s]


107

Figure 5.11: Fuel path lines colored by particle ID

Figure 5.12: Contours of static temperature [K]


108

Figure 5.13: Contours of mass fraction of O2

Figure 5.14: Contours of mass fraction of CO2


109

Figure 5.15: Contours of mass fraction of H2O

Figure 5.16: Contours of mass fraction of H2


110

Figure 5.17: Contours of mass fraction of CO

5.5 Validation

The process of obtaining a converged solution was of critical importance in these numerical

simulations. To monitor this process, residuals for each equation and for each 100 iterations were

monitored during the simulation, with careful attention to the convergence of the energy equation

owing to the fourth order source terms, and resulting from the radiation dominated heat transfer

inside the gasifier [109]. The residuals are a measure of how closely each finite difference

equation is balanced, given the current state of the solution. Residuals for each conservation

equation were computed by summing the imbalance in the equation for all cells in the domain.

The residuals of the calculation, which represent the accuracy of the results, were less than 10-5 at

the end of the calculations for all parameters throughout the simulations.


111

Figure 5.18: Vidir Best gasifier system for in-situ-experiments


112

Figure 5.19: In-situ particulate emission sampling

Once convergence was obtained with the proper mesh size, the temperatures in the primary and

secondary chamber from the simulation were compared to the experimental data obtained by

running the Vidir Best gasifier for validation. Details of the operating conditions for the base

case of the Vidir system for the gasification of the wheat straw can be found in Appendix F. The

results show that the temperature in the primary chamber was 900 K (1154.3oF) and the


113

temperature in the secondary chamber was 1356 K (1981oF). In addition, the results were

compared to the equilibrium model detailed in Section 5.1.

The primary and secondary temperature simulation results were in good agreement with the

equilibrium model and the experimental results as shown in Figure 5.20. The small variation can

be attributed in part to the uncertainty in the actual physical boundary conditions. Furthermore,

the approximation of the equilibrium model did not justify the use of a 1-D simplified model

over the more detailed 3-D calculation because design considerations required a careful

prediction of the variation of parameters throughout the gasifier.

Figure 5.20: Comparison of outlet temperatures [K] in primary and secondary chamber

with in-situ experiments with the gasifier and the equilibrium model predictions

The compositions of gases at the outlet were also compared and shown in Figure 5.21. These

results were also in fairly good agreement with the results predicted by the equilibrium model.

The measurements obtained from the experiment were limited to CO2, O2, CO and H2O because

the gas composition measuring device that was available was capable of measuring only these


114

gases (see Appendix E for details). In addition, the experimental results had several possible

errors which were difficult to quantify: unsteady effects, uncertainties with the boundary

conditions measurements, and variations in a field value across a duct that could not be

accurately measured with small sampling volume. Only the hydrogen composition was not well

predicted which can most likely be attributed to the reaction kinetics used for hydrogen in the

model.

Figure 5.21: Comparison of mass composition of gases [%] with in-situ experiments with

the gasifier and the equilibrium model predictions

5.6 Design improvement for air control

The motivation to develop a 3-D model that captures the physical phenomenon and thermal-fluid

complexities occurring inside a small-scale gasifier is to be able to deviate from the base

configuration and develop a predictive tool to understand the expected behaviour based on a

given geometry or process change. As any changes cause variations in the flow, heat transfer

and chemical reactions, it is critical to use an approach that models the physics properly, or at

least one that incorporates up-to-date models that have been validated for the intended use.

Rather than use trial and error by building numerous prototypes and test these during expensive

O2 CO2 H2O CH4 H2 CO


115

demonstrations, or even worst, experiment with new designs using a client’s system, the ability

to predict changes will lower costs and allow optimizing the system over a shorter period of

time. For small-scale renewable energy applications, flexible modeling is a critical aspect

because access to capital is scarce and failures occurring during demonstrations and first time

commercialisations are rapidly known within the industry and to potential future customers. This

section investigates the effect of changing geometry and operating parameters for the gasifier.

These new configurations could not be validated because they were exploratory and were

necessary to develop an understanding of how best to improve the controls of the gasifier or

make changes to the geometry. The following were investigated where bolded values represent

the base case conditions previously described. This test matrix resulted in 10 new simulations to

investigate design variation from the base case.

Table 5.6: Summary of parameters investigated

Aspect Parameter varied Comparison to base case

Moisture content

Moisture content (14%, 20%, 26%) Velocity magnitude (more water), secondary chamber temperature, calorific content, mass fraction of gases

Nozzle configuration

Angle of secondary air nozzle (30º, 45º and 90º )

Velocity magnitude, recirculation

Secondary to primary ratio (Primary air flow rate)

Primary air flow rate (0.16, 0.25 and 0.35 kg/s); secondary stays the same

Pressure drop across fuel bed, velocity magnitude, mass fraction of gases

Fuel bed height

Straw bed height ratio to cylindrical part of primary chamber height (0.6, 0.7, 0.8)

Temperature, mass fraction gases

Biomass types

Composition of biomass fuel (wheat straw, slough hay and wood chip)

Velocity magnitude, secondary chamber temperature, mass fraction of gases


116

5.6.1 Moisture Content Variation

Moisture variation in wheat straw is difficult to control, especially for small scale gasifier

implementations. Large combustors burning straw have tight control on the biomass delivery

process, ensuring that bales are within specified moisture content. For the Vidir gasifier which

uses large 1,000-kg bales, it is unrealistic to assume that even within even a single bale the

moisture content can be controlled. Often a wet patch is found which cannot be detected with a

simple moisture measurement probe that is inserted only a few inches within a bale. The gasifier

control systems need to be able to react to a relatively large moisture variation. Small-scale

implementation of a gasifier cannot rely on the user to implement sophisticated feedstock

moisture controls.

To study the effect of moisture content of the biomass, the amount of primary and secondary air

was fixed at 0.24 and 0.65 kg/s and the moisture content of the wheat straw was varied from 20%

and 26%. Figures 5.22 and 5.23 show the effect of moisture content on the reaction temperature,

the calorific value, and the composition of producer gas. The temperature in the primary chamber

dropped from 900 oK at 14% MC to 800.9 oK when the moisture content increased to 26.0%. The

change in temperature in the secondary chamber decreased from 1356 K for the base case to

1324 K and 1280 K for a MC 20.0% and 26.0%, respectively (Figure 5.22). The calorific value

also dropped from 4.76 MJ/Nm3 to 4.55 MJ/Nm3 (Figure 5.23). Hence, moisture content

negatively affects the calorific value of producer gas and causes the temperature distribution

within the gasifier to vary, impacting the thermal conversion process of the fuel. The controller

therefore needs to react to this change by first noticing that the temperature in the chamber has

decreased and develop a mitigation strategy which can include a change in primary air, addition

of more fuel or implement flue gas recirculation.

The composition of gases variation as a result of the different moisture contents is shown in

Figure 5.24. The CO2 decreased from 11.0% to 6.0% with increasing moisture content while CH4

had a low percentage in the producer gas, though it showed an increase from 0.15% to 0.19%.

For O2, it decreased from 11.3% to 9.8%. In the case of CO2, an inverse tendency was shown by

the simulation results. In regards to composition of mass fraction of gases, shown in Figure 5.24,

the fraction of H2 increased from 1.4% to 1.9% when the moisture content increased from 14.0%


117

to 26.0%. The contour plots of velocity magnitude, temperature, and O2 are shown in Figures

5.25 to 5..27 for the three moisture contents under consideration.

The moisture content of the straw had an obvious influence on the operation of the gasifier:

increases in moisture content resulted in an increasing equivalence ratio and gas yield and

aberrant temperature fluctuations. When a biomass with high moisture content is used, an

increase in the air supplied to the system is required to keep the reaction temperature. However,

increasing the amount of air randomly causes disadvantages and one of them is the mass fraction

reduction of useful gases which consequently reduce the heating value of the syngas. Therefore,

a careful adjustment to get the optimum air supply has to be implemented to enhance the thermal

decomposition characteristics of the biomass, thus higher calorific value syngas is produced.

Figure 5.22: Effect of MC on caloric value of producer gas in primary chamber


118

Figure 5.23: Effect of MC on the secondary temperature [K]

Figure 5.24: Effect of MC on the producer gas composition



119

Figure 5.25: Contours of velocity magnitude [m/s] with variation in MC: a) 14%, b) 20% and c) 26%

(a)

(b)

(c)


120

Figure 5.26: Contours of temperature [K] with variation in MC: a) 14, b) 20 and c) 26%

(a)

(b)

(c)


121

Figure 5.27: Contours of mass fraction of O2 with variation in MC: a) 14%, b) 20% and c) 26%

(a)

(b)

(c)


122

5.6.2 Nozzle configuration

The velocity field and the combustion reaction pattern in the secondary chamber are governed by

configuration of air injection nozzles, which represents an important means of controlling the

combustion process of the producer gas. The nozzle configuration was varied from 90o to 45o and

30o degree since the air jets provided favourable reaction conditions in terms of oxygen and high

turbulence levels ensuring good mixing of the oxidant with carbon monoxide. Figure 5.28 shows

the secondary air inlet nozzle configuration at different angles.

Figure 5.28: An angled nozzle configuration: a) 90o, b) 45o and c) 30o

(a)

(b)

(c)


123

The maximum velocity changed from 68.5 m/s to 143.0 m/s as a result of a decrease in the

nozzle angle to 45o. The comparisons of velocity magnitude are shown in Figure 5.29. The high

velocity was caused as a result of all inlet stream flows adding up while at 90 degrees the stream

flow from each inlet is disturbed by each other and hence slowing the fluid flow down. The

contours of velocity magnitude for the three cases are shown in Figure 5.30. Three factors are

necessary for combustion to occur: oxygen, combustibles and high temperatures—all at the same

location for a certain time. The 90o configuration ensured proper mixing of gaseous combustibles

and oxygen. Furthermore, good mixing will ensures convection of heat into the combustion zone.

The angle has an effect on the formation of swirl in the duct connecting the secondary to primary

chamber. The cross sectional contour plot of velocity magnitude near the secondary air inlet

nozzle is shown in Figure 5.31. It is hence evident that configuration of the nozzle affects the

mixing rate and turbulence intensities. The relatively poor mixing in the furnace between the

bulk flow of flue gases and the secondary air jets was also responsible for the high concentration

of CO and unburnt carbon in the fly ash.

The proper choice of nozzle configuration is also important to enhance the deposition of liquid

silica on the inside wall of the secondary chamber and not create an excessive hot zone to favour

thermal NOx formation and precipitate wear of thermal insulation. Figure 5.32 shows flow

pathlines colored by velocity, where red and green indicate higher velocities and blue indicates

low velocities for the cases studied. It could be seen that the flow conditions in the chamber were

highly three dimensional and complex. When the jet impinged on the opposite wall, the

velocities were significantly reduced. Figure 5.33 showed the vector plots of velocity magnitude

as the air jet entered the secondary chamber. The flow diverged in all directions and complex

circulation patterns could be observed. It could be seen that the flow fields for the 45° nozzles

were similar to the 30o nozzle configuration but differed from the 90o configuration except for

one similarity. In both the 45o and 90o cases, a large recirculation zone occurred above the jet,

and a second, smaller circulation zone occurred below the jet in the central plane. The

visualizations of the simulation results showed higher normalized velocities for the 45o jet. In

contrast to the 45o and 30o nozzle, the 90° nozzle showed a large circulation region indicating

significantly high mixing efficiency (Figure 5.33). It is important to see also that high velocity

will also result in high impact inertia of the particles driven with gases to the wall surface of the


124

secondary chamber. The impact inertia will cause the particles to turn sharp corners thereby

increasing the chances of the particles to stick to the wall causing deposition. Therefore, the

significance of varying the nozzle angle has an effect on the deposition of particles, including

silica which is one of the problems associated with straw gasification.

The tendency of these designs as shown from the simulation results was characterized by the gas

flow dynamics that differentially concentrated the ash particle streams and alter the angle of

impact onto the secondary chamber in directions that further increased the sticking rates. An

alternative configuration for the air inlet nozzles can be suggested to increase fuel-air mixing and

reduce unburnt fuel particle carryover from the chamber. Figure 5.34 shows a possible design

for the nozzles. The nozzles are tangential to the primary to secondary connecting duct (see

Appendix A for detail geometry) rather than perpendicular, with alternating orientation to

facilitate the mixing phenomena. As a result the air jet strikes the flue gases coming out from the

primary chamber perpendicular and hence swirl, the air coming to increase the turbulence, which

is an important factor, to improve the mixing between the bulk flue gas flow and the air being

injected through the secondary air ports.

Figure 5.29: Effect of nozzle angle on velocity magnitude


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Figure 5.30: Contours of velocity magnitude [m/s] with variation in secondary nozzle angle:

a) 90o, b) 45o and c) 30o

(a)

(b)

(c)


126

Figure 5.31: Vector plot of velocity magnitude [m/s] with variation of nozzle angle:

a) 90o, b) 45o, and c) 30o

(a)

(b)

(c)


127

Figure 5.32: Pathlines colored by ID with variation of secondary air nozzle angle: a) 90o, b) 45o and c) 30o

(a)

(b)

(c)


128

Figure 5.33: Vector plot of velocity magnitude [m/s] with variation of nozzle angle: a) 90o, b) 45o and c) 30o

(a)

(b)

(c)


129

Figure 5.34: Flow pattern tangential (a) versus perpendicular (b) to duct nozzle

5.6.3 Secondary to primary air ratio

The gasifier system under investigation relies on a negative draft pressure above the bed for

controls. The negative pressure in turn affects the hydrodynamics, heat transfer and reaction rates

during gasification processes. In addition, maintaining the system at below atmospheric pressure

avoids “blow back,” a potentially dangerous condition when flue gas is blown outside the

gasifier through view ports or the fuel feeder. Therefore, knowledge about the secondary to

primary air ratio that depends on primary air flow helped to make the adjustment that is required

to deliver sufficient air to maintain the desired straw bed height and maintain the lower primary

chamber region in a substoichiometric (insufficient air for complete combustion) environment.

To investigate the effect of primary air flow rate, the primary air flow rate was spanned from

0.16 kg/s to 0.35 kg/s (25%, 40% and 60% of the secondary air respectively) without preheat

(320 K) and with a moisture level of 14%. The pressure drop across the fuel bed for the three

secondary-to-primary ratios were compared, as shown in Figure 5.35. The pressure drop

increased with air flow increase. The contours of the velocity magnitude are shown in

Figure 5.36. The recirculation zones both at the exit from primary chamber and inlet to the

secondary chamber showed significant variation. The velocity contours also showed that the

velocity magnitude in the secondary mixer (see Section 5.3.2) increased as the primary air flow

rate increased.

The air flow rate influenced both the temperature profile and the distribution of gaseous

products. The mass fractions of the gases are compared for the three cases in Figure 5.37. The

(a) (b)


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temperature contour plots using various flow rates of primary air (oxidation agent) are shown in

Figure 5.38. The higher the flow rate of air was introduced in the gasifier, the shorter the time

was required to reach high temperatures and maximum concentrations of the gaseous products.

At a fixed moisture level, the burning rate increased as the airflow rate increased until a critical

point is reached, beyond which a further increase in the air flow resulted in the fall of the burning

rate. The reason for this result is attributed to the fact that as the air flow increases beyond a

certain point, air channeling occurs. Relatively high temperature in the upper section of the

primary chamber and an unusually low temperature in the secondary chamber was observed

when the primary air flow rate is raised to 0.35 kg/s. The rapid consumption of biomass in

combustion at a high flow rate of air caused a fast decrease in the concentrations of gaseous

products. At lower flow rates of primary air, the time needed to heat up the reactor and to obtain

maximum concentrations of gaseous products is longer; the consumption of biomass was also

slower. The temperature on the top part of the secondary chamber was low when the primary air

flow was 0.16 kg/s. As a result, the favorable condition for the conversion of CO to CO2 is not

present when the primary air flow rate was set to 0.16 kg/s. The contour for mass fraction of O2

is also given on Figure 5.39. An increase of mass fraction of CO from less than 0.5% to 6.4%

showed the lack of conversion of CO to CO2. As a result, the temperature was low in at the top

part of the secondary chamber.

Figure 5.35: Effect of primary air flow on pressure drop


131

Figure 5.36: Contours of velocity magnitude [m/s] with variation in primary air: a) 0.16 kg/s, b) 0.24 kg/s and c) 0.35 kg/s

(a)

(b)

(c)


132

Figure 5.37: Effect of primary air flow rate on composition of gases at the secondary exit

Primary air flow rate [kg/s] O2 CO2 H2O CH4 H2 CO


133

Figure 5.38: Contours of temperature [K] with variation in primary air flow rate

(a)

(b)

(c)


134

Figure 5.39: Contours of mass fraction of O2 with variations in primary air flow rate: a) 0.16 kg/s, b) 0.25 kg/s and c) 35 kg/s

(a)

(b)

(c)


135

5.6.4 Straw bed height

The bed height is responsible for the variation of density and porosity. Therefore, its effect was

simulated to understand its impact on the gasification process. The fuel bed for the base case is

70% (ratio of 0.7) of the cylinder part of the primary chamber, 104.5 cm (for dimensions refer to

Appendix A). A fuel bed height of 0.8 and 0.6 times the primary cylinder is investigated with

bed heights of 119.4 cm and 89.5 cm, respectively (Figure 5.40). The effects on the composition

of the gases are shown in Figure 5.42. The variation in bed height resulted in the syngas yield

increase, particularly the mass fraction of H2, CO, CO2 and methane because for a given

temperature, higher fuel bed heights increase the time that the fuel will be available for reactions

to occur (residence time).

Figure 5.40: Bed height as ratio of primary chamber cylinder part

Height = 119.4 cm Height = 104.5 cm Height = 89.5 cm


136

Figure 5.41: Effect of bed height on mass composition of gases: a) 0.6, b) 0.7 and 0.8 times cylinder part of primary chamber

5.6.5 Variation of biomass

Different biomass fuels were investigated during gasification. The biomasses used for the study

in addition to straw were slough hay and wood chips because these are prevalent in the Prairies.

The inputs for the simulation were obtained based on the ultimate and proximate analyses of

different biomass fuels. The ultimate and proximate analyse for these biomasses are given on

Tables 5.7 and 5.8, respectively.


137

Table 5.7: Ultimate analysis for slough hay and wood chips

Sample Basis MC (%)

Ash (%)

VM (%) FC (%)

CV kJ/kg

Slough hay A.R. 31.56 5.23 51.70 11.51 12433

A.D. 4.03 7.33 72.50 16.14 17435 Dry ----- 7.64 75.54 16.82 18167

Wood Chips

A.R. 19.57 0.38 67.95 12.11 16309

A.D. 2.21 0.46 82.61 14.72 19829 Dry ----- 0.47 84.48 15.05 20277

Table 5.8: Proximate analysis results

Sample Basis MC (%)

C (%)

H (%)

Ash (%)

N (%)

O (%)

Slough hay A.R. 31.56 33.14 3.69 5.23 0.88 25.34 A.D. 4.03 46.48 5.17 7.33 1.23 35.54 Dry ----- 48.43 5.39 7.64 1.28 37.03

Wood chips

A.R. 19.57 42.70 4.91 0.38 0.49 31.94 A.D. 2.21 51.92 5.96 0.46 0.60 38.84 Dry ----- 53.09 6.10 0.47 0.61 39.71

It is worthy to note the variation of volatile matter, moisture content and calorific value of these

biomasses. Even though the other operating conditions were maintained the same as the

operating conditions used for wheat straw gasification, the composition of the fuel had a

significant effect on the outcome of the gasification process. It was also difficult to show a direct

comparison of the parameters in question since the effects of each are not linear.

The effect of biomass variation on gas composition is shown on Figure 5.43. The CO2 and H2O

mass fractions showed a significant decrease. The secondary temperature comparison is provided

in Figure 5.44. The temperature in the secondary chamber during the simulation of gasification

of wood chip is significantly higher than the one obtained for slough hay and wheat straw. The

contours plot for the three temperatures is shown in Figure 5.45. The reason for a higher

temperature for the wood chips is that at a fixed air flow rate, wood chips had a higher burning

rate and therefore had a higher flame temperature. The small diameter of wood chips also


138

favours the preheating and pyrolysis process and hence contributed to the high temperature in the

primary chamber (Figure 5.45). Figure 5.46 shows the contours of velocity magnitudes for the

three fuels and a slight drop in the velocity magnitude was observed.

Figure 5.42: Effect of biomass variation on composition of gases at secondary outlet

Figure 5.43: Effect of biomass type on outlet temperature in secondary chamber



139

Figure 5.44: Contours of temperature [K] with variation of biomass: a) wheat straw, b) slough hay and c) wood chip

(a)

(b)

(c)


140

Figure 5.45: Contours of velocity magnitude [m/s] with variation of biomass: a) wheat straw, b) slough hay, and c) wood chips

(a)

(b)

(c)


141

5.7 Impact on control strategy

The simulations above produced results of practical use for biomass gasification design. CFD

simulations were relatively simple to perform once the base case model had been formulated and

validated with experimental data and the equilibrium model. The results can be used for design

and optimization. These simulations can also be used for system scaling-up purposes. As for the

Vidir best gasifier, the CFD showed how the controller should react to changes in secondary-to-

primary air ratio and moisture content because these were found to be the most influencing factors

in the gasification processes.

It is necessary to have the correct air-to-fuel ratio to achieve complete gasification. With lower

values of this ratio an excess of charcoal and tar is produced, and with higher values charcoal is

depleted and product gas is burnt. Hence, it is crucial to have a means to get the optimum ratio for

an efficient and safe operation of the gasifier.

The air flow control system has a direct effect on the temperature. The gasifier system is

controlled by negative draft pressure above the bed. The negative pressure in turn affects the

hydrodynamics, heat transfer and reaction rates during gasification processes. In addition,

maintaining the system at below atmospheric pressure avoids “blow back” and maintains the

gasifier under negative pressure to prevent potential dangerous conditions, for unattended

operation. Therefore, control of the secondary-to-primary air ratio that depends on the primary air

flow helps to make the required adjustment to deliver sufficient air to maintain the desired pile

height and maintain the lower primary chamber region in a substoichiometric (insufficient air for

complete combustion) environment. Results from the simulation have shown that the pressure

drop across the fuel bed increases with air flow increase. As a result the variation in pressure drop

has influenced both the temperature profile and the distribution of gaseous products. Therefore,

having a means to control the secondary-to-primary ratio that amounts to having the necessary

oxygen enter the system plays a significance role in controlling the temperature and hence the

whole gasification process.

Oxygen monitoring, which is directly related to secondary-to-primary air ratio is the most

effective approach because it has a measurable single-value relationship with excess air, it is


142

insensitive to other flue gases, and it is independent of fuel composition. There are certain

precautions to observe. The sampling location must be carefully selected to minimize effects of

stratification and infiltration of damp air, which will falsely raise the O2 reading. To ensure safe

burner conditions, it is usually necessary to control flue gas oxygen to 8% or higher to ensure

complete combustion of fuel. When a shorter time is required to reach high temperatures and

maximum concentrations of the gaseous products, the air introduced in to the gasifier has to be

dialled up. At a fixed moisture level, the burning rate increases as the airflow rate increases until a

critical point is reached, beyond which a further increase in the air flow results in the fall of the

burning rate as a result of air channelling. When a biomass with low moisture content is used,

turning the air flow rate to low helps to increase the time needed to heat up the reactor and also

slows down the consumption of biomass. As a consequence, temperature in the gasifier can be

kept below the critical temperature to help reduce corrosion and air pollution by minimizing

formation of NOx.

Monitoring CO coming out of an exhaust stack can provide very close to stoichiometric

combustion by ensuring the absolute minimum of excess air. This is because CO is a direct

measure of combustion. The ideal CO level from the exhaust stack is near 200 ppm, (parts per

million) which is less than 0.02% CO. This low level requires relatively expensive sensing

techniques and computerized signal conditioning, which may only be justified on large boilers or

process heaters.

The results from the simulation indicated that fuels with high moisture content lower the reactor

temperatures due to the amount of energy needed to dry, vaporize and superheat the water, which

results in the production of lower energy syngas. The amount of required air increases when

moisture content increases to maintain the required reaction temperature. However, increasing the

amount of air randomly causes many disadvantages and one of which is the mass fraction

reduction of useful gases that consequently reduce the heating value. Use of preheated air

promotes drying of the fuel bed and also provides additional energy in the pre-destruction stage,

which enhances the thermal decomposition characteristics of the biomass, thus higher calorific

value syngas is produced.


143

Combustible gases released from the solid fuel have to mix first with the secondary air before their

combustion can take place, i.e. the burning of the gases fuel is limited not only by the reaction

kinetics but also by the mixing rate of producer gases with the secondary air. A simple, adjustable

nozzle to optimize the flow pattern of the jet mixing into the bulk gas based on secondary

temperature can be implemented that, in turn, can be controlled by the air flow.


144

Chapter 6. Conclusion and recommendations

6.1 Conclusion

The use of agricultural residues as feedstock for gasification process has been presented.

FLUENT® was used for comprehensive simulations of the four different stages of gasification

processes namely drying, pyrolysis, gasification and combustion taking place inside a 900-kWth

two-stage gasifier. Compositions of combustible gases released from straw, slough hay and

wood chips gasification were predicted. The model was divided into transport and chemistry

equations. The equations were numerically solved simultaneously to yield the solution for the

profiles of gas composition and temperature along the gasifier chambers. The results were then

compared with experimental data to show the accuracy of the model. The effects of feedstock on

this process were explained. It was found that the major physical properties affecting this process

are size, shape, size distribution and density of feedstock. Size, form, and size distributions of

feedstock mainly affect the porosity and hence the pressure-drop across the gasifier chamber bed.

It was observed that in order to deliver sufficient air to maintain the desired bed height and

maintain the lower primary chamber region in a substoichiometric (insufficient air for complete

combustion) environment, between 40% and 60% of the required air must be supplied. Thermo-

chemical properties, heating value, chemical composition, moisture content, volatile matter and

ash content influenced the producer gas production. The moisture content of biomass had a great

impact on its effectiveness as a fuel source since considerable energy was consumed (2,444

kJ/kg) to evaporate this water without doing any useful work in the process. High moisture

content decreased calorific value impacting operations. Operating parameters of the gasifier such

as temperature, air-to-mass ratio, and primary-to-secondary ratio were also found critical to the

composition of gases and the rate of gasification.

Ash composition of the biomass materials is crucial to agglomeration problems which ultimately

results in fouling of the gasifier. Depending on the ash composition and the local bed

temperatures, a degree of sintering or fusion of the bed ash may occur. This is often exacerbated

by imperfect fuel distribution over the grate. If the degree of fusion of the ash is excessive,

relatively large ash agglomerates may form, and this can interfere with the distribution of the


145

combustion air through the fuel bed and affects burnout of the char and the quality of the gaseous

and gas-borne emission levels.

The temperature profiles of the producer gas and inside the chambers provided an understanding

of the phenomena taking place for the gasification process. It was shown that the temperature

levels in the gasifier affected the gas composition and the calorific value of the producer gas.

Increasing the air velocity, while maintaining a specified amount of biomass fed into the reactor

results in a higher burning rate as the oxygen penetrates farther into the bed. This consumes char

at a greater rate, increasing the overall temperature of the bed, which increases the rate of heat

transfer to the fuel. This in turn increases the drying and pyrolysis and gasification rate of the

fuel. The optimisation of the gasification process can result in higher carbon conversion rates,

and a corresponding reduction in the total particulate levels and the carbon content of the

particulates. Consequently, the knowledge of the temperature profiles, which is directly related

to the amount of air delivered to the system, is necessary for optimizing the performance of the

gasifier.

6.2 Recommendations

The simulated results provided a realistic impression of the gasification process, and enabled the

discussion of controlling parameters in this complex physical and reaction behaviour in a two-

stage gasifier for a better operation of the gasifier, which depends on an optimum air control

system. The following recommendations are put forward for consideration:

• Preheating of the primary air would increase the flame propagation speed by minimizing the

convective heat loss from the drying or burning fuel particles.

• Since the knowledge of the temperature is directly related to the amount of air delivered to

the system it is necessary to optimize the performance of the gasifier. When burning dry fuel

it would be helpful to equip the system with a fuel conditioning system: water spray.

Inserting a thermocouple into the neck of the primary chamber with its output going to the

PLC would actuate the fuel conditioning system. When the chamber temperature is reached

to an operator-set temperature, it can activate the system opening a solenoid. This simple

function does not actually increase the moisture content of the fuel, but rather it coats the fuel


146

with just enough water to slow the gasification process and reduce the gasifier temperature to

acceptable levels.

• The combustion system relies on a negative draft pressure. Given that the under grate air

supply follows the fuel, both are controlled through the PLC and act linearly. Therefore,

there has to be a means to control the fuel bed and the pressure separately so that the fan is

not only reacting to pressure changes but rather to gasification process changes: as more fuel

is required and delivered, the oxygen level would decrease causing the fan’s variable

frequency drive to increase the fan speed thus increasing the primary airflow.

• A gas analyzer probe at the exit of primary chamber is recommended. An analyzer

specialized for the purpose of measuring the components in flue gas and calculating the

relevant results from the temperature and gas readings would help to make adjustments on

the gasifier system and the effects could be seen immediately. The readings from the

analyzer would also help improve the validation of the CFD model in a more accurate way.


147

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158

Appendix A. Gasifier dimensions and FLUENT® model set up

Figure A.1: Dimensions of 900-kWth Vidir proprietary gasifier modelled


159

Table A.1: Solid straw and combusting straw particles properties

Straw Units

Material type solid

Density 410 kg/m3

Cp 1250 J/kg K

Thermal conductivity 0.05 W/m K

Straw-particles Units

Material type combusting-particle

Density 410 kg/m3

Cp 1250 J/kg K


Vaporization temperature 400 kg/m3

Volatile comp. fraction 79.28 %

Binary diffusivity 4.00E-05 m2/s

Swelling coefficient 1

Burnout stoichiometric ratio 1.333

Combustible fraction 16.03 %

Heat of rxn for burnout 9202211 J/kg

Devolatilization model single rate 1/s

Combustion model diffusion-limited


160

Table A.2: Straw-volatiles and straw-vol-air properties

Straw-volatiles Units

Material type fluid

Mixture straw-volatiles-air

Cp 1500 J/kg K

Molecular weight 32.83027 kg/kmol

Standard state enthalpy -32443550 J/kmol

Standard state entropy 0 J/kmol

Reference temperature 298.15 K

Straw-volatiles-air Units

Material type mixture

Density incompressible-ideal-gas

Cp mixing law J/kg K


Viscosity 1.72E-05 kg/m. s

Mass diffusivity 2.88E-05 m2/s

Absorption coefficient 0 1/m

Scattering coefficient 0 1/m

Scattering phase function isotropic


161

Table A.3: CH4 and CO properties

CH4 Units

Material type fluid


Cp piecewise-polynomial J/kg K


Standard state enthalpy -7.49E+07 J/kmol

Standard state entropy 186043.9 J/kmol

Reference temperature 298 K

CO Units

Material type fluid








162

Table A.4: H2O and CO2 properties

H2O Units

Material type fluid







CO2 Units

Material type fluid








163

Table A.5: H2, N2 and O2 properties

H2 Units Material type fluid Mixture straw-volatiles-air Cp piecewise-polynomial J/kg K Molecular weight 2.01588 kg/kmol Standard state enthalpy -1882.081 J/kmol Standard state entropy 130581.7 J/kmol Reference temperature 298 K N2 Units Material type fluid Mixture straw-volatiles-air Cp Piecewise polynomial J/kg K Molecular weight 28.0134 kg/kmol Standard state enthalpy -2930.741 J/kmol Reference temperature 298 K O2 Units Material type fluid Mixture straw-volatiles-air Cp piecewise-polynomial J/kg K Molecular weight 31.9988 kg/kmol Standard state enthalpy -5244.882 J/kmol Standard state entropy 205031.1 J/kmol Reference temperature 298 K

Table A.6: Fluent sub-models set up and inputs summary

Solver Species model Solver segregated model species transport Space 3-D reactions volumetric Velocity formulation absolute inlet diffusion on Gradient option cell-based diffusion energy source on Formulation implicit full multi-component diffusion off Time steady thermal diffusion off

Porous formulation superficial velocity mixture material straw-volatiles-air

number of volumetric species 8 turb-chemical interaction eddy-dissipation


164

Table A.7: Fluent® sub-models set up and inputs summary, (continued)

Multiphase model Discrete phase model

Model off interaction with cont. phase on

Energy update DPM sources every iterat. off

Energy equation on num cont. phase iteration DPM iteration 150

Viscous model unsteady particle tracking off

Model k-epsilon max. number of steps 2000

k-epsilon model standard step length factor 10

Near-wall treatment standard wall functions drag law

Udf drag_force_particle

Viscous heating off physical models all off

Cmu 0.09 UDF body force

Udf

body_force_particle

C1-Epsilon 1.44 scalar update none

C2-Epsilon 1.92 source none

TKE Prandtl number 1 DPM time step none

TDR Prandtl number 1.3 number of scalars 0

Energy Prandtl number 0.85 accuracy control on

Wall Prandtl number 0.85 tolerance 1.00E-05

Turb. Schmidt number 0.7 max refinements 20

UDFs none automated tracking scheme select on

Table A.8: Fluent® sub-models set up and inputs summary, (continued)

Radiation high order scheme trapezoidal

Model DOM low order scheme implicit

Solar load off coupled heat-mass solution off


165

Table A.9: Chemical reactions

Reaction 1: Straw_vol CO + CO2 + CH4 + H2

Species reactant/ product stoich. coefficient rate exponent

straw_vol reactant 1 1

CO product 0.6 0

CO2 product 0.254 0

CH4 product 0.146 0

H2 product 1.238 0

Reaction 2: CO + H2O + O2 CO2 +H2O


CO reactant 1 1

O2 reactant 0.5 0.25

H2O reactant 1 0.5

CO2 product 1 0

H2O product 1 0

Reaction 3: CH4 +O2 CO + H2O


CH4 reactant 1 0.7

O2 reactant 1.5 0.8

CO product 1 0

H2O product 2 0


166

Table A.10: Chemical reactions, (continued)

Reaction 4: H2 + O2 H2O


H2 reactant 1 1.5 O2 reactant 0.5 1 H2O product 1 0 Reaction 5: H2O + CO H2 + CO2


H2O reactant 1 1 CO reactant 1 1 H2 product 1 1 CO2 product 1 1

Table A.11: Operating conditions

Operating conditions Units

Operating pressure 101325 Pa

Ref P location X 0 m

Ref P location Y 0 m

Ref P location Z 0 m

Gravity On

Gravity acceleration. X 0 m/s2

Gravity acceleration. Y -9.81 m/s2

Gravity acceleration. Z 0 m/s2

Bous. operating temp 288.16 K

Specific operating density Off


167

Table A.12: Boundary conditions: zone, air

Boundary condition Zone name air Type fluid Source terms off Fixed values off Porous zone off Laminar zone off Reaction on Motion type stationary Reaction mechanism mechanism-1

Table A.13: Injection of particles

Injection-0 Injection type file Particle type combusting Custom laws off Material straw-particles Oxidizing species O2 Evaporating species H2O Devolatilizing species straw-volatiles Product species CO Point properties N/A Discrete random walk off Random eddy lifetime off Cloud model off Wet combustion model on Liquid material water-liquid Liquid fraction 0.046938 Initialization UDF none Multiple reactions N/A


168

Table A.14: Boundary conditions: primary air inlet

Boundary condition Units

Zone name primary_air_inlet

Type mass-flow-inlet

Mass flow spec. method mass flow rate

Mass flow rate 0.24 kg/s

Total temperature 320 K

Supersonic/Init gauge P 0 Pa

Direction spec. method normal to boundary

Reference frame absolute

Turbulence spec. method intensity and H. diameter

Turbulence intensity 10 %

Hydraulic diameter 0.060325 m

O2 mass fraction 0.23

CO2 mass fraction 0

H2O mass fraction 0

straw_vol mass fraction 0

CH4 mass fraction 0

CO mass fraction 0

H2 mass fraction 0

Ext. black body temp method boundary temperature

Internal emissivity 1

Discrete phase BC type escape


169

Table A.15: Boundary conditions: secondary air inlet


Zone name secondary_air_inlet





Supersonic/init gauge P 0 Pa







CO2 mass fraction 0

H2O mass fraction 0

straw_vol mass fraction 0

CH4 mass fraction 0

CO mass fraction 0

H2 mass fraction 0

Ext. black body temp method boundary temperature




170

Table A.16: Boundary conditions: fuel inlet


Zone name fuel_in





Supersonic/init gauge P 0 Pa







CO2 mass fraction 0

H2O mass fraction 0

straw volatiles mass Fraction 0

CH4 mass fraction 0

CO mass fraction 0

H2 mass fraction 0

Ext. black body temp Method boundary temperature




171

Table A.17: Boundary conditions: fuel bed


Zone name fuel bed

Type fluid

Source terms off

Porous zone on

Reaction on

Motion type stationary

Reaction mechanism mechanism-1

Direction-1 vector X 1

Direction-1 vector Y 0

Direction-1 vector Z 0

Direction-1 vector X 0

Direction-1 vector Y 1

Direction-1 vector Z 0

Viscous R direction-1 0 1/m2



Internal R direction-1 1/m



Power law model C0 0

Power law model C1 0

Fluid porosity Udf_ porosity_profile

Solid material name straw


172

Table A.18: Boundary conditions: outlet

Boundary condition

Zone name outlet

Type outflow

Flow rate weighting 1

Ext. black body temp method

boundary temperature



Table A.19: Boundary conditions: default-interior

Boundary condition

Zone name default-interior

Type interior

Boundary condition

Zone name default-interior:008

Type interior

Boundary condition

Zone name default-interior:010

Type interior


173

Table A.20: Boundary conditions: walls


Zone name wall

Type wall

Adjacent cell zone air

Thermal conditions heat flux

Heat flux 0 W/m2

Internal emissivity 0.5

Wall thickness 0 m

Heat generation rate 0 W/m3

Material name calcium sulphate

Boundary Condition Units

Zone name wall:001

Type wall

Adjacent cell zone fuel bed

Thermal conditions heat flux

Heat flux 0 W/m2

Internal emissivity 0.5

Wall thickness 0 m

Heat generation rate 0 W/m3

Material name calcium-sulphate


174

Table A.21: Solution controls

Solution controls

Pressure-velocity coupling SIMPLE

Skewness correction 0

Pressure URF 0.08

Density URF 0.08

Body Force URF 0.08

Momentum URF 0.08

Turbulent kinetic energy URF 0.1

Turbulent dissipation rate URF 0.1

Turbulent viscosity URF 0.1

O2 URF 0.1

CO2 URF 0.1

H2O URF 0.1

straw_vol URF 0.1

CH4 URF 0.1

CO URF 0.1

H2 URF 0.1

Energy URF 0.1

P1 URF 0.1

Discrete phase sources URF 0.1


175

Table A.22: Solution controls, (continued)

Solution controls

Pressure discretization standard

Density discretization first order upwind

Body force discretization first order upwind

Momentum discretization first order upwind

Turbulent kinetic energy discretization first order upwind

Turbulent dissipation rate discretization first order upwind

Turbulent viscosity discretization first order upwind

O2 discretization first order upwind

CO2 discretization first order upwind

H2O discretization first order upwind

straw_vol discretization first order upwind

CH4 discretization first order upwind

CO discretization first order upwind

H2 discretization first order upwind

Energy discretization first order upwind

P1 discretization first order upwind

Discrete phase sources discretization first order upwind


176

Table A.23: Solution initialization

Solution initialization Units

Gauge pressure 0 Pa

X velocity 0 m/s

Y velocity 0 m/s

Z velocity 0 m/s

Turbulence kinetic energy 0.1 m2/s2

Turbulence dissipation Rate 0.1 m2/s2

O2 0.1

CO2 0

H2O 0

straw_vol 0

CH4 0.03

CO 0.03

H2 0

Temperature 320 K


177

Table A.24: Residual controls

Residual Monitors

Continuity 1.00E-05

x-velocity 1.00E-05

y-velocity 1.00E-05

z-velocity 1.00E-05

energy 1.00E-05

k 1.00E-05

ε 1.00E-05

O2 1.00E-05

CO2 1.00E-05

CO2 1.00E-05

H2O 1.00E-05

straw_vol 1.00E-05

CH4 1.00E-05

CO 1.00E-05

H2 1.00E-05

P1 1.00E-06


178

Appendix B. Sampling protocol for emission testing

1. Biomass Analysis

1.1 Principle

Before gasification of the biomass begins, a sample of the biomass being tested was collected. A

sample was sent to a proper lab where an approximate and ultimate analysis was done. The

calorimetric value of the biomass and its moisture content was determined.

1.2 Apparatus

Ziploc Bag: - a clean Ziploc bag large enough to accommodate the biomass sample.

1.3 Procedure

A sample of approximately one liter was required for the proper analysis. It was essential that

the sample is representative of the biomass being gasified (i.e. All parts of the plant must be

represented in the sample). It was important to place the biomass in a clean Ziploc bag to avoid

the loss of any volatiles before analysis.

2. Producer gas sampling

2.1 Principle

A sample of gas produced during the gasification of biomass was collected in a Tedlar bag. The

composition, heating value and molecular weight of the gas was determined in the lab. Sensors

were in place to measure the gas temperature and flow rate at the sampling location.

2.2 Apparatus

Sample Probe: a probe made from any material that is resistant to the high temperature gas

stream at the sampling location. The material must also be inert to all components within the gas

stream. Because there were no foreseeable corrosion problems, standard industrial steel was

suitable. The probe diameter had to be between ¼” and 3/8”. The length of the probe depended

upon the exit temperature of the gas. The sample probe had to be of long enough to cool the gas


179

below 120°F or within the limits of the Tedlar bag system. If high exit gas temperatures were

anticipated, the sample probe needed to be water-cooled. For stack temperatures that exceed

800oF, a quartz probe could be used. The probe diameter had to be between ¼” and 3/8”. The

length of the probe is dependent upon the exit temperature of the gas. The sample probe must be

of sufficient length to be capable of cooling the gas below 50°C or within the limits of the Tedlar

bag system. The probe entrance had to be enlarged to approximately 20 mm I.D. for a length of

10 cm. The downstream end should be fitted with a 90o elbow and a ball joint. The probe had be

heat-traced and insulated if necessary to prevent sample condensation.

Thermocouple: a calibrated thermocouple capable of measuring the gas temperature within 2%

of the absolute gas temperature. A K-type ungrounded thermocouple 12” in length and with a

diameter of ¼” was selected. The thermocouple had a stainless steel sheath and ceramic

connector that allowed it to withstand high temperature gas stream.

Digital multimeter: capable of reading voltage, current and resistance and has a type-K input for

the thermocouple use.

Pitot tube: a Stauscheibe (type-S) with a known coefficient which is constant within ± 5% over

the entire working range.

Pressure Gauge: an inclined Manometer in the same range as the velocity and static pressures

being measured in the stack. It had to be capable of measuring the pitot tube velocity pressure

and the pressure drop across the orifice to within 0.1 mm (0.005 in) H20 on the 0 to 25 mm (1 in)

H20 scale and 1 mm (0.05 in) H20 on the 25-250 mm (1 -10 in) H20 scale.

Vacuum chamber: a heavy-duty vacuum tight case to accommodate a 10 L size Tedlar sample

bag. The chamber must be at a negative pressure to fill the sample bag directly.

Sample bag: a bag of non-reactive Tedlar material with a capacity of 10 L. Teflon tubing with a

diameter that could accommodate the sample bag was required.

Personal sampling pump: a pump capable of maintaining a sampling rate of 1 L/min to 2 L/min

while withdrawing a portion of the stack gas through the sampling train. It had to be equipped

with a flexible connecting tube to attach to the chamber.


180

2.3 Procedure

It was important to run the gasifier for at least 20 min before a gas sample could be collected and

make sure it was running at a constant and proper load. Temperature and flow rate sensor

preparation required that the faces of both openings were perpendicular to the airflow

(Figure B.1).

Figure B.1: S-type pitot tube specifications and orientation

Proper thermocouple placement to prevent interference was required while assembling the pitot

tube and thermocouple (Figure B.2). Dt had to be between 0.48 cm and 0.95 cm (3/16 in and 3/8

in).


181

Figure B.2: Pitot tube and thermocouple placement

It was also important to properly assemble the Pitot tube and the sampling probe as shown on

Figure B.3.

Figure B.3: Assembling pitot tube and sampling probe

The minimum pitot-sample probe separation needed to prevent interference was also given by

Dt, where Dt had to be between 0.48 cm and 0.95 cm (3/16 in and 3/8 in). Figure B.4 shows the

assembling of probe with pitot tube and thermocouple.

Figure B.4: Probe with pitot tube and thermocouple

The next step was to prepare the vacuum chamber using the following two steps:


182

• Insert a 10 L tedlar bag inside the vacuum chamber and connect it to the sample

valve.

• On the outside of the chamber, connect the Teflon tubing to the sample valve.

This was the sampling line that attached to the open end of the sample probe.

Finally, the sample port location and specifications had to be determined. To obtain accurate

results when sampling a gas stream, the location of the sampling port was important. A location

where the gas flow was well mixed and the infiltration of ambient air is at a minimum is

preferred. The flow of the gas stream is also required to be laminar with no cyclonic or

stratification patterns at the sampling location.

To meet the following criteria, the sample port had to be located in a straight section of the pipe.

It had to be located eight or more stack diameters in length downstream and two or more stack

diameters upstream from any flow disturbance (bend, elbow, etc.). Figure B.5 below illustrates

the minimum standards that must be met for the location of the sample port.

Figure B.5: a) Location of sample port and b) Distance away from duct wall


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The sampling port was made from standard industrial pipe, because there were no foreseeable

corrosion problems. The inside diameter of the port has be at least 3- ½ in. and extend 3 inches

outwards from the exterior stack of the wall. The port had to be threaded to be able to

accommodate a cap. The cap had to be used when the port is not being used.

3. Gas sampling

The sampling procedures were as follows:

1. Attach the sample line to the pump inlet valve on the vacuum chamber.

2. Wait for 15–30 s for the sample line to fill with the producer gas and then reattach the

sample line to the sample valve.

3. Attach the personal sampling pump to the chamber In/Out valve and start the sample

pump at a rate between 1 L/min and 2 L/min.

4. Make sure that the sampling rate is recorded and held constant throughout the sampling.

5. When the bag is ¾ full, disconnect the sample line and shut off the sample pump.

It was important to measure and record temperature and flow rate throughout sampling using the

digital multimeter and manometer.

10 L Tedlar Bag

Teflon Tubing

Pump

Vacuum Chamber

Digital Multimeter

Manometer

Pitot Tube

Thermocouple

Sample Probe

Figure B.6: Producer gas sample train


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4. Sampling of PM

4.1 Introduction

Particulate matter is not an absolute quantity; rather, it is a function of temperature and pressure.

Therefore, to prevent variability in particulate matter emission regulations and/or associated test

methods, the temperature and pressure at which particulate matter was to be measured had to be

carefully defined. Of the two variables (i.e., temperature and pressure), temperature had the

greater effect on the amount of particulate matter in an effluent gas stream; in most stationary

source categories, the effect of pressure appeared to be negligible. In Method E of Environment

Protection Agency (EPA) standard, 120oC ± 14oC (250oF ± 25oF) was established as a nominal

reference temperature. Thus, where Method 5 was specified in an applicable subpart of the

standards, particulate matter was defined with respect to temperature. To maintain a collection

temperature of 120oC ± 14oC (250oF ± 25oF), Method 5 employed a heated sample probe and a

heated filter holder.

4.2 Principle

Particulate matter was withdrawn isokinetically from the source and collected on a glass fiber

filter maintained at stack temperature. The particulate mass was determined gravimetrically after

uncombined water was removed.

4.3 Applicability

This method applied to the determination of particulate emissions from stationary sources for

determining compliance with new source performance standards, only when specifically

provided for in an applicable subpart of the standards. This method was not applicable to stacks

that contain liquid droplets or were saturated with water vapor.

4.4 Apparatus

The sampling train consisted of a sample probe, thermocouple, digital multimeter, pitot tube, and

pressure gauge with the same type and specification used in the producer gas sampling train. In

addition, the following apparatus were used.


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Nozzle: -a nozzle constructed of stainless steel or quartz and connected to a heated glass probe

liner with a sharp, tapered leading edge. The angle of the taper had to be 30o and the taper had to

be on the outside to preserve a constant internal diameter. The probe nozzle shall be of the

button-hook or elbow design. Nozzle diameters had to be sized to allow for isokinetic sampling.

Filter holder: a Teflon-coated stainless steel filter holder containing a quartz glass filter.

The probe line was connected to a Teflon-coated stainless steel filter holder containing a quartz

glass filter. The filter had to be heated in a hot box to 120oC ± 14oC (250oF ± 25oF). The back

half of the filter holder was connected to a series of impingers with insulated Teflon tubing.

Heating compartment heating system: a heating system capable of maintaining the temperature

of the filter holder compartment at 120oC ± 14oC (25oF ± 25oF) was needed. A temperature

gauge accurate to within 3oC (5.4oF) shall be installed such that the temperature around the filter

holder could be monitored during sampling.

Impingers: one standard and three modified (the tips and impaction plates of the standard design

were replaced with a 13 mm (0.5 in) ID glass tube extending to within 13 mm (0.5 in) of the

bottom of the impinger) Greenberg-Smith impingers with vertical and side ports (Figure B.9).

Cooling system: an ice bath was used to contain the impingers in order to cool them.

Dry test meter: a 175 CFH dry test meter accurate within ± 2% of the true volume and equipped

with a thermometer to measure the inlet and outlet temperature.

Vacuum Pump: a leak-free vacuum pump capable of maintaining a 28.7 L/min (1.0 CFM) flow

rate at 380 mm Hg (15 inches of mercury) or a sampling rate of 1-2 L/min while continuously

withdrawing a portion of the stack gases through the sampling train. The pump had to a sample

rate control valve and a vacuum gauge attached to the inlet.

Barometer: a barometer that was accurate to within 2.5mm Hg (± 0.1 inches of Hg).

Orifice: a calibrated orifice connected to the outlet of the dry gas meter.


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Connections: the connection between the filter and the first impinger had to be able to withstand

stack temperatures. All connections leading up to the filter were to be constructed of Teflon

and/or borosilicate glass and/or quartz.

Analytical Balance: an analytical balance capable of measuring condensate weights to the nearest

0.1 gram is acceptable.

4.5 Pre- Sampling Procedure

Prior to sampling, traverse points were selected on the basis of EPA Method A requirements.

Then, Pitot tube, thermocouple and the sample nozzle were assembled properly as shown on

Figure B.7.

Figure B.7: Pitot tube-sampling nozzle

Proper pitot tube-sampling nozzle configuration to prevent aerodynamic interference was

required (Figure B.8) with button-hook type nozzle where centers of nozzle and pitot opening

aligned and with Dt between 0.48 cm and 0.95 cm (3/16 in and 3/8 in.).


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Figure B.8: Pitot tube-sampling nozzle configuration

The sample nozzle was determined to the known moisture content of the flue gas. Next, the

impinger boxes and filters were assembled and weighed in an onsite trailer as illustrated in EPA

Method E (Figure B.9).

Modified Standard Train of 4 Impingers

Figure B.9: Impinger assembly

The first two impingers were charged with approximately 100 mL of deionized or distilled water

while leaving the third impinger dry and the fourth to be charged with a known amount

(approximately 200 g) silica gel to remove any residual water.


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The volume or weight of each impinger was recorded on the moisture analysis data sheet. The

total weight was given by

Weight of total water collected= [a] + [b] +[c] + [d] g

Finally, the sampling train was set up as illustrated in EPS Method E (Figure B.10) first by

adjusting the filter compartment and the probe heating systems to maintain a temperature of

120oC ± 14oC (250oF ± 25oF) to prevent the blinding of the filter due to condensation.

Figure B.10: Sampling train set up

Then, a mandatory pre-test leak check of the sampling train was conducted by plugging the

nozzle inlet and pulling a vacuum of 380 mm Hg (15 in Hg) for at least one minute. The leakage

rate had to be < 0.57 L/min (0.002 ft3/min) or 4 % of the estimated average sampling rate,

whichever was less.

4.6 Sampling operation

The following steps were performed to do the sampling operation.

1. Clean the portholes prior to the test run to minimize the chance of sampling the

deposited material. To begin sampling, remove the nozzle cap and verify that the

Pitot tube and probe extension are properly positioned.


189

2. Position the nozzle at the first traverse point with the tip pointing directly into the

gas stream.

3. Immediately start the pump and adjust the flow to isokinetic conditions.

Nomographs are available, which aid in the rapid adjustment to the isokinetic

sampling rate without excessive computations. These nomographs are designed for

use when the type S-pitot tube coefficient is 0.85 ± 02, and the stack gas equivalent

density (dry molecular weight) is equal to 29 ± 4.

4. Sample for at least five min at each traverse point, the sampling rate being the same

for every point.

5. Traverse the stack cross-section and maintain isokinetic sampling throughout the

test.

6. When it is necessary to halt sampling temporarily to dismantle the sampling train

during port changeover or to change a train component, turn off the pump and

immediately withdraw the probe from the stack.

7. Conduct a mandatory post-test leak-check on the sampling train by plugging the

nozzle and pulling a vacuum equal to or greater than the maximum value observed

during sampling.

8. If the leakage rate is >0.57 L/min (0.002 ft3/min) or 4% of the estimated average

sampling rate, the test is invalid.

The following steps have to be considered for a successful operation.

1. Add more water and ice to the impinger box, as required, to maintain the

temperature of the last impinger exit in the range of 0ºC -20ºC.

2. Record instrumentation readings every 5 min (sampling duration), before and after

a leak check and when sampling is halted.

3. If, during the sampling run, a component (e.g., filter assembly or impinger) change

becomes necessary, a leak-check shall be conducted immediately before the change

is made.


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5. Residual ash collection

5.1 Principle

Upon completion of gasification, a sample of the residual ash produced was collected. The

sample was analyzed and its composition determined. The mass of the total amount of residual

ash produced from the biomass was determined. From this information, the amount of ash that

became residual ash and the amount that became fly ash during gasification could be determined.

5.2 Apparatus

Ziploc bag: a clean Ziploc bag that was the proper size for the residual ash sample.

Scoop/shovel: a small scoop or shovel that could fit in the ash disposal area and collect a proper

sample of the residual ash.

Small scale: a scale capable of weighing 0.1 mg.

5.3 Procedure

All gasifiers have an area where a large portion of the ash collected during gasification. To avoid

contamination of the sample, this area had to be thoroughly cleaned before testing could occur.

Most of the ash was removed first with the compressed air hose and/or a shovel.

For small gasifiers or smaller biomass sample sizes, about 10% of the total residual ash produced

was to be collected (around 200 g). When collecting the sample it was important that the ash

collected represented the ash produced by the gasifier. A homogeneous, well mixed sample

should be taken. It could be scooped out of the ash disposal area and placed into the Ziploc bag.

The Ziploc bag was to be stored at room temperature in a dry area till the analysis can be

preformed.

Calibration of measuring devices was required and the results had to be recorded. The following

instruments needed to be calibrated.

Probe Nozzles: - probe nozzles had to be calibrated before their initial use in the field. Using a

micrometer, the inside diameter of the nozzle was measured to the nearest 0.025 mm (0.001 in.).

Three separate measurements using different diameters each time was required, and the average


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of the measurements was obtained. The difference between the high and low numbers should not

exceed 0.1 mm (0.004 in.). When nozzles become nicked, dented, or corroded, they should be

reshaped, sharpened, and recalibrated before use. Each nozzle had to be permanently and

uniquely identified.

Pitot tube: if the pitot tube was placed in an interference-free arrangement with respect to the

other probe assembly components, its baseline (isolated tube) coefficient had be determined

Metering system: the metering system included a vacuum gauge, leak-free pump, thermometers

capable of measuring temperature to within 3oC (5.4oF), dry gas meter capable of measuring

volume to within 2 percent. Before its initial use in the field, the metering system had to be

calibrated according to the procedure outlined in APTD-0576. Instead of physically adjusting the

dry gas meter dial readings to correspond to the wet test meter readings, calibration factors could

be used to mathematically correct the gas meter dial readings to the proper values.

Before calibrating the metering system, it was suggested that a leak-check be conducted. For

metering systems having diaphragm pumps, the normal leak-check procedure would not detect

leakages within the pump. For these cases the following leak-check procedure was suggested:

make a 10-minute calibration run at 0.00057 m3/min (0.02 cfm); at the end of the run, take the

difference of the measured wet test meter and dry gas meter volumes; divide the difference by

10, to get the leak rate. The leak rate should not exceed 0.00057 m3/min (0.02 cfm).

After each field use, the calibration of the metering system had to be checked by performing

three calibration runs at a single, intermediate orifice setting (based on the previous field test),

with the vacuum set at the maximum value reached during the test series. To adjust the vacuum,

insert a valve between the wet test meter and the inlet of the metering system. Calculate the

average value of the calibration factor. If the calibration changed by more than 5 percent,

recalibrate the meter over the full range of orifice settings.

If the dry gas meter coefficient values obtained before and after a test series differ by more than

5 percent, the test series had to either be voided, or calculations for the test series had be

performed using whichever meter coefficient value (i.e., before or after) gave the lower value of

total sample volume.


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Temperature gauges: dial thermometers, such as those used for the dry gas meter and condenser

outlet had to be calibrated against mercury-in-glass thermometers.

Leak check of metering system: the portion of the sampling train from the pump to the orifice

meter should be leak checked prior to initial use and after each shipment.

Figure B.11: Leak free check

Leakage after the pump would result in less volume being recorded than is actually sampled. The

following procedure was suggested (see Figure B.11). Close the main valve on the meter box.

Insert a one-hole rubber stopper with rubber tubing attached into the orifice exhaust pipe.

Disconnect and vent the low side of the orifice manometer. Close off the low side orifice tap.

Pressurize the system to 13 to 18 cm (5 to 7 in.) water column by blowing into the rubber tubing.

Pinch off the tubing and observe the manometer for one minute. A loss of pressure on the

manometer indicates a leak in the meter box; leaks, if present, must be corrected.

Barometer: wass calibrated against a mercury barometer.


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Appendix C. Measurements of gas composition report

1. Emission source

Gasification is a process by a solid carbonaceous material, containing mostly chemically bound

carbon, hydrogen, oxygen, and a variety of inorganic and organic constituents, is reacted with air

and oxygen. The reactions provide sufficient exothermic energy to produce a primary gaseous

product containing mostly CO, H2, CO2, H2O (gas), and light hydrocarbons laced with volatile

and condensable organic and inorganic compounds. Most of the inorganic constituents in the

feedstock are chemically altered and either discharged as bottom ash or entrained with the raw

product gas as fly-ash. The gasifier features a first stage of updraft gasification followed by a

second stage of downdraft char gasification. The second stage also serves to crack the tars

contained in the first stage producer gas as a result of the updraft gasification of the wet wheat

straw.

2. Method and references

The measurement was undertaken using the Model 375K / 375WP – portable flue gas analyzer

(Figure C.1) manufactured by Nova Analytical Systems Inc in accordance with the main

procedural requirements given within the acceptable Canadian standard methods as tabulated in

Table C.1.

Table C.1: Applicable methods and references

Method # parameter

EPA Method A traverse points

EP A Method B velocity & flow rate

EPA Method C gas molecular weight

EPA Method D gas moisture


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The following steps are followed based on the protocol developed:

1. The sampling port location is checked for compliance to the criteria imposed by E.P.A.

Method A (Environment Canada, 2001).

2. An integrated gas sample for CO, CO2, O2, and by difference N2 is taken during each

individual test run using a NOVA analyzer ,and the calculations from Appendix G is used

for stack gas molecular weight. .Stack gas moisture content is determined by the increase

in volume of the Impinger and the increase in weight of the drying tube and calculations

in Appendix G.

3. Each gas concentration reading is taken on a one minute interval from the gas analyzer.

Figure C.1: MODEL 375K / 375WP – Portable flue gas analyzer http://www.nova-gas.com

In operation, a built-in sample pump draws in the flue gas sample through a probe, 12 ft sample

hose, filter/condensate trap, secondary filter and flow meter then on to the four sensors. The

method of detection is an electrochemical for oxygen, CO and NO sensors and a solid state infra

red detector for CO2. The ranges for these detectors are 0–30% for O2, 0–20.0% for CO2 and

0–4.0% (0-2000 ppm) for CO, and NOx. The detected O2, CO2, CO and NOx are displayed on

the LCD digital meters.


195

3. Measuring location

The test site was visited prior to undertaking the sampling procedure, even if testing had

previously been undertaken at the site. During this visit, the two 3 inch diameter sample ports

were found ideally located (8 and 2 diameter criteria) on the 12-inch diameter exhaust stack. In

addition, the permanent working platform, its access and safety precautions were found to be

satisfactory.

4. Results

Under continuous operating conditions, five measurement readings were carried out for each

moisture content of wheat straw to assess the concentration of gases from the exhaust stack. The

operation conditions for the whole period of measuring are in Appendix E. The readings for all

four gases, O2, CO2, CO and NOx, are also in Appendix E. The summary of the results obtained

from this testing is tabulated below in Table C.2.

Table C.2: Summary of combustion gas concentration

Vidir Best gasifier, exhaust stack, Arborg, MB

Date Moisture content %

O2 CO CO2 NOx

July 10th, 2007

% mg/m3 ppm mg/m3 % mg/m3 ppm mg/m3

14 6.34 829.8 100.0 114.5 15.58 2803.8 533.0 1002

26 7.26 950.2 157.8 180.7 14.66 2638.2 541.6 1017

20 2.96 387.4 3340.0 3824.9 18.18 3271.7 261.6 492


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Appendix D. Measurement of particulate emission sampling and

testing

On 07 June 2006, particulate emission testing was undertaken by University of Manitoba, on the

Vidir Best plant at Vidir Machine Inc., MB. During the test, the weather was noted as dry and

sunny with an ambient air temperature of 30oC (86oF). The purpose of the emission testing was

to quantify the total suspended particulate matter for the following reasons:

• to determine if a process unit complied with existing or proposed emission

regulations

• to ascertain the economics of materials or product losses from a source

• to obtain relevant data which could facilitate the selection and design of control

equipment

• to test the efficiency of installed control equipment

• to allow control of a process by continuous or frequent observation of one or more

constituents in the stack gas stream

• to provide more accurate data to develop air resources management programs,

control regulations and inventories

These measured emission rates were conducted with the gasifier operating with an approximate

feed rate of 500 lb/hr (227 kg/hr) of wheat straw.

1. Introduction

Emissions testing (i.e., stack sampling or stack monitoring, etc.) is the experimental process for

evaluating the characteristics of industrial waste gas stream emissions into the atmosphere.

Materials emitted to the air from these sources can be solid, liquid, or gas; organic or inorganic.

The effluent pollutants emitted to the atmosphere from a source may contain many different

pollutant materials. Effluent measurements and sampling procedures follow specific test methods

and protocols to ensure representative and accurate emission data. Test methods are pollutant

specific, sometimes industry specific, and originate from a variety of sources and jurisdictions


197

such as USEPA, Environment Canada, state agencies, provincial agencies, trade organizations,

etc.

2. Sampling and testing

2.1 Emission source

Gasification is a process by which a solid carbonaceous material, containing mostly chemically

bound carbon, hydrogen, oxygen, and a variety of inorganic and organic constituents, is reacted

with air and oxygen. The reactions provide sufficient exothermic energy to produce a primary

gaseous product containing mostly CO, H2, CO2, H2O (g), and light hydrocarbons laced with

volatile and condensable organic and inorganic compounds. Most of the inorganic constituents in

the feedstock were chemically altered and either discharged as bottom ash or entrained with the

raw product gas as fly-ash.

2.2 Test method and references

Isokinetic sampling of the contained emission sources was undertaken using the CLEAN AIR

EXPRESS® Instruments Inc Method 5 Isokinetic sampling apparatus in accordance with main

procedural requirements and within the following acceptable Canadian standard methods (Table

D.1)

Table D.1: Applicable methods and references

Method # Parameter

EPA Method A traverse points

EP A Method B velocity & flow rate

EPA Method C gas molecular weight

EPA Method D gas moisture

EPA Method E (1 to 8) particulate matter

All measuring devices including, but not limited to, pitot tubes, meters, gauges, and

thermocouples were properly calibrated and maintained to provide accurate data. The sampling


198

location and number of traverse points were determined by EPA Method A. EPA Method B and

equations in Appendix G were used to determine stack gas velocity. An integrated gas sample

for CO, CO2, O2, and by difference N2 was taken during each individual test run using a Nova

analyser manufactured by Nova Analytical Systems Inc and the calculations from Appendix G

were used for stack gas molecular weight. Then, stack gas moisture content determined by the

increase in volume of the impinger and the increase in weight of the drying tube is calculated.

Each performance test consisted of three separate and valid one-hour test runs and for the

purpose of determining compliance with any applicable standard; the results of each valid test

run (Appendix F) were considered.

2.3 Sampling Location

The site was visited prior to undertaking the sampling procedure, even if testing had previously

been undertaken at the site. During this visit, the two three-inch diameter sample ports were

found to be ideally located (8 and 2 diameter criteria) on the 12-inch diameter exhaust stack. In

addition, the permanent working platform, its access and safety precautions were found to be

satisfactory.

2.4 Sample Train

The Clean Air Express Method 5 Train (Figure D.1) is which is the benchmark of the industry,

was used to do the sampling. It is suited to perform isokinetic particulate testing referencing

federal EPA 40 CFR 60, Appendix A, Method E “Determination of Particulate Emissions from

Stationary Sources.”


199

Figure D.1: Method 5 isokinetic sampling Train (http://www.cleanair.com)


200

The parts that are consisted in the sampling train are listed in table.

Table D.2: Clean Air Express® Method 5 train

1 Isokinetic control console with pump

2 Sample oven compartment

3 Impinger compartment

4 3” Glassware kit Ball joint or ball joint O-Ring

5 Stainless steel impinger outlet

6 50’ Umbilical cable with stainless steel quick connects

7 6’ Stainless steel heated Method 5 probe with liner and geometric calibration report

8 Stainless steel probe port adaptor seal assembly

9 Complete set of stainless steel nozzles (1/8” 3/16” 1/4" 5/16” 3/8”, and 1/2") with nuts

and ferrules

10 Sample recovery kit

11 8.5 cm glass fiber filters (box of 100)

2.5 Sampling Procedure

Isokinetic source sampling is achieved when the velocity of gas entering the sampling nozzle is

exactly equal to the velocity of the approaching gas stream. This provides a uniform, unbiased

sample of the pollutants being emitted by the source. Isokinetic source sampling most closely

evaluates and defines various parameters in the stack as they actually exist at the time of

sampling. Preliminary measurements were made in order to determine the appropriate sized

nozzle for isokinetic sampling across the velocity profile of the stack. Minor adjustments

required to maintain an isokinetic condition at each test point were accomplished by regulating

the sample flow rate.


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2.6 Sample collection

A leak check was carried out before and after sampling to confirm all the suction is had been

drawn through the nozzle. With the required isokinetic flow rates known, the sample probe is

inserted into the stack at 90o to the gas flow so that any particulate matter impinging on the filter

before sampling could be stopped. The filter head and probe were allowed to reach the stack gas

temperature. The initial gas meter volume was noted and the suction device and timer started.

The correct flow rate for isokinetic sampling is set and the nozzle positioned to face parallel to

the gas flow. Sampling was then carried out for the planned duration and number of sample

points, recording all the necessary data for final calculations. On completion, the suction device

and the timer were stopped and the final gas meter volume recorded. The probe was removed

from the process stack and a further leak test carried out before removing the filter, which was

subsequently removed and placed in a storage container. In addition, any residual particulate

upstream of the filter was washed with water and acetone into an appropriate beaker.

The above procedures were then repeated to obtain duplicate samples. At all times during the

sampling procedure the sampling person was in contact with the process operator to ensure that

the plant was in full production and there were no changes in the process that might affect the

representative nature of the samples collected.

2.7 Validation of test

Main conditions for compliance with ISO 9096:2003 are listed below. A single tick in the "fail"

column indicates that this test did not comply with the full provisions of ISO 9096:2003. Due to

site/sampling locations it was not always practically possible for all the conditions to be met.

Best practical means were employed to try to achieve a representative result.


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Table D.3: Test validation chart

Preliminary Velocity Survey Pass Fail

Direction of gas flow within 15o of flue axis *

Pitot-static pressure differential greater than 5 Pa (3 m/s) *

Ratio of highest to lowest pitot-static readings less than 9:1 *

Sampling procedure *

Sampling plane is correctly positioned *

Sampling centroids of equal area *

Nozzle is facing upstream to within + 10o *

Leak check performed *

Constant 'at' during cumulative sampling *

Post sampling operations *

Leak test performed *

Isokinetic rate 90% to 110 % * *

*Any test run during which the average percent isokinetic sampling rate (Appendix F) is less

than 90% or greater than 110% considered to be invalid.

2.8 Preliminary sampling results

Three articulate tests were carried out for the different moisture content of the given biomass,

under continuous operating conditions, to assess the emission concentration in the exhaust gases.

The sample time of each test was one hour after which the samples were recovered from the

sampling train and sent for an analysis to the Norwest Lab, Winnipeg, MB.


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Table D.4: Operating conditions during the measurement (14% moisture)

Average operating conditions obtained from Vidir Smartfire remote monitor

data logging (14% moisture)

Moisture

content

14%

primary

chamber

temp [oF]

secondary

chamber

temp [oF]

boiler temp

[oF]

boiler in

temp [oF]

exhaust

temp [oF]

1055.8

21389.0

141.8

114.1

364.9

Flue gas

oxygen %

system_

pressure

[psi]

primary

air inflow

[ft/sec]

primary air in

temp. [oF]

secondary

air inflow

[ft/sec]

secondary

air in temp.

[oF]

5.9

-1.1

7.7

71.3

5.3

73.9

Table D.5: Operating conditions during the measurement period (26% moisture).


data logging (26% moisture content)

Moisture

content

26%

primary

chamber

temp [oF]

secondary

chamber

temp [oF]

boiler temp

[oF]

boiler in

temp [oF]

exhaust

temp [oF]

1148.1

2014.5

137.5

110.0

380.5

Flue gas

oxygen %

system_

pressure

[psi]

primary

air inflow

[ft/sec]

primary air in

temp [oF]

secondary

air inflow

[ft/sec]

secondary

air in temp

[oF]

5.0

–1.0

11.5

67.9

17.4

70.3


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Table D.6: Operating conditions during the measurement (20% moisture)


data logging (20% moisture content)

Moisture

content

20%

primary

chamber

temp [oF]

secondary

Chamber

temp [oF]

boiler temp

[oF]

boiler in

temp [oF]

exhaust

temp [oF]

948.1 2214.0 145.8 117.2 367.5

Flue gas

oxygen %

System_

pressure

[psi]

primary

air inflow

[ft/sec]

primary air in

temp [oF]

secondary

air inflow

[ft/sec]

secondary

air in temp.

[oF]

5.6 -1.0 9.8 69.9 5.5 72.3

3. Emission testing results

The results from the emission testing are summarized in Table D.7. The total particulate matter

ranged from 0.391–0.539 mg/m3 while the corresponding emission rates were between 0.189–

0.249 gr/s. The average concentration of the total particulate matter and emission rate for the

gasification of wheat straw using the Vidir Best gasifier were 0.443 mg/m3 and 0.211 gr/s

respectively.


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Table D.7: Particulate emissions from Vidir Best gasifier exhaust

Result Summary of emission testing for Wheat Straw Area of stack 0.785375 ft2

Reference conditions T =298oK (25oC), P=101.325Kpa

Emission rate E[lb / hr]

Emission rate E[gr/s] Test No.

Us [ft/sec]

Mn [mg] Grain/dscf

Csd [mg/m^3]

Csd@ 12%CO2

Moisture content 14% 38.04 485.2 0.249 0.539 0.415 2.092 0.249 Moisture content 26% 39.68 359.8 0.185 0.400 0.308 1.436 0.192 Moisture content 20% 39.90 352.3 0.181 0.391 0.301 1.503 0.189 Average 39.21 399.1 0.205 0.443 0.334 1.669 0.211


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Appendix E. Gas composition data sheets

Table E.1: Data recording sheet for wheat straw

Gas analysis (wheat straw moisture content = 14% ) Vidir Best, Arborg, MB

Sampling duration: every 12 min %CO2 %O2

(%N2+ %CO)

CO ppm NOx ppm

1 14.6 8 77.4 100 489

2 15.7 6.2 78.1 100 618

3 16.2 6.4 77.4 100 513

4 15.9 5.6 78.5 100 520

5 15.5 5.5 79 100 525

Average 15.58 6.34 78.08 100 533

Dry molecular Wt. (Md) 30.7464 lb/lb-mole

Wet molecular Wt. (Ms) 28.70698 lb/lb-mole


207

Table E.2: Gas analysis wheat straw of moisture content = 20% summary

Table E.3: Gas analysis for wheat straw of moisture content = 26% summary

Gas analysis (wheat straw of Moisture content = 20%) Vidir Best, Arborg, MB Sampling duration: every 12 min %CO2 %O2

(%N2 + %CO)

CO ppm NOx ppm

1 15 6.1 78.9 50 623 2 13.7 8.7 77.6 56 630 3 13.8 7.4 78.8 79 539 4 14.3 8.7 77 430 379 5 16.5 5.4 78.1 174 537 Average 14.66 7.26 78.08 157.8 541.6 Dry molecular Wt. (Md) is 30.636 lb/lb-mole Wet molecular Wt. (Ms) is: 28.61424 lb/lb-mole

Gas analysis (wheat straw of Moisture Content = 26%) Vidir Best, Arborg, MB

Sampling duration every 12min %CO2 %O2

(%N2 + %CO)

CO ppm NOx ppm

1 18.8 2.8 78.4 3500 190

2 18.3 2.9 78.8 2100 243

3 18.5 2.5 79 7300 298

4 17.5 4.3 78.2 2100 360

5 17.8 2.3 79.9 1700 217

Average 18.18 2.96 78.86 3340 261.6

Dry molecular Wt. (Md) 31.0272 lb/lb-mole

Wet molecular Wt. (Ms) 28.94285 lb/lb-mole


208

Appendix F. Particulate emission sampling and testing data

sheets

Table F.1: Preliminary stack test data sheet (Run 1)

Preliminary stack test data: wheat straw ( Run 1)

Company: Vidir Best Inc. Facility: Arborg

Location: Box 600, Arborg, MB Date: 07 /06 /07

Data collected by: Daniel Balcha Ambient temperature : 84oF

Equivalent stack diameter (in.): 12 Est. moisture: 0.16

Diameters downstream : 8 Stack temperature : 3250F

Diameters upstream: 2 Barometric: 29.9 in. Hg

Number of sample ports: 2 Static press: 29.9 in. Hg

Sampling points needed: 12 Average ΔH reading: 0.97 in. W.C.

Orifice: ΔH@: 1.727 Maximum √ΔP: 0.62 in. W.C.

Pitot: Cp : 1 Average. √ΔP: 0.595 in. W.C.

Nozzle Diameter: 0.25 in Estimated meter temperature 98oF

Meter: Y-factor : 1


209

Table F.2: Particulate emission sampling data recording sheet-MC = 14%

RUN # 1 Moisture Content 14%

Sample

Dry Gas

Meter

Pitot

ΔP Orifice Pump

Temperatures [oF]

Port &

Reading

[ft3]

in

H2O Pressure Vacuum DGM Stack Probe Filter

Imp.

Exit

point ΔH[inH2O] Inlet Outlet %Iso

Us

[fps]

1, 1 157.0 0.33 0.880 0 92 74 225 250 250 62 92.27 36.7

1, 2 159.6 0.33 0.860 3 93 76 267 255 249 67 94.79 37.8

1, 3 161.5 0.34 0.890 3 92 74 315 250 250 64 96.69 39.61

1, 4 165.4 0.34 0.800 4 93 73 289 248 250 68 95.04 38.9

1, 5 168.1 0.32 0.860 3 93 78 228 247 251 61 93.47 36.2

1, 6 170.5 0.32 0.870 4 92 77 310 252 252 58 99.07 38.3

2, 7 173.2 0.31 0.890 4 93 78 290 250 258 62 99.16 37.2

2, 8 175.7 0.33 0.880 4 92 74 285 251 256 63 96.23 38.3

2, 9 179.2 0.33 0.880 5 90 73 275 249 252 64 95.84 38.0

2, 10 182.6 0.34 0.870 4 93 75 265 247 251 66 93.35 38.3

2, 11 185.9 0.34 0.875 4 90 77 310 250 248 64 96.29 39.5

2, 12 188.2 0.33 0.880 4 93 78 265 250 253 65 94.49 37.7

AVER 0.33 0.870 3.5 92.2 75.6 277.0 249.9 251.7 63.7 95.56 38.0

Tot.Gas

Meter

reading 31.20


210


Preliminary stack test data wheat straw (Run 2)




Equivalent stack diameter (in.): 12 Estimated moisture: 0.16

Diameters downstream: 8 Stack temperature : 325oF

Diameters upstream: 2 Barometric: 29.9 in. Hg

Number of sample ports: 2 Static pressure: 29.9 in. Hg

Sampling points needed: 12 Average ΔH reading: 0.97 in. WC

Orifice: ΔH@: 1.727 Maximum √ΔP: 0.62 in. WC

Pitot: Cp : 1 Average √ΔP: 0.595 in. WC

Nozzle diameter : 0.25 in Estimated meter temperature 98oF

Meter: Y-factor : 1


211



Sample

Dry Gas

Meter

Pitot

ΔP Orifice Pump

Temperatures [oF]

Port &

Reading

[ft3] in H2O Pressure Vacuum DGM Stack Probe Filter

Imp.

Exit

Point ΔH[inH2O] Inlet Outlet %Iso

Us

[fps]

1, 1 188.2 0.32 0.900 0 93 77 375 225 256 58 103.08 39.9

1, 2 190.6 0.34 0.880 4 92 78 379 228 253 59 100.23 41.2

1, 3 193.4 0.32 0.880 4 93 78 380 231 251 62 103.29 40.0

1, 4 196.1 0.32 0.860 5 92 79 383 228 250 54 103.47 40.1

1, 5 198.5 0.34 0.880 5 88 79 307 229 231 58 96.102 39.4

1, 6 201.4 0.33 0.890 4 87 79 315 232 237 57 98.147 39.0

2, 7 205.8 0.32 0.921 4 86 79 338 241 249 55 101.24 39.0

2, 8 208.2 0.34 0.860 4 85 79 350 244 251 56 99.027 40.5

2, 9 211.8 0.34 0.880 5 85 79 351 245 252 55 99.093 40.5

2, 10 214.9 0.32 0.875 5 87 78 357 247 251 56 102.42 39.5

2, 11 217.8 0.32 0.880 5 86 77 316 248 252 58 100.01 38.6

2, 12 220.3 0.32 0.840 4 87 78 325 251 253 62 100.39 38.7

AVER 0.33 0.879 4.1 88.4 78.3 348 237.4 248.8 57.5 100.54 39.7

Total

Gas

Meter

reading 32.10


212


Preliminary stack test data wheat straw ( Run 3)




Equivalent stack diameter (in.): 12 Estimated moisture: 0.16

Diameters downstream: 8 Stack temperature : 325oF

Diameter upstream: 2 Barometric: 29.9 in. Hg

Number of sample ports: 2 Static pressure: 29.9 in. Hg

Sampling points needed: 12 Average ΔH reading: 0.97 in. WC

Orifice: ΔH@: 1.727 Maximum √ΔP: 0.62 in. WC

Pitot: Cp : 1 Average √ΔP: 0.595 in. WC

Nozzle diameter : 0.25 in Estimated meter temperature 98oF

Meter: Y-factor: 1


213



Sample

Dry Gas

Meter

Pitot

ΔP Orifice Pump

Temperatures [oF]

Port &

Reading

[ft3]

in

H2O Pressure Vacuum DGM Stack Probe Filter

Imp.

Exit

Point ΔH[inH2O] Inlet Outlet %Iso

Us

[fps]

1, 1 220.3 0.32 0.92 0 83 77 375 225 256 58 104.04 39.9

1, 2 222.7 0.33 0.86 4 84 77 379 228 253 59 102.58 40.6

1, 3 225.3 0.31 0.87 5 85 78 392 231 251 62 106.46 39.7

1, 4 229.4 0.31 0.88 5 85 77 401 228 250 54 107.13 39.9

1, 5 232.7 0.33 0.98 6 86 77 417 229 231 58 104.72 41.5

1, 6 235.8 0.33 0.9 6 85 78 325 232 237 57 99.054 39.3

2, 7 238.5 0.32 0.91 6 85 77 338 241 249 55 101.52 39.0

2, 8 241.2 0.34 0.84 5 86 78 350 244 251 56 99.022 40.5

2, 9 243.6 0.33 0.86 5 87 78 351 245 252 55 100.49 39.9

2, 10 246.1 0.32 0.885 5 85 78 357 247 251 56 102.62 39.5

2, 11 248.7 0.34 0.89 5 86 77 326 248 252 58 97.646 39.9

2, 12 251.8 0.33 0.82 5 87 78 325 251 253 62 98.852 39.3

AVER 0.33 0.885 4.75 85.3 77.5 361.3 237.4 248.8 57.5 102.01 39.9

Total

Gas

Meter

reading 31.50


214

Appendix G. Derivation of equations for gas and emission testing

1. Calculations for determining moisture content, gas density and molecular weight of the stack

a. Percent moisture by volume in stack gas is determined by the formula:

where,

b. The specific gravity of the flue gas (Gd) equals the ratio of the molecular weight of the stack gas to the molecular weight of air (28.95).

c. Molecular weight of the stack gas is calculated from Nova gas analyzer data and moisture content by the formula:

* Percent expressed as decimal

2. Calculation of concentration of constituent gases

a. To convert the percentage reading into ppm:

b. To calculate the concentration of each gas in mg/m3

100% ×+

=mv

v

VVVMoisture

( )

⎟⎠⎞

⎜⎝⎛ Δ

+

+××=

6.13

46000267.0HP

TVVb

mmv

( ) ( ) ( ) ( ) ( )[ ] ( )BW18%N28%O32%CO28%CO44BW1M *22

**2s ×+×+×+×+××−=

95.28sMGd =

10000(%) ×= percentagePPM

⎥⎦

⎤⎢⎣

⎡ ×=⎥⎦

⎤⎢⎣⎡

45,24. 2

32COMWPMM

mmgCoConc

⎥⎦

⎤⎢⎣

⎡ ×=⎥⎦

⎤⎢⎣⎡

45,24. 2

32OMWPMM

mmgOConc


215

3. Derivation of % isokinetic sampling

a.

b.

c.

Substitute c for An in Equation (b) to obtain Equation (d)

d.

e.

%100×=s

n

UU

SamplingIsokinetic

( )( ) ( ) ⎟

⎠⎞

⎜⎝⎛××

=⎟⎠⎞

⎜⎝⎛

.min.sec60.min..

...sec

.

tftsqA

ftcuVftUn

tn

( ) ( ) ( ) ( )22

005454.0

..

..1444

..... nn

n D

ftsqinsq

insqDftsqA =

⎟⎟⎠

⎞⎜⎜⎝

⎛×

×=π

( ) ( ) tDV

tDVU

n

t

n

tn

×=

××= 22

0558.360005454.0

( )

( ) ( ) sm

bsm

t PBWT

HPTVV

×−×+

⎟⎠⎞

⎜⎝⎛ Δ

+×+×=

14606.13

460

⎥⎦

⎤⎢⎣

⎡ ×=⎥⎦

⎤⎢⎣⎡

45,24. 3

xNOx

MWPMMmmgNoConc

⎥⎦

⎤⎢⎣

⎡ ×=⎥⎦

⎤⎢⎣⎡

45,24. 3

COMWPMMmmgCoConc


216

Substitute Equation (e) for Vt in Equation (d) to obtain Equation (f)

f.

Substitute f for Un in Equation (a) to obtain Equation (g)

g.

4. Determination of emission concentration

a. Emission Concentration (Cs) for a test run is calculated by the formulas:

Grains = Total sample weight (grams) x 15.43

where,

( )

( ) ( ) ( ) smn

bsm

n PtBWTD

HPTVU

××−×+×

⎟⎠⎞

⎜⎝⎛ Δ

+×+××=

14606.13

4600558.3

2

( )

( ) ( ) ( ) ssmn

bsm

PUtBWTD

HPTVSamplingIsokinetic

×××−×+×

⎟⎠⎞

⎜⎝⎛ Δ

+×+××=

14606.13

4600558.3% 2

( )⎥⎦⎤

⎢⎣

⎡+

××=46092.29

530

m

smstd T

PVV6.13

staticbs

PPP +=

mStds V

SampledGrainsTotalC

__=

Documents

By Daniel A. Balcha A thesis submitted to the …graduate students: Amir Hossein Birjandi, Dave Gaden, Godwin Tay, Kwadjo Poku Owusu, James Arthur, Jonathan Mawuli Tsikata, Moftah