BIOMETHANATION OF NON-EXTRACTABLE

COAL: AN ENGINEERING-ECONOMIC MODEL OF

SUSTAINABLE ENERGY AND ENVIRONMENT

SECURITY

A Thesis

SUBMITTED TO

BIRLA INSTITUTE OF TECHNOLOGY

FOR THE AWARD OF THE DEGREE OF

DOCTOR OF PHILOSPHY

in

ENGINEERING

By

UMESH PRASAD SINGH

DEPARTMENT OF BIOTECHNOLOGY

BIRLA INSTITUTE OF TECHNOLOGY

MESRA: RANCHI, INDIA

2011

This thesis is dedicated to the memory of my wife

Meera Singh (7th November 1956 2nd January 2011)

i

APPROVAL OF THE GUIDE

Recommended that the thesis entitled Biomethanation of Non-Extractable

Coal: An Engineering-Economic Model of Sustainable Energy and Environment

Security prepared by Mr. U. P. Singh under my supervision and guidance be

accepted as fulfilling this part of the requirements for the degree of Doctor of

Philosophy.

To the best of my knowledge, the contents of this thesis did not form a basis

for the award of any previous degree to anybody else.

Date:

(Dr. A. S. Vidyarthi)

Guide

Professor& Head

Department of Biotechnology

BIT, Mesra, Ranchi

India

ii

ACKNOWLEDGEMENTS

I accolade my highest respect to Dr. A. S. Vidyarthi, Professor and Head,

Department of Biotechnology, BIT, Mesra and Chairperson of my doctoral

committee for his acceptance to guide an inter-disciplinary subject, immense

encouragement, necessary facilities, constant monitoring, instant availability to

support and guide during the course of my research work and preparation of this

manuscript.

I tender my sincere thanks to Dr. A Sachan, Dr. Usha Jha, and Dr. S. K. Bose

members of my doctoral committee for giving their continued invaluable suggestions

to improvise my research work, encouragement, and support to cross the inter-

disciplinary hurdles since beginning to date.

I am thankful to Dean, PG & Research, for his time to time suggestions to

make this study more focused and purposeful.

I thank Prof. Ashok Mishra for valued discussions that helped in giving an

academic shape to this inter-disciplinary industrial problem and Miss Meenakshi

Singh for her tips on conversion of concepts to graphics. My candid thanks are to

Prabhat Ji, Amit Ji, and Mr. Sankaracharya for their constant support and co-

operations.

This work is effort of many people. I thank all of them. In last 23 years of my

association with coal industry, I had gone through a number of books, research

papers, conferences/ seminars proceedings, paper clippings, web sites, etc.; and had

personal discussions with a number of people (experts in their own fields) to

conceptualize this multi-disciplinary coal biomethanation project. I acknowledge

contributions from all these corners that directly or indirectly helped me in coming to

this stage. I express my gratitude to all of them.

Last but not the least, I am thankful to BIT for giving me an opportunity to

take up this research work here.

(Umesh Prasad Singh)

iii

TABLE OF CONTENTS

Page Nos.

Approval of the Guide i

Acknowledgement ii

Table of Contents iii-x

Abstract xi-xii

Abbreviations xiii-xv

Notations Used xvi- xx

List of Figures xxi-xxiii

List of Tables xxiv

Chapter 1: Introduction 1-12

1.1 Energy Scenario of India 1-4

1.1.1 Energy Demand 1

1.1.2 Energy Supply 1-2

1.1.3 Options to Bridge the Gap 2

1.1.4 Augmentation of Resources 2-4

1.1.5 Coal Mainstay Fuel 4

1.2 Energy Security of India 5

1.1.6 Environment Security under Threat 5

1.1.7 Challenges 5

1.3 Non-Extractable Coal 6 7

1.3.1 CBM Recovery 6

1.3.2 In-situ Thermal UCG 6-7

1.3.3 Non-Extractable Coal as a CO2 Sink 7

1.4 In-situ Biomethanation of Non-Extractable Coal 7 -8

1.4.1 Hypothesis 8

1.5 Objectives of the Research work 8 -9

1.6 Scope and Organization of the Research Work 9 -12

Chapter 2: Literature Review 13-53

2.1 Biomethanation of Coal 13-19

2.1.1 Methanogenesis Pathways 15-17

2.1.1.1 Hydrogen Pathways 17

iv

Page Nos.

2.1.2 Microbial Growth and Environment 17

2.1.3 In-situ Coal Biomethanation 17-18

2.1.4 Sources of Microbial Consortia 18-19

2.2 Coal Chemistry 19-34

2.2.1 Macromolecular Geopolymers 20-21

2.2.2 Structure of Coal 21-26

2.2.2.1 Structural Changes with Rank 22-25

2.2.2.2 Lithotypes 25-26

2.2.3 Constituents of Coal 26-27

2.2.4 Oxygen in Coal 27-28

2.2.5 Water in Coal 28-29

2.2.6 Coal Porosity and Other Related Properties 29- 33

2.2.6.1 Cleat System and Natural fracturing 29-31

2.2.6.2 Microbial Accessibility of Coal 31-32

2.2.6.3 Apparent Density of Coal 33

2.2.6.4 Swelling of Matrix 33

2.2.7 Microbial Uniqueness of Coal 33-34

2.3 CO2 Sequestration 34-35

2.4 Anaerobic Hydrolysis of Complex Substrate 35-52

2.4.1 Enzymatic Hydrolysis Process 35-37

2.4.2 Factors Affecting Anaerobic Hydrolysis 37-43

2.4.2.1 Environmental Factors 37-38

2.4.2.2 Substrate Related Factors 39-40

2.4.2.3 Inhibitors 40-43

2.4.3 Hydrolysis Acceleration 43-45

2.4.3.1 Pre-Treatment 43-44

2.4.3.2 Enzyme Addition 44

2.4.3.3 Amorphogenesis 44-45

2.4.4 Hydrolysis Kinetics 45-50

2.4.5 Hydrolysis of Lignocellulosic Substrates 50-52

2.5 Conclusions 52-53

v

Page Nos.

Chapter 3: Coal Hydrolysis 54-63

3.1 Mechanism 54-56

3.1.1 Oxic and Anoxic 55

3.1.2 Attachment 55-56

3.1.2.1 Biofilm Formation 55-56

3.2 Depolymerization 56-58

3.3 Solubilization 58-60

3.3.1 Solvents and Surfactants 59-60

3.4 Products of Hydrolysis 60-62

3.5 Hydrolysis Rate Limiting Step in Coal

Biomethanation 62

3.6 Methodology for Hydrolysis Assessment: Substrate

Utilization Rate 63

3.7 Conclusions 63

Chapter 4: CoalBioreactor: An Economical and Accelerated in-situ

Coal Biomethanation Mechanism 64-90

4.1 Design 64-69

4.1.1 Fractured Coal within Impermeable Boundary 64-65

4.1.2 Purpose 65

4.1.3 Aim 65

4.1.4 Features and Components 66-67

4.1.5 Facilities 67-69

4.1.5.1 Subsurface 67-68

4.1.5.2 Surface 68-69

4.1.5.3 Subsurface and Surface Connectivity 69

4.2 Construction 69-79

4.2.1 Site Selection 70

4.2.1.1 Data Collection 70

4.2.1.2 Data Generation: Seismic Survey and

Reservoir Imaging 70-71

4.2.2 Technologies and Techniques Selection 71

4.2.3 Well Development 71-73

4.2.3.1 Diameter, Casing Size, and Material 72-73

vi

Page Nos.

4.2.4 Fracturing 73-77

4.2.4.1 Perforating 75

4.2.4.2 Jetting 76

4.2.4.3 Fracture Gradient 76

4.2.4.4 Coal Subsidence 76

4.2.4.5 Fracturing Fluids 76-77

4.2.5 Impermeable Boundary 77-79

4.2.6 Sump 79

4.3 Connectivity with Surface 79-81

4.3.1 Hydraulic Fracturing Pipe/ Production Pipe

for Recovery of Biomethane 79

4.3.2 Spray of Growth Medium, Nutrients, and

Other amendments 80

4.3.3 CO2 Injection Pipe 80

4.3.4 Recovery of Spent Medium 81

4.4 Operation 81-88

4.4.1 Biofilm Formation 81-82

4.4.1.1 Retention of Microbes 82

4.4.2 Biomethane Generation (Harvesting) 83-84

4.4.3 Measurement and Control of Parameters 84-85

4.4.4 Sequence of Injecting Fluids, Microbes, and

Nutrients 86-88

4.5 CO2 Sequestration 88-89

4.6 Advancement of Construction Sites 89

4.7 Conclusions 90

Chapter 5: CoalBioreactor Kinetics 91-118

5.1 Microbial Growth: Substrate Limited 91-93

5.1.1 Quantity of Primary Substrate (Coal) in

CoalBioreactor 91

5.1.2 Quantity of Coal Carbon Polymers 91

5.1.3 Quantity of Actual Substrate (Hydrolysable

Coal Carbon Polymers) 92-93

5.2 Hydrolysis: Rate Limiting Step 93

5.3 Acidogens in CoalBioreactor 94-101

vii

Page Nos.

5.3.1 Inoculation of Single Seed Concentration of

Consortium (SSCC) 94

5.3.2 Attachment of Acidogens to Coal Surfaces 94-98

5.3.2.1 Biofilm Formation on Coal Surfaces 95-97

5.3.2.2 Contact-Inhibited Growth of Acidogens 97-98

5.3.3 Hydrolysis Mechanism 98-101

5.3.3.1 Accumulation of Enzymes in

Hydrodynamic Boundary Layer 98

5.3.3.2 Diffusion of Enzymes in Coal Pores 98-99

5.3.3.3 Diffusion of Enzymes in Crystalline

Coal Macerals 99

5.3.3.4 Actual Substrate-Enzyme Surface Area

and Enzyme Penetration Rate 99-101

5.4 Kinetic Parameters: SBK Model 101-116

5.4.1 Hydrolysis Rate 112

5.4.2 Biomethanation Potential (BMP) 112-115

5.4.2.1 Enhanced BMP 114

5.4.2.1 Commercial BMP 114-115

5.4.3 CO2 Sequestration Potential (CSP) 115

5.4.4 Commercial Energy Availability 115-116

5.4.5 Project Carbon Neutrality 116

5.4.6 CoalBioreactor Starting Period 116

5.4.4.1Best Case Scenario (BCS) 116

5.4.4.2 Worst Case Scenario (WCS) 116

5.5 Conclusions 117-118

Chapter 6: Engineering-Economic Model 119-144

6.1 Challenges 119-123

6.1.1 Uncertain Parameters 119-120

6.1.1 Project full of Risk and Experimentation 120-121

6.1.2 Is Non-Extractable Coal Biomethanation

Project Worth It? 121-123

6.2 Development of an Engineering-Economic Model 123-125

6.2.1 Major Reasons 124

6.2.2 Main Objectives 124-125

viii

Page Nos.

6.3 Basic Components 125-128

6.3.1 Engineering Model 125-126

6.3.1.1 Design and Construction of an in-situ

CoalBioreactor 125

6.3.1.2 CoalBioreactor Kinetics 125

6.3.1.3 Mathematical Model 126

6.3.2 Economic Model 126-128

6.3.2.1 Net Present Value 126

6.3.2.2 Costs 127

6.3.2.3 Benefits 127

6.3.2.4 Financial Adjustments 127

6.3.2.5 Benefit-Cost Analysis 127-128

6.4 Mathematical-Economic Models 128-142

6.4.1 Inputs 128-137

6.4.1.1 Validation of Data 129

6.4.1.2 Input Assumptions 129-137

6.4.2 Simulation 137

6.4.3 Parameters Forecasted 137-138

6.4.4 Frequency Distribution Forecasted Results 138-139

6.4.5 Monte Carlo Simulation for Risk Analysis 139-140

6.4.6 Software Used: Excel Spreadsheet with

Crystal Ball 140-141

6.4.7 Model Assumptions Made 141

6.4.8 Mean Standard Error 142

6.4.9 Correlations 142

6.5 Compromise Made 142-144

6.6 Conclusions 144

Chapter 7: Results and Discussions 145-167

7.1 Deterministic Exploratory SSCC Model 145-146

7.2 Stochastic Exploratory SSCC Model 146-154

7.2.1 Project: Time Duration 149-151

7.2.1.1 Dynamic Time Duration of Project 149

ix

Page Nos.

7.2.1.2 Fixed Buildup Phase Time Duration

(FBPTD) 149-150

7.2.1.3 Sensitivity Analysis for Identification

of Inputs Values 150-151

7.2.2 Stochastic SSCC Model with FBPTD and VIV 151-154

7.3 Impact of Common Apprehensions, Inhibitions, and

Hopes 154-160

7.3.1 Apprehensions 154-155

7.3.2 Inhibitions 155

7.3.3 Hopes 155

7.3.4 Findings 155-159

7.3.4.1 20% Reduction in Bioavailability of

Actual Substrate 157

7.3.4.2 20% Reduction in Actual substrate-

Enzyme Surface Area 157-158

7.3.4.3 20% Reduction in Enzyme Penetration

Rate 158

7.3.4.4 20% Reduction in Specific Growth Rate

of Acidogens 158

7.3.4.5 20% Enhanced Coal to Biomethane

Conversion Factor due to ECHRMP 158-159

7.3.5 Wax Accumulation Problem 159-160

7.4 Gross Sensitivity Analysis and Stress Testing 160-163

7.4.1 Impact of Varying Microbial Geological

Biomethanation Conditions 161-162

7.4.2 Real Challenges: Feasibility of WMGBC

Project 162-163

7.5 End Result: Hydrolysis Rate Constant for Coal 163-164

7.6 Impact of Non-Extractable Coal Biomethanation

on Economy 164-166

7.6.1 Economics 165

7.6.2 Energy Security 165

7.6.3 Environment Security 165

7.6.4 Sustainability of Energy and Environment

Security 166

7.7 Conclusions 166-167

x

Page Nos.

Chapter 8: Verification & Validation of Models 168-176

8.1 Model Verification and Validation Process 169

8.2 Verification 169-170

8.3 Validation 171-176

8.3.1 Modified View Points on Validation 172-174

8.3.2 Model Implemented is the Model Intended 175-176

8.3.2.1 Development of Scientific

Understanding 175

8.3.2.2 Testing the Effect of Changes in the

System 175

8.3.2.3 Decision Making Aid 176

8.4 Conclusions 176

Chapter 9: Conclusions 177-179

Future Scope of Works 180

References 181-201

List of Patents & Publications 202

xi

Biomethanation of Non-Extractable Coal: An Engineering-Economic

Model of Sustainable Energy and Environment Security

Abstract

Biomethanation of coal in-situ is an innovative approach to utilize non-

extractable coal as a source of energy as well as a sink for CO2 sequestration to add in

energy and environment security of an economy. Though scientists and technologists

are aware of chemical energy potential of in-situ coal, however to date there is no

basis even to guess how far and how fast this energy would be available commercially

at surface in form of biogenic methane (biomethane). Therefore, decision makers, at

present, are apprehensive about success (techno-economic feasibility) of an in-situ

coal biomethanation project.

The natural process of biomethanation can be accelerated in such coals that

have favourable microbial geological biomethanation conditions (MGBC), and there

are some proposals and schemes for the same. However, there is no proposal and

scheme for biomethanation of coals that lack natural favourable MGBC.

This study hypothesizes that biomethanation of all types of coal in-situ is

techno-economically feasible.

To test the hypothesis, a mechanistic engineering-economic model has been

developed that assesses prospect of an envisaged in-situ biomethanation project in any

and all types of coal. Basic components of engineering model are: (a) design and in-

situ construction of a CoalBioreactor initially on a part or total of the target coal, (b)

CoalBioreactor kinetics, and (c) a mathematical model. CoalBioreactor is an envisaged

engineered, natural, at times imitated, trouble-free, cost-effective in-situ mechanism to

make biomethanation of all types of coal in-situ possible. It considers hydrolysis as the

rate limiting step in biomethanation of solid, dry, insoluble complex coal substrate in-

situ. CoalBioreactor kinetics formulates operational effectiveness of CoalBioreactor.

CoalBioreactor and its kinetics combined together provide the basic framework for

development of the mathematical model. For economic analysis of this engineering

model, an economic model has been developed that is basically a benefit-cost-analysis

(BCA) model that helps in rational decision making regarding Is an in-situ non-

extractable coal biomethanation project to date in a target coal worth it?

xii

The mathematical-economic (CoalBioreactor Biomethanation) model is a

dynamic, quantitative, lumped, stochastic, mechanistic, continuous, sub-model based,

verified, and virtually validated (V & VV) model. It has been developed in Microsoft

Excel Spreadsheet with Crystal Ball add-in that has used Monte Carlo simulation for

risk analysis. Model inputs are based on abstraction and generalization of limited

empirical hard data documented in literature. It also includes secondary data derived

from these primary hard data based on mechanistic logical conclusion, and assumption

of some missing inputs (initially). It computes forecasted parameters (outputs) on

discrete points in time. Exploratory analysis of model rudimentary estimates

(forecasts) of buildup phase time duration (i.e. the time acidogens take to fully cover

the entire coal surfaces exposed to them), and inputs values help in identifying more

rational values of inputs. Simulation run of the model with these validated inputs

values estimates the ultimate probability distribution of the forecasted parameters.

Decision makers know the certainty level attached with each forecasted result, and

therefore, the risk associated in accepting a forecast.

Model could be used as an exploratory e-laboratory to tackle the real world

challenges of in-situ coal biomethanation. Based on substrate depletion rate, the

surface based hydrolysis rate constant, in an envisaged CoalBioreactor, is in the range

of 2.06 to 2.67 gm m-2

d-1

with 2.506 gm m-2

d-1

as the most likely value. This estimates

commercial energy availability (CEA) in the range of 56.83 to 75.35% with a mean

value of 66%, project carbon neutrality (PCN) of 72.54%, and 100% certainty of

commercial viability of a project in a most probable MGBC coal. By identifying and

suggesting most appropriate technology mix, model demonstrates techno-economic

feasibility of a biomethanation project even in a worst MGBC coal. This verifies the

hypothesis that biomethanation of all types of coal is possible by designing and

constructing a CoalBioreactor in the target coal.

Impact analysis of in-situ non-extractable coal biomethanation on economy

(Indian) suggests average net present earning of more than 340 INR (in the range of 70

to 810 INR) per tonne of in-situ coal. It alone has potential of meeting primary energy

requirement of the country for more than 37 years and mitigating total emission for

more than 40 to about 60 years, considering 2031-32 as the base year.

xiii

Abbreviations

ABCDE : Alkaline substances Biocatalysts Chelators Detergents Esterases

AD : Anaerobic Digestion

ASTM : American Society for Testing and Materials

BAU : Business as Usual

BCA : Benefit Cost Analysis

BCM : Billion Cubic Meters

BCS : Best Case Scenario

BMGBC : Best Microbial Geological Biomethanation Conditions

BMP : Biomethanation Potential

BT : Billion Tonne

BTU : British Thermal Unit

CBCF : Coal to Biomethane Conversion Factor

CBM : Coalbed Methane

CDM : Clean Development Mechanism

CEA : Commercial Energy Availability

CER : Certified Emission Reduction

CHRMP : CO2-H2 Reduction Methanogenesis Pathways

CIL : Coal India Limited

CMPDIL : Coal Mine Planning & Design Institute Limited

CSP : CO2 Sequestration Potential

CT : Coiled Tubing

DNA : Deoxyribonucleic Acid

DSM : Demand Side Management

ECBCF : Enhanced Coal to Biomethane Conversion Factor

ECBM : Enhanced Coalbed Methane

ECHRMP : Enhanced CO2-H2 Reduction Methanogenesis Pathways

EMPCF : Enhancement in Microbial Porosity of Coal through Fracturing

ENPV : Expected Net Present Value

EPR : Enzyme Penetration Rate

EPS : Extracellular Polymeric Substances

FAL : Fulvic Acid Like

FBPTD : Fixed Buildup Phase Time Duration

xiv

FTIR : Fourier Transform Infrared Spectroscopy

GCV : Gross Calorific Value

GDP : Gross Domestic Product

GHG : Green House Gas

GOI : Government of India

GSI : Geological Survey of India

HAL : Humic Acid Like

HEC : Hydroxethyl Cellulose

HMMW : Horizontal Multi-lateral Multi-seam Well

INR : Indian Rupee

IPCC : Intergovernmental Panel on Climate Change

LHW : Liquid Hot Water

LNG : Liquefied Natural Gas

LWD : Logging While Drilling

MCM : Million Cubic Meters

MECoM : Microbially Enhanced Coalbed Methane

MF : Methano Furan

MGBC : Microbial Geological Biomethanation Condition

MPa : Mega Pascal

MPMGBC : Most Probable Microbial Geological Biomethanation Condition

MPS : Most Probable Scenario

MPT : Methano Pterin

MSE : Mean Standard Error

MT : Million Tonne

Mtoe : Million Tonne Oil Equivalent

MW : Mega Watt

NIR-X ray : Near Infra Red X-ray

NMR : Nuclear Magnetic Resonance

NPV : Net Present Value

PAC : Poly Anionic Cellulose

PAH : Polycyclic Aromatic Hydrocarbons

PCN : Project Carbon Neutrality

PD : Probability Distribution

xv

PDF : Probability Distribution Function

PERT : Program Evaluation and Review Technique

py-FIMS : Pyrolysis-Field Ionization Mass Spectrometry

py-GC : Pyrolysis Gas Chromatography

RASESA : Reduction in Actual Substrate Enzyme Surface Area

RBAS : Reduction in Bioavailability of Actual Substrate

RE : Renewable Energy

REPR : Reduction in Enzyme Penetration Rate

RSGRA : Reduction in Specific Growth Rate of Acidogens

SBK : Surface Based Kinetics

SEM : Scanning Electron Microscope

SME : Subject Matter Expert

SP : Submergible Pump

SSCC : Single Seed Concentration of Consortium

TERI : The Energy Resources Institute

THF : Tetra Hydro Furan

TPCES : Total Primary Commercial Energy Supply

TPES : Total Primary Energy Supply

TPNCES : Total Primary Non Commercial Energy Supply

UCG : Underground Coal Gasification

UNFCCC : United Nation Framework Convention on Climate Change

VFA : Volatile Fatty Acid

VIV : Validated Input Value

V&V : Verified and Validated

V&V V : Verified and Virtually Validated

WCS : Worst Case Scenario

WMGBC : Worst Microbial Geological Biomethanation Condition

xvi

Notations Used

a [L] : Radius of acidogens fractional/partial patch on spherical coal

particles

A : Pre-exponential factor

A [L2] : Area of the surface available for hydrolysis

A0

[L] : Angstrom

A0 [L] : Acidogens population per patch at t = 0

Aas-e [L2] : Actual substrate-enzyme surface area

Ah [L2] : Area of hydrodynamic boundary layer

Amax-cp : Maximum number of acidogens required to cover surface area

of a coal particle

Amax-cs : Maximum number of acidogens required to cover all exposed

surfaces available in one cubic meter of coal

App-cs : Acidogens population per patch on flat surface

App-cs-l : Number of acidogens required to cover the side surface of coal

matrix block

App-cs-s : Number of acidogens required to cover the front or back surface

of coal matrix block (patch area)

avcrc [L3] : Total volume of CO2 adsorbed in residual coal

: A constant

B : Cos-1

(1-20) (An assumption)

bbtu : Fraction of biomethane recovered getting discounted for BTU

adjustment

bcs : Fraction of biomethane recovered getting used for operation of

machineries at site

BMPumc [L3M

-1] : Biomethane (volume) recovered per unit mass of coal

: Ratio of patch area to total area of sphere

0 : Ratio of patch area to total area of sphere at time t0

C1 : Swelling Factor

Ca [L2] : Surface area of coal per unit mass

ca [L] : Cleat aperture size

cbio [M] : Bioavailable coal carbon

cbecf : Coal to biomethane energy conversion factor

CBl [L] : CoalBioreactor length

CBw [L] : CoalBioreactor width

xvii

CBh [L] : CoalBioreactor height/ thickness

CBv [L3] : CoalBioreactor volume

cc [%] : Carbon content in coal

cch [%] : Hydrolysable coal carbon

cfb : Commercial biomethane factor

cfcb : Conversion factor for coal to biomethane

cfcco2 : Conversion factor for coal to CO2

cfco2b : Conversion factor for CO2 to biomethane due to enhanced CO2-

H2 reduction methanogenesis pathways (ECHRMP)

ci-mpi-f [L3/L

3] : Increase in original porosity of coal due to fracturing

cmpi [L3/L

3] : Effective porosity (microbial) of coal (void/per cubic meter

volume of coal) initial

cps [L] : Coal pore size

CSPc [M] : CO2 sequestration potential (mass) per unit mass of coal

CSPm [M] : CO2 sequestration potential (mass)

CSPrc [L3] : CO2 sequestration potential (volume) per unit mass of residual

coal

CSPv [M] : CO2 sequestration potential (volume)

cs [L] : Cleat spacing

csa [L] : Coal surface area

csai [L2] : Initial exposed coal surface area for microbes/cubic meter of

coal

Cx : Hydrolytic enzymes

d [L] : Depth of coal

da [L] : Diameter of acidogen

Dc [ML-3

] : Coal density

Dca [ML-3

]

: Density of actual substrate

DCH4 [ML-3

] : Density of biomethane

DCO2 [ML-3

] : Density of CO2

Dcp [ML-3

] : Density of primary substrate

dcp [L] : Coal particles diameter

Dh [L2T

-1] : Diffusion constant of hydrolases

Dpores [L2T

-1] : Diffusion constant of actual substrate

dpp-cp [L] : Diameter of acidogens patch on spherical coal particle

Ea [JMol-1

] : Activation Energy

xviii

ecb : CoalBioreactor efficiency

ep [LT-1

] : Enzyme penetration rate in actual substrate-enzyme surface area

F [ML-3

] : Fracture gradient

fhole : Fraction of maximum density of possible acidogens in a biofilm

hole opening

fholeeq : Fraction of cell missing in the confluent areas in the centre of

the patches

GCVbm [JL-3

] : Gross calorific value of unit volume of biomethane

GCVc [JM-1

] : Gross calorific value of unit mass of coal

h [L2] : Patch area on coal particles covered by acidogens

H4MPT : Tetra hydro methanopterin

Has-bio [ML-3

] : Concentration of hydrolyzed actual substrate in biofilm

bioavailable to acidogens

Has-pores [ML-3

] : Concentration of hydrolyzed actual substrate in pores

Hc-hbl [ML-3

] : Concentration of hydrolases in hydrodynamic boundary layer

Hc-pores [ML-3

] : Concentration of hydrolases in pores

hems [L] : Hydrolyzing enzyme molecule size

iam [M] : Inoculated acidogens mass

J [Joule] : Joule

k [MolL-3T-1] : Reaction rate constant that depends on temperature

k [T-1

] : Acidogens 1st order death rate constant

K [T-1

] : Hydrolyzed substrate transport first order rate coefficient

Kh [ML-2

T-1

] : Hydrolysis rate constant

kh [T-1

] : 1st order hydrolysis rate constant

Ksbk [ML-2

T-1

] : Surface based hydrolysis constant

Ksi [ML-1

] : Half saturation constant of limiting substrate

Kx1 : Contois constant

m [M] : Mass of substrate

ma [M] : Acidogens Mass per cell

Mhbp [M] : Mass of actual substrate hydrolyzed during buildup phase

Nc : Number of face and butt cleats (average)/per sq. meter of coal

ncp : Number of coal particles (average 8 m size) in CoalBioreactor

Nm : Number of coal matrix block per cubic meter of coal

np : Number of patches on exposed coal surface

xix

m [L] : Nano meter

P [ML-3

] : Concentration of microbes in bulk liquid

p [psi] : Wellbore pressure

pa [L2] : Area of a fully grown patch

pabp [L2] : Fractional patch area (size) covered with acidogens at any point

of time

pas-e [L2] : Actual substrate-enzyme surface area per unit area of coal

exposed to acidogens

Pav-pd [L] : Average enzyme penetration rate in coal surface

PD1 : Probability distribution representing quantity of coal in

CoalBioreactor

PD2 : Probability distribution representing % of coal carbon polymers

in coal in CoalBioreactor

PD3 : Probability distribution representing % of hydrolysable coal

carbon polymers

pesa [L2] : Actual substrate-enzyme surface area per gm of coal

pwsa [L2] : Pore water surface area

[ML-3] : Apparent density of particle substrate

Qas-pores [M] : Mass of hydrolyzed actual substrate in pores

Qh [M] : Mass of hydrolases secreted by acidogens and accumulated in

hydrodynamic boundary layer

r [L] : Radius of acidogen patch on a coal particle i.e. dpp-cp/2

R [JK-1Mol-1] : Gas Constant

rfb : Biomethane recovery factor

Rg : Biomethane to CO2 ratio in a biogas

ri [MT-1

] : Rate of hydrolysis of primary substrate

R0 [L] : Particle mean radium, initial

Rt [L] : Particle mean radium at time t

s [ML-2

] : Overburden stress

S [ML-3

] : Substrate concentration

S0 [ML-3

] : Substrate concentration in bulk liquid

Shcr [MT-1

] : Rate of hydrolysis during plateau phase or rate of hydrolysis per

patch area

Shvr [MT-1

] : Rate of hydrolysis during buildup phase

Ssurf [L2] : Surface area of organic solid

xx

Sucr [MT-1

] : Substrate utilization at constant rate during plateau phase

Suvr [MT-1

] : Substrate utilization at varying rate during buildup phase

t [T] : Time

T [K] : Temperature

Tsucr [T] : Time taken by acidogens beyond Tsuvr to fully hydrolyze the

remaining (left out) actual substrate at constant rate i.e. plateau

phase duration of actual substrate hydrolysis at constant rate

Tsuvr [T] : Time taken by acidogens to fully cover the exposed coal

surface area under a patch i.e. buildup phase duration of actual

substrate hydrolysis at varying rate

Ttsu [T] : Total time of substrate utilization

: Fraction coverage of surface

[T-1

] : Specific growth rate of acidogens

i [T-1

] : Specific growth rate of respective species

mi [T-1

] : Maximum growth rate of respective species

net [T-1

] : 1st order net growth rate constant of acidogens

m [L] : Micro meter

v : Poissons ratio

vco2(volume) [L3] : Volume of CO2 produced

vco2(mass) [M] : Mass of CO2 produced

Vi [L3L

-3] : Natural porosity of coal

vvhc [L3] : Void created due to hydrolysis of coal

wms [L] : Water molecule size

wmc/mhc [M] : Water mass consumption per unit mass of hydrolyzed coal

x(1,2,..) [L] : Diffusion path length

Yi [MM-1

] : Microbial yield coefficient

xxi

List of Figures

Sl. Nos. Title Page Nos.

Fig. 1: Overview of the Thesis 12

Fig. 2: Schematic Representation of Various Trophic Groups of

Microorganisms Involved in Anaerobic Digestion of Organic

Matters of Coal to CH4 Production 13

Fig. 3: Proposed Mechanism of Stepwise Biodegradation of Original

Material in Coal. 14

Fig. 4: Methanogenesis Pathways: Hydrogentrophic, Acetoclastic

and, Methyltrophic 16

Fig. 5: A Representative Structure of the Chemical Groups in a

Bituminous Coal 23

Fig. 6: Coal Compounds Models based on FTIR, NIR-X ray

Diffraction, NMR, py-FIMS Spectroscopy, and py-GC,

Solvent Swelling and Extraction 24

Fig. 7: Schematic of Major Constituents of Coal: Organic Material,

Inorganic Inclusions, and an Extensive Pore Network 26

Fig. 8: Oxygen in Functional Groups 27

Fig. 9: Influence of Rank on Capacity Moisture 28

Fig. 10: Brittleness of Coal 30

Fig. 11: Coal Ranks Determine Porosity 30

Fig. 12: Cleat Aperture in Coal 31

Fig. 13: Different Types of Pore in Coal 31

Fig. 14: Apparent Density of Coal 33

Fig. 15: Illustration of Hydrolysis Inhibition (A) No Inhibition, (B)

Competitive Inhibition, and (C) Non-Competitive Inhibition 41

Fig. 16: Schematic of Architectural Arrangements of Lignocellulose 51

Fig. 17: Craters on Substrate Surface due to Assimilation of

Hydrolyzed Substrate by Acidogens 54

Fig. 18: A Hypothetical Coal Structure indicating all Possible Bonds in

Coal 57

Fig. 19: The So-Called ABCDE-Mechanism of Biological Conversion

of Brown Coal 58

Fig. 20: Products of Coal Hydrolysis Shown as Intermediary Products 61

Fig. 21: Schematic of CoalBioreactor Vertical Well indicating Injection

& Collection Pipes with Representative Dimensions 73

xxii

Sl. Nos. Title Page Nos.

Fig. 22: Schematic of Fractures and Porosity in (a) Natural Coal, and

(b) After Fracturing indicating that natural porosity of coal can

be Enhanced by Inducing Fractures in coal 74

Fig. 23: Schematic of Injection of Growth Medium and Microbes in

CoalBioreactor, indicating Induced Fractures in Coal, Their

Propagation in Fractures, and Their Placement even at the

Farthest End of the CoalBioreactor 74

Fig. 24: Schematic of Coal Fractures in CoalBioreactor indicating

Pushing of Growth Medium, Microbes, & Nutrients to the

Farthest Fractured End wherein Growth Medium itself is

Acting as a Fracturing Fluid 75

Fig. 25: Schematic of Fracturing of Coal in CoalBioreactor with

Traditional Hydraulic Fracturing Fluids First and Pushing of

Growth Medium, Microbes, & Nutrients Later on 75

Fig. 26: Schematic of a CoalBioreactor indicating Impermeable

Boundaries Surrounding All Sides of the CoalBioreactor, and

a Sump with Representative Dimesnsions 78

Fig. 27: Schematic of Injection Pipes and Spent Growth Medium

Collection Pipe in the CoalBioreactor 80

Fig. 28: Schematic of Different Stages of Biofilm Formation in the

CoalBioreactor 81

Fig. 29: Schematic of Flow of Spent Growth Medium and Gases in

CoalBioreactor 83

Fig. 30: Schematic of Measurement and Control in the CoalBioreactor 85

Fig. 31: CO2 Biomethanation Pathways 89

Fig. 32: Schematic of Attached Acidogens in Coal Fractures in the

CoalBioreactor 94

Fig. 33: Schematic of Growing Monolayer Patches of Hydrolyzing

Acidogens, Acetogens Patch, and Methanogens Patch 96

Fig. 34: Schematic of Coal Matrix Blocks and Pores 99

Fig. 35: (A) Schematic of Secretion of Hydrolases by Acidogens on

Biofilm; (B) Diffusion of Hydrolases Molecules into Macro

and Some of the Mesopores of Coal Matrix Blocks; (C)

Hydrolysis of Actual Substrate in Coal Matrix Block Pores

and Diffusion of Hydrolyzed Actual Substrate; and (D)

Subsequent Acidogenesis, Acetogenesis, and Methanogenesis 100

Fig. 36: Schematic of Coal Matrix Blocks indicating Pores of Some

Selected Blocks 104

Fig. 37: Plan View of an Acidogen Patch on a Flat Coal Surface 105

xxiii

Sl. Nos. Title Page Nos.

Fig. 38: Side View of Acidogens Patch on a Coal Particle 107

Fig. 39: Growth of a Patch with Acidogen/ Coal Particle Diameter 108

Fig. 40: (a) Schematic of Enzymes in Actual Substrate-Enzyme

Surface Area, Aas-e, and (b) Schematic of Average Rate of

Enzymes Penetration (pav-pd) (Rate) in Coal Primary Substrate

Area, Ah 110

Fig. 41: Spectrum of Non-Extractable Coal Biomethanation Study 121

Fig. 42: Line Diagram of in-situ Biomethanation of Non-Extractable

Coal Study Undertaken 122

Fig. 43: Schematic of Concept of a Model 123

Fig. 44: Schematic of Engineering-Economic Model indicating

Components of Engineering Model and Mathematical

Economic Model 125

Fig. 45: Schematic of Mathematical Model of Biomethanation of Coal

in CoalBioreactor 126

Fig. 46: Forecasted Range and Statistics of Ttsu of Stochastic

Exploratory SSCC Model for Complete Hydrolysis of Actual

Substrate 147

Fig. 47: Forecasted Range and Statistics of NPVs of Stochastic

Exploratory SSCC Model 147

Fig. 48: Certainty Percentage of Mean NPV of Stochastic-SSCC

version of Model 148

Fig. 49: Certainty Percentage of Mean BMP of Stochastic-SSCC

Version of Model 148

Fig. 50: Probability Distribution of Tsuvr of Stochastic Exploratory

SSCC Model indicating (a= 10.01, b= 25.19, and m= 15.3

years) 149

Fig. 51: Sensitivity Charts of Stochastic-SSCC Version of Model for

Tsuvr and Tsucr 150

Fig. 52: BMP of VIV-FBPTD-S-SSCC Model 152

Fig. 53: NPV of VIV-FBPTD-S-SSCC Model 152

Fig. 54: (a) Sensitivity Chart of BMP of VIV-FBPTD-S-SSCC Model 152

(b) Sensitivity Chart of NPV of VIV-FBPTD-S-SSCC Model 153

Fig. 55: Estimates of Rate of Anaerobic Hydrolysis of Coal in-situ

during Buildup and Plateau Phase or Estimates of Proportional

Quantity of Substrate Depleted during Buildup and Plateau

Phase Duration 164

Fig. 56: Model Verification and Validation Process in a Simpler Form 168

xxiv

List of Tables

Sl. Nos. Title Page Nos.

Table 1: Total Primary Energy Requirement 2

Table 2: Indias Hydrocarbon Reserves 2

Table 3: Scenario Summaries for 8% Growth Fuel Mix in year 2031-32 3

Table 4: Ranges of Commercial Energy Requirement, Domestic

Production, and Imports for 8% Growth for year 2031-32 4

Table 5: Chemical Structural Comparison of Coals of Various Ranks 22

Table 6: Chemical Composition of a Typical (C100H85O21N1S0.3) Low

Rank Sub-bituminous Coal 26

Table 7: Functional Groups of Oxygen in Coal 27

Table 8: Oxygen Functional Group Distribution Reflects Coal Rank 28

Table 9: Water Surface Area in Different Raw Coals of Various Rank 29

Table 10: Kinetic Models in the Literature for Describing Hydrolysis 46

Table 11: First Order Hydrolysis Rate Constant 47-48

Table 12: Process Based Cellulose Degradation Models 49

Table 13: Chemical Composition (%) of Different Lignocellulosic

Biomass 92

Table 14: Composition of Various Potential Lignocellulosic Biomass 92

Table 15: Fact-Sheet of the CoalBioreactor Biomethanation Project 129

Table 16: Data-Sheet of the CoalBioreactor Biomethanation Model 130-134

Table 17: Cost-Sheet of the CoalBioreactor Biomethanation Model 135-136

Table 18: Estimated Parameters Values by Different VIV-FBPTD-S-

SSCC Model Prototypes 156

Table 19: Forecasted Parameters Values of Coal Biomethanation Project

under Varying Percentage Reduction in ASESA and EPR 159

Table 20: Impact of Varying Microbial Geological Biomethanation

Conditions (MGBC) of Coal 161

Table 21: Forecasted Parameters of 3 Prototypes of WMGBC Model:

General, 20% ECBCF-ECHRMP, and 20% ECBCF-ECHRMP

and EMPCF 162

1

CHAPTER 1: INTRODUCTION

Energy powers the nations industries, vehicles, homes, and offices.

Therefore, energy, especially in the form of electricity, is the lifeline of modern

civilization, and a country cannot be economically and militarily powerful unless it

has ensured not only uninterrupted supply of its energy requirement but full energy

security i.e. supply of safe, convenient, and environment benign energy to all its

citizens to satisfy their various needs at affordable costs, at all times, with prescribed

confidence level, considering shocks & disruptions that can be reasonably expected.

1.1 Energy Scenario of India

India has been endowed with both fossil and non-fossil energy resources

including renewable, exhaustible, and nuclear energy. Though country has large

reserve of thorium, reserves of oil & gas, and uranium are meager. Coal is abundant

but of poor grade. Hydro potential is significant (1, 50,000 MW) but small compared

to countrys needs. Renewable energy (RE) and non-conventional energy sources are

having great potential, if getting exploited on commercial basis, but most of them are

still at laboratory stage only.

1.1.1 Energy Demand

To deliver a sustained growth rate of 8 % through 2031-32 and to meet the

lifeline energy needs of all citizens, India needs, at the very least, 1836 to 2043

Million Tonne of Oil Equivalent (Mtoe) total primary energy (Table 1) and 8,00,000

mega watt (MW) of electricity generation capacity (Parikh, 2006).

1.1.2 Energy Supply

Energy supply is constrained by countrys energy resources. Hydro-Carbon

Energy Reserves indicated in Table 2 reveals that India is not well endowed with

natural energy resources. Though coal is abundant (276.810 Billion Tonne - BT, as

on 1.4.2010) and has low sulfur, but it has high toxic elements (Masto et al., 2007).

Moreover, it is regionally concentrated and is of low calorie and high ash content.

2

Table 1: Total Primary Energy Requirement (Mtoe) (adopted from Parikh, 2006)

Year TPCES TPNCES TPES

8% 9% 8% 9% 8% 9%

2006-07 389 397 153 153 542 550

2011-12 496 546 169 169 665 715

2016-17 665 739 177 177 842 916

2021-22 907 1011 182 181 1089 1192

2026-27 1222 1378 184 183 1406 1561

2031-32 1651 1858 185 185 1836 2043

Table 2: Indias Hydrocarbon Reserves (Mtoe) (adopted from Parikh, 2006)

Resources Unit Proved Inferred Indicated Production

in 2004-05

Net Import

in 2004-05

Reserve/

Production Ratio

(P) (I) (Q) (M) P/Q (P+I)/Q

Coal (As on

1.1.2005

Mtoe 38114 48007 15497 - - - -

Extractable

Coal

Mtoe 13489 9600-15650 157 16 86 147.18

6

Lignite (As

on 1.1.2005)

Mtoe 1220 3652 5772 - - - -

Extractable

Lignite

Mtoe 1220 - - 9 - 136 136

Oil (2005) Mt 786 - - 34 87 23 23

Gas (2005) Mtoe 1101 - - 29 3 (LNG) 38 38

Coalbed

Methane

Mtoe 765 - 1260-2340 - - - -

In-situ Coal

Gasification

? ?

1.1.3 Options to Bridge the Gap

India must expand its energy resource base, seek new and emerging energy

sources, and pursue technologies that maximize energy efficiency, demand side

management, and conservation.

In Integrated Energy Policy of Planning Commission, Govt. of India (GOI),

11 numbers of energy mix (scenarios) (Table 3) has been envisaged to meet the

above energy demand. These scenarios indicate 29 to 59% energy import

dependence (Table 4).

1.1.4 Augmentation of Resources

Energy resources can be augmented by exploration to find more coal, oil, and

gas, or by recovering a higher percentage of the in-place reserves.

3

Table 3: Scenario Summaries for 8% Growth Fuel Mix in year 2031-32 (adopted from Parikh, 2006)

Scenario No. 1 2 3 4 5 6 7 8 9 10 11

Scenario

Description

Coal

Dominant

Case

Forced

Hydro

Forced

Nuclear

Forced

Nuclear

+

Hydro

Forced

Nuclear

+

Hydro

+

Gas

Forced

Nuclear

+

Hydro

+

Gas

+

DSM

Forced

Nuclear

+

Hydro

+

Gas

+

Coal eff.

Forced

Nuclear +

Hydro +

Gas +

DSM +

Coal eff.

Forced

Nuclear +

Hydro +

Gas +

DSM +

Coal eff. +

Rail share

up

Forced

Nuclear +

Hydro +

Gas +

DSM +

Coal eff. +

Rail share up +

Transport eff.

Scenario 10

+

Forced

Renewable

A. Mtoe

Crude Oil 486 485 486 485 486 486 485 485 447 361 350

Natural Gas 104 105 104 105 197 174 191 171 171 171 150

Coal 1022 953 998 929 835 715 818 698 701 707 632

Hydro 13 35 13 35 35 35 35 35 35 35 35

Nuclear 76 76 98 98 98 98 98 98 98 98 98

Renewable 2 2 2 2 2 2 2 2 2 2 87

Non-

commercial

185 185 185 185 185 185 185 185 185 185 185

Total 1887 1842 1885 1839 1837 1695 1813 1673 1639 1558 1536

Total without

Non-

Commercial

1702 1655 1700 1554 1652 1510 1628 1488 1454 1373 1351

B. Percentage

Crude Oil 25.7% 26.4% 25.8% 26.4% 26.4% 28.7% 26.8% 29.0% 27.3% 23.2% 22.8%

Natural Gas 5.5% 5.7% 5.5% 5.7% 10.7% 10.3% 10.5% 10.2% 10.5% 11.0% 9.8%

Coal 54.1% 51.8% 52.9% 50.5% 45.5% 42.2% 45.1% 41.7% 42.8% 45.4% 41.1%

Hydro 0.7% 1.9% 0.7% 1.9% 1.9% 2.0% 1.9% 2.1% 2.1% 2.2% 2.2%

Nuclear 4.0% 4.1% 5.2% 5.3% 5.3% 5.8% 5.4% 5.9% 6.0% 6.3% 6.4%

Renewable 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 5.6%

Non-

Commercial

9.8% 10.1% 9.8% 10.1% 10.1% 10.9% 10.2% 11.4% 11.3% 11.9% 12.0%

Total 100 100 100 100 100 100 100 100 100 100 100

4

Developing the thorium cycle for nuclear power and exploiting non-

conventional energy, especially solar power, offer possibilities for energy

independence beyond 2050.

Table 4: Ranges of Commercial Energy Requirement, Domestic Production, and Imports for

8% Growth for year 2031-32 (adopted from Parikh, 2006)

Fuel Range of

Requirement

in Scenario

(R)

Assumed

Domestic

Production

(P)

Range of

Imports*

(I)

Import

(Percent)

(I/R)

Oil (Mt) 350-486 35 315-451 90-93

Natural Gas (Mtoe)

including CBM

100-197 100 0-97 0-49

Coal (Mtoe) 632-1022 560 72-462 11-45

TPCES 1351-1702 - 387-1010 29-59

*Range of Imports is calculated across all scenario as follows:

Lower bound = Minimum requirement Maximum domestic production

Upper Bound = Maximum requirement Minimum domestic production

Domestic production of oil & gas will depend critically on new findings.

With a concerted push and 40-fold increase in contribution to primary energy,

renewable may account for only 5 to 6% of their contribution in Indias energy mix

by 2031-32.

Therefore, under the emerging energy mix, coal would remain the main stay

fuel for the country till 2031-32 and possibly beyond, despite rising environmental

concerns and contribution of almost 40% of the green house gases (GHGs) (Parikh,

2006).

1.1.5 Coal Mainstay Fuel

At present, coal accounts for over 50% of Indias commercial energy

consumption and 78% of domestic coal production are dedicated to power

generation, which is likely to increase in future. In this coal based development the

total demand for coal will increase to 2700 MT (Million Tonne) (i.e. equal to 1080

Mtoe) by 2031-32, which might be as high as 2842 MT, if 5% quality deterioration

over next 25 years is being considered, whereas the projected coal production is only

about 1400 Mt by 2031-32 for coal & lignite combined together.

5

1.2 Energy Security of India

Above discussion reveals that countrys energy is under supply, market, and

technical threats/risks.

1.2.1 Environment Security under Threat

Continuance with fossil fuels, especially with coal causes serious threats to

the environment. Combustion of coal contributes adverse environmental impacts of

local concern such as deforestation, land degradation, water & air pollution, and of

global concern such as GHGs. Though India is not required to curtail its green house

gas (GHG) emissions, as a signatory to the UNFCCC (Framework Convention on

Climate Change) and a country that has acceded to the Kyoto protocol, and emerges

as a relatively low carbon economy by global comparison, offsetting emissions in

line with the performance of the global economy (Anonymous, 2007), however,

India is very active in proposing Clean Development Mechanism (CDM) projects.

Moreover, the impact on the countrys poor, due to climate change, could be

serious. In a study conducted by The Energy Resources Institute (TERI) in 1997, the

costs of environmental damage in India, measured in terms of loss in potential gross

domestic product (GDP), have been estimated to be in excess of 10% (Anonymous,

2002a). Obviously coal is the main contributor to this damage, combustion of which

is expected to emit about 1 BT at present, to 5.5 BT per year by 2031-32 (Parikh,

2006).

Thus, under envisaged coal based energy development model, both energy

and environment securities of India are under threats (Parikh, 2006; and Anonymous,

2002a).

1.2.2 Challenges

To overcome these threats, challenges with country are to grow cleaner

energy supply from coal or/and other alternative sources.

6

1.3 Non-Extractable Coal

Out of total Indian coal geological resources, the coal reserve to extractable

coal reserves ratio is 4.7:1 (Anonymous, 2001a), thus, only 21.27 % of the

geological reserves are extractable under business as usual (BAU) scenario.

Even under envisaged best-case scenario (BCS) the percentage of extractable

reserves are likely to enhance say by another 50%. Though it is an optimistic

estimate but even then also extraction of only about 30% of the total coal geological

resources would be possible. 70% of the coal would be non-extractable, thus the

energy contained therein would either remain untapped and unrecovered forever or at

the most would be underutilized.

The scenario is likely to be the same worldwide, may be with a bit varying

environmental damage cost, and changed geological resources to extractable reserves

ratio.

The very high reserves of non-extractable coal contain a huge quantity of

energy in solid form (coal), gaseous form (methane i.e. coalbed methane, CBM), and

geo-thermal form. At the same time these are a good CO2 sink as well. Therefore,

these coals are an obvious and natural target for extraction of cleaner energy to

improve energy and environment security.

Adoption of coalbed methane (CBM) and thermal underground coal

gasification (UCG) technologies are efforts in this direction.

1.3.1 CBM Recovery

CBM recovery technology extracts energy available only in gaseous methane

form.

1.3.2 In-situ Thermal UCG

Extraction of energy available in solid coal form is possible through thermal

UCG. UCG is a method to utilise non-extractable coal resources between 30 to 800

meters depth for extracting energy available in solid coal form. It involves controlled

combustion of in-situ underground coal by injecting air, or oxygen, and steam down

a borehole from the land surface, and collecting the resultant combustion product i.e.

7

synthetic or syn gas consisting of CO, H2, CH4, CO2, H2O & N2 from a nearby

borehole. The method is ecologically very favourable because residual of coal after

combustion are being left in the deposit itself. Capture of higher concentrated CO2

not to be emitted in atmosphere, would be much more cost effective that could be

sequestered in the coal mines itself or elsewhere. Moreover, thermal UCG with or

without sequestration would be eligible for carbon credit.

This deceptive simplicity of thermal UCG has attracted many countries, and

off and on, for more than 100 years, it has been practiced by almost all-major coal

producing countries. However, its application, as a large-scale coal to syn gas

conversion technology for energy extraction, proves more difficult

(Anonymous,

2001b). UCG is only suitable for those non-extractable coal deposits that have low

water content. It is not suitable for aquatic coal deposits. Its suitability and

applicability is narrow and limited. While extracting effectively only a low

percentage of the energy available in coal; an UCG operation spoils the target coal

completely (Gayko, 2004), and the residual coal loses its further utility fully. Though

efforts are going on to commercialize this technology, however, its application, as a

large-scale coal to synthetic gas conversion technology

for energy extraction at

commercial scale, has not yet been deployed anywhere in the world (Parikh, 2006).

Therefore, it is imperative to identify, examine, and develop more efficient,

natural, and nature friendly innovative energy extraction technologies to extract

energy available in solid coal form in non-extractable coal deposits.

1.3.3 Non-Extractable Coal as a CO2 Sink

Carbon dioxide (CO2) has greater affinity to the solid surface than methane.

CO2 gets preferentially adsorbed over methane by coal, and therefore, non-

extractable coal is likely to be a good CO2 sink with subsequent conversion of a

fraction of CO2 to CH4 (biomethane) using designer microbes or bio-mimetic

systems (Beecy et al., 2007w).

8

1.4 In-situ Biomethanation of Non-Extractable Coal

Biomethanation or methanogenesis is a natural, ongoing, and complex

phenomenon of conversion of coal to biogenic methane (termed as biomethane now

onwards) by indigenous microbes. The conversion process occurs at a slow pace but

it would likely be accelerated to its optimum level by biostimulation and/or

bioaugmentation of non-extractable coal that have favourable environment for

sustainable microbial growth. It is expected that this optimum conversion

(biomethane harvesting) rate would be adequate to support commercial production of

harvested biomethane. Several agencies worldwide are currently exploring this

innovative possibility in search of a viable technical and economical solution. Luca

Technologies, Microbially Enhanced Coalbed Methane (MECoM) programme,

Alberta Research Council, and other agencies, based on lab experimentations, have

indicated harvesting of 0.03 to 30 m3 methane/ day/ tonne of coal (Anonymous,

2007wc; and Budwill, 2007). This indicates that technical feasibility of coal

biomethanation has been established in lab.

There are some documented proposals and schemes for acceleration of

existing natural biomethanation process in-situ. However, at present, there is no

documented proposal and scheme for in-situ biomethanation of coal that lacks

favourable environment for microbial growth.

To improve energy and environment security of an economy further,

application of innovative approach of in-situ coal biomethanation needs to be made

possible in all types of coal including non-extractable coal. This is proposed under

this study.

1.4.1 Hypothesis

Biomethanation of all types of coal in-situ is techno-economically feasible.

1.5 Objectives of the Research Work

To prove this hypothesis, it is imperative to increase knowledge and

understanding about in-situ coal biomethanation process, its imitation in coal that

lacks favourable natural environment, and mechanism of biomethanation

acceleration. It is likely that all combined and applied together would make

9

biomethanation of all types of coal feasible. Study of this inter-disciplinary research

work, a system or project for which is yet not existing, is proposed through following

objectives:

(i) Development of a mechanism for economical and accelerated methane

harvesting in non-extractable coal in-situ, and

(ii) Development of an engineering-economic model of envisaged

biomethanation project to carry out benefit-cost-analysis and risk analysis

using Monte Carlo simulation for identification of best technology mix to

optimize sustainable methane energy recovery and CO2 reduction potential.

1.6 Scope and Organization of the Research work

The thesis starts with discussing energy & environment security scenario of

the country under non-utilization or wasteful utilization of non-extractable coal. It

suggests application of innovative approach of biomethanation of non-extractable

coal in-situ for improving the energy and environment security. It hypothesizes that

biomethanation of all types of coal in-situ is techno-economically feasible. The

study is proposed through development of an in-situ mechanism for biomethane

harvesting and a mechanistic engineering-economic model of an envisaged in-situ

non-extractable coal biomethanation project, which would use this mechanism. All

these have been discussed in chapter 1. Coal as a substrate for biomethanation is

very complex and poorly understood. Literature review in chapter 2 suggests that in

anaerobic digestion of a solid, dry, and insoluble complex substrate like coal

(primary substrate), hydrolysis is likely the rate limiting step. Therefore, coal

hydrolysis has been discussed separately in detail in chapter 3 that indicates that

though depolymerization and solubilization are the two most important steps in coal

hydrolysis, however, release of enzymes by acidogens (microbes that make

anaerobic hydrolysis of coal possible) and mechanisms involved in the enzymatic

catalysis of coal under anaerobic condition are yet to be fully understood. However,

an important factor for improving hydrolysis is to bring the hydrolytic enzymes in

close contact with the minutest hydrolysable coal carbon polymers (actual substrate).

This is possible through development of a mechanism, which leads to design, in-situ

10

construction, and operation of a CoalBioreactor (unregistered trade mark of the

mechanism) in a target coal. This has been discussed in chapter 4. The design and

construction of the CoalBioreactor is such that it makes in situ biomethanation

possible in all types of coal including extractable, non-extractable, aquatic, coal with

a low water content, and coal in which natural microbial geological biomethanation

conditions (MGBC) are whether favourable or completely absent.

CoalBioreactor is an engineered-natural, at times imitated, accelerated,

trouble-free, and cost-effective in-situ biomethanation mechanism that consider

hydrolysis as the rate limiting step. It can be applied gainfully in any and all types of

coal for commercial recovery of harvested biomethane in a reasonably carbon-

neutral way. It is a low operational cost, single stage, mesophilic, slow rate,

heterogeneous, over-pressured, batch bioreactor that is surrounded by an

impermeable boundary. The purpose of the CoalBioreactor design with impermeable

boundary is to ensure (i) free flow and spread of growth medium, microbes,

nutrients inside the CoalBioreactor, so that (ii) microbial population grow and cover

maximum surfaces of coal exposed to them for its effective biomethanation, and (iii)

biomethane generated (harvested) flow freely and accumulates in CoalBioreactor

production well, for its (iv) maximum recovery (v) by sequestering produced CO2,

harvested during coal biomethanation, back in the CoalBioreactor itself; but at the

same time, not to allow (vi) flow and spread of growth medium, microbes, nutrients,

and biomethane to outside (surroundings) from the CoalBioreactor, and (vii) flow of

other things (materials) inside the CoalBioreactor from the surroundings.

CoalBioreactor kinetics (operational effectiveness of CoalBioreactor) has been

discussed in chapter 5. It formulates buildup phase time duration (i.e. the time

acidogens take to fully cover the entire coal surfaces exposed to them) of substrate

depletion/ utilization at varying rate (Tsuvr) on basis of which other parameters like

plateau phase time duration of substrate depletion/ utilization at constant rate (Tsucr),

total time of substrate utilization (Ttsu), biomethanation potential (BMP), CO2

sequestration potential (CSP), commercial energy availability (CEA), CO2 produced,

project carbon-neutrality (PCN), net present value (NPV), etc. have been

formulated. The multi-disciplinary in-situ non-extractable coal biomethanation

project to date is an early stage, high risk, uncertain, technology based project in

11

which the core of the project i.e. in-situ coal biomethanation is a less defined,

complex, and uncertain process. Obviously people at present are apprehensive about

success of an in-situ coal biomethanation project. This apprehension has been

attempted to be cleared through development of a mathematical-economic simulation

model. Model inputs are based on abstraction and generalization of limited empirical

hard data documented in literature. It also includes secondary data derived from these

primary hard data based on mechanistic logical conclusion, and initial assumption of

some of the missing inputs. Mathematical model computes forecasted parameters

(outputs) on discrete points in time. All these have been discussed under engineering-

economic model in chapter 6. CoalBioreactor discussed in chapter 4, its kinetics

discussed in chapter 5, and mathematical model discussed in chapter 6 are the

integral part of the engineering model. CoalBioreactor and its kinetics together

provide the basic framework for development of the mathematical model. Economic

model estimates economics (NPV) of the envisaged coal biomethanation project by

carrying out benefit cost analysis (BCA) of the project. The CoalBioreactor

Biomethanation model (name used for mathematical-economic model) is a dynamic,

quantitative, lumped, stochastic, mechanistic, continuous, sub-model based,

simulation model developed in Microsoft Excel Spreadsheet with Crystal Ball add-in

that use Monte Carlo simulation for risk analysis. Chapter 7 is results and

discussions of this thesis. Exploratory analysis of model rudimentary estimates

(forecasts) of buildup phase time duration, and inputs values help in identifying more

rational values of inputs. Simulation run of the model with these validated inputs

values estimates the ultimate probability distribution of the forecasted parameters.

Decision makers know the certainty level attached with each forecasted result, and

therefore the risk associated in accepting a decision. Model has been used as an

exploratory e-laboratory to get answers of all queries that relate to in-situ

biomethanation of non-extractable coal including apprehensions, inhibitions, and

expected hopes for further improvement in CoalBioreactor operational performance.

By identifying appropriate operational technology mix, model demonstrates techno-

economic feasibility of biomethanation project even in a worst MGBC coal. This

verifies the hypothesis. Analysis of impact of non-extractable coal on economy

suggests average net earnings of more than 340 INR per tonne of coal in-situ.

12

Considering 2031-32 as the base year, CEA of 66% of energy contained in coal alone

would meet primary energy requirement of country for more than 37 years and PCN

of 72.54% would mitigate total emission of country for more than 40 to about 60

years. Breakthrough of designer microbial consortium formulation is likely to pave

way for sustainable energy and environment security. Chapter 8 examines the model

and finds that it is a verified and virtually validated (V & VV) model. Chapter 9

concludes the thesis indicating that with design and construction of an in-situ

CoalBioreactor, it is possible to imitate and harvest biomethane in any and all types

of coal, and recover it commercially in a reasonably carbon-neutral way to add to

energy and environment security of an economy. A complete overview of thesis is

given in Fig. 1.

Test the Effects of Changes in the Coal Biomethanation Project

(Perform Experiments)

Ad

just

Da

ta C

olle

ctio

n

CoalBioreactor Kinetics

Coal Biomethanation in

CoalBioreactor Math

em

ati

cal-

Eco

no

mic

Mo

del

Co

alB

iore

acto

r-

Bio

meth

an

ati

on

Mo

del

Economic Model

Inputs from Literature and

Industry (CBM &

Biotechnology)

Co

al B

iom

eth

an

ati

on

Pro

ject

Data Collection

Mathematical Model Scientific Understanding

(Present Knowledge - What we know,

and what we do not know)

Decision Making

Strategic Decision by Planners

Tactical Decision by Decision Makers

Non-Extractable CoalWasteful and under Utilized

- Energy and Environment

Security - Problem

Chapter -1

Biomethanation of Non-Extractable CoalAn Innovative Technology and Proven Process

under Favourable Environment

Biomethantion of All Types of Coal in-situ

Feasible - Hypothesis

Literature Review for Enhanced Understanding

Chapter - 2Biomethanation of Coal

CoalCO

2 Sequestration

Anaerobic Hydrolysis of Complex Substrate

Chapter - 3Coal (Solid, Dry, Insoluble Complex

Substrate) Hydrolysis

Chapter - 4

Chapter - 5

Engineering- Economic ModelChapter - 6:

Chapter - 7: Results and Dicussions

Chapter - 8: Verification and Validation of Models

Chapter - 9: Conclusions

En

gin

eeri

ng

Mo

del

Future Scope of Works

References: Separately indicating Websites (sffixed w after the year of publication/ access)

List of Patents and Publications

Fig. 1: Overview of the Thesis

Future scope of work, references separately indicating websites (suffixed w

after the year of publication for convenience), and list of patents and publications

related to this research are given at the end.

13

CHAPTER 2: LITERATURE REVIEW

The bio-availability of coal carbon polymers, the presence of a microbial

community to convert coal carbon to biomethane, and an environment supporting

microbial growth and methanogenesis; all combined control coal deposits

biomethanation (Elizabeth et al., 2008). Therefore, to understand coal

biomethanation, it is prudent to discuss related topics like coal chemistry, anaerobic

hydrolysis of complex substrate that would likely help in understanding

bioavailability of coal carbon polymers, and CO2 sequestration to improve recovery

of biomethane.

2.1 Biomethanation of Coal

Biomethanation of coal is an anaerobic digestion (AD) process by microbes.

The microbiology of anaerobic digestion of coal is complicated (Gavala et al., 2003).

Microbes that are involved in biomethane production have synergistic and syntrophic

relationships among them (Anonymous, 2008wa). Several microbial groups are

involved in AD. Each group performs a specific role in the overall degradation

process. Figure 2 and 3 illustrate the usual four steps of hydrolysis, acidogenesis,

acetogenesis, and methanogenesis involved in AD of coal.

Fig. 2: Schematic Representation of Various Trophic Groups of Microorganisms Involved in

Anaerobic Digestion of Organic Matters of Coal to CH4 Production

Methanogenic Microbes

H2 Consuming and Acetotrophic Methanogens

Coal Complex Polymers (Polycyclic Aromatics, Phenols,

Long Chain Aliphatic)

Colloidal Polymeric

Substances

Monomers (Fatty Acids, Sugars, Amino Acids,

NH3, HS-, CO2, Acetate, H2)

Acetate, H2, CO2

CH4 + CO2

Carbonate Reduction CO2+4H2 = CH4 +2H2O

Acetoclastic

CH3COO- + H2O = CH4 + HCO3

-

or CH3COO

- + H

+ = CH4 + CO2

Hydrolytic Fermentative Microbes

Hyd

roly

tic F

erm

en

tativ

e M

icro

be

s

Syntrophic Acetogenic

Microbes

14

Though coal is extremely rich in complex organic matter, however, coal is a

solid rock, often dominated by recalcitrant, partially aromatic, and largely lignin

derived macromolecules that are relatively resistant to biodegradation. The organic

materials of coal (plant constituents or macerals) exhibit varying degree of resistance

to microbial degradation. Carbohydrates, proteins, and certain lipids get degraded

readily, whereas resins, lignins, and terpens are perhaps more stable to microbial

attack (Crawford, 1992).

Fig. 3: Proposed Mechanism of Stepwise Biodegradation of Original Material in Coal

(Macromolecular Coal and Coal Derived Oil), Annotated with Microbes Related to

Those Found in the Clone Library and Potentially Capable of Performing the Indicated

Process: (i) Defragmentation of Coal Geomolecular Structure Predominated by Fermentation Targeted at Oxygen-Linked Moieties and Oxygen Containing Functional Groups (This

Process Detaches Some of the Oxygen-Linked Aromatic Rings and Generates some Short

Organic Acids); (ii) Anaerobic Oxidation of Available Aromatic and Aliphatic Moieties,

Derived from Coal Defragmentation or From Dispersed Oil; (iii) Fermentation of Products

Available From Step (i) Described Above to H2, CO2, and Acetate; and (iv) Methanogenesis

Utilizing H2 and CO2 likely Predominating Over Homoacetogenesis and Acetoclastic

Methanogenesis. The Dark Area Represents a Droplet of Oil. (Adopted from Strapoc et al.,

2008)

Figure 2 and 3 suggest that hydrolyzing and fermenting microorganisms

(anaerobic acidogens carry out anaerobic hydrolysis) are responsible for initial attack

on macromolecular polymers found in coal and produce colloidal polymeric

substances that reduce into monomers subsequently. Under methanogenic conditions,

the paradigm for microbial conversion of complex organic matter to methane

involves the primary fermentation of polymers and monomers to fatty acids, organic

1Spirochaeta, 2Sporomusa, 3Cytophaga, 4Acidominococcus, 5Flavobacterium, 6Methanocopusculum, 7Rhodobacter

15

acids (e.g., lactate, succinate, acetate), alcohols (e.g., methanol), and hydrogen and

carbon dioxide. Degradation subsequently follows via secondary fermenting

microbes (syntrophs); homoacetogenic microbes; and acetoclastic, methyltrophic,

and hydrogentrophic methanogens (Schink, 2006). The same investigators have

hypothesized that this metabolic model is applicable to the bioconversion of coal to

biomethane as well.

In addition to the above fermentation products, coal matrices are also a source

of other substrates such as methylamines, methylsulfides, ethanol, and carbon

monoxide. The requisite methanogenic pathways can differ among basins, fields, and

wells and can depend on the physicochemical properties of the microenvironment

(Strapoc et al., 2010).

Biomethane generation from coal by microbial consortia has been

documented previously. Microflora, present in water, leached from coal mines were

shown to generate biomethane (Thielemann et al., 2004). A biomethane generating

consortium, extracted from coal was observed to grow on low volatile bituminous

coal as a sole carbon source (Shumkov et al., 1999).

2.1.1 Methanogenesis Pathways

Although there is one known methanogenic species, Methanosacineae,

metabolically and physiologically the most versatile methanogens, that can utilize up

to nine substrates (Anonymous 2011wa) like formate, CO, CO2, methanol, acetate,

methylated amines, short-chained alcohols, methyl mercaptan, etc. through three

methanogenic pathways; however, most other methanogens are highly specialized

and are capable of metabolizing only one or two substrates. However, extensive

biochemical studies have led to four pathways of methanogenesis (Paul and Metcalf,

2005), hydrogentrophic, acetoclastic, methyltrophic, and methyl reduction (Welander

and Metacalf, 2005), which can be represented by equations as given below (Zamri,

2010):

Hydrogentrophic 42 + 2 = 4 + 22 0 = 1311 (1)

Acetoclastic 31 + + = 4 + 2 0 = 36 1 (2)

16

Methyltrophic 43 + 22 = 34 + 2 + 42 0 = 36 1 (3)

Methyl Reduction 3 + 2 = 4 + 2 0 = 36 1 (4)

Out of these methanogenesis pathway most common are CO2-H2 reduction

(hydrogentrophic) and acetoclastic. Hydrogentrophic pathway using CO2 and H2 as

substrate is the widest spread, being found in all methanogenic orders.

Hydrogentrophic, acetoclastic, and methyltrophic methanogenesis pathways are

shown in Fig. 4 (Bapteste et al., 2005).

formate CO2

W-containing formyl-MF dehydronase (fwdHFGDACB)MF + XH2

Methanofuran

H2O + X Mo-containing formyl-MF dehydronase (fmdECB)

formyl - MF

formyl-MF: H4MPT formyltransferase (ftr)

H4MPT

MF

Methanopterin (mpt)

formyl - H4MPT

H2O

methenyl-H4MPT cyclohydrolase

(mch)

methenyl-H4MPT

F420

- H2 or H2

F420

Coenzyme F420

(cof) F420 - reducing methylene - H4MPT dehydrogenase (mtd)

H2-forming methylene-H

4MPT dehydrogenase (hmd)

methylene -H4MPT

F420 - H2

F420 methylene -H

4MPT reductase (mer)

methyl -H4MPT acetate

methylene -H4MPT methyl transferase (mtrEDCBAFGH)

CoM-SH

H4MPT

Coenzyme M (com)

methyl -CoM methyl-amines

methanol

methyl-sulfidesCoB-SH

CoM-S-S-CoB

CH4

methyl coenzyme M reductaseI (mcrBDCGA)

methyl coenzyme M reductase II (mrtBDGA)

Fig. 4: Methanogenesis Pathways: Hydrogentrophic (solid black arrows), Acetoclastic (double-

black arrows), and Methyltrophic (broken black arrows) (adopted from Bapteste et al., 2005)

17

2.1.1.1 Hydrogen Pathways

In the absence of methanogens and other microorganisms that consume

hydrogen, the bioconversion of coal into hydrogen is possible. Fermentative

hydrogen producers, that may be facultative or obligate anaerobes, are reported in

many natural environments including the nutrient-poor Sargasso Sea (Steven et al.,

1987), from flowers, and organic waste using anaerobic bacteria (Dreszer, 2004).

There are reports of hydrogen gases being produced from coalbeds suggesting that

undiscovered fermentative hydrogen producers are present in some coal beds and

possibly in other organic-rich substrates. Fermentative hydrogen producers such as

these can be collected and added to the microbial consortia injected into the

subsurface to increase hydrogen production, which in the presence of methanogens,

will result in increased methane generation.

2.1.2 Microbial Growth and Environment

The survival and growth of microbes depend on different activities such as

nutrient assimilation, catabolism, and the synthesis of living cytoplasm and all

cellular structures. Tested nutrient additions include ammonia, phosphate, yeast

extract, tryptone, milk, agar, trace metals, and vitamins (Jin et al., 2007; Pfeiffer et

al., 2010). In-situ microbially enhanced CBM stimulation performed in the Powder

River Basin showed an increase in methane production after nutrient treatment (e.g.,

phosphate) compared with the expected production decline curve. Microbes produce

different kind of enzymes. A specific enzyme performs a specific activity. Each of

these enzymes functions best in the presence of the optimal environmental conditions

of temperature, pH, osmotic pressure, and redox potential. The optimum conditions

vary for each enzyme (Anonymous, 2007wa). Therefore, every microbe has a

preferred unique environment that suits it best and provides maximum survival

potential. Conditions of preferred environment are the optimal growth conditions for

that microbe.

2.1.3 In-situ Coal Biomethanation

Coalification process (conversion of plant material to coal) involves the

burial of plant material to produce an anaerobic organic rich environment in some of

18

the regional aquifers. These aquifers may act like geobioreactor under favourable

environment for microbial activity for biomethane generation (Anonymous,

2007wc). Moreover, uplifting of higher rank coals and their exposure to meteoric

recharge can also transport microbes into permeable coalbeds to form geobioreactor

(Scott, 2001). Abandoned mines and other underground cavities frequently contain

water and in many cases are completely flooded with water to behave like a

geobioreactor. Collapse of roof and pillars expects to expose fresh coal surfaces for

microbial attack.

Several researchers have proposed subsurface enhancement of microbial

biomethane. For example, a patent by Menger et al. (2000) suggested digestion of

lignite in an underground chamber using termite micro flora composed of acid

formers and methanogens. Jin et al. (2007) suggested fracturing the reservoir for

better simultaneous nutrient delivery and enhanced surface area of coal. The only

description of a multi-well field trial has been presented in a patent application by

Pfeiffer et al. (2010).

Apart from these, biomethanation of non-extractable coal is likely to be much

more cost-effective, if being applied in CBM wells and practiced together with CBM

production to reduce drilling and other expenses, and enhancing permeability as well.

Chances of viability of in-situ coal biomethanation project are more in these

geobioreactor and CBM blocks. Therefore, these coals need to be considered as an

initial target for carrying out various R&D activities to establish in-situ coal

biomethanation feasibility. Once the technology matures there, this could be applied

cost effectively in rest of the non-extractable coals.

2.1.4 Sources of Microbial Consortia

Jin et al. (2007) have suggested and experimented with the addition of

selected microbial consortia. Coal biomethanation consortia comprises of many

types of microbial species from a dynamic microbial community. Although dominant

microbial species within the major groups of microbes may change over time, the

relative proportions of the major groups will remain relatively constant, indicating

that even dynamic communities may maintain a stable ecosystem function.

19

These consortia can be derived from: (1) commercial entities, which supply

known species of microbes, (2) undiscovered microbial species, which may be

obtained from underground coalbeds, and which have probably adapted genetically

to efficiently metabolize coal organic matter, and (3) genetically engineered bacterial

species or consortia highly adapted to convert organic compounds into methane. The

genetic material for these species may be obtained from known microbial species and

those discovered in subsurface environments. Examples of consortia used for

methanogenic inoculation with coal in laboratory settings include a cultivated

consortium indigenous to studied coal (Pfeiffer et al., 2010), a consortium obtained

from termite guts (Srivastava and Walia 1998; Menger et al., 2000), and a

consortium obtained from an abandoned coal mine used as sewage disposal in

Appalachian Basin (Volkwein, 1995).

In-situ biodegradation studies frequently show that contaminated sites will

select for organisms that can degrade the contaminant, and these indigenous

population will perform as well as or better than any foreign microbes that are

introduced to the site (Baker, 1994; and Cookson, 1995).

2.2 Coal Chemistry

Coal is a readily combustible rock containing of, more than 50% by weight

and more than 70% by volume, carbonaceous material (ASTM-American Society for

Testing and Materials). Coal is an organic rock derived from chemical and physical

transformations of plant biopolymer (cellulose, cutin, suberin, lignin, algaenon) due

to (i) micobially mediated enzymatic process (biodegradation) occurring during early

diagenis and peat formation that is peatification, and (ii) the effects of pressure and

temperature acting over long period of time following burial of the peat

(coalification) (Stach et al., 1982).

When plants, bacteria, and algae die and fall to the ground, a biochemical

transformation, mediated primarily by microbes, proceeds rapidly to utilize the

organic components as an energy source. In the presence of adequate oxygen, the

destruction is almost complete, giving rise to CO2, H2O, NO3, and SO4. However,

when the sediment is deposited in a sub-aquatic environment, the system quickly

20

goes anaerobic, and certain organic structures tend to survive the bacterial alteration

and are preserved in the sediment with minimal alteration (Stach et al., 1982).

Coal diagenesis differs i.e. different varieties of coal arise because of

differences in the kinds of plant material (coal type), degree of coalification (coal

rank), and range of impurities (coal grade). Structurally, coal is a complex system

consisting of organic material, fragment of plant debris (macerals), inorganic

inclusions, and an extensive pore network.

2.2.1 Macromolecular Geopolymers

Biopolymers in vascular plants are the most important starting materials in

the formation of coal. Especially important is the lignocellulose biopolymer, a

structural component of which is thought to provide the basic aromatic framework

for coal (Hatcher, 1990). Most of the cellulose and protein is converted to simple

organics and utilized as a food source by microbes. Lipids are the most resistant to

degradation. Trace secondary plant metabolites such as terpenes, steroids, and

alkaloids also tend to survive and become concentrated in the sediments. Complex

polymers produced by bacteria also become encapsulated in the sediment. Thus,

basically, the initial organic deposit is microbe excreta with occasional mummified

woody plant structures which by some event were pushed deep into the sediment and

escaped oxidation (Anonymous, 1993a).

In general coal geopolymers result from a random polymerization of a variety

of starting material. Although not necessarily derived from lignin, they tend to

resemble lignin in their chemical characteristics. Lignin is a structural plant polymer

that is abundant in plants and has an aromatic structure that consists of phenyl

propane subunits that are linked by C-C or C-O-C bonds (Atlas and Bartha, 1998;

Fakoussa and Hofrichter, 1999). Formation of coal starts with the decay of plant

material, which is then transformed into low rank coals (lignite and sub-bituminous).

Therefore, most low rank coal resembles lignin in structure and composition, and

have chemical structure very much like lignin, with a large number of C=O-OH and

C-OH bonds. Cations (K, Na, - - ) can substitute for H on the coal macromolecule.

21

The moisture within the coal structure is introduced from biological material

during coalification or as a result of water intrusion from the environment. Low rank

coals are relatively rich in moisture and oxygen content. With the time and right

conditions, lignite looses OH bonds and turn into higher rank coal i. e. bituminous

and ultimately forms condensed aromatic rings (free of oxygen) i.e. anthracite,

graphite. Different wet gases like ethane, propane, and other wet gases are also

sorbed in coal due to increased reservoir pressure created by artesian (well)

overpressure. The nitrogen and sulfur heterocyclic are the ones that make the coal

most environmentally unfriendly as these compounds are oxidized to SOX and NOX.

Thus, coal has heterogeneous nature and complex mixture of carbon, metal

compounds, and several other compounds such as hydrocarbons, hydrogen sulfur

compounds, hydrogen sulfide, ammonia water, and complex molecules such as tars.

Its composition varies widely according to location. Even within a coal resource, the

composition of coal may vary significantly along the x-y and z coordinates (Hatcher,

1990).

2.2.2 Structure of Coal

From a microbial CBM perspective, increasing coal maturity increases the

level of recalcitrance to biomethanation. Strapoc et al. (2011) observed a direct

negative correlation between coal m