computational fluid dinamics

COMPUTATIONAL FLUID DYNAMICS SIMULATION STUDY

ON HOT SPOT LOCATION IN A LONGWALL MINE GOB

by

Samuel Atta Lolon

A thesis submitted to the faculty of The University of Utah

in partial fulfillment of the requirements for the degree of

Master of Science

Department of Mining Engineering

The University of Utah

December 2008

COMPUTATIONAL FLUID DYNAMICS SIMULATION STUDY

ON HOT SPOT LOCATION IN A LONGWALL MINE GOB

by

Samuel Atta Lolon

A thesis submitted to the faculty of The University of Utah

in partial fulfillment of the requirements for the degree of

Master of Science

Department of Mining Engineering

The University of Utah

December 2008

THE UNIVERSITY OF UTAH GRADUATE SCHOOL

SUPERVISORY COMMITTEE APPROVAL

of a thesis submitted by

Samuel Atta Lolon

This thesis has been read by each member of the following supervisory comm ittee and by majori£)' vote has been found to be satisfactory.

Chair: Felipe CaIJzaya

Michael K. McCarter

D, Kip Solomon

THE UNIVERSITY OF UTAH GRADUATE SCHOOL

FINAL READING APPROVAL

To the Graduate Councj I of the Univers i ty of Utah:

I have read the thesis of Samuel Atta Lolon in its final fonn and have found that (1) its fonnat, citations, and bibliographic style are consistent and acceptabJe; (2) its illustrative materials includ i ng figures, tables, and charts are in place; and (3) the finaJ manuscript is satisfactory to the supervisory committee and is ready for submission to The Graduate School

.

Date Felipe Calizaya Chair: Supervisory Commltlee

Approved for the Major Department

Michael K. McCarter ChairlDean

Approved for the Graduate Council

. C'Q� ____ � David S. Chapman Dean of The Graduate School

..

ABSTRACT

Spontaneous combustion is one of the main sources for mine fires in underground

coal mines. Most of these fires are initiated in the longwall gob (caved area) by coal

oxidation. Because coal oxidation generates heat, this phenomenon is called the self-

heating process. This process will eventually create hot spots under conditions, i.e.,

oxygen concentrations of at least 5% (by volume) and gob temperatures of 100°C. Coal

properties, gob permeability, self-heating characteristics, and the ventilation system are

the key variables for the formation of these hot spots.

A study was carried out to identify the location of hot spots. The study is based on

mine ventilation surveys, laboratory experiments, and gob simulations using

Computational Fluid Dynamics (CFD). Ventilation surveys were conducted in an existing

longwall mine located in the western United States; the laboratory experiments were

performed on a physical gob model to investigate permeability (k) and airflow

distribution; and the CFD models were simulated to investigate the flow behavior in the

gob, the oxidation of coal, and heat transfer phenomena. Four CFD models were

formulated and solved, three utilized a bleeder ventilation system, and the fourth a

bleederless ventilation system. For these models, the gob length varied from 912 m to

2,445 m. The gob of each model was divided into 3 zones of different permeability:

unconsolidated (k = 4.68 xlO"7 m 2 ) , semi-consolidated (k= 3.15 x 10"8 m 2 ) , and

consolidated (k = 7.98 x 10"9 m 2 ) .

ABSTRACT

Spontaneous combustion is one of the main sources for mine fires in underground

coal mines. Most of these fires are initiated in the longwall gob (caved area) by coal

oxidation. Because coal oxidation generates heat, this phenomenon is called the self

heating process. This process will eventually create hot spots under conditions, i.e.,

oxygen concentrations of at least 5% (by volume) and gob temperatures of 100°C. Coal

properties, gob permeability, self-heating characteristics, and the ventilation system are

the key variables for the formation of these hot spots.

A study was carried out to identify the location of hot spots. The study is based on

mine ventilation surveys, laboratory experiments, and gob simulations using

Computational Fluid Dynamics (CFD). Ventilation surveys were conducted in an existing

longwall mine located in the western United States; the laboratory experiments were

performed on a physical gob model to investigate permeability (k) and airflow

distribution; and the CFD models were simulated to investigate the flow behavior in the

gob, the oxidation of coal, and heat transfer phenomena. Four CFD models were

formulated and solved, three utilized a bleeder ventilation system, and the fourth a

bleederless ventilation system. For these models, the gob length varied from 912 m to

2,445 m. The gob of each model was divided into 3 zones of different permeability:

unconsolidated (k= 4.68 xl0-7 m2), semi-consolidated (k= 3.15 x 10-8 m2

), and

consolidated (k = 7.98 x 10-9 m2).

The simulation results showed that in the models ventilated by a bleeder system,

the hot spot was located in the consolidated zone near the return side of the gob. Once

initiated, it propagated along the tailgate side as the gob progressed. The leakage flow

through the gob played an important role in determining the size and location of the hot

spot. In models ventilated by a bleederless system, the hot spot was located in the gob by

the face line. This is mainly caused by the air leakage from the headgate T junction (face)

and between the shields. It may extend further into the gob depending on the gob

permeability and the fan pressure.

In addition, these gob simulation exercises have shown that the hot spot areas in

all cases can be located accurately. This information can be used to develop suitable

control methods. The parametric studies have indicated that the ventilation system and

gob permeability are the major contributing factors for the formation of hot spots.

Although the gob models were developed for specific dimensions and ventilation system,

the results can be applied to other schemes with minor adjustments.

v

The simulation results showed that in the models ventilated by a bleeder system,

the hot spot was located in the consolidated zone near the return side of the gob. Once

initiated, it propagated along the tailgate side as the gob progressed. The leakage flow

through the gob played an important role in determining the size and location of the hot

spot. In models ventilated by a bleederless system, the hot spot was located in the gob by

the face line. This is mainly caused by the air leakage from the headgate T junction (face)

and between the shields. It may extend further into the gob depending on the gob

permeability and the fan pressure.

In addition, these gob simulation exercises have shown that the hot spot areas in

all cases can be located accurately. This information can be used to develop suitable

control methods. The parametric studies have indicated that the ventilation system and

gob permeability are the major contributing factors for the formation of hot spots.

Although the gob models were developed for specific dimensions and ventilation system,

the results can be applied to other schemes with minor adjustments.

v

To my parents: Jan and Elisabeth Lolon,

for their love and prayers

To my parents: Jan and Elisabeth Lolon,

for their love and prayers

TABLE OF CONTENTS

ABSTRACT iv

LIST OF TABLES x

LIST OF FIGURES xii

ACKNOWLEDGMENTS xv

CHAPTER

1. INTRODUCTION 1

1.1 Statement of Problems 1

1.2 Thesis Overview 4

2. BACKGROUND AND LITERATURE REVIEW 6

2.1 Longwall Mines in the United States 6 2.2 Ventilation Systems for Longwall Mines 10

2.2.1 U-Tube System 10 2.2.2 Y System 12 2.2.3 Wrap-Around System 14

2.3 Spontaneous Combustion in the Gob 15 2.3.1 Mechanism of Self-Heating Process 16 2.3.2 Requisites for Hot Spot Occurrence 18 2.3.3 Prediction of Spontaneous Combustion Potential 19 2.3.4 Contributing Factors to Self-Heating Process 23 2.3.5 Control Methods 26

2.4 Spontaneous Combustion Studies Using CFD 28 2.5 Porous Medium 30

2.5.1 Particle Size Distribution 30 2.5.2 Porosity 31 2.5.3 Specific Permeability 32

3. CHARACTERISTICS OF GOB MATERIAL 36

3.1 Longwall Mine Gob 36 3.2 Gob Material and Its Characteristics 40

TABLE OF CONTENTS

ABSTRACT .. . . . . . .. .. . . . . .. . . .. . .. . ... .. . .. . .. . .. .. . .. . . .. ... .. . . . . .. . . . .... . . .. .. .. .. .. . ..... ... IV

LIST OF TABLES . .... .. . .... ...... .. .. .. .. .. . ... . ... .. .. ... .. ... .. .. .... .. ... . ... . .... .. .. . . ... x

LIST OF FIGURES... .. ... .. ... . .......... . ... .. . ... .. . ....... ... ..... . . . .... ........ . ........ . XlI

ACKNOWLEDGMENTS.... ... . .. .. .. .. . .. . ..... ..... .. ......... ... ........................ xv

CHAPTER

1. INTRODUCTION.................... . ................... . ...... .. .... .. .... .. ..... .......... 1

1.1 Statement of Problems.... . ....... . .. .. .. .. ........ .. .... . .. . . ... .. .... . .... ...... ..... 1 1.2 Thesis Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ............ 4

2. BACKGROUND AND LITERATURE REVIEW ....... ....... ... .. ............ . . ... 6

2.1 Longwall Mines in the United States.................. ...... .......... ...... ...... 6 2.2 Ventilation Systems for Longwall Mines .. .. .. .... .. .. .. .. .. .. .. .. .... .. ...... .. . 10

2.2.1 U-Tube System... ... .. . ... ... ... ... ... . .... . ... .. . . . . ... . .. ......... . .. . ..... 10 2.2.2 Y System. ... ......... .. . ... .. .. . .. .. .. ... .... ............... .................. 12 2.2.3 Wrap-Around System.............. .............. .. ......................... 14

2.3 Spontaneous Combustion in the Gob .... . .. . ... , .. . . .. ... . . . . .. . . . .. .... . . .. .. . . .. 15 2.3.1 Mechanism of Self-Heating Process .... .... .... ... .. .. ............... ... 16 2.3.2 Requisites for Hot Spot Occurrence. .. . .. .. . . .. ... . .. .. .... . . .. . .... .. .. . 18 2.3.3 Prediction of Spontaneous Combustion Potential. .. ... . .. .. . .. ... ... .. 19 2.3.4 Contributing Factors to Self-Heating Process.............. ........ .. .... 23 2.3.5 Control Methods........ .. ... ... .......... ..... . ... . .... ............. ........ 26

2.4 Spontaneous Combustion Studies Using CFD .. ...... ........ ...... .. .. ......... 28 2.5 Porous Medium. ....... ..... .. . ... ... .. .. . . ... .. . . .......... ... ... ..... . .... ... .. . .. .. 30

2.5.1 Particle Size Distribution. ..... . .......... .... ...... . .. ................. .... 30 2.5.2 Porosity........................ . .. ... . .. .. ..... .. .. .... .... ... .. ... . . . ......... 31 2.5.3 Specific Permeability..................... . ....... .. ...... .. .. ... . .. ........ 32

3. CHARACTERISTICS OF GOB MATERIAL ............................ ........ ...... 36

3.1 Longwall Mine Gob....... ...... ............. .. .. .. . . . ... ........................... . 36 3.2 Gob Material and Its Characteristics............................ ........ ........... 40

3.2.1 Particle Size Selection 40 3.2.2 Packing and Particle Shape 41

3.3 Permeability Tests 42 3.3.1 Sample Preparation 43 3.3.2 Water-Based Method 44 3.3.3 Air-Based Method 50

3.4 Specific Permeability of Gob Material 55

4. RESEARCH METHODOLOGIES 58

4.1 Physical Model 58 4.1.1 Simulated Airway 60 4.1.2 Fan and Regulator 65

4.2 Computational Fluid Dynamics Model 66 4.2.1 Introduction 66 4.2.2 Airflow Simulation (Without Oxidation) 68

4.3 Model Similitude 74 4.3.1 Similitude Concept 74 4.3.2 Similitude Validation 76 4.3.3 Model Calibration 77

5. HOT SPOT LOCATION - CFD SIMULATION EXERCISES 79

5.1 Basic Assumptions 80 5.1.1 Longwall Mine Geometry 80 5.1.2 Input Parameters 83 5.1.3 Flow Distribution - A Base Case 89

5.2 Simulation Exercises 91 5.2.1 Bleeder Ventilation System: Models A, B, and C 91 5.2.2 Bleederless Ventilation System: Model D 100

5.3 Preliminary Conclusions 103

6. DISCUSSION OF GOB SIMULATION STUDIES 106

6.1 Physical Model 106 6.1.1 Limitations 106 6.1.2 Fluid Effects on Permeability 108 6.1.3 Permeability - Particle Size Relationship 110

6.2 Computational Fluid Dynamics Model 112 6.2.1 Limitations 112 6.2.2 Hot Spot Locations 114 6.2.3 Effect of Permeability on Hot Spot Formation 118 6.2.4 Effect of Gob Width on Hot Spot Formation 122 6.2.5 Hot Spot Control through Gas Injections 124

viii

3.2.1 Particle Size Selection.................................. ........ ............ 40 3.2.2 Packing and Particle Shape.................................. ............... 41

3.3 Permeability Tests............................................................ ........ 42 3.3.1 Sample Preparation........................................ .................. 43 3.3.2 Water-Based Method............................................ ........... 44 3.3.3 Air-Based Method........................................................... 50

3.4 Specific Permeability of Gob Material...... ........ .......... .................... 55

4. RESEARCH METHODOLOGIES .................................... '" .. . .......... .. 58

4.l Physical Model ... '" ........................................................... " .... 58 4.1.1 Simulated Airway. .. ... .. .... ....... .. ... .. .. . .... . .. . .. ......... . .... .. .. .... 60 4.l.2 Fan and Regulator ............................................................ 65

4.2 Computational Fluid Dynamics Model ............... '" .. . .. .... . .... .. ...... . ... 66 4.2.1 Introduction................................................................... 66 4.2.2 Airflow Simulation (Without Oxidation) ................................ .

4.3 Model Similitude ................................................................... .. 68 74 74 76 77

4.3.1 Similitude Concept .......................................................... . 4.3.2 Similitude Validation ....................................................... . 4.3.3 Model Calibration ........................................................... .

5. HOT SPOT LOCATION - CFD SIMULATION EXERCISES ..................... 79

5.1 Basic Assumptions................................................................... 80 5.l.1 Longwall Mine Geometry.................................................. 80 5.1.2 Input Parameters............................................................... 83 5.1.3 Flow Distribution - A Base Case.................. ........................ 89

5.2 Simulation Exercises ................................................................ , 91 5.2.1 Bleeder Ventilation System: Models A, B, and C ........................ 91 5.2.2 Bleederless Ventilation System: Model D ................................. 100

5.3 Preliminary Conclusions ............................................................. 103

6. DISCUSSION OF GOB SIMULATION STUDIES .................................. 106

6.1 Physical Model. . . . . . . . . ... . ... ...... .. ... .. .. . .... . . . .. . ... . .. . .. . . . . .. .. . ... . .. ..... 106 6.l.1 Limitations.................................................................... 106 6.1.2 Fluid Effects on Permeability........ .... .. .... ........ ...... .... .. ...... .... 108 6.1.3 Permeability - Particle Size Relationship........... . .... .. . .. ........ .. .. 110

6.2 Computational Fluid Dynamics Model ............................................ 112 6.2.1 Limitations ................................................................... , 112 6.2.2 Hot Spot Locations ........................................................... 114 6.2.3 Effect of Permeability on Hot Spot Formation........................... 118 6.2.4 Effect of Gob Width on Hot Spot Formation............................. 122 6.2.5 Hot Spot Control through Gas Injections .................................. 124

V111

7. CONCLUSIONS AND RECOMMENDATIONS 129

7.1 Conclusions 129

7.2 Recommendations for Future Research 131

APPENDICES

A PERMEABILITY TEST DATA 133

B SAMPLE OF PERMEABILITY CALCULATIONS 138

C CALIBRATION OF CFD MODEL 141

D CALCULATION OF COAL INJECTION RATE 147

E PHASES INVOLVED IN SELF-HEATING PROCESS 150

REFERENCES 153

ix

7. CONCLUSIONS AND RECOMMENDATIONS .. ... ... ... .. .. . . .................... 129

7.1 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 7.2 Recommendations for Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 131

APPENDICES

A PERMEABILITY TEST DATA ... ... .. . .. .. .. .. ... . . . . .. .. . . .. .. . . . ... .. . . . .. . ... .. 133

B SAMPLE OF PERMEABILITY CALCULATIONS ............. . .... . ....... .. . 138

C CALIBRATION OF CFD MODEL . .. ... . .. . .............. ............ ............. . 141

D CALCULATION OF COAL INJECTION RATE ..... .. ................... .. .. .. 147

E PHASES INVOLVED IN SELF-HEATING PROCESS ... .. . . . . .......... . ..... 150

REFERENCES .. . . .... ... .. ... ... . . .. ..... ........ ... .. .. ......... .... . .. .. ..... .... .. . ......... 153

IX

LIST OF TABLES

Table Page

2.1. Parameters for SPONCOM Program 22

2.2. Experimental specific permeability of Utah coals 34

2.3. Experimental specific permeability of broken rocks 35

3.1. Specific permeability for rock and coal samples using water-based tests .... 50

3.2. Specific permeability for rock samples using air-based tests 55

3.3. Specific permeability for simulated gob materials 57

4.1. Leakage percentage through crosscuts 63

4.2. Type of regulators used for ventilation controls 65

4.3. Input parameters used in Fluent for airflow simulations 69

4.4. Ventilation survey data for Mine A and Physical model 77

5.1. Input parameters used for a single-phase model 84

5.2. Input parameters used for a two-phase model 86

5.3. Input parameters for the self-heating process 89

6.1 Summary of hot spot locations - Models A through D 115

6.2 Specific permeabilities used for parametric studies 118

6.3 Input parameters for injection simulations 125

Al . Water-based test data for 0.28-mm diameter samples 134

A2. Water-based test data for 3.22-mm diameter samples 134

LIST OF TABLES

2.1. Parameters for SPONCOM Program........... . .... ......... ..... ... . ... .. . ......... 22

2.2. Experimental specific permeability of Utah coals . ... . ............ .. ... ........ .. 34

2.3 . Experimental specific permeability of broken rocks... ....... ... ... .. ......... .. 35

3.1. Specific permeability for rock and coal samples using water-based tests. ... 50

3.2. Specific permeability for rock samples using air-based tests .. . ...... .. . .... ... . 55

3.3. Specific permeability for simulated gob materials . .... . ..... . .. . . .. . ........... . 57

4.1. Leakage percentage through crosscuts. .. . .. . .. . .. . .. .. . . . . . .. . . . . .. .. ... . ...... . . .. 63

4.2. Type of regulators used for ventilation controls. .. . . . . .. .. . . .. .. . . . . .. .. ... . .. ... . 65

4.3. Input parameters used in Fluent for airflow simulations. . . .... ... .. ...... ... . ...... 69

4.4. Ventilation survey data for Mine A and Physical model.. . . . . ..... .. ..... .. ..... . 77

5.1. Input parameters used for a single-phase model .. .. .... .. .. ............ ...... .. ... 84

5.2. Input parameters used for a two-phase model ........ .. .... .......... .. .. .... ...... 86

5.3. Input parameters for the self-heating process.......... ........ .................. . .. 89

6.1 Summary of hot spot locations - Models A through D . . . ....... ........... .. ... 115

6.2 Specific permeabilities used for parametric studies. .. .. . . . . . .. . . . . .. . .. . . . . . ... . 118

6.3 Input parameters for injection simulations . ... . ....... . .......... ... .. ... . . ....... 125

AI. Water-based test data for 0.28-mm diameter samples.......... .. ............... 134

A2. Water-based test data for 3.22-mm diameter samples.... .... .. .... ........ ...... 134

A3. Water-based test data for 5.74-mm diameter samples 135

A4. Air-based test data for 5.74-mm diameter rock samples 135




B1. Sample data for permeability calculation 139

CI. Measured data for the physical model 142

C2. Calculated air velocity 144

C3. Reynolds Number (NR) of airflow 144

C4. Parameters used for validation in Fluent 145

C5. CFD modeling results 145

C6. Comparison of results - Physical model versus CFD model 146

Dl . Coal injection parameters 149

E l . Primary and mixture phase properties 151

E2. Secondary phase and gob material properties 152

xi

A3. Water-based test data for 5.74-mm diameter samples.......................... . 135

A4. Air-based test data for 5.74-mm diameter rock samples........ .. .............. 135

A5. Air-based test data for 7.73-mm diameter rock samples.. . ............ . ...... . . 136

A6. Air-based test data for 8.72-mm diameter rock samples.. . ....... .. .. ........ .. 136

A7. Air-based test data for 9.71-mm diameter rock samples.......... .......... .... 137

B 1. Sample data for permeability calculation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 139

Cl. Measured data for the physical model.................................. ...... ..... 142

C2. Calculated air velocity....................................... . ....... . ............... 144

C3. Reynolds Number (NR) of airflow.......................................... .... .... 144

C4. Parameters used for validation in Fluent.. ........ ............................ .... 145

C5. CFD modeling results ...................................... .. .... .. ...... ............ . 145

C6. Comparison of results - Physical model versus CFD model. ... ... . .. . .. .. ..... 146

Dl. Coal injection parameters................................ .. .... ..... . ... ............. 149

E 1. Primary and mixture phase properties ........... . . ..... . .... . .................... . 151

E2. Secondary phase and gob material properties................................ ..... 152

Xl

LIST OF FIGURES

Figure Page

2.1. Typical longwall mine layout used in the United States 7

2.2. Longwall equipment and coal transportation system 9

2.3. Typical U-tube ventilation system 11

2.4. Typical Y ventilation system 13

2.5. Typical Wrap-Around ventilation system 14

2.6. Schematic of fire triangle 16

2.7. SPONCOM result for the sample mine 23

3.1. Gob and strata zones in a longwall mine section 38

3.2. Permeability test network for water-based method 45

3.3. Water head-flow rate relationships for coal and rock samples 48

3.4. Longwall mine ventilation model at the University of Utah 51

3.5. The permeameter for air-based test 52

3.6. Particle size effect on broken rock sample permeability using air-based tests 56

3.7. Specific permeability distribution in gob 57

4.1. Mine ventilation model schematic 59

4.2. Pressure gradients for the physical model 62

4.3. Leakage percentage through four crosscuts 64

4.4. Type of regulator for physical model used in this study 66

4.5. The CFD model created in Gambit 69

LIST OF FIGURES

Figure

2.1 . Typicallongwall mine layout used in the United States ....... . . ...... ......... 7

2.2. Longwall equipment and coal transportation system ... . . . . .. .. . . .. . . . .. . . .. . . .. 9

2.3. Typical U-tube ventilation system............................. .............. ....... 11

2.4. Typical Y ventilation system...... ............ .. .. .. .. .. ...... .... .. .... .... .. .... .. 13

2.5. Typical Wrap-Around ventilation system........................................ . 14

2.6. Schematic of fire triangle.................... .. .................. .. ........ .. ........ . 16

2.7. SPONCOM result for the sample mine...................... .......... ...... ...... 23

3.1. Gob and strata zones in a longwall mine section ...... ............ .......... ...... 38

3.2. Permeability test network for water-based method........................... .. .. 45

3.3. Water head-flow rate relationships for coal and rock samples...... ... ... ..... 48

3.4. Longwall mine ventilation model at the University of Utah. . . . .... .. . .. .. . .. . . 51

3.5. The permeameter for air-based test.. .. .......................................... .. . 52

3.6. Particle size effect on broken rock sample permeability using air-based tests 56

3.7. Specific permeability distribution in gob. . ...... .. .... ... . . ...... . ... .. ......... .. 57

4.1. Mine ventilation model schematic............................................ ....... 59

4.2. Pressure gradients for the physical model.... .. .. .... ...... ...... .. .... .... ....... 62

4.3. Leakage percentage through four crosscuts...................................... . 64

4.4. Type of regulator for physical model used in this study. .. .. . . .. ... . .. . . . .. . .. .. 66

4.5. The CFD model created in Gambit...... .. .. ...... ...................... ........... 69

4.6. Velocity contours for the sample model 71

4.7. Velocity contours for the U-section 71

4.8. Velocity profiles for two simulated openings 72

4.9. Static pressure contours for the sample model 72

4.10. Static pressure contours for the U-section 73

4.11. Pressure drop through porous medium 74

5.1. Model schematic for a typical longwall mine 81

5.2. Location of injection ports in the simulated mine gob 82

5.3. Base case of airflow distribution 90

5.4. Velocity vectors in gob for a bleeder system 92

5.5. Oxygen concentration contours for model A 93

5.6. Temperature contours for model A 94

5.7. Potential hot spot location for model A 94

5.8. Oxygen concentration contours for model B 96

5.9. Temperature contours for model B 96

5.10. Potential hot spot location for model B 97

5.11. Oxygen concentration contours for model C 98

5.12. Temperature contours for model C 99

5.13. Potential hot spot locations for model C 100

5.14. Velocity vectors in gob for a bleederless system 101

5.15. Oxygen concentration contours for model D 102

5.16. Temperature contours for model D 102

5.17. Potential hot spot location for model D 103

xiii

4.6. Velocity contours for the sample model....................... . .. .. .......... ... .. 71

4.7. Velocity contours for the V-section. . .. .. ....... .. . ......... .... ... ...... .......... 71

4.8. Velocity profiles for two simulated openings..................... ........... .... . 72

4.9. Static pressure contours for the sample model . .. . .. .. . . ..................... .... , 72

4.10. Static pressure contours for the V-section.............. ... ... ... . . . .. ... . ..... .... 73

4.11. Pressure drop through porous medium.. ... . .. . .. . . . . ... .. . . .. . . . . .. .. . .. . ... ... . . 74

5.1. Model schematic for a typicallongwall mine .... ...... .... ... ................ .. . . 81

5.2. Location of injection ports in the simulated mine gob........... .. ........... . .. 82

5.3. Base case of airflow distribution.. .. .. . ... .. . ... ................. . ...... .... ........ 90

5.4. Velocity vectors in gob for a bleeder system ......... . .... . .. . . .. ........... ... .. ' 92

5.5. Oxygen concentration contours for model A ........... . .. . . .... .. . ... . ....... .. . 93

5.6. Temperature contours for model A ................................................. 94

5.7. Potential hot spot location for model A .. .. ......... . . .. ..... . ................. . .. ' 94

5.8. Oxygen concentration contours for model B ...... .. ........... ........ . .. . ... ... 96

5.9. Temperature contours for model B ...... ... ............ . .... ... .... .. .... . .. .. .. . .. 96

5.10. Potential hot spot location for model B ..................... . .... . ...... ........... 97

5.11. Oxygen concentration contours for model C ............................... ... ... ' 98

5.12. Temperature contours for model C .......... .. ........ .. .......... . . .. ... .... ... . .. 99

5.13. Potential hot spot locations for model C .. .. ... .. ................. .. .... .. . ....... , 100

5.14. Velocity vectors in gob for a bleederless system ................................. 101

5.15. Oxygen concentration contours for model D .. .. .... ....... .. ... ... .............. 102

5.16. Temperature contours for model D ...... ... .. ... ...... ... . .... ... ... ............... 102

5.17. Potential hot spot location for model D ........ .. .. . ... ... ... ..... . .. . . . .......... , 103

Xlll

6.1. Fluid effects on rock sample permeability 108

6.2. Velocity contours through the extended permeameter I l l

6.3. Pressure contours through the extended permeameter I l l

6.4. Particle size effect on broken rock sample permeability 112

6.5. Oxygen concentration contours for case 1 119

6.6. Temperature contours for case 1 120

6.7. Oxygen concentration contours for case 2 121

6.8. Temperature contours for case 2 121

6.9. Oxygen concentration contours for model E 123

6.10. Temperature contours for model E 123

6.11. Temperature contours for model A with a vertical injection 126

6.12. Nitrogen concentration contours for model D with horizontal injection holes 126

6.13. Temperature contours for model D with horizontal injection holes 127

CI. Mine ventilation model schematic 143

D l . Assumed gob shape and dimensions 148

xiv

6.1. Fluid effects on rock sample permeability. .. ... . . . . . . . .... . . . . . . .. .. . . .. . ... . ... . . 108

6.2. Velocity contours through the extended permeameter .... . ... . . . .... . ... . .. . . . . . 111

6.3. Pressure contours through the extended permeameter ...... . ............. ... . . . . 111

6.4. Particle size effect on broken rock sample permeability. . . . .... . . . .. ...... ..... 112

6.5. Oxygen concentration contours for case 1 ...... . .. ... . ....... . .... .. .. ... .... ..... 119

6.6. Temperature contours for case 1 . . ......... ............ . . . . ... . ... ... . .... . .. ...... . 120

6.7. Oxygen concentration contours for case 2 ... . . ... . .. ... . . .. ... ... . . ......... . ..... 121

6.8. Temperature contours for case 2 .... . .. .... ....... . .. . ..... . ... . . ... ... . .. . .... . . ... 121

6.9. Oxygen concentration contours for model E . . . . . . . . ......... ....... .... ...... .. . . 123

6.10. Temperature contours for model E ..... .. .............. . . . . ..... . . . ... ... . .. . ..... . 123

6.11. Temperature contours for model A with a vertical injection...... ..... ... . .. . . 126

6.12. Nitrogen concentration contours for model D with horizontal injection hole1 126

6.13. Temperature contours for model D with horizontal injection holes . . .... .. . . . 127

C1. Mine ventilation model schematic. .. . .. . .. . . .. . . .... . .... ...... . ... .. ...... . . .. ... 143

D 1. Assumed gob shape and dimensions ................................... ......... . . . 148

XIV

ACKNOWLEDGMENTS

This thesis would not have been possible without the financial support of the

William C. Browning Graduate Scholarship. I would like to express my sincere

appreciation to my advisor, Dr. Felipe Calizaya, for his constant encouragement

throughout this study and his invaluable advices on the research work. I gratefully

acknowledge the helpful guidance, advice and comments of my thesis committee

members: Dr Michael K. McCarter and Dr. D. Kip Solomon. Recognition is also due

to Pamela Hoffman of the Mining Engineering Department for helping me with

paperwork and administration, and Robbie for his assistance in performing the

experiments. I also sincerely appreciate the assistance and friendship given by all

graduate fellows of the Mining Engineering Department, Sonny Suryanto and his

family, and Darrel Cameron for reviewing some sections of this thesis. Finally,

special thanks are given to my parents, Jan and Elisabeth; my brothers and sisters,

Elyezer, Daniel, Olivia, Yunita; and the last but not the least, Zilva.

ACKNOWLEDGMENTS

This thesis would not have been possible without the financial support of the

William C. Browning Graduate Scholarship. I would like to express my sincere

appreciation to my advisor, Dr. Felipe Calizaya, for his constant encouragement

throughout this study and his invaluable advices on the research work. I gratefully

acknowledge the helpful guidance, advice and comments of my thesis committee

members: Dr Michael K. McCarter and Dr. D. Kip Solomon. Recognition is also due

to Pamela Hoffman of the Mining Engineering Department for helping me with

paperwork and administration, and Robbie for his assistance in performing the

experiments. I also sincerely appreciate the assistance and friendship given by all

graduate fellows of the Mining Engineering Department, Sonny Suryanto and his

family, and Darrel Cameron for reviewing some sections of this thesis. Finally,

special thanks are given to my parents, Jan and Elisabeth; my brothers and sisters,

Elyezer, Daniel, Olivia, Yunita; and the last but not the least, Zilva.

CHAPTER 1

INTRODUCTION

Spontaneous combustion in underground coal mines has become a serious

problem, particularly in the caved area (gob). Recent statistics have shown that

approximately 17% of a total of 87 underground coal mine fires in the United States are

attributed to spontaneous combustion (De Rosa, 2004). Spontaneous combustion results

from a self-heating process in exothermic conditions. The accumulated heat, if not

removed, is conducive to the rapid increase of temperature and may result in mine fires or

explosions. The incidence of such fires is expected to increase in the future as wider

panels and deeper coal seams are mined, and increased consumption of low rank coals

becomes more prevalent. The effects of spontaneous combustion are often associated

with loss of life and damage to property. The crucial step in reducing these effects is

locating the ignition point of spontaneous combustion (hot spot). This study is an effort to

obtain potential hot spot locations in mine gobs from the best gathered information.

1.1 Statement of Problems

In the past decades, much has been written on the subject of spontaneous

combustion. The characteristics of coal, including self-heating temperature and rank of

coal, have been the subjects of many experiments. In the late 1980s, the Bureau of Mines

CHAPTERl

INTRODUCTION

Spontaneous combustion in underground coal mines has become a serious

problem, particularly in the caved area (gob). Recent statistics have shown that

approximately 17% of a total of 87 underground coal mine fires in the United States are

attributed to spontaneous combustion (De Rosa, 2004). Spontaneous combustion results

from a self-heating process in exothermic conditions. The accumulated heat, if not

removed, is conducive to the rapid increase of temperature and may result in mine fires or

explosions. The incidence of such fires is expected to increase in the future as wider

panels and deeper coal seams are mined, and increased consumption of low rank coals

becomes more prevalent. The effects of spontaneous combustion are often associated

with loss of life and damage to property. The crucial step in reducing these effects is

locating the ignition point of spontaneous combustion (hot spot). This study is an effort to

obtain potential hot spot locations in mine gobs from the best gathered information.

1.1 Statement of Problems

In the past decades, much has been written on the subject of spontaneous

combustion. The characteristics of coal, including self-heating temperature and rank of

coal, have been the subjects of many experiments. In the late 1980s, the Bureau of Mines

2

performed extensive studies on this matter, developed an empirical expression of coal's

self-heating temperature, and identified several contributing factors (Smith and Lazarra,

1987). It is widely accepted that lower rank coals are more susceptible to spontaneous

combustion than higher rank coals mainly due to their innate properties. However, such

studies merely appear to explain the role of coal properties in spontaneous combustion.

Since this combustion often originates in the gob area, then the problem is more complex

than just a rank-related phenomenon. The permeability of the gob material is the major

contributing factor for the self-heating process. The resistances of the porous media

change over time. This is the result of stress changes during the mining process. A better

understanding of gob permeability must be developed to simulate the mine gob and

determine the possible location of self-heating areas. However, a thorough knowledge of

permeability is impossible because the gob is inaccessible. A number of studies have

been devoted to determining the characteristics of gob material. Brunner (1985)

constructed a model that was correlated to measured field data. Later research by Pappas

and Mark (1993) included a photoanalysis approach and laboratory tests on gob material.

A more recent study developed by Balusu (2002) used a tracer gas (SF6) to predict gob

caving characteristics. However, the results of these studies are crude estimates of gob

profiles and the problem calls for further investigation.

In foreign countries, limiting the oxygen supply to the gob using a bleederless

ventilation system has been chosen as the best alternative to control spontaneous

combustion (Koenning, 1989). The regulations in the Unites States require longwall

mines to utilize a bleeder system to dilute and remove gases generated in the gob (30

CFR section 75.334). The bleeder system, if not maintained correctly, may cause a

2

perfonned extensive studies on this matter, developed an empirical expression of coal's

self-heating temperature, and identified several contributing factors (Smith and Lazarra,

1987). It is widely accepted that lower rank coals are more susceptible to spontaneous

combustion than higher rank coals mainly due to their innate properties. However, such

studies merely appear to explain the role of coal properties in spontaneous combustion.

Since this combustion often originates in the gob area, then the problem is more complex

than just a rank-related phenomenon. The penneability of the gob material is the major

contributing factor for the self-heating process. The resistances of the porous media

change over time. This is the result of stress changes during the mining process. A better

understanding of gob penneability must be developed to simulate the mine gob and

detennine the possible location of self-heating areas. However, a thorough knowledge of

penneability is impossible because the gob is inaccessible. A number of studies have

been devoted to detennining the characteristics of gob material. Brunner (1985)

constructed a model that was correlated to measured field data. Later research by Pappas

and Mark (1993) included a photoanalysis approach and laboratory tests on gob material.

A more recent study developed by Balusu (2002) used a tracer gas (SF6) to predict gob

caving characteristics. However, the results of these studies are crude estimates of gob

profiles and the problem calls for further investigation.

In foreign countries, limiting the oxygen supply to the gob using a bleederless

ventilation system has been chosen as the best alternative to control spontaneous

combustion (Koenning, 1989). The regulations in the Unites States require longwall

mines to utilize a bleeder system to dilute and remove gases generated in the gob (30

CFR section 75.334). The bleeder system, ifnot maintained correctly, may cause a

3

substantial volume of coal left in the gob to be exposed to critical conditions under which

sufficient quantity of air is supplied to promote oxidation, but inadequate to remove heat.

The system may induce the self-heating of coal due to an improper utilization of

ventilation air, thus creating favorable conditions to sustain spontaneous combustion.

The ventilation practice of coursing air into the gob becomes more complex when

the dynamic aspects of longwall mining are considered. The overburden depth and the

mining rate determine the gob compaction behind the shields and the entry resistances to

the airflow. In the gob, the caved material expands to fill the void and the roof pressure is

transferred to the gob, thus reducing the gob porosity and increasing the airway

resistances. This dynamic aspect has been barely considered in the past.

The self-heating mechanism, the gob permeability, the required bleeder ventilation

system, and the dynamic aspects of the longwall mining method have magnified the

problem of spontaneous combustion, making it difficult to solve empirically. However,

with the advent of supercomputers, the problem can be investigated easily in more detail.

The application of numerical methods to simulate these phenomena has produced better

and more accurate results. Computational fluid dynamics has been used successfully to

model caved areas (Balusu et a l , 2002), gob wells (Ren and Edwards, 2000), and air

leakage through stoppings and seals (Calizaya, Duckworth, and Wallace, 2004). Such

simulation studies provided a better approximation of certain components of longwall

mining but not the self-heating mechanism of coal. The final goal of this study is locating

potential self-heating sources within a longwall gob. A series of experiments consisting

of physical models, field investigations, and computer simulation exercises have been

conducted to determine these locations. An evaluation performed in this study highlights

3

substantial volume of coal left in the gob to be exposed to critical conditions under which

sufficient quantity of air is supplied to promote oxidation, but inadequate to remove heat.

The system may induce the self-heating of coal due to an improper utilization of

ventilation air, thus creating favorable conditions to sustain spontaneous combustion.

The ventilation practice of coursing air into the gob becomes more complex when

the dynamic aspects of longwall mining are considered. The overburden depth and the

mining rate determine the gob compaction behind the shields and the entry resistances to

the airflow. In the gob, the caved material expands to fill the void and the roof pressure is

transferred to the gob, thus reducing the gob porosity and increasing the airway

resistances. This dynamic aspect has been barely considered in the past.

The self-heating mechanism, the gob permeability, the required bleeder ventilation

system, and the dynamic aspects of the longwall mining method have magnified the

problem of spontaneous combustion, making it difficult to solve empirically. However,

with the advent of supercomputers, the problem can be investigated easily in more detail.

The application of numerical methods to simulate these phenomena has produced better

and more accurate results. Computational fluid dynamics has been used successfully to

model caved areas (Balusu et aI., 2002), gob wells (Ren and Edwards, 2000), and air

leakage through stoppings and seals (Calizaya, Duckworth, and Wallace, 2004). Such

simulation studies provided a better approximation of certain components of longwall

mining but not the self-heating mechanism of coal. The final goal of this study is locating

potential self-heating sources within a longwall gob. A series of experiments consisting

of physical models, field investigations, and computer simulation exercises have been

conducted to determine these locations. An evaluation performed in this study highlights

4

the importance of using the Computational Fluid Dynamics (CFD) program to simulate

all the involved phenomena in the self-heating process.

1.2 Thesis Overview

This thesis develops a method used to simulate the location of a hot spot in a

longwall mine gob ventilated by either a bleeder system or a bleederless system using

CFD. Parameters such as gob permeability, panel geometry, self-heating coefficients, and

ventilation system are the major factors that affect the development of a hot spot. These

parameters are obtained from field surveys of existing mines and laboratory experiments.

The effectiveness of both bleeder and bleederless ventilation systems to control hot spot

are analyzed. As a reminder, the simulation results presented herein are valid to the

conditions stated in this study. To simulate different gob geometries or ventilation

systems, the base model should be modified accordingly.

After collecting background information and defining the parameters, computer

simulations are carried out to show the distribution of airflow inside the gob and to

predict the potential locations of hot spot. In this step, four CFD models are built to

represent a longwall mine with different gob lengths and ventilation systems. The results

of stress redistribution on the gob are simulated by zones of different permeability. The

gob zone adjacent to the face, filled with less consolidated material, is characterized by a

porous medium of high permeability. This permeability decreases with the distance of the

zone from the face; the further the distance, the lower the permeability. The permeability

of each zone is determined based on laboratory tests, field surveys, and computer

the importance of using the Computational Fluid Dynamics (CFD) program to simulate

all the involved phenomena in the self-heating process.

1.2 Thesis Overview

4

This thesis develops a method used to simulate the location of a hot spot in a

longwall mine gob ventilated by either a bleeder system or a bleederless system using

CFD. Parameters such as gob permeability, panel geometry, self-heating coefficients, and

ventilation system are the major factors that affect the development of a hot spot. These

parameters are obtained from field surveys of existing mines and laboratory experiments.

The effectiveness of both bleeder and bleederless ventilation systems to control hot spot

are analyzed. As a reminder, the simulation results presented herein are valid to the

conditions stated in this study. To simulate different gob geometries or ventilation

systems, the base model should be modified accordingly.

After collecting background information and defining the parameters, computer

simulations are carried out to show the distribution of airflow inside the gob and to

predict the potential locations of hot spot. In this step, four CFD models are built to

represent a longwall mine with different gob lengths and ventilation systems. The results

of stress redistribution on the gob are simulated by zones of different permeability. The

gob zone adjacent to the face, filled with less consolidated material, is characterized by a

porous medium of high permeability. This permeability decreases with the distance of the

zone from the face; the further the distance, the lower the permeability. The permeability

of each zone is determined based on laboratory tests, field surveys, and computer

5 simulations. Based on these data and information, the hot spot location is primarily

defined by two parameters: temperature and oxygen concentration.

A detailed analysis of the collected data, the geometry of the panel, and the

parameters used in the simulations are presented; the locations of potential hot spots in

the gob are identified and the effect of ventilation systems and gob characteristics on

these locations are discussed. Finally, some conclusions and recommendations for future

work in this area are presented.

simulations. Based on these data and infonnation, the hot spot location is primarily

defined by two parameters: temperature and oxygen concentration.

A detailed analysis of the collected data, the geometry of the panel, and the

parameters used in the simulations are presented; the locations of potential hot spots in

the gob are identified and the effect of ventilation systems and gob characteristics on

these locations are discussed. Finally, some conclusions and recommendations for future

work in this area are presented.

5

CHAPTER 2

BACKGROUND AND LITERATURE REVIEW

2.1 Longwall Mines in the United States

Longwall mining is the most efficient method of mining coal. The most recent

report issued by the U.S. Energy Information Administration shows that longwall mines

accounted for 49% of the 2006 nation's underground coal output. Today, approximately

51 underground coal mines in the United States utilize the longwall method. If the trend

for more energy sources prevails, there will be a higher demand for coal, thus calling for

a longer and wider panel to increase recovery and decrease the cost.

The longwall method is utilized for horizontal or nearly flat seams that have

relatively uniform thickness and are fairly free from discontinuities. According to the

Code of Federal Regulations in the United States, a three- or four-entry panel in

development is used in longwall mines, although a two-entry panel is allowed under

special circumstances. A typical longwall layout of a three-entry system is shown in

Figure 2.1. The geometry of a panel is generally 330 m (1,000 ft) wide and 3100 m

(10,000 ft) long. Development work usually requires 9 months to 1 year, depending on

the size of the panel. In contrast to the advancing-type method used in Europe, a

retreating method is widely used in the United States. With this method, coal extraction

starts from the farthest end of the panel and proceeds toward the main entries.

CHAPTER 2

BACKGROUND AND LITERATURE REVIEW

2.1 Longwall Mines in the United States

Longwall mining is the most efficient method of mining coal. The most recent

report issued by the U.S. Energy Information Administration shows that longwall mines

accounted for 49% of the 2006 nation's underground coal output. Today, approximately

51 underground coal mines in the United States utilize the longwall method. If the trend

for more energy sources prevails, there will be a higher demand for coal, thus calling for

a longer and wider panel to increase recovery and decrease the cost.

The longwall method is utilized for horizontal or nearly flat seams that have

relatively uniform thickness and are fairly free from discontinuities. According to the

Code of Federal Regulations in the United States, a three- or four-entry panel in

development is used in longwall mines, although a two-entry panel is allowed under

special circumstances. A typical longwalllayout of a three-entry system is shown in

Figure 2.1. The geometry of a panel is generally 330 m (1,000 ft) wide and 3100 m

(10,000 ft) long. Development work usually requires 9 months to 1 year, depending on

the size of the panel. In contrast to the advancing-type method used in Europe, a

retreating method is widely used in the United States. With this method, coal extraction

starts from the farthest end of the panel and proceeds toward the main entries.

• • • • • • ! • • • • • • • • • • • • • •

D • • • • • • •

Main entries

Figure 2.1 Typical longwall mine layout used in the United States

Main entries

Mining ;t----, direction ~

Intake air ...-- Return air ~ Bleeder air

Belt conveyor .......- Pennanent stopping

D

Figure 2.1 Typicallongwall mine layout used in the United States

Seal

R Regulator """'* Overcast

C Curtain

entries

The development entries are connected at the back of the panel by another set of

entries called "bleeder entries." Each entry is about 3 m (10 ft) high and 6 m (20 ft) wide.

The entries are connected in regular intervals by crosscuts and separated by a number of

pillars with an average dimension of 24 m (80 ft) wide and 50 m (165 ft) long, depending

on seam and cover conditions. The set of entries used for transportation of coal, workers,

and equipment is called a "headgate." These entries are also used to deliver intake air. On

the opposite side of the panel, a "tailgate" is used for return air. The main equipment used

to extract coal from the face is illustrated in Figure 2.2. It includes a shearer going back

and forth across the face, a set of shields, and a chain conveyor. For an average panel of

330 m wide, a continuous trip of coal cutting from headgate to tailgate takes about 45

minutes. As the shearer moves along the face, the cutting drums detach coal from the

face. The broken fragments are gathered and pushed onto the chain conveyor by a ramp

plate as the shields advance forward. A chain conveyor transports the broken coal to a

loading point on a stage loader and to a belt conveyor, which delivers coal to surface.

The support system, a side-by-side arrangement of hydraulic shields, is used not

only to hold the roof during the extraction and push the face conveyor, but also to provide

a safe workspace. Today, longwall mines utilize more than 100 shields per panel. After a

panel has been mined out completely, the relocation of equipment takes from 3 to 4

weeks. This is the major regular delay to production in longwall mining. With this

method, the overlying strata are allowed to cave behind the shield as soon as the coal is

extracted. The caved area is later referred to as the "gob." The void due to coal seam

extraction produces abutment pressure heaped up around the gob. Under this condition,

8

The development entries are connected at the back of the panel by another set of

entries called "bleeder entries." Each entry is about 3 m (10 ft) high and 6 m (20 ft) wide.

The entries are connected in regular intervals by crosscuts and separated by a number of

pillars with an average dimension of24 m (80 ft) wide and 50 m (165 ft) long, depending

on seam and cover conditions. The set of entries used for transportation of coal, workers,

and equipment is called a "headgate." These entries are also used to deliver intake air. On

the opposite side of the panel, a "tailgate" is used for return air. The main equipment used

to extract coal from the face is illustrated in Figure 2.2. It includes a shearer going back

and forth across the face, a set of shields, and a chain conveyor. For an average panel of

330 m wide, a continuous trip of coal cutting from headgate to tailgate takes about 45

minutes. As the shearer moves along the face, the cutting drums detach coal from the

face. The broken fragments are gathered and pushed onto the chain conveyor by a ramp

plate as the shields advance forward. A chain conveyor transports the broken coal to a

loading point on a stage loader and to a belt conveyor, which delivers coal to surface.

The support system, a side-by-side arrangement of hydraulic shields, is used not

only to hold the roof during the extraction and push the face conveyor, but also to provide

a safe workspace. Today, longwall mines utilize more than 100 shields per panel. After a

panel has been mined out completely, the relocation of equipment takes from 3 to 4

weeks. This is the major regular delay to production in longwall mining. With this

method, the overlying strata are allowed to cave behind the shield as soon as the coal is

extracted. The caved area is later referred to as the "gob." The void due to coal seam

extraction produces abutment pressure heaped up around the gob. Under this condition,

Figure 2.2 Longwall equipment and coal transportation system (after Oitto, 1979; Ramani, 1981; and Peng, 1984) Figure 2.2 Longwall equipment and coal transportation system (after Oitto, 1979; Ramani, 1981 ; and Peng, 1984)

10

the caved area expands laterally to the nearest entry of outward gob (Peng 1985). The

area is mainly filled with coal, caved-in roof, and heaved-up floor materials representing

media with different porosities. Its consolidation behavior changes over time as a

response to changes in stress pattern. It is accepted that the gob further from the face

becomes more consolidated over time and has less porosity than that behind the shields.

2.2 Ventilation Systems for Longwall Mines

The ventilation system is described as the lifeblood of underground mines. The

system ensures safe working conditions in the mine by providing airflow in sufficient

quantity and quality. In addition, ventilation air also dilutes contaminants and hazardous

gases to safe levels. Importantly, the mining and geologic conditions need to be examined

to determine the proper ventilation system. The primary function of a ventilation system

in an underground coal mine is to dilute methane gas to less than 1% by volume and keep

the respirable dust levels below 2 mg/m3 in all work areas.

For longwall mines, various ventilation systems have been developed. In the

United States, three systems are commonly applied: U-tube, Y system, and Wrap-around

(McPherson, 1993). It is a common practice to use the same system for the entire panel

life. The main features of each system and their layout are presented in this section. The

legend shown in Figure 2.1 applies to all longwall mines.

2.2.1 U-Tube System

In the U-tube ventilation system, air is brought to the face from the headgate and

is exhausted through the tailgate. The airflow schematic is shown in Figure 2.3. This

10

the caved area expands laterally to the nearest entry of outward gob (Peng 1985). The

area is mainly filled with coal, caved-in roof, and heaved-up floor materials representing

media with different porosities. Its consolidation behavior changes over time as a

response to changes in stress pattern. It is accepted that the gob further from the face

becomes more consolidated over time and has less porosity than that behind the shields.

2.2 Ventilation Systems for Longwall Mines

The ventilation system is described as the lifeblood of underground mines. The

system ensures safe working conditions in the mine by providing airflow in sufficient

quantity and quality. In addition, ventilation air also dilutes contaminants and hazardous

gases to safe levels. Importantly, the mining and geologic conditions need to be examined

to determine the proper ventilation system. The primary function of a ventilation system

in an underground coal mine is to dilute methane gas to less than 1 % by volume and keep

the respirable dust levels below 2 mglm3 in all work areas.

For longwall mines, various ventilation systems have been developed. In the

United States, three systems are commonly applied: U-tube, Y system, and Wrap-around

(McPherson, 1993). It is a common practice to use the same system for the entire panel

life. The main features of each system and their layout are presented in this section. The

legend shown in Figure 2.1 applies to alliongwall mines.

2.2.1 U-Tube System

In the U-tube ventilation system, air is brought to the face from the headgate and

is exhausted through the tailgate. The airflow schematic is shown in Figure 2.3. This

11

• • • • • • • • • • •

0 0 • • • • • • Figure 2.3 Typical U-tube ventilation system

system is preferred to limit the leakage flow to the gob and reduce the number of seals. It

is used in Australian and European mines.

A modified U-tube ventilation system is used in the United States. This system is

sometimes referred to as "bleederless" system. In this modified system, seals are

constructed in entries and crosscuts to isolate the gob. Air is directed up the headgate

entries, across the face, and back along the return. The entry in the headgate adjacent to

the longwall panel can be used either as intake or return. Most mines use this entry as

secondary intake since it is also the belt entry. Only two entries in the tailgate are

11

Figure 2.3 Typical U-tube ventilation system

system is preferred to limit the leakage flow to the gob and reduce the number of seals. It

is used in Australian and European mines.

A modified U-tube ventilation system is used in the United States. This system is

sometimes referred to as "bleederless" system. In this modified system, seals are

constructed in entries and crosscuts to isolate the gob. Air is directed up the headgate

entries, across the face, and back along the return. The entry in the headgate adjacent to

the longwall panel can be used either as intake or return. Most mines use this entry as

secondary intake since it is also the belt entry. Only two entries in the tailgate are

12

available for ventilation since the outer entry is caved during mining of the previous

panel. These two entries are used to exhaust the return air. This system is considered

simpler and more cost-effective compared to the "Y system" related to the number of

utilized seals. It is preferred in mines with the potential problem of spontaneous

combustion. The main disadvantage of this system is that in gassy mines, methane could

accumulate at the back corner of the gob in the tailgate side. Therefore, this system is

more suitable for non-gassy mines.

2.2.2 Y System

The Y system, sometimes called a "bleeder" system, utilizes both panel entries

outside the face as intakes and a tailgate bleeder as return. Figure 2.4 shows a typical

setup of this system. The fresh air flushes the face from the headgate to tailgate, and the

contaminated air exits through the outside return entry of the tailgate. This system also

allows some portions of fresh air to flow across the gob to dilute gasses generated inside

the caved area. It also provides an additional quantity of fresh air to the face near the

tailgate. It is used for gassy mines to control the gas concentrations in the tailgate corner.

A bleeder fan installed on the surface as exhauster creates the pressure difference to

ventilate the panel.

Despite the fact that most of the longwall mines employ this system, the Y

method is less suitable than other methods to control spontaneous combustion in mine

gobs. If the air flushing the gob does not have enough velocity to carry away the heat of

the self-heating process, it could be trapped inside the gob, creating "dead-lock" pockets

of air that would initiate a continuous oxidation of coal.

12

available for ventilation since the outer entry is caved during mining of the previous

panel. These two entries are used to exhaust the return air. This system is considered

simpler and more cost-effective compared to the "Y system" related to the number of

utilized seals. It is preferred in mines with the potential problem of spontaneous

combustion. The main disadvantage of this system is that in gassy mines, methane could

accumulate at the back comer of the gob in the tailgate side. Therefore, this system is

more suitable for non-gassy mines.

2.2.2 Y System

The Y system, sometimes called a "bleeder" system, utilizes both panel entries

outside the face as intakes and a tailgate bleeder as return. Figure 2.4 shows a typical

setup of this system. The fresh air flushes the face from the headgate to tailgate, and the

contaminated air exits through the outside return entry of the tailgate. This system also

allows some portions of fresh air to flow across the gob to dilute gasses generated inside

the caved area. It also provides an additional quantity of fresh air to the face near the

tailgate. It is used for gassy mines to control the gas concentrations in the tailgate comer.

A bleeder fan installed on the surface as exhauster creates the pressure difference to

ventilate the panel.

Despite the fact that most of the longwall mines employ this system, the Y

method is less suitable than other methods to control spontaneous combustion in mine

gobs. If the air flushing the gob does not have enough velocity to carry away the heat of

the self-heating process, it could be trapped inside the gob, creating "dead-lock" pockets

of air that would initiate a continuous oxidation of coal.

13

Bleeder fan on the surface

9 • • ' • • • • • • • •

•?r_p n n n r j

• Ci • [ f • Ci • [ • C: • [ • [ • [ Li I • D

GOB

G ] G G ] ] ] ] a

c GOB

_=DT[!JC: F^GD D D D D D D Q D

•

-e

Figure 2.4 Typical Y ventilation system

Besides the spontaneous combustion problem, another disadvantage of the Y

system is that the effectiveness of this method relies on the conditions of bleeder entries.

In practice, these entries will become high-resistance airways as the panel retreats. The

overburden weight could causes roof and pillar failures. The difficulties to maintaining

the initial entry conditions may extend to other entries and increase the airway

resistances, thus demanding greater pressure of the bleeder fan. In some cases, additional

pillars are set up to keep the return paths open, thus reducing the quantity of air circulated

through the face. However, the presence of pillars may increase the overall resistance of

the mine, thus decreasing the total flow rate.

13

~====;DDDDDDDD surface

Figure 2.4 Typical Y ventilation system

Besides the spontaneous combustion problem, another disadvantage of the Y

system is that the effectiveness of this method relies on the conditions of bleeder entries.

In practice, these entries will become high-resistance airways as the panel retreats. The

overburden weight could causes roof and pillar failures. The difficulties to maintaining

the initial entry conditions may extend to other entries and increase the airway

resistances, thus demanding greater pressure of the bleeder fan. In some cases, additional

pillars are set up to keep the return paths open, thus reducing the quantity of air circulated

through the face. However, the presence of pillars may increase the overall resistance of

the mine, thus decreasing the total flow rate.

14

Figure 2.5 Typical Wrap-Around ventilation system

2.2.3 Wrap-Around System

In the wrap-around system, the bleeder entries are located at the back of the

mined-out panels. These entries are used to ventilate the gob. Similar to the Y-type

system, a bleeder or exhaust fan is used to create the pressure difference. Figure 2.5

shows a typical layout of this system. Permanent ventilation controls such as stoppings

and seals are required to isolate the gob. The entries of the headgate are used as intake

and escape paths. The air is then split. Part of it is used to ventilate the face, and the

remainder directed through the gob and the bleeder entries. The major advantage of this

system is that the distance between the fan and the panel decreases as the panel retreats,

2.2.3 Wrap-Around System

In the wrap-around system, the bleeder entries are located at the back of the

mined-out panels. These entries are used to ventilate the gob. Similar to the Y -type

system, a bleeder or exhaust fan is used to create the pressure difference. Figure 2.5

shows a typical layout of this system. Permanent ventilation controls such as stoppings

and seals are required to isolate the gob. The entries of the headgate are used as intake

and escape paths. The air is then split. Part of it is used to ventilate the face, and the

remainder directed through the gob and the bleeder entries. The major advantage of this

system is that the distance between the fan and the panel decreases as the panel retreats,

Figure 2.5 Typical Wrap-Around ventilation system

14

15 thus increasing the quantity at the face. However, the flow rates through the gob and

bleeder entries may suffer due to the difficulties of maintaining narrow entries, especially

under coal seams of deep cover.

The success of all ventilation systems depends on the geologic conditions and

mining practices. Well-maintained entries and regularly inspected control devices are

essential to provide all workplaces with the required quantities of air. Ventilation

simulators such as VnetPC can be used to optimize the design parameters. VnetPC, a

commercial program developed between the 1960s and 1990s by McPherson, allows

users to evaluate alternatives and select the most efficient one (McPherson, 1993). An

evaluation of alternatives is crucial to ventilation planning since longwall mining is a

dynamic process. However, the application of such a simulator is restricted to fixed

resistance networks. Longwall mine gobs are difficult to simulate, though some efforts of

doing so have been reported (Brunner, 1985; Prosser and Oswald, 2006). Finite volume

programs such as Fluent are now used to study the flow distribution in the gob. This will

be discussed in more detail in the following sections.

2.3 Spontaneous Combustion in the Gob

Spontaneous combustion is a major safety concern in underground coal mines. It

accounts for approximately 17% of the total number of fires recorded in the United States

since 1990. Spontaneous combustion of coal is most likely initiated by a self-heating

process. This process is well described as the temperature rise due to oxidation of coal.

This process is a complex phenomenon involving a wide range of physical and chemical

processes. In longwall mines, the problem becomes complex mainly because the

15

thus increasing the quantity at the face. However, the flow rates through the gob and

bleeder entries may suffer due to the difficulties of maintaining narrow entries, especially

under coal seams of deep cover.

The success of all ventilation systems depends on the geologic conditions and

mining practices. Well-maintained entries and regularly inspected control devices are

essential to provide all workplaces with the required quantities of air. Ventilation

simulators such as VnetPC can be used to optimize the design parameters. VnetPC, a

commercial program developed between the 1960s and 1990s by McPherson, allows

users to evaluate alternatives and select the most efficient one (McPherson, 1993). An

evaluation of alternatives is crucial to ventilation planning since longwall mining is a

dynamic process. However, the application of such a simulator is restricted to fixed

resistance networks. Longwall mine gobs are difficult to simulate, though some efforts of

doing so have been reported (Brunner, 1985; Prosser and Oswald, 2006). Finite volume

programs such as Fluent are now used to study the flow distribution in the gob. This will

be discussed in more detail in the following sections.

2.3 Spontaneous Combustion in the Gob

Spontaneous combustion is a major safety concern in underground coal mines. It

accounts for approximately 17% of the total number of fires recorded in the United States

since 1990. Spontaneous combustion of coal is most likely initiated by a self-heating

process. This process is well described as the temperature rise due to oxidation of coal.

This process is a complex phenomenon involving a wide range of physical and chemical

processes. In longwall mines, the problem becomes complex mainly because the

16

Fuel

Figure 2.6 Schematic of fire triangle

processes take place inside the gob, thus restricting field investigations. The processes,

contributing factors, and spontaneous combustion control methods are described below.

2.3.1 Mechanism of Self-Heating Process

The spontaneous combustion follows the principle of the fire triangle, as shown in

Figure 2.6. The legs of the triangle represent three elements of fire. These are oxygen,

fuel, and ignition source. In the self-heating process, carbon, pyrite, and other

combustible matters left in the gob represent the fuel. The oxygen element is delivered to

the gob by the ventilation system, influenced by mining and geologic conditions. The

contact of oxygen and combustible matters initiates the exothermic oxidation of coal. The

rapid increase of heat, at last, can ignite the fuel and eventually develop a fire.

Today, it is well accepted that the interaction between oxygen and coal substances

is the main cause for spontaneous combustion. There has been much diversity of opinion

about the tendency of various components of coal to react with oxygen. However, it is

agreed that some factors such as pyrite, moisture, and bacteria play a secondary role to

the self-heating of coal. Therefore, they are not included in this study.

processes take place inside the gob, thus restricting field investigations. The processes,

contributing factors, and spontaneous ccmbustion control methods are described below.

2.3.1 Mechanism of Self-Heating Process

16

The spontaneous combustion follows the principle of the fire triangle, as shown in

Figure 2.6. The legs of the triangle represent three elements of fire. These are oxygen,

fuel, and ignition source. In the self-heating process, carbon, pyrite, and other

combustible matters left in the gob represent the fuel. The oxygen element is delivered to

the gob by the ventilation system, influenced by mining and geologic conditions. The

contact of oxygen and combustible matters initiates the exothermic oxidation of coal. The

rapid increase of heat, at last, can ignite the fuel and eventually develop a fire.

Today, it is well accepted that the interaction between oxygen and coal substances

is the main cause for spontaneous combustion. There has been much diversity of opinion

about the tendency of various components of coal to react with oxygen. However, it is

agreed that some factors such as pyrite, moisture, and bacteria playa secondary role to

the self-heating of coal. Therefore, they are not included in this study.

Ignition

Fuel

Figure 2.6 Schematic of fire triangle

17

C + 0 2 -> C 0 2 + heat (65°- 94°C)

CQ 2 + C -> 2CO + heat (100°- 150°C)

(2.1)

(2.2)

The presence of pyritic sulfur, FeS2, can initiate the spontaneous heating of coal

(Banerjee, 2000). Such a process is represented by:

2FeS2 + 7 0 2 + 16H 2 0 ^ 2 H 2 S 0 4 + 2FeS0 4 • 7 H 2 0 + 316 kcal (heat) (2.3)

This reaction, however, is not that frequent because the amount of pyritic sulfur in coal is

usually less than 1%.

As indicated in Equations 2.1 and 2.2, the carbon (C), constituent of coal, reacts

with oxygen (0 2 ) within the temperature range of 65 to 94 C producing carbon dioxide

(C0 2 ) and heat. Subsequent reaction of C 0 2 and C at higher temperature generates CO

and heat. Both processes occur in exothermic states. The process temperature, once above

o

100 C, begins to accelerate, though the heating can still be interrupted. The reaction

Coal oxidation occurs as coal comes into contact with air. The process is suitably

explained in terms of heat transfer, chemical surface absorption, and energy balance

related to inherent properties of coal. According to Wang et al. (2003), the oxidation

process of coal involves oxygen transport to the surface of coal particles, chemical

interaction between coal and oxygen, and release of heat and gaseous products.

Chamberlain and Hall (1973), and also Cliff and Bofmger (1998) have confirmed the

complexity of such phenomena; however, the overall reaction can be simplified using the

following reactions as suggested by Mitchell (1996):

17

Coal oxidation occurs as coal comes into contact with air. The process is suitably

explained in terms of heat transfer, chemical surface absorption, and energy balance

related to inherent properties of coal. According to Wang et al. (2003), the oxidation

process of coal involves oxygen transport to the surface of coal particles, chemical

interaction between coal and oxygen, and release of heat and gaseous products.

Chamberlain and Hall (1973), and also Cliff and Bofinger (1998) have confirmed the

complexity of such phenomena; however, the overall reaction can be simplified using the

following reactions as suggested by Mitchell (1996):

C + O2 -7 CO2 + heat (650

_ 94°C)

CO2 + C -7 2CO + heat (1000

_ 150°C)

The presence of pyritic sulfur, FeS2, can initiate the spontaneous heating of coal

(Banerjee, 2000). Such a process is represented by:

(2.1)

(2.2)

2FeS2 + 702 + 16H20 -7 2H2S04 + 2FeS04 . 7H20 + 316 kcal (heat) (2.3)

This reaction, however, is not that frequent because the amount of pyritic sulfur in coal is

usually less than 1 %.

As indicated in Equations 2.1 and 2.2, the carbon (C), constituent of coal, reacts

with oxygen (02) within the temperature range of65 to 94°C producing carbon dioxide

(C02) and heat. Subsequent reaction of CO2 and C at higher temperature generates CO

and heat. Both processes occur in exothermic states. The process temperature, once above

100°C, begins to accelerate, though the heating can still be interrupted. The reaction

18

process accelerates as temperature climbs beyond 150 C and then a spontaneous ignition

ensues. The temperature at which the coal reaches thermal runaway is called the self-

heating temperature (SHT) (Smith and Lazarra, 1987; Koenning, 1989). Equations 2.1

and 2.2 clearly imply the dependency of the reaction on temperature. The relationship

between reaction rate and temperature obeys Arrhenius' law, which is given by:

Rate = A [exp] (-E/RT) (2.4)

where

A = pre-exponential factor, K s"1

E = activation energy of coal, kJ mol"1

R = molar gas constant, 8.314472 JK"1 mol"1

T = temperature, K

Wiemann (1985), Smith and Lazarra (1987), and Mitchell (1996) noted that the rate

of coal oxidation does not produce a significant rise in temperature, as long as the oxygen

concentration in the air mixture is below 5% by volume. This finding, together with SHT

values, is used in the following sections to explain the hot spot occurrence.

2.3.2. Requisites for Hot Spot Occurrence

The term "hot spot" used in this study refers to a potential location for an ignition

source due to spontaneous combustion. Hot spot is a result of the self-heating process.

This condition is characterized by a high temperature produced by continuous oxidation.

Energy released from this exothermic reaction is in the forms of heat and reaction

18

process accelerates as temperature climbs beyond 150°C and then a spontaneous ignition

ensues. The temperature at which the coal reaches thermal runaway is called the self-

heating temperature (SHT) (Smith and Lazarra, 1987; Koenning, 1989). Equations 2.1

and 2.2 clearly imply the dependency of the reaction on temperature. The relationship

between reaction rate and temperature obeys Arrhenius' law, which is given by:

Rate = A [exp] (-E/RT) (2.4)

where

A pre-exponential factor, K S-1

E = activation energy of coal, kJ mor l

R = molar gas constant, 8.314472 JKI mor l

T temperature, K

Wiemann (1985), Smith and Lazarra (1987), and Mitchell (1996) noted that the rate

of coal oxidation does not produce a significant rise in temperature, as long as the oxygen

concentration in the air mixture is below 5% by volume. This finding, together with SHT

values, is used in the following sections to explain the hot spot occurrence.

2.3.2. Requisites for Hot Spot Occurrence

The term "hot spot" used in this study refers to a potential location for an ignition

source due to spontaneous combustion. Hot spot is a result ofthe self-heating process.

This condition is characterized by a high temperature produced by continuous oxidation.

Energy released from this exothermic reaction is in the forms of heat and reaction

19 products. Exothermic reaction implies that the higher the temperature, the more rapid the

reaction. Once the reaction temperature climbs above 100 C, it progresses so intensely

that it produces spontaneous combustion (Mitchell, 1996). This finding suggests a

o

minimum temperature of 100 C for hot spot occurrence in the simulation. Several other

experiments of thermal runaway beyond coal's SHT in oxidation present a solid

foundation for this study.

Oxygen, one of the two reactants in Equation 2.2, also represents an important

factor to the oxidation process. A minimum supply of oxygen should be available to

ensure continuation of oxidation. The oxygen concentration must be at least 5% by

volume in the air mixture. Methane, gases from oxidation, and the volume of fresh air

supply determine the oxygen concentration in the gob.

These two parameters, temperature and oxygen concentration, are used to

determine a hot spot. For simulation purposes, the area where temperature and oxygen o

concentration are above 100 C and 5%, respectively, obviously becomes a hot spot. Other

factors mentioned in section 2.3.4 such as moisture content are included in simulating the

hot spot under input variables of CFD. Chapter 5 describes the details of these variables.

2.3.3. Prediction of Spontaneous Combustion Potential

The self-heating temperature was proved to have a direct relationship with the

rank of coal (Chamberlain, 1973; Smith and Lazarra, 1987; Koenning, 1989). It is widely

accepted that spontaneous combustion of coal is a rank-related phenomenon, meaning

that young coals such as sub-bituminous or lignite are more susceptible to spontaneous

combustion than higher rank coals such as anthracite.

19

products. Exothermic reaction implies that the higher the temperature, the more rapid the

reaction. Once the reaction temperature climbs above 100°C, it progresses so intensely

that it produces spontaneous combustion (Mitchell, 1996). This finding suggests a

minimum temperature of 100°C for hot spot occurrence in the simulation. Several other

experiments of thermal runaway beyond coal's SHT in oxidation present a solid

foundation for this study.

Oxygen, one of the two reactants in Equation 2.2, also represents an important

factor to the oxidation process. A minimum supply of oxygen should be available to

ensure continuation of oxidation. The oxygen concentration must be at least 5% by

volume in the air mixture. Methane, gases from oxidation, and the volume of fresh air

supply determine the oxygen concentration in the gob.

These two parameters, temperature and oxygen concentration, are used to

determine a hot spot. For simulation purposes, the area where temperature and oxygen

concentration are above 100°C and 5%, respectively, obviously becomes a hot spot. Other

factors mentioned in section 2.3.4 such as moisture content are included in simulating the

hot spot under input variables of CFD. Chapter 5 describes the details of these variables.

2.3.3. Prediction of Spontaneous Combustion Potential

The self-heating temperature was proved to have a direct relationship with the

rank of coal (Chamberlain, 1973; Smith and Lazarra, 1987; Koenning, 1989). It is widely

accepted that spontaneous combustion of coal is a rank-related phenomenon, meaning

that young coals such as sub-bituminous or lignite are more susceptible to spontaneous

combustion than higher rank coals such as anthracite.

20 A number of laboratory tests and computer simulation exercises have been

developed to predict the propensity of coal to spontaneous combustion. However, none is

generally agreed upon and used universally (Cliff and Bofmger, 1998). There are many

unanswered questions about the reliability of such tests to represent real conditions

because the spontaneous combustion problem is not merely a coal rank problem. It also

depends on other factors such as geologic condition, mining method, and mine

ventilation. A most recent report on spontaneous combustion justified these facts (Cliff

and Bofinger, 1998). However, a global fact shown in these experiments indicates that

spontaneous combustion may be a significant cause for mine fires and explosions.

2.3.3.1. Development of SPONCOM Program

In the 1990s, the U.S Bureau of Mines developed a ranking method to predict the

combustion propensity of coal based on the temperature. The experiment was carried out

to determine the minimum temperature at which coal starts generating heat that is

retained until flaming. Coals with minimum self-heating temperature of below 70 C are

considered to have a high propensity to spontaneous combustion; those with temperatures

o t o

between 70 and 100 C, a medium propensity; and those with temperatures above 100 C,

low propensity. In general, the rank of coal is agreed to have a high correlation with this

propensity. According to Smith (1992), if the coal is lignite or sub-bituminous, the coal is

automatically assigned a high spontaneous combustion potential. If the rank is anthracite,

the coal is a low spontaneous combustion potential. For bituminous coal, Smith suggested

that the self-heating temperature can be approximated by:

20

A number of laboratory tests and computer simulation exercises have been

developed to predict the propensity of coal to spontaneous combustion. However, none is

generally agreed upon and used universally (Cliff and Bofinger, 1998). There are many

unanswered questions about the reliability of such tests to represent real conditions

because the spontaneous combustion problem is not merely a coal rank problem. It also

depends on other factors such as geologic condition, mining method, and mine

ventilation. A most recent report on spontaneous combustion justified these facts (Cliff

and Bofinger, 1998). However, a global fact shown in these experiments indicates that

spontaneous combustion may be a significant cause for mine fires and explosions.

2.3.3.1. Development ofSPONCOM Program

In the 1990s, the U.S Bureau of Mines developed a ranking method to predict the

combustion propensity of coal based on the temperature. The experiment was carried out

to detennine the minimum temperature at which coal starts generating heat that is

retained until flaming. Coals with minimum self-heating temperature of below 70°C are

considered to have a high propensity to spontaneous combustion; those with temperatures

between 70 and 100°C, a medium propensity; and those with temperatures above 100°C,

low propensity. In general, the rank of coal is agreed to have a high correlation with this

propensity. According to Smith (1992), if the coal is lignite or sub-bituminous, the coal is

automatically assigned a high spontaneous combustion potential. If the rank is anthracite,

the coal is a low spontaneous combustion potential. For bituminous coal, Smith suggested

that the self-heating temperature can be approximated by:

SHT, °C = 139.7 - [6.6 x 0 2 , %(DAF)]

21 (2.5)

where 0 2 is the oxygen percentage in the coal on a dry-ash free basis (DAF). As

indicated, the equation shows a correlation between the combustion risk through self-

heating temperature and the oxygen content.

Based on field and laboratory studies, the former U. S. Bureau of Mines

developed an expert system called SPONCOM (Smith et al., 1996). This program,

written in ANSI C language, is designed to assess the spontaneous combustion potential

of coal based on coal properties, geologic conditions, and mining practices. Since the

mining methods used in the United States are longwall and room-and-pillar, this program

is limited to those methods. The program output includes the spontaneous combustion

potential of the coal, its rank, and its self-heating temperature. The results of this program

are crucial for mine operators at the planning and production stages.

2.3.3.2. SPONCOM Program - A Case Study

This section demonstrates the application of SPONCOM to assess the

spontaneous combustion of coal samples taken from a longwall mine located in the

western U.S. In this mine, the coal seam has an average thickness of 3 m and a cover

ranging between 335 m and 700 m. This mine is in production since 1941, initially as a

room-and-pillar coal mine and more recently as a longwall operation. Currently, the

annual production is 7.9 Mt of clean coal. For a comprehensive analysis, data are also

gathered from references and reports conducted by the third-parties such as USGS core

drilling analysis, geologic properties, etc. (www.energy.er.usgs.gov). Several

SHT, °c = 139.7 - [6.6 x O2, %(DAF)]

21

(2.5)

where O2 is the oxygen percentage in the coal on a dry-ash free basis (DAF). As

indicated, the equation shows a correlation between the combustion risk through self

heating temperature and the oxygen content.

Based on field and laboratory studies, the former U. S. Bureau of Mines

developed an expert system called SPONCOM (Smith et a1., 1996). This program,

written in ANSI C language, is designed to assess the spontaneous combustion potential

of coal based on coal properties, geologic conditions, and mining practices. Since the

mining methods used in the United States are longwall and room-and-pillar, this program

is limited to those methods. The program output includes the spontaneous combustion

potential of the coal, its rank, and its self-heating temperature. The results of this program

are crucial for mine operators at the planning and production stages.

2.3.3.2. SPONCOM Program - A Case Study

This section demonstrates the application of SPONCOM to assess the

spontaneous combustion of coal samples taken from a longwall mine located in the

western U.S. In this mine, the coal seam has an average thickness of 3 m and a cover

ranging between 335 m and 700 m. This mine is in production since 1941, initially as a

room-and-pillar coal mine and more recently as a longwall operation. Currently, the

annual production is 7.9 Mt of clean coa1. For a comprehensive analysis, data are also

gathered from references and reports conducted by the third-parties such as USGS core

drilling analysis, geologic properties, etc. (www.energy.er.usgs.gov). Several

http://www.energy.er.usgs.gov

22

Table 2.1 Parameters for SPONCOM Program

1. Coal Properties (as received): 3. Geologic Properties:

Proximate Analysis: Concentration rating of: Moisture (%) 6.32 Joints 50

Volatile Matter (%) 35.43 Channel Deposits 50

Fixed Carbon (%) 45.92 Dikes 0

Ash (%) 12.33 Clay Veins 0

Ultimate Analysis: Coal seam thickness (ft): Hydrogen (%) 5.12 Max. 19.5 Carbon (%) 64.28 Min. 6.7 Nitrogen (%) 1.16 Seam gradient (%): 13.1 Sulfur (%) 0.5 Max. 3.0 Oxygen (%) 16.21 Min. 1.0 Ash (%) 12.33 Average 2.0

BTU/lb 11,302 Overburden range (ft): 1001-1500 Pyritic sulfur 0.13 Presence of rider seam in roof No Coal contains impurities such as resins Yes Presence of rider seam in floor Yes Coal bed show signs of previous oxidation Yes Distance from coal bed (ft) 4 Friability rating (0 - 100) 25 Presence of pyrite in roof No

Presence of pyrite in floor No 2. Mining Conditions Face cleats (Number/ft) 10

Rating of floor heave in the mine entries 0 Butt cleats (Number/ft) 10 Rating of rib sloughage in mine entries 25 Presence of geothermal sources No Ambient temperature of mine air ( F) 78 Presence of burn zones Yes Have you encountered self-heating events in: Presence of significant faults Yes

Gobs/worked out areas No Entries or gateroads No 4. Mining Practices Pillars No Mining technique used: Longwall Other in-mine areas No Average seam thickness (ft) 8.3 Transport No Longwall production rate 22700 Stockpiles or silos No (ton/day) 22700

Quantity of ventilating air Longwall rate of 75.0 on face near headgate (cfm) 50,000 advance/retreat (ft/day)

75.0

in tailgate return (cfm) 5,000 Longwall panel dimension: Caving height of gob (ft) 15.0 width (ft) 906

length (ft) 18,240

assumptions are also made based on experience and field survey data (Calizaya and

Miles, 2006). Table 2.1 lists all parameters used in the program.

22

assumptions are also made based on experience and field survey data (Calizaya and

Miles, 2006). Table 2.1 lists all parameters used in the program.

Table 2.1 Parameters for SPONCOM Program

1. Coal Properties (as received): 3. Geologic Properties:

Proximate Analysis: Concentration rating of:

Moisture (%) 6.32 Joints 50

Volatile Matter (%) 35.43 Channel Deposits 50

Fixed Carbon (%) 45.92 Dikes 0

Ash (%) 12.33 Clay Veins 0

Ultimate Analysis: Coal seam thickness (ft):

Hydrogen (%) 5.12 Max. 19.5

Carbon (%) 64.28 Min. 6.7

Nitrogen (%) 1.16 Seam gradient (%): 13.1

Sulfur (%) 0.5 Max. 3.0

Oxygen (%) 16.21 Min. 1.0

Ash (%) 12.33 Average 2.0

BTUllb 11,302 Overburden range (ft): 1001-1500

Pyritic sulfur 0.13 Presence of rider seam in roof No

Coal contains impurities such as resins Yes Presence of rider seam in floor Yes

Coal bed show signs of previous oxidation Yes Distance from coal bed (ft) 4

Friability rating (0 - 100) 25 Presence of pyrite in roof No

Presence of pyrite in floor No

2. Mining Conditions Face cleats (Number/ft) 10

Rating of floor heave in the mine entries 0 Butt cleats (Number/ft) 10

Rating of rib sloughage in mine entries 25 Presence of geothermal sources No

Ambient temperature of mine air ( F) 78 Presence of bum zones Yes

Have you encountered self-heating events in: Presence of significant faults Yes

Gobs/worked out areas No

Entries or gateroads No 4. Mining Practices

Pillars No Mining technique used: Longwall

Other in-mine areas No Average seam thickness (ft) 8.3

Transport No Longwall production rate 22700

Stockpiles or silos No (ton/day)

Quantity of ventilating air Longwall rate of 75.0

on face near headgate (cfm) 50,000 advance/retreat (ft/day)

in tailgate return (cfm) 5,000 Longwall panel dimension:

Caving height of gob (ft) 15.0 width (ft) 906

length (ft) 18,240

23

m Jsl«l Company Name : Company X

U s e r Name : SL Date : N/ft

Mine : Mine X C o a l b e d : N/ft

< 70 deg C

Coal Rank : High v o l a t i l e B EitrntrtTrot*©..;;/^ S e l f - H e a t i n g Temperature :/1>4 deg C "X S p o n t a n e o u s Combust ion P o t e n t i a l : HIGH )

The f o l l o w i n g p a r a m e t e r s were i d e n t i f i e d a s can i n c r e a s e t h e r i s k of s e l f - h e a t i n g :

f a c t o r s t h a t

RATING RISK

P r e s s any k e y

Figure 2.7 SPONCOM result for the sample mine

The output of the SPONCOM program is shown in Figure 2.7. An evaluation of

these data reveals that coal in this mine is highly susceptibility to spontaneous

combustion. The coal rank is classified as high volatile B with a self-heating temperature

of 54°C (less than the critical temperature of 70°C). This result is crucial to determine the

appropriate ventilation system in planning and also evaluating the effectiveness of the

current system.

2.3.4. Contributing Factors to Self-Heating Process

The problem of spontaneous combustion results from the exothermic oxidation of

coal. The oxidation process depends on intrinsic and extrinsic factors. The intrinsic

factors are represented by the inherent properties of coal, including self-heating

temperature, pyrites and moisture contents, volatile matter, friability, and particle size.

23

The output of the SPONCOM program is shown in Figure 2.7. An evaluation of

these data reveals that coal in this mine is highly susceptibility to spontaneous

combustion. The coal rank is classified as high volatile B with a self-heating temperature

of 54 DC (less than the critical temperature of 70DC). This result is crucial to determine the

appropriate ventilation system in planning and also evaluating the effectiveness of the

current system.

2.3.4. Contributing Factors to Self-Heating Process

The problem of spontaneous combustion results from the exothermic oxidation of

coal. The oxidation process depends on intrinsic and extrinsic factors. The intrinsic

factors are represented by the inherent properties of coal, including self-heating

temperature, pyrites and moisture contents, volatile matter, friability, and particle size.

Figure 2.7 SPONCOM result for the sample mine

24

These combustible properties influence the oxidation and heat generation process leading

to a fire. The extrinsic factors are represented by the ventilation system, geologic

conditions, and mining practices. Since the properties of coal are unalterable, the problem

of spontaneous combustion may be solved by providing a good ventilation system

compatible with the mining practice. The fire triangle, as illustrated in Figure 2.6,

requires both intrinsic and extrinsic factors to initiate a fire. The absence of either one

may stop the process, at least temporarily.

The self-heating is found through experiments to vary with the rank of coal. Coals

most susceptible to self-heating are found in the low-rank classification, namely the sub-

bituminous and lignite, containing high pyrite, moisture, and oxygen contents which have

self-heating temperatures below 70°C. Pyrite content was initially suspected to be a

major constituent to initiate the oxidation. Later, it was found that pyrite only enhances

the reaction of the coal by generating heat during the oxidation process. It may assist the

oxidation of carbonaceous matrix by breaking down coal into smaller fragments and

exposing larger surface area to the air (Banerjee, 2000).

The amount of moisture contained in coal is also a contributing factor to

oxidation. Groundwater and extraneous moisture known as adventitious moisture are

readily evaporated. Moisture held within the coal itself, known as inherent moisture, is

analyzed and shown in Table 2.1 (Ward, 1984). In the oxidation process, the heat-of-

wetting stage begins with adsorption of moisture. For coals capable of self-heating, the

evolved heat from moisture can be as much as 2.5 times greater than in dry air, and the

heat of wett ing can be greater than of oxidation (Kuchta et a l , 1980). If moisture drains,

adsorbed gases will replace the void space. If oxygen is adsorbed, more heat must be

24

These combustible properties influence the oxidation and heat generation process leading

to a fire. The extrinsic factors are represented by the ventilation system, geologic

conditions, and mining practices. Since the properties of coal are unalterable, the problem

of spontaneous combustion may be solved by providing a good ventilation system

compatible with the mining practice. The fire triangle, as illustrated in Figure 2.6,

requires both intrinsic and extrinsic factors to initiate a fire. The absence of either one

may stop the process, at least temporarily.

The self-heating is found through experiments to vary with the rank of coal. Coals

most susceptible to self-heating are found in the low-rank classification, namely the sub

bituminous and lignite, containing high pyrite, moisture, and oxygen contents which have

self-heating temperatures below 70°C. Pyrite content was initially suspected to be a

major constituent to initiate the oxidation. Later, it was found that pyrite only enhances

the reaction of the coal by generating heat during the oxidation process. It may assist the

oxidation of carbonaceous matrix by breaking down coal into smaller fragments and

exposing larger surface area to the air (Banerjee, 2000).

The amount of moisture contained in coal is also a contributing factor to

oxidation. Groundwater and extraneous moisture known as adventitious moisture are

readily evaporated. Moisture held within the coal itself, known as inherent moisture, is

analyzed and shown in Table 2.1 (Ward, 1984). In the oxidation process, the heat-of

wetting stage begins with adsorption of moisture. For coals capable of self-heating, the

evolved heat from moisture can be as much as 2.5 times greater than in dry air, and the

heat of wetting can be greater than of oxidation (Kuchta et aI., 1980). If moisture drains,

adsorbed gases will replace the void space. If oxygen is adsorbed, more heat must be

25

dissipated (Cliff et al., 1996) and the system grows to an exothermic condition and

induces potential for combustion.

The surface area of coal plays an important role in the oxidation process. Winmill

(1915-16) observed that the rate of oxidation increased with the fineness of coal. A study

conducted by Smith and Lazarra (1987) using an adiabatic heating oven also confirms

this statement. The self-heating process potential increases with the increase of surface

area or decrease of particle size.

Mining practices may contribute to self-heating mainly by extending the

production period and increasing the combustible matter left in the gob. The mining rate

determines the incubation period for a particular mine gob in which self-heating of coal

may develop. A reduction in mining rate, often caused by frequent delays, gives

sufficient t ime for heat buildup in the gob. The location of critical velocity where the self-

heating tends to occur is predicted to be near the face (Koenning, 1989). A rapid retreat

mining avoids the development of spontaneous combustion. The combustible materials

left in the gob may also increase the oxidation rate. In addition, t imber sets and steel

wrecks may cause voids inside the gob, creating paths for airflow. Air leakage is often

cited as the cause for initiating the spontaneous combustion process (Koenning, 1989).

Another extrinsic factor that contributes to spontaneous combustion is the

ventilation air. Current regulations require the use of a bleeder ventilation system for

longwall mines. The system allows the ventilation air to travel through the gob and

remove harmful gases and the heat of oxidation. This injection of air may cause a

substantial volume of broken coal to be oxidized. In foreign countries, a conventional

bleeder system has been recognized as being hazardous in a mine with high spontaneous

dissipated (Cliff et aI., 1996) and the system grows to an exothermic condition and

induces potential for combustion.

25

The surface area of coal plays an important role in the oxidation process. Winmill

(1915-16) observed that the rate of oxidation increased with the fineness of coal. A study

conducted by Smith and Lazarra (1987) using an adiabatic heating oven also confirms

this statement. The self-heating process potential increases with the increase of surface

area or decrease of particle size.

Mining practices may contribute to self-heating mainly by extending the

production period and increasing the combustible matter left in the gob. The mining rate

determines the incubation period for a particular mine gob in which self-heating of coal

may develop. A reduction in mining rate, often caused by frequent delays, gives

sufficient time for heat buildup in the gob. The location of critical velocity where the self

heating tends to occur is predicted to be near the face (Koenning, 1989). A rapid retreat

mining avoids the development of spontaneous combustion. The combustible materials

left in the gob may also increase the oxidation rate. In addition, timber sets and steel

wrecks may cause voids inside the gob, creating paths for airflow. Air leakage is often

cited as the cause for initiating the spontaneous combustion process (Koenning, 1989).

Another extrinsic factor that contributes to spontaneous combustion is the

ventilation air. Current regulations require the use of a bleeder ventilation system for

longwall mines. The system allows the ventilation air to travel through the gob and

remove harmful gases and the heat of oxidation. This injection of air may cause a

substantial volume of broken coal to be oxidized. In foreign countries, a conventional

bleeder system has been recognized as being hazardous in a mine with high spontaneous

26

combustion potential (Oitto, 1979). The bleeder system may prevent the self-heating

process if the quantity of air course passing through the gob is large enough to remove

the heat of oxidation. However , the difficulty of supplying sufficient airflow quantity to

all gob areas may result in preferable local conditions for a continuous self-heating of

coal and heat buildup. Air leakage through the stoppings is another factor that contributes

to the self-heating process. The leakage flow can be minimized by using heavy duty

doors and regulators. In panels with severe geologic structures, faults and joints provide

courses for airflow. These also contribute to spontaneous combustion.

2.3.5. Control Methods

Early detection of spontaneous combustion is a preventive method. Monitoring

combustion products of CO, CO2, and the oxygen deficiency in the gob is carried out to

detect a mine fire. However , this practice may not be very effective in early prevention of

fire. Time is not a friend in a mine fire (Mitchell, 1996). Currently, there are four control

methods to reduce the risk of spontaneous combustion: utilization of an inhibitor,

inertization, mining practices, and a ventilation system.

An inhibitor is a chemical substance that can be used to prevent the physical

contact between oxygen and combustible materials. The inhibitors used in mines include

inorganic chlorides such as NaCl and CaCl. Using the same principle of protecting steel

products from corrosion, an inhibitor is injected in liquid form into coal seams through a

borehole before mining. This substance propagates to the coal seam. The success of this

application depends on the borehole depth, injection pressure, and the presence of

fissures in the seam. It is then expected that this substance will protect the coal from

26

combustion potential (Oitto, 1979). The bleeder system may prevent the self-heating

process if the quantity of air course passing through the gob is large enough to remove

the heat of oxidation. However, the difficulty of supplying sufficient airflow quantity to

all gob areas may result in preferable local conditions for a continuous self-heating of

coal and heat buildup. Air leakage through the stoppings is another factor that contributes

to the self-heating process. The leakage flow can be minimized by using heavy duty

doors and regulators. In panels with severe geologic structures, faults and joints provide

courses for airflow. These also contribute to spontaneous combustion.

2.3.5. Control Methods

Early detection of spontaneous combustion is a preventive method. Monitoring

combustion products of CO, CO2, and the oxygen deficiency in the gob is carried out to

detect a mine fire. However, this practice may not be very effective in early prevention of

fire. Time is not a friend in a mine fire (Mitchell, 1996). Currently, there are four control

methods to reduce the risk of spontaneous combustion: utilization of an inhibitor,

inertization, mining practices, and a ventilation system.

An inhibitor is a chemical substance that can be used to prevent the physical

contact between oxygen and combustible materials. The inhibitors used in mines include

inorganic chlorides such as NaCI and CaC!. Using the same principle of protecting steel

products from corrosion, an inhibitor is injected in liquid form into coal seams through a

borehole before mining. This substance propagates to the coal seam. The success of this

application depends on the borehole depth, injection pressure, and the presence of

fissures in the seam. It is then expected that this substance will protect the coal from

27

being oxidized during and after the panel extraction. In addition, a cover of limestone or

bentonite spread in a foam-liquid solution reduces the surface exposed to air, thus

reducing the oxidation of coal (Chamberlain, 1973; Banerjee, 1985).

Inertization is the process of injecting an inert gas into the gob to replace the

oxygen content in the affected area. Nitrogen and carbon dioxide are the common inert

gasses used for this purpose. In longwall mine gobs, nitrogen is preferred to carbon

dioxide for safety reasons (Banerjee, 2000). San Juan Coal mine is the only longwall

mine that continues to apply gob inertization in the United States. The mine requires a

continuous supply of 0.007 m 3 / s of nitrogen. Pipes of 100 - 150 m m in diameter are

normally used to deliver nitrogen gas into the gob from crosscuts (Bessinger et al., 2005).

The development of wider and longer panels increases the mining period,

providing more t ime for coal oxidation. This extension allows for a longer incubation

period (Koenning, 1989). Hazardous situations may result due to the exponential

relationship between t ime and temperature rise (Wang and Dlugogorski , 2003). A little

increase in t ime could cause a thermal runaway and result in a fire. A larger panel also

gives a chance for the development of a hot spot.

An adequate ventilation system can be used to reduce spontaneous combustion in

the gob. If bleeder or wrap-around entries are used, oxygen is allowed to percolate

through the gob, thus supporting the oxidation of coal. If this condition is allowed to

occur for a long period of time, oxidation may result in heat buildup and spontaneous

combustion. Therefore, this system should be used only when the gob condition permits

ventilation air to pass through the gob without any chance for heat buildup. The

bleederless system should be considered if the resistance to airflow is so high that it can

27

being oxidized during and after the panel extraction. In addition, a cover of limestone or

bentonite spread in a foam-liquid solution reduces the surface exposed to air, thus

reducing the oxidation of coal (Chamberlain, 1973; Banerjee, 1985).

Inertization is the process of injecting an inert gas into the gob to replace the

oxygen content in the affected area. Nitrogen and carbon dioxide are the common inert

gasses used for this purpose. In longwall mine gobs, nitrogen is preferred to carbon

dioxide for safety reasons (Banerjee, 2000). San Juan Coal mine is the only longwall

mine that continues to apply gob inertization in the United States. The mine requires a

continuous supply of 0.007 m3/s of nitrogen. Pipes of 100 - 150 mm in diameter are

normally used to deliver nitrogen gas into the gob from crosscuts (Bessinger et aI., 2005).

The development of wider and longer panels increases the mining period,

providing more time for coal oxidation. This extension allows for a longer incubation

period (Koenning, 1989). Hazardous situations may result due to the exponential

relationship between time and temperature rise (Wang and Dlugogorski, 2003). A little

increase in time could cause a thermal runaway and result in a fire. A larger panel also

gives a chance for the development of a hot spot.

An adequate ventilation system can be used to reduce spontaneous combustion in

the gob. If bleeder or wrap-around entries are used, oxygen is allowed to percolate

through the gob, thus supporting the oxidation of coal. If this condition is allowed to

occur for a long period of time, oxidation may result in heat buildup and spontaneous

combustion. Therefore, this system should be used only when the gob condition permits

ventilation air to pass through the gob without any chance for heat buildup. The

bleederless system should be considered if the resistance to airflow is so high that it can

28

create critical conditions for self-heating of coal in the gob. In the bleederless systems,

the gob is isolated by means of seals and stoppings, thus reducing the risk of self-heating.

However, the self-heating of coal can still occur near the face. An adequate ventilation

design can reduce the number of potential locations of heat buildup without neglecting its

main function in providing fresh air to working areas (Hartman et al., 1997; McPherson,

1993; Banik et al., 1994; Cliff, Rowlands, and Sleeman, 1996).

2.4 Spontaneous Combustion Studies Using CFD

Many studies have been done on spontaneous combustion, but only a few utilize

Computational Fluid Dynamics (CFD) software in their investigations. CFD has initially

been used in a wide variety of fluid mechanics-related engineering applications. It

provides numerous options for modeling laminar and turbulent flows, studying

multiphase fluids, representing complex chemical reactions, etc. Often, results are

achieved by applying user-defined F O R T R A N subroutines. For combustion studies, CFD

is a powerful tool to simulate conductive, convective, and radiative processes.

In Australia, a CFD modeling investigation was carried out by the

Commonweal th Scientific and Industrial Research Organization (CSIRO) to develop

airflow patterns for spontaneous combustion control (Balusu et al., 2002). The studies

involved CFD modeling, validation, and calibration of initial models using data obtained

from field studies. One of the models showed that the oxygen distribution in the gob

ranging from 2 % to 2 1 % . A low concentration of up to 2 % was detected in the

consolidated zone. This zone was characterized by low insitu permeability. Although no

explanation was presented on this permeability, the value used was about 1 x 1 0 " 1 7 m 2

28 create critical conditions for self-heating of coal in the gob. In the bleederless systems,

the gob is isolated by means of seals and stoppings, thus reducing the risk of self-heating.

However, the self-heating of coal can still occur near the face. An adequate ventilation

design can reduce the number of potential locations of heat buildup without neglecting its

main function in providing fresh air to working areas (Hartman et aI., 1997; McPherson,

1993; Banik et aI., 1994; Cliff, Rowlands, and Sleeman, 1996).

2.4 Spontaneous Combustion Studies Using CFD

Many studies have been done on spontaneous combustion, but only a few utilize

Computational Fluid Dynamics (CFD) software in their investigations. CFD has initially

been used in a wide variety of fluid mechanics-related engineering applications. It

provides numerous options for modeling laminar and turbulent flows, studying

multiphase fluids, representing complex chemical reactions, etc. Often, results are

achieved by applying user-defined FORTRAN subroutines. For combustion studies, CFD

is a powerful tool to simulate conductive, convective, and radiative processes.

In Australia, a CFD modeling investigation was carried out by the

Commonwealth Scientific and Industrial Research Organization (CSIRO) to develop

airflow patterns for spontaneous combustion control (Balusu et aI., 2002). The studies

involved CFD modeling, validation, and calibration of initial models using data obtained

from field studies. One of the models showed that the oxygen distribution in the gob

ranging from 2% to 21 %. A low concentration of up to 2% was detected in the

consolidated zone. This zone was characterized by low insitu permeability. Although no

explanation was presented on this permeability, the value used was about 1 x 10-17 m2

29

similar to insitu coal permeabili ty determined for western coals by Hucka (1992). This

information is substantial to determine the susceptibility of coal to spontaneous

combustion. Yet, the study did not specify areas with potential heat buildup.

In the U.K., Lowndes et al. (2002) also used CFD model ing to improve the design

of surface gob wells for degasification while minimizing the leakage of air, which may

lead to the danger of spontaneous combustion of coal. Importantly, the permeability of

gob material was discussed in this study. An experimental method was developed for

measuring the permeabili ty of scaled-down rock fragments under increasing stress.

FLAC, a two-dimensional finite difference modeling package, was used to simulate strata

behavior in association with permeability changes. The permeabili ty used in this

simulation ranged from 1 x 10"8 to 1 x 10"1 4 m 2 . Three 0.18-m boreholes spaced 150 m

apart with a suction pressure of -4,000 Pa were found to yield the opt imum results for the

degasification study. Even though they have no direct correlation with spontaneous

combustion, these results can be taken into consideration when simulating inert gas

injection to reduce oxygen level in the gob. Pressurized air or inert gas can be used to

reduce or eliminate the heat buildup in the gob due to oxidation.

In the U.S. , a recent study conducted at the National Institute for Occupational

Safety and Health (NIOSH) utilized CFD to investigate the self-heating process of coal.

Yuan et al. (2006 - 2007) studied the ventilation flow paths in the gob and the likelihood

of spontaneous heating in longwall gob. Gob permeability, as the important input variable

for simulation, was obtained from FLAC. Using the results of the FLAC model, the

permeabilities for the five zones were determined to be between 1 x 10"9 and 5 x 10"1 2 m 2 .

In this study, the preferable condition for spontaneous combustion was analyzed in terms

similar to insitu coal permeability determined for western coals by Hucka (1992). This

information is substantial to determine the susceptibility of coal to spontaneous

combustion. Yet, the study did not specify areas with potential heat buildup.

29

In the u.K., Lowndes et al. (2002) also used CFD modeling to improve the design

of surface gob wells for degasification while minimizing the leakage of air, which may

lead to the danger of spontaneous combustion of coal. Importantly, the permeability of

gob material was discussed in this study. An experimental method was developed for

measuring the permeability of scaled-down rock fragments under increasing stress.

FLAC, a two-dimensional finite difference modeling package, was used to simulate strata

behavior in association with permeability changes. The permeability used in this

simulation ranged from 1 x 10-8 to 1 X 10-14 m2• Three 0.18-m boreholes spaced 150 m

apart with a suction pressure of -4,000 Pa were found to yield the optimum results for the

degasification study. Even though they have no direct correlation with spontaneous

combustion, these results can be taken into consideration when simulating inert gas

injection to reduce oxygen level in the gob. Pressurized air or inert gas can be used to

reduce or eliminate the heat buildup in the gob due to oxidation.

In the U.S., a recent study conducted at the National Institute for Occupational

Safety and Health (NIOSH) utilized CFD to investigate the self-heating process of coal.

Yuan et al. (2006 - 2007) studied the ventilation flow paths in the gob and the likelihood

of spontaneous heating in longwall gob. Gob permeability, as the important input variable

for simulation, was obtained from FLAC. Using the results of the FLAC model, the

permeabilities for the five zones were determined to be between 1 x 10-9 and 5 x 10-12 m2•

In this study, the preferable condition for spontaneous combustion was analyzed in terms

30

of critical velocity. Critical airflow is defined as insufficient airflow to remove the heat

due to oxidation, but sufficient to maintain the oxidation process. This study confirmed

the existence of a critical velocity zone behind the shields in the gob for a bleederless

system. In addition, for a three-entry bleeder system, the critical velocity zone may also

occur at the back end of the gob.

Although these studies outlined the areas with spontaneous combustion potential,

they did not specify the location of the hot spots in the gob. Besides critical velocity,

other parameters such as oxygen concentration and temperature should be considered in

the simulation study.

2.5 Porous Medium

Porous medium simply can be defined as the solid or loose body that contains

open cavities. A solid body refers to a packed form of bound material while a loose body

consists of granular particles. The interconnected pores in a porous system are often

called effective pores. In practice, the effective pores play an important role in fluid flow

through porous media. The detailed description of porous medium is sometimes intuitive,

so that the exact properties are difficult to describe (Scheidegger, 1957). A statistical

review of porous medium, including particle-size distribution, porosity, and permeability,

as described by Bear (1972), is necessary to understand mine gob characteristics.

2.5.1. Particle Size Distribution

Granular materials are best described by their particle-size distribution. It is

generally accepted that irregular material particle size cannot be easily defined as a

of critical velocity. Critical airflow is defined as insufficient airflow to remove the heat

due to oxidation, but sufficient to maintain the oxidation process. This study confirmed

the existence of a critical velocity zone behind the shields in the gob for a bleederless

system. In addition, for a three-entry bleeder system, the critical velocity zone may also

occur at the back end of the gob.

30

Although these studies outlined the areas with spontaneous combustion potential,

they did not specify the location of the hot spots in the gob. Besides critical velocity,

other parameters such as oxygen concentration and temperature should be considered in

the simulation study.

2.5 Porous Medium

Porous medium simply can be defined as the solid or loose body that contains

open cavities. A solid body refers to a packed form of bound material while a loose body

consists of granular particles. The interconnected pores in a porous system are often

called effective pores. In practice, the effective pores play an important role in fluid flow

through porous media. The detailed description of porous medium is sometimes intuitive,

so that the exact properties are difficult to describe (Scheidegger, 1957). A statistical

review of porous medium, including particle-size distribution, porosity, and permeability,

as described by Bear (1972), is necessary to understand mine gob characteristics.

2.5.1. Particle Size Distribution

Granular materials are best described by their particle-size distribution. It is

generally accepted that irregular material particle size cannot be easily defined as a

31

sphere or cube. Each particle shape is unique. The measurement results depend on the

particle dimensions and the method of measurement. For particles larger than 0.06 mm,

sieve analysis can be used to determine the size distribution (Bear, 1972).

In sieve analysis, a pile of material is forced to pass through a sieve of a certain

opening size. A number of sieves are used to define the particle distribution graphs.

However, using this method, particles with lengths larger than the sieve opening may slip

through and alter the particle distribution. Side assessments are necessary to eliminate

such a possibility, for example, by screening the material twice with the same sieve.

2.5.2. Porosity

The major properties required to simulate a mine gob are porosity and

permeability. Porosity is defined as the ratio of void volume to the total volume of a

packed body. Mathematically, it is given by:

n = —z- x 100 % (2.6) V

where

n = the porosity

VV = the pore volume

VT = the total volume

For consolidated materials, the porosity depends on the degree of cementation,

while the porosity of unconsolidated or loose material depends on the packing of the

grains, their shape and size distribution (Bear, 1972). Depending on their arrangement,

31

sphere or cube. Each particle shape is unique. The measurement results depend on the

particle dimensions and the method of measurement. For particles larger than 0.06 mm,

sieve analysis can be used to determine the size distribution (Bear, 1972).

In sieve analysis, a pile of material is forced to pass through a sieve of a certain

opening size. A number of sieves are used to define the particle distribution graphs.

However, using this method, particles with lengths larger than the sieve opening may slip

through and alter the particle distribution. Side assessments are necessary to eliminate

such a possibility, for example, by screening the material twice with the same sieve.

2.5.2. Porosity

The major properties required to simulate a mine gob are porosity and

permeability. Porosity is defined as the ratio of void volume to the total volume of a

packed body. Mathematically, it is given by:

where

n the porosity

Vv the pore volume

VT the total volume

n = Vv x 100 % VT

(2.6)

For consolidated materials, the porosity depends on the degree of cementation,

while the porosity of unconsolidated or loose material depends on the packing of the

grains, their shape and size distribution (Bear, 1972). Depending on their arrangement,

32

non-uniform-sized particles may change the porosity of the total volume. Small particles

may occupy the space between the large particles, and reduce the porosity. Compaction

and consolidation are other factors that affect porosity. In the case of gob material,

compaction is caused by the pressure of overlying strata varying with the depth of

overburden and age of the gob.

2.5.3. Specific Permeabili ty

Another parameter used to characterize the porous media is the specific

permeability, sometimes just called permeability. This parameter indicates the ability of

consolidated or unconsolidated material to transmit fluids. Specific permeabili ty is of

great importance in determining the airflow behavior in the gob. A common unit for

permeability is the darcy (D), or more commonly the millidarcy (mD), in which 1 darcy

equals 9.87 x 10 m 2 . Darcy ' s law is expressed by:

Q = C A Ah

(2.7) L

where

Q airflow rate, m 3 / s

C hydraulic conductivity, m/s

A cross sectional area of porous sample, m

Ah pressure difference between two points, m

L length of porous sample, m

32

non-uniform-sized particles may change the porosity of the total volume. Small particles

may occupy the space between the large particles, and reduce the porosity. Compaction

and consolidation are other factors that affect porosity. In the case of gob material,

compaction is caused by the pressure of overlying strata varying with the depth of

overburden and age of the gob.

2.5.3. Specific Permeability

Another parameter used to characterize the porous media is the specific

permeability, sometimes just called permeability. This parameter indicates the ability of

consolidated or unconsolidated material to transmit fluids. Specific permeability is of

great importance in determining the airflow behavior in the gob. A common unit for

permeability is the darcy (D), or more commonly the millidarcy (mD), in which 1 darcy

equals 9.87 x to- 13 m2• Darcy' s law is expressed by:

where

Q airflow rate, m3 Is

!1h Q=CA

L

C hydraulic conductivity, mls

A cross sectional area of porous sample, m2

Llh pressure difference between two points, m

L = length of porous sample, m

(2.7)

33

k = (2.8) 7

where

k = specific permeability, m 2

y = specific weight of fluid, N / m 2

= dynamic viscosity of fluid, Ns/m

Since porosity and specific permeability measure the structure of porous media,

they ought to be related. Many investigators have studied the relationship between these

two parameters. An empirical correlation was proposed by Carman in 1937. A modified

version of this work is known as the Carman-Kozeny equation (Scheidegger, 1957):

d2 n3

k* = - (2.9) 180 (1 - n)2

where

k* = theoretical specific permeability, m 2

dm = the mean particle size, m

Equation 2.9 clearly indicates that specific permeabili ty is dependent on the mean

particle diameter and porosity, and is theoretically obtained by assuming uniform-size

particles are arranged in cubic packing.

Equation 2.7 is restricted for a laminar flow condition. The proportionality

constant, C, is also known as Darcy ' s velocity. Based on this coefficient, the specific

permeability is given by:

33

Equation 2.7 is restricted for a laminar flow condition. The proportionality

constant, C, is also known as Darcy's velocity. Based on this coefficient, the specific

permeability is given by:

k =

where

k = specific permeability, m2

f.1 C

r

y specific weight of fluid, N/m2

Il dynamic viscosity of fluid, Ns/m2

(2.8)

Since porosity and specific permeability measure the structure of porous media,

they ought to be related. Many investigators have studied the relationship between these

two parameters. An empirical correlation was proposed by Carman in 1937. A modified

version of this work is known as the Carman-Kozeny equation (Scheidegger, 1957):

k* = d; n 3

180 (1 - n) 2 (2.9)

where

k* theoretical specific permeability, m2

dm the mean particle size, m

Equation 2.9 clearly indicates that specific permeability is dependent on the mean

particle diameter and porosity, and is theoretically obtained by assuming uniform-size

particles are arranged in cubic packing.

34

Mine Coal Seam Permeability ( x 10" 1 7 m 2 ) Mine Coal Seam Parallel to bedding Perpendicular to bedding

Castle Gate Sub 3 4.1 3.9

Soldier Creek Rock Canyon 0.5 1.4 Soldier Creek Sunnyside 6.3 9.6

Sunnyside L. Sunnyside 1.6 1.1

Using Darcy ' s law, Hucka (1992) found the specific permeabili ty values for

Utah 's coals (Table 2.2). The coal samples used by Hucka were taken from coal mines

and tested in the laboratory with nitrogen as a fluid. The permeabili ty values were found

to be influenced by the cleat direction and whether the coal sample is parallel or

perpendicular to the bedding, fracture, and other geological structures.

To characterize the gob material, it is necessary to consider the specific

permeability of the broken coal-rock mixture behind the face. Therefore, the gob specific

permeability should be much higher than the specific permeabili ty of fresh coal shown in

Table 2.2. Investigations of experimental specific permeabili ty were conducted at the

University of Utah. The results are shown in Table 2.3. These values have been adjusted

for air as fluid rather than nitrogen. The materials used were the broken rock with various

sizes and are tested by X-ray microtomography and constant-head techniques (Gold,

2004; Lin, 2005; Videla, 2008). These values should be comparable with those used in

this simulation.

Table 2.2 Experimental specific permeability of Utah coals (Hucka, 1992)

34

Table 2.2 Experimental specific permeability of Utah coals (Hucka, 1992)

Mine Coal Seam Permeability ( x 10-17 m2

)

Parallel to bedding Perpendicular to bedding Castle Gate Sub 3 4.1 3.9

Soldier Creek Rock Canyon 0.5 1.4

Sunnyside 6.3 9.6 Sunnyside L. Sunnyside 1.6 1.1

Using Darcy's law, Hucka (1992) found the specific permeability values for

Utah's coals (Table 2.2). The coal samples used by Hucka were taken from coal mines

and tested in the laboratory with nitrogen as a fluid. The permeability values were found

to be influenced by the cleat direction and whether the coal sample is parallel or

perpendicular to the bedding, fracture, and other geological structures.

To characterize the gob material, it is necessary to consider the specific

permeability of the broken coal-rock mixture behind the face. Therefore, the gob specific

permeability should be much higher than the specific permeability of fresh coal shown in

Table 2.2. Investigations of experimental specific permeability were conducted at the

University of Utah. The results are shown in Table 2.3. These values have been adjusted

for air as fluid rather than nitrogen. The materials used were the broken rock with various

sizes and are tested by X-ray microtomography and constant-head techniques (Gold,

2004; Lin, 2005; Videla, 2008). These values should be comparable with those used in

this simulation.

35

Particle Range Size Permeability (m2) Investigators Method Mesh Standard (mm) Permeability (m2) Investigators Method

No. 200 - 1 in. 0 .074-25.40 1.400 x 10"07 Lin et al. (2005)

X-Ray Tomography

No. 1 7 0 - 3 / 4 i n . 0 . 0 8 8 - 19.00 6.340 x 10"11

Gold (2004) Constant-head No. 40 - 1 in. 0 .420-25.40 3.450 x 10"11

Gold (2004) Constant-head

No. 1 0 0 - N o . 40 0 .149-0 .420 4.034 x 10"n

Videla (2008) Constant-head No. 4 0 - N o . 10 0.420 - 2.000 3.475 x 10"10 Videla (2008) Constant-head

No. 1 0 - 1 / 8 in. 2 .000-3 .175 2.023 x 10' 0 9

Videla (2008) Constant-head

Table 2.3 Experimental specific permeability of broken rocks

35

Table 2.3 Experimental specific permeability of broken rocks

Particle Range Size Permeability (m2

) Investigators Method Mesh Standard (mm)

No. 200 - 1 in. 0.074 - 25.40 1.400 x 10,07 Lin et al. X-Ray (2005) Tomography

No, 170 - 3/4 in. 0,088 - 19.00 6.340 x 10,11

No. 40 - 1 in. 0.420 - 25.40 3.450 x 10' " Gold (2004) Constant-head

No. 100 - No. 40 0.149 - 0.420 4.034 x 10' "

No. 40 - No. 10 0.420 - 2.000 3.475 X 10,10 Videla (2008) Constant-head

No. 10 - 118 in. 2.000 - 3.175 2.023 x 10'09

CHAPTER 3

CHARACTERISTICS OF GOB MATERIAL

Knowledge of longwall gob conditions is a critical element in the study of

spontaneous combustion. Currently, the interpretation of events taking place inside the

gob is unclear and, in some cases, merely guesses. Roof caving is one of the major causes

impeding investigators to search for further details on air-gas flow, although, recently,

much work has been done to understand this behavior. Some of these works are used as

the foundation of this study. Ventilation air distribution, panel dimensions, and particle

size distribution of gob material are important factors in the design of physical and

computer-based gob models . For this study, a number of tests have been conducted to

better understand the gob material characteristics. Results of these studies, assumptions

made and the significance of laboratory experiments are described in this section.

3.1. Longwall Mine Gob

The development of the gob in a longwall mine is influenced by several factors,

including the geologic conditions of the overlying and underlying strata, panel

dimensions, and the depth of the coal seam. The presence of joints, fractures, and any

other geologic features will change the characteristics of the gob, the caving time, and the

size of the broken material. The most important parameter of the gob considered in this

CHAPTER 3

CHARACTERISTICS OF GOB MATERIAL

Knowledge oflongwall gob conditions is a critical element in the study of

spontaneous combustion. Currently, the interpretation of events taking place inside the

gob is unclear and, in some cases, merely guesses. Roof caving is one of the major causes

impeding investigators to search for further details on air-gas flow, although, recently,

much work has been done to understand this behavior. Some of these works are used as

the foundation of this study. Ventilation air distribution, panel dimensions, and particle

size distribution of gob material are important factors in the design of physical and

computer-based gob models. For this study, a number oftests have been conducted to

better understand the gob material characteristics. Results of these studies, assumptions

made and the significance of laboratory experiments are described in this section.

3.1. Longwall Mine Gob

The development of the gob in a longwall mine is influenced by several factors,

including the geologic conditions of the overlying and underlying strata, panel

dimensions, and the depth of the coal seam. The presence of joints, fractures, and any

other geologic features will change the characteristics of the gob, the caving time, and the

size of the broken material. The most important parameter of the gob considered in this

37

study is specific permeability. This parameter is strongly affected by gob porosity and

particle size.

Peng (1984) states that coal extraction using the longwall mine method induces a

series of events: abutment pressure and roof-to-floor convergence in the entries and face

area, movement of rock strata, and surface subsidence. Figure 3.1 illustrates the typical

result of coal extraction in retreat longwall mining. The initial strata response to mining is

failure of the immediate roof, thus creating a void over the caved material. As the mined-

out span increases, the strata failure continues and the volume of the broken material

gradually fills the void space. Eventually the overlying strata rest on the caved material

which offers some degree of support. As the longwall face retreats further, the full weight

of the overlying strata will rest upon the gob material, reducing the void spaces in the

gob. An investigation by the U.S. Mine Safety and Health Administration in 2002

reported that the height of the caved zone may range from 1 to 10 times the mining

height, depending on the geologic condition of the roof. Other investigators state that the

caved zone may extend from 4 to 6 times the height of the coal bed (Mucho et al., 2000).

Above the caved zone, the strata do not detach from each other but are linked by

connecting cracks. This is called the dilated zone. This zone extends from 9 to 60 times

the mining height and may cause beam deformation. Above this is the fractured zone.

Surface fracture of about 50 ft deep may occur due to tension in the subsidence zone.

The gob materials, such as those from caved roof and heaved floor, can cause

variable resistance to airflow if a bleeder system is utilized. The amount of void spaces

and how they are connected affect the resistance. Research indicates that a significant

portion of voids is located in the area behind the shields. The max imum particle size of

study is specific permeability. This parameter is strongly affected by gob porosity and

particle size.

37

Peng (1984) states that coal extraction using the longwall mine method induces a

series of events: abutment pressure and roof-to-floor convergence in the entries and face

area, movement of rock strata, and surface subsidence. Figure 3.1 illustrates the typical

result of coal extraction in retreat longwall mining. The initial strata response to mining is

failure of the immediate roof, thus creating a void over the caved material. As the mined

out span increases, the strata failure continues and the volume of the broken material

gradually fills the void space. Eventually the overlying strata rest on the caved material

which offers some degree of support. As the longwall face retreats further, the full weight

of the overlying strata will rest upon the gob material, reducing the void spaces in the

gob. An investigation by the U.S. Mine Safety and Health Administration in 2002

reported that the height of the caved zone may range from 1 to 10 times the mining

height, depending on the geologic condition of the roof. Other investigators state that the

caved zone may extend from 4 to 6 times the height of the coal bed (Mucho et aI., 2000).

Above the caved zone, the strata do not detach from each other but are linked by

connecting cracks. This is called the dilated zone. This zone extends from 9 to 60 times

the mining height and may cause beam deformation. Above this is the fractured zone.

Surface fracture of about 50 ft deep may occur due to tension in the subsidence zone.

The gob materials, such as those from caved roof and heaved floor, can cause

variable resistance to airflow if a bleeder system is utilized. The amount of void spaces

and how they are connected affect the resistance. Research indicates that a significant

portion of voids is located in the area behind the shields. The maximum particle size of

s u r f a c e

Shearer/Shields Zone 1 Zone 2 Zone 3 Bleeder Entry

Figure 3.1 Gob and strata zones in a longwall mine section (after MSHA, 2002)

surface

Shearer/Shields Zone 1 Zone 2 Zone 3 Bleeder Entry

Figure 3.1 Gob and strata zones in a longwall mine section (after MSHA, 2002) w 00

39 gob material in this area is about 550 mm (Pappas and Mark, 1993). Conversely, smaller

particles are found near the bleeder entries. The reduction of void space is due to

compaction of the overlying strata; the longer the compaction process, the lower the void

space. The shape of the particles depends on the way these are arranged inside the gob.

Densely consolidated, blocky material tends to break into large slabs, and creates large

porous spaces. Laminated fragments tend to be more compacted than the blocky

materials, thus decreasing the void space. The initial shape of the large fragments

changes over time due to compaction.

Gob permeability depends on the void space distribution in the caved area. With

the knowledge of the material size, shape of broken fragments, and packing mode, the

gob can be divided into three permeability zones: unconsolidated, semiconsolidated, and

consolidated (Figure 3.1). These zones are characterized by their porosity as high,

medium, and low, respectively. The size, shape, and packing of gob material may change

and become more compact over time due to overburden weight. A number of studies

have found that the permeability of the gob material ranges from 1x10" to 1x10"

(Brunner, 1985; Ren et al., 1985; Ezterhuizen and Karacan, 2007). The experimental

values used in this study are presented in Section 3.4.

Although the step of dividing the gob into 3 zones is a fair assumption and

supported by several studies, the boundaries of each zone are difficult to define.

Longwall mining is a dynamic process. The gob permeability decreases gradually from

the face to the bleeder area over time. Therefore, more permeability zones are preferable

for simulation to reflect gradual permeability changes. However, iteration time and

complexity of the model are the limitations for having unlimited zones. Investigations

39

gob material in this area is about 550 mm (Pappas and Mark, 1993). Conversely, smaller

particles are found near the bleeder entries. The reduction of void space is due to

compaction of the overlying strata; the longer the compaction process, the lower the void

space. The shape of the particles depends on the way these are arranged inside the gob.

Densely consolidated, blocky material tends to break into large slabs, and creates large

porous spaces. Laminated fragments tend to be more compacted than the blocky

materials, thus decreasing the void space. The initial shape of the large fragments

changes over time due to compaction.

Gob permeability depends on the void space distribution in the caved area. With

the knowledge of the material size, shape of broken fragments, and packing mode, the

gob can be divided into three permeability zones: unconsolidated, semiconsolidated, and

consolidated (Figure 3.1). These zones are characterized by their porosity as high,

medium, and low, respectively. The size, shape, and packing of gob material may change

and become more compact over time due to overburden weight. A number of studies

have found that the permeability of the gob material ranges from lxlO -13 to lxl0-5

(Brunner, 1985; Ren et aI., 1985; Ezterhuizen and Karacan, 2007). The experimental

values used in this study are presented in Section 3.4.

Although the step of dividing the gob into 3 zones is a fair assumption and

supported by several studies, the boundaries of each zone are difficult to define.

Longwall mining is a dynamic process. The gob permeability decreases gradually from

the face to the bleeder area over time. Therefore, more permeability zones are preferable

for simulation to reflect gradual permeability changes. However, iteration time and

complexity of the model are the limitations for having unlimited zones. Investigations

40 conducted by Balusu et al. in 2002 and 2005 with tracer gas (SF6) and a gas monitoring

system presented information to characterize and determine the boundaries of each zone.

The unconsolidated zone, characterized by tracer gas, is hypothesized to extend up to 150

m behind the face. A lower concentration zone is assumed to extend from 150 m up to

600 m, and the third zone, almost degassed beyond 600 m. For simulation purposes, zone

1 extends up to 150 m from the face line, zone 2 from 150 m to 600 m, and zone 3 from

600 m up to the bleeder area. The schematic of these zones is presented in Section 5.1.

3.2. Gob Material and Its Characteristics

The reliability of physical and computational models in simulating hot spots

depends on how closely the simulated gob material emulates the real conditions. Even

though there is no simple way to quantify real gob conditions, some studies have been

conducted to approximate the air distribution through the gob. The gob is often

represented by a zone of fixed volume filled with particles of given size distribution. The

particle size and packing mode affect the airflow distribution and the self-heating process

of broken coal.

For this study, the gob material is represented by crushed rock and coal. Particle

size and packing modes are discussed in this section. These properties affect the porosity

and permeability of the porous media, and eventually the fluid transport process.

3.2.1 Particle Size Selection

In the field, the largest coal-rock particles are more likely to be located in the area

behind the shields. This material is freshly broken and unconsolidated. The size of the

40

conducted by Balusu et al. in 2002 and 2005 with tracer gas (SF6) and a gas monitoring

system presented information to characterize and determine the boundaries of each zone.

The unconsolidated zone, characterized by tracer gas, is hypothesized to extend up to 150

m behind the face. A lower concentration zone is assumed to extend from 150 m up to

600 m, and the third zone, almost degassed beyond 600 m. For simulation purposes, zone

1 extends up to 150 m from the face line, zone 2 from 150 m to 600 m, and zone 3 from

600 m up to the bleeder area. The schematic ofthese zones is presented in Section 5.1.

3.2. Gob Material and Its Characteristics

The reliability of physical and computational models in simulating hot spots

depends on how closely the simulated gob material emulates the real conditions. Even

though there is no simple way to quantify real gob conditions, some studies have been

conducted to approximate the air distribution through the gob. The gob is often

represented by a zone of fixed volume filled with particles of given size distribution. The

particle size and packing mode affect the airflow distribution and the self-heating process

of broken coal.

For this study, the gob material is represented by crushed rock and coal. Particle

size and packing modes are discussed in this section. These properties affect the porosity

and permeability of the porous media, and eventually the fluid transport process.

3.2.1 Particle Size Selection

In the field, the largest coal-rock particles are more likely to be located in the area

behind the shields. This material is freshly broken and unconsolidated. The size of the

41

broken particles in this area is based on a study carried out by analyzing images taken

from the area behind the shields in three coal mines in the United States (Pappas and

Mark, 1993). The results show that the maximum particle size in the gob area behind the

shields is about 550 mm with a mean of 122 mm. This average size is used in the present

study to determine the permeability values for the unconsolidated gob material.

For other zones, such as those located near the bleeder entries, the size should be

assessed through simulations. This is due to the lack of experimental information in these

gob zones. Through simulations, the mean particle sizes for the semi-consolidated and

consolidated zones were 0.02 and 0.006 m, respectively, smaller than those of the

unconsolidated zone. These were determined based on laboratory experiments,

permeability tests, and numerical simulations (Section 3.4).

3.2.2 Packing and Particle Shape

To understand the relationship between particle structure and porosity,

investigators have established the concept of stable packing (Scheidegger, 1957; Bear,

1972; Freeze and Cherry, 1979). Stable packing is approximated by a motionless

arrangement of uniform spheres. By studying the various modes of stable packing, a

correlation between grain size, structure, and porosity can be determined mathematically.

The uniform packing concept has been used by other investigators to generate computer

simulated porous media (Scheidegger, 1957; Bear, 1972). However, this concept only

approximates the natural condition of porous media. The natural condition includes

particles whose shape and size differs from that of spheres. They are seldom uniform in

41

broken particles in this area is based on a study carried out by analyzing images taken

from the area behind the shields in three coal mines in the United States (Pappas and

Mark, 1993). The results show that the maximum particle size in the gob area behind the

shields is about 550 mm with a mean of 122 mm. This average size is used in the present

study to determine the permeability values for the unconsolidated gob material.

For other zones, such as those located near the bleeder entries, the size should be

assessed through simulations. This is due to the lack of experimental information in these

gob zones. Through simulations, the mean particle sizes for the semi-consolidated and

consolidated zones were 0.02 and 0.006 m, respectively, smaller than those of the

unconsolidated zone. These were determined based on laboratory experiments,

permeability tests, and numerical simulations (Section 3.4).

3.2.2 Packing and Particle Shape

To understand the relationship between particle structure and porosity,

investigators have established the concept of stable packing (Scheidegger, 1957; Bear,

1972; Freeze and Cherry, 1979). Stable packing is approximated by a motionless

arrangement of uniform spheres. By studying the various modes of stable packing, a

correlation between grain size, structure, and porosity can be determined mathematically.

The uniform packing concept has been used by other investigators to generate computer

simulated porous media (Scheidegger, 1957; Bear, 1972). However, this concept only

approximates the natural condition of porous media. The natural condition includes

particles whose shape and size differs from that of spheres. They are seldom uniform in

42 size and shape. This nonuniformity permits the smaller particles to fill the spaces between

the larger ones, thus reducing the void space of the porous media.

In this study, both crushed coal and rock are used to represent the gob material.

Permeability tests have shown that though crushed coal and rock samples have identical

particle sizes, they may still have different values of porosity and permeability (Section

3.3). The way each particle is arranged in the porous media plays an important role in

defining the permeability of the porous media. The shape of coal particle is usually more

angular than that of rock. These factors cause coal particles to have a denser packing than

noncoal particles. However, the experiments carried out in this study indicate that, on the

average, the difference in permeability between coal and rock samples is within 20%.

While longwall gob does not exhibit spherical packing, computational simulators

such as Fluent use the spherical packing concept. Therefore, physical measurement and

computer modeling results are expected to differ to some degree. A calibration factor can

be used to convert physical rock or coal permeability to computer model permeability.

Then, this factor can be used to modify the Kozeny-Carman relationship used with Fluent

(Section 4.3.2).

3.3. Permeability Tests

A series of permeability tests were conducted at the University of Utah using

water and air as fluids. The objective of these tests was to determine the specific

permeability of simulated gob materials. These tests followed Darcy's concept of fluid

flow through porous media. During each test, laminar flow conditions were maintained in

the permeameter (container). Fluid flow rates, pressure differences, and room

42

size and shape. This nonunifonnity penn its the smaller particles to fill the spaces between

the larger ones, thus reducing the void space of the porous media.

In this study, both crushed coal and rock are used to represent the gob material.

Penneability tests have shown that though crushed coal and rock samples have identical

particle sizes, they may still have different values of porosity and penneability (Section

3.3). The way each particle is arranged in the porous media plays an important role in

defining the penneability of the porous media. The shape of coal particle is usually more

angular than that of rock. These factors cause coal particles to have a denser packing than

noncoal particles. However, the experiments carried out in this study indicate that, on the

average, the difference in penneability between coal and rock samples is within 20%.

While longwall gob does not exhibit spherical packing, computational simulators

such as Fluent use the spherical packing concept. Therefore, physical measurement and

computer modeling results are expected to differ to some degree. A calibration factor can

be used to convert physical rock or coal penneability to computer model penneability.

Then, this factor can be used to modify the Kozeny-Cannan relationship used with Fluent

(Section 4.3.2).

3.3. Penneability Tests

A series ofpenneability tests were conducted at the University of Utah using

water and air as fluids. The objective of these tests was to detennine the specific

penneability of simulated gob materials. These tests followed Darcy's concept of fluid

flow through porous media. During each test, laminar flow conditions were maintained in

the penneameter (container). Fluid flow rates, pressure differences, and room

43

temperatures were recorded systematically. These data were used to calculate specific

permeability of the material. This section describes the process of determining

permeability of broken coal and rock, data interpretation, and conclusions.

3.3.1 Sample Preparation

The granular materials such as crushed rock and coal are best described by their

particle-size distribution (Bear, 1972). Six sieve sizes were used to classify the rock and

coal samples: 150-um, 425-um, 1.70-mm, 4.75-mm, 6.73-mm, and 12.5-mm sizes. The

diameter of permeameter used to hold material defined the largest size. After sieving, the

crushed rock and coal samples were divided into 6 size ranges based on the sieves. The

mean sizes for each group were 0.28, 3.22, 5.74, 7.73, 8.72, and 9.71 mm, respectively.

Tests were conducted circulating either water or air through the permeameter.

ASTM Method D2434-68, a water-based standard used to measure the permeability of

granular soils, was followed for these tests using the "constant head method." For this

method, 30 tests were performed using 3 sample groups with mean sizes of 0.28, 3.22,

and 5.74 mm. The permeameter size restricted the tests for larger particles.

The air-based tests were carried out using the same constant-head method.

Permeameter dimensions used in this test were different than those used in water-based

test. Therefore, the sample groups were different. Thirty six tests using 4 sample groups

with mean sizes of 5.74, 7.73, 8.72, and 9.71 mm were performed. The first sample group

was tested using both fluids (air and water) to explore the effect of fluid to permeability.

A detailed description of both experiments is presented in the following sections.

temperatures were recorded systematically. These data were used to calculate specific

permeability of the material. This section describes the process of determining

permeability of broken coal and rock, data interpretation, and conclusions.

3.3.1 Sample Preparation

43

The granular materials such as crushed rock and coal are best described by their

particle-size distribution (Bear, 1972). Six sieve sizes were used to classify the rock and

coal samples: I50-l1m, 425-l1m, 1.70-mm, 4.75-mm, 6.73-mm, and I2.5-mm sizes. The

diameter of permeameter used to hold material defined the largest size. After sieving, the

crushed rock and coal samples were divided into 6 size ranges based on the sieves. The

mean sizes for each group were 0.28,3.22,5.74, 7.73, 8.72, and 9.71 mm, respectively.

Tests were conducted circulating either water or air through the permeameter.

ASTM Method D2434-68, a water-based standard used to measure the permeability of

granular soils, was followed for these tests using the "constant head method." For this

method, 30 tests were performed using 3 sample groups with mean sizes of 0.28, 3.22,

and 5.74 mm. The permeameter size restricted the tests for larger particles.

The air-based tests were carried out using the same constant-head method.

Permeameter dimensions used in this test were different than those used in water-based

test. Therefore, the sample groups were different. Thirty six tests using 4 sample groups

with mean sizes of 5.74, 7.73, 8.72, and 9.71 mm were performed. The first sample group

was tested using both fluids (air and water) to explore the effect of fluid to permeability.

A detailed description of both experiments is presented in the following sections.

44 3.3.2 Water-Based Method

The water-based method was used to measure the specific permeability of the

simulated gob material by maintaining constant water head (pressure). The pressure drop

through the porous medium is measured by the difference in height of two water

columns. To determine permeability using Darcy's law, constant flow must be

established first. This is achieved by maintaining the water column in the container

constant. The ASTM standard states stringent prerequisites for permeability tests: (a)

continuity of flow with no material volume change, (b) flow with the material voids

saturated with water and no air bubbles, and (c) steady state flow with no changes in

hydraulic gradient. These prerequisites are explained in the following sections.

3.3.2.1 Testing Apparatus

Figure 3.2 shows the apparatus used for the test. It includes a carbon dioxide gas

tank, a water container, a material column (permeameter), a flask, and tubings. At the

beginning of each test, carbon dioxide was flushed through the permeameter to eliminate

air bubbles trapped in the material voids. This gas was selected due to its inertness and

safety. A valve attached to the tank outlet controlled gas flow rate. The maximum gas

pressure in the tank was 689 kPa (100 psi). However, only 3.5 kPa (0.5 psi) of gage

pressure was used to flush the permeameter. It took from 10 to 15 minutes to flush out the

air bubbles from the column. This was monitored visually.

The energy source was represented by a water container of fixed head. The

container was joined to the permeameter through control valves and tubings. The

permeameter was filled with granular samples and saturated with distilled water. The

44

3.3.2 Water-Based Method

The water-based method was used to measure the specific permeability of the

simulated gob material by maintaining constant water head (pressure). The pressure drop

through the porous medium is measured by the difference in height of two water

columns. To determine permeability using Darcy's law, constant flow must be

established first. This is achieved by maintaining the water column in the container

constant. The ASTM standard states stringent prerequisites for permeability tests: (a)

continuity of flow with no material volume change, (b) flow with the material voids

saturated with water and no air bubbles, and (c) steady state flow with no changes in

hydraulic gradient. These prerequisites are explained in the following sections.


Figure 3.2 shows the apparatus used for the test. It includes a carbon dioxide gas

tank, a water container, a material column (permeameter), a flask, and tubings. At the

beginning of each test, carbon dioxide was flushed through the permeameter to eliminate

air bubbles trapped in the material voids. This gas was selected due to its inertness and

safety. A valve attached to the tank outlet controlled gas flow rate. The maximum gas

pressure in the tank was 689 kPa (100 psi). However, only 3.5 kPa (0.5 psi) of gage

pressure was used to flush the permeameter. It took from 10 to 15 minutes to flush out the

air bubbles from the column. This was monitored visually.

The energy source was represented by a water container of fixed head. The

container was joined to the permeameter through control valves and tUbings. The

permeameter was filled with granular samples and saturated with distilled water. The

Figure 3.2 Permeability test network for water-based method

r--~ Gas Pressure Gauge

Relief pressure taps

Top screen

Water Container Permeameter

Flask

Bottom screen

Figure 3.2 Penneability test network for water-based method

46

3.3.2.2 Testing Procedure

The permeability of crushed samples (coal and rock) was determined

experimentally using the following procedure:

1. Place crushed material in the permeameter and setup the network (Figure 3.2).

2. Flush the specimen with carbon dioxide at a gage pressure of 3.5 kPa (0.5 Psi).

3. Once the air bubbles are removed, close the gas control valve and open the water

valve.

4. Maintain the water level in the container constant by feeding it continuously.

5. Collect the fluid overflow in the flask and record the water volume (V). Also,

record the collection time (t).

6. Measure the difference in water head (Ah) and sample length in permeameter (L).

permeameter cylinder was 60 mm in diameter. This was 8 to 12 times larger than the

maximum particle size as prescribed by the ASTM standard. Two porous screens with

openings smaller than the particle size were attached to two ends of the specimen. The

screen openings were larger than the material voids but smaller than the particle diameter

to prevent the movement of particles. The permeameter had two taps to allow water to

flow. The specimen height in the permeameter was at least twice the diameter of the

cylinder. A metal spring was attached to the top screen to avoid changes in specimen

height during the test. Two pressure relief valves in the permeameter lid are used to

eliminate pressure buildup between the water level and the permeameter lid. The

overflow water collected in the flask was used to determine the flow rate through the

specimen.

46

permeameter cylinder was 60 mm in diameter. This was 8 to 12 times larger than the

maximum particle size as prescribed by the ASTM standard. Two porous screens with

openings smaller than the particle size were attached to two ends of the specimen. The

screen openings were larger than the material voids but smaller than the particle diameter

to prevent the movement of particles. The permeameter had two taps to allow water to

flow. The specimen height in the permeameter was at least twice the diameter of the

cylinder. A metal spring was attached to the top screen to avoid changes in specimen

height during the test. Two pressure relief valves in the permeameter lid are used to

eliminate pressure buildup between the water level and the permeameter lid. The

overflow water collected in the flask was used to determine the flow rate through the

speCImen.


The permeability of crushed samples (coal and rock) was determined

experimentally using the following procedure:

1. Place crushed material in the permeameter and setup the network (Figure 3.2).

2. Flush the specimen with carbon dioxide at a gage pressure of3.5 kPa (0.5 Psi).

3. Once the air bubbles are removed, close the gas control valve and open the water

valve.

4. Maintain the water level in the container constant by feeding it continuously.

5. Collect the fluid overflow in the flask and record the water volume (V). Also,

record the collection time (t).

6. Measure the difference in water head (~h) and sample length in permeameter (L).

47 7. Record the room temperature and atmospheric pressure.

8. Repeat steps 1 through 7 for different flow rates and particle sizes.

Thirty water-based tests were performed using the above procedure. The gathered

data from these tests are presented in Appendix A.

3.3.2.3 Testing Results

For the water-based method, 30 experiments were considered large enough to

produce reliable results. Besides the sample size, another concern was laminar condition

requirement for the experiments. Regression analysis was performed to check this

condition. Using a standard permeameter, and measuring the water heads, quantity of

overflow, and the Darcy's law, the specific permeability (A:) for material samples can be

calculated. An example of such a calculation can be found in Appendix A.

Figure 3.3 shows the results of 30 permeability tests conducted for the same flow

conditions. These were carried out using crushed rock and coal samples of three different

particle sizes. This figure also shows the relationship between the water head and flow

rates for rock and coal samples. The linear regression analysis on each graph shows an

upward trend of head with flow rates showing that the experimental conditions followed

the Darcy's concept.

Data points that lay outside these regression trends may indicate the presence of

turbulent flow. From the observations, such data generally occur at either very low or

high flow rates. The R-squared value (R ), called the correlation coefficient, represents

how well the regression line matches the original data points and ranges from 0 (no

match) to 1 (perfect match). The high R 2 values shown on the regression lines implied

47

7. Record the room temperature and atmospheric pressure.

8. Repeat steps 1 through 7 for different flow rates and particle sizes.

Thirty water-based tests were performed using the above procedure. The gathered

data from these tests are presented in Appendix A.


For the water-based method, 30 experiments were considered large enough to

produce reliable results. Besides the sample size, another concern was laminar condition

requirement for the experiments. Regression analysis was performed to check this

condition. Using a standard permeameter, and measuring the water heads, quantity of

overflow, and the Darcy's law, the specific permeability (k) for material samples can be

calculated. An example of such a calculation can be found in Appendix A.

Figure 3.3 shows the results of 30 permeability tests conducted for the same flow

conditions. These were carried out using crushed rock and coal samples of three different

particle sizes. This figure also shows the relationship between the water head and flow

rates for rock and coal samples. The linear regression analysis on each graph shows an

upward trend of head with flow rates showing that the experimental conditions followed

the Darcy's concept.

Data points that lay outside these regression trends may indicate the presence of

turbulent flow. From the observations, such data generally occur at either very low or

high flow rates. The R-squared value (R2), called the correlation coefficient, represents

how well the regression line matches the original data points and ranges from 0 (no

match) to 1 (perfect match). The high R2 values shown on the regression lines implied

48

A. Sample size: 0.3.5 - 0.42 mm

0.15 — — — • Rock

0.00 4 -t—

O.E+00 1..E-07 2.E-07 3.E-07 4.E-07 5.E-07 6.E-07 7.E-07 8.E-07 Flow rate, Q(m3 /s )

B. Sample size: 1.68 - 4.75 mm

0.15 r

0.10

0.05

0.00 O.E+00 l.E-06 2.E-06 3.E-06

Flow rate, Q { m 3 / s ) 4.E-06 5.E-06

G. Sample size: 4.75 - 6.73 mm

0.06

<5 0.04

I | m o.o2

0.00 0.E+O0

• Rock m Coal

Linear (Rock) Linear (Coal)

y = 1733.x R J = 0 ^ 9 6 1 ^ x

• ^ y= 1330.x R2 * 0.902

^ ^ ^ ^

5.E-06 l.E-05 2.E-05 2.E-05 3.E-05 Flow rate, Q ( m 3 / s )

3.E-05 4.E-05 4.E-05

Figure 3.3 Water head-flow rate relationships for coal and rock samples

A. Sample size: 0.15 - 0.42 mm

0 .15 .,---------------------------------------------, • Rock .. Coa l -- Linear (Rock) I

;§ • 0.10 ~

-- -.-.-.... Linear (Coal)

~ CII

:t: ::;;

y = 14282x R' = 0 .965

~ 0.0 5 1-·-·-.... ·--· .. · ........ · ...................... -·-.. · ...... · .. ·-· .. · ...... : .. · .............. :;;;;~~,,:.: ............. -.................... -............ -.... - ................ ---· .. · .. · .. · ........ -· ...... ·· .... · .. · .. ·_ .. -.. · .... · .. 1

:r:

0 .00 +-----.----.,---.........,-. O.E+OO l.E-0 7 2.E-07 3.E'()7 4.E-0 7 5.E .. 07 6.E .. 07 7.E-07 8 .E-07

Flow rate, Q (m3/s)

B. Sample size: 1.68 - 4.75 mm

0.15 .. ,.....------------------- --------------------------------, • Rock .. Coal

--- Linear (Rock)

y = 29705 • R' = 0.906 •

;§ 0 .10 .. ~ ........ -........ -.... -.-.-. L ..... in .... e ... _a.r •• (-c ... o .. a-.I-) .--.-.... --.. ------~:---.... -.-.-~~ .... - .• -~~<~, .. --.. --.... -.... --....... I ~ ~ ~ ~

y = 25103x R' = 0 .901

"" '" CII 0 .05 +-----------------;::>-"""--=--.,,:.:....----·--------·-------------.. - 1

:r:

I

0 .. 00

O.E+OO l.E-06

• III

2.E'()6 3.£ .. 06

Flow rate, Q (m3/s)

C. Sample size: 4 .75·6.73 mm

0 .06 • Rock .. Coal y " 173 3 .•

-- linear (Rock) Rl " 0.961 ...... · .. - .. linear (Coal )

4.E .. 06 5.E .. 06

• ;§ 0 .0 4 .......... ................................................... ................................. - ........ -;p ... ~ ............... M ................. ;,., _ :: .......................................... .. ..... . ... 1 cu' ~ ~ ~ ;g 0 .Q2 't>

'" .. :r:

0 .00

• •• • O.E+OO 5 .E .. 06 l.E·05

• III

2 .E-05 2.E-0 5 3.E·05

Flow rate, Q (m3/s)

y = 1330 .)( R' '" 0 .90 2

3 .E·0 5 4 .E .. 05 4.E .. 05

Figure 3.3 Water head-flow rate relationships for coal and rock samples

48

49

that the lines can be used to predict values that were not observed within the size ranges.

The graphs also illustrate the effect of particle size by showing the slope change

of the regression lines. In graph A, the rock and coal particle regression lines almost

overlap each other. The gap between the lines is larger with an increase in particle size.

This effect is shown in graphs B and C, implying that the smaller the grain size is, the

less significant angular shape is on packing mode. In other words, the angularity of

particles becomes smaller as the size decreases. This affects the properties of porous

media significantly. In the gob, the material directly behind the shields, made up by

large-size broken particles, will always have high porosity. In contrast, the gob area

adjacent to the bleeder entries due to compaction will have small porosities.

During the experiment, the prerequisites for the laminar flow conditions were

examined frequently. The test apparatus and its arrangement were checked for any

possibility of material volume changes, presence of air bubbles in the voids, and transient

state. Several external factors may still affect the results. The critical ones include the

following: (1) the permeameter specifications: length, specimen diameter, tap hole

diameters, and particle size; (2) presence of invisible air bubbles and tubing; and (3)

material placement in the permeameter. For coal, care must be taken to avoid abrading

the particles and washing out the fines during the test.

Table 3.1 shows a summary of specific permeabilities for crushed rock and coal

samples. A comparison of these results shows the specific permeability of coal specimen

is consistently higher than that of rock specimen.

49

that the lines can be used to predict values that were not observed within the size ranges.

The graphs also illustrate the effect of particle size by showing the slope change

of the regression lines. In graph A, the rock and coal particle regression lines almost

overlap each other. The gap between the lines is larger with an increase in particle size.

This effect is shown in graphs Band C, implying that the smaller the grain size is, the

less significant angular shape is on packing mode. In other words, the angularity of

particles becomes smaller as the size decreases. This affects the properties of porous

media significantly. In the gob, the material directly behind the shields, made up by

large-size broken particles, will always have high porosity. In contrast, the gob area

adjacent to the bleeder entries due to compaction will have small porosities.

During the experiment, the prerequisites for the laminar flow conditions were

examined frequently. The test apparatus and its arrangement were checked for any

possibility of material volume changes, presence of air bubbles in the voids, and transient

state. Several external factors may still affect the results. The critical ones include the

following: (1) the permeameter specifications: length, specimen diameter, tap hole

diameters, and particle size; (2) presence of invisible air bubbles and tubing; and (3)

material placement in the permearneter. For coal, care must be taken to avoid abrading

the particles and washing out the fines during the test.

Table 3.1 shows a summary of specific permeabilities for crushed rock and coal

samples. A comparison of these results shows the specific permeability of coal specimen

is consistently higher than that of rock specimen.

50

Particle Size Range (mm)

Mean size (mm)

Specific Permeability, k (m2) Particle Size Range (mm)

Mean size (mm) Rock Coal

0 .15-0.42 0.28 3.34 x 10"0 9 3.49 x 10"0 9

1.68-4.75 3.22 6.51 x 10"0 9 6.62 x 10~09

4 .75-6 .73 5.74 7.49 x 10 ' 0 9 8.54 x 10"0 9

3.3.3 Air-Based Method

This method is used to determine the specific permeability of rock particles by

passing air through a porous medium in a physical model. These tests were conducted by

applying the same principles used with the constant-head method. The prerequisites of

laminar flow conditions were also required in these tests. For this purpose, part of an

existing longwall model was modified to serve as a permeameter.

There are several advantages for conducting these tests: first, since the model

resembles a longwall panel, the expected results should better approximate real

conditions; second, air is circulated through the porous medium instead of water; third,

the permeameter can be used to measure the permeability of larger rock particles. The

combined results can predict the gob permeability more accurately. Finally, these results

can be compared with those of water-based tests for the same particle size. This

comparison can be used to determine the fluid effect on the specific permeability.


Figure 3.4 shows the ventilation model used for this test. This physical model was

constructed of PVC pipes and pressurized by a 1.75-kW blower fan. The maximum fan

speed was 60 rpm. The pipes were arranged in a U-shape system (Figure 3.5). It included

Table 3.1 Specific permeability for rock and coal samples using water-based tests

50

Table 3.1 Specific penneability for rock and coal samples using water-based tests

Particle Size Range Mean size Specific Permeability, k (m2)

(mm) (mm) Rock Coal 0.15-0.42 0.28 3.34 x lO-uli 3.49 x 10-Uli

1.68 - 4.75 3.22 6.51 x lO-uli 6.62 X 10-09

4.75 - 6.73 5.74 7.49 x 10-09 8.54 x 10-u~

3.3.3 Air-Based Method

This method is used to detennine the specific penneability of rock particles by

passing air through a porous medium in a physical model. These tests were conducted by

applying the same principles used with the constant-head method. The prerequisites of

laminar flow conditions were also required in these tests. For this purpose, part of an

existing longwall model was modified to serve as a penneameter.

There are several advantages for conducting these tests: first, since the model

resembles a longwall panel, the expected results should better approximate real

conditions; second, air is circulated through the porous medium instead of water; third,

the penneameter can be used to measure the penneability oflarger rock particles. The

combined results can predict the gob penneability more accurately. Finally, these results

can be compared with those of water-based tests for the same particle size. This

comparison can be used to detennine the fluid effect on the specific penneability.


Figure 3.4 shows the ventilation model used for this test. This physical model was

constructed of PVC pipes and pressurized by a 1. 75-kW blower fan. The maximum fan

speed was 60 rpm. The pipes were arranged in a U-shape system (Figure 3.5). It included

Figure 3.4 Longwall mine ventilation model at the University of Utah Figure 3.4 Longwall mine ventilation model at the University of Utah

Figure 3.5 The permeameter for air-based test

t o

Return Steel Screen

Stat 10

A B C D Simulated Gob 55.75 cm Fan Material

Stat 1 Stat 2 Stat 4 Stat 5

Intake Steel Screen

Pressure tap

Crosscut

Figure 3.5 The penneameter for air-based test

53 one intake, one return and four crosscuts (A, B, C and D). There were 10 pressure taps

(stat 1 to 10) to measure velocity and static pressures. Each crosscut had one slot where a

regulator of fixed resistance (porous medium) could be inserted. For the permeability

tests, the first three crosscuts were completely blocked while the last crosscut was

regulated. This arrangement represented a longwall panel. A detail description of the

model is presented in Chapter 4.

Figure 3.5 also shows the modified permeameter. It consists of a cylindrical

container 14 cm in diameter and 55.75 cm in length. It was filled with rock particles. Two

steel screens of 4.7 mm spacing were attached to the top and bottom ends of the

permeameter. The screen size was selected to minimize the resistance to airflow. This

limited the particle size that could be tested in the permeameter. The height of the

sample-column in the permeameter was at least twice its diameter (31.25 cm). The

pressure drop through the porous medium was measured by reading a manometer at

Stations 5 and 6. The resistances caused by two elbows were also measured and

considered in the calculation. The air quantity was determined based on velocity heads

monitored at Stations 4, 5 and 7.


An air-based test was carried out using the following procedure:

1. Inspect the model and the monitoring instruments (i.e. manometers, pitot tubes,

regulators, and tubings).

2. Disassemble the permeameter from the mine model. Fill it up with particles of

predetermined height.

53

one intake, one return and four crosscuts (A, B, C and D). There were 10 pressure taps

(stat 1 to 10) to measure velocity and static pressures. Each crosscut had one slot where a

regulator of fixed resistance (porous medium) could be inserted. For the permeability

tests, the first three crosscuts were completely blocked while the last crosscut was

regulated. This arrangement represented a longwall panel. A detail description of the

model is presented in Chapter 4.

Figure 3.5 also shows the modified permeameter. It consists of a cylindrical

container 14 cm in diameter and 55.75 cm in length. It was filled with rock particles. Two

steel screens of 4.7 mm spacing were attached to the top and bottom ends of the

permeameter. The screen size was selected to minimize the resistance to airflow. This

limited the particle size that could be tested in the permeameter. The height of the

sample-column in the permeameter was at least twice its diameter (31.25 cm). The

pressure drop through the porous medium was measured by reading a manometer at

Stations 5 and 6. The resistances caused by two elbows were also measured and

considered in the calculation. The air quantity was determined based on velocity heads

monitored at Stations 4, 5 and 7.


An air-based test was carried out using the following procedure:

1. Inspect the model and the monitoring instruments (i.e. manometers, pitot tubes,

regulators, and tubings).

2. Disassemble the permeameter from the mine model. Fill it up with particles of

predetermined height.

54 3. Install the top steel screen and reassemble the permeameter.

4. Energize the blower fan and set the initial frequency to 30 Hz. Run the fan for

about 2 minutes to reach a steady state condition.

5. Record static and velocity heads at Stations 1, 5, 6, and 7.

6. Measure the room temperature and barometric pressure.

7. Repeat the procedure for different specimen heights (312.5, 468.8, and 557.5 mm)

and fan frequencies (45 and 60 Hz).

Thirty seven experiments were carried out to determine a relationship between

particle size and permeability. Four different particles sizes were tested: 5.74, 7.73, 8.72

and 9.71 mm, respectively. The first experiment was carried out with an empty

permeameter to determine the model's resistance to airflow due to frictions and shock

losses. The model was inspected carefully for leakage. The remaining tests were carried

out with the permeameter filled with dried rock particles. There were nine tests for each

particle size (3 sample heights x 3 fan frequencies).


In these tests, rock particles were used as the porous medium. The particle size

ranging from 4.7 to 12.7 mm were divided into four groups. Their mean sizes were 5.74,

7.73, 8.72, and 9.71 mm. A parametric study was conducted by changing one of the three

variables at a time: particle size, specimen height, and fan speed. For example, the first

test was conducted with a fan frequency of 30 Hz, mean particle size of 5.74 mm, and

sample height of 3,125 mm. For the next test, the fan fequency was increased to 45 Hz

while maintaining the particle size and the sample height constant. Thirty six experiments

54

3. Install the top steel screen and reassemble the permeameter.

4. Energize the blower fan and set the initial frequency to 30 Hz. Run the fan for

about 2 minutes to reach a steady state condition.

5. Record static and velocity heads at Stations 1,5,6, and 7.

6. Measure the room temperature and barometric pressure.

7. Repeat the procedure for different specimen heights (312.5, 468.8, and 557.5 mm)

and fan frequencies (45 and 60 Hz).

Thirty seven experiments were carried out to determine a relationship between

particle size and permeability. Four different particles sizes were tested: 5.74, 7.73, 8.72

and 9.71 mm, respectively. The first experiment was carried out with an empty

permeameter to determine the model's resistance to airflow due to frictions and shock

losses. The model was inspected carefully for leakage. The remaining tests were carried

out with the permeameter filled with dried rock particles. There were nine tests for each

particle size (3 sample heights x 3 fan frequencies).


In these tests, rock particles were used as the porous medium. The particle size

ranging from 4.7 to 12.7 mm were divided into four groups. Their mean sizes were 5.74,

7.73,8.72, and 9.71 mm. A parametric study was conducted by changing one of the three

variables at a time: particle size, specimen height, and fan speed. For example, the first

test was conducted with a fan frequency of30 Hz, mean particle size of 5.74 mm, and

sample height of 3,125 mm. For the next test, the fan fequency was increased to 45 Hz

while maintaining the particle size and the sample height constant. Thirty six experiments

55

Table 3.2 Specific permeability for rock samples using air-based tests (fan frequency 45 Hz)

Mean size (mm)

Specific Permeability, k (x 10"8 m 2 ) Mean size (mm) (half-packed) (3/4-packed) (fully-packed) Average 5.74 1.109 1.011 1.231 1.117 7.73 1.017 1.185 1.281 1.161 8.72 1.229 1.137 1.748 1.371 9.71 1.118 1.261 1.830 1.403

were carried out to complete this study. Out of these, only 12 yielded reasonable results.

These were achieved by setting the fan frequency at 45 Hz. At speeds higher than this,

the air leakage became a problem and at lower speeds, the instrument accuracy became

questionable. Table 3.2 shows the results of this experiment. An evaluation of the figures

in this table shows that the permeability increases with the particle size and remains

unchanged with the sample height. This follows the Karman-Cozeny concept of

permeability and porosity relationship (Bear, 1972). For the sample sizes used in the

experiment (5.7 - 9.7 mm), the specific permeability varied between 1.117 x 10"8 and

1.405 x 10"8m2.

3.4. Specific Permeability of Gob Material

The specific permeability of gob material is one of the key parameters in the

spontaneous combustion study. A number of gob investigations have been carried out to

determine the gob permeability, including the use of tracer gas (Koenning, 1989;

Lowndes et al., 2002) and photoanalyses (Pappas and Mark, 1993). Such investigations

found that the gob permeability varies from 1x10 _ 1 3 to lxlO"5 m . This variation in

permeability is addressed in this study by using three permeability zones: unconsolidated,

semiconsolidated and consolidated, as explained in Section 3.1.

55 were carried out to complete this study. Out of these, only 12 yielded reasonable results.

These were achieved by setting the fan frequency at 45 Hz. At speeds higher than this,

the air leakage became a problem and at lower speeds, the instrument accuracy became

questionable. Table 3.2 shows the results of this experiment. An evaluation of the figures

in this table shows that the permeability increases with the particle size and remains

unchanged with the sample height. This follows the Karman-Cozeny concept of

permeability and porosity relationship (Bear, 1972). For the sample sizes used in the

experiment (5.7 - 9.7 mm), the specific permeability varied between 1.117 x 10-8 and

1.405 X 10-8 m2.

3.4. Specific Permeability of Gob Material

The specific permeability of gob material is one of the key parameters in the

spontaneous combustion study. A number of gob investigations have been carried out to

determine the gob permeability, including the use of tracer gas (Koenning, 1989;

Lowndes et aI., 2002) and photoanalyses (Pappas and Mark, 1993). Such investigations

found that the gob permeability varies from 1x10 -13 to 1x10-5 m2. This variation in

permeability is addressed in this study by using three permeability zones: unconsolidated,

semiconsolidated and consolidated, as explained in Section 3.1.

Table 3.2 Specific permeability for rock samples using air-based tests (fan frequency 45 Hz)

Mean size Specific Permeabilit , k (x 10-8 m2)

(mm) (half-packed) (3/4-packed) (fully-packed) Average 5.74 1.109 1.011 1.231 1.117

7.73 1.017 1.185 1.281 1.161 8.72 1.229 1.137 1.748 1.371 9.71 1.118 1.261 1.830 1.403

56

Pappas and Mark (1993) reported that an average particle size of gob material

behind the shields is 122 mm. The permeability of the unconsolidated zone was

determined based on this information. For other zones, semiconsolidated and

consolidated, their permeabilities were determined by CFD simulations (Section 5.1.3).

The permeability experiments carried out in this study were used to determine the particle

size - permeability relationship. This relationship, once adjusted for packing effect, was

then used to generate input parameters for CFD modeling. Figure 3.6 shows this

modified relationship for air-based permeability tests. Based on this relationship, for the

unconsolidated zone (particles size: 0.122 m), the specific permeability was estimated at

4.203 x 10-7 m 2 .

Due to limited information on material characteristics in zones 2 and 3, the

permeabilities for these zones were estimated using CFD simulations. Section 5.1.3

1.4E-07

0.0E+00 - - — —

0 0.01 0 .02 0 .03 0 .04 0 .05 0 .06

Particle mean size (m)

Figure 3.6 Particle size effect on broken rock permeability for air-based tests

56

Pappas and Mark (1993) reported that an average particle size of gob material

behind the shields is 122 mm. The permeability of the unconsolidated zone was

determined based on this information. For other zones, semiconsolidated and

consolidated, their permeabilities were determined by CFD simulations (Section 5.1.3).

The permeability experiments carried out in this study were used to determine the particle

size - permeability relationship. This relationship, once adjusted for packing effect, was

then used to generate input parameters for CFD modeling. Figure 3.6 shows this

modified relationship for air-based permeability tests . Based on this relationship, for the

unconsolidated zone (particles size: 0.122 m), the specific permeability was estimated at

Due to limited information on material characteristics in zones 2 and 3, the

permeabilities for these zones were estimated using CFD simulations. Section 5.1.3

1.4E-07

1.2E-07

.§. 1.05-07

.lI:

§ 8.0E-08

I 6.0E-08

.g 40E-08

1 I/) 2.0E-08

O.OE+OO

o

y= 2E-OSJ<'! + 1 E-06x + 6E-10

R2 = 0 .9993

--_ .. _ .... _--_._ .... __ ._._---_ ... __ ._ ... _-----_ .. _-------_._-----_._._._ .... _----0 .01 0 .02 0 .03 0 .04 0 .05 0 .06


Figure 3.6 Particle size effect on broken rock permeability for air-based tests

57

Table 3.3 Specific permeability for simulated gob materials

Specific Permeability, k (m )

Unconsolidated Semi-consolidated Consolidated Reference values

4.203 x 10"7 2.83 x10"8 7.17 x 10"9 1 x 10~5 to 1 x 10~13

Figure 3.7 Specific permeability distribution in gob

describes this work in more detail. Using this approach, the specific permeabilities for

zones 2 and 3 were estimated at 2.83 x 10"8 and 7.17 x 10"9 m 2 , respectively. Particle

sizes in these zones were 0.02 and 0.006 m, respectively. Table 3.3 summarizes the

permeability for the three zones: unconsolidated, semiconsolidated, and consolidated.

Figure 3.7 illustrates the permeability contour lines for the simulated mine gob. As

shown in Table 3.3, the highest permeability is found in the unconsolidated zone (behind

the faceline) and gob perimeter.

57

describes this work in more detail. Using this approach, the specific permeabilities for

zones 2 and 3 were estimated at 2.83 x 10-8 and 7.17 x 10-9 m2, respectively. Particle

sizes in these zones were 0.02 and 0.006 m, respectively. Table 3.3 summarizes the

permeability for the three zones: unconsolidated, semiconsolidated, and consolidated.

Figure 3.7 illustrates the permeability contour lines for the simulated mine gob. As

shown in Table 3.3, the highest permeability is found in the unconsolidated zone (behind

the faceline) and gob perimeter.

Table 3.3 Specific permeability for simulated gob materials

Specific Permeability, k (m2)

Unconsolidated Semi-consolidated Consolidated Reference values

4.203 x 10.7 2.83 X 10-8 7.17 X 10-9 1 X 10-5 to 1 X 10-13

Unconsolidated zone

Semi consolidated zone

Consolidated zone

Figure 3.7 Specific permeability distribution in gob

CHAPTER 4

RESEARCH METHODOLOGIES

Two laboratory-scale methodologies are used to study the development of hot

spots in a mine gob: physical modeling and modeling using Computational Fluid

Dynamic. The physical model is a small-scale longwall mine representation. The CFD

model is a numerical representation of a longwall mine. It is presented in the latter

section of this chapter. To simulate a real case, both models are designed to resemble a

longwall panel, which includes intake and return entries, crosscuts, and a gob. This

chapter discusses the details of each model, the similitude principles, and ventilation

systems. Validation tests for both models reflecting the similitude principles are also

presented. Laboratory tests and simulation exercises are described to better understand

the air-gas behavior in the gob.

4.1 Physical Model

A ventilation model was expanded to include a longwall mine gob at the

University of Utah. Figure 4.1 shows the main components of the model, including a

blower fan, intake and return ducts, crosscuts, and a simulated mine gob. This model was

built to resemble an existing longwall mine panel in geometry and airflow characteristics,

and was equipped with high precision instruments to conduct ventilation surveys.

CHAPTER 4

RESEARCH METHODOLOGIES

Two laboratory-scale methodologies are used to study the development of hot

spots in a mine gob: physical modeling and modeling using Computational Fluid

Dynamic. The physical model is a small-scale longwall mine representation. The CFD

model is a numerical representation of a longwall mine. It is presented in the latter

section ofthis chapter. To simulate a real case, both models are designed to resemble a

longwall panel, which includes intake and return entries, crosscuts, and a gob. This

chapter discusses the details of each model, the similitude principles, and ventilation

systems. Validation tests for both models reflecting the similitude principles are also

presented. Laboratory tests and simulation exercises are described to better understand

the air-gas behavior in the gob.

4.1 Physical Model

A ventilation model was expanded to include a longwall mine gob at the

University of Utah. Figure 4.1 shows the main components of the model, including a

blower fan, intake and return ducts, crosscuts, and a simulated mine gob. This model was

built to resemble an existing longwall mine panel in geometry and airflow characteristics,

and was equipped with high precision instruments to conduct ventilation surveys.

Return

Fan Stat. 1 Stat. 2 Stat. 3 (1.5 kW)

Intake

Figure 4.1 Mine ventilation model schematic

Return

Stat. 10

Fan Stat. 1 Stat. 2 Stat. 3 Stat. 4 Stat. 5

(1.5 kW)

Intake

Figure 4.1 Mine ventilation model schematic

60 4.1.1 Simulated Airway

The physical model was initially designed to simulate the airflow behavior in a

longwall mine and determine experimental values for friction factors, shock losses, and

the resistance of ventilation controls such as stoppings and regulators. The longwall mine

model resembles a U-tube shape and includes: two entries (intake and return) and four

crosscuts (A, B, C, and D). Crosscuts A, B, and C are used to simulate stoppings and

seals; crosscut D is used for the face, and the U-section is used for the simulated mine

gob.

The monitored parameters during a test were air velocity and pressure. Ten

pressure taps distributed along the pipes are used to measure these parameters. The

physical model is made of PVC pipes of about 10.63 m long and 1.2 m high. The inside

diameter of the pipes used for main airways is 14 cm, and 7 cm is used for the crosscuts.

In this model, a variable speed fan is used to pressurize the air and simulate different

ventilation scenarios. The head losses are determined through measurements and cross

checked by applying the steady-state energy equation. This equation is expressed by:

Y 2S + Z, = ^ + ̂ + Z 2 +H

7 2g (4.1)

where

P = absolute air pressure, Pa

V = air velocity, m/s

y = specific weight of the air, kg/m

Z = measuring point elevation, m

60

4.1.1 Simulated Airway

The physical model was initially designed to simulate the airflow behavior in a

longwall mine and determine experimental values for friction factors, shock losses, and

the resistance of ventilation controls such as stoppings and regulators. The longwall mine

model resembles a U-tube shape and includes: two entries (intake and return) and four

crosscuts (A, B, C, and D). Crosscuts A, B, and C are used to simulate stoppings and

seals; crosscut D is used for the face, and the U-section is used for the simulated mine

gob.

The monitored parameters during a test were air velocity and pressure. Ten

pressure taps distributed along the pipes are used to measure these parameters. The

physical model is made of PVC pipes of about 10.63 m long and 1.2 m high. The inside

diameter ofthe pipes used for main airways is 14 cm, and 7 cm is used for the crosscuts.

In this model, a variable speed fan is used to pressurize the air and simulate different

ventilation scenarios. The head losses are determined through measurements and cross-

checked by applying the steady-state energy equation. This equation is expressed by:

V2 V2 !!..l + _1_ + Z = P2 + _2_ + Z + H Y 2g 1 Y 2g 2 L

(4.1)

where

p absolute air pressure, Pa

V air velocity, mls

y specific weight of the air, kg/m3

Z measuring point elevation, m

61

Hs = — = RQ2 (4.3) r

v2

Hv=— (4.4) 2g

where

Hs = static head, m

Hv - velocity head, m

R = duct resistance, Ns /m

Q = airflow rate, V A, m3/s

In Equation 4.2, the measuring point elevations are omitted. This version is

correct as long as all head measurements are made on a gage-pressure basis (Hartman,

1997). Figure 4.2 shows the pressure gradient along a single circuit (from fan to

discharge) of this model. The pressures are obtained by multiplying the corresponding

heads with specific weight of air. In this figure, the total pressure is the sum of velocity

and static pressures. The head loss shown in Equation 4.2 consists of two components:

friction loss, Hf, and shock loss, Hx. Frictional head losses are caused by the surface

Hi = head loss, m

The subscripts 1 and 2 denote two individual measurement stations. Accepting the

energy conservation principle, Equation 4.1 can also be written using gage-pressure basis

(McPherson 1993) as follows:

HsJ + HvJ=Hs2 + Hv2+HL (4.2)

61

HL = head loss, m

The subscripts 1 and 2 denote two individual measurement stations. Accepting the

energy conservation principle, Equation 4.1 can also be written using gage-pressure basis

(McPherson 1993) as follows:

(4.2)

(4.3)

2g (4.4)

where

Hs static head, m

Hv velocity head, m

R duct resistance, Ns2/m8

Q airflow rate, V A, m 3/s

In Equation 4.2, the measuring point elevations are omitted. This version is

correct as long as all head measurements are made on a gage-pressure basis (Hartman,

1997). Figure 4.2 shows the pressure gradient along a single circuit (from fan to

discharge) of this model. The pressures are obtained by multiplying the corresponding

heads with specific weight of air. In this figure, the total pressure is the sum of velocity

and static pressures. The head loss shown in Equation 4.2 consists of two components:

friction loss, HI, and shock loss, Hx. Frictional head losses are caused by the surface

62

2500

-500 j-

-1000 -Distance from Fan (m)

Figure 4.2 Pressure gradients for the physical model

resistance (gradual decrease in total pressure lines) whereas the shock losses are due to

changes in flow direction or air velocity in the duct.

This figure shows a pressure reduction at a distance of about 9.5 m. This is caused

by two 90° elbows (Stations 5 and 6). The shock losses represent more than 80% of the

total head loss in this section. The difference between the total and static pressure is the

velocity pressure. In this graph, this parameter remains fairly constant, indicating near

zero leakage.

To simulate the airflow behavior in mine entries and leakage paths, all crosscuts

were blocked by a set of identical regulators. Regulators of predefined size were inserted

into slots at four crosscuts. Air quantities and head losses were determined using a pitot

tube and a manometer at 11 pressure taps. The collected data were also used to determine

62

2500 -

-+- Total 2000 -

1500 -,-... ro

Po< 1000 '-"" Q) I-< ::s CJ) 500 CJ) Q) I-<

Po< 0

2 4 6 8 10 12 14 16 18 20 -500

-1000

Distance from Fan (m)

Figure 4.2 Pressure gradients for the physical model

resistance (gradual decrease in total pressure lines) whereas the shock losses are due to

changes in flow direction or air velocity in the duct.

This figure shows a pressure reduction at a distance of about 9.5 m. This is caused

by two 90 0 elbows (Stations 5 and 6). The shock losses represent more than 80% of the

total head loss in this section. The difference between the total and static pressure is the

velocity pressure. In this graph, this parameter remains fairly constant, indicating near

zero leakage.

To simulate the airflow behavior in mine entries and leakage paths, all crosscuts

were blocked by a set of identical regulators. Regulators of predefined size were inserted

into slots at four crosscuts. Air quantities and head losses were determined using a pitot

tube and a manometer at 11 pressure taps. The collected data were also used to determine

63

Table 4.1 Leakage percentage through crosscuts

Regulator Leakage percenta^ yd at crosscut: Type A B C D # 1 14.63 11.79 8.14 4.19 # 2 7.57 4.17 3.59 2.42 #3 2.97 0.75 0.33 0.16

the leakage flow through each crosscut, which is a common problem in longwall mines.

A common practice to determine leakage flow through a stopping is to measure two flow

quantities: upstream and downstream of the stopping. For example, the leakage at

crosscut A was calculated by subtracting the air quantity at Station 2 from that at Station

1. The result was cross-checked with readings at Stations 9 and 10. Then, the leakage rate

is obtained by (Calizaya & Miles 2006):

% L = Q ~®2 .xlOO (4.5) Gi

where Qj and Q2 are the upstream and downstream flow rate from the split. Table 4.1

shows the summary of leakage percentages calculated using Equation 4.5 for a fan

frequency of 60 Hz. The leakage rates were determined for 3 different regulators.

Regulator #1 is more porous than that of either regulator #2 or #3.

Figure 4.3 shows the changes in leakage percentage with the location of crosscuts

from the fan. It shows that the closer the crosscut is to the pressure source, the higher the

leakage percent. For any given circuit, the air will always choose the path of the least

resistance. This behavior yields a higher leakage rate at crosscut A than at any other

crosscut.

63

the leakage flow through each crosscut, which is a common problem in longwall mines.

A common practice to determine leakage flow through a stopping is to measure two flow

quantities: upstream and downstream of the stopping. For example, the leakage at

crosscut A was calculated by subtracting the air quantity at Station 2 from that at Station

1. The result was cross-checked with readings at Stations 9 and 10. Then, the leakage rate

is obtained by (Calizaya & Miles 2006):

(4.5)

where QJ and Q2 are the upstream and downstream flow rate from the split. Table 4.1

shows the summary ofleakage percentages calculated using Equation 4.5 for a fan

frequency of 60 Hz. The leakage rates were determined for 3 different regulators.

Regulator #1 is more porous than that of either regulator #2 or #3.

Figure 4.3 shows the changes in leakage percentage with the location of crosscuts

from the fan. It shows that the closer the crosscut is to the pressure source, the higher the

leakage percent. For any given circuit, the air will always choose the path of the least

resistance. This behavior yields a higher leakage rate at crosscut A than at any other

crosscut.

Table 4.1 Leakage percentage through crosscuts

Regulator Leakage percentage at crosscut: Type A B C D # 1 14.63 11.79 8.14 4.19 #2 7.57 4.17 3.59 2.42 #3 2.97 0.75 0.33 0.16

64

20

15

Q) S> 10 3 CO 0

• R e g #1 0 R e g #2 • R e g #3

B C Crosscut

Figure 4.3 Leakage percentage through four crosscuts

By modifying the U-section to become a permeameter (gob), permeability tests

can be performed. The modified model is featured in Figure 3.5. During a test, the first

three crosscuts, A, B, and C, were blocked while D was kept open. This arrangement

resembles a longwall panel with crosscut D as the working face and the U-section as the

mine gob.

A precise reading of head loss due to porous medium is very important. The

pressure drop was determined from gage readings at Stations 5 a and 6. An initial test

without the gob material was conducted to determine the shock losses due to two elbows

and four joints. These losses are crucial to assess the pressure drop through a porous

7 2 8

medium. However, the calculated resistance due to elbows was only 5.46 x 10"'Ns7m°,

which is negligible in this study compared to that of rock particles (10" to 10" Ns /m ).

64

20

15 ......... ~ 0 --Q) 0> 10 ~ ctl Q)

....I 5

o A B c D

Crosscut

Figure 4.3 Leakage percentage through four crosscuts

By modifying the U-section to become a penneameter (gob), penneability tests

can be perfonned. The modified model is featured in Figure 3.5. During a test, the first

three crosscuts, A, B, and C, were blocked while D was kept open. This arrangement

resembles a longwall panel with crosscut D as the working face and the U-section as the

mine gob.

A precise reading of head loss due to porous medium is very important. The

pressure drop was detennined from gage readings at Stations 5a and 6. An initial test

without the gob material was conducted to detennine the shock losses due to two elbows

and four joints. These losses are crucial to assess the pressure drop through a porous

medium. However, the calculated resistance due to elbows was only 5.46 x 10-7 Ns2/m8,

which is negligible in this study compared to that ofrock particles (10-2 to 10-1 Ns2/m8).

65

Table 4.2 Type of regulators used for ventilation controls

Regulator Holes Porosity Resistance Type Number Diameter (cm) (%) (Ns2/m8)

#0 1 7 100 8.44 x 10-06

#1 37 0.6 27.18 6.59 x 10 0 4

#2 21 0.6 15.43 1.89 x 10'0 3

#3 21 0.3 3.86 2.43 x 10"02

#4 21 0.15 0.96 8.11 x 10"02

#5 0 0 0 3.90 x 10"01

4.1.2 Fan and Regulator

In a ventilation system, airflow occurs due to a pressure difference between two

points. A blower fan is used to raise the air pressure in a duct, thus creating a pressure

difference. The 1.5-kW fan can produce a gage pressure of up to 1, 245 Pa (5" water

gage), and is driven by an AC electric motor. The motor can supply power of up to 2.24

kW (3 HP). It is equipped with a digital inverter to vary the fan speed. The maximum fan

speed is 3600 Rpm (60 Hz). On the average, the fan can circulate as much as 0.45 m /s

(945 cfm) of air through the system. Several experiments were conducted to verify this

quantity.

Regulators were used to control the airflow in the model. They were used to

simulate mine stoppings, doors, and curtains. Physically, a regulator is a perforated plate

of 7.62 cm wide and 0.3 cm thick. It is designed to closely fit a prefabricated slot. The

number of holes on each plate determines the equivalent size of a regulator. There were 6

different regulators available for this study. These were labeled as Regulator #0 for fully

open, #5 for a solid plate, and the remainder (#1 through #4) for perforated plates with

various numbers of holes and diameters of holes. Table 4.2 describes these regulators.

65

4.1.2 Fan and Regulator

In a ventilation system, airflow occurs due to a pressure difference between two

points. A blower fan is used to raise the air pressure in a duct, thus creating a pressure

difference. The 1.5-kW fan can produce a gage pressure of up to 1,245 Pa (5" water

gage), and is driven by an AC electric motor. The motor can supply power of up to 2.24

kW (3 HP). It is equipped with a digital inverter to vary the fan speed. The maximum fan

speed is 3600 Rpm (60 Hz). On the average, the fan can circulate as much as 0.45 m3/s

(945 cfm) of air through the system. Several experiments were conducted to verify this

quantity.

Regulators were used to control the airflow in the model. They were used to

simulate mine stoppings, doors, and curtains. Physically, a regulator is a perforated plate

of 7.62 cm wide and 0.3 cm thick. It is designed to closely fit a prefabricated slot. The

number of holes on each plate determines the equivalent size of a regulator. There were 6

different regulators available for this study. These were labeled as Regulator #0 for fully

open, #5 for a solid plate, and the remainder (#1 through #4) for perforated plates with

various numbers of holes and diameters of holes. Table 4.2 describes these regulators.

Table 4.2 Type of regulators used for ventilation controls

Regulator Holes Porosity Resistance Type Number Diameter (cm) (%) (Ns2/m8

)

#0 1 7 100 8.44 X 10-06

#1 37 0.6 27.18 6.59 x 10-04

#2 21 0.6 15.43 1.89 x 10-03

#3 21 0.3 3.86 2.43 x 10-02

#4 21 0.15 0.96 8.11 x 10-02

#5 0 0 0 3.90 X 10-01

66

7.62 cm

#0 # l - # 4 #5

Figure 4.4 Type of regulator for physical model used in this study

Figure 4.4 shows three sample regulators: fully open (0), partially open (#1- #4) and

fully closed (#5). These regulators were used to simulate the leakage flow through

stopping and doors. They were also used to determine a parameter to characterize these

control devices. This parameter is called regulator resistance. This resistance was

determined experimentally (Table 4.2). These were determined from pressure-quantity

measurements applying Atkinson's relationship (Equation 4.3).

4.2 Computational Fluid Dynamics Model

4.2.1 Introduction

Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses

numerical methods and algorithms to solve and analyze problems that involve fluid

flows. Computers are used to perform millions of calculations required to simulate the

interaction of fluids and gases within complex systems.

66

Figure 4.4 shows three sample regulators: fully open (0), partially open (#1- #4) and

fully closed (#5). These regulators were used to simulate the leakage flow through

stopping and doors. They were also used to determine a parameter to characterize these

control devices. This parameter is called regulator resistance. This resistance was

determined experimentally (Table 4.2). These were determined from pressure-quantity

measurements applying Atkinson's relationship (Equation 4.3).


4.2.1 Introduction

Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses

numerical methods and algorithms to solve and analyze problems that involve fluid

flows. Computers are used to perform millions of calculations required to simulate the

interaction of fluids and gases within complex systems.

7.62 em

I

},

#0 #1- #4 #5

Figure 4.4 Type of regulator for physical model used in this study

67 Fluent version 6.1, the most widely used CFD software (Fluent Inc. 2003) was

chosen to simulate the air-gas and heat flow in a ventilation model. Fluent uses a

numerical method to discretize the spatial domain into small cells to form a volume mesh

or grid, and then apply a suitable algorithm to solve the equations of motion. Three

governing equations are solved using this software: conservation of mass, conversation of

momentum, and conservation of energy. The conservation of mass states that mass of

fluid remains constant when moving from one location to another. The conservation of

momentum states that the magnitude of momentum (the mass of an object multiplied by

the velocity of the object) remains constant but changes only through the action of forces.

The conservation of energy states that within a domain, the amount of energy remains

constant. Energy can be converted from one form to another but the total energy within

the domain remains the same (Versteeg, 1955; Adler, 1992; Thomas, 1992).

In CFD, the solution to a problem is found in two steps: (1) defining the

geometries and boundary conditions of the problem, and (2) solving the governing

equations iteratively. The first stage involves the use of Gambit software. Gambit allows

the users to create the geometry of the problem or import it from a CAD package. The

geometry is then divided into small elements. This process is called meshing. It is

performed using a menu-driven routine. Defining the boundary conditions completes this

stage. The second stage involves exporting the geometries created by Gambit into Fluent

software. This stage also involves defining the fluid properties and boundary conditions,

and solving the fluid flow problem through iteration. The CFD modeling procedure

consists of the following steps:

1. Create geometry of the model.

67

Fluent version 6.1, the most widely used CFD software (Fluent Inc. 2003) was

chosen to simulate the air-gas and heat flow in a ventilation model. Fluent uses a

numerical method to discretize the spatial domain into small cells to form a volume mesh

or grid, and then apply a suitable algorithm to solve the equations of motion. Three

governing equations are solved using this software: conservation of mass, conversation of

momentum, and conservation of energy. The conservation of mass states that mass of

fluid remains constant when moving from one location to another. The conservation of

momentum states that the magnitude of momentum (the mass of an object multiplied by

the velocity of the object) remains constant but changes only through the action of forces.

The conservation of energy states that within a domain, the amount of energy remains

constant. Energy can be converted from one form to another but the total energy within

the domain remains the same (Versteeg, 1955; Adler, 1992; Thomas, 1992).

In CFD, the solution to a problem is found in two steps: (1) defining the

geometries and boundary conditions of the problem, and (2) solving the governing

equations iteratively. The first stage involves the use of Gambit software. Gambit allows

the users to create the geometry of the problem or import it from a CAD package. The

geometry is then divided into small elements. This process is called meshing. It is

performed using a menu-driven routine. Defining the boundary conditions completes this

stage. The second stage involves exporting the geometries created by Gambit into Fluent

software. This stage also involves defining the fluid properties and boundary conditions,

and solving the fluid flow problem through iteration. The CFD modeling procedure

consists of the following steps:

1. Create geometry of the model.

68

2. Divide the volume occupied by the fluid into discrete cells. The meshing may be

uniform or nonuniform. There are various mesh types available in Gambit.

3. Define the fluid properties and input parameters.

4. Define boundary conditions. This involves specifying the fluid properties at the

boundaries of the model.

5. Solve the continuity equations iteratively either at steady-state or transient

conditions.

In the postprocessing stage, the simulation results can be visualized as contour lines,

vector graphs, velocity profile, etc.

Currently, CFD is used to solve different types of problems, including gas and

particles flow problems under both turbulent and laminar conditions, and heat flow

transfer. In this study, this tool is used to investigate the airflow behavior in a mine gob,

oxidation heat, and heat transfer through conduction, convection, and radiation.

4.2.2 Airflow Simulation (Without Oxidation)

A complete airflow study has been conducted on the physical model (duct

diameter: 16 cm). Based on this, a CFD model was created and simulated using Gambit

and Fluent. The CFD model included a porous medium at the end of the U-section. This

section was assigned with a permeability of a represented mine gob material. This

permeability was determined based on the laboratory tests (section 3.3.3) and field

measurements.

Figure 4.5 shows the geometry of a 2-D gob model created in Gambit. Gambit is a

preprocessor that is used to build the model geometry and mesh the cells. A map type

68

2. Divide the volume occupied by the fluid into discrete cells. The meshing may be

uniform or nonuniform. There are various mesh types available in Gambit.

3. Define the fluid properties and input parameters.

4. Define boundary conditions. This involves specifying the fluid properties at the

boundaries of the model.

5. Solve the continuity equations iteratively either at steady-state or transient

conditions.

In the postprocessing stage, the simulation results can be visualized as contour lines,

vector graphs, velocity profile, etc.

Currently, CFD is used to solve different types of problems, including gas and

particles flow problems under both turbulent and laminar conditions, and heat flow

transfer. In this study, this tool is used to investigate the airflow behavior in a mine gob,

oxidation heat, and heat transfer through conduction, convection, and radiation.

4.2.2 Airflow Simulation (Without Oxidation)

A complete airflow study has been conducted on the physical model (duct

diameter: 16 cm). Based on this, a CFD model was created and simulated using Gambit

and Fluent. The CFD model included a porous medium at the end of the U-section. This

section was assigned with a permeability of a represented mine gob material. This

permeability was determined based on the laboratory tests (section 3.3.3) and field

measurements.

Figure 4.5 shows the geometry of a 2-D gob model created in Gambit. Gambit is a

preprocessor that is used to build the model geometry and mesh the cells. A map type

69

meshing with an interval size of 4 cm was selected. In Gambit, the permeameter was

defined as a "face" characterized by a porous jump. Other relevant parameters include

pressure inlet for inlet duct, porous jump for crosscuts, and pressure outlet for return. The

model was calibrated using laboratory data to account for duct roughness (Section 4.3.2).

All input parameters and boundary conditions were quantified before running the

Fluent program. Table 4.3 shows the input parameters used for this sample model.

In Fluent, the permeameter, representing the porous medium, is characterized by 3 major

parameters: viscous resistance (Ci), inertial resistance (C2), and porosity (£). These

parameters were obtained through physical experiments and field data. These parameters

Table 4.3 Input parameters used in Fluent for airflow simulations

Label Boundary Boundary Condition Remarks Inlet pressure inlet 1145 Pa Gage pressure Return pressure outlet 0 Gage pressure

A, B, C wall - Blocked Face porous jump 0 Open

Gob face C^T^le+OTm" 2 Viscous resistance

Gob face C2 = 14700 m'1 Inertia resistance Gob face 8 = 0.240389 Porosity

wall wall 0.0922 m Roughness constant wall wall 0.00198 m Roughness height

69

Return

A B C Face Gob

Inlet

Figure 4.5 The CFD model created in Gambit

meshing with an interval size of 4 cm was selected. In Gambit, the permeameter was

defined as a "face" characterized by a porous jump. Other relevant parameters include

pressure inlet for inlet duct, porous jump for crosscuts, and pressure outlet for return. The

model was calibrated using laboratory data to account for duct roughness (Section 4.3.2).

All input parameters and boundary conditions were quantified before running the

Fluent program. Table 4.3 shows the input parameters used for this sample model.

In Fluent, the permeameter, representing the porous medium, is characterized by 3 major

parameters: viscous resistance (C l ), inertial resistance (C2), and porosity (£). These

parameters were obtained through physical experiments and field data. These parameters

Table 4.3 Input parameters used in Fluent for airflow simulations

Label Boundary Boundary Condition Remarks Inlet pressure inlet 1145 Pa

Gage pressure Return pressure outlet 0 A, B,C wall - Blocked

Face porous jump 0 Open C, = 7.91e+07 m-2 Viscous resistance

Gob face C2 = 14700 m-' Inertia resistance € = 0.240389 Porosity

wall wall 0.0922 m Roughness constant

0.00198 m Roughness height

—

70

are interrelated by the following equations:

C = 1 1 k

(4.5)

3.5 ( 1 - g ) d s3

m

(4.6)

where

k 9

= specific permeability, m

= porosity, dimensionless

d, •m the mean particle size, m

Fluent is equipped with different postprocessing tools. For qualitative analysis,

the tools available are contoured plots and vector plots, which can be used to display

parameters such as static pressure, velocity, species concentration, etc. For quantitative

analysis, Fluent offers XY-plots in the plane of choice. Figures 4.6 through 4.11 show

some of these results. Figures 4.6 and 4.7 show the air velocity profiles through the

system and at the U- section. For the sample model, the first three crosscuts are blocked

near zero flow, the fourth is open to represent the face, and the U-section represents a

porous medium. Figure 4.8 shows velocity plot for two settings: face (crosscut D) and

gob. Figures 4.9 and 4.10 show the static pressure profile for the sample model. The zone

with the lowest velocity has the highest static pressure. This finding is explained by the

energy conservation law. The pressure drop due to the porous medium is shown by the

gradual color change in the U-section (Figure 4.10). This can be quantified by a

longitudinal cut along the porous medium (line 1 -2 ) . For the sample case conditions, the

70

are interrelated by the following equations:

C1

1 (m -2) (4.5) = -k

C - 3.5 (1- £) (m-1) (4.6) 2 -

d", £3

where

k specific penneability, m2

E porosity, dimensionless

dm the mean particle size, m

Fluent is equipped with different postprocessing tools. For qualitative analysis,

the tools available are contoured plots and vector plots, which can be used to display

parameters such as static pressure, velocity, species concentration, etc. For quantitative

analysis, Fluent offers XY -plots in the plane of choice. Figures 4.6 through 4.11 show

some of these results. Figures 4.6 and 4.7 show the air velocity profiles through the

system and at the U- section. For the sample model, the first three crosscuts are blocked

near zero flow, the fourth is open to represent the face, and the U-section represents a

porous medium. Figure 4.8 shows velocity plot for two settings: face (crosscut D) and

gob. Figures 4.9 and 4.10 show the static pressure profile for the sample model. The zone

with the lowest velocity has the highest static pressure. This finding is explained by the

energy conservation law. The pressure drop due to the porous medium is shown by the

gradual color change in the U-section (Figure 4.10). This can be quantified by a

longitudinal cut along the porous medium (line 1 - 2). For the sample case conditions, the

71

I 34.5 32.6 30.8 29 27.2 25.4 23.6 21.8 19.9 18.1 16.3 14.5 12.7 10.9 9.07 7.25 5.44 3.63 1.81 0

I I I

C o n t o u r s of Ve loc i ty Magni tude (m/s) D e c 04 , 2 0 0 7 F L U E N T 6.2 (2d , s e g r e g a t e d , ske )

Figure 4.6 Velocity contours for the sample model

34.5 32.6 30.8 29 27.2 25.4 23.6 21.8 19.9 18.1 16.3 14.5 12.7 10.9 9.07 7.25 5.44 3.63 1.81 0

C o n t o u r s of Ve loc i ty Magni tude (m/s) D e c 04 , 2 0 0 7 F L U E N T 6.2 (2d , s e g r e g a t e d , s k e )

Figure 4.7 Velocity contours for the U-section

34.5

32.6

30.8

29

27 .2

25.4

23.6

21 .8

19.9

18.1

16.3

14 .5

12.7

10.9

9 .07

7 .25

5.44

3.63

1 .81

o

Contours of Velocity Magnitude (m/s) Dec 04, 2007 FLUENT 6 .2 (2d, segregated, ske)

Figure 4.6 Velocity contours for the sample model

34.5

32.6

30.8

29

27.2

25.4

23.6

21 .8

19.9

18.1

16.3

14.5

12.7

10.9

9 .07

7 .25

5.44

3 .63

1 .81

o

Contours of Velocity Magnitude (m/s) Dec 04, 2007 FLUENT 6 .2 (2d, segregated, ske)

Figure 4.7 Velocity contours for the U-section

71

72

2.25e+01

2.00e+01

1.75e+01

1.50e+01

1.25e+01 V e l o c i t y

M a g n i t u d e i . o o e + 0 1

( m / s ) 7 . 5 0 e + 0 0

5.00e+00

2.50e+00

0.00e+00

Face

i • • 1 ' i • ' ' — ' i ' ' — ' ' i

16.8

Gob

17 17.3 17.5 17.8 18 18.3 18.5 18.8 19 19.3 P o s i t i o n ( m )

Veloc i ty Magni tude D e c 04 , 2 0 0 7 F L U E N T 6.2 (2d , s e g r e g a t e d , ske )

Figure 4.8 Velocity profiles for two simulated openings

1.01e+03

772 713 654

Contours of Static Pressure (pascal) Dec 04, 2007 FLUENT 6.2 (2d, segregated, ske)

Figure 4.9 Static pressure contours for the sample model

2 .25e+01

(: 2.00e+01 I Face I 1.75e+01 • •

• 1.50e+01 • •

• • 1.25e+01 • Velocity • Magnitude 1.00e+01 • •

(m/s) • • 7.50e+00 • • •

5.00e+00 • • ~ 2. 50e+00 • • • •

O.OOe+OO -16.8 17 17.3 17.5 17.8 18 18.3 18.5 18.8 19 19. 3

Position (m)

Velocity Magnitude Dec 04, 2007 FLUENT 6 .2 (2d, segregated, ske)

Figure 4.8 Velocity profiles for two simulated openings

1.01e+03

948

889

831

772

713

654

72

595

536

477

419

360

301

I

242

183

124

65.6

6 .75

-52.1

-111

~

Contours of Static Pressure (pascal) Dec 04, 2007 FLUENT 6 .2 (2d, segregated, ske)

Figure 4.9 Static pressure contours for the sample model

73

Figure 4.10 Static pressure contours for the U-section

static pressure in this section decreases from 1,011 to 598 Pa (Figure 4.11).

An evaluation of the above results shows that Fluent software can be used to

study the airflow behavior in a longwall mine. The air pressure-quantity distributions in

the system can be detected qualitatively and quantitatively. If field or laboratory data

were available, these can be used to calibrate the CFD models and improve the accuracy

of the results. More complex scenarios involving oxidation of coal, heat transfer, and

multigas phenomena can also be investigated. However, this requires changes in the

modeling process. These simulations are presented in Chapter 5.

1 .01e+03

948

889

831

772

713

654

595

536

477

419

360

301

242

183

124

65.6

6 .75

-52.1

-111

Contours of Static Pressure (pascal) Dec 04 , 2007 FLUENT 6.2 (2d , segregated, ske)

73

Figure 4.10 Static pressure contours for the U-section

static pressure in this section decreases from 1,011 to 598 Pa (Figure 4.11).

An evaluation of the above results shows that Fluent software can be used to

study the airflow behavior in a longwall mine. The air pressure-quantity distributions in

the system can be detected qualitatively and quantitatively. If field or laboratory data

were available, these can be used to calibrate the CFD models and improve the accuracy

of the results. More complex scenarios involving oxidation of coal, heat transfer, and

multi gas phenomena can also be investigated. However, this requires changes in the

modeling process. These simulations are presented in Chapter 5.

74

1.05e+03

1.00e+03 H

9.50e+02

9.00e+02 -\

8.50e+02

Static 8.00e+02 Pressure (pascal) 7 - 5 0 e + 0 2 H

7.00e+02

6.50e+02

6.00e+02 H

5.50e+02 10.2 10.3 10.4 10.5 10.6 10.7

Position (m) 10.8 10.9

Static Pressure Dec 04, 2007 FLUENT 6.2 (2d, segregated, ske)

Figure 4.11 Pressure drop through porous medium

4.3 Model Similitude

4.3.1 Similitude Concept

Similitude is an important application of a nondimensional characteristic (i.e.,

scale) used in engineering modeling. The object of interest is the relationship between the

real application and a test model . Similitude is achieved when test conditions are created

such that their results are applicable to real-world conditions. In fluid dynamics, the

criteria to be fulfilled are geometric, kinematic, and dynamic similarities (Murphy, 1950;

Szucs, 1980). The geometric similarity may be simply achieved by scaling down the real

dimension. The kinematic similarity requires, for both physical and CFD models , similar

streamline properties for similar time rates. The dynamic similarity requires constant

ratios for all forces acting on corresponding fluid particles and boundary conditions.

1.05e+03 1

1.00e+03 • • • 9.50e+02 •

• 9.00e+02 •

• 8.50e+02

Static 8.00e+02 Pressure (pascal) 7.50e+02

7.00e+02

6.50e+02

6.00e+02

5.50e+02 10.2 10.3 10.4

Static Pressure

• •

• •

• •

• •

• •

• • • 2

10.5 10.6 10.7 10.8 10.9 11

Position (m)

Dec 04, 2007 FLUENT 6 .2 (2d, segregated, ske)

Figure 4.11 Pressure drop through porous medium

4.3 Model Similitude

4.3.1 Similitude Concept

Similitude is an important application of a nondimensional characteristic (i.e.,

74

scale) used in engineering modeling. The object of interest is the relationship between the

real application and a test model. Similitude is achieved when test conditions are created

such that their results are applicable to real-world conditions. In fluid dynamics, the

criteria to be fulfilled are geometric, kinematic, and dynamic similarities (Murphy, 1950;

Szucs, 1980). The geometric similarity may be simply achieved by scaling down the real

dimension. The kinematic similarity requires, for both physical and CFD models, similar

streamline properties for similar time rates. The dynamic similarity requires constant

ratios for all forces acting on corresponding fluid particles and boundary conditions.

75

D h = ^ ~ (4-7) Per

where

A = cross sectional area of noncircular duct, m 2

Per = inside perimeter of noncircular duct, m

This CFD model is drawn based on the best information available from the

physical model , i.e., shape, dimensions, boundary conditions, pressure, velocity, etc. It is

often difficult to achieve strict similitude during the tests. Therefore, model ing is based

on key parameters only; some aspects of similitude may be neglected. In fluid dynamics,

the most common dimensionless parameter used to analyze similitude is the Reynolds

Number (NR). If the Reynolds Number is satisfied, the geometric and kinematic criteria

are also satisfied. This number is obtained by the following formula (McPherson 1993):

pDV DV N R = = (4.8)

JU V

In this study, the similitude between the physical model and real longwall panel

are investigated. The physical model is built to represent a 1:29 scale of a longwall mine

entry. It is designed with an equivalent hydraulic diameter so that the characteristics of

air flow through the pipes are the same as those through mine openings. The hydraulic

diameter, Dh, is a common term used when handling fluid flow in noncircular ducts

(Murphy, 1950).

75

In this study, the similitude between the physical model and reallongwall panel

are investigated. The physical model is built to represent a 1 :29 scale of a longwall mine

entry. It is designed with an equivalent hydraulic diameter so that the characteristics of

air flow through the pipes are the same as those through mine openings. The hydraulic

diameter, Dh, is a common term used when handling fluid flow in noncircular ducts

(Murphy, 1950).

4A Dh =

Per (4.7)

where

A cross sectional area of noncircular duct, m2

Per inside perimeter of noncircular duct, m

This CFD model is drawn based on the best information available from the

physical model, i.e., shape, dimensions, boundary conditions, pressure, velocity, etc. It is

often difficult to achieve strict similitude during the tests. Therefore, modeling is based

on key parameters only; some aspects of similitude may be neglected. In fluid dynamics,

the most common dimensionless parameter used to analyze similitude is the Reynolds

Number (NR). If the Reynolds Number is satisfied, the geometric and kinematic criteria

are also satisfied. This number is obtained by the following formula (McPherson 1993):

pDV

11

DV

V (4.8)

. 1 1

76

where

p = density of the fluid, kg /m 3

V = relative velocity of the fluid, m/s

D = diameter of conduit, m

ju = dynamic viscosity, Pa.s

V = kinematic viscosity, m / s

For any fluid flow conditions, the NR can be determined from measurements and

Equation 4.8. A laminar flow condition (NR < 2000) is expected within the porous

medium, but not within the ducts. Turbulent flow conditions prevail in most mine

openings except the caved area (Hartman et al., 1997). Another aspect worth mentioning

is that mine airways are not smooth but rough. Therefore, an assessment on conduit

roughness is necessary to achieve similitude.

4.3.2 Similitude Validation

To represent a longwall panel entry, the intake and return ducts are 6 m wide and

3 m high. For this cross-section, the hydraulic diameter is 4 m. Other parameters were

obtained through ventilation surveys at Mine A. The results of these surveys are shown in

Table 4.4 (Calizaya and Miles, 2006). Based on these results, the Reynolds Number is

6.16 x 10 5 (turbulent flow). For a 1:29 scale physical model and the measurement shown

in Table 4.4, a Reynolds Number of 6.45 x 10 5 was calculated. A comparison of these

two parameters shows good agreement between the mine data and those of the physical

model. The difference in their Reynolds Numbers is within 5%.

76

where

p density of the fluid, kg/m3

V relative velocity of the fluid, mls

D diameter of conduit, m

f1 dynamic viscosity, Pa.s

V kinematic viscosity, m2/s

For any fluid flow conditions, the NR can be determined from measurements and

Equation 4.8. A laminar flow condition (NR < 2000) is expected within the porous

medium, but not within the ducts. Turbulent flow conditions prevail in most mine

openings except the caved area (Hartman et aI., 1997). Another aspect worth mentioning

is that mine airways are not smooth but rough. Therefore, an assessment on conduit

roughness is necessary to achieve similitude.

4.3.2 Similitude Validation

To represent a longwall panel entry, the intake and return ducts are 6 m wide and

3 m high. For this cross-section, the hydraulic diameter is 4 m. Other parameters were

obtained through ventilation surveys at Mine A. The results of these surveys are shown in

Table 4.4 (Calizaya and Miles, 2006). Based on these results, the Reynolds Number is

6.16 x 105 (turbulent flow). For a 1 :29 scale physical model and the measurement shown

in Table 4.4, a Reynolds Number of 6.45 x 105 was calculated. A comparison of these

two parameters shows good agreement between the mine data and those of the physical

model. The difference in their Reynolds Numbers is within 5%.

77

Parameters Mine A Physical Model Air density (kg/m ) 1.12 0.99 Airflow quantity (m3/s) 46.62 1.61 Airway diameter (m) 4 0.14

Kinematic viscosity (m2/s) 24.1 x 10"6 22.64 x 10"6

4.3.3 Model Calibration

To check the similarity between the physical model and CFD model , experimental

data from Section 4.2.2 were used. In the physical model , the permeameter was filled

with rock particles of 9.71 m m in diameter. The permeabili ty of this material was 1.403 x

10" m (Table 3.2). Other relevant parameters for the CFD model are shown in Table 4.3.

For both models , three factors needed to be checked at each pressure tap: velocity,

pressure, and NR.

In C F D modeling, the laboratory conditions were replicated. This was achieved

by changing the airway resistances in the numerical model for the same airflow

conditions (calibration). In Fluent, the airway resistances are changed by modifying two

parameters: roughness constant and roughness height. Following this, the simulation

converged to 5 % accuracy after approximately 531 iterations. This accuracy indicates a

good correlation of results for the two models (Appendix C).

In the next step, the U-section of the CFD model was modified to include a

porous medium characterized by a permeability of 1.56 x 10" m . This permeability was

determined iteratively to represent the laboratory results for the same particle size. After a

few trials, a calibration factor ( a ) of 0.898 was established. Then, the corrected

permeability (&CFD) for the broken material in the CFD model is given by:

Table 4.4 Ventilation survey data for Mine A and Physical model

77

Table 4.4 Ventilation survey data for Mine A and Physical model

Parameters Mine A Physical Model Air density (kg/mj) 1.l2 0.99 Airflow quantity (m3/s) 46.62 1.61

Airway diameter (m) 4 0.14

Kinematic viscosity (m2/s) 24.1 x 10-6 22.64 X 10-6

4.3.3 Model Calibration

To check the similarity between the physical model and CFD model, experimental

data from Section 4.2.2 were used. In the physical model, the permeameter was filled

with rock particles of9.71 mm in diameter. The permeability of this material was 1.403 x

10-8 m2 (Table 3.2). Other relevant parameters for the CFD model are shown in Table 4.3.

For both models, three factors needed to be checked at each pressure tap: velocity,

pressure, and N R .

In CFD modeling, the laboratory conditions were replicated. This was achieved

by changing the airway resistances in the numerical model for the same airflow

conditions (calibration). In Fluent, the airway resistances are changed by modifying two

parameters: roughness constant and roughness height. Following this, the simulation

converged to 5% accuracy after approximately 531 iterations. This accuracy indicates a

good correlation of results for the two models (Appendix C).

In the next step, the U-section of the CFD model was modified to include a

porous medium characterized by a permeability of 1.56 x 10-8 m2• This permeability was

determined iteratively to represent the laboratory results for the same particle size. After a

few trials, a calibration factor (a) of 0.898 was established. Then, the corrected

permeability (kCFD) for the broken material in the CFD model is given by:

78

This relationship has been verified for all experiments using different particle

sizes. This factor (0.898) compensates for the differences in packing mode and particle

shape used in CFD. Finally, the modified permeability relationship (Equation 2.9) for

broken rock becomes:

ir = in " (A in) physical 4 4 { l _ n ) 2 K • )

This equation was used to determine permeability of the simulated mine gob throughout

the study.

, = Khysic^ ( 4 9 )

C F D 0.898

78

k k physical

CFD = 0.898

(4.9)

This relationship has been verified for all experiments using different particle

sizes. This factor (0.898) compensates for the differences in packing mode and particle

shape used in CFD. Finally, the modified permeability relationship (Equation 2.9) for

broken rock becomes:

d 2 n 3

k physical = 200.4~ (1 _ n) 2 (4.10)

This equation was used to determine permeability of the simulated mine gob throughout

the study.

CHAPTER 5

HOT SPOT LOCATION - CFD SIMULATION EXERCISES

From a safety point of view, a preventive control method such as locating

potential hot spots in gobs is more effective than measuring combustion products. This

would reduce the fire hazard considerably. Since the gob is inaccessible, locating the hot

spot is a challenge for the mine operators. This section explores the conditions under

which CFD could be used to determine the potential hot spot locations in longwall mines.

The hot spot occurrence is a complex process involving oxidation, heat transfer,

gas replacement, etc. (Chamberlain, 1972; Koenning, 1989; Cliff and Banik et al., 1993;

McPherson, 1993; Smith et a l , 1996; Saghafi and Carras, 1997). This complexity and the

nature of gob make CFD modeling more suitable for hot spot investigation than other

techniques.

This chapter presents the hot spot simulations using CFD. Assumptions used in

each exercise, including the panel geometry, ventilation system, self-heating properties,

are presented in the following sections. The prerequisites for hot spot development in a

gob are temperature and oxygen concentration. Gob temperature of about 100°C with at

least 5 % oxygen (by volume) will ensure a thermal runaway state which will eventually

ignite coal and sustain combustion. Specific mining method schemes and ventilation

CHAPTERS

HOT SPOT LOCATION - CFD SIMULATION EXERCISES

From a safety point of view, a preventive control method such as locating

potential hot spots in gobs is more effective than measuring combustion products. This

would reduce the fire hazard considerably. Since the gob is inaccessible, locating the hot

spot is a challenge for the mine operators. This section explores the conditions under

which CFD could be used to determine the potential hot spot locations in longwall mines.

The hot spot occurrence is a complex process involving oxidation, heat transfer,

gas replacement, etc. (Chamberlain, 1972; Koenning, 1989; Cliff and Banik et aI., 1993;

McPherson, 1993; Smith et aI., 1996; Saghafi and Carras, 1997). This complexity and the

nature of gob make CFD modeling more suitable for hot spot investigation than other

techniques.

This chapter presents the hot spot simulations using CFD. Assumptions used in

each exercise, including the panel geometry, ventilation system, self-heating properties,

are presented in the following sections. The prerequisites for hot spot development in a

gob are temperature and oxygen concentration. Gob temperature of about 100°C with at

least 5% oxygen (by volume) will ensure a thermal runaway state which will eventually

ignite coal and sustain combustion. Specific mining method schemes and ventilation

80

system are used for each simulation. The results are expected to show potential hot spot

locations for simulated cases.

5.1 Basic Assumptions

5.1.1 Longwall Mine Geometry

Figure 5.1 shows the mine schematic used in this study. It represents a typical

longwall mine found in the western U.S. This schematic was used to create CFD models

using Gambit. Gambit is a C A D software that allows the users to create the required

geometry to solve a fluid flow problem. It is also used to mesh the cells and define the

boundary conditions of the model .

The simulated panel is about 3,100 m long and 330 m wide, and utilizes two sets

of entries: one for intake (headgate) and another for return (tailgate). The work area is

represented by a single airway (face). The caved area (gob) is divided into three zones:

freshly broken, semiconsolidated, and consolidated. The permeabili ty of each zone is

shown in Table 3.3. Zone 1 represents the area behind the shields and the gob perimeter.

This zone is filled with freshly caved material. Zones 2 and 3 are filled with more

consolidated particles than those of Zone 1. The gob perimeter represents an area with

freshly broken material. A bleeder system is used to ventilate the gas emissions from the

gob. Four models are presented in this chapter: three using a bleeder ventilation system

and one using a bleederless system.

A typical longwall entry 6 m wide and 3 m high is used for all airways except the

face and bleeder entries. These are characterized by high resistance airways. The width of

the face entry is 3 m while that of the gob perimeter is 2 m. The wall roughness for these

80

system are used for each simulation. The results are expected to show potential hot spot

locations for simulated cases.

5.1 Basic Assumptions

5.1.1 Longwall Mine Geometry

Figure 5.1 shows the mine schematic used in this study. It represents a typical

longwall mine found in the western U.S. This schematic was used to create CFD models

using Gambit. Gambit is a CAD software that allows the users to create the required

geometry to solve a fluid flow problem. It is also used to mesh the cells and define the

boundary conditions of the model.

The simulated panel is about 3,100 m long and 330 m wide, and utilizes two sets

of entries: one for intake (headgate) and another for return (tailgate). The work area is

represented by a single airway (face). The caved area (gob) is divided into three zones:

freshly broken, semiconsolidated, and consolidated. The permeability of each zone is

shown in Table 3.3. Zone 1 represents the area behind the shields and the gob perimeter.

This zone is filled with freshly caved material. Zones 2 and 3 are filled with more

consolidated particles than those of Zone 1. The gob perimeter represents an area with

freshly broken material. A bleeder system is used to ventilate the gas emissions from the

gob. Four models are presented in this chapter: three using a bleeder ventilation system

and one using a bleederless system.

A typicallongwall entry 6 m wide and 3 m high is used for all airways except the

face and bleeder entries. These are characterized by high resistance airways. The width of

the face entry is 3 m while that of the gob perimeter is 2 m. The wall roughness for these

3,100 m

Bleeder Entries Gob perimeter Headgate

330 m Zone 3 Zone 2 •Zone 1

Injection Face

BS <g> I

Gob perimeter Tailgate

l_. I I

Model A j

Model B _>! (x= 1,524)

Model C > ! ( x = 2,445)

J M J B

Tl T2

• > X

BS = Bleeder shaft E = Escape entry M = Main entry

B = Belt entry T l = Tailgate 1 T2 = Tailgate 2

Figure 5.1 Model schematic for a typical longwall mine

3,100 m

Bleeder Entries ;? Gob perimeter

......................................................................................................................... ? .. ~:: : ................ ~ ..................... ~~~~ ................ ~!'. """""""" .: !. ...................... ! , ................... • ::~; ................ (~~. """""""""" ~'.!:~ -. :'-'; ... .

Zone 3 Zone 2 r:: : .... ',, : .'-'. ~ : .... : .... : C::

I~ection

~~~:: : .............. ~ . :.; ................. ~ .; .~ .............. :~ ~ ................ ~. ::: ................... ~.:.; ................................................................. ~~:.... ..

.'-'. : .. :

Zone 1

Headgate

Face

E M B

......................................................................................................................... W=~~~~~~~~~~~~~~~~\~~~~~~====~~~~~~Tl

: : ~ Gob perimeter Tailgate - T2

Model A

ModelB

Model C

y

Lx

I I I I

______ >:(x=912) :

I I(X = 1,524)

--------------------~I I I I(x = 2,445)

-----------------------------------------71

BS= E M =

Bleeder shaft Escape entry Main entry

Figure 5.1 Model schematic for a typicallongwall mine

B = Tl= T2 =

Beitentry Tailgate 1 Tailgate 2

00 .......

82

entries is at 0.1 m. Although a panel is developed using a 3-entries system, after mining,

only two entries are left at the headgate side and one at the tailgate end. This is due to

caving of the strata in the gob area.

The coal presence in gob is simulated by several particle injection points. In the

model, these points are evenly distributed (Figure 5.2). Each point is 1 m in diameter.

There are 3 points along the width of the gob, and several along its length. It is assumed

that 10-28% volume of gob area is occupied by the left-over coal.

The simulated models are represented by four letters: A, B , C, and D. The first

three models represent three production stages of the same panel where the gob is

ventilated by a bleeder system. The fourth model represents a gob area with a bleederless

Face line Mining direction

85 m

Headgate Tailgate > 100 m

6 o

Injection poit ( 0 = l m ) j

Panel start line

Figure 5.2 Location of injection ports in the simulated mine gob

82

entries is at 0.1 m. Although a panel is developed using a 3-entries system, after mining,

only two entries are left at the headgate side and one at the tailgate end. This is due to

caving of the strata in the gob area.

The coal presence in gob is simulated by several particle injection points. In the

model, these points are evenly distributed (Figure 5.2). Each point is 1 m in diameter.

There are 3 points along the width of the gob, and several along its length. It is assumed

that 10-28% volume of gob area is occupied by the left-over coal.

The simulated models are represented by four letters: A, B, C, and D. The first

three models represent three production stages of the same panel where the gob is

ventilated by a bleeder system. The fourth model represents a gob area with a bleederless

Headgate

Face line

, 85 m ' ,

- - --- --~- ------c>--------, ,

Mining direction

, , ,Tailgate , ,

100m:

------~-------~-------~-------£' , , , , , , , , 'I'" , , njechon port , : (0 = 1 m) : , , , , ,

Panel start line

Figure 5.2 Location of injection ports in the simulated mine gob

83

ventilation system. In model A, the gob length is one-third of the panel length. This stage

may be reached after 3 to 4 months of operation. In model B , the gob takes up one-half of

panel length. This stage may be reached after 6 to 7 months of operation. Model C

simulates the panel condition near the end of production schedule. The gob lengths for

these three stages are 912 m, 1,524 m, and 2,445 m, respectively (Figure 5.1). These three

models are intended to address the dynamic aspect of the mining sequence. Model D is

used to investigate the effect of the bleederless ventilation system onto the hot spot

development. This model replicates model A in which the panel is ventilated by a

bleederless system. The simulation result can be used to analyze the effectiveness of

these systems to control the hot spot.

5.1.2 Input Parameters

These parameters are used to specify the boundary conditions for CFD models.

The models are formulated to simulate two fluid flow scenarios: (1) single phase model

(without coal oxidation) and (2) two phase model (with coal oxidation). The parameters

are divided into two groups: ventilation and self-heating. The ventilation parameters are

used to determine the air flow behavior in the gob and the self-heating parameters to

determine the location of potential fire sources (hot spots).

5.1.2.1 Single-Phase Model

A single-phase model is used to simulate the air flow distribution in the gob

without coal oxidation. The absence of coal (in solid phase) ensures no oxidation in the

gob. The ventilation air is the only fluid phase used in the model . The main objectives of

83

ventilation system. In model A, the gob length is one-third of the panel length. This stage

may be reached after 3 to 4 months of operation. In model B, the gob takes up one-half of

panel length. This stage may be reached after 6 to 7 months of operation. Model C

simulates the panel condition near the end of production schedule. The gob lengths for

these three stages are 912 m, 1,524 m, and 2,445 m, respectively (Figure 5.1). These three

models are intended to address the dynamic aspect of the mining sequence. Model D is

used to investigate the effect of the bleederless ventilation system onto the hot spot

development. This model replicates model A in which the panel is ventilated by a

bleederless system. The simulation result can be used to analyze the effectiveness of

these systems to control the hot spot.

5.1.2 Input Parameters

These parameters are used to specify the boundary conditions for CFD models.

The models are formulated to simulate two fluid flow scenarios: (1) single phase model

(without coal oxidation) and (2) two phase model (with coal oxidation). The parameters

are divided into two groups: ventilation and self-heating. The ventilation parameters are

used to determine the air flow behavior in the gob and the self-heating parameters to

determine the location of potential fire sources (hot spots).

5.1.2.1 Single-Phase Model

A single-phase model is used to simulate the air flow distribution in the gob

without coal oxidation. The absence of coal (in solid phase) ensures no oxidation in the

gob. The ventilation air is the only fluid phase used in the model. The main objectives of

84

Table 5.1 Input parameters used for a single-phase model

Parameters Values Ventilation Pressure Inlet (Pa):

Main entry 250 Belt entry -250 Escape entry 250

Pressure outlet (Pa): Return at tailgate -100 Bleeder fan -2500

Doors: Face permeability (m2) 4.67e-07 Pressure-Jump coefficient (1/m) 1800

Curtains/Regulators: Face permeability (m2) 2.47e-05 Pressure-Jump coefficient (1/m) 96.8

Minimum air quantity at face (m3/s) 14.16 Minimum mean air velocity at face (m/s) 0.3 Operation Temperature (K) 293 Gob Specific Permeability (m2)

Zone 1 (unconsolidated) 4.68 x 10-7

Zone 2 (semi-consolidated) 3.15 x 10- 8

Zone 3 (consolidated) 7.98 x 10'9

this exercise are to determine the ventilation control parameters (i.e., regulator

resistances), and to represent the initial airflow distribution in the mine. These are defined

iteratively to replicate the airflow distribution of the base case (Section 5.1.3). The input

parameters used with the single phase model are shown in Table 5.1 and in Appendix E.

These parameters are for a gob model ventilated by a bleeder system and include:

1. Pressure inlet for intake entries

2. Pressure outlet for return entries and bleeder fan

3. Porous j u m p for regulators and stoppings

4. Porous medium for gob permeability zones

84

this exercise are to determine the ventilation control parameters (i.e., regulator

resistances), and to represent the initial airflow distribution in the mine. These are defined

iteratively to replicate the airflow distribution of the base case (Section 5.1.3). The input

parameters used with the single phase model are shown in Table 5.1 and in Appendix E.

These parameters are for a gob model ventilated by a bleeder system and include:

1. Pressure inlet for intake entries

2. Pressure outlet for return entries and bleeder fan

3. Porous jump for regulators and stoppings

4. Porous medium for gob permeability zones

Table 5.1 Input parameters used for a single-phase model

Parameters Values Ventilation Pressure Inlet (Pa):

Main entry 250 Belt entry -250 Escape entry 250

Pressure outlet (Pa): Return at tailgate -100 Bleeder fan -2500

Doors: Face permeability (m2

) 4.67e-07 Pressure-Jump coefficient (l/m) 1800

Curtains/Regulators: Face permeability (m2

) 2.47e-05 Pressure-Jump coefficient (11m) 96.8

Minimum air quantity at face (m3/s) 14.16 Minimum mean air velocity at face (mls) 0.3 Operation Temperature (K) 293 Gob S~ecific Permeability (m2

)

Zone 1 (unconsolidated) 4.68 x 10-7

Zone 2 (semi-consolidated) 3.15 x 10-8

Zone 3 (consolidated) 7.98 x 10-9

85

At the headgate side, the inlet pressure (main and escape) was set at 250 Pa. The

belt entry was used as a return with an inlet pressure of -250 Pa. At the face, the air was

split; about 3 0 % was directed to the face and the remainder to the gob and bleeder

entries. At the tailgate side, the outlet pressure was set at -100 Pa. The gob was

represented by porous media and a ventilation control by a parameter called porous jump.

Three permeabili ty zones, as described in Section 3.4, were used to characterize the gob.

Their permeabilities, after being corrected (Section 4.3.2), were 4.68 x 10"7, 3.15 x 10"8,

9 2

and 7.98 x 10" m for zones 1, 2, and 3, respectively. These values reflected three

degrees of gob consolidation.

To regulate the ventilation air in the gob, four regulators and six doors were used.

Furthermore, the calculated air velocity for the face was compared against the minimum

requirements (30 CFR Part 75 Section 325-326). The average air velocity was at least 0.3

m/s. Permeabili ty and pressure-jump coefficients for the ventilation controls were

determined based on ventilation surveys, laboratory tests and CFD modeling. These are

described in detail in Chapters 3 and 4. When the gob model was ventilated by a

bleederless system, two changes were made to the model with bleeder system: (1) the

bleeder fan was removed, and (2) the bleeder entries inside the face were blocked.

Since the single-phase model did not include coal oxidation, the parameters used

to define this process were omitted. The parameters shown in Table 5.1 are the

ventilation-related parameters only. In the multiphase model , a solid phase (coal) was

included, and its properties were added as the input parameters, and the simulation

became more complex than that of the single phase model .

85

At the headgate side, the inlet pressure (main and escape) was set at 250 Pa. The

belt entry was used as a return with an inlet pressure of -250 Pa. At the face, the air was

split; about 30% was directed to the face and the remainder to the gob and bleeder

entries. At the tailgate side, the outlet pressure was set at -100 Pa. The gob was

represented by porous media and a ventilation control by a parameter called porous jump.

Three permeability zones, as described in Section 3.4, were used to characterize the gob.

Their permeabilities, after being corrected (Section 4.3.2), were 4.68 x 10-7, 3.15 x 10-8

,

and 7.98 x 10-9 m2 for zones 1,2, and 3, respectively. These values reflected three

degrees of gob consolidation.

To regulate the ventilation air in the gob, four regulators and six doors were used.

Furthermore, the calculated air velocity for the face was compared against the minimum

requirements (30 CFR Part 75 Section 325-326). The average air velocity was at least 0.3

m/s. Permeability and pressure-jump coefficients for the ventilation controls were

determined based on ventilation surveys, laboratory tests and CFD modeling. These are

described in detail in Chapters 3 and 4. When the gob model was ventilated by a

bleederless system, two changes were made to the model with bleeder system: (1) the

bleeder fan was removed, and (2) the bleeder entries inside the face were blocked.

Since the single-phase model did not include coal oxidation, the parameters used

to define this process were omitted. The parameters shown in Table 5.1 are the

ventilation-related parameters only. In the multiphase model, a solid phase (coal) was

included, and its properties were added as the input parameters, and the simulation

became more complex than that of the single phase model.

86

Table 5.2 Input parameters used for a two-phase model

Parameters Values Parameters Values Ventilation Coal Particles Pressure Inlet (Pa): Properties:

Main entry 250 Moisture (%) 10.00 Belt entry -250 Volatile matter (%) 35.43 Escape entry 250 Fixed Carbon (%) 45.92

Pressure outlet (Pa): Ash (%) 12.33 Return at tailgate -100 Density (kg/m3) 1,324 Bleeder fan -2500 Injection ports:

Airflow condition at face: Particle diameter (cm) 0.5 Minimum air quantity (m3/s) 14.16 Injection rate (kg/s) 2.4 Minimum mean air velocity (m/s) 0.3 Gob

Doors: Specific Permeability Zone (m2) Face permeability (m2) 4.67e-07 Zone 1 (unconsolidated) 4.68 x 10"7

Pressure-Jump coefficient (1/m) 1800 Zone 2 (semi-consolidated) 3.15 x 10~8

Curtains/Regulators: Zone 3 (consolidated) 7.98 x 10'9

Face permeability (m2) 2.47e-05 Gob materials: Pressure-Jump coefficient (1/m) 96.8 Density (kg/m3) 2800

Operation Temperature (K) 293

5.1.2.2 Two-Phase Model

A mult iphase model is used when simulation involves a mixture of two or more

species: liquid, gas, or solid particles. In this study, the mixture consists of the following:

primary phase (ventilation air) and secondary phase (coal particles). Table 5.2 shows the

input parameters for a two-phase model ventilated by a bleeder system.

In Fluent, the pr imary phase was represented by atmospheric air with a density of

1.12 kg/m and an initial temperature of 20°C. Phase 2 was represented by high-volatile

coal particles added to the system. The chemical reaction (oxidation) between both

phases resulted in a mixture that consisted of combustion products: carbon dioxide (CO2),

carbon monoxide (CO), and water-vapor (F^Og). The stoichiometric parameters of this

reaction are not specified yet in this model. These will be specified as self-heating

86

5.1.2.2 Two-Phase Model

A multiphase model is used when simulation involves a mixture of two or more

species: liquid, gas, or solid particles. In this study, the mixture consists of the following:

primary phase (ventilation air) and secondary phase (coal particles). Table 5.2 shows the

input parameters for a two-phase model ventilated by a bleeder system.

In Fluent, the primary phase was represented by atmospheric air with a density of

1.12 kg/m3 and an initial temperature of 20T. Phase 2 was represented by high-volatile

coal particles added to the system. The chemical reaction (oxidation) between both

phases resulted in a mixture that consisted of combustion products: carbon dioxide (C02).

carbon monoxide (CO), and water-vapor (H20 g). The stoichiometric parameters of this

reaction are not specified yet in this model. These will be specified as self-heating

Table 5.2 Input parameters used for a two-phase model

Parameters Values Parameters Values Ventilation Coal Particles Pressure Inlet (Pa): Properties:

Main entry 250 Moisture (%) 10.00 Belt entry -250 V olatile matter (%) 35.43 Escape entry 250 Fixed Carbon (%) 45.92

Pressure outlet (Pa): Ash (%) 12.33 Return at tailgate -100 Density (kg/m3) 1,324 Bleeder fan -2500 Injection ports:

Airflow condition at face: Particle diameter (cm) 0.5 Minimum air quantity (m3/s) 14.16 Injection rate (kg/s) 2.4 Minimum mean air velocity (m/s) 0.3 Gob

Doors: Specific Permeability Zone (m2)

Face permeability (m2) 4.67e-07 Zone 1 (unconsolidated) 4.68 x 10-7

Pressure-Jump coefficient (11m) 1800 Zone 2 (semi-consolidated) 3.15 x 10-8

CurtainslRegulators: Zone 3 (consolidated) 7.98 x 10-9

Face permeability (m2) 2.47e-05 Gob materials:

Pressure-Jump coefficient (11m) 96.8 Density (kg/m3) 2800

Operation Temperature (K) 293

87

parameters in Section 5.1.2.3. Appendix E shows the properties of all compounds used in

this two-phase model .

The primary phase, atmospheric air, is composed of 2 1 % O2 and 7 9 % N 2 . Other

constituents such as argon (0.93%) and carbon dioxide (0.038%) are neglected in this

study. The properties of each gas such as density, thermal conductivity, viscosity, etc.,

are imported from the Fluent database that contains properties of about 6,000 other

materials (www. fluent.com). These imported properties can be customized, if necessary,

based on the ventilation survey data. The ventilation parameters for a two-phase model

are the same as those of single-phase model except that the presence of coal injections

will change the airflow distribution in the gob.

The secondary phase, coal particles, is characterized by two-mixture fractions:

fuel stream and secondary stream. The fuel stream fraction represents the char and the

secondary stream the volatiles. The char is represented by fixed carbon and the volatile

matter by a mixture of hydrocarbons and some sulphur excluding the moisture content.

The simulated fractions for fuel stream and secondary stream were 45 .92% and 35.43%,

respectively (Table 5.2). These data are based on the coal chemical properties presented

in Table 2 .1 . The ash content of coal is the noncombustible residue left after coal is burnt.

This parameter took up 12 .33% of coal content. The ash and moisture contents were also

entered as input parameters.

The coal left in the gob was represented by a set of particle injection ports (Figure

5.2). This is the best way to represent the broken coal in a 2D model . A stream of

combustible particles, with 0.5 cm in diameter, was injected at the rate of 2.4 kg/s. This

represented approximately 10-28% of the total gob volume. The selected particle

87

parameters in Section 5.1.2.3. Appendix E shows the properties of all compounds used in

this two-phase model.

The primary phase, atmospheric air, is composed of 21 % O2 and 79% N2. Other

constituents such as argon (0.93%) and carbon dioxide (0.038%) are neglected in this

study. The properties of each gas such as density, thermal conductivity, viscosity, etc.,

are imported from the Fluent database that contains properties of about 6,000 other

materials (www.fluent.eom). These imported properties can be customized, if necessary,

based on the ventilation survey data. The ventilation parameters for a two-phase model

are the same as those of single-phase model except that the presence of coal injections

will change the airflow distribution in the gob.

The secondary phase, coal particles, is characterized by two-mixture fractions:

fuel stream and secondary stream. The fuel stream fraction represents the char and the

secondary stream the volatiles. The char is represented by fixed carbon and the volatile

matter by a mixture of hydrocarbons and some sulphur excluding the moisture content.

The simulated fractions for fuel stream and secondary stream were 45.92% and 35.43%,

respectively (Table 5.2). These data are based on the coal chemical properties presented

in Table 2.1. The ash content of coal is the noncombustible residue left after coal is burnt.

This parameter took up 12.33% of coal content. The ash and moisture contents were also

entered as input parameters.

The coal left in the gob was represented by a set of particle injection ports (Figure

5.2). This is the best way to represent the broken coal in a 2D model. A stream of

combustible particles, with 0.5 em in diameter, was injected at the rate of 2.4 kg/so This

represented approximately 10-28% of the total gob volume. The selected particle

http://fluent.com

88

diameter of 0.5 cm was obtained from spontaneous combustion studies (Smith and

Lazarra, 1987). Sample calculations of simulated flow rates and number of injections

holes are presented in Appendix D. The reaction between these particles and oxygen

releases heat, thus increasing the gob temperature and decreasing the oxygen

concentration in the air.

In contrast to single phase, the two-phase model must include the properties of all

phases i.e., ventilation air, coal particles, and mixture products. The simulation process

for the two-phase model takes more processing t ime than that of the single-phase model.

Further, additional processing t ime is required to simulate the coal-oxygen reaction

process. The required parameters to initiate coal reaction are called self-heating

parameters. These are explained in the following section.

5.1.2.3 Self-Heating Process

The self-heating process in a gob is simulated using the oxygen-coal particle

mixture fraction routine. In Fluent, the properties of these substances are entered

interactively. The reaction parameters such as gas molecular weights and oxygen-coal

burnout ratio used to characterize the mixture are also entered interactively.

Table 5.3 summarizes the parameters used to simulate the formation of a hot spot.

In addition to these, the parameters shown in Table 5.2 are also required. The coal

properties (proximate and ultimate analyses) were determined from a coal sample

brought from an existing mine. Other parameters, including released heat from the coal

oxidation, Arrhenius rate, latent heat of water content, and carbon-oxygen burnout ratio,

were obtained from reliable sources (Smith and Lazarra, 1987; Wang et al., 2003).

diameter of 0.5 em was obtained from spontaneous combustion studies (Smith and

Lazarra, 1987). Sample calculations of simulated flow rates and number of injections

holes are presented in Appendix D. The reaction between these particles and oxygen

releases heat, thus increasing the gob temperature and decreasing the oxygen

concentration in the air.

88

In contrast to single phase, the two-phase model must include the properties of all

phases i.e., ventilation air, coal particles, and mixture products. The simulation process

for the two-phase model takes more processing time than that of the single-phase model.

Further, additional processing time is required to simulate the coal-oxygen reaction

process. The required parameters to initiate coal reaction are called self-heating

parameters. These are explained in the following section.

5.1.2.3 Self-Heating Process

The self-heating process in a gob is simulated using the oxygen-coal particle

mixture fraction routine. In Fluent, the properties of these substances are entered

interactively. The reaction parameters such as gas molecular weights and oxygen-coal

burnout ratio used to characterize the mixture are also entered interactively.

Table 5.3 summarizes the parameters used to simulate the formation of a hot spot.

In addition to these, the parameters shown in Table 5.2 are also required. The coal

properties (proximate and ultimate analyses) were determined from a coal sample

brought from an existing mine. Other parameters, including released heat from the coal

oxidation, Arrhenius rate, latent heat of water content, and carbon-oxygen burnout ratio,

were obtained from reliable sources (Smith and Lazarra, 1987; Wang et aI., 2003).

89

5.1.3 Flow Distribution - A Base Case

To investigate the factors affecting the development of hot spots, a fundamental

understanding of the airflow distribution in the gob is necessary. To facilitate this, the

Table 5.3 Input parameters for the self-heating process

Parameters Value Coal Burnout

Heat from Burnout (J/kg) 2.628 x 107

Latent Heat (J/kg-k) 2.25 x 106

Burnout Stoichiometric Ratio 2.664 Arrhenius Rate:

Pre-exponential factor 2.48 x 108

Activation energy (kJ/kg) 85.411 Reference temperature (K) 293

Gob materials: Heat capacity (J/kg-k) 856 Thermal conductivity (w/m-k) 1.25

The simulation process considers coal devolatilization and char burnout as the

only source of heat. The latent heat in Table 5.3 is the heat required to vaporize the

volatiles and water content. The pyritic sulfur (FeS2) contained in coal is neglected.

Based on Equation 2.2, the heat of coal oxidation is 4 orders of magnitude greater than

that of pyritic sulfur. Furthermore, an adiabatic wall condition is assigned to the model

limit, pillar, and unmined coal. This condition restricts the oxidation heat to the gob.

These self-heating and ventilation parameters are the key figures required for hot

spot simulations. The self-heating process between oxygen and coal particles changes the

temperature and oxygen concentration in the gob. These changes are evaluated to

determine the location of hot spots. Details of these simulation exercises are presented in

Section 5.2.

89

The simulation process considers coal devolatilization and char burnout as the

only source of heat. The latent heat in Table 5.3 is the heat required to vaporize the

volatiles and water content. The pyritic sulfur (FeS2) contained in coal is neglected.

Based on Equation 2.2, the heat of coal oxidation is 4 orders of magnitude greater than

that of pyritic sulfur. Furthermore, an adiabatic wall condition is assigned to the model

limit, pillar, and unmined coal. This condition restricts the oxidation heat to the gob.

These self-heating and ventilation parameters are the key figures required for hot

spot simulations. The self-heating process between oxygen and coal particles changes the

temperature and oxygen concentration in the gob. These changes are evaluated to

determine the location of hot spots. Details of these simulation exercises are presented in

Section 5.2.

5.1.3 Flow Distribution - A Base Case

To investigate the factors affecting the development of hot spots, a fundamental

understanding of the airflow distribution in the gob is necessary. To facilitate this, the

Table 5.3 Input parameters for the self-heating process

Parameters Value Coal Burnout

Heat from Burnout (J/kg) 2.628 x 107

Latent Heat (J/kg-k) 2.25 x 106

Burnout Stoichiometric Ratio 2.664 Arrhenius Rate:

Pre-exponential factor 2.48 x 108

Activation energy (kJ/kg) 85.411 Reference temperature (K) 293

Gob materials: Heat capacity (J/kg-k) 856 Thermal conductivity (w/m-k) 1.25

90

Figure 5.3 Base case of airflow distribution (after Brunner, 1982)

airflow distribution in a mine gob suggested by Brunner (1982) is adopted. This

distribution was determined from a detailed ventilation survey conducted by Mine

Ventilation Services, Inc. in an operating mine (Brunner, 1982).

Based on this model , for a longwall panel, the following flow percentages were

established: 3 1 % of the total flow was directed to the working face, 2 9 % entered the gob

at the headgate junct ion, 3 0 % leaked through the stoppings in the headgate side, and the

remaining 10% circulated through bleeder entries (Figure 5.3).

There are two major benefits of having this information: first, it is used to

calibrate the CFD model and determine the parameters to represent regulators and other

control devices and second, to determine the gob permeability for zones 2 and 3 through

trial and error simulations.

For zone 1, permeability, porosity, and particle size were determined from

laboratory tests and Equation 4.9. Based on the information provided in Section 3.4, the

90

airflow distribution in a mine gob suggested by Brunner (1982) is adopted. This

distribution was determined from a detailed ventilation survey conducted by Mine

Ventilation Services, Inc. in an operating mine (Brunner, 1982).

Based on this model, for a longwall panel, the following flow percentages were

established: 31 % of the total flow was directed to the working face, 29% entered the gob

at the headgate junction, 30% leaked through the stoppings in the headgate side, and the

remaining 10% circulated through bleeder entries (Figure 5.3).

There are two major benefits of having this information: first, it is used to

calibrate the CFD model and determine the parameters to represent regulators and other

control devices and second, to determine the gob permeability for zones 2 and 3 through

trial and error simulations.

For zone 1, permeability, porosity, and particle size were determined from

laboratory tests and Equation 4.9. Based on the information provided in Section 3.4, the

Bleeder Entries

10%

Headgate

Tailgate

Figure 5.3 Base case of airflow distribution (after Brunner, 1982)

91

permeability for zone 1 was estimated at 4.68 x 10"7 m 2 . For Zones 2 and 3, their

permeabilities were determined by assigning with two best estimations and checking the

airflow patterns. These should be similar to those of the base. After a few trials, the

following permeabilit ies were found: 3.15 x 10"8 m 2 for zone 2 and 7.98 x 10"9 m 2 for

zone 3.

5.2 Simulation Exercises

The results of four gob simulation exercises are presented in this section. Three of

these utilize a bleeder ventilation system and one a bleederless system. The governing

variables for all models are kept constant except those for the gob. Temperature and

oxygen concentration are the two parameters monitored carefully for self-heating

phenomena. The thermal run-away constant of coal is determined experimentally. A

thermal run-away starts when the reaction temperature is above the SHT of coal (Smith

and Lazarra, 1987). Once this temperature reaches 100°C, the possibility of stopping the

self-heating process is very small (Mitchell, 1996). Another requirement for this

phenomenon is that the oxygen concentration in the gob should be above 5 % by volume.

5.2.1 Bleeder Ventilation System: Models A, B, and C

5.2.1.1 Model A: Gob Length = 912 m

The primary difference between the models of this section is the gob length. In

model A, the gob is about 912 m long, which is approximately one-third of the panel

length. This length may be reached within 3 or 4 months of operation. This is called the

91

pelmeability for zone 1 was estimated at 4.68 x 10-7 m2. For Zones 2 and 3, their

pelmeabilities were determined by assigning with two best estimations and checking the

airflow patterns. These should be similar to those of the base. After a few trials, the

following pelmeabilities were found: 3.15 x 10-8 m2 for zone 2 and 7.98 x 10-9 m2 for

zone 3.

5.2 Simulation Exercises

The results of four gob simulation exercises are presented in this section. Three of

these utilize a bleeder ventilation system and one a bleederless system. The governing

variables for all models are kept constant except those for the gob. Temperature and

oxygen concentration are the two parameters monitored carefully for self-heating

phenomena. The thermal run-away constant of coal is determined experimentally. A

thelmal run-away starts when the reaction temperature is above the SHT of coal (Smith

and Lazarra, 1987). Once this temperature reaches 100o e, the possibility of stopping the

self-heating process is very small (Mitchell, 1996). Another requirement for this

phenomenon is that the oxygen concentration in the gob should be above 5% by volume.

5.2.1 Bleeder Ventilation System: Models A, S, and e

5.2.1.1 Model A: Gob Length = 912 m

The primary difference between the models of this section is the gob length. In

model A, the gob is about 912 m long, which is approximately one-third of the panel

length. This length may be reached within 3 or 4 months of operation. This is called the

92

• 0.1000

0.0950

0.0900

0.0850

0.0800

0.0750

0.0700

0.0651

0.0601

0.0551

0.0501

0.0451

0.0401

0.0351

0.0301

0.0251

0.0201

Q.0151

0.0101

0.0051

0.0002

Zone 3 Zone 2 Zone 1 Intake

Bleeder shaft

Return

Velocity Vectors Co lored By Velocity Magnitude (m/s) Jan 30. 200S F L U E N T 6.2 (2d. segregated, spe. ske>

Figure 5.4 Velocity vectors in gob for a bleeder system

Base Case Model. Escape and main entries are used as intakes, while the bleeder and

tailgate entries are the returns. The belt entry is used as an auxiliary return with very

small flow rate. The air flow distribution plays an important role in hot spot development.

Figure 5.4 shows the air velocity vectors for the gob model. This pattern

represents an initial flow distribution in gob without the coal injection points. The intake

air from the headgate is split into 3 directions: face, gob, and bleeder entries. This

distribution replicates Brunner's base case model. The ventilation air exits the mine

through a bleeder shaft and two return entries.

Figure 5.5 shows the contours of oxygen concentration in the gob. The coal

oxidation causes reduction in oxygen concentration. This concentration ranges from 11 to

21%. Due to the continuous supply of combustible particles through injection ports, the

92

Base Case Model. Escape and main entries are used as intakes, while the bleeder and

tailgate entries are the returns. The belt entry is used as an auxiliary return with very

small flow rate. The air flow distribution plays an important role in hot spot development.

Figure 5.4 shows the air velocity vectors for the gob model. This pattern

represents an initial flow distribution in gob without the coal injection points. The intake

air from the headgate is split into 3 directions: face, gob, and bleeder entries. This

distribution replicates Brunner's base case model. The ventilation air exits the mine

through a bleeder shaft and two return entries.

Figure 5.5 shows the contours of oxygen concentration in the gob. The coal

oxidation causes reduction in oxygen concentration. This concentration ranges from 11 to

21 %. Due to the continuous supply of combustible particles through injection ports, the

0.1000

0.0950

0.0900

0 .0850

0 .0800

0 .0750

0 .0700

0.0651

0 .0601

0.0551

0.0501

0 .0451

0.0401

0 .0351

0.0301

0.02 51

0 .0 201

0 .0151

0 .0101

0 .0051

0.0002

Zone3

Velocity Vectors Colored By Velocity Magnitude (m/s ~

Zone 2 Zone I Intake

Jan 30, 2008 FLUENT 6 .2 (2d . seg.regated, spe. ske )

Figure 5.4 Velocity vectors in gob for a bleeder system

93

0.21

0.20

0.19

0.18

0.17

0.16

0.15

0.14

0.13

0.12

0.11

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0.00

Zone 2 Zone 1

Bleeder shaft

Contours of V o l u m e fraction of o2 Jan 05. 2008 F L U E N T 6.2 (2d. segregated, spe. ske)

Figure 5.5 Oxygen concentration contours for model A

lowest oxygen concentration is likely to be found at about these points. Heat transfer

through radiation (space between porous), conduction (gob material), and convection

(water content) may increase the gob temperature, or decrease it if the heat is carried

away by ventilation air. Figure 5.6 shows the results of the heat transfer mechanisms in

the gob. Figure 5.7 shows the area where the coal temperature increases from 365 to 400

K. Based on the isotherms of this graph, the area with potential heat buildup is located in

Zone 3. A careful inspection showed that the hot spot is located at the back of gob on the

tailgate side near the bleeder shaft (shaded area; x=140 m, y=128 m). In this area, the gob

temperature reached 385 K (112°C), i.e., 12 K above the critical temperature.

0 .21

0 .20

0 .. 19

0 .16

0.17

0 .1/i

0 .15

0.14

0 .1 3

0.12

0.1 '1

0.0,9

0 .08

0.07

0 .06

0 .05

0 .04

0 .03

0 .0 2

0 .01

0 .00

Zone3 Zone 2

93

Zone 1

Contours of Volume fraction of 02 Jan 05.2008 FLUENT 6.2 (2d. segregated. spe. ske)

Figure 5.5 Oxygen concentration contours for model A

lowest oxygen concentration is likely to be found at about these points. Heat transfer

through radiation (space between porous), conduction (gob material), and convection

(water content) may increase the gob temperature, or decrease it if the heat is carried

away by ventilation air. Figure 5.6 shows the results of the heat transfer mechanisms in

the gob. Figure 5.7 shows the area where the coal temperature increases from 365 to 400

K. Based on the isotherms of this graph, the area with potential heat buildup is located in

Zone 3. A careful inspection showed that the hot spot is located at the back of gob on the

tailgate side near the bleeder shaft (shaded area; x=140 m, y=128 m). In this area, the gob

temperature reached 385 K (l12°C), i.e., 12 K above the critical temperature.

94

Contours of Total Temperature (k) Dec 29, 2007 F L U E N T 6.2 (2d, segregated, spe, ske)

Figure 5.6 Temperature contours for model A

Contours of Total Temperature (k) Jan 05, 2008 F L U E N T 6.2 (2d, segregated, spe, ske)

Figure 5.7 Potential hot spot location for model A

400

394

389

383

378

372

366

361

355

350

344

338

333

327

322

316

310

305

299

294

288

Zone3

Bleeder shaft

Contours of Total Temperature (k)

Zone 2 Zone I

Combustion products buildup

94

Dec 29.2007 FLUENT 6 .2 (2d, segregated. spe, ske)

Figure 5.6 Temperature contours for model A

400

398 397

395 393

391

390 388

386

384 383

381

379

Zone 3

... ------, I .... ------ ------: t c :p- ..c:~ "'. , , , ,

".'

c;,'

, \ , , , ,

Zone 2

II' ~ .- .f'

. ,,' . 1"

p

~~ .J~ •

.t'

..

, \ ,

Zone 1

377 y=128 m 376 ~~~~~~~~~~~~~~~~~~~~~~~----~~~~~~~~

374

372 370 36"9

36"7

365

Bleeder shaft


Potential hot spot (373 - 385 K)

Jan 05.2008 FLUENT 6 .2 (2d, segregated. spe, ske)

Figure 5.7 Potential hot spot location for model A

95

5.2.1.2 Model B: Gob Length = 1,524 m

In model B, the gob is about 1,524 m long, which is approximately one-half of the

panel length. In practice, this length may be reached after 6 or 7 months of operation. The

permeability zones are elongated as the gob length increases. The gob perimeter also has

higher resistance than that of model A due to roof failure. Escape and main entries are

kept as intakes, and the bleeder and tailgate entries as returns. The belt entry is used as

auxiliary return.

Although the gob length in model B is longer than that of model A, the air flow

patterns for both models are similar to each other. However, as the gob length increases,

the gob permeability decreases due to compaction. This decrease in permeability results

in airflow reduction to the gob, thus reducing its oxygen concentration. The reduction in

oxygen is mainly caused by coal oxidation. Figure 5.8 illustrates part of this effect. A

comparison of the oxygen concentrations depicted in Figures 5.5 and 5.8 shows wider

areas of oxidation in model B than that for model A. In model B, the oxygen

concentration in gob ranges from 5 to 21%. The lowest oxygen concentration is found

along the tailgate side. This is caused by the absence of high air velocities within the gob.

The quantity of air passing through the gob (leakage) is not sufficient to remove the

oxidation products. These products replace the oxygen and reduce its concentration.

Figure 5.9 illustrates the temperature contours for model B. An inspection of this

graph demonstrates the heat buildup in the gob near the tailgate end. Locations with

temperatures greater than 373 K are found in zones 2 and 3. In zone 2, part of the heat is

carried away by the ventilation air, and the remainder is absorbed by the coal which

95

5.2.1.2 Model B: Gob Length = 1,524 m

In model B, the gob is about 1,524 m long, which is approximately one-half of the

panel length. In practice, this length may be reached after 6 or 7 months of operation. The

permeability zones are elongated as the gob length increases. The gob perimeter also has

higher resistance than that of model A due to roof failure. Escape and main entries are

kept as intakes, and the bleeder and tailgate entries as returns. The belt entry is used as

auxiliary return.

Although the gob length in model B is longer than that of model A, the air flow

patterns for both models are similar to each other. However, as the gob length increases,

the gob permeability decreases due to compaction. This decrease in permeability results

in airflow reduction to the gob, thus reducing its oxygen concentration. The reduction in

oxygen is mainly caused by coal oxidation. Figure 5.8 illustrates part of this effect. A

comparison of the oxygen concentrations depicted in Figures 5.5 and 5.8 shows wider

areas of oxidation in model B than that for model A. In model B, the oxygen

concentration in gob ranges from 5 to 21 %. The lowest oxygen concentration is found

along the tailgate side. This is caused by the absence of high air velocities within the gob.

The quantity of air passing through the gob (leakage) is not sufficient to remove the

oxidation products. These products replace the oxygen and reduce its concentration.

Figure 5.9 illustrates the temperature contours for model B. An inspection of this

graph demonstrates the heat buildup in the gob near the tailgate end. Locations with

temperatures greater than 373 K are found in zones 2 and 3. In zone 2, part of the heat is

carried away by the ventilation air, and the remainder is absorbed by the coal which

96

0.21

0.20

0.19

0.18

0.17

0.16

0.15

0.14

0.13

0.12

0.11

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0.00

Zone 3 Zone 2 Zone 1

Bleeder shaft

Contours of Volume fraction of o2 Jan 05. 2008 FLUENT 6.2 (2d, segregated, spe, ske)

Figure 5.8 Oxygen concentration contours for model B

400

394

389

383

378

372

366

361

355

350

344

338

333

327

322

316

310

305

299

294

288

> Ventilated gob

Bleeder shaft Combustion

products buildup

Contours of Total Temperature (k) Jan 05. 2008 FLUENT 6.2 (2d. segregated, spe, ske)

Figure 5.9 Temperature contours for model B

0 .21

0.20

0.19

0 .18

0.17

0.16

0 .15

0 .14

0.13

0.12

0 .11

0 .09

0 .08

0 .07

0.06

0.05

0.04 Bleeder shaft 0.03

0.02

0.01

0 .00

Contours of Volume fraction of 02

Zone3

96

Zone 2 Zone 1

Jan 05. 2008 FLUENT 6.2 (2d. segregated. spe. ske)

Figure 5.8 Oxygen concentration contours for model B

400

394

389

383

378

372

366

361

355

350

344

338

333

321

322

316

310

305

299

294

288

Bleeder shaft

Ventilated gob

Combustion products buildup

Contours of Total Temperature (k) Jan 05, 2008 FLUENT 6.2 (2d. se.gregated. spe, ske)

Figure 5.9 Temperature contours for model B

97

ultimately increased its self-heating temperature. In zone 3, this ventilation effect of

ventilation air is not shown. This is due to its low permeability.

Figure 5.10 shows the area where the gob temperature ranges from 365 to 400 K.

Based on the isotherms of this graph, the area with greater potential heat buildup is

located along the tailgate side (shaded area). This area starts from the back of gob near

the bleeder shaft to the end of zone 3 (mid-gob area). This can be considered as an

extension of the hot spot of model A. The gob temperature in this area reaches 386 K

(113°C), i.e., 13 K above the critical temperature. In addition, another hot spot location is

identified near the face-tailgate junction. This site may be regarded as the beginning of a

new hot spot in zone 2.

400

398

397

395

393

391

390

388

386

384

383

381

379

377

376

374

372

370

369

367

365


1 i 1 i

Bleeder shaft

Potential hot spots (373 - 386 K)


Figure 5.10 Potential hot spot location for model B

97

ultimately increased its self-heating temperature. In zone 3, this ventilation effect of

ventilation air is not shown. This is due to its low permeability.

Figure 5.10 shows the area where the gob temperature ranges from 365 to 400K.

Based on the isotherms of this graph, the area with greater potential heat buildup is

located along the tailgate side (shaded area). This area starts from the back of gob near

the bleeder shaft to the end of zone 3 (mid-gob area). This can be considered as an

extension ofthe hot spot of model A. The gob temperature in this area reaches 386 K

(113°e), i.e., 13 K above the critical temperature. In addition, another hot spot location is

identified near the face-tailgate junction. This site may be regarded as the beginning of a

new hot spot in zone 2.

400

398

397

395

393

391

390

388

386

384

383

381

379

377

376

374

372

370

369

367

365

Bleeder shaft

Contours of' Total T empe.rature (k }

Zone 3 Zone 2


Zone I

Jan 05. 2008 FLUENT 6.2 (2d, se.gregated, spe, ske)

Figure 5.1 0 Potential hot spot location for model B

_ — 1

Contours of Volume fraction of o2 Jan 05. 2008 FLUENT 6.2 (2d, segregated, spe, ske)

Figure 5.11 Oxygen concentration contours for model C

98

5.2.1.3 Model C: Gob Length = 2, 445 m

In model C, the gob is about 2,445 m long, which is approximately two-thirds of

the panel length. In practice, this length may be reached after 10 or 11 months of

operation. This simulates the condition at which mining approaches the recovery room.

Escape and main entries are kept as intakes, and the bleeder entries as returns. The belt

entry is kept almost neutral. In contrast to models A and B, one tailgate entry is utilized

as an auxiliary intake to dilute the mine gases and eliminate the heat buildup near the

tailgate corner.

Figure 5.11 shows the oxygen concentration contours. The oxygen concentration

ranges from 3 to 2 1 % throughout the gob. Due to continuous oxidation, the lowest

oxygen concentration is found along the tailgate side of the gob zone 3. This is caused by

the absence of higher air velocities in these areas (zones 2 and 3), a favorable condition

98

5.2.1.3 Model C: Gob Length = 2, 445 m

In model C, the gob is about 2,445 m long, which is approximately two-thirds of

the panel length. In practice, this length may be reached after 10 or 11 months of

operation. This simulates the condition at which mining approaches the recovery room.

Escape and main entries are kept as intakes, and the bleeder entries as returns. The belt

entry is kept almost neutral. In contrast to models A and B, one tailgate entry is utilized

as an auxiliary intake to dilute the mine gases and eliminate the heat buildup near the

tailgate comer.

Figure 5.11 shows the oxygen concentration contours. The oxygen concentration

ranges from 3 to 21 % throughout the gob. Due to continuous oxidation, the lowest

oxygen concentration is found along the tailgate side of the gob zone 3. This is caused by

the absence of higher air velocities in these areas (zones 2 and 3), a favorable condition

0 .21

0.2 0

0 .19

0.18

0.17

0.16

0.15

0 .14

0 . 13

0.12

0 .11

0 .09

0 .0 8

0.07

0 .06

0.05

0 .04

0 .03

0 .0 2

0 .01

0 .00


Zone 3 Zone 2

Oxidized area (02

::; 20%)

Zone 1

Jan 05. 2008. FLUENT 6.2 (2d, segr egated, spe, ske}

Figure 5.11 Oxygen concentration contours for model C

99

| 394

j 389

383


Figure 5.12 Temperature contours for model C

for coal oxidation. Sufficient supply of oxygen with very low air velocities ensures a heat

buildup in the gob. The combustion products also affect the concentration profiles. This

effect produces higher oxygen concentrations on the headgate side than those of the

tailgate.

Figure 5.12 shows temperature contours in the gob. The highest temperature is

found in zone 3. The ventilation air enters, through the stoppings, the gob from the

headgate junction. It removes most of the combustion products, thus eliminating the heat

buildup in zones 1 and 2.

Figure 5.13 shows the temperature contour lines for the 371 to 400 K range.

Based on these graphs, the areas with a potential hot spot are located on the tailgate side

in zone 3 (shaded areas). In this model, the hot spot area in zone 3 is longer than those of

99

for coal oxidation. Sufficient supply of oxygen with very low air velocities ensures a heat

buildup in the gob. The combustion products also affect the concentration profiles. This

effect produces higher oxygen concentrations on the headgate side than those of the

tailgate.

Figure 5.12 shows temperature contours in the gob. The highest temperature is

found in zone 3. The ventilation air enters, through the stoppings, the gob from the

headgate junction. It removes most of the combustion products, thus eliminating the heat

buildup in zones 1 and 2.

Figure 5.13 shows the temperature contour lines for the 371 to 400 K range.

Based on these graphs, the areas with a potential hot spot are located on the tailgate side

in zone 3 (shaded areas). In this model, the hot spot area in zone 3 is longer than those of

I

Air temperature about 293 K (20 ' C)

400

394

389

383

378

372

366

361

355

350

344

3 38

3 33

32 7

\ .. - -- - .. - -- - .. - -- - .. - -- - .. - --- .. - --

322 Bleeder shaft 316

310

305

299

294

288

c.ontours of' Total Temp erature (k )

Air temperature :::: 366 K (93 ' C)

Jan 05. 2008 FLUENT 6.2 (2d . segregated . spe .. ske }

Figure 5.12 Temperature contours for model C

100

400

399

397

396

394

393

391

390

388

387

386

384

383

381

380

378

377

375

374

372

371


Bleeder shaft

-> Potential hot spots ^_ (373 - 3 8 8 K)

Contours of Total Temperature (k) Jan 05. 2008 FLUENT 6.2 (2d. segregated, spe. ske)

Figure 5.13 Potential hot spot locations for model C

models A and B. Zone 3 has higher resistance to airflow, thus providing a favorable

condition for the build-up of combustion products. This figure shows two hot spot areas:

one located near the back corner of the gob (A) and another, larger one, in the mid-area

(B). The irregular thermal patterns shown on the headgate side are caused by the leakage

flow through stoppings. The gob temperature in both areas reached 388 K, i.e., 15 K

greater than the critical temperature.

5.2.2 Bleederless Ventilation System: Model D

Model D replicates model A, but utilizes a bleederless ventilation system. The

gob is about 912 m long, which is approximately one-third of the panel length. The input

parameters are the same as those of model A except the headgate entries are used as

400

399

397

3 96

394

3 93

:391

3,90

,3,88

387

386

384

3 8 3

3 81

380

378

377

375

374

372

371

Bleeder shaft

Zone 3


B

100

Zone 2 Zone I

---------------____ - ,f , I 4

I • f if , • J

I

,, __ ' ___ ' __ f

Contours of Total Temperature (k) Jan 05. 2008 FLUENT 6.2 (2d . segregated. spe . ske)

Figure 5.13 Potential hot spot locations for model C

models A and B. Zone 3 has higher resistance to airflow, thus providing a favorable

condition for the build-up of combustion products. This figure shows two hot spot areas:

one located near the back corner of the gob (A) and another, larger one, in the mid-area

(B). The irregular thermal patterns shown on the headgate side are caused by the leakage

flow through stoppings. The gob temperature in both areas reached 388 K, i.e. , 15 K

greater than the critical temperature.

5.2.2 Bleederless Ventilation System: Model D

Model D replicates model A, but utilizes a bleederless ventilation system. The

gob is about 912 m long, which is approximately one-third of the panel length. The input

parameters are the same as those of model A except the headgate entries are used as

101

• 0O08O

00076

00072

00068

00064

00060

00056

00052

00048

00044

00040

00036

00032

00028

00024

00020

00016

00012

00008

00004

00000


Seal

Velocity Vectors Co lo red By Velocity Magni tude (m/s) Jan 3 1 . 2008

F L U E N T 6.2 (2d, segregated, spe. ske}

Figure 5.14 Velocity vectors in gob for a bleederless system

intakes, and the tailgate entries as returns. The belt entry is still used as an auxiliary

return. The entries inside the face line are sealed completely. However, some ventilation

air is expected to leak through the stoppings and seals.

Figure 5.14 shows the air flow distribution for model D. With a bleederless

system, the ventilation air follows a "U" pattern from headgate (i.e., main and escape

entries) to tailgate. Seals, constructed inside the face, are used to direct the air to the face

(Section 2.2). In practice, a substantial amount of air is lost in the form of leakage

through stoppings and seals. Also, a significant amount of air enters the gob from the

headgate corner and between the shields.

Figure 5.15 shows the oxygen concentration contours in the gob. This

concentration ranges from 0 to 21%. The lowest concentration is found in the center area

and covers the three zones. Areas with higher oxygen concentrations are located near the

gob perimeter. Figure 5.16 shows the temperature contours in the gob. The lowest

101

intakes, and the tailgate entries as returns. The belt entry is still used as an auxiliary

return. The entries inside the face line are sealed completely. However, some ventilation

air is expected to leak through the stoppings and seals.

Figure 5.14 shows the air flow distribution for model D. With a bleederless

system, the ventilation air follows a "U" pattern from headgate (i.e., main and escape

entries) to tailgate. Seals, constructed inside the face, are used to direct the air to the face

(Section 2.2). In practice, a substantial amount of air is lost in the form of leakage

through stoppings and seals. Also, a significant amount of air enters the gob from the

headgate comer and between the shields.

Figure 5.l5 shows the oxygen concentration contours in the gob. This

concentration ranges from 0 to 21 %. The lowest concentration is found in the center area

and covers the three zones. Areas with higher oxygen concentrations are located near the

gob perimeter. Figure 5.16 shows the temperature contours in the gob. The lowest

0 .0008 0

0 .00076

0 .00072

0 .0006 8

0 .00064

0 .0006 0

0 .00056

0 .00052

0 .00048

0 .00044

0 .00040

0 .00036

0 .00032

0 .00028

0 .000 24

0 .000 2 0

0 .00016

0.0001 2

0 .00008

0 .00004

0 .00000

Zone 3

, " .:. '. 'd" :-.~ . .. i • .' · b .

",", . .. .. . . -', ~.

Zone 2

Veloc ity Vec tors C olore d B y V eloc ity M a gnitude (m /s )

Zone 1

Seal

J a n 3 1. 200 8 FLUENT 6 .2 ( 2 d . segre.g a ted . s pe. ske )

Figure 5.14 Velocity vectors in gob for a bleederless system

102

0.21

0.20

0.19

0.18

0.17

0.16

0.15

0.14

0.13

0.12

0.10

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0.00

Zero-oxygen area of oxidation

Zero-oxygen area of low permeability

Contours of V o l u m e fraction of o2 Jan 18. 2008 FLUENT 6.2 (2d, segregated, spe, ske)

Figure 5.15 Oxygen concentration contours for model D

400 395 389 384 379 373 368 363 357 352 347 341 336 330 325 320 314 309 304 298 293

Leakage

Oxidized area (T > 363 K)

Contours of Total Temperature <k) Jan 18. 2008 FLUENT 6.2 (2d. segregated, spe. ske)

Figure 5.16 Temperature contours for model D

0.21

0.20

0 .19

0.18

0 .17

0.16

0.15

0.14

0.13

0.12

0.10

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0.00

Zero-oxygen area of oxidation

Zero-oxygen area of low permeability

102

Contours of Volume fraction of 02 Jan 18. 2008 FLUENT 6.2 (2d. segregated. spe. ske)

400

395

389

384

379

373 368

363

357

352

347

341

336

330 325.

320 Jt4

309

304

298

293

Figure 5.15 Oxygen concentration contours for model D

I I ", , , ,

o· I

~,

C

Oxidized area (T 2: 363 K)

Leakage

Contours of Total Temperature (k ) Jan 18. 2008 FLUENT 6.2 (2d. segregated. spe. ske)

Figure 5.16 Temperature contours for model D

103

400

399

397

396

394

393

391

390

388

387

385

384

382

381

373

378

376

375

373

372

370


Potential hot spot (373 -398 K)

Contours of Total Temperature (k) Jan 19, 200S FLUENT 6.2 (2d. segregated, spe. ske)

Figure 5.17 Potential hot spot location for model D

temperature of about 350 K is found in zone 3. The areas with higher temperatures are

located in zones 1 and 2. Figure 5.17 shows the areas where the gob temperature ranges

between 373 and 400 K. Based on the isotherms of this graph, the area with a potential

hot spot is located along the face line behind the shields (shaded area). The area starts at

the headgate, extends to the inner section, and ends at the tailgate junction in zone 1. It

may also extend to zone 2, depending on its permeability. The gob temperature in this

area reached 398 K (125°C), i.e., 25 K above the critical temperature.

5.3 Preliminary Conclusions

Four gob oxidation models have been simulated in this study: three, labeled as A,

B, and C, represent a longwall mine equipped with a bleeder ventilation system, and one,

103

temperature of about 350 K is found in zone 3. The areas with higher temperatures are

located in zones 1 and 2. Figure 5.17 shows the areas where the gob temperature ranges

between 373 and 400 K. Based on the isotherms of this graph, the area with a potential

hot spot is located along the face line behind the shields (shaded area). The area starts at

the headgate, extends to the inner section, and ends at the tailgate junction in zone 1. It

may also extend to zone 2, depending on its permeability. The gob temperature in this

area reached 398 K (125°C), i.e., 25 K above the critical temperature.

5.3 Preliminary Conclusions

Four gob oxidation models have been simulated in this study: three, labeled as A,

B, and C, represent a 10ngwaU mine equipped with a bleeder ventilation system, and one,

400

399

397

396

394

393

391

390

388

387

385

384

382

381

379

378

376

375

373

372

370

Zone 3 Zone 2

... - - --- - - - -, I _--------

: " ~ I I I I I ~ I I I

, It> \

o I

I I I

, \

I I

, ~~.- ... ... " ,--~~~~~~~~~~~~~-----------------------,

Potential hot spot (373 -398 K)

Zone 1

Contours of Total Temperature (k ) Jan 19 . 2008 FLUENT 6.2 (2d. segregated, spe, ske)

Figure 5.17 Potential hot spot location for model D

104 model D, with bleederless ventilation. The primary difference between these models is

the gob size. The gob length for model A, B, and C are one-third, one-half, and two-

thirds of the panel length, respectively. Model D practically replicates model A except for

its ventilation, which was replaced by a bleederless system. These models were

formulated to investigate the effect of longwall mining on the hot spot location.

An evaluation of the simulations results shows that in a longwall mine vented by a

bleeder ventilation system, the hot spot will start and develop in the consolidated zone

near the return side of the gob. Model A shows a potential hot spot location at the back of

the gob near the bleeder shaft (zone 3). In model B, the hot spot is still located in zone 3.

However, as the panel becomes longer, the size of the hot spot increases, covering a

larger area along the tailgate (parallel to the x-axis). In addition, model B reveals that a

new hot spot area would develop at the tailgate side near the face in zone 2. Model C

shows two separated hot spots. This is the effect of air leakage on the gob from the

headgate stoppings. In model C, one tailgate entry is used as an auxiliary intake. This

modification successfully eliminates the heat buildup at the comer of tailgate junction. In

summary, with a bleeder ventilation system, the hot spot will start and develop in zone 3

along the tailgate. Further, with this system, the leakage flow will always play an

important role in the development of a hot spot.

When the longwall mine is ventilated by a bleederless ventilation system, the

development of a hot spot is governed by the supply of oxygen. Due to the lack of

oxygen, the chance for the development of a hot spot in zone 3 is practically nil.

However, by using this model, the hot spot may still occur near the face, behind the

shields where the oxygen is present. The leakage flow, characterized by its low velocity,

104

model D, with bleederless ventilation. The primary difference between these models is

the gob size. The gob length for model A, B, and C are one-third, one-half, and two

thirds of the panel length, respectively. Model D practically replicates model A except for

its ventilation, which was replaced by a bleederless system. These models were

formulated to investigate the effect of longwall mining on the hot spot location.

An evaluation of the simulations results shows that in a longwall mine vented by a

bleeder ventilation system, the hot spot will start and develop in the consolidated zone

near the return side of the gob. Model A shows a potential hot spot location at the back of

the gob near the bleeder shaft (zone 3). In model B, the hot spot is still located in zone 3.

However, as the panel becomes longer, the size of the hot spot increases, covering a

larger area along the tailgate (parallel to the x-axis). In addition, model B reveals that a

new hot spot area would develop at the tailgate side near the face in zone 2. Model C

shows two separated hot spots. This is the effect of air leakage on the gob from the

headgate stoppings. In model C, one tailgate entry is used as an auxiliary intake. This

modification successfully eliminates the heat buildup at the comer of tailgate junction. In

summary, with a bleeder ventilation system, the hot spot will start and develop in zone 3

along the tailgate. Further, with this system, the leakage flow will always play an

important role in the development of a hot spot.

When the longwall mine is ventilated by a bleederless ventilation system, the

development of a hot spot is governed by the supply of oxygen. Due to the lack of

oxygen, the chance for the development of a hot spot in zone 3 is practically nil.

However, by using this model, the hot spot may still occur near the face, behind the

shields where the oxygen is present. The leakage flow, characterized by its low velocity,

enhances the hot spot development because this is sufficient for coal oxidation but not

enough to remove the generated heat. The leakage air can permeate further into zones 2

and 3, depending on the fan pressure and gob permeability. The simulation exercises

have shown that the hot spot can extend all the way to zone 2 and result in a larger hot

spot area than those found in gobs vented with bleeder ventilation systems.

105

enhances the hot spot development because this is sufficient for coal oxidation but not

enough to remove the generated heat. The leakage air can permeate further into zones 2

and 3, depending on the fan pressure and gob permeability. The simulation exercises

have shown that the hot spot can extend all the way to zone 2 and result in a larger hot

spot area than those found in gobs vented with bleeder ventilation systems.

CHAPTER 6

DISCUSSION OF GOB SIMULATION STUDIES

This chapter discusses the initial condition and results of four gob models. These

discussions are aimed at understanding the capabilities of the physical and CFD models,

and limitations encountered. This chapter also discusses the results of parametric studies

that were conducted to analyze the effect of gob dimensions and permeability changes on

hot spot location. These were performed to determine the behavior of the hot spot under

different schemes of gob width and permeability. Finally, two spontaneous combustion

control methods were evaluated: (1) pressurized air, and (2) inert gas injections through

vertical and horizontal boreholes. Based on these studies, it is found that potential hot

spot location strongly depends on gob characteristics and ventilation methods.

6.1 Physical Model

6.1.1 Limitations

The laboratory model was built based on the best available information. This was

intended to closely represent the real condition that is simulated. However, some

limitations still exist due to the model's fixed dimensions. These limitations restrict the

model for a wide range of airflow simulations and permeability tests.

CHAPTER 6

DISCUSSION OF GOB SIMULATION STUDIES

This chapter discusses the initial condition and results of four gob models. These

discussions are aimed at understanding the capabilities of the physical and CFD models,

and limitations encountered. This chapter also discusses the results of parametric studies

that were conducted to analyze the effect of gob dimensions and permeability changes on

hot spot location. These were performed to determine the behavior of the hot spot under

different schemes of gob width and permeability. Finally, two spontaneous combustion

control methods were evaluated: (1) pressurized air, and (2) inert gas injections through

vertical and horizontal boreholes. Based on these studies, it is found that potential hot

spot location strongly depends on gob characteristics and ventilation methods.

6.1 Physical Model

6.1.1 Limitations

The laboratory model was built based on the best available information. This was

intended to closely represent the real condition that is simulated. However, some

limitations still exist due to the model's fixed dimensions. These limitations restrict the

model for a wide range of airflow simulations and permeability tests.

107 First, the limitation is due to a fixed diameter of permeameter. According to the

ASTM D2434 standard, for a container with a diameter of 14 cm, the largest particle size

to be tested is 9.71 mm (Vs times container diameter). This restricts the permeability test

for particle sizes larger than 9.71 mm diameter. However, the CFD model can be used to

perform permeability tests for larger particles (Section 6.1.3).

Second, limitation is due to a laminar flow condition. This condition is associated

with physical model in permeability test. This limits the fluid flow rate through porous

media because a turbulence flow is established by a high flow rate. The laminar condition

can be tested by two methods: Darcy's law and Reynolds Number. With Darcy's law, the

pressure difference through a porous medium is directly proportional to the flow rate

(Equation 2.8). A data plot of airflow rates on one axis and pressure differences on the

other yields a linear relationship (Figure 3.4).

Reynolds Number can also be used to test the laminar flow condition. This

number depends on the fluid velocity, duct diameter, and viscosity. For laminar flow, the

number is usually less than 2,000 (McPherson, 1993; Hartman et al., 1997). All test

results presented in this study have been verified using the Reynolds Number (Equation

4.8). Another way to test the laminar condition is by using Fluent. Figure 4.12 shows an

example of Fluent output showing the Reynolds Number for the physical model. This

graph shows a maximum Reynolds Number of 115 for fluid flow through porous media.

The diameter of the permeameter and fluid flow conditions are limitations, but

fundamental, for permeability tests. The validity of these tests determines the validity of

CFD simulations. Examination of such limitations is strongly recommended. In some

cases, while the laboratory model is limited by the physical dimension of the duct work,

107

First, the limitation is due to a fixed diameter of permeameter. According to the

ASTM D2434 standard, for a container with a diameter of 14 cm, the largest particle size

to be tested is 9.71 mm (Ys times container diameter). This restricts the permeability test

for particle sizes larger than 9.71 mm diameter. However, the CFD model can be used to

perform permeability tests for larger particles (Section 6.1.3).

Second, limitation is due to a laminar flow condition. This condition is associated

with physical model in permeability test. This limits the fluid flow rate through porous

media because a turbulence flow is established by a high flow rate. The laminar condition

can be tested by two methods: Darcy's law and Reynolds Number. With Darcy's law, the

pressure difference through a porous medium is directly proportional to the flow rate

(Equation 2.8). A data plot of airflow rates on one axis and pressure differences on the

other yields a linear relationship (Figure 3.4).

Reynolds Number can also be used to test the laminar flow condition. This

number depends on the fluid velocity, duct diameter, and viscosity. For laminar flow, the

number is usually less than 2,000 (McPherson, 1993; Hartman et a1., 1997). All test

results presented in this study have been verified using the Reynolds Number (Equation

4.8). Another way to test the laminar condition is by using Fluent. Figure 4.12 shows an

example of Fluent output showing the Reynolds Number for the physical mode1. This

graph shows a maximum Reynolds Number of 115 for fluid flow through porous media.

The diameter of the permeameter and fluid flow conditions are limitations, but

fundamental, for permeability tests. The validity of these tests determines the validity of

CFD simulations. Examination of such limitations is strongly recommended. In some

cases, while the laboratory model is limited by the physical dimension of the duct work,

108

6.1.2 Fluid Effects on Permeability

Two fluids were available for this study: water and air; both were selected for the

physical laboratory tests. For CFD modeling, air was chosen as the fluid for the primary

phase. The water-based tests were performed for comparison purposes only. Water-based

and air-based tests were described in Chapter 3, and their corresponding results

summarized in Tables 3.1 and 3.2, respectively.

Figure 6.1 shows the results of both air-based and water-based tests. This figure

shows that the sample permeability for the air-based test is consistently higher than that

1.60E-08

1.40E-08

1.20E-08

g 1.00E-08 m

fj 8.00E-09

6.00E-09

1 4.00E-09

2.00E-09

0.00E+00

y*7.79E«07x + 6 . 4 2 E « 0 9 ~ ^ R 2 - 0.837 _

L22E-07X + 3.14E-09 R2» 0 J 2 5 " * "

A Air-based test • g^ater-based test

0.002 0.004 0.006 0.008 0.01 Particle mean size (m)

0.012

Figure 6.1 Fluid effects on rock sample permeability

the CFD model is practically unrestricted. Permeability test with large permeameter can

be simulated easily in CFD.

108

the CFD model is practically unrestricted. Permeability test with large permeameter can

be simulated easily in CFD.

6.1.2 Fluid Effects on Permeability

Two fluids were available for this study: water and air; both were selected for the

physical laboratory tests. For CFD modeling, air was chosen as the fluid for the primary

phase. The water-based tests were performed for comparison purposes only. Water-based

and air-based tests were described in Chapter 3, and their corresponding results

summarized in Tables 3.1 and 3.2, respectively.

Figure 6.1 shows the results of both air-based and water-based tests. This figure

shows that the sample permeability for the air-based test is consistently higher than that

N E

L60E-OB

L40E-OB

:;:; L20E-OB

~ :s LOOE-OB n:I <II

E B_00E-09

[-.~~~:~~~-'~~===~~::~~~~=".~-=~=~::::~::~~~=:-~:-'::~~::.---- :--:~~~~~---:-----y ::: 7.79E-07x + 6.42E-09

____ , ____ R.:.=.2;§37., _________ , _~'-"--.. '-.--""' __ -

,-------_ ... -

i ~ 6,00E-09 +-------.:::;;!I!;;...c::'------------------i 'u ~

\1'1 4.00E-09 = B.22E-07x + 3.14E-09

"'·"""---·-··~w_=:tf.'915····"-···"·"·-·-·----"-·--··-··"-·'··.---.. --..•... " .•. - .•. -.... "-,,~ ... ,. . ..... , ..•• ---""--'"

2.00E-09 fj, Air-based test • lWIater-based test

O.OOE+OO -l----".-----........,.------,----'-r-----r------i

o 0.002 0.004 0.006 0.008 0,01 0 .012 Particle mean size (m)

----------"""----,-------------_._---------------------------

Figure 6.1 Fluid effects on rock sample permeability

109

of the water-based test (approximately 2.6 times). As an example, for the same material

and particle size (5.74 mm) but different fluids, the permeability tests resulted in the

o 9 2

following specific permeabilities: 1.117 x 10" (air-based) and 7.49 x 10" m (water-

based).

For comparison purposes, the permeabilities obtained using water-based tests

have been adjusted using air properties (i.e., viscosity and specific weight). The variation

of permeability with the type of fluid was reported by other investigations (Klinkenberg,

1941; Scheidegger, 1957; Bear, 1972). Scheidegger suggested applying a factor to correct

permeability measurements carried out using different fluids. This approach is best

explained by the slip phenomenon or Klinkenberg effect (Klinkenberg, 1941). Laminar

flow condition assumes no friction at the solid-fluid interface. In practice, however,

friction may occur in microscopic scale. This phenomenon depends on many factors,

including the fluid viscosity. High viscosity produces more shears at the solid-fluid

interface, thus increasing the path resistance to fluid flow. The effect of this phenomenon

on permeability could be avoided by using air as the circulating fluid.

For CFD simulations, air was used as the primary phase, similar to that used in

mine ventilation. Air properties were determined based on a ventilation survey (Miles

and Calizaya, 2006). These properties include barometric pressure, dry-bulb, and wet-

bulb temperatures. Other properties needed for CFD modeling were retrieved from

ventilation references (McPherson, 1993; Hartman et al., 2002). Some of these properties

are constant while the others are functions of temperature. In Fluent, these are adjusted

using polynomial equations (www.fluent.com).

109

of the water-based test (approximately 2.6 times). As an example, for the same material

and particle size (5.74 mm) but different fluids, the permeability tests resulted in the

following specific permeabilities: 1.117 x 10-8 (air-based) and 7.49 x 10-9 m2 (water

based).

For comparison purposes, the permeabilities obtained using water-based tests

have been adjusted using air properties (i.e., viscosity and specific weight). The variation

of permeability with the type of fluid was reported by other investigations (Klinkenberg,

1941; Scheidegger, 1957; Bear, 1972). Scheidegger suggested applying a factor to correct

permeability measurements carried out using different fluids. This approach is best

explained by the slip phenomenon or Klinkenberg effect (Klinkenberg, 1941). Laminar

flow condition assumes no friction at the solid-fluid interface. In practice, however,

friction may occur in microscopic scale. This phenomenon depends on many factors,

including the fluid viscosity. High viscosity produces more shears at the solid-fluid

interface, thus increasing the path resistance to fluid flow. The effect of this phenomenon

on permeability could be avoided by using air as the circulating fluid.

For CFD simulations, air was used as the primary phase, similar to that used in

mine ventilation. Air properties were determined based on a ventilation survey (Miles

and Calizaya, 2006). These properties include barometric pressure, dry-bulb, and wet

bulb temperatures. Other properties needed for CFD modeling were retrieved from

ventilation references (McPherson, 1993; Hartman et aI., 2002). Some of these properties

are constant while the others are functions of temperature. In Fluent, these are adjusted

using polynomial equations (www.fluent.com).

http://www.fluent.com

110

6.1.3 Permeability - Particle Size Relationship

The rock samples were divided into 4 groups by sieving. The maximum mean size

tested with the physical model was 9.71 mm. Twelve out of 36 air-based tests were

considered for this study. Each size group was tested three times with three different

sample lengths (Table 3.2). The results of these tests indicate that the larger the particle

size, the higher the permeability. Figure 3.7 shows the results of four air-based tests.

These results show a linear trend between permeability and particle size. However, lack

of information on permeability for larger particles weakens this conclusion. More

experiments with larger particle size are needed to check the validity of this relationship.

Since the dimensions of the physical model were fixed, then Fluent was used to simulate

experiments for larger particles. The permeameter in CFD model was enlarged to the

allowable diameter (i.e., 3 times larger than the diameter of the physical model). This

increased diameter allowed tests for larger particles of up to 53 mm.

Figures 6.2 and 6.3 show the velocity and pressure profiles, respectively, for the

simulation model using 50-mm particles. The values of velocity and pressure drop in the

permeameter are used to determine the specific permeability using Darcy's law. This

value also needs to be corrected by factor 0.898 (Equation 4.9). The experiment was

repeated for two smaller particle sizes (20 and 30 mm). With these additional tests, there

were 7 size groups available. Figure 6.4 shows the permeability - particle size

relationship for these experiments. The first four experiments yield the linear equation of

k = (8 x 10"07) (dm) + (6 x 10"0 9). The k and dm are the specific permeability and particle

mean size, respectively. Using three additional tests, the graph is represented by a second

power function as follows:

110

6.1.3 Permeability - Particle Size Relationship

The rock samples were divided into 4 groups by sieving. The maximum mean size

tested with the physical model was 9.71 mm. Twelve out of36 air-based tests were

considered for this study. Each size group was tested three times with three different

sample lengths (Table 3.2). The results of these tests indicate that the larger the particle

size, the higher the permeability. Figure 3.7 shows the results of four air-based tests.

These results show a linear trend between permeability and particle size. However, lack

of information on permeability for larger particles weakens this conclusion. More

experiments with larger particle size are needed to check the validity of this relationship.

Since the dimensions of the physical model were fixed, then Fluent was used to simulate

experiments for larger particles. The permeameter in CFD model was enlarged to the

allowable diameter (i.e., 3 times larger than the diameter of the physical model). This

increased diameter allowed tests for larger particles of up to 53 mm.

Figures 6.2 and 6.3 show the velocity and pressure profiles, respectively, for the

simulation model using 50-mm particles. The values of velocity and pressure drop in the

permeameter are used to determine the specific permeability using Darcy's law. This

value also needs to be corrected by factor 0.898 (Equation 4.9). The experiment was

repeated for two smaller particle sizes (20 and 30 mm). With these additional tests, there

were 7 size groups available. Figure 6.4 shows the permeability - particle size

relationship for these experiments. The first four experiments yield the linear equation of

k = (8 x 10-07) (dm) + (6 x 10-09

). The k and dm are the specific permeability and particle

mean size, respectively. Using three additional tests, the graph is represented by a second

power function as follows:

I l l

Contours of Velocity Magnitude (m/s) Feb 04, 2008 FLUENT 6.2 (2d, segregated, ske)

Figure 6.2 Velocity contours through the extended permeameter

Contours of Total Pressure (pascal) Feb 04, 2008 FLUENT 6.2 (2d, segregated, ske)

Figure 6.3 Pressure contours through the extended permeameter

21

20

19 18

17

16

15 14 13

12

11

10

9

8

6

5

4

3

2

o

Contours of Velocity Magnitude (m/s)

111

Feb 04, 2008 FLUENT 6.2 (2d, segregated, ske)

Figure 6.2 Velocity contours through the extended permeameter

1295 1231 1167 1102

1038

974 910

845

781 717 653

588

524 460 396

331 267 203

139 74 10

Contours of Total Pressure (pascal) Feb 04, 2008 FLUENT 6.2 (2d, segregated, ske)

Figure 6.3 Pressure contours through the extended permeameter

112

1.4E-07

0.06


Figure 6.4 Particle size effect on broken rock sample permeability

k= [(2 x 10~5) (dm

2)] + [(1 x 10- 6) (dm)} + (6 x 10-1 0) (6.1)

This finding agrees with the Kozeny-Carman relationship (Equation 2.7) and Fair

and Hatch (1993) formula. This nonlinear relationship is used to obtain various

permeabilities as a function of particle size.


6.2.1 Limitations

In Chapter 5, the versatility of Fluent in solving fluid flow and heat transfer

problems has been demonstrated. However, simulation studies using Fluent are limited

by the processing time and the computer storage capacity. These can be overcome by

112

1.4E-07

1.2E-07 ............................ -... _.

y = 2E-OS,t + 1 E-06x + 6E-1 0 -N R2 = 0.9993 .§. 1.0E-07

~

~ 8.0E-08

:c l'O Q)

E 6.0E-08

8. c.J

4 .0E-08 Ii=

.~ If) 2.0E-08

O.OE+OO

0 0.01 0 .02 0.03 0.04 0.05 0.06


Figure 6.4 Particle size effect on broken rock sample permeability

(6.1)

This finding agrees with the Kozeny-Carrnan relationship (Equation 2.7) and Fair

and Hatch (1993) formula. This nonlinear relationship is used to obtain various

permeabilities as a function of particle size.


6.2.1 Limitations

In Chapter 5, the versatility of Fluent in solving fluid flow and heat transfer

problems has been demonstrated. However, simulation studies using Fluent are limited

by the processing time and the computer storage capacity. These can be overcome by

113

applying simulation control routines. These routines include utilization of two-

dimensional, scaled-down, and coarser-mesh models.

Two-dimensional models were used in this study. The models were constructed as

simple as possible without sacrificing the basic element of a longwall mine. A 2D model

requires less processing time than a 3D model. In one case (Section 5.2.1.1), the

following processing times were recorded: using a 3D longwall gob model, the

calculations converged after 72 hours; using a 2D model for the same geometry (with

125k elements) nearly the same results were obtained in 24 hours (66% reduction).

Utilizing a scaled-down model can also reduce the processing time. However, in this

study, scaling down a 3D model does not reduce the processing time significantly. For

the previous case, when the 3D model was scaled down by half, the processing time

decreased from 72 hours to 48 hours (33% reduction).

In some cases, meshing a 3D model could be more difficult than meshing a 2D

model. For example meshing injection points in a 2D model only requires defining a few

parameters (meshing type and spacing) whereas in 3D, this step requires creating and

defining new volumes and faces, thus increasing the processing time both in Gambit

(additional geometry) and Fluent (additional number of iterations). The results from both

models are about the same except for the buoyancy effect.

The computer storage capacity can also restrict the simulation. A 3D model

requires more CPU capacity than a 2D model. In one experimental, using a 3D model, the

simulator crashed due to the computer shortage capacity. For these reasons, only 2D

models were used in this study.

applying simulation control routines. These routines include utilization of two

dimensional, scaled-down, and coarser-mesh models.

113

Two-dimensional models were used in this study. The models were constructed as

simple as possible without sacrificing the basic element of a longwall mine. A 2D model

requires less processing time than a 3D model. In one case (Section 5.2.1.1), the

following processing times were recorded: using a 3D longwall gob model, the

calculations converged after 72 hours; using a 2D model for the same geometry (with

125k elements) nearly the same results were obtained in 24 hours (66% reduction).

Utilizing a scaled-down model can also reduce the processing time. However, in this

study, scaling down a 3D model does not reduce the processing time significantly. For

the previous case, when the 3D model was scaled down by half, the processing time

decreased from 72 hours to 48 hours (33% reduction).

In some cases, meshing a 3D model could be more difficult than meshing a 2D

model. For example meshing injection points in a 2D model only requires defining a few

parameters (meshing type and spacing) whereas in 3D, this step requires creating and

defining new volumes and faces, thus increasing the processing time both in Gambit

(additional geometry) and Fluent (additional number of iterations). The results from both

models are about the same except for the buoyancy effect.

The computer storage capacity can also restrict the simulation. A 3D model

requires more CPU capacity than a 2D model. In one experimental, using a 3D model, the

simulator crashed due to the computer shortage capacity. For these reasons, only 2D

models were used in this study.

114

6.2.2 Hot Spot Locations

In CFD modeling, hot spots are determined based on two parameters: (1) gob

temperature and (2) oxygen concentration. These parameters ensure a thermal run-away

state that sustains a self-heating process. This section discusses the formation of hot spots

in a longwall mine gob for 3 different gob lengths. The simulated conditions are:

1. Gob length is lA of panel length (models A and D).

2. Gob length is lA of panel length (model B).

3. Gob length is 2A of panel length (model C).

Models A, B, and C are ventilated using a bleeder system and model D using a

bleederless system. Table 6.1 summarizes the conditions and the results achieved.

Detailed discussions of these results, including the effect of gob length on hot spot

location, are presented in the following sections.

6.2.2.1 Effect of Gob Length

Models A, B, and C were used to evaluate the effect of gob length on the hot spot

location. All other parameters, including permeability and coal properties, were kept

constant. These models were executed under the same ventilation conditions and their

results evaluated against pre-established standards of oxygen concentration (> 5%) and

gob temperature (>100°C).

In model A, the oxygen concentration varied between 11 and 21%. This range

covered almost the whole gob area. In all 3 cases, the highest concentration of 2 1 % was

found only on the headgate side near the gob perimeter. In models B and C, the oxygen

concentration in some spots of the tailgate side dropped to below 11%. The consolidation

114

6.2.2 Hot Spot Locations

In CFD modeling, hot spots are determined based on two parameters: (1) gob

temperature and (2) oxygen concentration. These parameters ensure a thermal run-away

state that sustains a self-heating process. This section discusses the formation of hot spots

in a longwall mine gob for 3 different gob lengths. The simulated conditions are:

1. Gob length is Y3 of panel length (models A and D).

2. Gob length is Yz of panel length (model B).

3. Gob length is 73 of panel length (model C).

Models A, B, and C are ventilated using a bleeder system and model D using a

bleederless system. Table 6.1 summarizes the conditions and the results achieved.

Detailed discussions of these results, including the effect of gob length on hot spot

location, are presented in the following sections.

6.2.2.1 Effect of Gob Length

Models A, B, and C were used to evaluate the effect of gob length on the hot spot

location. All other parameters, including permeability and coal properties, were kept

constant. These models were executed under the same ventilation conditions and their

results evaluated against pre-established standards of oxygen concentration (> 5%) and

gob temperature (> 100°C).

In model A, the oxygen concentration varied between 11 and 21 %. This range

covered almost the whole gob area. In all 3 cases, the highest concentration of 21 % was

found only on the head gate side near the gob perimeter. In models Band C, the oxygen

concentration in some spots of the tailgate side dropped to below 11 %. The consolidation

Table 6.1 Summary of hot spot locations - Models A through D

Model Ventilation

System Gob Length (m)

Oxygen Concentration

(%)

Hot Spot Temperature

(K) Hot Spot Location Location Schematic

Bleeder 912 (1/3 of panel length)

1 1 - 2 1 373 - 3 8 5

One hot spot. Zone 3; At the back corner of the gob on tailgate side, near the bleeder shaft.

B Bleeder 1,524

(1/2 of the panel length) 5 - 2 1 373 - 3 8 6

Two hot spots. Zone 2; On return side, near the tailgate corner Zone 3; Similar to model A, hot spot is elongated along the tailgate side.

C Bleeder 2,445

(2/3 of the panel length) - 2 1 373 - 3 8 8

Two hot spots. Zone 3; Both hot spots located along the tailgate end.

D Bleederless (U-type)

912 (1/3 of panel length)

0 - 2 1 398

One hot spot. Zone 1; Hot spot located near the headgate-face intersection It may extends further into zone 2 (gob center)

- —^"

on

Table 6.1 Summary of hot spot locations - Models A through D

Ventilation Oxygen Hot Spot

Model System

Gob Length (m) Concentration Temperature Hot Spot Location Location Schematic (%) (K)

One hot spot.

A Bleeder 912 11 - 21 373 - 385

Zone 3; At the back comer of the (1 /3 of panel length) gob on tailgate side, near the bleeder

shaft.

Two hot spots.

1,524 Zone 2; On return side, near the

B Bleeder 5 - 21 373 - 386 tailgate comer (1 /2 of the panel length)

Zone 3; Similar to model A, hot spot is elongated along the tailgate side.

Two hot spots. 2,445

C Bleeder (2/3 of the panel length)

3 - 21 373 - 388 Zone 3; Both hot spots located along the tailgate end.

One hot spot.

Bleederless 912 Zone 1; Hot spot located near the

D 0 - 21 373 - 398 head gate-face intersection It (U-type) (1 /3 of panel length)

may extends further into zone 2 (gob center)

--VI

116 of the gob material is the main reason for this reduction. The consolidated area (zone 3)

in model A covered almost 25% of gob area, in model B about 30%, and in model C

more than 50%. This effect is mainly due to the compaction of the overlying strata as the

gob length increases over time. These factors of compaction and panel length changed the

airflow pattern and reduced the amount of air passing through the gob, creating favorable

conditions for the development of hot spots. In all simulation cases, the potential hot

spots were found in the consolidated area (zone 3).

In summary, a comparison of the simulation results (models A, B, and C) shows

that the gob length strongly affects the hot spot size. The potential hot spot location is

influenced by the consolidated area; the larger the consolidated area, the larger the size of

the hot spot. Therefore, from a fire hazard point of view, the worst case scenario is likely

to occur when mining approaches the end of the panel.

6.2.2.2 Effect of Ventilation System

Models A and D were used to evaluate the effect of ventilation on the hot spot

location. Two common ventilation systems were simulated: a bleeder system (model A)

and a bleederless system (model D). The same ventilation and gob characteristics were

used in both models. In the bleeder system, the gob was ventilated with fresh air from

bleeder entries. In the bleederless system, the bleeder entries were blocked by means of

seals and stoppings. This practice reduced the airflow rate to the gob considerably. With

these changes, the airflow patterns in both models (A and D) were completely different

(Figures 5.4 and 5.14).

] 16

of the gob material is the main reason for this reduction. The consolidated area (zone 3)

in model A covered almost 25% of gob area, in model B about 30%, and in model C

more than 50%. This effect is mainly due to the compaction of the overlying strata as the

gob length increases over time. These factors of compaction and panel length changed the

airflow pattern and reduced the amount of air passing through the gob, creating favorable

conditions for the development of hot spots. In all simulation cases, the potential hot

spots were found in the consolidated area (zone 3).

In summary, a comparison of the simulation results (models A, B, and C) shows

that the gob length strongly affects the hot spot size. The potential hot spot location is

influenced by the consolidated area; the larger the consolidated area, the larger the size of

the hot spot. Therefore, from a fire hazard point of view, the worst case scenario is likely

to occur when mining approaches the end of the panel.

6.2.2.2 Effect of Ventilation System

Models A and D were used to evaluate the effect of ventilation on the hot spot

location. Two common ventilation systems were simulated: a bleeder system (model A)

and a bleederless system (model D). The same ventilation and gob characteristics were

used in both models. In the bleeder system, the gob was ventilated with fresh air from

bleeder entries. In the bleederless system, the bleeder entries were blocked by means of

seals and stoppings. This practice reduced the airflow rate to the gob considerably. With

these changes, the airflow patterns in both models (A and D) were completely different

(Figures 5.4 and 5.14).

117 Since model A utilizes a bleeder system, part of the fresh air is allowed to

percolate through the gob. Under this condition, gob permeability plays an important role

in determining the airflow distribution. This is a critical factor for the development of hot

spots. In this model, the hot spot is found in zone 3 at the tailgate side. On one hand, the

air leaking eliminates the heat buildup in the gob by flushing the combustion products to

the tailgate side (Figure 5.6). On the other hand, this air, due to its low velocity (caused

by low gob permeability), contributes to the generation of the hot spot. As a result, the

oxidation heat will build up in zone 3.

Contrary to the sources of heat buildup considered in model A, the main factor for

the formation of hot spot in model D is the leakage flow rate. With the bleederless

ventilation system, the gob is isolated from ventilation air paths. The main source of

oxygen is the leakage air from the face line. Therefore, the gob is almost free of oxygen

except the area behind the shields (Figure 5.15). This also affects the temperature field in

the gob.

An evaluation on Figures 5.15 and 5.16 shows that the low-oxygen areas in zones

1 and 2 are due to intense oxidation, whereas those in zone 3 are due to low gob

permeability. This finding shows that the hot spot location shifts from zone 3, in model

A, to zones 1 and 2, in model D. Since the leakage along the face line is unavoidable in a

bledeerless system, the hot spot is likely to develop throughout the life of the panel. Once

the self-heating process is initiated, it may extend further into the gob depending on its

permeability.

From this comparison, it is concluded that ventilation system plays an important

role in the development and breakup of hot spot in the gob. The bleederless ventilation

117

Since model A utilizes a bleeder system, part of the fresh air is allowed to

percolate through the gob. Under this condition, gob permeability plays an important role

in determining the airflow distribution. This is a critical factor for the development of hot

spots. In this model, the hot spot is found in zone 3 at the tailgate side. On one hand, the

air leaking eliminates the heat buildup in the gob by flushing the combustion products to

the tailgate side (Figure 5.6). On the other hand, this air, due to its low velocity (caused

by low gob permeability), contributes to the generation of the hot spot. As a result, the

oxidation heat will build up in zone 3.

Contrary to the sources of heat buildup considered in model A, the main factor for

the formation of hot spot in model D is the leakage flow rate. With the bleederless

ventilation system, the gob is isolated from ventilation air paths. The main source of

oxygen is the leakage air from the face line. Therefore, the gob is almost free of oxygen

except the area behind the shields (Figure 5.15). This also affects the temperature field in

the gob.

An evaluation on Figures 5.15 and 5.16 shows that the low-oxygen areas in zones

1 and 2 are due to intense oxidation, whereas those in zone 3 are due to low gob

permeability. This finding shows that the hot spot location shifts from zone 3, in model

A, to zones 1 and 2, in model D. Since the leakage along the face line is unavoidable in a

bledeerless system, the hot spot is likely to develop throughout the life of the panel. Once

the self-heating process is initiated, it may extend further into the gob depending on its

permeability.

From this comparison, it is concluded that ventilation system plays an important

role in the development and breakup of hot spot in the gob. The bleederless ventilation

system shifts the hot spot location from the consolidated area, in a bleeder system, to the

unconsolidated area. In a bleeder system, the critical condition for hot spot development

is determined by the gob characteristics, whereas in the bleederless system, this is

determined by the leakage air flow rate. In both cases, it is found that the air velocity in

hot spot areas ranges between 2 x 10"4 and 1x10" m/s. Another important finding is that

using a bleeder system to eliminate the heat buildup, the leakage flow rate to the gob

should be maximized. With a bleederless system, this rate should be minimized to

prevent oxidation.

6.2.3 Effect of Permeability on Hot Spot Formation

Gob permeability is one of the key parameters for the development of hot spots.

Studies on gob permeability have shown mixed results about this parameter. The main

reason for the uncertainty is the inaccessibility of the gob.

This section introduces a parametric study to investigate the effect of permeability

on the hot spot generation. The parameter of interest is the particle size. To investigate

their effect, the input parameters of model A (base case) were modified to reflect three

particle sizes: 0.122, 0.02, and 0.006 m, respectively. Table 6.2 shows the summary of

Table 6.2 Specific permeabilities used for parametric studies

Gob zone

Case 1 (higher, m 2 )

Base Case/Model A (initial, m 2 )

Case 2 (lower, m2) Zone Condition

1 2.10 x 10"5 4.68 x 10"7 1.26 x 10~8 Unconsolidated

2 3.06 x 10"7 3.15 x 10"8 1.62 x 10 "9 Semiconsolidated

3 1.26 x 10"8 7.98 x 10"9 7.02 x lO- 1 0 Consolidated

118

system shifts the hot spot location from the consolidated area, in a bleeder system, to the

unconsolidated area. In a bleeder system, the critical condition for hot spot development

is determined by the gob characteristics, whereas in the bleederless system, this is

determined by the leakage air flow rate. In both cases, it is found that the air velocity in

hot spot areas ranges between 2 x 10-4 and 1 x 10-3 m/s. Another important finding is that

using a bleeder system to eliminate the heat buildup, the leakage flow rate to the gob

should be maximized. With a bleederless system, this rate should be minimized to

prevent oxidation.

6.2.3 Effect of Permeability on Hot Spot Formation

Gob permeability is one of the key parameters for the development of hot spots.

Studies on gob permeability have shown mixed results about this parameter. The main

reason for the uncertainty is the inaccessibility of the gob.

This section introduces a parametric study to investigate the effect of permeability

on the hot spot generation. The parameter of interest is the particle size. To investigate

their effect, the input parameters of model A (base case) were modified to reflect three

particle sizes: 0.122, 0.02, and 0.006 m, respectively. Table 6.2 shows the summary of

Table 6.2 Specific permeabilities used for parametric studies

Gob Case 1 Base CaselModel A Case 2 Zone Condition

zone (higher, m2 ) (initial, m2

) (lower, m2)

1 2.10 x 10-5 4.68 X 10-7 1.26 X 10-8 Unconsolidated

2 3.06 x 10-7 3.15 X 10-8 1.62 x 10-9 Semi consolidated

3 1.26 x 10-8 7.98 X 10-9 7.02 X 10-10 Consolidated

119

0.21 0.20 0.19 0.18 0.17 0.16 0.15 0.14 0.13 0 .12 0.11 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0.00

Zone 3

Bleeder shaft

Zone 2 Zone 1

Contours of V o l u m e fraction of o2 Feb 03, 2008 FLUENT 6.2 (2d. segregated, spe, ske)

Figure 6.5 Oxygen concentration contours for case 1

permeability values for three cases: higher (case 1), base case, and lower (case 2). Figure

6.5 shows the oxygen concentration contours for case 1. The oxygen concentration in gob

ranges from 14 to 21%. In model A, this ranged from 11 to 21%. The higher specific

permeability increased the oxygen level in the gob mainly because it increased the

airflow rate through the gob. The lowest oxygen concentration was found near the

combustible particle injection points, particularly in zones 2 and 3. Although abundant

supply of oxygen may ensure coal oxidation, the temperature contours (Figure 6.6) do not

picture any heat buildup, as shown in model A (Figure 5.7). The ventilation air carried

away most of the heat and combustion products so that the maximum gob temperature

decreased in relation to the base case (369 versus 385 K). This declining trend is expected

to continue for any model with higher permeability values. In case 2 (with lower

119

permeability values for three cases: higher (case 1), base case, and lower (case 2). Figure

6.5 shows the oxygen concentration contours for case 1. The oxygen concentration in gob

ranges from 14 to 21 %. In model A, this ranged from 11 to 21 %. The higher specific

permeability increased the oxygen level in the gob mainly because it increased the

airflow rate through the gob. The lowest oxygen concentration was found near the

combustible particle injection points, particularly in zones 2 and 3. Although abundant

supply of oxygen may ensure coal oxidation, the temperature contours (Figure 6.6) do not

picture any heat buildup, as shown in model A (Figure 5.7). The ventilation air carried

away most of the heat and combustion products so that the maximum gob temperature

decreased in relation to the base case (369 versus 385 K). This declining trend is expected

to continue for any model with higher permeability values. In case 2 (with lower

0. .2 1

0. .20.

0. .19

0. .18

0. . 17

0. .16

0..15

0. .14

0..13

0. .12

0. .11

0..0.9

0. .0.8

0. .0.7

0..0.6

0. .0.5

0..0.4

0. .0.3

0..0.2

0..0.1

0. .0.0.

Zone 3

Bleeder shaft

Contours 01 Volume 1raction of 0 2

Zone 2 Zone 1

Feb 03. 2008 FLUENT 6 .2 (2d , segregated. sp e. ske }


120

4 0 0 395 389 384 379 373 368 363 357 352 346 341 336 330 325 320 314 309 304 298 293

Zone 3

s> Bleeder shaft

Zone 2 Zone 1

No heat buildup

Contours of Total Temperature (k) Feb 05, 2008 FLUENT 6.2 (2d, segregated, spe, ske)

Figure 6.6 Temperature contours for case 1

permeability), the oxygen concentration drops to near zero level, particularly in zones 2

and 3 (Figure 6.7). In zone 2, the lower-oxygen area is due to intense oxidation, while in

zone 3 this is due to low gob permeability. Figure 6.8 shows the temperature contours for

case 2. These contours range between 293 and 383 K with the highest temperature found

in zone 2 (shaded area). The low permeability condition shifts the hot spot location from

zone 3 in model A (base case) to the zone 2. The hot spot temperature in case 2 reaches

383 K. This is 10 K higher than the critical temperature.

In summary, this study shows that an increase in gob permeability does not

change the hot spot development. This is because the model allows sufficient quantity of

air to carry away the combustion products and maintain the gob temperature below the

critical one. When lower gob permeability is considered, the hot spot initially found in

400

395 .38 9

384

379

.373

368

36 3

357

352

34 6

34 1

336

3 30

325

320

3 14

309

304

298

29 3

Zone 3

Bleeder shaft

Contours of Total Temperature (k )

Zone 2

120

Zone 1

No heat buildup

Feb 05. 2008 FLUENT 6 .2 (2d . segregated. spe. ske)


permeability), the oxygen concentration drops to near zero level, particularly in zones 2

and 3 (Figure 6.7). In zone 2, the lower-oxygen area is due to intense oxidation, while in

zone 3 this is due to low gob permeability. Figure 6.8 shows the temperature contours for

case 2. These contours range between 293 and 383 K with the highest temperature found

in zone 2 (shaded area). The low permeability condition shifts the hot spot location from

zone 3 in model A (base case) to the zone 2. The hot spot temperature in case 2 reaches

383 K. This is 10K higher than the critical temperature.

In summary, this study shows that an increase in gob permeability does not

change the hot spot development. This is because the model allows sufficient quantity of

air to carry away the combustion products and maintain the gob temperature below the

critical one. When lower gob permeability is considered, the hot spot initially found in

121

0.21 0.20 0.19 0.18 0.17 0.16 0.15 0.14 0.13 0.12 0.10 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0.00

Oxidation areas

low-oxygen areas due to low permeability

Contours of V o l u m e fract ion of o2 Feb 03. 2008 FLUENT 6.2 (2d, segregated, spe, ske)


4 0 0 395 389 384 379 373 368 363 357 352 346 341 336 330 325 320 314 309 304 298 293


s> Bleeder shaft


Contours of Total Temperature (k) Feb 03, 2008 FLUENT 6.2 <2d, segregated, spe, ske)


0.21

0.20

0.19

0 .18

0 .17

0 .16

0.15

0.14

0 .13

0 .12

0 .10

0 .09

0 .08

0 .07

0.06

0.05

0.04

0 .03

0 .02

0.01

0.00


Oxidation areas

low-oxygen areas due to low permeability

121

Feb 03. 2008 FLUENT 6.2 (2d. segregated, spe, ske)


400

395

389

384

379

373

368

363

357

352

346

341

336

330

325

320

314

309

304

298

2 93

Zone 3


Zone 2 Zone 1


Feb 03.2008 FLUENT 6 .2 (2d, se.gregated, spe., ske)


122

zone 3 shifted towards the face line. The hot spot is now located in zone 2, and zone 3

becomes inert with near zero-oxygen. When the gob permeability is much lower than this

case, the hot spot will move even closer to the face line.

6.2.4 Effect of Gob Width on Hot Spot Formation

To fulfill an increased demand for coal, mine operators would prefer to develop

wider panels. On one hand, a wide panel can increase the production by reducing the

operation down time due to equipment setup. On the other hand, this application can

increase the risk of initiating a self-heating process. This section describes a parametric

study conducted to investigate the effect of gob width on hot spot formation. A new

model (E) was formulated to investigate this problem. The width of this model was

increased from 330 to 450 m. The model was then executed under the same ventilation

and self-heating conditions as those in model B.

Figure 6.9 shows the oxygen concentration contours for this model. For the new

geometry, the oxygen concentration ranged between 0 and 21%. In model B, this ranged

between 5 and 21%. The reduction in oxygen content indicates an intense oxidation in the

gob, especially in zone 3. For the same pressure, an increased gob resistance to airflow

results in lower velocities, thus creating a favorable condition for hot spot development.

Figure 6.10 shows the temperature contours for this model. These contours show

a hot spot in the mid-area where the temperature ranges between 373 and 386 K. In

model B, the hot spot was found along the tailgate side because the ventilation air

eliminated the heat buildup in the mid-area (Figure 5.10). The increased gob width

reduced the airflow through zone 3. As a result, the heat buildup takes place in this zone

122

zone 3 shifted towards the face line. The hot spot is now located in zone 2, and zone 3

becomes inert with near zero-oxygen. When the gob permeability is much lower than this

case, the hot spot will move even closer to the face line.

6.2.4 Effect of Gob Width on Hot Spot Formation

To fulfill an increased demand for coal, mine operators would prefer to develop

wider panels. On one hand, a wide panel can increase the production by reducing the

operation down time due to equipment setup. On the other hand, this application can

increase the risk of initiating a self-heating process. This section describes a parametric

study conducted to investigate the effect of gob width on hot spot formation. A new

model (E) was formulated to investigate this problem. The width of this model was

increased from 330 to 450 m. The model was then executed under the same ventilation

and self-heating conditions as those in model B.

Figure 6.9 shows the oxygen concentration contours for this model. For the new

geometry, the oxygen concentration ranged between 0 and 21 %. In model B, this ranged

between 5 and 21 %. The reduction in oxygen content indicates an intense oxidation in the

gob, especially in zone 3. For the same pressure, an increased gob resistance to airflow

results in lower velocities, thus creating a favorable condition for hot spot development.

Figure 6.10 shows the temperature contours for this model. These contours show

a hot spot in the mid-area where the temperature ranges between 373 and 386 K. In

model B, the hot spot was found along the tailgate side because the ventilation air

eliminated the heat buildup in the mid-area (Figure 5.1 0). The increased gob width

reduced the airflow through zone 3. As a result, the heat buildup takes place in this zone

123

0.21

0.20

0.19

0.18

0.17

0.16

0.15

0.14

0.13

0.12

0.11

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0.00

Bleeder shaft

Oxidation area

Contours of V o l u m e fraction of o2 Feb 03, 2008 FLUENT 6.2 (2d, segregated, spe, ske)

Figure 6.9 Oxygen concentration contours for model E

400

395

389

384

379

373

368

363

357

352

346

341

336

330

325

320

314

309

304

298

293

Bleeder shaft

> Potential hot spot (373 - 3 8 6 K)

Contours of Total Temperature (k) Feb 03. 2008 FLUENT 6.2 (2d, segregated, spe. ske)

Figure 6.10 Temperature contours for model E

0.21

0.20

0 .19

0.18

0.17

0.16

0.15

0 .14

0 .13

0.12

0 .11

0 .09

0 .08

0 .07

0.06

0 .05 Bleeder shaft 0 .04

0 .03

0 .02

0 .01

0 .00


1,524 m

123

450 m

Oxidation area

Feb 03,2008 FLUENT 6.2 (2d, segregated, spe, ske)

Figure 6.9 Oxygen concentration contours for model E

400

395

389

384

379

373

368

363

357

352

346

341

336

330

325

320

314

309

304

298

293

Bleeder shaft


Contours of Total Temperature (k) Feb 03,2008 FLUENT 6.2 (2d , segregated, spe, ske.)

Figure 6.10 Temperature contours for model E

124

and creates a large hot spot (shaded area). This area is expected to grow along the width

as the gob becomes wider.

6.2.5 Hot Spot Control through Gas Injections

Several gob inertization methods have been used by mine operators to control

spontaneous combustion (Section 2.3.5). Two preliminary studies of gas injections are

described in this section: injection through a vertical hole for a bleeder system and

horizontal pipes for a bleederless system. The vertical hole is drilled to the hot spot center

from surface prior mining and equipped with a gas injection system. The horizontal

injection points, represented by high pressure pipes, are built in stoppings and seals

located near the headgate. The injection method was determined based on the hot spot

location. If the hot spot was located on the head gate side, a horizontal injection system

was used; otherwise, a vertical injection system was chosen. Both methods are used in

coal mines where gob degasification and inertization techniques are required (Ren and

Edwards, 2002; Balusu et a l , 2005; Bessinger, 2005).

Models A and D were used as the basis for these studies. For model A, which

utilizes a bleeder system, compressed air was injected through a 0.3-m-diameter vertical

hole. The gage pressure at the injection point was only 4,000 Pa. The hole was located at

the center of the hot spot area (Figure 5.7). For model D, which utilizes a bleederless

system, nitrogen gas was injected through two horizontal pipes at a gage pressure of

1,500 Pa each. In reality, the pressure could be one or two orders of magnitude greater

than this pressure (Bessinger, 2005). These pipes, 0.1 m in diameter, were spaced 100 m

from each other. Table 6.3 summarizes the input parameters for both methods.

124

and creates a large hot spot (shaded area). This area is expected to grow along the width

as the gob becomes wider.

6.2.5 Hot Spot Control through Gas Injections

Several gob inertization methods have been used by mine operators to control

spontaneous combustion (Section 2.3.5). Two preliminary studies of gas injections are

described in this section: injection through a vertical hole for a bleeder system and

horizontal pipes for a bleederless system. The vertical hole is drilled to the hot spot center

from surface prior mining and equipped with a gas injection system. The horizontal

injection points, represented by high pressure pipes, are built in stoppings and seals

located near the headgate. The injection method was determined based on the hot spot

location. If the hot spot was located on the head gate side, a horizontal injection system

was used; otherwise, a vertical injection system was chosen. Both methods are used in

coal mines where gob degasification and inertization techniques are required (Ren and

Edwards, 2002; Balusu et aI., 2005; Bessinger, 2005).

Models A and D were used as the basis for these studies. For model A, which

utilizes a bleeder system, compressed air was injected through a 0.3-m-diameter vertical

hole. The gage pressure at the injection point was only 4,000 Pa. The hole was located at

the center of the hot spot area (Figure 5.7). For model D, which utilizes a bleederless

system, nitrogen gas was injected through two horizontal pipes at a gage pressure of

1,500 Pa each. In reality, the pressure could be one or two orders of magnitude greater

than this pressure (Bessinger, 2005). These pipes, 0.1 m in diameter, were spaced 100 m

from each other. Table 6.3 summarizes the input parameters for both methods.

125

Parameters Bleeder system (Model A) Bleederless system (Model D)

Injected fluid Pressurized air Nitrogen (99% N 2 )

Injection method One vertical hole

from surface to the hot spot. Two horizontal holes

from headgate side; near the face

Diameter hole 0.3 m (12") 0.1 m(4")

Gage Pressure 4,000 Pa 1,500 Pa each

Location Distance from the back corner of gob, tailgate side: x=140 m; y=128 m

Along the headgate side; hole spacing 100 m; starting from the face-headgate corner.

Figure 6.11 shows the temperature contours for model A. These contours show

that the pressurized air affects the hot spot area within a 40-m radius from the hole. With

this injection system, the maximum temperature drops from 385 K to 369 K, i.e., 4

degree lower than the critical temperature; thus, neutralizing the hot spot.

In model D, the air leakage through the stoppings and between the shields contributes to

the hot spot development. Nitrogen is utilized to inertize this area and reduce the oxygen

content of the leakage air. Since the hot spot is located near the working face (Figure

5.17), the most suitable locations for the injection pipes are the crosscuts located on the

headgate side, near the face.

Figure 6.12 shows the nitrogen concentration contours in the gob after an

injection period. The injected nitrogen covers zones 1 and 2 and nearly replaces the

oxygen level. These are the zones where the hot spot was located in model D. A small

area with nitrogen levels of about 7 8 % along the face line indicates the presence of

leakage between the shields. Figure 6.13 shows the temperature contours for model D

with a max imum temperature of 363 K. In absence of these injection points, this

Table 6.3 Input parameters for injection simulations

125

Table 6.3 Input parameters for injection simulations

Parameters Bleeder system (Model A) Bleederless system (Model D)

Injected fluid Pressurized air Nitrogen (99% N2)

One vertical hole Two horizontal holes Injection method from surface to the hot spot. from headgate side; near the face

Diameter hole 0.3 m (12") 0.1 m (4")

Gage Pressure 4,000 Pa 1,500 Pa each

Distance from the back comer of Along the headgate side; hole Location gob, tailgate side: x=140 m; spacing 100 m; starting from the

y=128 m face-headgate comer.

Figure 6.11 shows the temperature contours for model A. These contours show

that the pressurized air affects the hot spot area within a 40-m radius from the hole. With

this injection system, the maximum temperature drops from 385 K to 369 K, i.e., 4

degree lower than the critical temperature; thus, neutralizing the hot spot.

In model D, the air leakage through the stoppings and between the shields contributes to

the hot spot development. Nitrogen is utilized to inertize this area and reduce the oxygen

content of the leakage air. Since the hot spot is located near the working face (Figure

5.17), the most suitable locations for the injection pipes are the crosscuts located on the

head gate side, near the face.

Figure 6.12 shows the nitrogen concentration contours in the gob after an

injection period. The injected nitrogen covers zones 1 and 2 and nearly replaces the

oxygen level. These are the zones where the hot spot was located in model D. A small

area with nitrogen levels of about 78% along the face line indicates the presence of

leakage between the shields. Figure 6.13 shows the temperature contours for model D

with a maximum temperature of 363 K. In absence of these injection points, this

126

379 Zone 3 Zone 2 Zone 1

C o n t o u r s o f To ta l T e m p e r a t u r e (k) Feb 05 , 2 0 0 8 F L U E N T 6.2 (2d , s e g r e g a t e d , s p e , ske)

Figure 6.11 Temperature contours for model A with a vertical injection

0.99 0.94 0.89 0.84 0.79 0.74 0.69 0.64 0.59 0.54 0.50 0,45 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00

Horizontal holes


Contours of Volume fraction of n2 Feb 06, 2008 FLUENT 6.2 (2d. segregated, spe, ske)

Figure 6.12 Nitrogen concentration contours for model D with horizontal injection holes

400

395

389

384

379

373

368

363

3 57

352

346

341

336

330

325

320

314

309

304

298

293

Bleeder shaft


Zone3 Zone 2

126

Zone 1

Feb 05. 2008 FLUENT 6.2 (2d. segregated. spe. ske)

Figure 6.11 Temperature contours for model A with a vertical injection

0 .99

0 .94

0 .89

0.84

0 .79

0 .74

0 .69 I , --,

0 .64 , , I ,

0 .59 I , I ,

0.54 I

0 .50

0.45

0 .40 I \ ,

0 .35 , '-,

0 .30

0 .25

0.20

0.15 Zone 3 0 .10

0.05

0.00

Contours of Volume fraction of n2

Zone 2

Horizontal holes

Zone 1

Feb 06. 2008 FLUENT 6.2 (2d. segregated. spe, ske)

Figure 6.12 Nitrogen concentration contours for model D with horizontal injection holes

127

400 395 389 384 379 373 368 363 357 352 346 341 336 330 325 320 314 309 304 298 293

Horizontal holes


Contours of Total Temperature (k) Feb 06 , 2 0 0 3 F L U E N T 6.2 ( 2d , s e g r e g a t e d , s p e , ske)

Figure 6.13 Temperature contours for model D with horizontal injection holes

temperature reached 398 K. These contours also show that the injection points avoided

the leakage air from percolating further into the gob. The gas injection reduces the

oxygen supply to zones 1 and 2, thus reducing the oxidation of coal and heat buildup in

the gob.

Based on this study, for panels ventilated by a bleeder system, pressurized air

injected through vertical holes would be preferred to horizontal holes especially for

longwall mines that practice a degasification program. This is an advantage of this

method. In gassy mines, the holes used for methane drainage can be utilized for gas

injection to control spontaneous combustion. The location of these holes in relation to the

hot spot area determines the effectiveness of this method. For panels ventilated by a

bleederless system that restricts air flow to the gob, nitrogen injection through pipes is a

400

395

389

384

379

373

368

3.63

357

352

346

.341

336

330

325

320

314

309

304

298

293

Zone3 Zone 2

127

Horizontal holes

Zone 1

Contours of Total Temperature (k) Feb 06,2008 FLUENT 6 .2 (2d, segregated, spe, ske)

Figure 6.13 Temperature contours for model D with horizontal injection holes

temperature reached 398 K. These contours also show that the injection points avoided

the leakage air from percolating further into the gob. The gas injection reduces the

oxygen supply to zones 1 and 2, thus reducing the oxidation of coal and heat buildup in

the gob.

Based on this study, for panels ventilated by a bleeder system, pressurized air

injected through vertical holes would be preferred to horizontal holes especially for

longwall mines that practice a degasification program. This is an advantage of this

method. In gassy mines, the holes used for methane drainage can be utilized for gas

injection to control spontaneous combustion. The location of these holes in relation to the

hot spot area determines the effectiveness of this method. For panels ventilated by a

bleederless system that restricts air flow to the gob, nitrogen injection through pipes is a

128

suitable technique to control spontaneous combustion. The location of hot spots near the

working face suggests the use of horizontal injection pipes on the headgate side.

Although this technique is expensive and time consuming, it reduces the risk of hot spot

development considerably.

In conclusion, both vertical and horizontal gas injection methods can be used to

control the onset of spontaneous combustion potential provided that the ventilation

control devices are included in the mine planning and maintained properly.

128

suitable technique to control spontaneous combustion. The location of hot spots near the

working face suggests the use of horizontal injection pipes on the headgate side.

Although this technique is expensive and time consuming, it reduces the risk of hot spot

development considerably.

In conclusion, both vertical and horizontal gas injection methods can be used to

control the onset of spontaneous combustion potential provided that the ventilation

control devices are included in the mine planning and maintained properly.

CHAPTER 7

CONCLUSIONS AND RECOMMENDATIONS

7.1 Conclusions

A hot spot, an initial condition for spontaneous combustion, is developed from the

exothermic oxidation of coal. In a longwall mine gob, the heat of oxidation, if not

removed, can ignite the coal and eventually initiate fires and explosions. In this study, the

hot spot location is determined as a function of oxygen concentration and gob

temperature. The critical values for these variables are the following: 5 % (by volume) for

oxygen and 100°C for gob temperature.

Permeabili ty is one of the key parameters considered in this study. Three

permeability values are used to characterize the gob: 4.68 x 10"7 m 2 (for the

unconsolidated zone), 3.15 x 10"8 m 2 (semiconsolidated), and 7.98 x 10"9 m 2

(consolidated). These values are at least 3 orders of magnitude higher than those utilized

by other authors (Ren et a l , 1 x 10" 1 5 m 2 and Yuan et al., 1 x 10" 1 2 m 2 ) .

In Fluent, the permeabili ty of porous media is determined from the Kozeny-

Carman equation. In this equation, permeability is determined as a function of particle

size and porosity of spherical particles arranged uniformly. In the mine gob and

permeameter, particles are of irregular shape with chaotic arrangement. Therefore, the

permeability of gob material determined based on laboratory experiments is lower than

CHAPTER 7

CONCLUSIONS AND RECOMMENDATIONS

7.1 Conclusions

A hot spot, an initial condition for spontaneous combustion, is developed from the

exothermic oxidation of coal. In a longwall mine gob, the heat of oxidation, if not

removed, can ignite the coal and eventually initiate fires and explosions. In this study, the

hot spot location is determined as a function of oxygen concentration and gob

temperature. The critical values for these variables are the following: 5% (by volume) for

oxygen and 100°C for gob temperature.

Permeability is one of the key parameters considered in this study. Three

permeability values are used to characterize the gob: 4.68 x 10-7 m2 (for the

unconsolidated zone), 3.15 x 10-8 m2 (semiconsolidated), and 7.98 x 10-9 m2

(consolidated). These values are at least 3 orders of magnitude higher than those utilized

by other authors (Ren et aI., 1 x 10-15 m2 and Yuan et aI., 1 x 10-12 m\

In Fluent, the permeability of porous media is determined from the Kozeny

Carman equation. In this equation, permeability is determined as a function of particle

size and porosity of spherical particles arranged uniformly. In the mine gob and

permeameter, particles are of irregular shape with chaotic arrangement. Therefore, the

permeability of gob material determined based on laboratory experiments is lower than

130

permeability in Fluent simulations under the same conditions. To account for these

factors, this equation was modified by a calibration constant of 0.898.

This study includes 3 gob models ventilated by a bleeder system (A, B, and C). In

these models , hot spots always start in the consolidated area near the bleeder shaft. As the

panel becomes longer, the hot spot becomes larger along the tailgate side. The increased

size of hot spot is followed by the increased temperature of the gob. The risk for the hot

spot development increases with the gob length. When the gob length was equal or

greater than 5 0 % of the panel length, two hot spots were observed: one near the bleeder

shaft and the other near the face. These were the effect of leakage flow from headgate

crosscuts. The advantage of this flow in flushing the heat buildup should be maximized.

When a bleederless ventilation system is used (model D), the consolidated area is

practically kept free of oxygen; then, the hot spot can only be developed in the

unconsolidated area along the face line where the oxygen is still present due to leakage

flow. The hot spot area extends for about 200 m from the face line. Within this area, the

gob temperature reaches 125°C (the highest of all the models) representing a greater

potential risk for fire initiation. To reduce this risk in panels ventilated by a bleederless

system, the leakage air quantity should be minimized.

To understand the effect of the mining practices on hot spots, two parameters

were investigated: gob permeabili ty and panel width. The study on permeabili ty was

performed on model A using three different values. The results showed that the higher

the permeability, the higher the leakage quantity percolating through the gob. This

quantity is sufficient to remove the heat of oxidation and reduce the potential for the

development of spontaneous combustion. However, when lower permeabili ty values

permeability in Fluent simulations under the same conditions. To account for these

factors, this equation was modified by a calibration constant of 0.898.

130

This study includes 3 gob models ventilated by a bleeder system CA, B, and C). In

these models, hot spots always start in the consolidated area near the bleeder shaft. As the

panel becomes longer, the hot spot becomes larger along the tailgate side. The increased

size of hot spot is followed by the increased temperature of the gob. The risk for the hot

spot development increases with the gob length. When the gob length was equal or

greater than 50% of the panel length, two hot spots were observed: one near the bleeder

shaft and the other near the face. These were the effect of leakage flow from headgate

crosscuts. The advantage of this flow in flushing the heat buildup should be maximized.

When a bleederless ventilation system is used (model D), the consolidated area is

practically kept free of oxygen; then, the hot spot can only be developed in the

unconsolidated area along the face line where the oxygen is still present due to leakage

flow. The hot spot area extends for about 200 m from the face line. Within this area, the

gob temperature reaches 125°C (the highest of all the models) representing a greater

potential risk for fire initiation. To reduce this risk in panels ventilated by a bleederless

system, the leakage air quantity should be minimized.

To understand the effect ofthe mining practices on hot spots, two parameters

were investigated: gob permeability and panel width. The study on permeability was

performed on model A using three different values. The results showed that the higher

the permeability, the higher the leakage quantity percolating through the gob. This

quantity is sufficient to remove the heat of oxidation and reduce the potential for the

development of spontaneous combustion. However, when lower permeability values

131

were simulated, the hot spot shifted from the consolidated to the semiconsolidated zone.

This is caused by the decreased permeability, which reduces the leakage quantities to the

consolidated zone.

The study on panel width was performed on model B where the width was

increased by 50%. The results indicated that the hot spot extended from the tailgate side

toward the mid-gob area. The increased width produced higher gob resistance, thus

reducing the leakage flow. This reduced quantity was sufficient to sustain intense

oxidation of coal but insufficient to remove the heat, thus increasing the likelihood for

hot spot development in the gob.

In addition to ventilation, two other hot spot control methods were considered:

compressed air injection through a vertical hole and nitrogen injection through pipes.

Based on hot spot locations, the first method is appropriate for a bleeder ventilation

system and the second for a bleederless system. With a vertical injection system, the

compressed air is directed to the hot spot; this flushes the affected area and eliminates the

heat buildup. When horizontal pipes are used, the nitrogen was injected through two

pipes located at the headgate side near the working face. The nitrogen replaced the

oxygen from leakage air, thus reducing the potential for oxidation of coal behind the

shields. In general, both methods are useful techniques to reduce the risk of spontaneous

combustion in longwall mine gobs.

7.2 Recommendations for Future Research

Based on the results of this thesis, further studies are recommended in the

following areas: (1) permeabili ty tests for larger particle sizes, (2) laboratory experiments

131

were simulated, the hot spot shifted from the consolidated to the semi consolidated zone.

This is caused by the decreased permeability, which reduces the leakage quantities to the

consolidated zone.

The study on panel width was performed on model B where the width was

increased by 50%. The results indicated that the hot spot extended from the tailgate side

toward the mid-gob area. The increased width produced higher gob resistance, thus

reducing the leakage flow. This reduced quantity was sufficient to sustain intense

oxidation of coal but insufficient to remove the heat, thus increasing the likelihood for

hot spot development in the gob.

In addition to ventilation, two other hot spot control methods were considered:

compressed air injection through a vertical hole and nitrogen injection through pipes.

Based on hot spot locations, the first method is appropriate for a bleeder ventilation

system and the second for a bleederless system. With a vertical injection system, the

compressed air is directed to the hot spot; this flushes the affected area and eliminates the

heat buildup. When horizontal pipes are used, the nitrogen was injected through two

pipes located at the headgate side near the working face. The nitrogen replaced the

oxygen from leakage air, thus reducing the potential for oxidation of coal behind the

shields. In general, both methods are useful techniques to reduce the risk of spontaneous

combustion in longwall mine gobs.

7.2 Recommendations for Future Research

Based on the results of this thesis, further studies are recommended in the

following areas: (1) permeability tests for larger particle sizes, (2) laboratory experiments

132

using a large-scale gob model , and (3) CFD-based gob simulation exercises using

enhanced 3D models .

Permeabili ty tests using larger particle sizes are required to validate the

permeability-particle size relationship presented in this study. This information can be

obtained using a large permeameter (e.g., at least 14 cm in diameter). This will improve

the accuracy of the permeabili ty estimates determined using the Kozeny-Carman

equation. In longwall mines, the use of tracer gas is recommended to approximate the

permeability of gob material.

In this study, the longwall mine gob is represented by a container 14-cm long and

62.5-cm in diameter. To improve the accuracy of results, a larger-scale gob model of

rectangular shape attached to the far end of the existing coal mine model is suggested.

The recommended dimensions for this gob model are 0.20 x 1.25 m in cross section and

3.75 m long (1:29 scale reduction).

The gob simulation exercises were carried out using 2D models . Although these

models produced satisfactory results for flow behavior, coal oxidation, and self-heating

process, they have a few limitations such as the following: inability to represent the

buoyancy effect of combustion gases, inability to simulate variations of gob permeability

in vertical direction, etc. Part of these problems can be overcome using 3D models.

However, this requires longer processing t ime and a computer with at least one Giga-byte

of memory.

using a large-scale gob model, and (3) CFD-based gob simulation exercises using

enhanced 3D models.

132

Permeability tests using larger particle sizes are required to validate the

permeability-particle size relationship presented in this study. This information can be

obtained using a large permeameter (e.g., at least 14 cm in diameter). This will improve

the accuracy of the permeability estimates determined using the Kozeny-Carman

equation. In longwall mines, the use of tracer gas is recommended to approximate the

permeability of gob material.

In this study, the longwall mine gob is represented by a container 14-cm long and

62.5-cm in diameter. To improve the accuracy of results, a larger-scale gob model of

rectangular shape attached to the far end of the existing coal mine model is suggested.

The recommended dimensions for this gob model are 0.20 x 1.25 m in cross section and

3.75 m long (I :29 scale reduction).

The gob simulation exercises were carried out using 2D models. Although these

models produced satisfactory results for flow behavior, coal oxidation, and self-heating

process, they have a few limitations such as the following: inability to represent the

buoyancy effect of combustion gases, inability to simulate variations of gob permeability

in vertical direction, etc. Part of these problems can be overcome using 3D models.

However, this requires longer processing time and a computer with at least one Giga-byte

of memory.

APPENDIX A

PERMEABILITY TEST D A T A

APPENDIX A

PERMEABILITY TEST DATA

134

A. l Water-Based Permeabili ty Test

Table A l . Water-based test data for 0.28-mm diameter samples

Material Type Tes t# Sample length

L, m Water head difference

Ah, m Time t, sec

Outflow V, ml

Flow rate Q, ml/s

Rock

1 0.080 0.105 765 500 0.653

Rock 2 0.080 0.043 1953 500 0.256

Rock 3 0.080 0.085 798 500 0.626 Rock 4 0.080 0.054 1435 500 0.348

Rock

5 0.080 0.036 2015 500 0.248

Coal

6 0.080 0.076 869 500 0.575

Coal 7 0.080 0.037 2095 500 0.238

Coal 8 0.080 0.105 728 500 0.687 Coal 9 0.080 0.040 1924 500 0.259

Coal

10 0.080 0.086 792 500 0.631

Table A2. Water-based test data for 3.22-mm diameter samples

Material Type Test# Sample length


Ah, m Time t, sec

Outflow V,ml

Flow rate Q, ml/s

1 0.090 0.125 114 500 4.385 2 0.090 0.027 372 500 1.344

Rock 3 0.090 0.096 214 556 2.598 4 0.090 0.061 253 500 1.976 5 0.090 0.037 327 500 1.529 6 0.100 0.081 146 500 3.425 7 0.100 0.026 289 500 1.730

Coal 8 0.100 0.131 105 500 4.762 9 0.100 0.059 179 500 2.793 10 0.100 0.100 134 500 3.731

134

A.1 Water-Based Permeability Test

Table Al. Water-based test data for O.28-mm diameter samples

Material Test #

Sample length Water head difference Time Outflow Flow rate Type L,m ~h,m t, sec V,ml Q, mIls

1 0.080 0.105 765 500 0.653 2 0.080 0.043 1953 500 0.256

Rock 3 0.080 0.085 798 500 0.626 4 0.080 0.054 1435 500 0.348 5 0.080 0.036 2015 500 0.248 6 0.080 0.076 869 500 0.575 7 0.080 0.037 2095 500 0.238

Coal 8 0.080 0.105 728 500 0.687 9 0.080 0.040 1924 500 0.259 10 0.080 0.086 792 500 0.631


Material Test #

Sample length Water head difference Time Outflow Flow rate Type L,m ~h,m t, sec V,ml Q, mIls

1 0.090 0.125 114 500 4.385 2 0.090 0.027 372 500 1.344

Rock 3 0.090 0.096 214 556 2.598 4 0.090 0.061 253 500 1.976 5 0.090 0.037 327 500 1.529 6 0.100 0.081 146 500 3.425 7 0.100 0.026 289 500 l.730

Coal 8 0.100 0.131 105 500 4.762 9 0.100 0.059 179 500 2.793 10 0.100 0.100 134 500 3.731

135

Material Type Test# Sample length


Ah, m Time t, sec

Outflow V, ml

Flow rate Q, ml/s

1 0.200 0.055 16 500 31.250 2 0.200 0.028 38 500 13.157

Rock 3 0.200 0.005 102 500 4.902 Rock 4 0.200 0.011 66 500 7.576 5 0.200 0.007 85 500 5.882 6 0.200 0.016 44 500 11.364 1 0.180 0.055 27 1000 37.037

Coal 2 0.180 0.028 45 1000 22.222

Coal 3 0.180 0.011 69 1000 14.493 4 0.180 0.005 115 1000 8.695

A.2 Air-Based Permeability Test

Table A4. Air-based test data for 5.74-mm diameter rock samples

Moto r frequency

45 H z

Sample Height 312.5 m m 468.8 m m 557.5 m m

Pressure T a p # Stat 1 Stat 5 Stat 7 Stat 1 Stat 5 Stat 7 Stat 1 Stat 5 Stat 7

8.890 0.203 9.144 10.414 0.178 9.398 9.525 0.203 9.525

10.160 0.203 10.414 10.922 0.178 10.160 10.414 0.178 9.779

Veloci ty Head , 10.160 0.178 10.414 11.684 0.152 10.414 10.668 0.127 9.906 H v (mm) 10.922 0.127 11.684 11.176 0.127 12.065 11.430 0.127 11.176

11.430 0.127 11.176 10.160 0.127 11.684 9.906 0.102 11.430

9.144 0.102 9.652 8.890 0.076 10.160 8.890 0.102 10.922

Static Head, H s (mm)

114.300 111.760 9.652 116.840 110.490 8.636 115.570 111.760 8.382

AP 5 a -6 (Pa) 846.60 834.15 846.60

Air proper t ies : T d = 29.72 °C Pb = 85084.02 Pa

Table A 3 . Water-based test data for 5.74-mm diameter samples

135


Material Test #

Sample length Water head difference Time Outflow Flow rate Type L,m ~h,m t, sec Y,ml Q, mIls

1 0.200 0.055 16 500 31.250 2 0.200 0.028 38 500 13.157

Rock 3 0.200 0.005 102 500 4.902 4 0.200 0.011 66 500 7.576 5 0.200 0.007 85 500 5.882 6 0.200 0.016 44 500 11.364 1 0.180 0.055 27 1000 37.037

Coal 2 0.180 0.028 45 1000 22.222 3 0.180 0.011 69 1000 14.493 4 0.180 0.005 115 1000 8.695

A.2 Air-Based Permeability Test


Motor 45 Hz

frequency

Sample Height 312.5 mm 468.8 mm 557.5 mm

Pressure Tap # Stat I Stat 5 Stat 7 Stat I Stat 5 Stat 7 Stat I Stat 5 Stat 7

8.890 0.203 9.144 10.414 0.178 9.398 9.525 0.203 9.525

10.160 0.203 10.414 10.922 0.178 10.160 10.414 0.178 9.779

Velocity Head, 10.160 0.178 10.414 11.684 0.152 10.414 10.668 0.127 9.906

Hv (mm) 10.922 0.127 11.684 11.176 0.127 12.065 11.430 0.127 11.176

11.430 0.127 11.176 10.160 0.127 11.684 9.906 0.102 11.430

9.144 0.102 9.652 8.890 0.076 10.160 8.890 0.102 10.922

Static Head, Hs 114.300 111.760 9.652 116.840 110.490 8.636 115.570 111.760 8.382

(mm)

i1PSa•6 (Pa) 846.60 834.15 846.60

Air properties: Td = 29.72 'C Pb = 85084.02 Pa

136

Moto r frequency

45 H z

Sample He igh t 312.5 m m 468.8 m m 557.5 m m


9.398 0.254 9.906 9.398 0.127 9.906 10.16 0.254 9.144

10.668 0.203 10.668 10.287 0.127 10.160 10.16 0.152 9.652

Veloci ty Head, 10.414 0.127 10.922 10.668 0.076 10.160 11.43 0.152 10.159 H v ( m m ) 10.414 0.076 11.430 10.795 0.076 10.668 11.176 0.152 12.192

9.398 0.076 10.159 10.287 0.025 11.684 10.668 0.152 11.684

9.398 0.025 10.159 8.890 0.025 9.652 9.144 0.127 10.160

Static Head, Hs (mm)

114.300 109.220 11.684 116.840 113.030 9.144 115.570 113.030 8.636

AP 5 a -6 (Pa) 859.05 846.60 859.05

Air proper t ies : T d = 22.22 °C P b = 85097 .56 Pa


Moto r frequency

45 H z



9.398 0.254 9.906 9.398 0.127 9.906 10.160 0.254 9.144

10.668 0.203 10.668 10.287 0.127 10.160 10.160 0.152 9.652

Veloci ty Head , 10.668 0.127 10.922 10.668 0.076 10.160 11.430 0.152 10.160 Hv (mm) 10.414 0.076 11.430 10.668 0.076 10.668 11.176 0.147 12.192

9.398 0.076 10.160 10.160 0.025 11.684 10.668 0.152 11.684

9.398 0.025 10.160 8.890 0.025 9.652 9.144 0.127 10.160

Static Head , Hs (mm)

116.840 110.236 12.700 118.110 111.760 10.160 115.570 113.030 8.636

AP 5 a -6 (Pa) 846.60 859.05 871.50 Air proper t ies : T d = 21.11 °C P b = 85973.96 Pa

Table A5 . Air-based test data for 7.73-mm diameter rock samples

136


Motor 45 Hz

frequency


Pressure Tap # Stat 1 Stat 5 Stat 7 Stat 1 Stat 5 Stat 7 Stat 1 Stat 5 Stat 7

9.398 0.254 9.906 9.398 0.127 9.906 10.16 0.254 9.144

10.668 0.203 10.668 10.287 0.127 10.160 10.16 0.152 9.652

Velocity Head, 10.414 0.127 10.922 10.668 0.076 10.160 11.43 0.152 10.159

Hv (mm) 10.414 0.076 11.430 10.795 0.076 10.668 11.176 0.152 12.192

9.398 0.076 10.159 10.287 0.025 11.684 10.668 0.152 11.684

9.398 0.025 10.159 8.890 0.025 9.652 9.144 0.127 10.160

Static Head, Hs 114.300 109.220 11.684 116.840 113.030 9.144 115.570 113.030 8.636

(mm)

~P5a-6 (Pa) 859.05 846.60 859.05

Air properties: Td = 22.22 °C Pb = 85097.56 Pa


Motor 45 Hz

frequency


Pressure Tap # Stat I Stat 5 Stat 7 Stat I Stat 5 Stat 7 Stat 1 Stat 5 Stat 7

9.398 0.254 9.906 9.398 0.127 9.906 10.160 0.254 9.144

10.668 0.203 10.668 10.287 0.127 10.160 10.160 0.152 9.652

Velocity Head, 10.668 0.127 10.922 10.668 0.076 10.160 11.430 0.152 10.160 Hv (mm) 10.414 0.076 11.430 10.668 0.076 10.668 11.176 0.147 12.192

9.398 0.076 10.160 10.160 0.025 11.684 10.668 0.152 11.684

9.398 0.025 10.160 8.890 0.025 9.652 9.144 0.127 10.160

Static Head, Hs 116.840 110.236 12.700 118.110 111.760 10.160 115.570 113.030 8.636

(mm)

~P5a-6 (Pa) 846.60 859.05 871.50

Air properties: Td=21.11°C Pb = 85973.96 Pa

137

Moto r frequency

45 H z



Veloci ty Head , H v ( m m )

9.398 0.254 9.906 9.398 0.127 9.906 10.160 0.279 9.652


10.668 0.203 10.668 10.287 0.127 10.160 10.668 0.178 9.652


10.414 0.127 10.922 10.668 0.076 10.160 11.176 0.152 10.16 Veloci ty Head , H v ( m m ) 10.414 0.076 11.430 10.795 0.076 10.668 11.430 0.152 12.192


9.398 0.076 10.160 10.287 0.025 11.684 10.668 0.127 11.684


9.398 0.025 10.160 8.890 0.025 9.652 9.652 0.127 11.684

Static Head , Hs (mm)

114.300 109.220 11.684 116.840 113.030 9.144 116.840 114.300 8.636

AP 5 a -6 (Pa) 859.05 866.52 859.05

Air proper t ies : T d = 22.77 °C P b = 89167.33 Pa

Table A7 . Air-based test data for 9.71-mm diameter rock samples

137


Motor 45 Hz

frequency


Pressure Tap # Stat 1 Stat 5 Stat 7 Stat 1 Stat 5 Stat 7 Stat 1 Stat 5 Stat 7

9.398 0.254 9.906 9.398 0.127 9.906 10.160 0.279 9.652

10.668 0.203 10.668 10.287 0.127 10.160 10.668 0.178 9.652

Velocity Head, 10.414 0.127 10.922 10.668 0.076 10.160 11.176 0.152 10.16

Hv (mm) 10.414 0.076 11.430 10.795 0.076 10.668 11.430 0.152 12.192

9.398 0.076 10.160 10.287 0.025 11.684 10.668 0.127 11.684

9.398 0.025 10.160 8.890 0.025 9.652 9.652 0.127 11.684

Static Head, Hs 114.300 109.220 11.684 116.840 113.030 9.144 116.840 114.300 8.636

(mm)

~P5a-6 (Pa) 859.05 866.52 859.05

Air properties: Td = 22.77·C P b = 89167.33 Pa

APPENDIX B

S A M P L E OF PERMEABILITY C A L C U L A T I O N S

APPENDIXB

SAMPLE OF PERMEABILITY CALCULATIONS

Table B l . Sample data for permeabili ty calculation1

Symbols Descriptions Units Equations Values

D Permeameter diameter m - 0.140

A Cross-sectional area m 2 A = -7t(D)2

4 0.015

L Sample length m - 0.5575 td

Dry-bulb temperature °C - 22.780 Pb Barometric pressure Pa - 89,168

P Air density kg/m3 Pb

P ~ 287.04 (273+ td) 1.050

M Dynamic viscosity Ns/m2 - 1.541 x 10"5

V, Velocity m/s

Stat 1 Stat 5 Stat 7

V, Velocity m/s

where velocity pressure: Pv=Uwx 9.803

13.774 2.282 13.425

V, Velocity m/s


14.114 1.823 13.425 V, Velocity m/s


14.446 1.685 13.774 V, Velocity m/s


14.609 1.685 15.088 V, Velocity m/s

where velocity pressure: Pv=Uwx 9.803 14.114 1.540 14.771

V, Velocity m/s


13.425 1.540 14.771

V Average Velocity m/s y - I ^ lt\ n

14.080 1.759 14.209

Pinlet Pressure Inlet Pa P m iet = H s s t a t l x 9.803 1,145

Qporous Airflow through porous

medium m3/s Qporous — ' stat 5 X A 0.0264

*Data of Table A 7 (L = 557.5 m m ) were used for calculat ion

Symbols

D

A

L

Pb

p

v

Qporous

Table B 1. Sample data for permeability calculation*

Descriptions

Permeameter diameter

Cross-sectional area

Sample length Dry-bulb temperature Barometric pressure

Air density

Dynamic viscosity

Velocity

Average Velocity

Pressure Inlet

Airflow through porous medium

Units

m

m

Pa

m/s

m/s

Pa

Equations

Pb p=-------

287.04 (273 + t d )

Vi = ~2 Xp P v

where velocity pressure:

Pv= Hv x 9.803

v' = t ~ 1='1 n

Pinlet = HSstat 1 x 9.803

Qporous = V stat 5 X A

*Data of Table A7 (L = 557.5 rum) were used for calculation

Values

0.140

0.015

0.5575 22.780 89,168

1.050

1.541 X 10-5

Stat 1 Stat 5 Stat 7 13.774 2.282 13.425 14.114 1.823 13.425 14.446 l.685 13.774 14.609 1.685 15.088 14.114 l.540 14.771

13.425 l.540 14.771

14.080 l.759 14.209

1,145

0.0264

140

and, the specific permeability, k, by (Equation 2.8):

7

( 1 . 568 x 10 " 5 ) ( 0 . 0 1 2 )

(1 .050 x 9 .81 ) k =

= 1.830 x 10"8 m 2

B. l Calculation

Head loss due to porous medium (Ah)

M = ( A ^ )

(pxg)

where

Ah = Head loss due to porous medium, m

APsa-6 = Pressure drop due to porous medium, Pa

p = Air density, kg /m 3

g = gravity acceleration, m/s

For AP5a.6 = 859.05 Pa and p = 1.050 kg /m 3 , the head loss (Ah) is 83.39 m. Then, the

hydraulic conductivity, C, is given by (Equation 2.7):

Q = C A — L

83 39 0 .0264 = C ( 0 . 0 1 5 4 ) :

0 . 5575

C= 0.012 m/s

B.1 Calculation

Head loss due to porous medium (!J..h)

where

!J..h = (Msa-6 ) (p x g)

!J..h = Head loss due to porous medium, m

M 5a-6 = Pressure drop due to porous medium, Pa

p = Air density, kg/m3

g = gravity acceleration, mls2

For M 5a-6 = 859.05 Pa and p = 1.050 kg/m3, the head loss (!J..h) is 83 .39 m. Then, the

hydraulic conductivity, C, is given by (Equation 2.7):

Q = C A !J..h

L

83 .39 0.0264 = C (0.0154 )

0.5575

c = 0.012 mls

and, the specific permeability, k, by (Equation 2.8):

k = Jl C r

k = (1.568 x 10 -5)(0.012 )

(1.050 x 9.81)

140

APPENDIX C

CALIBRATION OF CFD M O D E L

APPENDIXC

CALIBRATION OF CFD MODEL

C.l Experiments Using the Physical Model

Table C I . Measured data for the physical model

Pressure Static Pressure, Ps (Pa) Velocity Pressure, Pv (Pa)

Pressure tap Regulator # 1 Regulator # 2 Regulator # 1 Regulator # 2

Stat 1 1,170 1,282

435 445 445 498 522 498 189

411 460 460 454 435 398 373

Stat 2 1,157 1,232

Stat 3 1,145 1,194 -Stat 4 1,132 1,157

Stat 5 1,120 1,107

187 194 274 274 194 187 129

249 274 323 336 323 286 212

Stat 6 771 622

Stat 7 647 523

Stat 8 485 373

Stat 9 311 236

Stat 10 62 47

C . l . l Measured Data

Table CI shows the measured data to determine the airflow rates (without porous

medium) in SI units. The duct is 0.14 m in diameter equipped with 10 pressure taps

(Figure C I ) . The experiment was performed at 13.33°C wet temperature and 21.67°C dry

temperature, and at a barometric pressure of 85,235.4 Pa.

142

C.l Experiments Using the Physical Model

C.l.l Measured Data

Table Cl shows the measured data to determine the airflow rates (without porous

medium) in SI units. The duct is 0.14 m in diameter equipped with 10 pressure taps

(Figure Cl). The experiment was performed at 13.33°C wet temperature and 21.6TC dry

temperature, and at a barometric pressure of 85,235.4 Pa.

Table Cl. Measured data for the physical model

Static Pressure, Ps (Pa) Velocity Pressure, Pv (Pa) Pressure

tap Regulator # 1 Regulator # 2 Regulator # 1 Regulator # 2

435 411 445 460 445 460

Stat 1 1,170 1,282 498 454 522 435 498 398 189 373

Stat 2 1,157 1,232

Stat 3 1,145 1,194 -Stat 4 1,132 1,157

187 249 194 274 274 323

Stat 5 1,120 1,107 274 336 194 323 187 286 129 212

Stat 6 771 622

Stat 7 647 523 -

Stat 8 485 373

Stat 9 311 236

Stat 10 62 47

143

Figure C I . Mine ventilation model schematic

C.1.2 Calculation of Airflow Parameters

Two parameters of physical model and CFD model are investigated for their

similitude: velocity and Reynolds Numbers . The pressure inlet is kept constant. These

parameters are calculated as follows:

a. Air density (p)

P Pb

287.04 (273+ td)

where p is the air density ( k g / m 3 ) , td is the dry temperature (°C), and Pb is barometric

pressure (Pa). Therefore,

P = Pb

287.04 (273 + td) = 1.01 kg/m3

b . Airflow velocity (V)

V = 2 x AP

Stat. 9 Stat. 8 Stat. 7 Stat. 6

Fan Stat. I Stat. 2 Stat. 3 Stat. 4 Stat. 5

Figure Cl. Mine ventilation model schematic

C.l.2 Calculation of Airflow Parameters

Two parameters of physical model and CFD model are investigated for their

similitude: velocity and Reynolds Numbers. The pressure inlet is kept constant. These

parameters are calculated as follows:

a. Air density (P)

Pb p=------

287.04 (273 + t d )

143

where p is the air density (kg/m3) , td is the dry temperature CC), and Pb is barometric

pressure (Pa). Therefore,

Pb P = = 1.01 kg/m3

287.04 (273 + t d )

b. Airflow velocity (V)

144

Table C2. Calculated air velocity

Regulator # 1 Regulator # 2

No. Stat 1 Stat 5 Stat 1 Stat 5 No. AP V AP V AP V AP V (Pa) (m/s) (Pa) (m/s) (Pa) (m/s) (Pa) (m/s)

1. 435 29.4 187 19.2 411 28.6 249 22.2 2. 445 29.7 194 19.6 460 30.3 274 23.3 3. 445 29.7 274 23.3 460 30.3 323 25.4 4. 498 31.4 274 23.3 454 30.1 336 25.8 5. 522 32.2 194 19.2 435 29.4 323 25.3 6. 498 31.4 187 19.2 398 28.1 286 23.9 7. 189 19.4 129 16.0 373 27.2 212 20.5

V 29.1 m/s 20 m/s 29.2 m/s 23.8 m/s

Table C3 . Reynolds Number (NR) of airflow

Regulator # 1 Regulator # 2 Stat 1 Stat 5 Stat 1 Stat 5

NR 275,270 189,189 276,216 225,135

Based on the above equation and AP measurements at station 1 and 5, the velocity of

air passing through the duct is calculated and shown in Table C2.

Reynolds Numbers

The Reynolds Number (TVR) for stations 1 and 5 are calculated from Equation 4.8, as

follows:

DV NR =

V

where D is the duct diameter (m), F i s the velocity (m/s), and u is the kinematic

• 2 5

viscosity (m /s). For air, u = 1.48 x 10" at 21.67°C. For the two regulator settings, the

results are shown in Table C3 .

144

Based on the above equation and M measurements at station 1 and 5, the velocity of

air passing through the duct is calculated and shown in Table C2.

c. Reynolds Numbers

The Reynolds Number (NR) for stations 1 and 5 are calculated from Equation 4.8, as

follows:

where D is the duct diameter (m), V is the velocity (m/s), and D is the kinematic

viscosity (m2/s). For air, D = 1.48 x 1O-5at 21.6TC. For the two regulator settings, the

results are shown in Table C3.

Table C2. Calculated air velocity

Regulator # 1 Regulator # 2

No. Stat 1 Stat 5 Stat 1 Stat 5 M V M V M V M V (Pa) (mls) (Pa) (mls) (Pa) (mls) (Pa) (mls)

1. 435 29.4 187 19.2 411 28.6 249 22.2 2. 445 29.7 194 19.6 460 30.3 274 23.3 3. 445 29.7 274 23.3 460 30.3 323 25.4 4. 498 31.4 274 23.3 454 30.1 336 25.8 5. 522 32.2 194 19.2 435 29.4 323 25.3 6. 498 31.4 187 19.2 398 28.1 286 23.9 7. 189 19.4 129 16.0 373 27.2 212 20.5

V 29.1 mls 20 mls 29.2 mls 23.8 mls

Table C3. Reynolds Number (NR) of airflow

Regulator # 1 Regulator # 2 Stat 1 Stat 5 Stat 1 Stat 5

NR 275,270 189,189 276,216 225,135

145

Table C4. Parameters used for validation in Fluent

Parameter Symbol Unit Value

Air density P kg/m3 1.006

Fluid kinematic viscosity V m2/s 1.48 x 10"5

Static Pressure at Station 1 Condition 1 Condition 2

P Pa 1,170 1,282

Hydraulic diameter (=duct diameter) D m 0.14

Turbulence intensity - % 10

Table C5. CFD modeling results

Parameter Regulator #1 Regulator #2

Wall conditions

Roughness height (m) 0.00198 0.00198

Roughness constant 0.0922 0.0922

Regulator porosity 0.2718 0.1543

Computation results

Velocity at Stat 1 (m/s) 29.15 29.21

Velocity at Stat 5 (m/s) 19.99 23.77

Static pressure at Stat l(Pa) 1170 1282

Static pressure at Stat 5 (Pa) 1087 1082

Reynolds Number at Stat 1 276,836 276,650


C.2 Experiments Using the CFD model

The airflow conditions emulated in Fluent are basically the same as those used in

physical experiments. The governing parameters are given in Table C4 and the

simulation results are shown in Table C5. The experimental results of both physical and

CFD models are shown in Table C6. Their differences were within 5 % accuracy.

145

C.2 Experiments Using the CFD model

The airflow conditions emulated in Fluent are basically the same as those used in

physical experiments. The governing parameters are given in Table C4 and the

simulation results are shown in Table C5. The experimental results of both physical and

CFD models are shown in Table C6. Their differences were within 5% accuracy.

Table C4. Parameters used for validation in Fluent

Parameter Symbol Unit Value

Air density p kg/m3 1.006

Fluid kinematic viscosity v m2/s 1.48 x 10-5

Static Pressure at Station 1 Condition 1 P Pa 1,170 Condition 2 1,282

Hydraulic diameter (=duct diameter) D m 0.14

Turbulence intensity - % 10

Table C5. CFD modeling results

Parameter Regulator #1 Regulator #2

Wall conditions

Roughness height (m) 0.00198 0.00198

Roughness constant 0.0922 0.0922

Regulator porosity 0.2718 0.1543

Computation results

Velocity at Stat 1 (rn/s) 29.15 29.21

Velocity at Stat 5 (rn/s) 19.99 23.77





Table C6. Comparison of results - Physical model versus CFD model

Airflow Parameters CFD model Laboratory model

% Difference

Velocity At Stat 1 (m/s) 29.15 29.10 0.17

it'

)L i

f. (m/s) At Stat 5 (m/s) 19.99 20.00 0.05 R

egul

ate

Static pressure at Stat 5 (Pa) 1087 1120 2.95 R

egul

ate

NR

Stat 1 276,830 275,270 0.56

Reg

ulat

e

NR

Stat 5 190,315 189,189 0.59

alat

or #

2 Velocity At Stat 1 29.21 29.20 0.03

alat

or #

2

(m/s) At Stat 5 23,77 23.8 0.13

alat

or #

2

Static pressure at Stat 5 (Pa) 1082 1107 2.26 bD CD

NR

Stat 1 276, 650 276,216 0.16 bD CD

NR

Stat 5 226,125 225,135 0.44

146

Table C6. Comparison of results - Physical model versus CFD model

Airflow Parameters CFD model Laboratory %

model Difference

Velocity At Stat 1 (mls) 29.15 29.10 0.17 ...... =tt: (mls) At Stat 5 (mls) 19.99 20.00 0.05 I..; 0 ~ Static pressure at Stat 5 (Pa) 1087 1120 2.95 -;:::! C1} Stat 1 276,830 275,270 0.56 (\)

~ NR Stat 5 190,315 189,189 0.59

Velocity At Stat 1 29.21 29.20 0.03 N =tt: (mls) At Stat 5 23,77 23.8 0.13 I..; 0 ......

Static pressure at Stat 5 (Pa) 1082 1107 2.26 ell -;:::! C1}

Stat 1 276,650 276,216 0.16 (\)

~ NR Stat 5 226,125 225,135 0.44

APPENDIX D

C A L C U L A T I O N OF C O A L INJECTION R A T E

APPENDIXD

CALCULATION OF COAL INJECTION RATE

D.l Data and Assumptions

148

a. Gob geometry

Model A is the sample of calculation. The gob is represented by a flat volume of

rectangular cross section as shown in Figure D l .

b . Gob dimensions

Length (L): 912 m

Width (W): 330 m

Height (H): 15 m (5 times the height of mined coal which is 3 m)

c. Coal properties

Specific gravity: 1.32 (average broken bituminous coal)

Density: 1,324 kg /m 3

d. Coal left in the gob take up 10% of the gob volume.

e. Total number of particle injection points is 24.

Figure D l . Assumed gob shape and dimensions

D.1 Data and Assumptions 148

a. Gob geometry

Model A is the sample of calculation. The gob is represented by a flat volume of

rectangular cross section as shown in Figure D 1.

b. Gob dimensions

Length (L): 912 m

Width (W): 330 m

Height (H): 15 m (5 times the height of mined coal which is 3 m)

c. Coal properties

Specific gravity: 1.32 (average broken bituminous coal)

Density: 1,324 kg/m3

d. Coal left in the gob take up 10% of the gob volume.

e. Total number of particle injection points is 24.

L

Figure D1. Assumed gob shape and dimensions

149

D.2 Calculations

a. Total gob volume (Vol g 0 b) :

= 912 x 330 x 15

= 4 , 5 1 4 , 4 0 0 m 3

b . Gob volume rilled with leftover coal ( V o l c o a i ) :

= 10% x 4,514,400 m 3

= 4 5 1 , 4 4 0 m 3

c. Leftover coal in gob:

= V 0 l C 0 a l X P

= (451 , 440 m 3 ) x ( 1 , 3 2 4 kg /m 3 )

= 5 9 7 , 7 0 6 , 5 6 0 kg

~ 5 . 9 8 x l 0 8 k g

d. Coal injection rate for model A for a four month production period (t = 10.368 x 10 6

seconds). The coal injection rate per each injection port is given by:

5.98 J C I O 8 kg

(24 injections x l 0 . 3 6 8 x l 0 6 s)

= 2 .4kg / s

Table D l shows the summary of the calculated injection rates.

Table D l . Coal injection parameters

Models A and D Model B Model C Model E Gob dimensions L : W : H (m) 912 : 330 : 15 1,524:330: 15 2,445 : 330 : 15 1,524:450: 15

Percentage of coal in gob (% volume) 10 19 28 19

Simulated time operation (month)

4 7 10 7

Number of injection holes 24 45 72 60

Injection rate per hole (kg/s) 2.4 2.4 2.4 2.4

a. Total gob volume (Volgob) :

912 x 330 x 15

4,514,400 m3

D.2 Calculations

b. Gob volume filled with leftover coal (VOlcoal) :

10% x 4,514,400 m3

451 , 440 m 3

c. Leftover coal in gob:

Volcoa1 x P

(451,440 m3) x (1,324 kg/m3

)

597, 706, 560 kg

;::::: 5.98x108 kg

149

d. Coal injection rate for model A for a four month production period (t = 10.368 x 106

seconds). The coal injection rate per each injection port is given by:

5.98 x 108 kg

(24 injections x 10.368 x 106 s)

2.4 kg/s

Table D1 shows the summary of the calculated injection rates.

Table D 1. Coal inj ection parameters

Models A and D Model B Model C Model E

Gob dimensions 912: 330 : 15 1,524: 330 : 15 2,445 : 330 : 15 1,524: 450: 15

L: W: H (m) Percentage of coal

10 19 28 19 in gob (% volume) Simulated time

4 7 10 7 operation (month) Number of

24 45 72 60 injection holes Injection rate per

2.4 2.4 2.4 2.4 hole (kg/s)

APPENDIX E

P H A S E S INVOLVED IN SELF-HEATING P R O C E S S

APPENDIXE

PHASES INVOLVED IN SELF-HEATING PROCESS

Table E l . Primary and mixture phase properties

Parameters Unit Air N 2 H 2 0( vap0r) o 2 CO c o 2

Density kg/m3 1.12 1.17 0.5542 1.33 1.17 1.84

Cp (Specific heat)* j/kg-k

1006.43 1040.67 2014 919.31 1043 840.37

Cp (Specific heat)* j/kg-k - -

834.826480 0.29295799 -0.00014956 3.4139e-05 -2.2781e-10

-

968.389770 0.44878769

-0.001152217 1.65683e-06

-7.34637e-10

429.92883 1.8744713 -0.001966 1.297e-06

-3.999e-10

Thermal conductivity w/m-k 0.0242 0.0242 0.0261 0.0246 0.025 0.0145

Viscosity kg/m-s 1.7894e-05 1.663e-

05 1.34e-05 1.919e-05 1.75e-05 1.37e-05

Molecular Weight kg/kg mol 28.966 28.0134 18.01534 31.9988 28.01055 44.00995

Standard State Enthalpy

j/kg mol 0 0 -2.4184e+08 0 -1.1054e+8 -3.9353e+08

Standard State Entropy j/kg mol-k 0 191494.7 8 188696.44 205026.86 197531.64 213720.2

Reference Temperature K 293 293 293 293 293 293

2 n row Cp values represent polynomial constants for mixture phase

Table El. Primary and mixture phase properties

Parameters Unit Air N2 H2O(v3POr) O2

Density kg/m3 1.12 1.17 0.5542 1.33

1006.43 1040.67 2014 919.31 834.826480

Cp (Specific heat)* j/kg-k 0.29295799 - - -0.00014956 -

3.413ge-05 -2.2781e-10

Thermal conductivity w/m-k 0.0242 0.0242 0.0261 0.0246

Viscosity kg/m-s 1.7894e-05 1.663e-

1.34e-05 1.91ge-05 05

Molecular Weight kg/kg mol 28.966 28.0134 18.01534 31.9988

Standard State j/kg mol 0 0 -2.4184e+08 0

Enthalpy

Standard State Entropy j/kg mol-k 0 191494.7

188696.44 205026.86 8

Reference K 293 293 293 293

Temperature

* 2nd row Cp values represent polynomial constants for mixture phase

CO

1.17

1043 968.389770 0.44878769

-0.001152217 1.65683e-06

-7.34637e-10

0.025

1.75e-05

28.01055

-1.1054e+8

197531.64

293

CO2

1.84

840.37 429.92883 1.8744713 -0.001966 1.297e-06

-3.99ge-10

0.0145

1.37e-05

44.00995

-3 .9353e+08

213720.2

293

...... Vl ......

152

Parameters Unit Coal Particles Gob Material

Density kg/m3 1,324 2800

Specific Heat j/kg-k 1100 856

Thermal Conductivity w/m-k 0 1.25

Latent Heat j /kg 2.25e+6 -

Devolatilization Heat J/kg 1.34e+4

Vaporization Temperature K 373 -

Char Component Fraction % 45.92 -

Binary Diffusivity m2/s 2.9999e-05 -

Swelling Coefficient - 1.5 -

Burnout Stoichiometric Ratio - 2.66 -Combustible Fraction (100% - ash content) % 87.67 -

Heat of Reaction for Burnout J/kg 2.62e+07 -Reaction Heat Fraction Absorbed by Gob Material % - 15

Burnout Stoichiometry

1. Chemical reaction (per mol of carbon)

Reaction 1: 1 C + 1 0 2 + 1 H 2 0 -> 1 C 0 2 + 1 H 2 0 + heat

Reaction 2: 1 C 0 2 + 1 C -> 2 CO + heat

2. Burnout ratio (ratio of coal mass to oxygen needed for a complete oxidation):

Atomic weight of C = 12.01 Molecular weight of 0 2 = 31.99

Mass 0 2 = 1.00 gC f ImolC >

,12.01 g C y

1 mol 02

1 mol C

31.99 g02

1 mol O 2.664 g 02

2 J

So, to consume 1.0 gram of carbon, 2.66 grams of oxygen from ventilation air are

required.

Table E2. Secondary phase and gob material properties

152

Table E2. Secondary phase and gob material properties

Parameters Unit Coal Particles Gob Material

Density kg/m3 1,324

Specific Heat j/kg-k 1100

Thermal Conductivity w/m-k 0

Latent Heat j/kg 2.25e+6

Devolatilization Heat j/kg 1.34e+4

Vaporization Temperature K 373

Char Component Fraction % 45.92

Binary Diffusivity m2/s 2.999ge-05

Swelling Coefficient - l.5

Burnout Stoichiometric Ratio - 2.66

Combustible Fraction % 87.67

(100% - ash content)

Heat of Reaction for Burnout j/kg 2.62e+07

Reaction Heat Fraction Absorbed by Gob % -

Material

Burnout Stoichiometry

1. Chemical reaction (per mol of carbon)

Reaction 1:

Reaction 2:

1 C + 1 O2 + 1 H20 ~ 1 CO2 + 1 H20 + heat

1 CO2 + 1 C ~ 2 CO + heat

2800

856

l.25

-

-

-

-

-

-

-

-

15

2. Burnout ratio (ratio of coal mass to oxygen needed for a complete oxidation):

Atomic weight ofC = 12.01 Molecular weight of O2 = 31.99

Mass O2 = (1.00 g C) ( 1 mol C J (1 mol O2 J (31.99 g O2 J = 2.664 g O2

1 12.01 g C 1 mol C 1 mol O2

So, to consume 1.0 gram of carbon, 2.66 grams of oxygen from ventilation air are

required.

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