copyright by fahad mohammed alfadhli 2012

Copyright

by

Fahad Mohammed Alfadhli

2012

The Dissertation Committee for Fahad Mohammed Alfadhli Certifies that this is the

approved version of the following dissertation:

Reducing Environmental Impacts of Petroleum Refining: A Case Study

of Industrial Flaring

Committee:

David T. Allen, Supervisor

Thomas F. Edgar

Elena McDonald-Buller

Michael Baldea

Vincent M. Torres



by

Fahad Mohammed Alfadhli, B.Ch.E; M.S.E.

Dissertation

Presented to the Faculty of the Graduate School of

The University of Texas at Austin

in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

The University of Texas at Austin

August 2012

Dedication

Dedicated to my parents, wife, lovely daughter Shaikha and family

v

Acknowledgements

I would like to thank my supervisor, Dr. David T. Allen, for his guidance and

support; it has been a privilege to work with him. His guidance and patience have helped

me grow scientifically and professionally. My sincere gratitude is also goes to Dr.

Yosuke Kimura for his the technical supports he offered. My gratitude also extended to

Dr. Thomas Edgar, Dr. Elena McDonald-Buller, Dr. Michael Baldea and Mr. Vincent M.

Torres for serving on my committee.

I gratefully thank my parents, Mohammed Al-Fadhli and Shaikha Al-Khemsan

for their love, continuous encouragement, and motivational support throughout my life. I

am most thankful to my wife Asmaa Al-Fadgham and my daughter Shaikha for their

support and patience. My sincere thanks go to my family and friends for believing in me.

This work would not be possible without them.

Finally, a special thanks to Kuwait University for giving me the opportunity to

pursue my graduate study at the University of Texas at Austin.

vi



Fahad Mohammed Alfadhli, Ph.D.

The University of Texas at Austin, 2012

Supervisor: David T. Allen

Industrial flaring can have negative impacts on regional air quality and recent

studies have shown that flares are often operated at low combustion efficiency, which

exacerbates these air quality impacts. This thesis examines industrial flaring with the

objectives of (1) assessing the air quality impacts of flares operating at a variety of

conditions, (2) examining the extent to which improvements in flare operations could

reduce emissions, (3) identifying opportunities for recycling flared gases in fuel gas

networks, and (4) identifying opportunities for reducing the generation of flared gases,

using the improved control of catalytic cracking operations as a case study.

The work presented in this thesis demonstrates that flares operating at low

combustion efficiency can increase localized ambient ozone concentrations by more than

15 ppb under some conditions. The impact of flares on air quality depends most strongly

on combustion efficiency, the flow rates to the flares and the chemical composition

(photochemical reactivity) of the emissions. Products of incomplete combustion and

nitrogen oxides emissions from flaring generally had a smaller impact on air quality than

unburned flare gases.

vii

The combustion efficiency at which a flare can operate can be constrained by the

flare’s design. In a case study of an air-assisted flare, it was demonstrated that choices in

blower configurations could lead to emissions that were orders of magnitude greater or

less than those predicted using an assumed combustion efficiency of 98%. Designing

flares with air-assist rates that can be finely tuned can significantly reduce emissions.

Similarly, flaring can be reduced by integrating sources of waste gases into fuel gas

networks. Analyses for a petroleum refinery indicated that this integration can often be

accomplished with little net cost by expanding boiler capacities. Finally, flared gases can

be reduced at their source. A case study of a fluid catalytic cracking indicated that using

better temperature control could significantly minimize flared gases.

viii

Table of Contents

List of Tables ........................................................................................................ xii

List of Figures .................................................................................................... xviii

CHAPTER 1: Introduction ...................................................................................1

CHAPTER 2: Literature Review..........................................................................8

2.1 Impact of flare emissions on air quality .................................................8

2.2 Flare destruction efficiency ..................................................................12

2.3 Reducing flaring through fuel gas networks ........................................18

CHAPTER 3: Impact of Flare Destruction Efficiency and Products of Incomplete

Combustion on Ozone Formation in Houston, Texas ............................................23

3.1 Introduction ..........................................................................................23

3.2 Methodology ........................................................................................25

3.2.1 Industrial Flares ..........................................................................25

3.2.1.1 Petroleum refinery flares ..............................................25

3.2.1.2 Olefin flares ..................................................................28

3.2.2 Flare chemical compositions.......................................................31

3.2.3 Emissions scenarios ....................................................................33

3.2.3.1 VOC emissions .............................................................33

3.2.3.2 NOx emissions ..............................................................35

3.2.4 Photochemical modeling .............................................................35

3.3 Results and Discussion ........................................................................40

3.3.1 Base Case ....................................................................................40

3.3.2 Flare Emission Scenarios ............................................................42

3.3.2.1 Refinery Flare 1 ............................................................42

3.3.2.2 Refinery Flares 2 and 3 ................................................46

3.3.2.3 Olefin Flares .................................................................49

3.4 Conclusion ...........................................................................................52

ix

CHAPTER 4: Impact of Emissions of Nitrogen Oxides from Flares on Ozone

Formation in Houston, Texas .................................................................................53

4.1 Introduction ..........................................................................................53

4.2 Methods................................................................................................55

4.2.1 Emission factor ...........................................................................55

4.2.2 Air quality ...................................................................................58

4.2.2.1 Flare selection ..............................................................58

4.2.2.2 NOx emission scenarios ................................................58

4.2.2.3 Photochemical modeling ..............................................59


4.4 Conclusion ...........................................................................................67

CHAPTER 5: Impacts of Air-Assist Flare Blower Configurations on Flaring

Emissions……… ...................................................................................................69

5.1 Introduction ..........................................................................................69

5.2 Methods................................................................................................71

5.2.1 Air-assisted flare experimental tests ...........................................71

5.2.2 Stochastic models........................................................................73


5.3.1 Vent gas mixture with LHV of 350 Btu/scf ................................78

5.3.2 Vent gas mixture with LHV of 560 Btu/scf ................................83

5.4 Conclusion ...........................................................................................84

CHAPTER 6: Minimization of Refinery Flaring Through Integration with Fuel Gas

Networks……… ....................................................................................................85

6.1 Introduction ..........................................................................................85

6.2 Methods................................................................................................86

6.2.1 Flow rates ....................................................................................89

6.2.2 Energy demand ...........................................................................90

6.2.3 Non-isothermal and non-isobaric operations ..............................91

6.2.4 Fuel quality .................................................................................93

6.2.5 Physical features .........................................................................97

6.3 Case study of a petroleum refinery ....................................................101

x

6.3.1 Minimizing waste gases ............................................................106

6.3.1.1 Turbine fuels ..............................................................106

6.3.1.2 Flare gas and boiler fuel .............................................108

6.4 Conclusion .........................................................................................113

CHAPTER 7: Effect of Temperature Excursions in Catalytic Cracking Units on the

Generation of Flared gases ...................................................................................114

7.1 Introduction ........................................................................................114

7.2 Three-Lump model ............................................................................117

7.2.1 Kinetic model ............................................................................117

7.2.2 Comparing the Three-lump model with experimental data ......120

7.2.3 Temperature effect ....................................................................121

7.2.4 Coke formation .........................................................................122

7.3 Light gases scenarios .........................................................................123

7.4 Comparison between scenario results and FCC flare data .................127

7.5 Conclusion .........................................................................................137

CHAPTER 8: Findings, Outcomes and Recommmedations ............................138

8.1 Findings..............................................................................................138

8.1.1 Air quality impacts of flaring operations ..................................138

8.1.2 Improving flare operation .........................................................139

8.1.3 Minimization of refinery flaring through integration with fuel gas

networks ....................................................................................139

8.1.4 Impact of temperature excursions of FCC units on the light

gas/flared gas production. .........................................................140

8.2 Outcomes ...........................................................................................140

8.3 Future work ........................................................................................141

8.3.1 Air quality impacts of flaring operations ..................................141

8.3.2 Improving flare operation .........................................................141

8.3.3 Minimization of refinery flaring through integration with fuel gas

networks ....................................................................................141

8.3.4 Impact of temperature excursions of FCC units on the light

gas/flared gas production. .........................................................141

xi

Appendix A ..........................................................................................................142

Appendix B ..........................................................................................................158

Appendix C ..........................................................................................................160

Appendix D ..........................................................................................................181

References ............................................................................................................185

Vita .......................................................................................................................192

xii

List of Tables

Table ‎3-1: The 24 refinery flares with the highest VOC emissions, as reported

through a month-long 2006 inventory ......................................................... 26

Table ‎3-2: Petroleum refinery flares selected for photochemical modeling analyses ...... 26

Table ‎3-3: The 17 olefin manufacturing flares with the highest VOC emissions, as

reported through a month-long 2006 inventory ........................................... 29

Table ‎3-4: Olefin manufacturing flares selected for photochemical modeling analyses .. 29

Table ‎3-5: Composition of flared gases ............................................................................ 32

Table ‎3-6: Photochemical modeling scenarios performed for each flare ......................... 34

Table ‎3-7: daily maximum mass flow rate for the selected flares, daily maximum one-

hour average ozone concentrations during period of 2006 SI and whether

simulation files for 2 kilometers domain are exist or not ............................ 39

Table ‎3-8: Summary of Maximum ozone concentrations (ppb) for all DRE scenarios

applied on Refinery Flare 1 ......................................................................... 46

Table ‎3-9 : The difference in wide region maxima one-hour average ozone

concentrations (in ppb) and the maximum difference in one-hour

average ozone concentrations (in ppb) for all the flare DRE scenarios

applied to the three refinery flares compared to the base case .................... 48

Table ‎3-10 : The difference in wide region maxima one-hour average ozone

concentrations (in ppb) and the maximum difference in one-hour

average ozone concentrations (in ppb) for all the flare DRE scenarios

applied to the two olefin flares compared to the base.................................. 49

Table ‎3-11: The mass of the predicted ozone (ton) for all scenarios when the

maximum daily flow rates were used for the five flares. The maximum

xiii

daily flow rates for Refinery Flare 1, 2, 3, Olefin Flare 1 and 2 are 25.3,

4, 3.9, 3.5 and 4.1 tons/ hr respectively. The mass of the predicted ozone

was based on MIR values (Carter, 2011) ..................................................... 51

Table ‎3-12 : The absolute product all the scenarios in Table 3-11 divided by the base

case value ..................................................................................................... 51

Table ‎4-1: Photochemical modeling scenarios performed for Refinery Flare 1 ............... 59

Table ‎4-2 : The difference in maximum ozone concentration (DMOC), maximum

difference in ozone concentration (MaxDOC), and minimum difference

in ozone concentration (MinDOC) for all the scenarios with reduced

NOx compared to the base case. All ozone concentrations are in ppb. ........ 66

Table ‎4-3: The difference in maximum ozone concentration (DMOC), maximum

difference in ozone concentration (MaxDOC) and minimum difference

in ozone concentration (MinDOC) for all the scenarios with NOx based

on AP-42 compared to the base case with NOx based on AP-42 value. ...... 67

Table ‎5-1: Categorization of petroleum refinery flares in 2006 SI (Pavlovic et al.,

2012b). ......................................................................................................... 74

Table ‎5-2: Air-assist rates (ft3/min) for each air-assist design under different

stoichiometric air conditions ........................................................................ 76

Table ‎6-1: Data of the sources in the refinery problem .................................................. 103

Table ‎6-2: Data of the sinks in the refinery problem ...................................................... 104

Table ‎6-3: CAPEX and OPEX of auxiliary equipment and pipelines in the multi-

mode FGN.................................................................................................. 105

Table ‎6-4: CAPEX of the pipelines in the multi-mode FGN .......................................... 105

Table ‎6-5: optimum operating conditions for the first problem ..................................... 107

xiv

Table ‎6-6: Scenario results of the impact of hydrocarbon penalty constraint on the

TAC and the flaring amount ...................................................................... 109

Table ‎6-7: Scenario results of the impact of the sink flexibility on the TAC and the

flaring amount ............................................................................................ 110

Table ‎6-8: Scenario results of the impact of expanding the boiler capacity on the TAC

and the flaring amount ............................................................................... 111

Table ‎6-9: Scenario results of the impact of installing a new boiler on the TAC and

flaring amount ............................................................................................ 112

Table ‎6-10: Impact of utilizing the additional high pressure steam on the TAC. ........... 113

Table ‎7-1: The values of rates of reactions and catalyst decay coefficient (Weekman

Jr and Nace, 1970). .................................................................................... 121

Table ‎7-2: Mass rates of additional light gases from a FCC unit for different

temperature excursion scenario when the tc =5 min ................................. 126

Table ‎7-3 : Mass rates of additional light gases from a FCC unit for different

temperature excursion scenario when the tc = 1.25min ............................ 126

Table ‎7-4: The maximum temperature, the minimum temperature, and the maximum

temperature excursion for each scenario when tc =5 min ......................... 132

Table ‎7-5: The maximum temperature, the minimum temperature, and the maximum

temperature excursion for each scenario when tc =5 min ......................... 135

Table A-1: Summary of emission scenarios simulated for each flare ............................ 142

Table A-2: Summary of VOC emissions (tons/hr) for the base case, scenarios A, C, E

and G for Refinery Flare 1 ......................................................................... 143

Table A-3: Summary of NOx, unburned hydrocarbon (UHC) and products of

incomplete combustion (PICs) for the base case, scenarios B, D, F and

H for Refinery Flare 1 ............................................................................... 144

xv





H for Refinery Flare 2 ............................................................................... 146




incomplete combustion (PICs) for the base case, scenarios B, D, F and H

for Refinery Flare 3.................................................................................. 148

Table A-8 : Summary of VOC emissions (tons/hr) for the base case, scenarios A, C, E

and G for Olefin Flare 1 ............................................................................. 149



H for Olefin Flare1 .................................................................................... 150

Table A-10: Summary of VOC emissions (tons/hr) for the base case, scenarios A, C,

E and G for Olefin Flare 2 ......................................................................... 151



H for Olefin Flare 2 ................................................................................... 152

Table A-12: Average ratios of PICs to propylene (unburned flared gas) emissions in

air -assisted flare tests (lbs / lbs Propene) as function of DRE. The feed

to the flare was 80% Propene and 20% of Tulsa natural gas (Allen and

Torres, 2011b). ........................................................................................... 153

xvi

Table A-13: Average ratios of PICs to propylene (unburned flared gas) emissions in

steam-assisted flare tests (lbs / lbs Propene) as function of DRE. The

feed to the flare was 80% propylene and 20% of Tulsa natural gas (Allen

and Torres, 2011b). .................................................................................... 153

Table B-1: Photochemical modeling scenario performed for Refinery Flare 1 .............. 158

Table B-2: Summary of VOC emissions (tons/hr) for the base case, scenarios A, C, E


Table C-1: Flaring emission of flare type 1 (natural, process, and fuel-fired equipment

flares–low variability) for all the average flow scenarios (vent gas with

350 Btu/scf)................................................................................................ 166

Table C-2: Flaring emission of flare type 2 (Natural, process, and fuel-fired

equipment flares–medium variability) for all the average flow scenarios

(vent gas with 350 Btu/scf) ........................................................................ 166


equipment flares–high variability) for all the average flow scenarios


Table C-4: Flaring emission of flare type 4 (Fluid catalytic cracking flares) for all the

average flow scenarios (vent gas with 350 Btu/scf) .................................. 167

Table C-5: Flaring emission of flare type 5 (Unclassified process flares–low

variability) for all the average flow scenarios (vent gas with 350 Btu/scf) 168

Table C-6: Flaring emission of flare type 6 (Unclassified process flares–high



equipment flares–low variability) for all the average flow scenarios


xvii


equipment flares–medium variability) for all the average flow scenarios



equipment flares–high variability) for all the average flow scenarios


Table C-10: Flaring emission of flare type 4 (Fluid catalytic cracking flares) for all

the average flow scenarios (vent gas with 560 Btu/scf) ............................ 170

Table C-11: Flaring emission of flare type 5 (Unclassified process flares–low


Table C-12: Flaring emission of flare type 6 (Unclassified process flares–high


Table D-1: Flow rates and LHV of the available sources in the refinery problem ......... 181

Table D-2: Flow rates and bounds on the LHV of the avaiable sink (Boiler) ................ 181

xviii

List of Figures

Figure ‎1-1: The geographical locations of petroleum refineries in the U.S. (DOE,

2006). ............................................................................................................. 2

Figure ‎1-2: Conceptual diagram of a typical refinery (DOE, 2007) ................................... 3

Figure ‎2-1: Flare CE versus steam to vent gas ratio when the LHV is 350 Btu/scf

(Torres et al., 2012a). ................................................................................... 15

Figure ‎2-2: Flare CE versus steam to vent gas ratio when the LHV is 600 Btu/scf

(Torres et al., 2012a). ................................................................................... 15

Figure ‎2-3: Flare CE versus steam to vent gas ratio when the LHV is 350 Btu/scf and

with no center steaming (Torres et al., 2012a). ........................................... 16

Figure ‎2-4: Flare CE versus air to vent gas ratio when the LHV is 350 Btu/scf. ............. 17

Figure ‎2-5: Flare CE versus air to vent gas ratio when the LHV is 560 Btu/scf. ............. 17

Figure ‎2-6: The superstructure of the FGN. ...................................................................... 20

Figure ‎3-1: Monitored hourly flow rate time series for Refinery Flare 1. ........................ 27



Figure ‎3-4: Monitored hourly flow rate time series for Olefin Flare 1. ............................ 30

Figure ‎3-5: Monitored hourly flow rate time series for Olefin Flare 2. ........................... 30

Figure ‎3-6: Full domain used in this study. The East US, East Texas, Houston-

Galveston-Beaumont-Port Arthur (HGBPA), and Houston Galveston

(HG) nested domains had 36, 12, 4 and 2 km resolution, respectively; in

this work the 2 km grid was flexi-nested to a 1 km resolution (TCEQ,

2010b). ......................................................................................................... 37

xix

Figure ‎3-7: Maximum one-hour ozone concentrations over HG (the red region in

Figure ‎3-6) for the base cases on August 20, 22 and September 4, 2006. ... 41

Figure ‎3-8: Ozone spatial distribution for base cases on August 20, 22 and September

4, 2006, where the white dots are the flare locations (3 flares on August

20, 1 flare on August 22 and 1 flare on September 4). Wind was from

the south-east on August 20 and 22, 2006 and from the northeast on

September 4, 2006. ...................................................................................... 41

Figure ‎3-9: (a) Maximum one-hour average ozone concentrations on August 22, 2006

resulting from applying different flare DRE on the Refinery Flare 1.

(b)The difference in the region-wide maxima one-hour average ozone

concentrations on August 22, 2006 resulting from applying different

flare DREs on Refinery Flare 1. .................................................................. 43

Figure ‎3-10: The spatial distribution for the differences in ozone concentrations from

8:00 am through 3:00 pm between the scenario when Refinery Flare 1

has 50% DRE and the base case, on August 22, 2006................................. 44

Figure ‎3-11: The maximum change in one-hour ozone concentrations compared to the

base case on August 22, 2006 resulting from applying different flare

DREs to Refinery Flare 1............................................................................. 45

Figure ‎4-1: The estimated NOx emission factor versus the combustion efficiency for

the steam-assisted flare. ............................................................................... 57

Figure ‎4-2: The estimated NOx emission factor versus the combustion efficiency for

the air-assisted flare. .................................................................................... 57

Figure ‎4-3: The changes in the region-wide maximum one-hour average ozone

concentrations on August 22, 2006, resulting from applying the three

scenarios with the reduced NOx to Refinery Flare 1. .................................. 60

xx

Figure ‎4-4: The spatial distribution for the differences in ozone concentration from

00:00 am through 7:00 am between the scenario of 98% DRE and NOx

reduced to 50% of the AP-42 value and the base case on August 22,

2006. ............................................................................................................ 62


1:00 am through 8:00 am between the scenario of 90% DRE and NOx


2006. ............................................................................................................ 63


8:00 am through 3:00 pm between the scenario of 75% DRE and NOx


2006. ............................................................................................................ 64

Figure ‎4-7: (a) The maximum positive changes in one-hour average ozone

concentrations compared to the base case on August 22, 2006 resulting

from applying the three scenarios to Refinery Flare 1. (b) The minimum

negative changes in one-hour ozone average concentrations compared to

the base case on August 22, 2006 resulting from applying the three

scenarios to Refinery Flare 1. ...................................................................... 65

Figure ‎5-1: Destruction removal efficiency (DRE) versus air-to-vent gas ratio for

flared gases with a lower heating value (LHV) of 560 Btu/Scf (upper)

and 350 Btu/Scf (lower) (Torres et al., 2012a). ........................................... 72

Figure ‎5-2: Flaring emission of flare type 2 of refinery flares (natural, process, and

fuel-fired equipment flares–medium variability) based on 98% DRE and

using single fixed speed and dual fixed speed blowers. The vent gas has

a LHV of 350 Btu/scf................................................................................... 80

xxi

Figure ‎5-3: Hourly emission rate of flare type 2 (maximum flow 1% of maximum

design capacity) based on DRE of 98% and LHV of 350 Btu/scf (upper)

and hourly emission rate of the same scenario when the single fixed

speed blower configuration is used (lower). ................................................ 81

Figure ‎5-4: Flaring emission of flare type 6 (unclassified process flares–high

variability) based on 98% DRE and using all the air-assist designs for all

the flow scenarios. The vent gas has a LHV of 350 Btu/scf. ....................... 82

Figure ‎5-5: Flaring emission of flare type 6 (unclassified process flares–high

variability) based on 98% DRE and using all the air-assist designs for all

the flow scenarios. The vent gas has a LHV of 560 Btu/scf. ....................... 83

Figure ‎6-1: The superstructure of the multi-mode FGN. .................................................. 87

Figure ‎6-2: Waste gases Flow rates time series from FCCU over a month of

operation. ................................................................................................... 102

Figure ‎7-1: Fluid Catalytic Cracking (FCC) process (DOE, 2007). ............................... 116

Figure ‎7-2: reaction scheme of catalytic cracking of the heavy gas oil. ......................... 118

Figure ‎7-3: The probability of the FCC’s reactor temperature for the base case. .......... 124

Figure ‎7-4 :The probability of the FCC’s reactor temperature for the scenario where

the hot spots average temperature is 830 K and represent 10% of the

reactor volume. .......................................................................................... 125

Figure ‎7-5: Monitored hourly flow rate time series of FCC flare over a month of

operation. ................................................................................................... 128

Figure ‎7-6 : Mass cumulative distribution function for the FCC flare flows. ................ 128

Figure ‎7-7: Histogram and time cumulative distribution for the FCC flare flows for a

month of operation. .................................................................................... 129

xxii

Figure ‎7-8: Hot spot temperature versus the production of additional light gases at

tc=5 min. ..................................................................................................... 130

Figure ‎7-9: Hot spot temperature versus the production of additional light gases at

tc=1.25min. ................................................................................................ 130

Figure ‎7-10: The cumulative distributions of the hot spot temperatures at three

different volume fractions of 2, 5 and 10% at tc of 5 min. ........................ 131

Figure ‎7-11: Time cumulative distribution and histogram of the temperature of the hot

spots at volume fraction of 2% when the tc =5 min. .................................. 132


spots at volume fraction of 5% when the tc =5 min. .................................. 133


spots at volume fraction of 10% when the tc =5 min. ................................ 133

Figure ‎7-14: The cumulative distributions of the hot spot temperatures at three

different volume fractions of 2, 5 and 10%, respectively, at catalyst

residence time of 1.25 min. ........................................................................ 134

Figure ‎7-15 : Time cumulative distribution and histogram of the temperature of the

hot spots at volume fraction of 2% when the tc =1.25 min. ...................... 135

Figure ‎7-16 : Time cumulative distribution and histogram of the temperature of the

hot spots at volume fraction of 5 % when the tc =1.25 min. ..................... 136


spots at volume fraction of 10 % when the tc =1.25 min. ......................... 136

Figure A-1: Maximum one-hour average ozone concentrations on August 30, 2006

resulting from applying different flare DRE on the Refinery Flare2. ....... 154

xxiii

Figure A-2:The difference in the wide-region maxima one-hour average ozone


flare DRE on the Refinery Flare2. ............................................................. 154

Figure A-3: The maximum change in one-hour ozone concentrations compared to the


DREs to the Refinery Flare 2. .................................................................... 154


resulting from applying different flare DRE on the Refinery Flare3. ....... 155

Figure A-5: The difference in the wide-region maxima one-hour average ozone


flare DRE on the Refinery Flare3. ............................................................. 155



DREs to the Refinery Flare 3. .................................................................... 155


resulting from applying different flare DRE on the Olefin Flare1. ........... 156



flare DRE on the Olefin Flare1. ................................................................. 156



DREs to the Olefin Flare1. ........................................................................ 156


resulting from applying different flare DRE on the Olefin Flare2. ........... 157

xxiv



flare DRE on the Olefin Flare2. ................................................................. 157

Figure A-12: The maximum change in one-hour ozone concentrations compared to

the base case on August 20, 2006 resulting from applying different flare

DREs to the Olefin Flare2. ........................................................................ 157

Figure C-1: Hourly emission rate of for flare type 2 (maximum flow 5% of maximum



speed blower configuration is used (lower). .............................................. 172

Figure C-2: Hourly emission rate of flare type 2 (maximum flow 10% of maximum












xxv



and hourly emission rate of the same scenario when the dual variable


















Figure D-1: Material reuse pinch diagram for the first operation mode in the refinery

problem using a flexible fixed capacity boiler of 0.2-0.25 MMscf/hr and

a flare as sinks. ........................................................................................... 182

xxvi

Figure D-2: Material reuse pinch diagram for the second operation mode in the

refinery problem using a flexible fixed capacity boiler of 0.2-0.25

MMscf/hr and a flare as sinks. ................................................................... 182

Figure D-3: Material reuse pinch diagram for the third operation mode in the refinery

problem using a flexible fixed capacity boiler of 0.2-0.25 MMscf/hr and

a flare as sinks.. .......................................................................................... 183

Figure D-4: Material reuse pinch diagram for the fourth operation mode in the

refinery problem using a flexible fixed capacity boiler of 0.2-0.25

MMscf/hr and a flare as sinks.. .................................................................. 183

1

CHAPTER 1: Introduction

Crude oil (petroleum) is a mixture of hydrocarbons in an unrefined form. This

mixture has no or little use in its crude form. In contrast, the refined products from crude

oil such as gasoline, kerosene and diesel fuels have multiple and important uses

worldwide. Petroleum derived fuels are the largest source of energy in the world, and

petroleum and its derivatives are the most traded commodities in the world (DOE, 2007).

In 2006, petroleum fuels accounted for 36 % of the total world’s energy consumption

(EIA, 2009a). In the United States, petroleum is the leading fuel source of energy,

representing approximately 38% of total energy consumption (EIA, 2009b).

The conversion of crude oil into a broad range of marketable products is called

petroleum refining and involves physical and chemical separations, molecular cracking,

physical and chemical treatments, and blending and reforming of organic hydrocarbon

molecules. A petroleum refinery is a complex, coupled and varied collection of processes

that are designed to be flexible in the products produced and the ability to manage a

variety of feedstocks (heavy or light oil), under a variety of market conditions and

environmental regulations.

The U.S. petroleum refining industry is the largest producer of petroleum

products in the world. In 2003, the total production of the U.S refining industry counted

for 23% of total world production (DOE, 2007). There are about 150 refining facilities in

the U.S., which are mapped in Figure 1-1. The majority of the large petroleum refineries

are distributed along the coast of the U.S. to facilitate access to sea transportation. The

total capacity of the U.S petroleum refineries is 18 million barrels per stream day of crude

2

oil. The capacity of processing the crude oil varies from 4,000 to 843,000 barrels per

stream day for refineries in the U.S. (DOE, 2006).

Figure ‎1-1: The geographical locations of petroleum refineries in the U.S. (DOE, 2006).

A petroleum refinery consists of many chemical process units. The types of

processes depend on the type of products desired and the available feedstocks, however,

the most common chemical processes are crude oil distillation, catalytic cracking,

alkylation, catalytic hydrotreating and catalytic reforming. These processes are shown in

a conceptual diagram of a typical refinery, in Figure 1-2.

3

Figure ‎1-2: Conceptual diagram of a typical refinery (DOE, 2007)

4

Most refineries have a crude oil distillation unit that receives crude oil as feed,

and then separates the crude oil into different mixtures or fractions of hydrocarbon (cuts)

according to their boiling points. Distillation is done at both atmospheric pressure and

under vacuum. Typically, atmospheric distillation is done first and the heavy residue

from the bottom of the atmospheric distillation is further processed by vacuum distillation

to recover gas oil under vacuum pressure (Parkash, 2003; Speight, 2005). Fractions

heavier than diesel or gasoline are often then chemically processed in the catalytic

cracking unit. Catalytic cracking breaks (cracks) heavy oil, with high molecular weight,

into lighter hydrocarbons, such as LPG, gasoline and diesel. The most common catalytic

cracking process used in petroleum refineries is fluid catalytic cracking. The catalyst is a

fine powder that behaves as fluid when it is mixed with vaporized feed. The catalyst

deactivates rapidly, and so is continuously circulated between a reactor and a regenerator

(DOE, 2006; Ertl et al., 2008; Parkash, 2003; Speight, 2005).

A variety of hydrotreating processes are used to remove nitrogen, sulfur and other

undesirable species from the refinery’s hydrocarbon products. The refinery streams to be

upgraded are contacted with hydrogen at high temperature and pressure in the presence of

catalysts (DOE, 2006; Speight, 2005). Other catalytic processes (e.g., catalytic reforming,

alkylation) are used to improve the octane number (or other fuel quality parameters) by

restructuring the molecules of the hydrocarbon compounds in the presence of catalysts

(DOE, 2006; Parkash, 2003).

Modern refineries are very efficient at converting increasingly heavy crude oils

into salable products, typically converting 99% of mass entering a refinery into products

that are either sold or used as fuels in the refining process (DOE, 2007; Speight, 2005).

Wastes (the 1% of the input material that is not converted into products) can be routinely

5

generated in processes such as distillation, fluid catalytic cracking and reforming (De

Carli et al., 2002). Wastes can also be associated with startup, shutdown, maintenance,

process upsets, and emergency releases. Wastes from refineries can be in the form of

liquids (mostly water), gases or solids. A typical waste distribution for a large refinery is

51% (by mass ) water with dilute contaminants, 33% solid and 12% gas (Allen and

Rosselot, 1997). Most of the waste gases such as flash gases, purged gas and off gas are

not commercial products and are frequently routed to flares for destruction.

The goal of this thesis will be to examine methods for reducing the environmental

impacts of waste gases from refineries. Waste gases are an appropriate area of focus

because they are typically combustible and therefore have heating value that can be used,

and because, as demonstrated later in this thesis, the emissions may have a significant

impact on regional air quality. The focus in the thesis will be on waste gases that are

currently flared.

Flares are essential units in petroleum refineries. Flares are designed to destroy

waste gases at very high efficiency (98-99% destruction) in a safe manner to protect the

environment from direct waste disposal. During process emergencies (process failure)

flares work as safety units to protect the plant operators and equipment by disposing of

the combustible gases (EPA, 1991). While safe operation of many chemical processes

(including refining) would not be feasible without flaring, previous studies have shown

that flaring emission have negative impacts on air quality (Murphy and Allen, 2005; Nam

et al., 2006; Nam et al., 2008; Pavlovic, 2009; Webster et al., 2007). These studies

demonstrate that flaring emissions can cause a significant increase in concentrations of

ground level ozone and other pollutants. Moreover, recent studies have shown that flare

destruction efficiency can often be significantly below the design value of 98-99%,

6

particularly at low flow conditions (Allen and Torres, 2011a; Cade and Evans, 2010;

Ewing et al., 2010). Most flares operate at low flow rate conditions for the majority of

their operating time and reach their maximum flow rate capacity just a few times every

few years (Cade and Evans, 2010; Ewing et al., 2010; Webster et al., 2007). Therefore,

these flares may work at poor destruction efficiency most of the time, potentially causing

significant air quality impacts.

The goals of this thesis will be:

i) Characterize the air quality impacts of flaring operations at a variety of

operating conditions

ii) Assess the extent to which improvements in flare operations could reduce

flaring emissions

iii) Identify opportunities for reusing currently flared gases in refinery or

ancillary processes

iv) Examine opportunities for reducing the generation of flared gases, using

the improved control of catalytic cracking operations as a case study

Chapter 2 will provide a literature review on industrial flaring operations.

Chapters 3 and 4 will describe the work on assessing the air quality impacts of flares.

Specifically, Chapter 3 will illustrate the impact of flare destruction removal efficiency

and products of incomplete combustion on the air quality. Chapter 4 will show the impact

of nitrogen oxide emissions from flares on regional air quality, using the area around

Houston, Texas as a case study. Chapter 5 will describe opportunities for improving flare

operation, and the emissions changes that could result from those improvements. Chapter

6 will report results on recycling of flared gases in petroleum refineries and Chapter 7

7

will describe preliminary analyses of the effect of temperature excursions in catalytic

cracking units on the generation of flared gases. Finally, Chapter 8 will summarize the

major findings of the work described in this thesis and will make recommendations for

future work.

8

CHAPTER 2: Literature Review

Flares are indispensable units in petroleum refineries that are used to dispose of

waste gases; however, flare emissions can have negative impacts on air quality.

Therefore, minimizing flaring is an important issue in petroleum refining. This chapter

reviews current understanding of the impacts of flare emissions on air quality, and the

potential for reduction of flaring.

2.1 IMPACT OF FLARE EMISSIONS ON AIR QUALITY

Although industrial flares are designed to handle waste gases from petrochemical

facilities safely, recent studies have shown that flaring emissions have the potential to

impact regional air quality. This section reviews recent studies of the impact industrial

flaring on air quality.

Murphy and Allen (2005) showed that emissions from industrial point sources,

such as flares, can exhibit significant temporal variability. Webster et al. (2007) studied

the impact of variability in flare emissions on regional air quality in the Houston-

Galveston (HG) area. For flares, the emissions were grouped into three categories: nearly

constant, routinely variable and allowable episodic emissions. A stochastic model was

developed based on data from a limited group of industrial point sources to generate

typical flare emissions (within the permitted level). Then, these variable emissions were

used in a photochemical model to simulate the impact of the emission variability on

ozone concentrations in the HG area. The air quality simulations showed that the

temporal variability in emissions could result in either a positive or negative change in

ozone concentrations, as compared to assumptions of continuous flare emissions.

9

Localized increases were up to 52 ppb and increases in the region wide ozone maxima

were up to 12 ppb (Webster et al., 2007).

The study of Webster et al. (2007) was based on only a few petrochemical flares.

Pavlovic et al. (2012b) extended the study of Webster et al. (2007) by characterizing the

temporal profiles of emissions from a much larger number of industrial flares. They

found that the emissions from virtually every flare examined had significant temporal

variability and stochastic behavior. Their study was focused on petroleum refinery flares

and flares at olefin manufacturing facilities. Their analysis was based on flared gas flow

rates from a data set referred to as the 2006 special inventory (2006 SI). The 2006 SI is a

collection of hourly data, from August 15, 2006 through September 15, 2006, of

emissions or emission surrogates for different types of emission point sources (flares,

stacks and fugitives) from 141 industrial sites in the HG area. For flares, the data set

reports hourly mass flow rates to the flares and emissions are estimated assuming either a

98% or 99% combustion efficiency of the flared gases. Pavlovic et al. (2012b) grouped

the flares based on industrial sector, process type and chemical compositions. Then,

flares were segregated into subcategories based on statistical parameters (relative

emission variability) of flare emissions. Pavlovic et al. (2012b) then developed a

stochastic model for each subcategory to generate flare emissions with the same temporal

variability as the actual flare flow rates.

Nam et al. (2008) used the stochastic models developed by Webster et al. (2007),

which were later refined by Pavlovic, et al. (2012b), to study the effectiveness of

emission control strategies on air quality in HG area. Specifically, they used regional

photochemical models to estimate the change in the one–hour average ozone

concentrations resulting from applying two approaches to reducing flaring emissions in

10

the HG area. The first method eliminated episodic emissions which are likely due to

activities such as start-ups and shut-downs. The second method decreased the nearly

constant emissions from flares. They also compared the impacts of reducing time varying

flare emissions (both episodic and continuous) to the estimated impacts of reducing

constant flare emissions. The simulations indicated that the benefits estimated from

reducing time varying emissions were higher than those estimated using a deterministic

inventory (constant average emissions), when the same total reduction in emissions was

simulated. Also, controlling episodic emissions was more effective than controlling the

nearly constant emissions in reducing very high localized ozone concentrations in HG

area (Nam et al., 2008).

These analyses all assumed that flare destruction efficiency is ideal (98-99%

destruction). However, there have been concerns that flares may not always perform at

the designed destruction efficiency. A group of full scale tests have demonstrated that, at

high flow rates, and under conditions above a threshold exit velocity, flares operate at

high combustion efficiencies (CE) (McDaniel, 1983; Pohl et al., 1986) However, a

number of field observations indicate that CE can fall below the targeted 98-99% values

under certain conditions. For example, combustion efficiencies below 85% were

measured for two flares (sweet and sour gas flares) in Alberta, under conditions of

relatively low flow and liquid carry-over (Strosher, 2000). During a field measurement

campaign in Houston, the CE of two flares, estimated using Solar Occultation Flux

techniques, were low at low flow rates (Mellqvist, 2001). Recent measurements of full

scale flares under controlled flaring conditions, reported by the University of Texas

(Allen and Torres, 2011a; Torres et al., 2012a; Torres et al., 2012b) have indicated that

for some types of flares, low flows and high steam or air assist rates lead to CEs

11

substantially below 98-99%. Low CEs were observed even under some conditions when

standard emission estimation algorithms would have predicted 98-99% CE. Recently,

field tests were conducted to measure the combustion efficiency of two industrial steam-

assisted flares at petroleum refineries in Texas City, Texas and Detroit, Michigan using

Passive Fourier Transform Infrared Spectroscopy. The tests showed results that were

qualitatively and quantitatively similar to the University of Texas studies. Increasing the

amount of steam assist at low flow can reduce the combustion efficiency dramatically

below 98% (Cade and Evans, 2010; Ewing et al., 2010). Computational studies have

shown similar results. Castiñeira and Edgar (2006) studied flare destruction removal

efficiency (DRE) using computational fluid dynamics simulations. The simulations

indicated that high steam/feed gas and air/feed gas ratios cause inefficient combustion

(decreasing the DRE). Waste gases with lower heating values (LHVs) below 200 Btu/scf

were predicted to cause a dramatic decrease in flare efficiency (Castiñeira and Edgar,

2006). Computational studies of the impact of wind speed indicated that cross winds

shortened the flame length, decreasing flame efficiency, and that increasing the exit

velocity of high momentum flames decreased the flare combustion efficiency under

crosswind conditions (Castiñeira and Edgar, 2008a; Castiñeira and Edgar, 2008b).

Overall, these studies indicate that low destruction efficiencies are possible in industrial

flaring, even when the flares are operated at conditions that may be expected to lead to

high DREs. These lower destruction efficiencies at low flows will influence the air

quality impacts of flare operation.

Al-Fadhli (2010) studied the impact of variation in flare CE on regional air

quality in the HG area. Stochastic models developed by Pavlovic et al. (2012b) were used

to generate variable VOC emissions for petroleum refinery and olefin flares. The VOC

12

emissions from stochastic models were initially based on ideal destruction efficiency (98-

99%). Then, based on assumptions that the CE would vary with flow rate, 100 different

forms of the relationship between CE and flow rate were applied to the hourly mass flow

rate of twenty-five flares in HG area to estimate the VOC emissions of theses flares. The

total VOC emissions resulting from applying different destruction efficiency scenarios

varied between 7.8 to 268 tons/day as compared to an average estimate of 6.3 tons/day of

VOC emissions for an assumed 98-99% CE. These new emissions were used in a

photochemical model to estimate air quality impacts. Meteorological data from August

25, 2000 was used in the model since this day is representative of conditions conducive to

ozone formation. The simulations results were compared to the base case scenario where

the flare destruction efficiency was assumed constant and equal to 98-99%. The air

quality results indicated that flare CE scenarios have the potential to increase ozone

concentrations from a few ppb up to 50 ppb (Al-Fadhli, 2009; Pavlovic et al., 2012a).

2.2 FLARE DESTRUCTION EFFICIENCY

The studies reviewed in the previous section have demonstrated that flare

emissions are highly variable and that both the temporal variability in flared gas flow and

the effectiveness of combustion in the flare can have significant impacts on regional air

quality. Reducing flare emissions will require an understanding of the processes that

generate flare gas and the operating conditions that lead to low combustion efficiencies.

This section addresses understanding the conditions that lead to poor combustion

efficiencies.

Understanding of the factors that lead to low combustion efficiencies in flares was

substantially improved through a recent measurement campaign conducted by the

13

University of Texas at the testing facilities of the John Zink Company. The campaign

examined flare CE and destruction removal efficiency (DRE, percentage of the flared

gases that react to form either products of complete or incomplete combustion) at low

flow rates under variety of industrial operation conditions. Two flares were tested in this

campaign: a steam-assisted flare and an air-assisted flare with flare tips of 36 inch and 24

inch diameters, respectively. The steam-assisted flare has both an upper ring and center

injecting nozzle to introduce stream into the combustion zone. The maximum design

capacity of the steam-assisted flare was 937,000 lb/hr while the air-assisted flare was

144,000 lb/hr. The flare tests were conducted at an outdoor flare facility under semi-

controlled environment (controlled flare conditions but uncontrolled weather conditions).

A series of tests were conducted at low flow conditions and with low heating value gases,

since previous studies, summarized in the last section, indicated that low flow of low

heating value gases represent particularly challenging conditions for flares. All the flare

operating tests were operated under conditions at which 98-99% combustion efficiency is

expected (i.e., in compliance with all criteria of 40 Code of Federal Regulation (CFR) §

60.18) (Allen and Torres, 2011a; Torres et al., 2012a).

A plume sample collector was used, suspended by a crane over the flare plume, to

extract gas samples from the flare plume. The extracted sample was sent to two sampling

vans that contained instruments that allowed measurement of unburned gases, and

products of complete and incomplete combustion. These measurements allowed CE and

DRE in the flare plume, downwind of the combustion zone, to be measured with a one

second resolution. Remote sensing technologies also were used to estimate CE. Vent gas

flow rates ranged from 0.1% to 0.25% of the maximum design capacity of the flares and

14

gases with heating values of 300-600 BTU/scf were used (Allen and Torres, 2011a;

Torres et al., 2012a; Torres et al., 2012b).

The campaign results showed that both flares could reach high CE and DRE at

low flow rates when they are working near the incipient smoke point (the point at which

smoke begins to persist multiple flame lengths from the flare tip); however, as the steam

or air to fuel ratio increases, the CE and DRE decrease dramatically which results in poor

flare CE and DRE (below 50%). Because operating a flare which produces smoke

violates regulations, most flare operators will not operate near the smoke point, and will

add steam or air until the smoke is extinguished. Unfortunately, reducing smoke can also

significantly lower CE and DRE. For the steam-assisted flare, the dependence of CE on

steam assist at low vent flow rates and relatively low LHV shows that CE greater than

98% for a limited range of steam to vent gas ratio. The CE decreases dramatically as

more steam is added. The ratio of steam to vent gas flow rate at which the CE and DRE

start to decline depends on the LHVs of the vent gas and the amount of steaming. As

shown in Figure 2-1, the flare CE starts to decline dramatically at a steam to vent gas

ratio of 0.5 when the LHV is 350 Btu/scf. In contrast, the flare CE starts to decrease at a

steam to vent gas ratio of 1 when the LHV is 600 Btu/scf as shown in Figure 2-2. Figure

2-3 shows the relation between the flare CE and steam to vent gas ratio at LHV of 350

Btu/scf but with no center steaming. Comparing Figure 2-1 with Figure 2-3 shows that

center steaming impacts flare CE more than upper steam, where center steam indicates

steam added with the vent gas and upper steam indicates steam added at the flare tip.

15

Figure ‎2-1: Flare CE versus steam to vent gas ratio when the LHV is 350 Btu/scf (Torres et al., 2012a).

Figure ‎2-2: Flare CE versus steam to vent gas ratio when the LHV is 600 Btu/scf (Torres et al., 2012a).

0

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16

Figure ‎2-3: Flare CE versus steam to vent gas ratio when the LHV is 350 Btu/scf and with no center steaming

(Torres et al., 2012a).

For air-assisted flares, CE is high at low air assist rates, however, the CE declines

linearly as air is added. Figures 2-4 and 2-5 illustrate the relationship between the CE and

the air-to-vent gas ratio at two different vent gas LHVs. Comparing Figure 2-4 with

Figure 2-5 indicates that the CE declines more slowly for the air-assisted flare as the

LHV of the vent gas increases (Allen and Torres, 2011a; Torres et al., 2012a).

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17

Figure ‎2-4: Flare CE versus air to vent gas ratio when the LHV is 350 Btu/scf.

Figure ‎2-5: Flare CE versus air to vent gas ratio when the LHV is 560 Btu/scf.

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18

The chemical composition of products of incomplete combustion (PICs) were also

reported. The PICs in the flare emissions include carbon monoxide formaldehyde,

ethylene, acetaldehyde, acrolein, acetylene, ethane, propylene-oxide, methanol, acetone,

propanol and butane isomers. However, the dominant species were carbon monoxide

formaldehyde, acetaldehyde, acetylene and ethylene (Allen and Torres, 2011a; Knighton

et al., 2012; Torres et al., 2012a). The chemical composition of the vent gas had some

impact on PICs, but little impact on the CE.

These detailed measurements provide new insights into methods that could be

used to reduce the air quality impacts of flares. This topic will be addressed in more

detail in Chapter 5.

2.3 REDUCING FLARING THROUGH FUEL GAS NETWORKS

The previous sections have demonstrated that flare emissions can have significant

air quality impacts. Those impacts can be mitigated by improving flare operation

(increasing CE and DRE), but a more effective strategy would be to prevent the gases

from being flared. One method for reducing flared gases is to incorporate the gases into

the fuel gas networks in refineries. This presents challenges, since as was documented in

previous sections, flared gas flows are highly variable. Nevertheless, as will be described

in Chapter 6, there are opportunities for integrating more flared gases into refinery fuel

gas networks. This section reviews previous work on fuel gas networks.

De Carli et al (2002) proposed a new management and control strategy to improve

the performance of the fuel gas network (FGN) without changing the existing

superstructure in a petroleum refinery. They discussed designing an advanced controller

for the management of the FGN with the following objectives: controlling pressure

19

variation within a prescribed range, minimizing the flared waste fuel gas, minimizing the

use of fuel oil and LPG, and stabilizing the fluid temperature. The simulations showed

good preliminary results on the performance of FGN after using the advanced controller

(De Carli et al., 2002).

Hasan et al.(2011) developed a fuel gas network (FGN) design methodology that

evaluates waste gases with fuel values from different sources (chemical processes) and

mixes them optimally to match the available fuel waste gases with various sinks ( boilers,

furnace, turbines, steam generator etc.) in a petroleum refinery or LNG plant. The

optimality of the mixing depends on the flows, chemical compositions, fuel quality of

waste gases, and fuel constraints (flow, chemical compositions and fuel qualities) of all

the potential sinks. These fuels can be used to provide steam, electricity and heat to onsite

and offsite units or plants. It is this approach to fuel gas network optimization that will be

used in this thesis to examine opportunities for minimizing flaring.

In the current formulation, the optimized FGN is a steady state model that

accounts for non-isothermal and non-isobaric operation, non-isothermal mixing,

treatment cost, utility and operating cost, the profit of using waste gases from all the

available sources and the cost of not using them. The superstructure of the FGN model

includes sources, sinks, headers (pools) for each sink and auxiliary equipment such as

heaters, coolers, compressors and valves to account for non-isothermal and non-isobaric

operation. Figure 2-6 shows the superstructure of the FGN. The header is used to mix the

fuels from different sources and supply the sinks with required amounts of gas. Also, a

standard fuel source was added to the superstructure of the FGN to assure the standard

fuel can be used, but with relatively higher cost, if there is any shortage in the waste

20

gases supply to meet energy requirements of the sinks. Any additional fuel waste gas that

cannot be consumed by the sinks is disposed as waste or sent to a flare.

The FGN network is optimized using a non-linear program (NLP) that

incorporates constraints concerning utilities (compressors, valves, heaters, and coolers),

system pressure and temperature, non-isothermal mixing and fuel gas properties. The fuel

gas properties that are included in the FGN model are: lower heating values, Wobbe

index, Methane number, temperatures, pressures, Flammability ratio and chemical

composition. The main objective of the FGN model is to integrate the waste gases from

different sources with the available sinks while minimizing the annual cost of the FGN.

The FGN model has been applied to a petroleum refinery case study and two LNG case

studies. The model saved approximately 40 to 50 % of total energy cost in the three case

studies (Hasan et al., 2011).

Figure ‎2-6: The superstructure of the FGN.

21

Broadly, the mixing and redistribution fuels in FGN is a well known problem in

the literature referred to as a pooling problem. In general, the pooling problem consists of

three main nodes. The first node represents the sources. The second node represents the

pools which are intermediate storage used to receive the streams from different sources

and distribute them to the products. These pools improve mixing flexibility; however,

impose more restrictions that introduce nonlinear constraints. The third node represents

the products. Connections among the sources, pools and the products are defined. Also,

the quality specifications of the source streams and the required characteristics of the

products are known. However, all flow rates among the sources, pools and product tanks

are unknown and are optimized subject to all the imposed constraints. Determining the

optimum flow rates between the three nodes to minimize the total cost or maximizing the

total revenue of the blending process is called the pooling problem.

Much research has been devoted to find the global solution of pooling problems

because of their importance in the petrochemical sector. Haverly (1978) proposed a

recursion approach to solve the pooling problem. However, whether the global optimum

will be found using this approach depends on the initial starting points (Haverly, 1978).

Lasdon et al. (1979) and his colleges solved the pooling problem proposed by Haverly

through nonlinear programming using generalized reduced gradient algorithms and

successive linear programming (Lasdon et al., 1979). A decomposition strategy to search

for the global optimum for the pooling problem with nonconvex bilinear terms was

proposed by Floudas and Aggarwal (1990). They applied their strategy to a pooling

problem introduced by Haverly. However, their approach cannot guarantee determining

the global optimum (Floudas and Aggarwal, 1990). Audet et al. (2004) formulated the

pooling problem into models that can be solved using the branch-and-cut quadratic

22

programming algorithm developed by Audet et al. (2000) (Audet et al., 2004; Audet et

al., 2000). Meyer and Floudas (2006) proposed a piecewise algorithm based on the

reformulation-linearization technique to determine the global solution of the pooling

problem. Their proposed approach has reduced the gap between the upper and the lower

bound to 1.2% when it was applied on a large complex industrial problem (Meyer and

Floudas, 2006). Pham et al.(2009) proposed a convex hull discretization approach to find

the global or the near global solution for the pooling problem. They solved the discretized

pooling problem as a mixed integer linear programming to determine the global

minimum flow rates among the sources, pools and the product tanks (Pham et al., 2009).

Minsener and Floudas (2010) used piecewise underestimation algorithms for the

nonconvex bilinear terms developed by(Wicaksono and Karimi, 2008), and (Gounaris et

al., 2009) to solve large scale pooling problems (Misener and Floudas, 2010)

This work will use the optimization approach utilized by Hasan, et al. (2011) to

minimize flared gases by incorporating these gases into the FGN in a refinery.

Specifically, this study will modify the FGN to accommodate the waste gases from a

fluidized catalytic cracking unit (FCCU) flare as a case study. Different scenarios will be

tested to minimize the flared from FCCU.

An alternative to incorporating flared gases into a FGN is to minimize the flared

gases at their source. As a case study of this approach, this thesis will examine

minimization of flared gases from FCC units. The approach will be to use kinetic models

of FCC units to estimate the quantity of gas generated at various operating temperatures,

then to use that information to assess the value (in minimized flare gases) of better

temperature control in FCC units. This topic is described in Chapter 7.

23

CHAPTER 3: Impact of Flare Destruction Efficiency and Products

of Incomplete Combustion on Ozone Formation in Houston, Texas

3.1 INTRODUCTION

Flares are designed to combust waste organic gases at very high efficiency. Most

flares are designed to have destruction removal efficiencies (DREs), defined as the

percentage of waste gas fed to the flare that is destroyed by complete or partial

combustion, of 98% or 99%. Flares are also designed to operate over a very large range

of flow rates. Emergency flares need to be able to handle the large volumes of gases that

may need to be disposed of very rapidly during a process upset or during a process start-

up or shut-down. Some of these emergency flares are also used to destroy much lower

flows of gases that occur during routine operation. Webster et al. (2007) have reported on

variability in the flow to a small sample of petrochemical flares; Pavlovic et al. (2012b)

have reported on flow variability for a much larger number of flares. Both of these

studies have confirmed that flow to flares varies over large ranges, and have presented

quantitative characterizations of variability in flare flows.

As described in Chapter 2, several studies have examined the impact of flow

variability in industrial flare emissions on ambient ozone concentrations, particularly

focusing on the Houston-Galveston area (Murphy and Allen, 2005; Nam et al., 2006;

Nam et al., 2008; Pavlovic, 2009; Webster et al., 2007). However, all of these studies

have assumed that the destruction efficiency in flares remains constant at 98-99%. As

also described in Chapter 2, low destruction efficiencies are possible in industrial flaring,

even when the flares are operated at conditions that may be expected to lead to high

DREs. These lower destruction efficiencies at low flows will influence the air quality

impacts of flare operation.

24

Previous analyses of the air quality impacts of flare emissions have assumed

constant destruction efficiencies; these studies have also generally assumed that unburned

hydrocarbons exiting the flare have the same composition as the waste gas. Recent

measurements have characterized products of incomplete combustion (PICs) in flares,

and these PICs include both highly reactive gases (e.g., formaldehyde and acetaldehyde)

and less reactive gases (e.g., CO) (Allen and Torres, 2011a; Herndon, 2011). These PICs

may also influence the air quality impacts of flare emissions.

This Chapter describes an extension of previous analyses of the air quality

impacts of flare emissions that uses results from recent studies of the DRE and PICs in

full scale flares. The impact on air quality of flare DREs that are less than 98-99% and

the impact on air quality, specifically ozone formation, of products of incomplete

composition (PICs), are examined.

25

3.2 METHODOLOGY

3.2.1 Industrial Flares

The first step in the analysis was to select specific flares for analysis. Flares from

petroleum refineries and from olefin manufacturing operations were chosen, as described

below.

3.2.1.1 Petroleum refinery flares

Detailed data on the flow rates to petroleum refinery flares have been examined

by Pavlovic (2009). These flares were classified into categories based on the sources of

the waste gas fed to the flare, the composition of the waste gas streams, and the

variability in the flow rates. Table 3-1 lists the 24 flares in the Houston-Galveston area,

reported with refinery source codes, with the highest reported flow rates. The flow data

are based on information from a month-long study period (August 15-September 15,

2006) during which hourly mass flows fed to the flares were reported. The flares are

grouped into five categories: natural gas flares, process gas flares, fuel fired equipment

flares, fluid catalytic cracking unit flares and unclassified flares.

This work will examine the potential air quality impacts of low destruction

efficiencies and PICs formation on three of these flares. These flares were selected based

on their relatively high average flow rates and the photochemical reactivity of the vent

gases. The three flares chosen are described in Table 3-2. Figures 3-1, 3-2 and 3-3 show

the monitored hourly flow rate time series for each of the flares selected for detailed

modeling, during the month-long data collection period starting on August 15 and ending

September 15, 2006 (768 hours).

26

Table ‎3-1: The 24 refinery flares with the highest VOC emissions, as reported through a month-long 2006

inventory

Process type Flow rate (Tons/32 day) Emissions, assuming

98 or 99% DRE (Tons/32day)

Natural Gas 2639.12 52.78

fuel fired equipment 1511.16 30.22

Process Gas 1515.42 15.15


Fluid Catalytic Cracking Unit 638.60 12.77





Unclassified 311.80 6.24













Fluid Catalytic Cracking Unit 14.60 0.29


DRE of 98 % or 99% can be assumed if a flare satisfies all criteria of 40 Code of Federal Regulation (CFR) § 60.18. If the flared gas is

alkane (butane +) and hydrogen, a DRE of 98% is assumed, and if the flared gas is propylene, propane, or ethylene, a DRE of 99% is

assumed (TCEQ, 2009).

Table ‎3-2: Petroleum refinery flares selected for photochemical modeling analyses

Flare identifier Flow rate

(Tons/32 day)

Emission assuming 98 or

99% DRE (Tons/32day)

Location

(lat, lon)

Average heat content

( Btu/Ib)

Refinery Flare 1 1511.16 30.22 29.717, -95.130 19300

Refinery Flare 2 1515.42 15.15 29.723, -95.209 19400

Refinery Flare 3 716.18 14.32 29.371, -94.927 19600 DRE of 98 % or 99% can be assumed if a flare satisfies all criteria of 40 Code of Federal Regulation (CFR) § 60.18. If the flared gas is alkane (butane +) and hydrogen, a DRE of 98% is assumed, and if the flared gas is propylene, propane, or ethylene, a DRE of 99% is

assumed (TCEQ, 2009)

27

Figure ‎3-1: Monitored hourly flow rate time series for Refinery Flare 1.


0

5

10

15

20

25

30

0 200 400 600

Flo

w r

ate

(to

ns/

hr)

Hour of operation

0

1

2

3

4

5

6

0 200 400 600

Flo

w r

ate

(to

ns/

hr)

Hour of operation

28


3.2.1.2 Olefin flares

Flares used in olefin (ethylene and propylene) manufacturing processes were

chosen for analysis because they have relatively high flow rates and because they emit

chemical species that have high photochemical reactivity. Table 3-3 lists the 17 flares

reported with olefin manufacturing source codes that had the highest flow rates during

the August 15 through September 15, 2006 reporting period (Pavlovic, 2009). Two of

these flares were chosen for detailed analysis (Table 3-4), based on their relatively high

flow rates and their physical location within a region in which photochemical models

with detailed spatial resolution were available. Figures 3-4 and 3-5 show the monitored

hourly flow rate time series for each of the flares selected for detailed photochemical

modeling.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 200 400 600

Flo

w r

ate

(t

on

s/h

r)

Hour of operation

29

Table ‎3-3: The 17 olefin manufacturing flares with the highest VOC emissions, as reported through a month-

long 2006 inventory

Process type Flow rate (Tons/32day) Emissions at 98-99%

DRE (Tons/32day)

Ethylene: General 1666.88 33.34


Propylene: General 287.95 5.76

Not Classified 206.52 4.13


Ethylene: Flue Gas Vent 119.20 2.38




Propylene: Fugitive Emissions 58.58 1.17



Propylene: Fugitive Emissions 27.55 0.55




Ethylene: General 2.09 0.04 DRE of 98 % or 99% can be assumed if a flare satisfies all criteria of 40 Code of Federal Regulation (CFR) § 60.18. If the flared gas is

alkane (butane +) and hydrogen, a DRE of 98% is assumed, and if the flared gas is propylene, propane, or ethylene, a DRE of 99% is

assumed (TCEQ, 2009).

Table ‎3-4: Olefin manufacturing flares selected for photochemical modeling analyses

Flare identifier Flow rate

(Tons/32day)

Emission assuming 98 or

99% DRE (Tons/32day)

Location

(lat, lon)

Average heat content

( Btu/Ib flow rate)

Olefin Flare 1 499.13 9.98 29.752, -95.009 19200

Olefin Flare 2 287.95 5.76 29.858, -94.911 19500 DRE of 98 % or 99% can be assumed if a flare satisfies all criteria of 40 Code of Federal Regulation (CFR) § 60.18. If the flared gas is alkane (butane +) and hydrogen, a DRE of 98% is assumed, and if the flared gas is propylene, propane, or ethylene, a DRE of 99% is

assumed (TCEQ, 2009)

30

Figure ‎3-4: Monitored hourly flow rate time series for Olefin Flare 1.

Figure ‎3-5: Monitored hourly flow rate time series for Olefin Flare 2.

0

2

4

6

8

10

12

0 200 400 600

Flo

w r

ate

(to

ns/

hr)

Hour of operation

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 200 400 600

Flo

w r

ate

(to

ns/

hr)

Hour of operation

31

3.2.2 Flare chemical compositions

The chemical composition of the inlet flow was assumed constant for each flare,

and was based on the monthly average composition. Also, the composition of the VOCs

reported as unclassified in the flare composition data (ranging from 0 to 54.6% mass

fraction) was assumed to have a composition identical to the average composition of

identified species. Table 3-5 shows chemical compositions for each flare. The flared

gases contain a mixture of olefins and saturated alkanes, and therefore, the emissions

from these flares are expected to have a broad range of chemical reactivity. This

variability in the composition of flared gases also indicated that the flares service very

different chemical and petroleum processing operations. Further, the labeling of these

flares as refinery or olefin manufacturing should be regarded a broad source identifier.

Operations serviced by these flares include integrated refining and chemical

manufacturing operations, and the identifications used in this work are based on the

source code identifiers used in reporting the emissions.

32

Table ‎3-5: Composition of flared gases

Mass percentages in waste gases

Chemical species Refinery

Flare 1

Refinery

Flare 2

Refinery

Flare 3 Olefin Flare 1

Olefin Flare 2

1,3-BUTADIENE 7.81% 0.00% 0.00% 9.75% 0.00%

ACETYLENE 0.03% 0.00% 0.00% 0.01% 0.00%

BENZENE 0.00% 0.00% 0.00% 6.25% 0.00%

N-BUTANE 4.91% 10.14% 7.14% 4.72% 21.69%

BUTENE 0.00% 1.31% 1.45% 0.00% 0.00%

1-BUTENE 2.47% 0.00% 0.00% 0.00% 0.00%

2-METHYL-1-BUTENE 0.00% 0.00% 0.00% 2.63% 0.00%

3-METHYL-1-BUTENE 0.00% 0.00% 0.00% 0.92% 0.00%

CIS-2-BUTENE 1.17% 0.42% 0.00% 1.13% 0.04%

ETHYL BENZENE 0.00% 0.00% 0.00% 0.18% 0.00%

ETHYLENE 7.17% 0.81% 0.00% 3.54% 0.45%

HEPTANE 0.00% 0.00% 0.00% 1.76% 0.00%

HEXANE 0.00% 0.00% 7.47% 5.44% 0.00%

INDENE 0.00% 0.00% 0.00% 0.04% 0.00%

ISOBUTANE 3.39% 9.81% 9.35% 1.43% 36.26%

ISO-BUTENE 2.48% 0.00% 1.45% 0.00% 0.00%

ISOBUTYLENE 0.00% 0.00% 0.00% 5.12% 0.22%

ISO-PENTANE 0.00% 0.00% 7.26% 15.29% 0.00%

ISOPRENE 0.00% 0.00% 0.00% 5.45% 0.00%

NONANE 0.00% 0.00% 0.00% 0.50% 0.00%

OCTANE 0.00% 0.00% 0.00% 1.22% 0.00%

(E)-1,3-PENTADIENE 0.00% 0.00% 0.00% 1.48% 0.00%

1,4-PENTADIENE 0.00% 0.00% 0.00% 1.61% 0.00%

PENTANE 0.00% 0.00% 7.26% 4.15% 16.25%

N-PENTANE 0.00% 0.00% 0.00% 9.74% 0.00%

1-PENTENE 0.00% 0.00% 0.00% 3.62% 0.00%

PROPANE 1.27% 20.26% 44.01% 2.21% 15.99%

PROPYLENE 13.14% 2.26% 3.65% 4.46% 9.05%

STYRENE 0.00% 0.00% 0.00% 0.11% 0.00%

TOLUENE 0.00% 0.00% 0.00% 1.18% 0.00%

TRANS-2-BUTENE 1.60% 0.56% 0.00% 1.14% 0.06%

TRANS-2-PENTENE 0.00% 0.00% 0.00% 1.52% 0.00%

VINYL TOLUENE 0.00% 0.00% 0.00% 0.06% 0.00%

VOC-UNCLASSIFIED 54.56% 54.44% 10.96% 3.35% 0.00%

33

3.2.3 Emissions scenarios

3.2.3.1 VOC emissions

Flare DRE and the extent of PICs formation could be influenced by many factors,

such as high cross wind speed, over steaming, over aerating, and low heating value of the

waste gases. This analysis will examine the impacts on air quality for four assumed levels

of DRE (95%, 90%, 75% and 50%) for each of the five flares chosen for photochemical

modeling analyses. These DRE values will be applied to the inlet flow rate data for the

five flares to determine the flare emissions. For each flare and each DRE level, two

scenarios for PIC emissions will be examined. The first case assumes that VOC

emissions are just unburned hydrocarbons (no PICs formation) while the second case

assumes that the emissions are a combination of unburned hydrocarbons and PICs. The

ratio of unburned hydrocarbon to PICs was based on results reported in the University of

Texas flare studies (Allen and Torres, 2011a), and depended on DRE. Tables A-12 and

A-13 in the Appendix A summarize the measurement results of the PICs reported by the

University of Texas. In this work, it will be assumed that, in cases where the DRE is 95

or 90%, the ratio of unburned hydrocarbon to PIC is 1:1. For 75 and 50 % DREs, the

ratios of unburned hydrocarbons to PICs are assumed to be 4:1 and 9:1, respectively. In

all cases, the PICs are assumed to be 93% (carbon basis) carbon monoxide (CO), 5%

formaldehyde, and 2% acetaldehyde. This is a simplification of the full results of PIC

composition reported by Knighton et al. (2012), but captures the main features of the PIC

composition analysis. Much of the mass of PICs is CO, but the remainder includes highly

photochemically reactive species such as aldehydes. Taking into account all of the

assumed flare emission scenarios, for each of the 5 flares chosen for analysis, 8 scenarios

34

(in addition to the base case of 98 or 99% DRE) were examined. Table 3-6 summarizes

the 8 scenarios examined for each flare.

Table ‎3-6: Photochemical modeling scenarios performed for each flare

Flare emissions

Scenario DRE%

Unburned hydrocarbons PIC Ratio of hydrocarbon to PIC

A 95 yes no 1 : 0

B 95 yes yes 1 : 1

C 90 yes no 1 : 0

D 90 yes yes 1 : 1

E 75 yes no 1 : 0

F 75 yes yes 4 : 1

G 50 yes no 1 : 0

H 50 yes yes 9 : 1

These levels of DREs (95%, 90%, 75% and 50%) are within the range of

measurements reported in the University of Texas flare tests. At world-scale facilities,

refinery and olefin emergency flares can accommodate on the order of 500 tons /hr of

flow. The flow rates of the five flares selected for this analysis do not exceed 25 tons/hr

and for most of the time, the reported flows do not exceed 4.5 tons/hr (<1% expected

maxima for emergency flares). As reported by Pavlovic et al. (2012b) and Webster et al.

(2007), many flares work primarily at low flow rate conditions and may reach maximum

flow only once in several years of operation. The available flow rate data of the flares

examined in this work is for one month. Therefore, the flow rates examined in this work

could represent low flow conditions, subject to over-steaming, over-aerating and cross

wind impacts.

35

3.2.3.2 NOx emissions

Flare NOx emissions are formed by the fixation of atmospheric nitrogen at high

temperature or the reaction of hydrocarbon free radical with nitrogen in the combustion

zone (Wark et al., 1998). NOx emissions for the flares were estimated using the emission

factor from AP-42, 0.068 lb/106 BTU (EPA, 1991). Detailed information about the flare

emissions for each emission scenario is reported in the Appendix A and a more detailed

examination of the impacts of NOx emissions on air quality impacts is presented in

Chapter 4.

3.2.4 Photochemical modeling

A photochemical model was used to evaluate the impact of the flare emission

scenarios on predicted one-hour average ozone concentration in Houston, Texas. The

photochemical model used in the simulations was the Comprehensive Air Quality Model

with extension (CAMx). This model simulates the dispersion, advection, and physical

and chemical processes of pollutants in the troposphere by solving the continuity

equation for each chemical species in nested 3 dimensional grids. CAMx was selected for

this analysis since it is the photochemical model used by the State of Texas in developing

air quality management plans (State Implementation Plan or SIP). The model and input

data used in the simulations, except for the emissions of the specific flares being

examined, were the same model formulation and inputs being used by the State of Texas

in its SIP for the Houston area and are available from the Texas Commission on

Environmental Quality (TCEQ) web site (TCEQ, 2011). In this work, CAMx ready input

files were downloaded from TCEQ. These input files included initial and boundary

conditions, metrological data, emissions inventories input files, and land use data (TCEQ,

36

2010a). This base case model has undergone extensive performance evaluation, as

described by the TCEQ (TCEQ, 2011). Figure 3-6 shows the spatial domain for the

simulation and maps the horizontal grid resolutions used in the model.

Previous work had shown that the air quality impacts of flaring can be sensitive to

the horizontal grid resolution used in the modeling in the region immediately surrounding

the flare (Nam et al., 2006; Pavlovic, 2009; Webster et al., 2007). Therefore, for this

work, the minimum practicable horizontal grid resolution, 1 km, was used in the regions

in which the flares were located. A 1 km resolution was achieved by flexi-nesting

(Environ International corporation (Environ), 2010) the 2 km resolution grid provided by

the TCEQ.

37

Figure ‎3-6: Full domain used in this study. The East US, East Texas, Houston-Galveston-Beaumont-Port Arthur

(HGBPA), and Houston Galveston (HG) nested domains had 36, 12, 4 and 2 km resolution, respectively; in this

work the 2 km grid was flexi-nested to a 1 km resolution (TCEQ, 2010b).

38

Photochemical model predictions were made for different days for the five flares.

Days to be simulated were chosen based on three criteria. First, only days when the daily

maximum one-hour average ozone concentration in Houston exceeded 85 ppb were

considered. These days were chosen because they are meteorologically conducive to

ozone formation, and flare emissions on these days will have the greatest impact on air

quality management decisions. A second criterion was that the flow rate to the flare

would approach the observed maximum for the 2006 modeling period which is still

assumed to potentially represent low flow conditions. Finally, the availability of a high

spatial resolution photochemical modeling base case for the day of interest was

necessary. Table 3-7 shows the daily maximum mass flow rate for the selected flares,

daily maximum one-hour average ozone concentrations during the period when hourly

emissions were available, and whether high spatial resolution photochemical models

were available. Based on these data, the best simulation day for Refinery Flare 1 was

August 22, 2006, and the best day for Refinery Flare 2 was September 4, 2006; the best

day for simulation for the other flares was August 20, 2006.

39

Table ‎3-7: daily maximum mass flow rate for the selected flares, daily maximum one-hour average ozone

concentrations during period of 2006 SI and whether simulation files for 2 kilometers domain are exist or not

Daily maximum flow rate(ton/hr)

Date

Maximum ozone concentration(ppb)

Refinery

Flare1

Refinery

Flare2

Refinery

Flare3 Olefin Flare1

Olefin Flare2

Files of 2 Km domain

8/15/2006 98 8.6 0.2 1.3 1.6 0.6 yes

8/16/2006 141 3.5 0.1 2.2 1.1 3.1 yes

8/17/2006 158 2.4 0.1 3.3 2.1 0.7 yes

8/18/2006 106 0.1 0.1 3.1 0.9 0.5 yes

8/19/2006 69 0.5 0.4 1.7 1.4 2.0 yes

8/20/2006 107 3.3 2.8 3.9 3.5 4.1 yes

8/21/2006 102 8.7 2.1 1.6 0.9 0.6 yes

8/22/2006 93 25.3 3.8 1.4 0.8 0.8 yes

8/23/2006 75 3.8 3.9 1.3 0.9 0.6 no

8/24/2006 93 4.9 4.0 0.3 10.5 2.7 no

8/25/2006 51 3.4 3.4 1.0 8.1 2.0 no

8/26/2006 30 3.2 3.6 1.3 1.5 0.9 no

8/27/2006 36 9.5 3.8 1.1 1.0 0.8 no

8/28/2006 47 5.4 4.9 1.5 0.7 1.0 no

8/29/2006 103 2.9 3.0 1.7 1.1 1.9 yes

8/30/2006 95 3.0 3.2 1.5 1.9 0.9 yes

8/31/2006 147 4.6 3.9 1.4 0.5 1.3 yes

9/1/2006 161 0.1 3.7 1.7 0.5 0.7 yes

9/2/2006 90 0.2 3.4 2.5 0.8 1.0 yes

9/3/2006 107 0.2 3.0 1.3 1.0 1.5 yes

9/4/2006 101 0.1 4.0 1.5 0.8 0.3 yes

9/5/2006 82 4.0 3.5 1.5 0.4 1.5 yes

9/6/2006 101 3.7 3.7 1.4 0.5 2.1 yes

9/7/2006 153 4.2 3.3 1.4 0.9 0.5 yes

9/8/2006 102 7.9 3.3 1.4 0.7 0.7 yes

9/9/2006 54 6.1 3.3 1.4 0.4 0.8 no

9/10/2006 62 5.9 3.4 1.4 0.4 0.9 no

9/11/2006 68 6.3 3.5 1.4 0.4 0.4 no

9/12/2006 83 4.6 3.2 1.5 1.0 0.7 yes

9/13/2006 109 2.8 3.7 1.5 0.5 0.8 yes

9/14/2006 165 1.7 3.6 1.2 0.7 1.0 yes

9/15/2006 73 3.0 0.0 1.7 0.7 2.1 no

40

3.3 RESULTS AND DISCUSSION

Results will be reported as differences in ozone concentrations between a

photochemical modeling base case with high (98-99%) DRE and a photochemical

modeling scenario with lowered DRE, with and without PIC formation. The impact of the

flare emission scenarios on the region-wide ozone maximum and the maximum

difference in the one-hour average ozone concentrations resulting from the flare emission

scenarios will both be reported.

3.3.1 Base Case

Base cases with high flare DRE (98-99%) were simulated for August 20, 22 and

September 4, 2006. Region-wide maximum one-hour ozone concentrations of 120, 145

and 101 ppb occurred at 3:00 pm on August 20, 4:00 pm on August 22 and 2:00 pm on

September 4, respectively. Figure 3-7 shows the region-wide maximum one-hour average

ozone concentrations for each hour for the base cases on August 20, 22 and September 4,

2006. Figure 3-8 shows the spatial distribution of one-hour average ozone concentrations

for the times on the 3 days when the region-wide ozone maxima occurred.

41

Figure ‎3-7: Maximum one-hour ozone concentrations over HG (the red region in Figure ‎3-6) for the base cases

on August 20, 22 and September 4, 2006.

Figure ‎3-8: Ozone spatial distribution for base cases on August 20, 22 and September 4, 2006, where the white

dots are the flare locations (3 flares on August 20, 1 flare on August 22 and 1 flare on September 4). Wind was

from the south-east on August 20 and 22, 2006 and from the northeast on September 4, 2006.

0

20

40

60

80

100

120

140

160

0 5 10 15 20 25

August 20,2006

August 22,2006

September 4,2006

42

3.3.2 Flare Emission Scenarios

Eight emission scenarios, described in Table 3-6, were considered for each flare.

Results for each of the 5 flares are reported.

3.3.2.1 Refinery Flare 1

Changes in region-wide maximum one-hour average ozone concentrations on

August 22, 2006, resulting from applying different flare DREs on Refinery Flare 1, are

shown in Figure 3-9. This analysis assumes no PICs are formed. The predicted difference

in the region-wide maximum one-hour average ozone concentrations reaches 31.6 ppb

when the DRE is assumed to be 50%. The spatial distribution for differences in ozone

concentration, between the scenario when the Refinery Flare 1 has 50% DRE and the

base case on August 22, 2006, over the course of the day, is shown in Figure 3-10. The

largest difference in ozone concentrations occurs at 10 am, when the difference reaches

40.9 ppb. Figure 3-11 summarizes the maximum change in one-hour averaged ozone

concentrations, compared to the base case, for all DRE scenarios applied to Refinery

Flare 1.

43

Figure ‎3-9: (a) Maximum one-hour average ozone concentrations on August 22, 2006 resulting from applying

different flare DRE on the Refinery Flare 1. (b)The difference in the region-wide maxima one-hour average

ozone concentrations on August 22, 2006 resulting from applying different flare DREs on Refinery Flare 1.

0

20

40

60

80

100

120

140

160

180

0 5 10 15 20

Max

imu

m O

zon

e c

on

cen

trat

ion

(pp

b)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

Base Case

( a )

0

5

10

15

20

25

30

35

0 5 10 15 20

Dif

fere

nce

in m

axim

um

ozo

ne

co

nce

ntr

atio

n(p

pb

)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

( b )

44

Figure ‎3-10: The spatial distribution for the differences in ozone concentrations from 8:00 am through 3:00 pm

between the scenario when Refinery Flare 1 has 50% DRE and the base case, on August 22, 2006.

45

Figure ‎3-11: The maximum change in one-hour ozone concentrations compared to the base case on August 22,

2006 resulting from applying different flare DREs to Refinery Flare 1.

The results in Figures 3-9 to 3-11 assumed that the flare emissions contained just

unburned hydrocarbons. Including PICs in the photochemical modeling scenarios had

relatively little impact on ozone formation chemistry. Table 3-8 shows the region-wide

maximum one-hour average ozone concentrations on August 22, 2006 for each of the

DRE scenarios, with and without PICs. Since the PICs include relatively reactive

aldehydes, and relatively unreactive CO, the net impact of replacing some of the

unburned flare gases with PICs had a relatively small impact on the reactivity of the

emissions for this flare, and in fact, lowered the estimated ozone formation by

approximately one ppb.

0

5

10

15

20

25

30

35

40

45

0 5 10 15 20

Max

imu

m d

iffe

ren

ce in

ozo

ne

co

nce

ntr

atio

n(p

pb

)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

46

Table ‎3-8: Summary of Maximum ozone concentrations (ppb) for all DRE scenarios applied on Refinery Flare 1

DRE % No PICs PICs

50 157.9 156.9

75 151.9 150.8

90 147.7 146.3

95 146.2 145.4

3.3.2.2 Refinery Flares 2 and 3

Predicted changes in region-wide maximum one-hour average ozone

concentrations on September 4, 2006, and August 20, 2006, resulting from applying

different flare DREs on Refinery Flares 2 and 3, respectively, are shown in Table 3-9.

Predicted ozone enhancements are much smaller than for Refinery Flare 1, with a

maximum difference in the one-hour ozone concentrations for the 50% DRE scenario of

4-5 ppb for both Refinery Flares 2 and 3. DRE scenarios with PICs and hydrocarbon

emissions have almost the same impact as DRE scenarios with no PICs emissions.

The difference in air quality impacts between Refinery Flare 1 and Refinery

Flares 2 and 3 is largely due to the chemical composition and the flow rate of flared

gases. A quantitative estimate of the relative role of the reactivity of flared gases can be

calculated based on the composition and the Maximum Incremental Reactivity (MIR) of

the individual gases. This approach has been used previously to assess the effectiveness

of reactivity-based emission trading programs in Houston (Wang et al., 2007). The

maximum incremental reactivity was calculated using data from the SAPRC-07 chemical

mechanism (Carter, 2011). The results indicate that each 100 g of Refinery Flare 1 gases

47

has the potential to produce 896 g of ozone. In contrast, each 100 g of Refinery Flare 2

and Refinery Flare 3 gases has the potential to form 207 and 153 g of ozone, respectively.

While these are likely not accurate absolute estimates of ozone production, previous work

has shown that, for Houston, they are reliable indicators of relative ozone formation

potential (Wang et al., 2007). Therefore, the Refinery Flare 1 gases are likely to be 4-6

times more reactive than Refinery Flares 2 or 3. This coupled with the higher flow rate of

Refinery Flare 1, accounts for much of the difference in ozone formation between the

flares. The remainder is likely due to different photochemistry on different days.

48

Table ‎3-9 : The difference in wide region maxima one-hour average ozone concentrations (in ppb) and the maximum difference in one-hour average ozone

concentrations (in ppb) for all the flare DRE scenarios applied to the three refinery flares compared to the base case

Difference in maximum ozone concentration(ppb)

Maximum difference in ozone concentration(ppb)

Refinery Flare 1

Refinery Flare 2

Refinery Flare 3

Refinery Flare 1

Refinery Flare 2

Refinery Flare 3

DRE %

No PIC

PIC No PIC

PIC No PIC

PIC No PIC

PIC No PIC

PIC No PIC

PIC

50 31.6 29.0 0.3 0.3 0.2 0.2 40.9 37.3 4.0 3.7 4.2 4.6

75 16.3 13.2 0.2 0.2 0.1 0.1 21.0 17.3 2.4 1.8 2.0 2.0

90 5.1 2.2 0.1 0.1 0.1 0.0 8.3 3.2 0.7 0.6 0.7 0.6

95 1.7 0.3 0.0 0.0 0.0 0.0 2.4 1.7 0.3 0.4 0.2 0.2

49

3.3.2.3 Olefin Flares

Results for the olefin flares were qualitatively similar to the results for the 3

refinery flares. One of the flares (Olefin Flare 1) exhibited relatively large impacts, while

the other flare had relatively low impacts. For Olefin Flare 1, the predicted maximum

difference in one-hour average ozone concentrations reach 17.2 ppb, compared to the

base case. Including PICs in the simulations reduced the maximum difference in one-

hour average ozone concentrations by a few ppb. In contrast, for Olefin Flare 2, the

maximum difference in ozone concentrations was 1.5 ppb. Table 3-10 summarizes the

difference in wide region maxima and maximum difference in one-hour average ozone

concentrations for two olefin flares, compared to the base case.

Table ‎3-10 : The difference in wide region maxima one-hour average ozone concentrations (in ppb) and the

maximum difference in one-hour average ozone concentrations (in ppb) for all the flare DRE scenarios applied

to the two olefin flares compared to the base

Difference in maximum ozone concentration(ppb)

Maximum difference in ozone concentration(ppb)

Olefin Flare1 Olefin Flare2 Olefin Flare1 Olefin Flare2

DRE % No PIC PIC No PIC PIC No PIC PIC No PIC PIC

50 2.8 2.5 0.4 0.4 17.2 15.7 1.3 1.5 75 1.1 0.9 0.2 0.2 6.5 5.5 0.8 0.8 90 0.4 0.2 0.1 0.1 2.0 1.0 0.4 0.4 95 0.1 0.0 0.0 0.0 0.5 0.2 0.3 0.3

This large difference in the air quality impacts of the two olefin flares could be

due to the same reasons as the refinery flares. Maximum incremental reactivity data

(Carter, 2011) for the flares gases from Olefin Flare 1 showed that each 100 g of flared

gases has the potential to form 515 g atmospheric ozone. In contrast, 100 g of vent gases

50

from Olefin Flare 2 has the potential to produce 211 g ozone. The flow rate of Olefin

Flare 1 is roughly double that of Olefin Flare 2. The remainder of the difference is likely

due to the different physical location of the flares.

Overall, flow and chemical reactivity explain much of the difference in ozone

formation differences among the flares considered in this work. Table 3-11 shows the

product of the mass of emissions, multiplied by the MIR of the emitted gases for each of

the flares. The maximum daily flow rates were used in the calculations. Table 3-12 shows

the absolute product of flow rate and reactivity (reported in Table 3-11) divided by the

base case value. This represents a relative increase in ozone formation potential. The

results suggest that the relative increase in ozone formation potential is reasonably

consistent among the flares as DRE is reduced and that a DRE of 50% can lead to an

increase in ozone formation potential of a factor of up to 20-50 (2000-5000%), relative to

the base case. (Al-Fadhli et al., 2012a)

51

Table ‎3-11: The mass of the predicted ozone (ton) for all scenarios when the maximum daily flow rates were used for the five flares. The maximum daily flow

rates for Refinery Flare 1, 2, 3, Olefin Flare 1 and 2 are 25.3, 4, 3.9, 3.5 and 4.1 tons/ hr respectively. The mass of the predicted ozone was based on MIR values

(Carter, 2011)

Refinery Flare1 Refinery Flare2 Refinery Flare3 Olefin Flare1 Olefin Flare2

DRE % No PIC PIC No PIC PIC No PIC PIC No PIC PIC No PIC PIC

50 113.4 103.8 4.1 3.9 3.0 2.9 9.1 8.4 4.4 4.2 75 56.7 47.0 2.0 1.9 1.5 1.4 4.5 3.9 2.2 2.0 90 22.7 13.0 0.8 0.7 0.6 0.5 1.8 1.1 0.9 0.7 95 11.3 6.5 0.4 0.3 0.3 0.3 0.9 0.6 0.4 0.4

Base Case 4.5 0.1 0.1 0.4 0.2

Table ‎3-12 : The absolute product all the scenarios in Table 3-11 divided by the base case value

Refinery Flare1 Refinery Flare2 Refinery Flare3 Olefin Flare1 Olefin Flare2

DRE % No PIC PIC No PIC PIC No PIC PIC No PIC PIC No PIC PIC 50 25.0 22.9 50.0 48.1 25.0 24.6 25.0 23.1 25.0 24.0 75 12.5 10.4 25.0 23.1 12.5 12.1 12.5 10.6 12.5 11.5 90 5.0 2.9 10.0 8.1 5.0 4.6 5.0 3.1 5.0 4.0 95 2.5 1.4 5.0 4.0 2.5 2.3 2.5 1.6 2.5 2.0

52

3.4 CONCLUSION

The impact of flare DRE on the ozone formation potential of flare emissions

varies among flares. Five flares were examined in this study. Two flares showed a

predicted increases in ozone concentrations in excess of 15 ppb when the DRE is

reduced. The other three flares showed smaller impacts on the ozone concentrations. The

flare DRE impact on ozone concentration depends on the amount of flare emissions and

chemical composition of the emissions. PICs have a modest impact on ozone

concentration because most of PIC mass is carbon monoxide which has low chemical

reactivity to form ozone.

The analyses reported here are intended to represent upper bounds on the ozone

formation potential of flare emissions. The flow rates analyzed were for days when flow

rates were reaching their maxima for the month-long study. Meteorological conditions

were conducive to ozone formation. Destruction efficiencies as low as 50% were

assumed, and while these low DREs have been observed, they may not be representative

for these flares at these flow conditions. VOC emissions that had unclassified

compositions were assumed to have the same composition as the average of the speciated

emissions; the true composition of these unclassified emissions may have a lower

reactivity than the speciated emissions. Nevertheless, the results indicate that for flares

burning photochemically reactive gases, ozone formation potential of flare emissions

could lead to increases in ozone concentrations in excess of 15 ppb. These results

reported here indicate that ensuring high DRE during flaring operations can be important

for regional air quality.

53

CHAPTER 4: Impact of Emissions of Nitrogen Oxides from Flares

on Ozone Formation in Houston, Texas

4.1 INTRODUCTION

Flares destruct waste gases by burning them at high temperature to produce

carbon dioxide and steam, with an efficiency that is designed to be 9899%. Oxides of

nitrogen (NOx, the sum of NO and NO2) are formed as a by-product of combustion (Wark

et al., 1998), and since NOx is a precursor for tropospheric ozone production (Seinfeld

and Pandis, 2006), emissions of NOx are inventoried in regions where ozone

concentrations are of concern. Typically, NOx emissions from flares are estimated using

methods outlined by the US Environmental Protection Agency (EPA) in the AP-42 series

of documents (EPA, 1991). Emissions are estimated by multiplying an emission factor by

an activity parameter:

( 4.1 )

where E is the emissions rate, A is the activity rate and EF is the emission factor.

For estimating NOx emissions from flares, current practice, based on information

documented in Section 13.5-1 of AP-42, is to assume a constant emission factor of 0.068

lb NOx per million BTU of heating value (lower heating value, LHV) of gases sent to the

flare. A slightly different set of emission factors is suggested in documentation from the

State of Texas (TCEQ, 2000). The Texas Commission on Environmental Quality (TCEQ)

suggests the use of one of four emission factors, two for low heating value gases (0.068

and 0.064 lb NOx per million BTU of heating value for steam- and air-assisted flares,

respectively) and two for high heating value gases (0.049 and 0.14 lb NOx per million

54

BTU of heating value for steam- and air-assisted flares, respectively). The emission

factors suggested by the TCEQ for low heating value gases are very similar to the AP-42

emission factor.

The activity factor in equation 4.1 is the flow rate of gases fed to the flare,

multiplied by their lower heating value. Emissions are estimated, as indicated in equation

4.1, by multiplying the activity factor by the emission factor. This approach to estimating

NOx emissions from flares, and the emission factors, are based largely on data collected

during full scale flare tests conducted in 1983, with flares generally operating at high

flow rates, with high heating value gases (McDaniel, 1983). During these tests,

combustion efficiencies (fraction of the feed gases converted to CO2 and water) were

high.

As stated in Chapter 2, if too much steam or air is added to flared gases (steam

and air-assist), particularly at low flow conditions and with low heating value gases being

flared, combustion efficiencies can be lower than the targeted values of 98-99%. Since

less heat of combustion is generated with low heating value gases, combustion

temperatures are lower than for high heating value gases, production of NOx may be

reduced. Further, if too much air or steam is added to the flared gases, destruction

efficiencies and combustion temperatures can be reduced, and NOx emissions may be

reduced. This Chapter presents NOx emission data for the steam-assisted and air-assisted

flare tests reported by Torres et al. (2012a) and Torres et al.(2012b). These tests were

conducted at low flow rates (0.1% to 0.25% of maximum flow), and with low heating

value gases (300-650 BTU LHV/scf). The NOx emission rates from the tests will be

shown to be generally lower than emissions calculated using existing procedures, and the

55

potential implications for region-wide NOx emission estimates and ozone formation in

the Houston area of these lower NOx emission rates from flares are quantified.

4.2 METHODS

4.2.1 Emission factor

NOx emissions from industrial flares have been estimated using an emission

factor of 0.068 lb of NOx/106 BTU of flared gas. Equation 4.2 (similar to equation 4.1)

uses the AP-42 NOx emission factor to estimate the NOx emission rate from burning

hydrocarbon gases using an industrial flare.

( 4.2 )

where Xi is the mass fraction of species i and LHVi is the lower heating value for species

i (BTU/lb).

The results of the University of Texas flare study showed that NOx emissions are

a function of the combustion efficiency of the flare. The NOx (lbmole) to total C

(lbmole) ratios were measured during flare tests done with propane as the flared gas

(Torres et al., 2012c). The propane was fed to the flares (steam- and air-assist) with

natural gas in a volume ratio of 4:1. The NOx emission factors were estimated based on

the measured NOx (lbmole) / C (lbmole) ratios using equation 4.3. The derivation of

equation 4.3 is described in detail in Torres et al. (2012c). The measured NOx (lbmole)

to C (lbmole) ratios showed that the NOx emission factor ranged from 10120% of the

factor reported in AP-42.

56

( 4.3 )

Figures 4-1 and 4-2 show the estimated NOx emission factor versus the

combustion efficiency of a steam-assisted flare and an air-assisted flare, respectively. As

shown in Figures 4-1 and 4-2, the air-assisted flare has higher NOx emissions than does

the steam-assisted flare. This suggests that the NOx emission factor for the air-assisted

flares should be higher than that for the steam-assisted flares. However, the NOx emission

factor is the same for both air- and steam-assisted flares in AP-42. Moreover, both

Figures (4-1and 4-2) indicate that the NOx emissions are high at high combustion

efficiency (9899%) and drop by a factor of 2 when combustion efficiency reaches 90%.

Then, the NOx emissions remain relatively constant as the combustion efficiency

decreases below 90%.

57

Figure ‎4-1: The estimated NOx emission factor versus the combustion efficiency for the steam-assisted flare.

Figure ‎4-2: The estimated NOx emission factor versus the combustion efficiency for the air-assisted flare.

-0.002

0.008

0.018

0.028

0.038

0.048

0.058

0.068

30 40 50 60 70 80 90 100

NO

x e

mis

sio

n f

acto

r (l

b N

Ox/

10

6 B

TU)

Combustion efficiency (%)

NOx factor based on AP-42

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

60 65 70 75 80 85 90 95 100

NO

x e

mis

sio

n f

acto

r (l

b N

Ox/

10

6 B

TU)

Combustion efficiency (%)

NOx factor based on AP-42

58

These results will be used to compare the impact of estimated NOx emissions,

based on the University of Texas flare study, with the impact of NOx emissions based on

the AP-42 emission factor, on the formation of ozone in Houston, Texas.

4.2.2 Air quality

4.2.2.1 Flare selection

Five industrial flares were considered for evaluating the impact of new NOx

emission factors on air quality. These are the same five flares examined in Chapter 3,

where they were used to estimate the impact of low flare DRE and products of

incomplete combustion on ozone formation in Houston, Texas. Of these five flares,

Refinery Flare 1 showed the highest increase in ozone concentrations (up to 40 ppb)

when its DRE was low. Therefore, Refinery Flare 1 is used in this analysis. Table 3-2

provides detailed information about Refinery Flare 1. The chemical composition of the

flared gases for Refinery Flare 1 is shown in Table 3-5.

4.2.2.2 NOx emission scenarios

To estimate the impact of the NOx emission factors for an industrial flare on

ozone formation, NOx emission scenarios were developed based on the results of the

University of Texas flare study. In this work, we assume that Refinery Flare 1 is a steam-

assisted flare because it is one of the largest refinery flares in the Houston area. Most of

the largest refinery flares are steam-assisted (EPA, 2000). Based on the results for the

steam-assisted flare reported in Figure 4-1, three NOx emission scenarios were developed

depending on the levels of the flare DRE. The first scenario assumes the NOx emission

factor is 50% of the AP-42 value at a DRE of 98%. The second and the third scenarios

59

assume that the NOx emissions are 25% of the estimated NOx emissions based on an AP-

42 value at flare DREs of 90% and 75%. These scenarios were applied to Refinery Flare

1. Table 4-1 summarizes the three scenarios. Detailed information about Refinery Flare 1

emissions for each NOx emission scenario is reported in Appendix B. A photochemical

model, described in Chapter 3, was used to predict changes in one-hour average ozone

concentrations resulting from each scenario.

Table ‎4-1: Photochemical modeling scenarios performed for Refinery Flare 1

Scenario DRE% NOx reduction compared to the AP-42 value

1 98 50%

2 90 25%

3 75 25%

4.2.2.3 Photochemical modeling

CAMx was used to predict one-hour average ozone concentrations resulting from

applying the NOx emission scenarios to Refinery Flare 1. Figure 3-6 (in Chapter 3) shows

the full spatial domain used by the photochemical model, where the red region is the

Houston area, which is modeled with 1 km grid resolution. In this work, all the input

files required by CAMx were downloaded from the TCEQ website, except the emission

from Refinery Flare 1. A simulation day of August 22, 2006 was selected based on three

criteria developed in Chapter 3. Additional information concerning the photochemical

model is also provided in Chapter 3.

60


The simulation results will be reported as the difference in ozone concentrations

between the examined scenarios and the base case. The base case assumes the DRE of

Refinery Flare 1 is 98% and the NOx emissions are based on the AP-42 factor. The

simulation results of the base case are discussed in detail in Chapter 3.

The differences in the region-wide maximum one-hour average ozone

concentrations resulting from applying the three scenarios with the reduced NOx to

Refinery Flare 1 are shown in Figure 4-3. The first scenario (DRE of 98% with reduced

NOx) shows no change in the region-wide maxima compared to the base case. In contrast,

the second and the third scenarios (DREs of 90% and 75%, respectively, with the reduced

NOx) show positive increases in the predicted difference in the region-wide maximum

one-hour average ozone concentrations up to 5.1 and 16.4 ppb, respectively.

Figure ‎4-3: The changes in the region-wide maximum one-hour average ozone concentrations on August 22,

2006, resulting from applying the three scenarios with the reduced NOx to Refinery Flare 1.

0

2

4

6

8

10

12

14

16

18

0 5 10 15 20 25

Dif

fere

nce

in m

axim

um

ozo

ne

co

nce

ntr

atio

n(p

pb

)

Time (hr)

DRE = 75%

DRE = 90%

DRE =98%

61

Figures 4-4, 4-5, and 4-6 show the spatial distribution for the differences in the

one-hour average ozone concentrations between the three scenarios with reduced NOx

and the base case, over the course of August 22, 2006. Figure 4-4 (98% DRE, reduced

NOx) shows that the largest predicted difference in ozone concentrations occurred at 2 am

and reached 6.6 ppb. However, the reduction in ozone concentration was highly

localized around Refinery Flare 1. The largest predicted difference in ozone

concentrations in Figure 4-5 (90% DRE, reduced NOx) reached up to 9.4 ppb, and it

occurred at 3 am. The largest predicted difference in ozone concentrations in Figure 4-6

(75% DRE, reduced NOx) reached up to 21.0 ppb, and it occurred at 11 am. Figure 4-7

summarizes the maximum positive and the minimum negative changes in one-hour

average ozone concentrations for all the three scenarios compared to the base case. Table

4-2 summarizes the maximum difference in wide region maxima, maximum difference in

one-hour average ozone concentrations, and minimum difference in one-hour average

ozone concentrations for the three scenarios compared to the base case.

62

Figure ‎4-4: The spatial distribution for the differences in ozone concentration from 00:00 am through 7:00 am

between the scenario of 98% DRE and NOx reduced to 50% of the AP-42 value and the base case on August 22,

2006.

63

Figure ‎4-5: The spatial distribution for the differences in ozone concentration from 1:00 am through 8:00 am


2006.

64

Figure ‎4-6: The spatial distribution for the differences in ozone concentration from 8:00 am through 3:00 pm


2006.

65

Figure ‎4-7: (a) The maximum positive changes in one-hour average ozone concentrations compared to the base

case on August 22, 2006 resulting from applying the three scenarios to Refinery Flare 1. (b) The minimum

negative changes in one-hour ozone average concentrations compared to the base case on August 22, 2006

resulting from applying the three scenarios to Refinery Flare 1.

0

5

10

15

20

25

0 5 10 15 20 25

Max

imu

m d

iffe

ren

ce in

ozo

ne

co

nce

ntr

atio

n(p

pb

)

Time (hr)

DRE = 75%

DRE = 90%

DRE =98%

-7

-6

-5

-4

-3

-2

-1

0

0 5 10 15 20 25

Min

imu

m d

iffe

ren

ce in

ozo

ne

co

nce

ntr

atio

n(p

pb

)

Time (hr)

DRE = 75%

DRE = 90%

DRE =98%

A

(a)

A

(b)

66

Table ‎4-2 : The difference in maximum ozone concentration (DMOC), maximum difference in ozone

concentration (MaxDOC), and minimum difference in ozone concentration (MinDOC) for all the scenarios with

reduced NOx compared to the base case. All ozone concentrations are in ppb.

DRE% DMOC MaxDOC MinDOC

98 0.0 1.0 -6.6

90 5.1 9.4 -1.2

75 16.4 21 -1.1

The simulation results of the second and the third scenarios (DREs of 90% and

75% with reduced NOx) can also be compared to the scenarios, discussed in Chapter 3,

with the same DRE but with NOx emissions based on the AP-42 value. These

comparisons will determine whether the increase in ozone concentrations for the second

and the third scenarios was due to the reduction of NOx emissions or the enhanced

hydrocarbon emission associated with low DRE.

As shown in Chapter 3, the predicted maximum change in ozone concentrations

for the scenarios of 90% and 75% DRE and NOx emissions based on the AP-42 value

was 8.3 ppb and 21.0 ppb, respectively, compared to the base case. Table 4-3 shows the

maximum difference in wide region maxima, maximum difference in ozone

concentrations and minimum difference in ozone concentrations for the scenarios

discussed in Chapter 3, with NOx based on the AP-42 value, compared to the base case.

Comparing the results of Table 4-2 with Table 4-3 suggests that the change in ozone

concentrations due to NOx emissions was small relative to the changes in ozone

concentrations associated with increased hydrocarbon emissions. For example, the

maximum increase in ozone concentrations is the same for 75% DRE scenarios, with

lowered NOx emissions and using the AP-42 factor, compared to the base case. At 90%

67

DRE, the positive change in the ozone concentrations is 9.4 ppb compared to the base

case, in which NOx emissions are lowered to 25% of the AP-42 value. Using the AP-42

NOx emission factor, at 90% DRE, results in an increase in ozone concentrations of up to

8.3 ppb, compared to the base case. Again, lowering the DRE of Refinery Flare 1 has a

greater impact on hydrocarbon emissions than on NOx emissions. In general, industrial

flares are located in complexes that have many sources of NOx emissions. Thus, the

hydrocarbon emissions from Refinery Flare 1 could react with NOx from other sources in

the facility or its NOx emissions.

Table ‎4-3: The difference in maximum ozone concentration (DMOC), maximum difference in ozone

concentration (MaxDOC) and minimum difference in ozone concentration (MinDOC) for all the scenarios with

NOx based on AP-42 compared to the base case with NOx based on AP-42 value.

DRE% DMOC MaxDOC MinDOC

90 5.1 8.3 -2.7

75 16.3 21 -1.0

4.4 CONCLUSION

The production of NOx from industrial flares is not constant. It depends on the

DRE of the flare. Lowering the flare DRE decreases the temperature of the combustion

zone. This reduction in the flame temperature leads to a reduction in formation of NOx

emissions. The results of the University of Texas flare study showed that the NOx

emission factor ranged from 10120% of the factor reported in the AP-42. Also, the

results showed that the air-assisted flare produced more NOx emissions than did the

68

steam-assisted flare. Photochemical modeling simulation indicated that lowering the NOx

emissions of a large refinery flare had small impacts on the ozone formation relative to

changes in ozone concentration associated with the enhanced hydrocarbon emissions at

low DRE. The change in ozone concentration resulting from changing the NOx emission

was highly localized around the refinery flare.

69

CHAPTER 5: Impacts of Air-Assist Flare Blower Configurations on

Flaring Emissions

5.1 INTRODUCTION

Flares burn waste gases at high temperature, in an open flame, to produce carbon

dioxide and steam. Flares are primarily safety devices designed to destroy waste gases

(vent gases) at a destruction removal efficiency (DRE, fraction of waste gases destroyed

by complete and incomplete combustion) of 9899%. As described in Chapter 2, flares

operating at low flow rates (<1% of designed maximum flow) and with low heating value

gases (<600 BTU/scf lower heating value (LHV)) can have low destruction efficiencies,

particularly when air-assist or steam-assist rates are high. Chapter 2 also described

studies that have demonstrated that, for vent gases with low heating values, there is, at

times, a narrow operating window of air assist-to-vent gas ratios and steam-to-vent gas

ratios that achieve both high destruction efficiencies and avoid flare generated smoke.

The purpose of the analyses presented in this Chapter is to examine how flare

configurations, particularly blower selection for air-assisted flares, may limit the ability

of a flare to operate in the targeted, but narrow, operating window that achieves low

smoke and high destruction efficiencies, and the potential implications for flare

emissions.

The air-assisted flare to be used as a case study in this work is the flare described

by Torres, et al (Allen and Torres, 2011a; TCEQ, 2010c; Torres et al., 2012a; Torres et

al., 2012b). This flare can accommodate a maximum vent gas flow rate of 144,000

pounds per hour when used with propylene and operated in compliance with 40 Code of

Federal Regulation §60.18. It would typically be operated with a blower or blowers that

can deliver 65,000 cfm of air assist (Kodesh, 2012). The blower(s) used in an air-assisted

70

flare can be either fixed or variable speed. Both fixed and variable speed blowers will be

considered in the analyses to be presented here, and two configurations of each type will

be examined. The fixed-speed motor designs are (a) a single blower (65,000 cfm) that has

a two-speed motor (off, half speed and full speed), and (b) dual blowers, each with two

speeds, delivering a combined maximum flow of 65,000 cfm. The variable-speed blowers

are assumed to operate with variable frequency drives (VFDs) capable of generating

flows of 5% to 100% of blower design capacity. Scenarios with single blowers and dual

blowers with VFDs are examined.

71

5.2 METHODS

5.2.1 Air-assisted flare experimental tests

Torres et al. reported full-scale air-assisted flare tests under low flow and low

heating value conditions (Torres et al., 2012a; Torres et al., 2012b). The air-assisted flare

used in the tests had a tip 24 inches in diameter and a maximum design capacity of

144,000 lb/hr. The chemical composition of the vent gas was a mixture of propylene and

natural gas, with nitrogen added as a diluent to reach low heating value conditions. The

volume ratio of the propylene to natural gas in the vent gas was 4:1 in all tests. Two

targeted LHVs (560 and 350 Btu/scf) were tested under low flow conditions (less than

0.7% of the maximum capacity). The test results showed that the air-assisted flare

achieves high DRE at low air-to-vent gas ratios, and the DRE drops linearly as the air-to-

vent gas ratio increases. Figure 5-1 shows the relationship between DRE and air-to-vent

gas ratio when the LHV was 560 Btu/scf and when the LHV was 350 Btu/scf.

72

Figure ‎5-1: Destruction removal efficiency (DRE) versus air-to-vent gas ratio for flared gases with a lower

heating value (LHV) of 560 Btu/Scf (upper) and 350 Btu/Scf (lower) (Torres et al., 2012a).

R² = 0.85

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200

DR

E%

Air-to-vent gas ratio (mass basis)

DRE = -0.11 *(Air-to-vent ratio) + 102.9 (Air-to-vent ratio> 26.4 ) DRE = 99.9 (Air-to-vent ratio< 26.4)

R² = 0.88

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140 160

DR

E %

Air to vent gas ratio (mass basis)

DRE = -0.25 *(Air-to-vent ratio) + 104.9 (Air-to-vent ratio> 19.6 ) DRE = 99.9 (Air-to-vent ratio< 19.6)

73

5.2.2 Stochastic models

To determine the DRE achievable with air-assisted flares with various blower

configurations, the air assist-to-vent gas ratio must be calculated (Figure 5-1). This

requires an understanding of the vent gas flow rates fed to the flare, along with the air

assist flows achievable using either fixed or variable speed blowers. In this study, models

of flare vent gas flows developed by Pavlovic et al. (2012b) for refinery flares are used to

estimate hourly flow rates of vent gas. These models were developed based on hourly

flow rates of a large number of refinery flares in Houston, Texas for a month-long period

in the summer of 2006. The modeled flows are therefore assumed to be representative of

the temporal variation in flows to refinery flares in Houston, Texas. Pavlovic et al.

(2012b) grouped refinery flares into three categories: (a) natural, process, and fuel-fired

equipment flares, (b) fluid catalytic cracking flares, and (c) unclassified process flares.

The natural, process, and fuel-fired equipment flares, and the unclassified process flares

were further divided into sub-categories based on their relative variability, defined as the

ratio of the standard deviation to the average flow rate. This results in the six groups of

refinery flares shown in Table 5-1.

74

Table ‎5-1: Categorization of petroleum refinery flares in 2006 SI (Pavlovic et al., 2012b).

Group

Number Group Name

1 Natural, process, and fuel-fired equipment flares–low variability

2 Natural, process, and fuel-fired equipment flares–medium variability

3 Natural, process, and fuel-fired equipment flares–high variability

4 Fluid catalytic cracking flares

5 Unclassified process flares–low variability

6 Unclassified process flares–high variability

In this analysis, the models for the hourly flow of vent gas to six types of refinery

flares were used to generate 240 hours (10 days) of simulated flow rates for each flare

type. Flow rates for ten days were generated to capture a broad range of variability in the

flow rates to each flare type. The models predicted normalized hourly flow rates (hourly

flow/time averaged flow), so hourly flare flow was predicted by multiplying normalized

hourly flow rates generated by the model by an average flow rate. In this work, five

average flow rate scenarios were developed for each of the 6 flare types. The first

scenario assumed that the maximum hourly flow (of the 240 hours of simulated flow)

was 1% of the maximum design capacity (1,440 lb/hr) of the air-assisted flare (maximum

design capacity of 144,000 lb/hr). The average flow for this scenario was calculated by

dividing the 1,440 lb/hr by the maximum normalized hourly flow predicted by the model

for the 240 hours of simulation. This average flow was then multiplied by the 240

normalized hourly flow rates to obtain the 240 hourly flow rates for this scenario.

Assuming that the maximum predicted flow over a 10 day period is 1% of flare capacity

75

is a feasible scenario because many flares only experience maximum flow once over a

period of multiple years. The flare vent gas flow data reported by Pavlovic, and which the

model is based on, are for only one month of operations, and it is unlikely that a flare

would reach its design flow limit during such a short sampling period. However,

assuming that the maximum flow achieved over one month of sampling is only 1% of

maximum flow may be too low. Therefore, four additional scenarios were examined

assuming that the maximum flows over 240 hours were 5, 10, 20 and 100%, respectively,

of the maximum design capacity of the air-assisted flare. Most of these scenarios were for

low flow conditions (maximum flow for the 240 simulated hours less than 20% of

maximum design capacity of 144,000 lb/hr) because most industrial flares work at low

flow conditions most of their operating time (ENVIRON International Corporation, 2009;

Pavlovic et al., 2012b). The 100% scenario was considered because during upset

conditions, flares may work near their maximum design flow; however, the primary focus

of this work is on low flow conditions. Overall, 5 scenarios for the flow to each flare

were considered, each with the same pattern of temporal variability, but scaled to

different average flows.

Calculating the air assist-to-vent gas ratio for each of the 30 flaring scenarios (6

flares, 5 average flow rates for each flare) requires that air assist rates be calculated. In

this work, it is assumed that the flare operator has perfect knowledge of the air

requirements of the vent gas being fed to the flare and the ability to respond to hourly

changes in vent gas flow rate, but that the ability to achieve the ideal air assist-to-vent gas

ratio is limited by blower configuration. For the single fixed speed blower, only three

options are available (0, 50% and 100% of full flow). For the dual fixed speed blowers,

with each blower sized to handle half of the total flow, five options are possible (0, 25,

76

50, 75 and 100% of full flow). The single variable speed blower can achieve between

10% and 100% of maximum flow. The dual variable speed blowers can achieve between

5% and 100% of maximum flow.

In this analysis, the desired air-assist flow rate was based on the stoichiometric air

required by the vent gas and the air-assist design. For example, if the stoichiometric air

required by the vent gas is less than 32,500 ft3/min (50% of maximum flow of 65,000

ft3/min) and the single fixed speed blower is used, then the air-assist rate is 32,500

ft3/min. For the same single fixed blower system, if the stoichiometric air required is

larger than 32,500 ft3/min, the air-assist rate is 65,000 ft

3/min. Table 5-2 summarizes all

conditions used to assign hourly air-assist rates for each air-assist design.

Table ‎5-2: Air-assist rates (ft3/min) for each air-assist design under different stoichiometric air conditions

Condition Single fixed

speed

Dual fixed

speed

Single variable

speed

Dual variable

speed

Stoichiometric air required is

less than 3,250 ft3/min

32,500 16,250 6,500 3,250


higher than 3,250 and less than

6,500 ft3/min

32,500 16,250 6,500

Stoichiometric

air



16,250 ft3/min

32,500 16,250

Stoichiometric

air

Stoichiometric

air



32,500 ft3/min

32,500 32,500

Stoichiometric

air

Stoichiometric

air



48,750 ft3/min

65,000 48,750

Stoichiometric

air

Stoichiometric

air


larger than 48,750 ft3/min 65,000 65,000

Stoichiometric

air

Stoichiometric

air

77

In summary, 240 hourly flow rates for each of the six refinery flare types and for

the five average flow rates (30 flaring scenarios) were simulated. For each of the 30

flaring scenarios, two cases were tested (60 sets of simulations). The first case assumed

the LHV of the vent gas was 350 Btu/scf, and the second case assumed the LHV was 560

Btu/scf. For each of these 60 sets of simulations, four blower configurations were used to

estimate emissions (240 emission scenarios). A sample calculation of estimating the

hourly DRE of an air-assisted flare with the dual fixed speed blowers is provided in

Appendix C.

78


The results for the 240 emission scenarios are presented in two major sections, the

first assuming the vent mixture has a LHV of 350 Btu/scf, and the second assuming the

vent mixture has a LHV of 560 Btu/scf. The focus is on presenting the main features

observed in the 240 emission scenarios. Emission estimates for all 240 emission

scenarios are presented in Appendix C.

5.3.1 Vent gas mixture with LHV of 350 Btu/scf

Typically, emissions from flares have been estimated assuming a DRE of 98%. In

this study, the estimated emissions based on a DRE of 98% are referred to as base case

emissions and alternative scenarios, limited by blower configurations, are compared to

the base case. Because the flare tests reported by Torres et al. (2012a, b) had DREs both

greater than and less than 98%, emission scenarios may have estimated emissions that are

greater than or less than the base case. In general, a greater ability to fine tune air assist

rates lowers emissions. Emission estimates based on using a fixed-speed motor results in

emission increases of more than an order of magnitude compared the base case at low-

flow conditions. For example, average flow scenario 1 (maximum flow is 1% of flare

design capacity) for group 2 of the refinery flares (natural, process, and fuel-fired

equipment flares–medium variability) results in an emission increase of 4200% when the

single fixed speed blower is used and 3600% when the dual fixed speed blower

configuration is used, compared to the base case over 10 days (240 hours). Figure 5-2

compares the estimated base case emissions of flare type 2 (natural, process, and fuel-

fired equipment flares–medium variability) to emissions based on using single and dual

fixed speed blowers for all of the average flow scenarios. Emissions are reported for a

79

time-integrated 10 day period. At low-flow conditions (maximum flow less than 20% of

maximum flare design capacity) emissions were lower in the base case than is achievable

using any of fixed speed blower configurations. This is because at low flows, the

minimum blower speed results in air assist-to-vent gas ratios that are higher than the

amount needed for 98% or higher DRE. However, at high flow conditions (maximum

flow is 100% of flare design capacity) the time averaged DRE of the air-assisted flare is

higher than 98%. For example, using the single fixed speed blower for flare type 2

(natural, process, and fuel-fired equipment flares–medium variability) at high flow

conditions results in a time averaged DRE of 99.1 % and using dual fixed speed blowers

results in a time averaged DRE of 99.7%. Figure 5-3 compares the base case emissions

time series for this flare type to the emissions predicted with maximum flow at 1% of

flare capacity and a single fixed speed blower. The comparison shows that relatively low

vent gas flow rates can lead to emissions that are predicted to be as high as the emissions

at maximum vent gas flow because of low DRE at high air assist-to-vent gas ratios.

Appendix C contains time series for many additional flaring and blower configuration

scenarios.

80

Figure ‎5-2: Flaring emission of flare type 2 of refinery flares (natural, process, and fuel-fired equipment flares–

medium variability) based on 98% DRE and using single fixed speed and dual fixed speed blowers. The vent gas

has a LHV of 350 Btu/scf.

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

0% 20% 40% 60% 80% 100%

Emis

sio

n (

Ib/1

0 d

ay)

Upper bound of flare flow compared to maximum design capacity

Emission using the single fixed speed blower

Emission using the dual fixed speed blowers

Emission based on DRE of 98%

81

Figure ‎5-3: Hourly emission rate of flare type 2 (maximum flow 1% of maximum design capacity) based on DRE

of 98% and LHV of 350 Btu/scf (upper) and hourly emission rate of the same scenario when the single fixed

speed blower configuration is used (lower).

0

1

2

3

4

5

6

7

0 50 100 150 200

Emis

sio

n (

Ib/h

r)

Time (hr)

0

10

20

30

40

50

60

70

80

90

0 50 100 150 200

Emis

sio

n (

Ib/h

r)

Time (hr)

82

Using single or dual variable speed blowers reduced predicted flaring emissions

compared to using the fixed speed blower(s) in all scenarios, however, low-flow

conditions (maximum flow 1% to 5% or 10% of design capacity) can still produce higher

emissions than the base case. Figure 5-4 compares base case, fixed speed blower and

variable speed blower flaring emissions for flare type 6 (unclassified process flares–high

variability). Figure 4 shows that at high flow conditions (maximum flow is 100% of flare

design capacity) the time averaged DRE of the air-assisted flare is predicted to be higher

than 98%. Using the variable speed blowers for this flare type results in a time averaged

DRE of 99.9%. Tables in Appendix C report predicted emissions for all of the flare types.

Figure ‎5-4: Flaring emission of flare type 6 (unclassified process flares–high variability) based on 98% DRE and

using all the air-assist designs for all the flow scenarios. The vent gas has a LHV of 350 Btu/scf.

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

0% 20% 40% 60% 80% 100%

Emis

sio

n (

Ib/1

0 d

ay)





Emission using the single variable speed blower

Emission using the dual variable speed blowers

83

5.3.2 Vent gas mixture with LHV of 560 Btu/scf

Results assuming a vent gas mixture of 560 Btu/scf were qualitatively similar to

the results based on a vent gas mixture of 350 Btu/scf. Using the single fixed speed

blower or the dual fixed speed blowers design resulted in a significant increase in

emissions compared to the base case at low-flow conditions, and using the variable speed

blowers reduced emissions compared to using a fixed-speed motor. Figure 5-5 shows the

estimated emissions of flare type 6 (unclassified process flares–high variability) for the

base case, fixed speed blowers and variable speed blowers. Emissions are lower than in

the 350 BTU/scf cases, however, operating with flows that peak at 1% of maximum

design capacity is still challenging. Using the dual variable speed blowers leads to

reductions in flaring emissions compared to other blower configurations. Tables

containing emission estimates for additional flare types are provided in Appendix C.

Figure ‎5-5: Flaring emission of flare type 6 (unclassified process flares–high variability) based on 98% DRE and

using all the air-assist designs for all the flow scenarios. The vent gas has a LHV of 560 Btu/scf.

0

5,000

10,000

15,000

20,000

25,000

0% 20% 40% 60% 80% 100%

Emis

sio

n (

Ib/1

0 d

ay)





Emission using the single variable speed blower


84

5.4 CONCLUSION

Air-assisted flares, operating under low flow conditions (<1% of maximum flow)

with low BTU gases, have relatively narrow bands of air-to-vent gas ratios that can

achieve DREs greater than 98%. If blower configurations are not able to operate within

these narrow bands, emissions may be greater than those predicted based on 98% DRE,

but if air-assist rates can be finely tuned, emissions much lower than those predicted

assuming 98% DRE are achievable. This work examined the potential impact on

emissions of using four different blower configurations (single fixed speed, dual fixed

speed, single variable speed, dual variable speed) on an air-assisted flare. Typical patterns

of flare vent gas flow rates were obtained from hourly data on vent gas flow rates from

Houston, Texas. The analyses indicate that flare emissions can be much greater than or

much less than the emissions assumed based on 98% DRE, depending on whether the

blowers are able to achieve desired assist air to vent gas ratios. Variable speed blower

configurations achieve lower predicted emissions than similarly sized fixed speed

blowers. The most cost effective blower configurations will depend on flared gas flow

rate, flow rate variability and composition. (Al-Fadhli et al., 2012b)

85

CHAPTER 6: Minimization of Refinery Flaring Through

Integration with Fuel Gas Networks

6.1 INTRODUCTION

Minimizing flaring emissions is an effective approach to reducing flaring impacts

on air quality. As will be described in this chapter, integrating flared gases into the fuel

gas network of a refinery can be an effective minimization strategy.

Refineries use internal fuel sources and purchase external fuel such as natural gas

to satisfy their needs. Waste gases with different pressures, temperatures, composition,

and heating values can be collected, rather than flared, and utilized as fuel in a refinery

fuel gas network. However, the quality (physical and chemical properties) and the

quantity of the waste gases must be matched to refinery needs. Designing a fuel network

that can consume the available waste gases and fuels in an optimal manner, can both

reduce flaring operation and the cost of purchasing fuels and consuming refinery fuel

products.

This chapter will examine the integration of time varying flare gas (waste gases)

flows into a refinery fuel gas network (Hasan et al., 2011). A modified version of the fuel

gas network (FGN) design methodology reported by Hasan et al (2011) will be used.

86

6.2 METHODS

Hasan et al. (2011) have developed a fuel gas network (FGN) design

methodology that matches multiple waste gas sources with refinery sinks. The optimal

configuration depends on the flows, chemical compositions, fuel quality of waste gases,

and fuel constraints (flow, chemical compositions and fuel qualities) of all the potential

sinks. The FGN designs reported by Hasan, et al (2011) assume steady state operation

and account for non-isothermal and non-isobaric operation, non-isothermal mixing,

treatment cost, utility and operating cost, the profit of using waste gases from all the

available sources and the cost of not using them. The superstructure of the FGN model

includes heaters, coolers, compressors, valves, sources, sinks and headers for each sink.

The model is a non-linear program (NLP) that incorporates constraints concerning

utilities (compressors, valves, heaters, and coolers), system pressure and temperature,

non-isothermal mixing and fuel gas properties.

As stated above, the model developed by Hasan et.al (2011) model was designed

for a static and steady state operation. However, operation in most petrochemical plants is

unsteady-state (dynamic). The flow rate of wastes gases is highly variable due to

variations in the operation modes. For example, previous chapters in this thesis have

shown that waste gases exhibit significant temporal variability. In this work the FGN is

modified to handle the dynamic behavior of waste gas flows.

The modified version of the FGN, called a multi-mode FGN, has been described

by Jagannath et. al (2012). This model assumes that a plant operates under P operating

modes. Each industrial operating mode is steady-state operation. Also, each operation

mode has a probability of occurrence during a year of operation.

87

The multi-mode FGN model consists of three main nodes. The first node consists

of all available sources with a fuel gas. The second node consists of mixing headers or

tanks, called pools, which are used to blend different source streams to meet the

requirements of the sinks. The third node consists of sinks where the fuel gases are used.

The model used in this work assumes that each pool is connected to only one sink.

Between the source and pool nodes, waste gases can pass through coolers or heaters,

valves or compressors. Figure 6-1 shows the superstructure of the multi-mode FGN,

where fuel gas from source (i) passes through utility exchangers (coolers and heaters) and

movers (compressors and valves) to pool (k) and then reaches to sink (k).

Figure ‎6-1: The superstructure of the multi-mode FGN.

88

In developing the mathematical formulation of the multi-mode FGN model,

several assumptions were made:

1. All source gases, internal and external fuels are ideal gases.

2. The lower heating value of the source gases, internal and external fuels is

constant and does not vary with temperature variation.

3. All gas expansions in the multi-mode FGN follow Joule–Thompson

expansion.

4. Only valves are used for expansion.

5. All gas compressions in the multi-mode FGN are adiabatic and single-

stage compressors are used.

6. No pressure drops in utility exchangers, mixers and transfer pipes.

7. No chemical reaction or phase change during the multi-mode FGN

operation.

8. Operation of the multi-mode FGN consist of several operating modes

(1,2,…p) with a known probability of occurrence during a year of

operation.

9. No limit on utility exchangers operation at any temperature.

10. All flow rates of gaseous fuel (MMscf/h) were calculated at 68 F and 14.7

psia.

The mathematical formulation of the multi-mode FGN imposes constraints on (i)

the flow rates of the sources and the sinks, (ii) the energy demand of the sinks, (iii)

pressure and temperature limits in the multi-mode FGN, (iv) fuel quality limits, and (v)

physical features of the multi-mode FGN. In specifying the mathematical formulation,

the index i is used to represent the source number while the index k is used to represent

89

the header/sink number. Also, index p is used represent the number of the operation

mode.

6.2.1 Flow rates

The multi-mode FGN must consume all the available fuel gas from a source i.

The multi-mode FGN distributes source gases among the available sinks including a

flare. The total available flow rate that can be used by the multi-mode FGN from the

source i during mode p is . Also, the flow from source i to sink k during the operating

mode p is denoted by .

Then:

( 6.1 )

The flow rate of source i is limited by the available fuel flow

( 6.2 )

where the

represent the minimum and the maximum flow limits of source i

during operating mode p. For fuel gas sources, equation 6.2 is written as the following

, to enforce the constraint that all the fuel gases from source i enter the

multi-mode FGN, so the model will choose between using waste gases as fuel in sink k or

dispose of them via flaring.

90

The flow rates of the fuel gas that is received by the sink k should be within its

flow limits.

( 6.3 )

where the

represent the minimum and the maximum flow limits of sink k in

period p.

6.2.2 Energy demand

There are two types of sinks. The first type can use a fixed amount of energy

(fixed sink) while the second can use excess energy above its energy demand (flexible

sink). To account for these two types of sinks, the following constraints are developed.

( 6.4 )

( 6.5 )

where is the lower heating value of the waste gaseous fuel from source i and is

the energy demand for sink k during operating mode p.

91

6.2.3 Non-isothermal and non-isobaric operations

The multi-mode FGN accounts for the enthalpy, temperature and pressure

changes in the network that could occur because of the existence of coolers, heaters,

compressors or valves. The following constraints account for the non-isothermal and non-

isobaric operations in the multi-mode FGN.

Total temperature/enthalpy change of waste gases from source i to sink k:

( 6.6 )

Enthalpy change across the valve:

( 6.7 )

Enthalpy change across the compressor:

( 6.8 )

Pressure limits of sink k:

( 6.9 )

92

Temperature/enthalpy limits of source i:

( 6.10 )

( 6.11 )

Energy balance across the mixing header k:

( 6.12 )

( 6.13 )

where

are the enthalpy changes across the compressor,

the valve, the heater and the cooler from source i to sink k during mode p, is the

heat capacity of waste gases from source i in mode p, Pkp and Pip are pressure of sink k

and source i during mode p, respectively, and

are the minimum and the

maximum allowable pressure limits in sink k during mode p. is the temperature of

93

source i in period p, and and

are the upper and lower allowable temperature

bounds of gaseous fuel from source i. is the temperature of sink k during mode p.

and are the minimum and the maximum acceptable temperature by sink k during

mode p. Also, µip is the Joule-Thompson coefficient, Ƞip is the adiabatic compression

efficiency of source i during operating mode p, and R is the ideal gas constant.

6.2.4 Fuel quality

Each sink can consume fuel gas that satisfies its fuel quality requirements.

Therefore, the FGN accounts for the fuel quality limits for each sink. The following

section discuses the constraints on several fuel properties.

Specific gravity of gaseous fuel is the density ratio of fuel to the air. Both

densities are measured at the same conditions (pressure and temperature). However, for

an ideal gas fuel, the specific gravity is just the ratio of the molecular weight of the ideal

gas to the molecular weight of air. The following constraints are used to satisfy the

specific gravity requirement for each sink.

( 6.14 )

( 6.15 )

94

where is the specific gravity of gaseous fuel from source i to sink k, is the

specific gravity of sink k, and

are the minimum and the maximum

acceptable specific gravity of sink k.

A lower heating value (LHV) is an important property that characterizes the

energy content of gaseous fuels depending on their chemical composition. Sinks require a

fuel with a minimum LHV to work with high performance. For example, flares require

waste gas with a minimum LHV of 300 BTU/scf in order to maintain high combustion

efficiency (TCEQ, 2012). The following constraints provide each sink with the required

LHV.

( 6.16 )

( 6.17 )

where is the lower heating value of sink k, and

are the

minimum and the maximum acceptable lower heating value of sink k in operating mode

p.

Several sinks constrain the presence of chemical species in gaseous fuels. The

presence of chemicals above the acceptable level may cause corrosion or destruction to

the sink. Also, it could violate an environmental regulation by allowing the sink (e.g.

turbine) to emit hazardous emissions above allowable levels. Therefore, the following

constraint is used to ensure that the multi-mode FGN delivers gaseous fuel with

acceptable contaminant levels.

95

( 6.18 )

where is the fraction of species s in source i, and

are the minimum

and the maximum acceptable fraction of species s in sink k during mode p.

The Wobbe index (WI) is a measure of energy flow and interchangeability of fuel

gas. The heating value of gaseous fuel flowing through an orifice in a specific period

increases as the WI increases. The WI combines the LHV with SG as follows:

Two gaseous fuels with different LHV and composition, but with same WI,

exhibit similar combustion characteristics. The WI is not a linear fuel property, so the

following constraint is developed to account for the WI requirement for each sink.

( 6.19 )

where

are the minimum and the maximum WI limits for sink k.

The methane number is a fuel property that characterizes the knocking resistance

of a gaseous fuel. When fuel is purely methane, the methane number is 100. In contrast, if

the fuel is hydrogen, the methane number is 0. If the fuel is a mixture of 80% methane

and 20% hydrogen, the methane number would be 80. The methane number is mostly

used for gas turbines. Gas turbines can commonly use fuel gas with a minimum Methane

96

number of 80. The following constraint is used to account for a Methane number

requirement.

( 6.20 )

where and are the mole fraction

of methane, ethane, propane, butane, pentane and higher alkanes in source i during mode

p.

Moreover, more constraints were developed to prevent any condensation of fuel

gas in sink k. The two minimum superheat requirements of sink k are:

( 6.21 )

( 6.22 )

where MDPkp and HDPkp are moisture and hydrocarbon dew point temperatures for sink

k during mode p.

97

The multi-mode FGN distributes the waste gases to several sinks. One of the sinks

is a flare. The flare disposes of all the waste gases that cannot be economically consumed

by the sinks. A hydrocarbon penalty is introduced through constraint 6.23 to discourage

flaring which leads to a reduction in flare environmental footprints. The hydrocarbon

penalty constraint allows a limited amount of hydrocarbon to be flared without a fee.

However, if the amount of hydrocarbon exceeds the allowable amount, constraint 6.23

incurs a cost of the excess flared hydrocarbon.

( 6.23 )

where is the amount of flared hydrocarbon with emission fee (kg/hr), is the

amount of hydrocarbon in source i (Kg/MMscf) and flared hydrocarbon with no

emission fee (kg/hr) in mode p. The cost of the hydrocarbon penalty ($/kg) will be

imposed in the objective function.

6.2.5 Physical features

Designing a FGN for each operation mode is not a practical solution. In contrast,

designing a FGN with the same physical features but with varying operational details (e.g

flow rates, temperature, pressure) in all the operation modes is practically preferable.

Therefore, the following design constraints are developed to address this issue.

( 6.24 )

( 6.25 )

98

( 6.26 )

( 6.27 )

( 6.28 )

where is the maximum flow capacity through transfer line from source i to sink k,

,

, and

are the maximum ,

, and

from

source i to sink k.

The main objective of the multi-mode FGN model is to integrate the waste gases

from different sources with the available sinks while minimizing the total annualized cost

(TAC) of the multi-mode FGN with P operation modes. The TAC is comprised of three

parts. The first and the second part are the capital expenditure (CAPEX) and operating

expenditure (OPEX) of the multi-mode FGN. The third part is flaring cost in terms of

hydrocarbon penalty. The following equation is the multi-mode FGN objective function.

99

( 6.29 )

Where

: is an annualization factor.

: is the capital expenditure of the pipelines.

: is the capital expenditure of the compressors.

: is the capital expenditure of the heaters.

: is the capital expenditure of the coolers.

: is the capital expenditure of the valves.

: is the cost of installing a new sink if needed.

: is the cost of upgrading the capacity of an existent sink.

: is the working time in hours of the plant per year.

: is the probability of occurrence of operation mode p during a year of operation.

100

: is the cost of using the fuel gas from source i in $/MMscf.

: is the revenue from surplus energy produced from sink k in $/BTU.

: is the treatment cost in sink k in $/MMscf.

: is the cost of flaring additional hydrocarbon above the allowable limit in $/kg.

: is the operating cost of the pipelines.

: is the operating cost of the compressors.

: is the operating cost of the heaters.

: is the operating cost of the coolers.

: is the operating cost of the valves.

Since the flare flow is highly variable in time, the objective in this work will be to

use the multi-mode FGN model to identify FGN designs that minimize flaring operations.

A case study of a petroleum refinery is adapted from Jagannath et. al (2012) to illustrate

scenarios that would minimize flaring operation.

101

6.3 CASE STUDY OF A PETROLEUM REFINERY

This case study is adapted from Jagannath et. al (2012) . A petroleum refinery has

four potential sources of fuel gases that are normally sent to a flare. Also, the refinery has

two fuel sources: internal and external. The internal fuels are products of the refinery

units and it is considered a fuel from product (FFP). The external source is a purchased

natural gas fuel. The refinery has five sinks: 4 turbines and a boiler.

Minimizing the waste gas from fluid catalytic cracking unit (FCCU) is the initial

focus of this study. The waste gas flow rate from FCCU is highly variable. Figure 6-2

illustrates the waste gas flow rate time series from an FCCU flare over a month of

operation. This FCCU flare will replace one of the sources in the petroleum refinery

problems that are reported by Jagannath et al. (2012). Pavlovic et al. (2011) modeled and

characterized the FCCU flow shown in Figure 6-2. They showed that this FCCU has four

modes of operations. The average flow rates of the four operation modes are 0.01, 0.013,

0.015 and 0.02 MMscf/hr with probability of occurrence of 0.59, 0.16, 0.12 and 0.13

respectively. Consequently, these flow rates with their occurrence probability will be

used in the refinery case study.

102

Figure ‎6-2: Waste gases Flow rates time series from FCCU over a month of operation.

In summary, the refinery problem has 6 sources (including FCCU) and 6 sinks

(including a flare). Table 6-1 provides the flow, temperature, pressure, LHV, and other

properties of all sources in the refinery problem. Table 6-2 shows the flow range,

temperature, pressure, and other specifications required by all sinks in the refinery

problem.

In the refinery problem, we assume that all flow rates from all sources are

constant except the waste gases from the FCCU flare. Moreover, the probability of

occurrence of the FCCU flare operation modes is the same as the probability of the

refinery operation modes. Also, it is assumed that this refinery works 8000 hours per year

and the annualization factor in this problem is 10% (straight line 10 year capital

depreciation). Finally, Tables 6-3 and 6-4 provide the cost parameters (CAPEX and

OPEX) of the pipelines, compressors, valves, heaters, and coolers that are used in the

multi-mode FGN.

0

500

1000

1500

2000

2500

3000

3500

0 100 200 300 400 500 600 700

Mas

s fl

ow

rat

e(I

b/h

r)

Hour of operation

103

Table ‎6-1: Data of the sources in the refinery problem

Spec/Parameter Source 1 Source 2 (FFCU) Source 3 Source 4 FFP Natural gas

Flow (MMscf/h) – Mode 1 0.04 0.01 0.18 0.09 ≤5.00 ≤5.00

Flow (MMscf/h) – Mode 2 0.04 0.013 0.18 0.09 ≤5.00 ≤5.00

Flow (MMscf/h) – Mode 3 0.04 0.015 0.18 0.09 ≤5.00 ≤5.00

Flow (MMscf/h) – Mode 4 0.04 0.02 0.18 0.09 ≤5.00 ≤5.00

Temperature (K) 373 400 350 380 320 320

Pressure (psia) 50 35 25 50 25 50

Cp (kJ/MMscf K) 42791 122142 42270 44403 100626 46157

µ (K/psia) 0.03 0.028 0.03 0.028 0.028 0.028

Ƞ 0.75 0.75 0.75 0.75 0.75 0.75

R/Cp 0.2 0.2 0.17 0.17 0.18 0.2

LHV (MMBtu/MMscf) 880 2628 850 807 2659 1100

SG 0.769 0.74 0.769 0.772 1.425 0.909

Hydrocarbon content (Kg/MMscf) 21400 61099 20622 25358 62701 21665

Methane (mol%) 88 0 88 62 0 85

Ethane (mol%) 2 0 2 5 2 5

Propane (mol%) 0.5 0 0 4 56 4

C3+ (mol%) 1 100 0 2 42 2

Hydrogen (mol%) 0.5 0 4 1 0 0

Carbon Monoxide (mol%) 1 0 3 1 0 0.05

Nitrogen (%) 7 0 3 25 0 3.95

Sulfur (ppm) 55 0 55 65 65 65

H2S (ppm) 0.05 0 0.05 198 198 198

Price ($/MMscf) 0 0 0 0 500 800

104

Table ‎6-2: Data of the sinks in the refinery problem

Spec/Parameter Turbine 1 Turbine 2 Turbine3 Turbine4 Boiler Flare

Flow Range (MMscf/h) 0.08-0.11 0.09-0.145 0.10-0.13 0.09-0.12 0.20-0.25 ≥0

Temperature (K) 273-800 273-800 273-800 273-800 273-800 273-800

Pressure (psia) 25-360 25-360 25-360 25-360 25-360 14-17

Demand (MMBtu/h) 120 140 110 110 180 ≥0

WI 750-1590 750-1590 750-1590 750-1590 750-1590 -

MN >80 >80 >80 >80 >80 -

MDP(K) 277 277 277 277 277 -

HDP(K) 277 277 277 277 277 -

LHV (MMBtu/MMscf) 500-2000 500-2000 500-2000 500-2000 500-2000 300-2000

SG 0.5-1 0.5-1 0.5-1 0.5-1 0.5-1 0.5-1

Methane (mol%) >85 >85 >85 >85 - -

Ethane (mol%) <15 <15 <15 <15 - -

Propane (mol%) <15 <15 <15 <15 - -

C3+ (mol%) <5 <5 <5 <5 - -

Carbon Monoxide(mol%) <10 <10 <10 <10 - -

Nitrogen (mol%) <15 <15 <15 <15 - -

Sulfur (ppm) <500 <500 <500 <500 - -

H2S (ppm) <200 <200 <200 <200 - -

Handling Cost ($/MMscf) 0 0 0 0 0 5

105

Table ‎6-3: CAPEX and OPEX of auxiliary equipment and pipelines in the multi-mode FGN

Unit CAPEX ($/kW) OPEX ($/kWh)

Compressor 100000 0.1

Heater 50000 0.01

Cooler 50000 0.02

Valve 5000 0.001

Pipeline - 0.5 $ /MMscf

Table ‎6-4: CAPEX of the pipelines in the multi-mode FGN

CAPEX ($-h/MMscf) Turbine1 Turbine 2 Turbine 3 Turbine 4 Boiler Flare

Source 1 62500 64000 65000 63000 62500 62500

Source 2 62500 66000 65500 62500 63500 63000

Source 3 61000 64000 60000 62000 63500 63000

Source 4 62500 63500 64500 65500 67000 68500

Source 5 62500 64000 65000 66000 66500 68000

Source 6 61500 61000 60500 64000 64500 66000

106

6.3.1 Minimizing waste gases

To minimize waste gases that are normally sent to the flare, for these scenarios,

the sources and the sinks were divided into two problems. The first problem contains the

natural gas and FFP sources with the four turbines. Turbines are sensitive to the chemical

composition of the fuel gas. The presence of heavy hydrocarbon gas in the fuel with

unknown composition could cause severe turbine damage or explosion (Elliott et al.,

2004). Data on flared gas composition showed that waste gases contain considerable

amounts of heavy hydrocarbon which could impact turbine performance. Therefore,

natural gas and FFP were the only sources of fuel for the four turbines in this case study.

The second problem contains the four sources of the waste gases with the boiler and the

flare sinks. This effectively converts the problem of minimizing flared gases to a boiler

design problem. In the following sections, the solution to the turbine fuel feed problem is

briefly described, but the main focus is on the flare gas and boiler design scenarios.

6.3.1.1 Turbine fuels

In the turbine fuel problem, the only sources of fuel are commercial natural gas

and FFP and the sinks are four turbines.

Using the FGN model to solve the turbine fuel problem showed that no heater,

cooler or compressor is need for this problem. Only a single valve is needed. The model

uses a combination of natural gas and FFP to satisfy all the four turbine demands. Table

6-5 gives the optimum operating parameters for the turbine fuel problem.

107

Table ‎6-5: optimum operating conditions for the first problem

Flow (MMscf/h)

Flow from

Natural gas (MMscf/h)

Flow from

FFP (MMscf/h) LHV

(MMBtu/MMscf) Wobbe Index (MMBTU/MMscf)

Energy Flow (MMBTU/hr)

Pressure (Psia)

Temperature (K)

Turbine 1 0.107 0.099 0.008 933 959 120 25 319

Turbine 2 0.124 0.115 0.009 943 969 140 25 319

Turbine 3 0.1 0.094 0.006 916 945 110 25 319

Turbine 4 0.098 0.09 0.007 934 965 110 25 319

108

6.3.1.2 Flare gas and boiler fuel

The flare gas and boiler fuel problem contains four sources of waste gases and

two sinks (a boiler and a flare). The flow rates of the waste gases from three sources are

constant and the flow rate from the fourth source (FCCU flare) is variable. The flow rate

from the FCCU flare can be divided into 4 operation modes with their known

probabilities of occurrence during a year of operation. The objective of this work is to

determine the best scenarios to utilize all the waste gases instead of flaring them.

Three methods will be tested for the second problem. Then, impact of different

parameters on the TAC and the amount of flaring will be examined. The parameters to

be tested are: minimum flared hydrocarbon without emission fee ( , hydrocarbon

penalty ( , and sink flexibility. In the first method, the existing boiler with a limited

capacity and a flare will be the only sinks for the waste gases that are produced by the

four sources. The model will be used to assess sensitivity to parameters such as emission

fees. In the second and third methods, the capacity of the existing boiler will be expanded

assuming either an upgrade to the existing boiler or the construction of a new boiler, to

accommodate more fuel and produce more steam. Again, the model will be used to assess

sensitivity to network parameters.

6.3.1.2.1 Fixed capacity boiler

The existing boiler and a flare were the only sinks for the waste gases for these

scenarios. The existing boiler has a limited fuel flow capacity range of 0.2 to 0.25

MMscf/hr. In contrast, the flare can accommodate any amount of waste gases. A variety

of different scenarios for hydrocarbon penalties were examined, as summarized in Table

6-6. The fuel gas network was relatively insensitive to any changes in flaring penalties.

109

Table ‎6-6: Scenario results of the impact of hydrocarbon penalty constraint on the TAC and the flaring amount

Scenario

(Kg/hr)

($/kg) TAC

($/YEAR) Flaring amount (MMscf/Year)

1 1000 0.1 1,379,015 858.1

2 500 0.1 1,779,015 858.1

3 0 0.1 2,179,015 858.1

4 3000 0.1 27,138 858.1

5 4000 0.1 8,503 846.8

6 1000 0.05 693,787 858.1

7 1000 0 8,503 846.8

8 NO NO 8,503 846.8

In the scenarios, summarized in Table 6-6, the boiler, in addition to having a flow

limit of 0.2 to 0.25 MMscf/hr, was a fixed sink with energy demand of 180 MMBtu/hr.

However, the boiler, at the existing upper flow constraint of 0.25 MMscf/hr, could

consume more energy than 180 MMBtu/hr, producing more steam. The additional steam

has the potential to generate revenue. For example, the extra steam could be used as a

feed to steam turbines to generate electricity.

To study the impact of making the existing boiler as a flexible sink, it is assumed

that any additional energy consumed by the boiler beyond 180 MMBtu/hr is used to

generate high pressure steam (515 psia). The revenue from the high pressure steam is

assumed to be $5.3/MMBtu. This value was obtained based on the cost of high pressure

steam ($3.8/MMBtu) in 2002 (Peters et al., 2003). The cost in 2002 was multiplied by the

ratio of the cost index of 2010 to cost index of 2002 to obtain the cost of high pressure

steam in 2010. The cost indexes were obtained from Marshall & Swift Cost Index

(Marshall & Swift Cost Index, 2011). Then, the scenarios reported in Table 6-6

110

(scenarios 1 to 8) were re-examined, assuming that the existing boiler is flexible. Table

6-7 shows the impact of the sink flexibility on the TAC and the flaring amount.

Table ‎6-7: Scenario results of the impact of the sink flexibility on the TAC and the flaring amount

Scenario

(Kg/hr)

($/kg)

TAC ($/YEAR)

Flaring amount (MMscf/Year)

9 1000 0.1 -1,789,230 579.0

10 500 0.1 -1,389,230 579.0

11 0 0.1 -989,226 579.0

12 3000 0.1 -2,321,370 579.0

13 4000 0.1 -2,321,370 579.0

14 1000 0.05 -1,987,220 579.0

15 1000 0 -2,321,370 579.0

16 NO NO -2,321,370 579.0

The results of scenarios 9-16 indicate that the sink flexibility has a significant

impact on the TAC and the flaring. However, with an upper flow rate bound of 0.25

MMscf/hr, the sink flexibility did not eliminate flaring, only reduced the flaring amount.

Once again, emission fees have a negligible impact on network design.

Material reuse pinch analyses (described in detail in Appendix D) confirmed that,

for this maximum boiler flow rate, the minimum possible amount of flaring is 579

MMscf per year. These results further confirm that increasing the hydrocarbon penalty

will not reduce flaring. This led to a series of additional case studies which increase the

capacity of the boiler.

111

6.3.1.2.2 Variable capacity boilers

Two alternatives for expanding boiler capacity were examined: adding additional

capacity to an existing boiler and adding a new boiler. For the first alternative

(expanding the existing boiler) the proposed new fuel capacity limits are 0.22-0.33

MMscf/hr. The cost of the upgrading depends on whether high or low pressure steam is

generated. The costs of high pressure and low pressure steam boiler expansions were

assumed to be $400,000 and $200,000, respectively. Also, the revenues from producing

high pressure and low pressure steam were assumed to be $5.3 and $3.6 / MMBtu,

respectively.

Expanding the boiler capacity showed a significant impact on flaring, eliminating

all flaring in each of the tested scenarios. Table 6-8 shows the impact of upgrading the

boiler capacity (high and low pressure steam) on the TAC and the amount of gases flared.

Table ‎6-8: Scenario results of the impact of expanding the boiler capacity on the TAC and the flaring amount

Scenari

o Cost of

Expanding Steam Type

(Kg/hr)

($/kg) TAC

($/YEAR)

Flaring amount

(MMscf/Year)

17 400,000 High pressure 1000 0.1 -4,754,970 0

18 400,000 High pressure 1000 0.05 -4,754,970 0

19 400,000 High pressure 1000 0 -4,754,970 0

20 400,000 High pressure NO NO -4,754,970 0

21 200,000 Low pressure 1000 0.1 -3,235,660 0

22 200,000 Low pressure 1000 0.05 -3,235,660 0

23 200,000 Low pressure 1000 0 -3,235,660 0

24 200,000 Low pressure NO NO -3,235,660 0

112

An alternative configuration, which differs only in the capital cost assumed for

the boiler expansion, is the installation of a new boiler. In this scenario, there are three

sinks (2 boilers and a flare) for the waste gases. The maximum capacity of the new boiler

is 0.08 MMscf/hr. The cost of the new boiler depends on whether high or low pressure

steam is generated, with the costs of new high pressure and low pressure steam boilers set

at $1,000,000 and $ 600,000, respectively.

Installing a new boiler with the proposed capacity eliminates flaring in scenarios

25-32 (Table 6-9). Hydrocarbon penalties had no impact on the amount flared

Table ‎6-9: Scenario results of the impact of installing a new boiler on the TAC and flaring amount

Scenario Cost of

installing Steam Type

(Kg/hr)

($/kg) TAC

($/YEAR) Flaring amount (MMscf/Year)

25 1,000,000 High pressure 1000 0.1 -4,694,970 0

26 1,000,000 High pressure 1000 0.05 -4,694,970 0

27 1,000,000 High pressure 1000 0 -4,694,970 0

28 1,000,000 High pressure NO NO -4,694,970 0

29 600,000 Low pressure 1000 0.1 -3,155,660 0

30 600,000 Low pressure 1000 0.05 -3,155,660 0

31 600,,000 Low pressure 1000 0 -3,155,660 0

32 600,000 Low pressure NO NO -3,155,660 0

Installing a new boiler capacity, whether expansion or through the addition of a

new boiler, shows a net operating profit if all of the steam can be sold. However, it is

possible that the refinery may not use all the additional steam. Table 6-10 reports the cost

impacts of using only various fractions of the generated steam. The results demonstrate

that even if only a few percent of the steam generated by burning waste gas in the boiler

can be used, the design still shows a profit.

113

Table ‎6-10: Impact of utilizing the additional high pressure steam on the TAC.

Percentage of additional steam used

TAC ($/year)

100% -4,694,970.0

90% -4,215,060

40% -1,815,550

30% -1,335,650

10% -375,841

8% -79,115

3% -39,909

2% 8,081

1% 83,893

0% 104,062

6.4 CONCLUSION

Integrating sources of waste gases with the multi-mode FGN is an effective

approach to reducing flaring impacts on air quality. In the case studies reported here,

configurations that expanded boiler capacity, using waste gases to generate steam, were

estimated to be profitable. Including a hydrocarbon penalty constraint on flaring showed

little effect. Overall, upgrading and installing new sinks can minimize or eliminate

flaring.(Jagannath et al., 2012)

114

CHAPTER 7: Effect of Temperature Excursions in Catalytic

Cracking Units on the Generation of Flared gases

7.1 INTRODUCTION

A petroleum refinery is a complex facility, and detailed processing configurations

vary among refineries, but a central operation in most refineries is the fluid catalytic

cracking (FCC) unit. FCC is a chemical process used to break down large hydrocarbon

molecules in gas oils into more valuable products. The main products of the FCC are

gasoline and liquefied petroleum gas (LPG). In 1942, the first FCC unit was introduced to

the world of petroleum refining for the production of aviation gasoline. It was installed in

Baton Rouge, Louisiana. FCC units allowed more gasoline production, and enhanced the

fuel quality (Ertl et al., 2008; Parkash, 2003). In modern refineries, approximately half of

motor gasoline is produced from the FCC process. In 2004, the total worldwide capacity

of FCC was above 2 million tons per day, which is about 17% of the total crude oil unit

capacity in the world (Ertl et al., 2008). In 2006, U.S refineries processed around 6.3

million barrels per day of heavy oil in catalytic cracking units (DOE, 2007). The oil feed

capacity for a typical FCC unit is about 10,000 tons/day (100,000 BBL/day) (Ertl et al.,

2008). The traditional feed of FCC is the heavy vacuum gas oil, which is the middle

product from the vacuum distillation column. This feed is catalytically cracked through

the FCC unit to produce useful fuel (mainly gasoline) (Ertl et al., 2008; Parkash, 2003).

Due to the flexible operation of the FCC unit, some refineries use residual oil, the bottom

product of vacuum distillation, which contains high concentrations of aromatics and

asphaltenes. The catalytic cracking of these compounds may result in high coke

formation and could affect the FCC unit performance. Also, existence of some

contaminants in the feed, such as nickel and vanadium, can promote undesirable chemical

reactions and negatively impact catalyst performance (Ertl et al., 2008; Parkash, 2003).

115

Therefore, some refineries install hydrotreating units to decontaminate the FCC feed,

which leads to improved fuel quality and reduced pollutants emissions such as SOx, NOx

from FCC units. In the United States, approximately 37% of the FCC feed is hyrotreated

which is expected to increase in the future due to stringent environmental regulations; 35

% of the FCC worldwide feed is hydrotreated (Ertl et al., 2008).

A typical FCC unit diagram is shown in Figure 7-1. The FCC oil feed is

preheated by series of heat exchangers and then mixed with very hot regenerated catalyst

which vaporizes the oil. The gas oil with the catalyst travels up to the catalyst riser

(reactor) where the cracking reactions take place. A typical catalyst to oil ratio is around

6. The cracking reactions take place at high temperature, approximately 810 K(Ertl et al.,

2008; Parkash, 2003). The gaseous products from the cracking reactions are separated

from the catalyst by cyclones. A small fraction of the products formed in the reactor is

coke, which deposits on the catalyst surface. The gaseous products (super heated vapors)

are sent to a fractionator. The fractionator separates the cracking products into different

cuts (such as light gases, gasoline and heavy cycle oil) based on their boiling point

temperature. The separated catalyst from the cyclones travels down to the catalyst

regenerator. The used catalyst is regenerated by using hot air to burn off the deposited

coke on the catalyst surface. The flue gas from the combustion is separated from the

regenerated catalyst by cyclones. The regenerated hot catalyst is mixed again with oil

feed and then the cycle is repeated. Moreover, the regenerator and reactor are thermally

integrated. The thermal energy from the combustion of deposited coke is used to provide

heat for cracking reactions in the reactor. Also, the reactor provides the combustion fuel

(coke) to the regenerator (Ertl et al., 2008; Pariyani et al., 2010; Parkash, 2003).

116

Figure ‎7-1: Fluid Catalytic Cracking (FCC) process (DOE, 2007).

Fresh catalyst is added daily to the FCC unit. However, the fresh catalyst rate

varies depending on the mode of operation and the feed type. For example, the fresh

catalyst rate could reach to 0.3 kg of catalyst per ton feed for feedstock with high metal

concentration but the rate may drop to 0.03 kg of catalyst per ton feed for feedstock with

low metal residual (Ertl et al., 2008).

For this work, the objective is to understand the production of light gases by the

FCC unit, and their impact on flaring. Light gas formation could be influenced by hot

spots in the reactor (e.g., from fresh catalyst addition), the level of coke formation

(influencing the energy demand in the regenerator) or other factors. A three–lump kinetic

model will be used to predict the impact of the reactor temperature on the conversion of

the gas oil charged. Specifically, the model is used to estimate the change in the

117

production of light gases (C2-C4) resulting from temperature excursions in the fluid bed

reactor.

7.2 THREE-LUMP MODEL

In 1970, Weekman and his colleagues reported a kinetic model for the catalytic

cracking process which is called the three-lump model. The Three-lump model is a

mathematical model to estimate the gas oil conversion and its yields in fixed, moving and

fluid bed reactors. The model assumes an isothermal reactor that is working at steady

state condition. Also, the model counts for the catalyst decay by adding a decay function

in the reaction rates. The model can estimate the gasoline, light gases and coke yields

based on reactor temperature, space velocity, catalyst to gas oil ratio and catalyst

residence time (Weekman and Nace, 1970).

A number of advancements have been made to FCC modeling since the

development of the three-lump model, and modern reaction models use a concept called

structure oriented lumping (SOL), first reported by Liguras and Allen (Liguras and Allen,

1989a; Liguras and Allen, 1989b). While SOL dramatically improves the ability to

predict gasoline quantity and quality, it does little to change predictions of light gas

formation, the focus of this work. Therefore, this work will use the simpler, and far less

computationally intensive, 3-lump model.

7.2.1 Kinetic model

In the three-lump model, the wide range of catalytic cracking feed and products

are lumped into three components; gas oil, gasoline, and light gases (C2-C4) and coke.

The interconversion of these components is determined by the process variables, kinetic

and parameters. Figure 7-2 shows the reaction scheme among the three components.

118

Figure ‎7-2: reaction scheme of catalytic cracking of the heavy gas oil.

where Y1 is the weight fraction of unconverted gas oil charged, Y2 is the weight fraction

of gasoline formed and Y3 is the weight fraction of light gases (C2-C4) and coke yields.

The catalytic cracking of gas oil charged results in gasoline and other products (light

gases and coke). The cracking of gas oil has been proven to be a second order reaction

(Weekman, 1968). Moreover, the cracking of gasoline to light gases and coke is assumed

to be a first order reaction because gasoline does not have as much diversity in the

molecule types as the gas oil (Weekman, 1979; Weekman and Nace, 1970). Equations

7.1 and 7.2 show the rate of disappearance of gas oil and the rate of change in weight

fraction of gasoline. Equation 7.3 calculates the weight fraction of light gases and coke.

( 7.1 )

( 7.2 )

( 7.3 )

where to is the oil residence time and Ф is the catalyst decay function.

119

The catalyst affects the rate of reaction. The activity of the catalyst decays as the

reaction proceeds because coke deposits on the catalyst surface (active sites). Voorhies

(1945) showed that the rate of coke deposition on the catalyst surface is highly dependent

on the catalyst residence time and independent of the gas oil conversion or the space

velocity (Nace et al., 1971; Voorhies 1945). Therefore, the catalyst activity is a function

of the catalyst residence time in the reactor. Also, it is assumed that the catalyst decay

functions for gas oil and gasoline cracking reactions are equal, because the same catalyst

active sites crack both the gas oil and the gasoline (Weekman and Nace, 1970). Using a

simple exponential form to represent the catalyst decay function (Ф) is adequate for

catalytic cracking reactions (equation 7.4) (Weekman, 1979).

( 7.4 )

where α is catalyst decay coefficient and tc is the catalyst residence time.

Solving equation 7.1 and equation 7.2 simultaneously is not easily possible, so the

best practice is to divide equation 7.2 by equation 7.1 which results in equation 7.5.

Equation 7.5 relates the weight fraction of the gas oil charged (Y1) and the gasoline (Y2).

At the beginning of the cracking reaction there is only gas oil (Y1=1) and no initial

gasoline (Y2=0). Using this as a boundary condition, equation 7.5 can be solved in terms

of K0, K1 and K2. As can be shown from equation 7.5, the relationship between Y1 and

Y2 does not depend on the space velocity, the catalyst activity function, or the catalyst

residence time but it depends only on the selectivity ratio (K1/K0) and the over cracking

ratio (K2/K0) (Weekman and Nace, 1970).

( 7.5 )

120

The derivation of the conversion equations for fixed, moving and fixed bed

reactor are described in detail by Weekman (1968). Equation 7.6 is the conversion

equation for a fluid bed reactor, which will be used for this work.

( 7.6 )

where AO is the extent of the reaction group, λ is the extend of the catalyst decay group, S

is the space velocity, K0=K1+K3, β is the catalyst-to-oil ratio, FO is the mass flow rate for

the oil charge, ρl is density of the liquid charge at room temperature and Vr is the volume

of the reactor.

7.2.2 Comparing the Three-lump model with experimental data

Laboratory experimental data of catalytic cracking of gas oil were used to test the

ability of the three-lump model to predict the conversion of the gas oil, weight fraction of

gasoline formed, and the weight fraction of light gases and coke yields. A wide range of

gas oils with different properties and molecular compositions were catalytically cracked.

The paraffins, naphthenes and aromatics concentrations were varied in all the tested gas

oil types. The model successfully estimated the weight fractions of the three components

(Y1, Y2 and Y3) under different reaction conditions for all types of charge stocks (gas

121

oils) (Nace et al., 1971). The Three-lump model requires four constants (K0, K1, K2 and

α) to be determined experimentally. The gas oil data that will be used for this work is the

data for a Mid-Continent blend with a boiling range of 423 to 904o F that has API =

29.6o. Also, an activity coefficient (α) for a typical zeolite catalyst will be used. The

laboratory experiments of catalytic cracking of the Mid-Continent blend with zeolite

catalyst were carried out at 900o F (Weekman and Nace, 1970). Table 7-1 shows the

values of the four constants at T=900o F.

Table ‎7-1: The values of rates of reactions and catalyst decay coefficient (Weekman Jr and Nace, 1970).

constant Value

K0 22.9

K1 18.1

K2 1.7 α 42.7

7.2.3 Temperature effect

All the reaction rate constants are function of temperature. Therefore, the relation

between Y1 and Y2 is a function of the temperature. The activation energy of the catalytic

cracking of gas oil and gasoline were assumed to be 10000 cal/gmole and 18000

cal/gmole, respectively (Weekman and Nace, 1970). The temperature dependence on K0,

K1 and K2 is shown in equations 7.7, 7.8 and 7.9.

( 7.7 )

( 7.8 )

( 7.9 )

122

The impact of the process variables on gas oil conversion, light gas and coke

yields, and gasoline yields can be determined by combining equations 7.3, 7.6, 7.7, 7.8,

7.9 and the solution of equation 7.5.

7.2.4 Coke formation

The amount of coke deposited on the catalyst surface can be calculated using the

Voorhees (1945) relation (equation 7.10), which relates coke formation to the catalyst

residence time.

( 7.10 )

where C is weight percent of carbon on the catalyst, tc is the catalyst residence time, and a

and b are constants. The value of a increases as the aromatics concentration in the gas oil

increases. Therefore, as the concentration of aromatics increases in feed stock, more coke

will deposit on the catalyst surface, which increases the catalyst deactivation (Nace et al.,

1971).

The weight percent of the total coke yield can be estimated using correlations.

Jacob et al. (1976) slightly modified Voorhees’s relation by testing 95 sets of coke data.

Then the coke yield was estimated by multiplying a modified form of relation 7.10 by 1.1

which counts for ratio of the hydrogen to carbon in coke (Jacob et al., 1976). In this work

it is assumed that the coke yield only depends on the composition of the feed stock and

the catalyst residence time and does not depend on the reactor temperature. In other

words, the impact of the reactor temperature excursion on the Y3 lump changes the

weight fraction of only the light gas yield, not the coke.

123

7.3 LIGHT GASES SCENARIOS

The typical average temperature of an FCC reactor is 810 K, (Ertl et al., 2008)

however, there are some hot spots in the reactor that have higher temperatures. Therefore,

different scenarios are developed to determine the impact of temperature excursions on

the additional amount of light gases produced.

For this work, it is assumed that the nominal temperature of the reactor is 810 K,

the total oil feed capacity to the FCC is 10,000 tons /day and the catalyst to oil ratio is 6

(Ertl et al., 2008). The base case scenario was developed by assuming that the average

reactor temperature is 810 K and no hot spots exist in the reactor. Moreover, it is assumed

that the temperature is normally distributed throughout the reactor volume. The

probability density function of the reactor temperature is calculated using equation 7.11.

The full width at half maximum (FWHM) of the temperature distribution in the reactor is

assumed to be 10. Using equation 7.12, the standard deviation (σ) of the temperature

distribution is 4.25. Figure 7-3 shows the temperature probability distribution of the FCC

reactor temperature for the base case.

( 7.11 )

( 7.12 )

where T is reactor temperature, Tav is the average reactor temperature, V is the volume

fraction of the temperature distribution in the FCC reactor (V=1 for the base case), σ is

the standard deviation of temperature distribution in the reactor, and FWHM is the full

width at half maximum (FWHM) of the temperature distribution in the reactor.

124

Figure ‎7-3: The probability of the FCC’s reactor temperature for the base case.

The weight fraction of the light gases (WFLG) at different temperatures can

determined using equations 7.3, 7.6, 7.7, 7.8, 7.9 and the solution of equation 7.5. Then

equation 7.13 is used to evaluate the WFLG for the base case.

( 7.13 )

The FCC reactor could have hot spots that have higher temperatures than the

average reactor temperature. These hot spots normally represent a small fraction of the

reactor volume. Therefore, different scenarios are developed depending on the

temperature of the hot spots and the volume percent of the hot spots in the reactor. For

example, one of the tested scenarios assumed that the hot spots have an average

temperature of 830 K and represent 10 % of the reactor volume. Figure 7-4 shows the

temperature probability distribution of the FCC reactor for this scenario. Then, equation

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

780 790 800 810 820 830 840

Tem

pe

ratu

re p

rob

abili

ty

T (K)

125

7.14 is used to calculate the WFLG for this scenario. Tables 7-2 and 7-3 show the

additional light gases of a FCC unit resulting from an oil feed of 10,000 tons /day for

different scenarios. The scenarios in Table 7-2 and 7-3 assume the catalyst residence

times (tc) are 5 min and 1.25 min, respectively.

( 7.14 )

Figure ‎7-4 :The probability of the FCC’s reactor temperature for the scenario where the hot spots

average temperature is 830 K and represent 10% of the reactor volume.

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

780 790 800 810 820 830 840 850 860

Tem

pe

ratu

re p

rob

abili

ty

T (K)

90 % of the reactor volume

10 % of the reactor volume

126

Table ‎7-2: Mass rates of additional light gases from a FCC unit for different temperature excursion scenario

when the tc =5 min

Average T of hot spots

Volume % of the reactor

Additional

WFLG % Mass rate of additional

light gases (Ib/hr) 830 10% 0.55894% 4658 830 5% 0.27947% 2329 830 2% 0.11179% 932 820 10% 0.27070% 2256 820 5% 0.13535% 1128 820 2% 0.05414% 451 815 10% 0.13310% 1109 815 5% 0.06655% 555 815 2% 0.02662% 222 813 10% 0.07931% 661 813 5% 0.03966% 330 813 2% 0.01586% 132 812 10% 0.05269% 439 812 5% 0.02635% 220 812 2% 0.01054% 88

Table ‎7-3 : Mass rates of additional light gases from a FCC unit for different temperature excursion scenario

when the tc = 1.25min

Average T of hot spots

Volume % of the reactor

Additional

WFLG % Mass rate of additional

light gases (Ib/hr) 830 10% 0.37673% 3139 830 5% 0.18836% 1570 830 2% 0.07535% 628 820 10% 0.18107% 1509 820 5% 0.09053% 754 820 2% 0.03621% 302 815 10% 0.08874% 740 815 5% 0.04437% 370 815 2% 0.01775% 148 813 10% 0.05282% 440 813 5% 0.02641% 220 813 2% 0.01056% 88 812 10% 0.03507% 292 812 5% 0.01754% 146 812 2% 0.00701% 58

127

7.4 COMPARISON BETWEEN SCENARIO RESULTS AND FCC FLARE DATA

A FCC unit is a central and continuous process in all modern refineries.

Therefore, waste gases from the FCC unit are expected to be continuously generated.

These waste gases are normally sent to either the fuel gas network or a flare. Figure 7-5

shows the monitored hourly flow rate times series for a FCC flare in Houston, Texas.

These data was extracted from 2006 SI. The total flow rate of the FCC flare is 1,277,000

lb for a month of operation with an average of 1,665.2 lb/hr. The cumulative mass

distribution for the FCC flare flows is shown in Figure 7-6. Figure 7-6 indicates that

almost 50% of total mass flow rate is generated at flow rates less than 1,600 lb/hr. The

histogram and the time cumulative distribution of the waste gas flow rates from the FCC

unit for a month of operation are shown in Figure 7-7. Figure 7-7 shows the waste gas

flow rates was ranged between 1300 to 1600 lb/hr for more than half of the operating

time (32 days) with a frequency of 415 hours out of 767 hours. Also, the flow rate of the

generated waste gases from the FCC unites was less than 1600 lb/hr for 62 % of the total

operating time.

128

Figure ‎7-5: Monitored hourly flow rate time series of FCC flare over a month of operation.

Figure ‎7-6 : Mass cumulative distribution function for the FCC flare flows.

0

500

1000

1500

2000

2500

3000

3500

0 100 200 300 400 500 600 700

Mas

s fl

ow

rat

e(I

b/h

r)

Hour of operation

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

90

0

10

00

11

00

12

00

13

00

14

00

15

00

16

00

17

00

18

00

19

00

20

00

21

00

22

00

23

00

24

00

25

00

26

00

27

00

28

00

29

00

30

00

31

00

Cu

mu

lati

ve D

istr

ibu

tio

n F

un

ctio

n (

%)

Mass flow rate (Ib/hr)

129

Figure ‎7-7: Histogram and time cumulative distribution for the FCC flare flows for a month of

operation.

Tables 7-2 and 7-3 showed the impact of the temperature and the volume fraction

of the hot spots inside the FCC’s reactor on the production of additional light gases at two

different catalyst residence times. Figures 7-8 and 7-9 illustrate the relation between the

hot spot temperatures and the production of the light gases at constant volume fractions

and catalyst residence times 5 and 1.25 min, respectively.

0 1 2 13

44

124

158

133

77

40 33

15 8 11 15

26 14 10

16 12 10 3 2 0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

20

40

60

80

100

120

140

160

≤ 9

9-1

0

10

-11

11

-12

12

-13

13

-14

14

-15

15

-16

16

-17

17

-18

18

-19

19

-20

20

-21

21

-22

22

-23

23

-24

24

-25

25

-26

26

-27

27

-28

28

-29

29

-30

30

-31

31

≤ Ti

me

Cu

mu

lati

ve D

istr

ibu

tio

n F

un

ctio

n %

Fre

qu

en

cy

Mass flow rate (102 Ib/hr)

Frequency

Time Cumulative Distribution Function %

130

Figure ‎7-8: Hot spot temperature versus the production of additional light gases at tc=5 min.

Figure ‎7-9: Hot spot temperature versus the production of additional light gases at tc=1.25min.

Figures 7-8 and 7-9 show that the relation between the hotspot temperatures and

the generation of additional light gases is a linear relationship. However, the slope of the

linear relationship depends on the volume fraction of the hot spots and the tc. The six

y = 46.917x - 38013 R² = 0.9998

y = 117.29x - 95033 R² = 0.9998

y = 234.58x - 190065 R² = 0.9998

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

810 815 820 825 830 835

Mas

s ra

te o

f ad

dit

ion

al li

ght

gase

s (I

b/h

r)

Temperature (K)

Volume fraction = 2%



y = 31.667x - 25659 R² = 0.9997

y = 79.168x - 64148 R² = 0.9997

y = 158.34x - 128295 R² = 0.9997

0

500

1000

1500

2000

2500

3000

3500

810 815 820 825 830 835

Mas

s ra

te o

f ad

dit

ion

al li

ght

gase

s (I

b/h

r)

Temperature (K)




131

scenarios in Figures 7-8 and 7-9 can be used to infer the characteristics (temperature and

volume) of the temperature hot spots that would be required to generate the waste gas

flow rates from the FCC unit reported in Figure 7-5. Figure 7-10 shows the cumulative

distribution of the hot spot temperatures at three different volume fractions of 2, 5 and

10% at tc of 5 min. When the scenario of 2% volume fraction of the hot spots is applied,

temperature excursions reached to of 30-70 K are required to generate the volumes of

waste gases observed. For the scenarios where the volume fractions are 5 and 10 %, the

much smaller temperature excursions (5-35 K) are required to generate the waste gases.

Note that all of these calculations assume that the base case leads to no flared gases.

Table 7-4 summarizes the maximum temperature, the minimum temperature, and the

maximum temperature excursion for each scenario, when tc =5 min, to replicate the

actual data of waste gases flow rates from FCC units in Figure 7-5. Figures 7-11, 7-12

and 7-13 show the time cumulative distribution and the histogram for the scenarios when

tc =5 min.

Figure ‎7-10: The cumulative distributions of the hot spot temperatures at three different volume fractions of 2, 5

and 10% at tc of 5 min.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

81

0

82

0

83

0

84

0

85

0

86

0

87

0

88

0

Cu

mu

lati

ve D

istr

ibu

tio

n F

un

ctio

n

(%)

Temperature (K)




132

Table ‎7-4: The maximum temperature, the minimum temperature, and the maximum temperature excursion

for each scenario when tc =5 min

Volume fraction of hotspots

2% 5% 10%

Maximum Temperature (K) 876 837 823 Minimum Temperature (K) 831 819 814 Maximum Temperature Excursion 66 27 13

Figure ‎7-11: Time cumulative distribution and histogram of the temperature of the hot spots at volume fraction

of 2% when the tc =5 min.

0 1 9

52

191 200

108

57

22 14 18

33 16 19 17

7 3 0 0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

50

100

150

200

250

≤ 8

29

82

9-8

32

83

2-8

35

83

5-8

38

83

8-8

41

84

1-8

44

84

4-8

47

84

7-8

50

85

0-8

53

85

3-8

56

85

6-8

59

85

9-8

62

86

2-8

65

86

5-8

68

86

8-8

71

87

1-8

74

87

4-8

77

87

7 ≤

Tim

e C

um

ula

tive

Dis

trib

uti

on

Fu

nct

ion

%

Fre

qu

en

cy

Temperature (K)

Frequency


133





0 1 8

24

120

180

158

73

49

23 11 12

18 28

16 15 12 14 3 2 0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

20

40

60

80

100

120

140

160

180

200

≤81

8

81

8-8

19

81

9-8

20

82

0-8

21

82

1-8

22

82

2-8

23

82

3-8

24

82

4-8

25

82

5-8

26

82

6-8

27

82

7-8

28

82

8-8

29

82

9-8

30

83

0-8

31

83

1-8

32

83

2-8

33

83

3-8

34

83

4-8

35

83

5-8

36

83

6-8

37

83

7≤

Tim

e C

um

ula

tive

Dis

trib

uti

on

Fu

nct

ion

%

Fre

qu

en

cy

Temperature (K)

Frequency


0 3

110

342

144

47 27

44 26 21

3 0 0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

50

100

150

200

250

300

350

400

≤ 8

14

81

4-8

15

81

5-8

16

81

6-8

17

81

7-8

18

81

8-8

19

81

9-8

20

82

0-8

21

82

1-8

22

82

2-8

23

82

3-8

24

82

4 ≤

Tim

e C

um

ula

tive

Dis

trib

uti

on

Fu

nct

ion

%

Fre

qu

en

cy

Temperature (K)

Frequency


134

Figure 7-14 shows the cumulative distribution of the hot spot temperatures at

three different volume fractions of 2, 5 and 10% at tc of 1.25 min. In these simulations,

larger temperature excursions are required to generate the waste gases since less coke is

formed at the shorter catalyst residence time. Table 7-5 summarizes the maximum

temperature, the minimum temperature, and the maximum temperature excursion for

each scenario, when tc =1.25 min, to replicate the actual data of waste gases flow rates

from FCC units in Figure 7-5. Figures 7-15, 7-16 and 7-17 show the time cumulative

distribution of and the histogram for the scenarios when the tc =1.25 min.

Figure ‎7-14: The cumulative distributions of the hot spot temperatures at three different volume fractions of 2, 5

and 10%, respectively, at catalyst residence time of 1.25 min.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

80

0

82

0

84

0

86

0

88

0

90

0

92

0

Cu

mu

lati

ve D

istr

ibu

tio

n F

un

ctio

n (

%)

Temperature (K)




135

Table ‎7-5: The maximum temperature, the minimum temperature, and the maximum temperature excursion

for each scenario when tc =5 min

Volume fraction of hotspots

2% 5% 10%

Maximum Temperature (K) 908 849 830 Minimum Temperature (K) 841 823 816 Maximum Temperature Excursion 98 39 20

Figure ‎7-15 : Time cumulative distribution and histogram of the temperature of the hot spots at volume fraction

of 2% when the tc =1.25 min.

0 1 5 14

64

132 141

126

69

39 33

14 9 9 13

25 14 13 10 12 13

6 3 2 0 0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

20

40

60

80

100

120

140

160

≤ 8

40

84

0-8

43

84

3-8

46

84

6-8

49

84

9-8

52

85

2-8

55

85

5-8

58

85

8-8

61

86

1-8

64

86

4-8

67

86

7-8

70

87

0-8

73

87

3-8

76

87

6-8

79

87

9-8

82

88

2-8

85

88

5-8

88

88

8-8

91

89

1-8

94

89

4-8

97

89

7-9

00

90

0-9

03

90

3-9

06

90

6-9

09

90

9 ≤

Tim

e C

um

ula

tive

Dis

trib

uti

on

Fu

nct

ion

%

Fre

qu

en

cy

Temperature (K)

Frequency


136

Figure ‎7-16 : Time cumulative distribution and histogram of the temperature of the hot spots at volume fraction

of 5 % when the tc =1.25 min.


of 10 % when the tc =1.25 min.

0 0 2

27

157

238

134

62

25 13

32 27 19 19

8 4 0 0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

50

100

150

200

250

≤ 8

20

82

0-8

22

82

2-8

24

82

4-8

26

82

6-8

28

82

8-8

30

83

0-8

32

83

2-8

34

83

4-8

36

83

6-8

38

83

8-8

40

84

0-8

42

84

2-8

44

84

4-8

46

84

6-8

48

84

8-8

50

85

0 ≤

Tim

e C

um

ula

tive

Dis

trib

uti

on

Fu

nct

ion

%

Fre

qu

en

cy

Temperature (K)

Frequency


0 1 20

143

240

148

66

24 14

29 29 18 21

10 4 0 0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

50

100

150

200

250

300

≤ 8

16

81

6-8

17

81

7-8

18

81

8-8

19

81

9-8

20

82

0-8

21

82

1-8

22

82

2-8

23

82

3-8

24

82

4-8

25

82

5-8

26

82

6-8

27

82

7-8

28

82

8-8

29

82

9-8

30

83

0 ≤

Tim

e C

um

ula

tive

Dis

trib

uti

on

Fu

nct

ion

%

Fre

qu

en

cy

Temperature (K)

Frequency


137

7.5 CONCLUSION

The results from these scenarios suggest that reasonable scenarios for temperature

excursions in FCC reactors lead to the types of waste gas volumes reported for FCC

flares. This suggests that better temperature controls on FCC units could result in the

elimination of flared gases. Although the development of temperature control schemes

for FCC units is beyond the scope of this work, this analysis has demonstrated that the

goal of minimization of flaring may create additional economic incentives for improved

FCC temperature control.

138

CHAPTER 8: Findings, Outcomes and Recommmedations

This thesis has examined the impacts of industrial flaring and methods for

minimizing those impacts. Four primary topics were investigated. This Chapter

summarizes the main findings reported in this thesis, outcomes of this work and

recommendations of the future work in four areas:

i) Air quality impacts of flaring operations

ii) Improving flare operation

iii) Minimization of refinery flaring through integration with fuel gas

networks

iv) Impact of temperature excursions of FCC units on the light gas/flared gas

production.

8.1 FINDINGS

8.1.1 Air quality impacts of flaring operations

Flares operating at low DRE can increase localized ambient ozone

concentrations by more than 15 ppb under some conditions. The impact of

flare DRE on the ozone formation potential of flare emissions depends

largely on flow rate and composition of flared gases; Products of

incomplete combustion play a secondary role in the photochemistry driven

by flare emissions.

The production of NOx from industrial flares is not constant and depends

on the DRE of the flare; current emission factors may overstate NOx

139

emissions, however, NOx emissions from flares play a much smaller role

in the photochemistry of flare emissions than unburned hydrocarbons.

8.1.2 Improving flare operation

The analyses of improving flare operation focused on the implications of the

narrow operating range in which flares operating at low flow rate can achieve both high

combustion efficiency and low smoke production. A case study of how blower design for

an air-assisted flare impacts the ability to operate in this narrow regime was examined.

Flare emissions can be more than an order of magnitude greater than or

less than a base case assuming 98% DRE, depending on whether the

blowers are able to achieve desired assist-air to vent gas ratios.

Variable speed blower configurations achieve lower predicted emissions

than similarly sized fixed speed blowers.

8.1.3 Minimization of refinery flaring through integration with fuel gas networks

The protential for recycling flared gases was examined using a case study of a

petroleum refinery.

By expanding boiler (sink) size, flaring can be reduced significantly, and

recovered steam may offset much or all of the cost of boiler expansion.

Including a hydrocarbon penalty constraint on flaring showed little effect

on flaring.

140

8.1.4 Impact of temperature excursions of FCC units on the light gas/flared gas

production.

Temperature excursions in FCC reactors lead to the types of waste gas

volumes reported for FCC flares.

Better temperature controls on FCC units could result in the elimination of

flared gases.

8.2 OUTCOMES

This thesis represents:

1. The most comprehensive air quality analysis done to date of the air quality

impact of flare emissions, including products of incomplete combustion

and NOx emissions

2. The first assessment of the impact of blower configurations on flare

emissions

3. The first integration of time varying flare flows into refinery fuel gas

networks (a dynamic pooling problem)

4. The first assessment of flare prevention potential of temperature

excursions on FCC units

Collectively, the work is the first time that a comprehensive examination of

flaring has been performed, incorporating an understanding of environmental impacts,

and assessments of the relative merits of better flare operation, flare gas recycling and

preventing flare gas generation.

141

8.3 FUTURE WORK

8.3.1 Air quality impacts of flaring operations

The air quality analyses in this thesis represent the upper bounds on the ozone

formation potential of flare emissions. Using hourly flow data of flares in Houston Area,

with hourly resolved chemical compositions and using University of Texas Flare study

results to estimate the hourly DRE will be the focus of the future work.

8.3.2 Improving flare operation

A case study of an air-assisted flare was examined to improve its operation. For

the future work, a case study of a steam-assisted flare could be studied to determine the

best practices that maintain high performance.

8.3.3 Minimization of refinery flaring through integration with fuel gas networks

Determining new sinks in petroleum refineries that can use the flared gases will

be the focus of the future work. Also the possibility of integrating the waste gases from

refinery sources with other industrial facilities will be explored in the near future.

8.3.4 Impact of temperature excursions of FCC units on the light gas/flared gas

production.

Developing a temperature control schemes for FCC units to minimize temperature

excursions will be explored in more detail in the future. The ultimate goal of the

temperature control for FCC units is minimizing the production of waste gases.

142

Appendix A

This appendix contains tables summarizing the air quality modeling scenarios

presented in Chapter 3 and emissions for each flare for each scenario, including both

VOC and NOx emissions. A summary of the emissions of products of incomplete

combustion (PICs) reported in full scale flare tests by the University of Texas is included.

Also, this appendix contains the air quality simulation results for all flares except

Refinery Flare1.

Table A-1: Summary of emission scenarios simulated for each flare

Flare emissions

Scenario DE%

Unburned

hydrocarbons PIC Ratio of hydrocarbon to PIC

A 95 yes no 1 : 0

B 95 yes yes 1 : 1

C 90 yes no 1 : 0

D 90 yes yes 1 : 1

E 75 yes no 1 : 0

F 75 yes yes 4 : 1

G 50 yes no 1 : 0

H 50 yes yes 9 : 1

143

Table A-2: Summary of VOC emissions (tons/hr) for the base case, scenarios A, C, E and G for Refinery Flare 1

Base case Scenario A Scenario C Scenario E Scenario G

Time

(hr)

Mass flow

rate

(tons/hr)

NOx

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

0 6.03 0.00793 0.121 0.301 0.603 1.51 3.01

1 25.3 0.0333 0.506 1.265 2.531 6.33 12.7

2 23.1 0.0304 0.462 1.154 2.309 5.77 11.5

3 16.2 0.0214 0.325 0.811 1.623 4.06 8.11

4 2.54 0.00334 0.0508 0.127 0.254 0.635 1.27

5 2.44 0.00321 0.0487 0.122 0.244 0.609 1.22

6 2.35 0.00309 0.0469 0.117 0.235 0.586 1.17

7 2.27 0.00298 0.0453 0.113 0.227 0.566 1.13

8 2.68 0.00353 0.0537 0.134 0.268 0.671 1.34

9 3.76 0.00495 0.0752 0.188 0.376 0.940 1.88

10 0.130 0.000171 0.00260 0.00649 0.0130 0.0324 0.0649

11 0.101 0.000133 0.00203 0.00507 0.0102 0.0254 0.0507

12 1.63 0.00215 0.0327 0.0817 0.163 0.409 0.817

13 1.80 0.00237 0.0361 0.0902 0.180 0.451 0.901

14 2.19 0.00288 0.0438 0.109 0.219 0.547 1.09

15 1.85 0.00243 0.0369 0.0923 0.185 0.462 0.923

16 1.79 0.00235 0.0358 0.0894 0.179 0.447 0.894

17 1.99 0.00262 0.0398 0.0996 0.199 0.498 0.996

18 2.54 0.00334 0.0507 0.127 0.254 0.634 1.268

19 2.12 0.00279 0.0425 0.106 0.212 0.530 1.06

20 1.93 0.00254 0.0387 0.0967 0.193 0.483 0.967

21 2.19 0.00289 0.0439 0.110 0.219 0.548 1.10

22 1.63 0.00215 0.0326 0.0816 0.163 0.408 0.816

23 6.64 0.00874 0.133 0.332 0.664 1.661 3.32

144

Table A-3: Summary of NOx, unburned hydrocarbon (UHC) and products of incomplete combustion (PICs) for the base case, scenarios B, D, F and H for Refinery

Flare 1

Scenario B Scenario D Scenario F Scenario H

Time

(hr)

Mass flow

rate

(tons/hr)

NOx

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

0 6.03 0.00793 0.151 0.151 0.301 0.301 1.206 0.301 2.71 0.301

1 25.3 0.0333 0.633 0.633 1.27 1.27 5.06 1.27 11.4 1.27

2 23.1 0.0304 0.577 0.577 1.15 1.15 4.62 1.15 10.4 1.15

3 16.2 0.0214 0.406 0.406 0.811 0.811 3.25 0.811 7.30 0.811

4 2.54 0.00334 0.0635 0.0635 0.127 0.127 0.508 0.127 1.14 0.127

5 2.44 0.00321 0.0609 0.0609 0.122 0.122 0.487 0.122 1.1 0.122

6 2.35 0.00309 0.0586 0.0586 0.117 0.117 0.469 0.117 1.06 0.117

7 2.27 0.00298 0.0566 0.0566 0.113 0.113 0.453 0.113 1.02 0.113

8 2.68 0.00353 0.0671 0.0671 0.134 0.134 0.537 0.134 1.21 0.134

9 3.76 0.00495 0.0940 0.0940 0.188 0.188 0.752 0.188 1.69 0.188

10 0.130 0.000171 0.00324 0.00324 0.00649 0.00649 0.0260 0.00649 0.0583 0.00649

11 0.101 0.000133 0.00254 0.00254 0.00507 0.00507 0.0202 0.00507 0.0457 0.00507

12 1.63 0.00215 0.0409 0.0409 0.0817 0.0817 0.327 0.0817 0.736 0.0817

13 1.80 0.00237 0.0451 0.0451 0.0901 0.0901 0.361 0.0901 0.811 0.0901

14 2.19 0.00288 0.0547 0.0547 0.109 0.109 0.438 0.109 0.985 0.109

15 1.85 0.00243 0.0462 0.0462 0.0923 0.0923 0.369 0.0923 0.830 0.0923

16 1.79 0.00235 0.0447 0.0447 0.0894 0.0894 0.358 0.0894 0.804 0.0894

17 1.99 0.00262 0.0498 0.0498 0.0996 0.0996 0.398 0.0996 0.896 0.0996

18 2.54 0.00334 0.0633 0.0633 0.127 0.127 0.507 0.127 1.14 0.127

19 2.12 0.00279 0.0531 0.0531 0.106 0.106 0.424 0.106 0.955 0.106

20 1.93 0.00254 0.0484 0.0484 0.0967 0.0967 0.387 0.0967 0.870 0.0967

21 2.19 0.00289 0.0548 0.0548 0.110 0.110 0.439 0.110 0.987 0.110

22 1.63 0.00215 0.0408 0.0408 0.0816 0.0816 0.326 0.0816 0.734 0.0816

23 6.64 0.00874 0.166 0.166 0.332 0.332 1.33 0.332 2.99 0.332

145



Time

(hr)

Mass flow

rate

(tons/hr)

NOx

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

0 2.74 0.00361 0.05474 0.137 0.274 0.684 1.37

1 3.02 0.00398 0.06036 0.151 0.302 0.754 1.51

2 3.12 0.00411 0.06231 0.156 0.312 0.779 1.56

3 3.95 0.00522 0.07908 0.198 0.395 0.988 1.98

4 2.74 0.00362 0.05486 0.137 0.274 0.686 1.37

5 3.14 0.00414 0.06280 0.157 0.314 0.785 1.57

6 2.95 0.00389 0.05891 0.147 0.295 0.736 1.47

7 2.93 0.00387 0.05858 0.146 0.293 0.732 1.46

8 3.10 0.00409 0.06201 0.155 0.310 0.775 1.55

9 3.28 0.00433 0.06567 0.164 0.328 0.821 1.64

10 3.16 0.00417 0.06320 0.158 0.316 0.790 1.58

11 3.08 0.00407 0.06166 0.154 0.308 0.771 1.54

12 3.16 0.00417 0.06313 0.158 0.316 0.789 1.58

13 2.98 0.00393 0.05956 0.149 0.298 0.744 1.49

14 2.94 0.00388 0.05874 0.147 0.294 0.734 1.47

15 3.15 0.00415 0.06290 0.157 0.315 0.786 1.57

16 3.08 0.00407 0.06166 0.154 0.308 0.771 1.54

17 2.83 0.00374 0.05668 0.142 0.283 0.708 1.42

18 3.09 0.00407 0.06175 0.154 0.309 0.772 1.54

19 2.94 0.00388 0.05885 0.147 0.294 0.736 1.47

20 3.28 0.00433 0.06566 0.164 0.328 0.821 1.64

21 3.39 0.00447 0.06777 0.169 0.339 0.847 1.69

22 3.48 0.00459 0.06957 0.174 0.348 0.870 1.74

23 2.91 0.00385 0.05829 0.146 0.291 0.729 1.46

146


Flare 2


Time

(hr)

Mass flow

rate

(tons/hr)

NOx

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

0 2.74 0.00361 0.0684 0.0684 0.137 0.137 0.547 0.137 1.232 0.137

1 3.02 0.00398 0.0755 0.0755 0.151 0.151 0.604 0.151 1.358 0.151

2 3.12 0.00411 0.0779 0.0779 0.156 0.156 0.623 0.156 1.402 0.156

3 3.95 0.00522 0.0989 0.0989 0.198 0.198 0.791 0.198 1.779 0.198

4 2.74 0.00362 0.0686 0.0686 0.137 0.137 0.549 0.137 1.234 0.137

5 3.14 0.00414 0.0785 0.0785 0.157 0.157 0.628 0.157 1.413 0.157

6 2.95 0.00389 0.0736 0.0736 0.147 0.147 0.589 0.147 1.325 0.147

7 2.93 0.00387 0.0732 0.0732 0.146 0.146 0.586 0.146 1.318 0.146

8 3.10 0.00409 0.0775 0.0775 0.155 0.155 0.620 0.155 1.395 0.155

9 3.28 0.00433 0.0821 0.0821 0.164 0.164 0.657 0.164 1.478 0.164

10 3.16 0.00417 0.0790 0.0790 0.158 0.158 0.632 0.158 1.422 0.158

11 3.08 0.00407 0.0771 0.0771 0.154 0.154 0.617 0.154 1.387 0.154

12 3.16 0.00417 0.0789 0.0789 0.158 0.158 0.631 0.158 1.420 0.158

13 2.98 0.00393 0.0745 0.0745 0.149 0.149 0.596 0.149 1.340 0.149

14 2.94 0.00388 0.0734 0.0734 0.147 0.147 0.587 0.147 1.322 0.147

15 3.15 0.00415 0.0786 0.0786 0.157 0.157 0.629 0.157 1.415 0.157

16 3.08 0.00407 0.0771 0.0771 0.154 0.154 0.617 0.154 1.387 0.154

17 2.83 0.00374 0.0709 0.0709 0.142 0.142 0.567 0.142 1.275 0.142

18 3.09 0.00407 0.0772 0.0772 0.154 0.154 0.617 0.154 1.389 0.154

19 2.94 0.00388 0.0736 0.0736 0.147 0.147 0.588 0.147 1.324 0.147

20 3.28 0.00433 0.0821 0.0821 0.164 0.164 0.657 0.164 1.477 0.164

21 3.39 0.00447 0.0847 0.0847 0.169 0.169 0.678 0.169 1.525 0.169

22 3.48 0.00459 0.0870 0.0870 0.174 0.174 0.696 0.174 1.565 0.174

23 2.91 0.00385 0.0729 0.0729 0.146 0.146 0.583 0.146 1.312 0.146

147



Time

(hr)

Mass flow

rate

(tons/hr)

NOx

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

0 1.88 0.00251 0.0376 0.0939 0.188 0.470 0.939

1 2.06 0.00276 0.0413 0.103 0.206 0.516 1.03

2 1.91 0.00256 0.0383 0.0957 0.191 0.479 0.957

3 2.11 0.00282 0.0423 0.10569 0.211 0.528 1.06

4 0.654 0.00087 0.0131 0.0327 0.0654 0.164 0.327

5 0.146 0.00020 0.00293 0.00732 0.0146 0.0366 0.0732

6 0.0179 0.00002 0.00036 0.00090 0.00179 0.00448 0.00895

7 0.297 0.00040 0.00594 0.0149 0.0297 0.0743 0.149

8 2.26 0.00301 0.0451 0.113 0.226 0.564 1.13

9 2.12 0.00284 0.0425 0.106 0.212 0.531 1.06

10 2.36 0.00315 0.0472 0.118 0.236 0.589 1.18

11 3.91 0.00522 0.0782 0.196 0.391 0.978 1.96

12 2.32 0.00309 0.0463 0.116 0.232 0.579 1.16

13 1.75 0.00234 0.0351 0.0877 0.175 0.439 0.877

14 0.662 0.00088 0.0132 0.0331 0.0662 0.165 0.331

15 0.755 0.00101 0.0151 0.0378 0.0755 0.189 0.378

16 0.654 0.00087 0.0131 0.0327 0.0654 0.164 0.327

17 0.568 0.00076 0.0114 0.0284 0.0568 0.142 0.284

18 0.535 0.00071 0.0107 0.0267 0.0535 0.134 0.267

19 0.654 0.00087 0.0131 0.0327 0.0654 0.163 0.327

20 0.616 0.00082 0.0123 0.0308 0.0616 0.154 0.308

21 0.755 0.00101 0.0151 0.0378 0.0755 0.189 0.378

22 0.869 0.00116 0.0174 0.0435 0.0869 0.217 0.435

23 1.45 0.00193 0.0289 0.0723 0.145 0.361 0.722

148


Flare 3


Time

(hr)

Mass flow

rate

(tons/hr)

NOx

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

0 2.06 0.00251 0.0470 0.0470 0.0939 0.0939 0.376 0.0939 0.845 0.0939

1 1.91 0.00276 0.0516 0.0516 0.103 0.103 0.413 0.103 0.928 0.103

2 2.11 0.00256 0.0479 0.0479 0.0957 0.0957 0.383 0.0957 0.861 0.0957

3 0.654 0.00282 0.0528 0.0528 0.106 0.106 0.423 0.106 0.951 0.106

4 0.146 0.00087 0.0164 0.0164 0.0327 0.0327 0.131 0.0327 0.294 0.0327

5 0.0179 0.00020 0.00366 0.00366 0.00732 0.00732 0.0293 0.00732 0.0659 0.00732

6 0.297 0.00002 0.00045 0.00045 0.00089 0.00089 0.00358 0.00089 0.00805 0.00089

7 2.255 0.00040 0.00743 0.00743 0.0149 0.0149 0.0594 0.0149 0.13370 0.0149

8 2.12 0.00301 0.0564 0.0564 0.113 0.113 0.451 0.113 1.01 0.113

9 2.36 0.00284 0.0531 0.0531 0.106 0.106 0.425 0.106 0.955 0.106

10 3.91 0.00315 0.0590 0.0590 0.118 0.118 0.472 0.118 1.06 0.118

11 2.32 0.00522 0.0978 0.0978 0.196 0.196 0.782 0.196 1.76 0.196

12 1.75 0.00309 0.0579 0.0579 0.116 0.116 0.463 0.116 1.04 0.116

13 0.662 0.00234 0.0439 0.0439 0.0877 0.0877 0.351 0.0877 0.789 0.0877

14 0.755 0.00088 0.0165 0.0165 0.0331 0.0331 0.132 0.0331 0.298 0.0331

15 0.654 0.00101 0.0189 0.0189 0.0378 0.0378 0.151 0.0378 0.340 0.0378

16 0.568 0.00087 0.0164 0.0164 0.0327 0.0327 0.131 0.0327 0.294 0.0327

17 0.535 0.00076 0.0142 0.0142 0.0284 0.0284 0.114 0.0284 0.256 0.0284

18 0.654 0.00071 0.0134 0.0134 0.0267 0.0267 0.107 0.0267 0.241 0.0267

19 0.616 0.00087 0.0163 0.0163 0.0327 0.0327 0.131 0.0327 0.294 0.0327

20 0.755 0.00082 0.0154 0.0154 0.0308 0.0308 0.123 0.0308 0.277 0.0308

21 0.869 0.00101 0.0189 0.0189 0.0378 0.0378 0.151 0.0378 0.340 0.0378

22 1.45 0.00116 0.0217 0.0217 0.0435 0.0435 0.174 0.0435 0.391 0.0435

23 2.06 0.00193 0.0361 0.0361 0.0722 0.0722 0.289 0.0722 0.650 0.0722

149

Table A-8 : Summary of VOC emissions (tons/hr) for the base case, scenarios A, C, E and G for Olefin Flare 1


Time

(hr)

Mass flow

rate

(tons/hr)

NOx

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

0 1.09 0.00143 0.0219 0.0547 0.109 0.273 0.547

1 3.52 0.00460 0.0703 0.176 0.352 0.879 1.76

2 2.35 0.00307 0.0469 0.117 0.235 0.586 1.17

3 1.10 0.00144 0.0221 0.0552 0.110 0.276 0.552

4 0.737 0.00096 0.0147 0.0368 0.0737 0.184 0.368

5 0.549 0.00072 0.0110 0.0275 0.0549 0.137 0.275

6 0.678 0.00089 0.0136 0.0339 0.0678 0.169 0.339

7 0.540 0.00071 0.0108 0.0270 0.0540 0.135 0.270

8 2.26 0.00296 0.0453 0.113 0.226 0.566 1.13

9 1.06 0.00138 0.0212 0.0529 0.106 0.265 0.529

10 0.929 0.00121 0.0186 0.0464 0.0929 0.232 0.464

11 0.745 0.00097 0.0149 0.0372 0.0745 0.186 0.372

12 0.667 0.00087 0.0133 0.0334 0.0667 0.167 0.334

13 0.590 0.00077 0.0118 0.0295 0.0590 0.148 0.295

14 0.604 0.00079 0.0121 0.0302 0.0604 0.151 0.302

15 0.596 0.00078 0.0119 0.0298 0.0596 0.149 0.298

16 0.558 0.00073 0.0112 0.0279 0.0558 0.140 0.279

17 0.527 0.00069 0.0105 0.0264 0.0527 0.132 0.264

18 0.516 0.00067 0.0103 0.0258 0.0515 0.129 0.258

19 0.476 0.00062 0.00952 0.0238 0.0476 0.119 0.238

20 0.465 0.00061 0.00930 0.0233 0.0465 0.116 0.233

21 0.480 0.00063 0.00960 0.0240 0.0480 0.120 0.240

22 0.420 0.00055 0.00840 0.0210 0.0420 0.105 0.210

23 0.431 0.00056 0.00863 0.0216 0.0431 0.108 0.216

150

Table A-9: Summary of NOx, unburned hydrocarbon (UHC) and products of incomplete combustion (PICs) for the base case, scenarios B, D, F and H for Olefin Flare1


Time

(hr)

Mass flow

rate

(tons/hr)

NOx

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

0 1.09 0.00143 0.0273 0.0273 0.0547 0.0547 0.219 0.0547 0.492 0.0547

1 3.52 0.00460 0.0879 0.0879 0.176 0.176 0.703 0.176 1.58 0.176

2 2.35 0.00307 0.0586 0.0586 0.117 0.117 0.469 0.117 1.06 0.117

3 1.10 0.00144 0.0276 0.0276 0.0552 0.0552 0.221 0.0552 0.497 0.0552

4 0.737 0.00096 0.0184 0.0184 0.0368 0.0368 0.147 0.0368 0.332 0.0368

5 0.549 0.00072 0.0137 0.0137 0.0275 0.0275 0.110 0.0275 0.247 0.0275

6 0.678 0.00089 0.0169 0.0169 0.0339 0.0339 0.136 0.0339 0.305 0.0339

7 0.540 0.00071 0.0135 0.0135 0.0270 0.0270 0.108 0.0270 0.243 0.0270

8 2.26 0.00296 0.0566 0.0566 0.113 0.113 0.453 0.113 1.02 0.113

9 1.06 0.00138 0.0265 0.0265 0.0529 0.0529 0.212 0.0529 0.476 0.0529

10 0.929 0.00121 0.0232 0.0232 0.0464 0.0464 0.186 0.0464 0.418 0.0464

11 0.745 0.00097 0.0186 0.0186 0.0372 0.0372 0.149 0.0372 0.335 0.0372

12 0.667 0.00087 0.0167 0.0167 0.0334 0.0334 0.133 0.0334 0.300 0.0334

13 0.590 0.00077 0.0148 0.0148 0.0295 0.0295 0.118 0.0295 0.266 0.0295

14 0.604 0.00079 0.0151 0.0151 0.0302 0.0302 0.121 0.0302 0.272 0.0302

15 0.596 0.00078 0.0149 0.0149 0.0298 0.0298 0.119 0.0298 0.268 0.0298

16 0.558 0.00073 0.0140 0.0140 0.0279 0.0279 0.112 0.0279 0.251 0.0279

17 0.527 0.00069 0.0132 0.0132 0.0264 0.0264 0.105 0.0264 0.237 0.0264

18 0.516 0.00067 0.0129 0.0129 0.0258 0.0258 0.103 0.0258 0.232 0.0258

19 0.476 0.00062 0.0119 0.0119 0.0238 0.0238 0.0952 0.0238 0.214 0.0238

20 0.465 0.00061 0.0116 0.0116 0.0233 0.0233 0.0930 0.0233 0.209 0.0233

21 0.480 0.00063 0.0120 0.0120 0.0240 0.0240 0.0960 0.0240 0.216 0.0240

22 0.420 0.00055 0.0105 0.0105 0.0210 0.0210 0.0840 0.0210 0.189 0.0210

23 0.431 0.00056 0.0108 0.0108 0.0216 0.0216 0.0862 0.0216 0.194 0.0216

151

Table A-10: Summary of VOC emissions (tons/hr) for the base case, scenarios A, C, E and G for Olefin Flare 2


Time

(hr)

Mass

flow rate

(tons/hr)

NOx

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

VOC

emissions

(tons/hr)

0 0.230 0.000306 0.00460 0.0115 0.0230 0.0575 0.115

1 0.217 0.000289 0.00435 0.0109 0.0217 0.0543 0.109

2 0.203 0.000271 0.00407 0.0102 0.0203 0.0508 0.102

3 0.189 0.000252 0.00379 0.00947 0.0189 0.0473 0.0946

4 0.180 0.000239 0.00359 0.00898 0.0180 0.0449 0.0898

5 0.239 0.000319 0.00479 0.0120 0.0239 0.0599 0.120

6 0.243 0.000324 0.00486 0.0122 0.0243 0.0608 0.122

7 0.180 0.000240 0.00360 0.00901 0.0180 0.0451 0.0901

8 0.204 0.000272 0.00408 0.0102 0.0204 0.0511 0.102

9 0.205 0.000273 0.00411 0.0103 0.0205 0.0513 0.103

10 0.401 0.000534 0.00802 0.0201 0.0401 0.100 0.201

11 0.216 0.000287 0.00432 0.0108 0.0216 0.0540 0.108

12 0.265 0.000352 0.00529 0.0132 0.0265 0.0661 0.132

13 0.420 0.000559 0.00839 0.0210 0.0420 0.105 0.210

14 4.13 0.005501 0.0827 0.207 0.413 1.03 2.07

15 2.43 0.00324 0.0487 0.122 0.243 0.608 1.22

16 0.956 0.00127 0.0191 0.0478 0.0956 0.239 0.478

17 0.603 0.000803 0.0121 0.0302 0.0603 0.151 0.302

18 0.464 0.000618 0.00929 0.0232 0.0464 0.116 0.232

19 0.422 0.000561 0.00843 0.0211 0.0422 0.105 0.211

20 0.291 0.000387 0.00581 0.0145 0.0291 0.0726 0.145

21 0.201 0.000268 0.00403 0.0101 0.0201 0.0503 0.101

22 0.137 0.000182 0.00274 0.00684 0.0137 0.0342 0.0684

23 0.150 0.000200 0.00301 0.00752 0.0150 0.0376 0.0752

152

Table A-11: Summary of NOx, unburned hydrocarbon (UHC) and products of incomplete combustion (PICs) for the base case, scenarios B, D, F and H for Olefin Flare

2


Time

(hr)

Mass flow

rate

(tons/hr)

NOx

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

UHC

(tons/hr)

PICs

(tons/hr)

0 0.230 0.000306 0.00575 0.00575 0.0115 0.0115 0.0460 0.0115 0.103 0.0115

1 0.217 0.000289 0.00543 0.00543 0.0109 0.0109 0.0435 0.0109 0.0978 0.0109

2 0.203 0.000271 0.00508 0.00508 0.0102 0.0102 0.0407 0.0102 0.0915 0.0102

3 0.189 0.000252 0.00473 0.00473 0.00946 0.00946 0.0379 0.00946 0.0852 0.00946

4 0.180 0.000239 0.00449 0.00449 0.00898 0.00898 0.0359 0.00898 0.0808 0.00898

5 0.239 0.000319 0.00599 0.00599 0.0120 0.0120 0.0479 0.0120 0.108 0.0120

6 0.243 0.000324 0.00608 0.00608 0.0122 0.0122 0.0486 0.0122 0.109 0.0122

7 0.180 0.000240 0.00451 0.00451 0.00901 0.00901 0.0360 0.0090 0.0811 0.00901

8 0.204 0.000272 0.00511 0.00511 0.0102 0.0102 0.0408 0.0102 0.0919 0.0102

9 0.205 0.000273 0.00513 0.00513 0.0103 0.0103 0.0411 0.0103 0.0924 0.0103

10 0.401 0.000534 0.0100 0.0100 0.0201 0.0201 0.0802 0.0201 0.181 0.0201

11 0.216 0.000287 0.00540 0.00540 0.0108 0.0108 0.0432 0.0108 0.0972 0.0108

12 0.265 0.000352 0.00662 0.00662 0.0132 0.0132 0.0529 0.0132 0.119 0.0132

13 0.420 0.000559 0.0105 0.0105 0.0210 0.0210 0.0839 0.0210 0.189 0.0210

14 4.13 0.005501 0.103 0.103 0.207 0.207 0.827 0.207 1.86 0.207

15 2.43 0.00324 0.06084 0.06084 0.122 0.122 0.487 0.122 1.10 0.122

16 0.956 0.00127 0.0239 0.0239 0.0478 0.0478 0.191 0.048 0.430 0.0478

17 0.603 0.000803 0.0151 0.0151 0.0302 0.0302 0.121 0.0302 0.272 0.0302

18 0.464 0.000618 0.0116 0.0116 0.0232 0.0232 0.0929 0.0232 0.209 0.0232

19 0.422 0.000561 0.0105 0.0105 0.0211 0.0211 0.0843 0.0211 0.190 0.0211

20 0.291 0.000387 0.00727 0.00727 0.0145 0.0145 0.0581 0.0145 0.131 0.0145

21 0.201 0.000268 0.00503 0.00503 0.0101 0.0101 0.0403 0.0101 0.0906 0.0101

22 0.137 0.000182 0.00342 0.00342 0.00684 0.00684 0.0274 0.00684 0.0616 0.00684

23 0.150 0.000200 0.00376 0.00376 0.00752 0.00752 0.0301 0.00752 0.0677 0.00752

153

Table A-12: Average ratios of PICs to propylene (unburned flared gas) emissions in air -assisted flare tests (lbs / lbs

Propene) as function of DRE. The feed to the flare was 80% Propene and 20% of Tulsa natural gas (Allen and Torres,

2011b).

Ratio of PICs to unburned propene (flared gas)

Species DRE>98% 95%<DRE<98 80%<DRE<95 DRE<80%

Acetylene 0.0625 0.0457 0.0433 0.0218

Ethylene 0.0307 0.0207 0.0180 0.0093

Propylene 1.0000 1.0000 1.0000 1.0000

Butene isomers 0.0002 0.0002 0.0017 0.0048

Formaldehyde 0.0463 0.0303 0.0247 0.0127

Acetaldehyde 0.0308 0.0192 0.0146 0.0069

Propanal 0.0010 0.0007 0.0007 0.0004

Acrolein 0.0186 0.0142 0.0099 0.0038

Methanol 0.0020 0.0014 0.0016 0.0012

Acetone 0.0012 0.0008 0.0008 0.0005

Propylene-Oxide 0.0014 0.0010 0.0006 0.0002

Methane 0.1318 0.1318 0.1318 0.1318

Ethane 0.0103 0.0103 0.0103 0.0103

Table A-13: Average ratios of PICs to propylene (unburned flared gas) emissions in steam-assisted flare tests (lbs / lbs

Propene) as function of DRE. The feed to the flare was 80% propylene and 20% of Tulsa natural gas (Allen and Torres,

2011b).

Ratio of PICs to unburned propylene (flared gas)

Species DRE>98% 95%<DRE<98 80%<DRE<95 DRE<80%

Acetylene 0.0228 0.0164 0.0162 0.0104

Ethylene 0.0388 0.0278 0.0199 0.0091

Propylene 1 1 1 1

Butene isomers 0.0003 0.0003 0.0021 0.0073

Formaldehyde 0.0588 0.0384 0.0265 0.0102

Acetaldehyde 0.0373 0.0226 0.0153 0.0054

Propanal 0.0011 0.0010 0.0009 0.0005

Acrolein 0.0289 0.0233 0.0125 0.0059

Methanol 0.0013 0.0013 0.0011 0.0008

Acetone 0.0013 0.0012 0.0010 0.0006

Propylene-Oxide 0.0016 0.0015 0.0007 0.0003

Methane 0.1318 0.1318 0.1318 0.1318

Ethane 0.0103 0.0103 0.0103 0.0103

154

Simulation results for Refinery Flare 2

Figure A-1: Maximum one-hour average ozone concentrations on August 30, 2006 resulting from applying

different flare DRE on the Refinery Flare2.

Figure A-2:The difference in the wide-region maxima one-hour average ozone concentrations on August 30,

2006 resulting from applying different flare DRE on the Refinery Flare2.

Figure A-3: The maximum change in one-hour ozone concentrations compared to the base case on August 30, 2006

resulting from applying different flare DREs to the Refinery Flare 2.

0

20

40

60

80

100

120

0 5 10 15 20 25

Max

imu

m O

zon

e

con

cen

trat

ion

(pp

b)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

Base Case

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 5 10 15 20

Dif

fere

nce

in m

axim

um

o

zon

e c

on

cen

trat

ion

(pp

b)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

0

1

2

3

4

5

0 5 10 15 20

Max

imu

m d

iffe

ren

ce in

o

zon

e c

on

cen

trat

ion

(pp

b)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

155

Simulation results for Refinery Flare 3


different flare DRE on the Refinery Flare3.

Figure A-5: The difference in the wide-region maxima one-hour average ozone concentrations on August 20,

2006 resulting from applying different flare DRE on the Refinery Flare3.

Figure A-6: The maximum change in one-hour ozone concentrations compared to the base case on August 20,

2006 resulting from applying different flare DREs to the Refinery Flare 3.

0

20

40

60

80

100

120

140

0 5 10 15 20

Max

imu

m O

zon

e

con

cen

trat

ion

(pp

b)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

Base Case

0

0.05

0.1

0.15

0.2

0.25

0 5 10 15 20

Dif

fere

nce

in m

axim

um

o

zon

e c

on

cen

trat

ion

(pp

b)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

0

1

2

3

4

5

0 5 10 15 20

Max

imu

m d

iffe

ren

ce in

o

zon

e c

on

cen

trat

ion

(pp

b)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

156

Simulation results for Olefin Flare 1


different flare DRE on the Olefin Flare1.


2006 resulting from applying different flare DRE on the Olefin Flare1.


2006 resulting from applying different flare DREs to the Olefin Flare1.

0

20

40

60

80

100

120

140

0 5 10 15 20

Max

imu

m O

zon

e

con

cen

trat

ion

(pp

b)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

Base Case

0

0.5

1

1.5

2

2.5

3

0 5 10 15 20

Dif

fere

nce

in m

axim

um

o

zon

e c

on

cen

trat

ion

(pp

b)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

0

5

10

15

20

0 5 10 15 20

Max

imu

m d

iffe

ren

ce in

o

zon

e c

on

cen

trat

ion

(pp

b)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

157

Simulation results for Olefin Flare 2


different flare DRE on the Olefin Flare2.


2006 resulting from applying different flare DRE on the Olefin Flare2.


2006 resulting from applying different flare DREs to the Olefin Flare2.

0

20

40

60

80

100

120

140

0 5 10 15 20

Max

imu

m O

zon

e

con

cen

trat

ion

(pp

b)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

Base Case

0

0.1

0.2

0.3

0.4

0.5

0 5 10 15 20

Dif

fere

nce

in m

axim

um

o

zon

e c

on

cen

trat

ion

(pp

b)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

0

0.5

1

1.5

0 5 10 15 20

Max

imu

m d

iffe

ren

ce in

o

zon

e c

on

cen

trat

ion

(pp

b)

Time (hr)

DRE = 50%

DRE = 75%

DRE = 90%

DRE =95%

158

Appendix B

This appendix contains tables summarizing the air quality modeling scenarios and

emissions for Refinery Flare 1 performed in Chapter 4, including both VOC and NOx

emissions.

Table B-1: Photochemical modeling scenario performed for Refinery Flare 1

Scenario DRE% NOx reduction compared to the AP-42 value

1 98 50%

2 90 25%

3 75 25%

159

Table B-2: Summary of VOC emissions (tons/hr) for the base case, scenarios A, C, E and G for Refinery Flare 1

Scenario 1 Scenario 2 Scenario 3

Time

Mass

flow rate

(tons/hr)

NOx

(tons/hr)

VOC

emissions

(tons/hr)

NOx

(tons/hr)

VOC

emissions

(tons/hr)

NOx

(tons/hr)

VOC

emissions

(tons/hr)

0 6.03 0.00397 0.121 0.00198 0.603 0.00198 1.507

1 25.31 0.01665 0.506 0.00832 2.531 0.00832 6.327

2 23.09 0.01519 0.462 0.00759 2.309 0.00759 5.772

3 16.23 0.01067 0.325 0.00534 1.623 0.00534 4.056

4 2.54 0.00167 0.051 0.00084 0.254 0.00084 0.635

5 2.44 0.00160 0.049 0.00080 0.244 0.00080 0.609

6 2.35 0.00154 0.047 0.00077 0.235 0.00077 0.586

7 2.27 0.00149 0.045 0.00075 0.227 0.00075 0.566

8 2.68 0.00177 0.054 0.00088 0.268 0.00088 0.671

9 3.76 0.00247 0.075 0.00124 0.376 0.00124 0.940

10 0.13 0.00009 0.003 0.00004 0.013 0.00004 0.032

11 0.10 0.00007 0.002 0.00003 0.010 0.00003 0.025

12 1.63 0.00108 0.033 0.00054 0.163 0.00054 0.409

13 1.80 0.00119 0.036 0.00059 0.180 0.00059 0.451

14 2.19 0.00144 0.044 0.00072 0.219 0.00072 0.547

15 1.85 0.00121 0.037 0.00061 0.185 0.00061 0.462

16 1.79 0.00118 0.036 0.00059 0.179 0.00059 0.447

17 1.99 0.00131 0.040 0.00065 0.199 0.00065 0.498

18 2.54 0.00167 0.051 0.00083 0.254 0.00083 0.634

19 2.12 0.00140 0.042 0.00070 0.212 0.00070 0.531

20 1.93 0.00127 0.039 0.00064 0.193 0.00064 0.483

21 2.19 0.00144 0.044 0.00072 0.219 0.00072 0.548

22 1.63 0.00107 0.033 0.00054 0.163 0.00054 0.408

23 6.64 0.00437 0.133 0.00218 0.664 0.00218 1.661

160

Appendix C

This Appendix demonstrates a sample calculation of estimating the hourly DRE

of an air-assisted flare with the dual fixed speed blowers and, summarizes predicted

emissions for all of the 240 emission scenarios described in Chapter 5. This Appendix

also contains time series of estimated emissions for all scenarios of flare type 2 (natural,

process, and fuel-fired equipment flares–medium variability) using the single fixed speed

blower configuration and the dual variable speed blower configuration.

161

Sample calculation

This sample calculation for flare type 3 (Natural, process and fuel fired flares-

highest variability) using duel fixed speed blowers and assuming a vent gas mixture of

560 Btu/scf.

1) Run the stochastic model to obtain normalized flow data for 10 days

2) Identify the maximum normalized flow during the 10 days. For this example, the

maximum normalized flow is 58.54

3) Calculate the average flow rate assuming the following scenarios

Scenario 1: Maximum normalized flow* Average flow rate = 1% of 144,000 lb/hr

Average flow rate = 24.60 lb/hr







162



4) Calculate the flow rate of each hour for each scenario by multiplying the average flow

rate by the hourly normalized flow

5) Assume the composition of the vent gas is identical to test A4 and A6, where the

heating value is 560 Btu/scf. The chemical composition of the test A4 and A6 is

propylene, methane (4:1 volume ratio) with nitrogen as diluent.

6) Calculate the mass fraction of propylene, methane and nitrogen as the following:

LHVCH4 = (21,450 Btu/lb) (16 lb/lb-mole) (0.0026 lb-mole/scf) = 892.32 Btu/scf

LHVC3H6= (19,683 Btu/lb) (42 lb/lb-mole) (0.0026 lb-mole/scf) = 2,149.4 Btu/scf

LHVN2= 0 Btu/scf

LHVvent gas = XN2*LHVN2 + XHC (4/5) LHVC3H6 + XHC (1/5) LHVCH4= 560 Btu/scf

XHC = 0.3 (mole fraction of hydrocarbon (propylene and methane))

XN2 = 0.7 (mole fraction of nitrogen)

XC3H6 = 0.24 (mole fraction of C3H6)

XCH4= 0.06 (mole fraction of CH4)

163

Mass fractions (Y) of the species were calculated as the following:

MWT = molecular weight

7) Then, calculated the stoichiometric air required as the following:

Assume 16.667 lb-mole vent gas basis (including nitrogen diluent)

4 C3H6 + 18 O2 + 67.7 N2 12 CO2 + 12 H2O + 67.7 N2

CH4 + 2 O2 + 7.5 N2 CO2 + 2 H2O + 7.5 N2

Total mass of air = (20*32 + 75.2*28) = 2745.6 lb

164

Therefore

5.38 lb of air required for 1 lb of vent gas

8) Then, multiply 5.38 by each hourly flow rate to get the amount of hourly air required

9) Select the air assist rate using the following cases

0 < Stoichiometric air < 16,250 CFM

Then air-assist flow is 16,250 CFM

16,250 CFM < Stoichiometric air < 32,500 CFM


32,500 CFM < Stoichiometric air < 48,750 CFM


Stoichiometric air > 48,750 CFM

Then air-assist flow is 65, 000 CFM

165

10) Use the DRE vs air-to-vent gas ratio data from Torres et al. (2012) to obtain the

following equation

11) Then , use the DRE equation to estimate the DRE for each hourly flow

12) Calculate the hourly emission and the total emission during the 10 days data

166

Table C-1: Flaring emission of flare type 1 (natural, process, and fuel-fired equipment flares–low variability) for all the average flow scenarios (vent gas with 350

Btu/scf)

Scenario

Upper bound of

flare flow compared

to maximum design

capacity

Vent flow rate

(lb/10 day)

Emission based

on DRE of 98%

(lb/10 day)

Single fixed

speed

(lb/10 day)

Dual fixed

speed

(lb/10 day)

Single

variable

speed

(lb/10 day)

Dual

variable

speed

(lb/10 day)

1 1% 99,650 457 14,854 8,005 2,716 890

2 5% 498,252 2,283 13,582 4,451 759 268

3 10% 996,505 4,566 8,902 2,318 535 316

4 20% 1,993,010 9,131 4,636 1,368 632 489

5 100% 9,965,050 45,657 3,162 2,444 2,292 2,283

Table C-2: Flaring emission of flare type 2 (Natural, process, and fuel-fired equipment flares–medium variability) for all the average flow scenarios (vent gas with

350 Btu/scf)

Scenario

Upper bound of

flare flow compared

to maximum design

capacity

Vent flow rate

(lb/10 day)

Emission based

on DRE of 98%

(lb/10 day)

Single fixed

speed

(lb/10 day)

Dual fixed

speed

(lb/10 day)

Single

variable

speed

(lb/10 day)

Dual

variable

speed

(lb/10 day)

1 1% 25,092 115 4,982 4,267 2,707 1,430

2 5% 125,460 575 13,534 7,149 2,379 843

3 10% 250,920 1,150 14,298 6,363 1,685 498

4 20% 501,841 2,299 12,726 4,805 996 389

5 100% 2,509,204 11,497 4,980 1,945 1,001 744

167

Table C-3: Flaring emission of flare type 3 (Natural, process, and fuel-fired equipment flares–high variability) for all the average flow scenarios (vent gas with

350 Btu/scf)

Scenario

Upper bound of

flare flow compared

to maximum design

capacity

Vent flow rate

(lb/10 day)

Emission

based on DRE

of 98% (lb/10

day)

Single fixed

speed

(lb/10 day)

Dual fixed

speed

(lb/10 day)

Single

variable

speed

(lb/10 day)

Dual

variable

speed

(lb/10 day)

1 1% 7,613 35 1,341 1,169 1,004 814

2 5% 38,064 174 5,021 4,068 2,482 1,552

3 10% 76,129 349 8,135 5,665 3,103 1,480

4 20% 152,257 698 11,330 7,345 2,960 1,167

5 100% 761,285 3,488 14,801 5,833 1,435 367

Table C-4: Flaring emission of flare type 4 (Fluid catalytic cracking flares) for all the average flow scenarios (vent gas with 350 Btu/scf)

Scenario

Upper bound of

flare flow compared

to maximum design

capacity

Vent flow rate

(lb/10 day)

Emission

based on DRE

of 98% (lb/10

day)

Single fixed

speed

(lb/10 day)

Dual fixed

speed

(lb/10 day)

Single

variable

speed

(lb/10 day)

Dual

variable

speed

(lb/10 day)

1 1% 166,394 762 18,118 8,133 2,137 279

2 5% 831,970 3,812 10,684 1,397 191 191

3 10% 1,663,941 7,624 2,793 386 381 381

4 20% 3,327,881 15,247 771 762 762 762

5 100% 16,639,405 76,237 3,812 3,812 3,812 3,812

168

Table C-5: Flaring emission of flare type 5 (Unclassified process flares–low variability) for all the average flow scenarios (vent gas with 350 Btu/scf)

Scenario

Upper bound of

flare flow compared

to maximum design

capacity

Vent flow rate

(lb/10 day)

Emission

based on DRE

of 98% (lb/10

day)

Single fixed

speed

(lb/10 day)

Dual fixed

speed

(lb/10 day)

Single

variable

speed

(lb/10 day)

Dual

variable

speed

(lb/10 day)

1 1% 61,219 280 8,743 4,824 2,145 1,134

2 5% 306,096 1,402 10,725 5,668 1,929 852

3 10% 612,192 2,805 11,337 4,964 1,704 680

4 20% 1,224,384 5,610 9,928 4,461 1,361 483

5 100% 6,121,922 28,049 6,803 2,414 1,447 1,407

Table C-6: Flaring emission of flare type 6 (Unclassified process flares–high variability) for all the average flow scenarios (vent gas with 350 Btu/scf)

Scenario

Upper bound of flare

flow compared to

maximum design

capacity

Vent flow rate

(lb/10 day)

Emission

based on DRE

of 98% (lb/10

day)

Single fixed

speed

(lb/10 day)

Dual fixed

speed

(lb/10 day)

Single

variable

speed

(lb/10 day)

Dual

variable

speed

(lb/10 day)

1 1% 30,524 140 3,647 2,815 1,977 1,323

2 5% 152,619 699 9,884 6,615 2,612 1,052

3 10% 305,238 1,399 13,230 6,761 2,105 680

4 20% 610,476 2,797 13,522 5,761 1,360 309

5 100% 3,052,378 13,985 6,798 1,545 703 699

169

Table C-7: Flaring emission of flare type 1 (Natural, process, and fuel-fired equipment flares–low variability) for all the average flow scenarios (vent gas with 560

Btu/scf)

Scenario


flow compared to

maximum design

capacity

Vent flow rate

(lb/10 day)

Emission based

on DRE of 98%

(lb/10 day)

Single fixed

speed

(lb/10 day)

Dual fixed

speed

(lb/10 day)

Single

variable

speed

(lb/10 day)

Dual

variable

speed

(lb/10 day)

1 1% 99,650 707 10,415 4,889 1,423 394

2 5% 498,252 3,536 7,117 1,969 414 247

3 10% 996,505 7,073 3,937 1,117 494 385

4 20% 1,993,010 14,145 2,234 1,120 770 716

5 100% 9,965,050 70,727 3,850 3,580 3,536 3,536

Table C-8: Flaring emission of flare type 2 (Natural, process, and fuel-fired equipment flares–medium variability) for all the average flow scenarios (vent gas with

560 Btu/scf)

Scenario


flow compared to

maximum design

capacity

Vent flow rate

(lb/10 day)

Emission based

on DRE of 98%

(lb/10 day)

Single fixed

speed

(lb/10 day)

Dual fixed

speed

(lb/10 day)

Single

variable

speed

(lb/10 day)

Dual

variable

speed

(lb/10 day)

1 1% 25,092 178 6,195 4,337 1,888 862

2 5% 125,460 890 9,439 4,308 1,294 430

3 10% 250,920 1,781 8,615 3,561 859 299

4 20% 501,841 3,562 7,122 2,501 597 330

5 100% 2,509,204 17,809 2,987 1,649 1,128 968

170

Table C-9: Flaring emission of flare type 3 (Natural, process, and fuel-fired equipment flares–high variability) for all the average flow scenarios (vent gas with

560 Btu/scf)

Scenario


flow compared to

maximum design

capacity

Vent flow rate

(lb/10 day)

Emission based

on DRE of 98%

(lb/10 day)

Single fixed

speed

(lb/10 day)

Dual fixed

speed

(lb/10 day)

Single

variable

speed

(lb/10 day)

Dual

variable

speed

(lb/10 day)

1 1% 7,613 54 1,769 1,583 1,162 794

2 5% 38,064 270 5,809 3,971 2,109 988

3 10% 76,129 540 7,943 5,082 1,976 816

4 20% 152,257 1,081 10,164 5,137 1,633 620

5 100% 761,285 5,403 8,164 3,100 718 292

Table C-10: Flaring emission of flare type 4 (Fluid catalytic cracking flares) for all the average flow scenarios (vent gas with 560 Btu/scf)

Scenario


flow compared to

maximum design

capacity

Vent flow rate

(lb/10 day)

Emission based

on DRE of 98%

(lb/10 day)

Single fixed

speed

(lb/10 day)

Dual fixed

speed

(lb/10 day)

Single

variable

speed

(lb/10 day)

Dual

variable

speed

(lb/10 day)

1 1% 166,394 1,181 10,648 4,456 783 70

2 5% 831,970 5,905 3,915 351 295 295

3 10% 1,663,941 11,810 703 590 590 590

4 20% 3,327,881 23,620 1,181 1,181 1,181 1,181

5 100% 16,639,405 118,098 5,905 5,905 5,905 5,905

171

Table C-11: Flaring emission of flare type 5 (Unclassified process flares–low variability) for all the average flow scenarios (vent gas with 560 Btu/scf)

Scenario


flow compared to

maximum design

capacity

Vent flow rate

(lb/10 day)

Emission based

on DRE of 98%

(lb/10 day)

Single fixed

speed

(lb/10 day)

Dual fixed

speed

(lb/10 day)

Single

variable

speed

(lb/10 day)

Dual

variable

speed

(lb/10 day)

1 1% 61,219 435 6,519 3,718 1,636 697

2 5% 306,096 2,173 8,178 3,487 1,162 511

3 10% 612,192 4,345 6,973 3,010 1,022 422

4 20% 1,224,384 8,690 6,020 2,685 845 486

5 100% 6,121,922 43,450 4,224 2,429 2,182 2,174

Table C-12: Flaring emission of flare type 6 (Unclassified process flares–high variability) for all the average flow scenarios (vent gas with 560 Btu/scf)

Scenario


flow compared to

maximum design

capacity

Vent flow rate

(lb/10 day)

Emission based

on DRE of 98%

(lb/10 day)

Single fixed

speed

(lb/10 day)

Dual fixed

speed

(lb/10 day)

Single

variable

speed

(lb/10 day)

Dual

variable

speed

(lb/10 day)

1 1% 30,524 217 4,213 3,231 1,774 877

2 5% 152,619 1,083 8,872 4,383 1,473 542

3 10% 305,238 2,166 8,766 3,915 1,083 303

4 20% 610,476 4,333 7,831 3,053 606 237

5 100% 3,052,378 21,664 3,030 1,183 1,083 1,083

172

Figure C-1: Hourly emission rate of for flare type 2 (maximum flow 5% of maximum design capacity) based on

DRE of 98% and LHV of 350 Btu/scf (upper) and hourly emission rate of the same scenario when the single

fixed speed blower configuration is used (lower).

0

5

10

15

20

25

30

35

0 50 100 150 200

Emis

sio

n (

Ib/h

r)

Time (hr)

0

10

20

30

40

50

60

70

80

90

0 50 100 150 200

Emis

sio

n (

Ib/h

r)

Time (hr)

173

Figure C-2: Hourly emission rate of flare type 2 (maximum flow 10% of maximum design capacity) based on



0

10

20

30

40

50

60

70

0 50 100 150 200

Emis

sio

n (

Ib/h

r)

Time (hr)

0

10

20

30

40

50

60

70

80

90

0 50 100 150 200

Emis

sio

n (

Ib/h

r)

Time (hr)

174




0

20

40

60

80

100

120

140

0 50 100 150 200

Emis

sio

n (

Ib/h

r)

Time (hr)

0

10

20

30

40

50

60

70

80

90

0 50 100 150 200

Emis

sio

n (

Ib/h

r)

Time (hr)

175




0

100

200

300

400

500

600

700

0 50 100 150 200

Emis

sio

n (

Ib/h

r)

Time (hr)

0

10

20

30

40

50

60

70

80

90

0 50 100 150 200

Emis

sio

n (

Ib/h

r)

Time (hr)

176


DRE of 98% and LHV of 560 Btu/scf (upper) and hourly emission rate of the same scenario when the dual

variable speed blower configuration is used (lower).

0

2

4

6

8

10

12

0 50 100 150 200

Emis

sio

n (

Ib/h

r)

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Appendix D

This appendix contains the results of the material reuse pinch analyses described

in Chapter 6 assuming a flexible boiler is used. The material reuse pinch calculations are

discussed in El-Halwagi (2006) and, Kazantzi and El-Halwagi (2005). (El-Halwagi,

2006; Kazantzi and El-Halwagi, 2005)..

Table D-1: Flow rates and LHV of the available sources in the refinery problem

Sources Flow rate in

mode 1 (MMscf/h)

Flow rate in mode 2

(MMscf/h)

Flow rate in mode 3

(MMscf/h)

Flow rate in mode 4

(MMscf/h)

LHV (MMBtu/MMscf)

Source 4 0.09 0.09 0.09 0.09 807

Source 3 0.18 0.18 0.18 0.18 850

Source 1 0.04 0.04 0.04 0.04 880

Source 2 0.01 0.013 0.015 0.02 2628

Table D-2: Flow rates and bounds on the LHV of the avaiable sink (Boiler)

sink Flow rate

(MMscf/h) Lower Bound on LHV

(MMBtu/scf) upper bound on LHV

(MMBtu/MMscf)

Boiler 0.25 500 2000

182

Figure D-1: Material reuse pinch diagram for the first operation mode in the refinery problem using a flexible

fixed capacity boiler of 0.2-0.25 MMscf/hr and a flare as sinks.

Figure D-2: Material reuse pinch diagram for the second operation mode in the refinery problem using a

flexible fixed capacity boiler of 0.2-0.25 MMscf/hr and a flare as sinks.

0

100

200

300

400

500

600

700

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ene

rgy

(MM

Btu

/h)

Flowrate (MMscf/h)

Sink composite curve

source composite curve

Minimum flaring = 0.07 MMscf/hr

0

100

200

300

400

500

600

700

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ene

rgy

(MM

Btu

/h)

Flowrate (MMscf/h)




183

Figure D-3: Material reuse pinch diagram for the third operation mode in the refinery problem using a flexible

fixed capacity boiler of 0.2-0.25 MMscf/hr and a flare as sinks..

Figure D-4: Material reuse pinch diagram for the fourth operation mode in the refinery problem using a flexible

fixed capacity boiler of 0.2-0.25 MMscf/hr and a flare as sinks..

0

100

200

300

400

500

600

700

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ene

rgy

(MM

Btu

/h)

Flowrate (MMscf/h)




0

100

200

300

400

500

600

700

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ene

rgy

(MM

Btu

/h)

Flowrate (MMscf/h)




185

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192

Vita

Fahad M. Al-Fadhli was born in Doha, Kuwait on March 3, 1983. After

completing his work at Doha High School, he attended Kuwait University. He received

the degree of Bachelor of Science in Chemical Engineering with Class Honors from

Kuwait University in August 2005. After graduation, he worked at the Ministry of

Electricity and Water in State of Kuwait for three years. In 2008, he was awarded a

scholarship from Kuwait University to perform his graduate studies at the University of

Texas at Austin with an invitation to return to Kuwait University as a professor. In May

2010, he received his Master’s Degree in Chemical Engineering.

Permanent e-mail : [email protected]

This dissertation was typed by the author.

copyright by fahad mohammed alfadhli 2012

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