study on proactive air-quality control by coupling … on proactive air-quality control by coupling...

Study on Proactive Air-quality Control by

Coupling Emission Source Reduction and

Air Quality Modeling

Dr. Qiang Xu

Dan F. Smith Department of Chemical Engineering Lamar University, Beaumont, TX 77710

2014 TARC Meeting/SETRPC Air Quality Symposium Lamar University, Beaumont, TX

August 8, 2014

Project Background

• “Strategic research in EI development should be focused on those emission categories that have the greatest uncertainties (industrial flaring) and that are likely to have the greatest impacts on ozone formation.”

• “provide necessary technical details to support legislative changes that will result in implementing quantified, cost-effective control strategies.”

• “improvements in the scientific understanding of emissions, atmospheric chemistry, and meteorology, and improvements to air quality modeling tools must be integrated into air quality management systems.”

Problems

Priority Research Needs

• The HGA/BPA experience highly localized and transient high ozone concentration events that violate NAAQS.

• Air emission from chemical plants’ turnarounds (start-up, shutdown, and process upsets) is one of the root causes.

Motivation and Study Scope

<1%

Ethylene Propylene 1,3-Butadiene Isobutylene Hydrogen

Methane Acetylene Ethane Methyl-Acetylene Propane N-Butane

Legend:

t = 0

Start-up time

t = 14 hr

t = 3 hr

<1%

33%

3%

20%

9%<1%

6%

9%

17%

1%

43%

37%

1%

17% 1% Others 1%

667.1 klb/hr

72.3 klb/hr

5%0.6%5%

36%

6%

9%

36%

NO NO2 OLE PAR TOL XYL FORM ALD2

ETH MEOH ETOH ISOP IOLE ALDX TERP NR CO

t = 14 hr

Legend:

t = 0

Start-up time

<0.01%

0.11%

0.13%

0.13%

0.02%0.30%

0.10%0.56%

0.09%

0.35%

238.5 kmol/hr

t = 3 hr

27.2 kmol/hr

Emission

Source

Accounting

Flare Emissions

Air Quality

Modeling

Emission Source

Minimization

Air-quality Conscious

& Cost-effective

Emission Control

Project Objectives

• Explore various in-plant control strategies for emission source

reduction and obtain dynamic emission data;

• Employ CAMx modeling to precisely study impacts on regional

ozone concentrations under different plant operation scenarios

(e.g., start-ups, shutdowns, upsets, and etc.);

• Optimize out-plant control strategies for air-quality impact

minimization via CAMx based simulation/optimization, such as

smart selection of date/starting time of turnarounds, scheduling

multi-plant turnarounds, and new plant site determination;

• Study cost-effective and air-quality conscious control strategies

for all stake holders, including TCEQ, chemical industry, and

residential communities.

Methodology

Framework

Process Modeling

and Validation

Detailed Process

and Control

Information

Reliable Process

Models

Meteorological Data

and Boundary

Conditions

Emission Impact

Minimization

Develop Plant-wide

Process Model

Model Validation

Satisfied?Model TuningNo

Conduct Dynamic

Simulation for Different

Scenarios

Start-up

Operation

Shutdown

Operation

Process Upset

Handling

Process

Retrofit...

Optimize In-plant

Controls for

Emission Reduction

Obtain Hourly Dynamic

Emission Data

Satisfied?No

Optimize Out-plant

Controls for Planned

Emission Events

Air Quality

Modeling and

Optimization

Update TCEQ EIs

Event Date

Selections

Turnaround

Starting Time

Selection

Multi-plant

Turnarounds

Scheduling

New Plant

Location

Selection

...

Emission Source

MinimizationOriginal Operation

Strategies

Yes

Process

Optimization

Yes

Obtain Spatial and Temporal

Air Quality Impacts

Obtain Best Air-quality

Control Strategies

2. Safety-Considered Dynamic Simulation

for Flare Emission Reduction

CGC HP and LP DeC3 DeC4 & DeC5

Cold Box

DeC1 DeC2 C2 Splitter &C2

Refrigeration C3 Splitter

Example: Modeling of C2 Splitter & C2

Refrigeration Subsystem

1. Faring Minimization during Plant Start-ups

(Different Startup Designs)

Furnance

Quench

DeC1 DeC2

Chilling

Train

DeC3 DeC4

C2+

CGC

Feed

Recycle

Heater

C3+ C4+ C5+

H2/C1 C2 C3 C4H2/C1

Furnance

Quench

DeC1 DeC2

Chilling

Train

DeC3 DeC4

C2+

CGC

Feed

Recycle

Heater

C3+ C4+ C5+

H2/C1 C2 C3 C4

Flare

Streams

Recycle

Streams

DeC4 Recycle

Streams

Ordinary Process

Streams

Legend:

H2/C1

Furnance

Quench

DeC1 DeC2

Chilling

Train

DeC3 DeC4

C2+

CGC

Feed

C3+ C4+ C5+

Base Design:

Optimal Design 1:

Optimal Design 2:

Different Start-up Operational Procedures

Operational Procedure 1:



Rec

ycle

Flo

w R

ate

(Klb

/hr)

0

50

100

150

200

250

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

H2/C1 Recycle

C2 Recycle

C3 Recycle

C4 Recycle

3rd – 7th Furnace

Start-up

0

150

300

450

600

750

Light Naphtha Feed

Heavy Naphtha Feed

Cra

cked

Gas

Fee

d Fl

ow R

ate

(Klb

/hr)

Time (Hr)

1st

Furnace

2nd

Furnace Waiting Waiting

Rec

ycle

Flo

w R

ate

(Klb

/hr)

0

50

100

150

200

250

0 1 2 3 4 5 6 7 8 9 10 11 12

H2/C1 Recycle

C2 Recycle

C3 Recycle

C4 Recycle

1st

Furnace

2nd

Furnace

3rd – 7th Furnace

Start-up

0

150

300

450

600

750

Heavy Naphtha Feed

Light Naphtha Feed

Time (Hr)

Cra

cked

Gas

Fee

d F

low

Rat

e (K

lb/h

r)

Waiting

13

Rec

ycle

Flo

w R

ate

(Klb

/hr)

0

50

100

150

200

250

0 1 2 3 4 5 6 7 8 9 10 11 12

H2/C1 Recycle

C2 Recycle

C3 Recycle

C4 Recycle

1st

Furnace

2nd

Furnace

3rd – 7th Furnace

Start-up

0

150

300

450

600

750

Heavy Naphtha Feed

Time (Hr)

Cra

cked

Gas

Fee

d F

low

Rat

e (K

lb/h

r)Light Naphtha Feed

Emission Source Reductions under

Different Start-up Strategies

Case Studies Procedure 1 Procedure 2 Procedure 3

Design 1 Case 1 Case 3 Case 5

Design 2 Case 2 Case 4 Case 6

Start-up

time(hrs)

Flared raw materials (kg) Emissions (kg)

Case Design Procedure H2 C1 C2 C3 C4+ CO2 CO NOx HRVOCs

1 1 1 14 26.7 410.2 1016.3 484.0 493.1 7188.2 19.6 3.6 34.0

2 2 1 14 26.7 410.6 1016.0 481.9 380.9 6837.1 18.7 3.4 32.1

3 1 2 14 25.1 386.0 966.1 451.5 458.3 6763.5 18.4 3.4 31.9

4 2 2 14 25.1 386.3 965.8 453.0 355.6 6451.7 17.6 3.2 30.2

5 1 3 11 24.6 380.0 953.8 443.7 449.8 6658.6 18.1 3.3 31.4

6 2 3 11 24.6 380.3 953.5 446.1 349.6 6357.8 17.4 3.2 29.7

historical best start-up 25 60.8 981.1 2526.1 1368.5 1261.9 22208.7 49.0 9.0 83.3

Reduction achieved compared with historical start-up (%)

Case 1 44.0 56.0 58.2 59.8 64.6 60.9 67.6 60.1 60.1 59.1

Case 2 44.0 56.0 58.2 59.8 64.8 69.8 69.2 61.8 61.8 61.4

Case 3 44.0 58.8 60.7 61.8 67.0 63.7 69.5 62.4 62.4 61.7

Case 4 44.0 58.8 60.6 61.8 66.9 71.8 70.9 64.0 64.0 63.7

Case 5 56.0 59.5 61.3 62.2 67.6 64.4 70.0 63.0 63.0 62.3

Case 6 56.0 59.5 61.2 62.3 67.4 72.3 71.4 64.5 64.5 64.3

Air Quality Modeling

● Scenario 1: Effect of Start-up Starting Time

- 9 Cases: 0, 1, 2, 3, 4, 5, 6, 7, 8 am

● Scenario 2: Effect of Plant Location

- 2 Cases: Deer Park (D.P.); Texas City (T.C.)

● Scenario 3: Effect of Start-up Date

- 2 Cases: August 17 and August 19, 2006

Comparison of Ozone Results

Ozone Concentration

without the Start-up

Ozone Concentration

with the Start-up

Scenario 1: Ozone Increments with

Different Turnaround Starting Time

Starting time: 0:00 am

Peak time: 2:00pm

Maximum Ozone: 17.7ppb


Peak time: 10:00pm



Peak time: 6:00pm


Deer Park August 17th, 2006

Domain Maximum 8-Hour-Ozone

Increments under Different Scenarios

Scenario 1

(August 17; D.P)

(ppb)

Scenario 2

(August 17; T.C.)

(ppb)

Scenario 3

(August 19; D.P.)

(ppb)

Case 1 5.5 13.6 5.2

Case 2 5.3 16.2 4.9

Case 3 4.7 16.8 4.3

Case 4 4.0 15.6 3.6

Case 5 3.3 13.6 2.9

Case 6 2.7 11.5 2.3

Case 7 2.1 9.2 1.9

Case 8 1.7 7.0 1.5

Case 9 1.3 5.4 1.2

2. Flare Minimization during Plant Shutdowns

i. Preparation of shutdown: reduce feed rate, cut off

unnecessary units, and reduce liquid/gas inventories;

ii. Shutting down the process: e.g., for olefin plants,

shutting down Charge Gas Compressor (CGC), then

cutting off furnace feed, and then other units;

iii. Decommissioning: liquid discharge, vapor discharge,

and N2 purge.

General Shutdown Procedure

Modeling Example: N2 Purge

0

100

200

300

400

500

600

700

0

1

2

3

4

5

6

7

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Molar flow

rate (kmol/hr)

Pres

sure

(bar

)

Time (hr)

Pressure Flowrate

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1 1.2 1.4

N2 M

ola

r F

ract

ion

(%

)

Time (hr)

Pressurizing Operation Depressurizing Operation

Legend:

Schedule for An Olefin Plant Shutdown

0 5 10 15 20 25 30

Furnaces

CGC

Chilling Train

& DeC1

DeC2

C2S

DeC3

C3S

DeC4

Time (hr)

Facility Shutdown

Liquid Discharge

Vapor Discharge

Nitrogen Purge

Emission Source

Characterization

during the Plant

Shutdown

Emissions

after Flaring

0

50

100

150

200

250

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Mo

lar

Flo

wra

te (k

mo

l/h

r)

Time (hr)

(a)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Mol

ar F

ract

ion

(%)

Time (hr)

NO NO2 OLE PAR TOL XYL FORM ALD2 ETH MEOH

ETOH ISOP IOLE ALDX TERP CH4 ETHA NR NASN CO

Legend:

(b)

Air Quality Modeling: Positions of the

Plant and Monitoring Station

JEFC,

C64

BMTC,

C2

S43S,

C643

PAWC,

C28

SEPA,

C628

S40S,

C640

WORA,

C9

S42S,

C642

NDLD ,

C1035

Domain Maximum Ozone Increase

due to the Plant Shutdown Emissions

Hourly

8-hr

case0 case2 case4 case6 case8 case10 case12 case14 case16 case18 case20 case22

6/3/2006 2.8 3.2 4.6 5.4 5.5 3.6 1.3 0.8 1.0 0.5 2.0 4.9

6/4/2006 1.8 3.4 5.3 5.3 4.0 5.1 1.9 1.2 0.9 1.0 2.9 4.6

6/5/2006 5.9 4.0 7.5 8.4 9.4 5.8 1.5 0.6 2.1 1.0 2.5 15.5

6/6/2006 9.1 8.2 11.1 11.0 8.9 4.7 2.6 0.6 1.4 1.3 3.0 4.6

6/7/2006 2.9 3.0 7.1 6.1 5.9 4.2 2.9 1.0 1.0 1.4 3.9 3.9

6/8/2006 2.3 2.9 3.9 4.2 5.8 6.1 2.8 0.6 0.9 1.2 2.2 6.3

6/9/2006 2.7 6.4 6.9 8.2 4.7 4.3 2.0 0.8 1.0 0.8 2.1 4.1

6/10/2006 2.6 4.0 5.5 7.0 6.5 2.8 1.3 1.1 1.7 0.8 1.0 2.7

6/11/2006 3.0 3.9 5.5 4.7 2.5 2.1 1.3 0.6 0.7 1.0 4.2 3.9

6/12/2006 5.2 4.5 6.2 7.9 6.1 4.3 1.9 1.2 1.1 0.9 1.3 4.5

6/13/2006 2.9 4.0 5.5 5.1 2.8 2.5 0.8 0.9 1.6 1.8 2.8 3.8

case0 case2 case4 case6 case8 case10 case12 case14 case16 case18 case20 case22

6/3/2006 0.8 1.2 1.8 2.2 2.1 1.1 0.3 0.3 0.2 0.2 0.5 1.0

6/4/2006 0.9 0.9 1.4 1.8 1.4 1.5 0.6 0.2 0.3 0.4 0.6 1.1

6/5/2006 1.9 1.6 2.9 4.7 3.8 1.8 0.4 0.3 0.4 0.5 1.2 4.2

6/6/2006 2.8 3.3 4.7 5.0 3.7 1.5 0.6 0.2 0.3 0.2 0.6 1.1

6/7/2006 0.9 1.5 2.5 3.6 2.8 1.3 0.5 0.4 0.3 0.4 0.5 0.8

6/8/2006 0.7 1.1 1.1 1.9 2.3 1.7 0.8 0.2 0.2 0.3 0.8 1.3

6/9/2006 1.3 1.7 2.5 3.2 2.4 1.6 0.6 0.2 0.2 0.4 0.9 1.0

6/10/2006 1.4 1.3 1.8 2.3 2.0 0.8 0.3 0.4 0.4 0.2 0.4 0.7

6/11/2006 0.6 0.8 1.0 1.1 1.1 0.7 0.4 0.2 0.3 0.3 0.7 1.0

6/12/2006 1.4 1.9 1.6 2.1 2.1 1.4 0.5 0.3 0.4 0.2 0.2 1.1

6/13/2006 0.8 1.1 1.6 1.3 0.7 0.6 0.2 0.2 0.4 0.4 0.9 0.8

Case 6 Ozone Increase at Various

Monitoring Stations on June 6, 2006

20060603 20060604 20060605

-2

-1

0

1

2

3

4

5

6

-2

-1

0

1

2

3

4

5

6

0 12 0 12 0 12

Ozo

ne

Incr

ease

(p

pb

)

Maximum Eight-Hour O3 Concentration(ppb) Increase at layer 1

C642 C9 C2 C1035 C643

C28 C628 C640 C64 Plant

OP1

Furn

ace

Furn

ace

Furn

ace

CGC

Stages

1-3 HP

-DeC

3

CGC

Stage 4

C2

Conver

ter

LP

-DeC

3

DeC

4

DeC

5

DeC

1 S

trip

per

DeC

1

Chilling Train 1st

DeC

2

2n

d D

eC2

C2 S

pli

tter

C3 S

pli

tter

Legend: Ordinary Process

Stream

New Stream for

Plant Start-up

Oil & Water

Quench

C2 Refrig.

System

C3 Refrig.

System

Furn

ace

Furn

ace

A Novel Shutdown Strategy for Flare

Minimization at An Olefin Plant

3. Flare Minimization under Process Upsets

Flare Minimization Support

• Short-time and small quantity of off-spec flaring source ---> to CGC suction.

• Long-time and large quantity of off-spec flaring source ---> to furnace feed with the consideration of fresh feed reduction.

• CGC dynamic simulation will determine the quantitative impacts.

Flare Minimization Strategy under

Process Upsets

C2 Reactor

Effluent

Off-spec Stream

Identification?

C2 Splitter

Overhead

Process Upsets

Identification?

Process Upsets

Identification?

STSQ Upsets LTLQ Upsets STSQ Upsets LTLQ Upsets

Recycle to CGC

InletRecycle to Furnace

System Inlet

Recycle to Both CGC and

Furnace System Inlets

F1 F3

F2 F4 F5 F1'

F3'

F2' F4'

F5'

Flare System

Fflared= F-F5 (F5')F > F5 F > F5’

Legend:

First Priority Choice Second Priority Choice: : Third Priority Choice: Others:

4. Air-quality Conscious Plant Site

Selection

Air-quality Conscious Plant Site

Selection (Cont’d)

270280

290300

310320

072

144216

288360

0

3

6

9

12

15

Ambient Temperature (K)

Wind Direction (degree)

Win

d S

peed (

m/s

)

270 275 280 285 290 295 300 305 310 315 3200

50

100

150

200

250

300

Temperature (K)

Count

200

400

600

800

30

210

60

240

90

270

120

300

150

330

180 0

0

0 2 4 6 8 10 12 14 16 180

500

1000

1500

2000

2500

3000

Wind Speed (m/s)

Count

Count

X (meter)

Y (

met

er)

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

E1

E2

E3

E4E5

E6

E7

E8

E9

01

23

45

01

23

450

20

40

60

80

100

120

X (kilometer)Y (kilometer)

Co

nce

ntr

atio

n (

pp

b)

0

20

40

60

80

100

120

Case Study 1: Domain Peak Concentration

Impact from Candidate Locations

X (meter)

Y (

met

er)

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

E1

E2

E3

E4E5

E6

E7

E8

E9

0

20

40

60

80

100

120

Case Study 2: Peak Concentration

Impact from Two New Plants

Location Node of New Plant A

Loca

tion N

ode

of

New

Pla

nt

B

1 13 25 37 49 61 73 851

13

25

37

49

61

73

85

70

75

80

85

90

95

65

5. Optimization for Smart Air-quality

Monitoring Station Allocation

X (meter)

Y (

mete

r)

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

E1

E2

E3

E4E5

E6

E7

E8

E9

10

15

20

5

5

Case Studies

X (x100m)

Y (

x100m

)

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

25

30

35

40

45

50

5

10

15

20

25

30

35

40

X (x100m)

Y (

x100m

)

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

25

30

35

40

45

50

5

10

15

20

25

30

35

40

45

X (x100m)

Y (

x100m

)

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

25

30

35

40

45

50

5

10

15

20

25

30

35

40

45

X (x100m)

Y (

x100m

)

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

25

30

35

40

45

50

5

10

15

20

25

30

35

40

45

New Emission

Profile

25 Units of

Budget

110 Units of

Budget

85 Units of

Budget

Optimization Pareto Frontier of Under

Different Budget Size

Summary of Current Progress and

Future Study Expectation

• Partially supported by the TARC project, 7 journal papers and 4 conference papers have been published;

• 12 conference presentations;

• Conduct more case studies for different chemical processes under various turnarounds;

• Integrate various industrial practices on emission reduction with air-quality modeling;

• Study cost-effective and air-quality-conscious emission control strategies.

Concluding Remarks

• Multi-scale dynamic simulation and optimization by coupling emission source reduction and air quality modeling is important to study Texas air quality;

• This project helps identify cost-effective and air-quality-conscious control strategies, which benefits Texas air quality control;

• Significant progresses have been made;

• Look forward to the success of the final project.

http://customersrock.files.wordpress.com/2007/04/thank-you.jpg

Cases with Different Starting Time

0

100

200

300

400

500

600

1 4 7 10 13 16 19 22

To

tal E

mis

sio

ns (

km

ol/

hr)

Time (hour)

Case 1

Case 2

Case 3

Case 4

Case 5

Case 6

Case 7

Case 8

Case 9

Starting Time

Peak Time

Hypothetical Start-up Days

Aug 17, 2006 Aug 19, 2006

Type High-Ozone Day Low-Ozone Day

Max 8-hour Ozone 113.6 ppb 51.6 ppb

Temperature 79 ~ 97 F 75 ~ 89 F

Wind Speed 3 mph 6 mph

Wind Direction SSW East

Precipitation 0.00 in 0.16 in

Plant Located in VOC Control Area

10:00 11:00 12:00

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