Evaluation Of Aeration Outline Control - WordPress.com · Evaluation Of Aeration Control PNCWA...

1

Evaluation Of Aeration

Control

PNCWA ANNUAL CONFERENCE 2004

Randal Samstag - Carollo EngineersCarol Nelson and Curtis Steinke – King County DNRP

2Pncwa2004Samstag.ppt

Outline

• Introduction

• Aeration control – Why is it important?

• Control system levels and strategies

• South Plant testing and modeling

Aeration Control - Simplified ProcessDiagram


Why is Aeration Control Important?

• High operating cost

• Crucial to the process

2

Typical Energy Consumption Profile


Aeration System Energy Consumption

• Aeration system: 50-70%

• All other systems: 30-50%


Aeration Energy Cost

• 20 mgd plant

• Annual energy cost: $750,000


Factors That Affect AerationPower Cost

• Water depth

• Type of diffuser

• Type of blower

• Friction losses

• Degree of control

3


Aeration Control System Levels

• Manual

• Auto control of common air header

• Auto control of each aeration basin

• Auto control of each aeration zone

Auto Control of Common Air HeaderAuto Control of Common Air Header

Auto Control of Each BasinAuto Control of Each Zone

4


General Summary andConclusions

• Aeration systems are the biggestconsumers of energy in a WWTP

• Energy consumption of aerationsystems can be optimized with proper levelof control


South Plant Testing and Modeling

• Within a single basin, where is the bestplace to control?

– Performed dye test to determine hydrauliccharacter of the aeration tank

– Used commercial simulation program tocalibrate air flows to demand distribution

– Used customized model to test impact ofcontrol at different locations


Tank Schematic

MLSS

PE

RAS

Air

AirDO

DO DO DO

DO

DO

DO DO DO

DO

DO

DO


Dye Test Results : End of Pass 4

COMPARISON OF EXPERIMENTAL DATA AND TANKS IN SERIES

MODEL PREDICTIONS

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0.00 50.00 100.00 150.00 200.00 250.00

Time (min)

C/C

Experimental Data Best Fit (N=53) N=30 N=70

5


Dye Test Results : End of Pass 2

COMPARISON OF EXPERIMENTAL DATA AND TANKS IN SERIES

MODEL PREDICTIONS

0.0

0.5

1.0

1.5

2.0

2.5

0.00 50.00 100.00 150.00 200.00 250.00

Time (min)

C/C

Experimental Data Best Fit (N=18) N=10 N=30


Schematic: 56-celled BioWinTM

Model

Secondary Influent Tank 2-4 Pass 1-1

Pass 2-14

Pass 3-1

Pass 4-14

ML Channel Secondary Effluent

RAS Mixing Box

WAS

Pass 1-2 Pass 1-3 Pass 1-4 Pass 1-5 Pass 1-6 Pass 1-7 Pass 1-8 Pass 1-9 Pass 1-10 Pass 1-11 Pass 1-12 Pass 1-13 Pass 1-14

Pass 2-1Pass 2-2Pass 2-3Pass 2-4Pass 2-5Pass 2-6Pass 2-7Pass 2-8Pass 2-9Pass 2-10Pass 2-11Pas 2-12Pass 2-13

Pass 3-2 Pass 3-3 Pass 3-4 Pass 3-5 Pass 3-6 Pass 3-7 Pass 3-8

Pass 4-13 Pass 4-12 Pass 4-11 Pass 4-10 Pass 4-9 Pass 4-8 Pass 4-7

Pass 3-9 Pass 3-10 Pass 3-11 Pass 3-12 Pass 3-13 Pass 3-14

Pass 4-6 Pass 4-5 Pass 4-4 Pass 4-3 Pass 4-2 Pass 4-1


Calibration: 56-celled BioWinTM

Model

56-Celled BioWin Model

0

500

1000

1500

2000

2500

3000

1 2 3 4 5 6 7 8 910

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

Air

Su

pp

lyp

er

cell

(cfm

)

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

DO

(mg

/L)

Measured Air Supply Rate Per Cell DO Measured DO Predicted Air Supply Per Tank Per Cell


BiowinTM Schematic – 12-celledSystem

Settled Sewage Probe 1

Probe 7

Probe 10

ML Channel Effluent

RAS Mixing Box

WAS

Filtrate

TO

Probe 2 Probe 3

Probe 5 Probe 6Probe 4

Probe 8 Probe 9

Probe 11 Probe 12

6


Calibration: 12-celled BioWinTM

Model

12-Celled BioWin Model

0

1000

2000

3000

4000

5000

6000

7000

8000P

robe

1

Pro

be

2

Pro

be

3

Pro

be

4

Pro

be

5

Pro

be

6

Pro

be

7

Pro

be

8

Pro

be

9

Pro

be

10

Pro

be

11

Pro

be

12

Air

Su

pp

lyp

er

Cell

(cfm

)

0

0.5

1

1.5

2

2.5

3

3.5

4

DO

(mg

/L)

Measured Air Supply Rate Per Cell DO Measured DO Predicted Air Supply Per Cell


Schematic: Custom ControlModel

PID

DO

Air

PID

DO

Air

PID

DO

Air


Control Modeling – Calibration to12-celled Model

12-Celled BioTank Model

0

1000

2000

3000

4000

5000

6000

Pro

be

1

Pro

be

2

Pro

be

3

Pro

be

4

Pro

be

5

Pro

be

6

Pro

be

7

Pro

be

8

Pro

be

9

Pro

be

10

Pro

be

11

Pro

be

12

Air

Su

pp

lyp

er

Cell

(cfm

)

0

0.5

1

1.5

2

2.5

3

3.5

4

DO

(mg

/L)

Measured Air Supply Rate Per Cell Simple Carbon Model Predicted DO

Measured DO Predicted Air Supply, Simple Carbon Model (cfm)


Control Modeling: Air Flowswith Single Control Point

020,00040,00060,00080,000

100,000120,000140,000160,000180,000

Each

Zone

Probe

1

Probe

3

Probe

5

Probe

7

Probe

9

Probe

11

Probe

12

Probe

6 and

12

Air

Flo

w(c

fm)

Total Air Flow (cfm)

7


Control Modeling: DO DistributionSingle Point Control

0.01.02.03.04.05.06.07.08.09.0

Each

Zone

Probe

1

Probe

3

Probe

5

Probe

7

Probe

9

Probe

11

Probe

12

Probe

6an

d12

DO

(mg

/L)

DO (mg/L) Cell 1 Cell 3 Cell 4 Cell 5

Cell 6 Cell 7 Cell 9 Cell 10

Cell 11 Cell 12


Control Modeling: Air Flows withTwo Points of Control

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

Probe

6 and

12

Probe

6 and

10

Probe

5 and

12

Probe

5 and

10

Probe

4 and

12

Probe

4 and

10

Probe

3 and

12

Probe

3 and

10

Probe

1 and

12

Air

Flo

w(c

fm)

Total Air Flow (cfm)


Control Modeling: DO with TwoPoints of Control

0123456789

Probe

6an

d12

Probe

6an

d10

Probe

5an

d12

Probe

5an

d10

Probe

4an

d12

Probe

4an

d10

Probe

3an

d12

Probe

3an

d10

Probe

1an

d12

DO

(mg

/L)

DO (mg/L) Cell 1 Cell 3 Cell 4 Cell 5

Cell 6 Cell 7 Cell 9 Cell 10

Cell 11 Cell 12


Conclusions South Plant Modeling

• Even though hydraulics indicate a 56-celledsystem, a 12-celled model is adequate formodeling control

• Optimal control is two point control

– Probe 6 and 12

– Probes 5 and 10

8


Acknowledgements

• King County Department of NaturalResources and Parks

• Carollo Engineers

Questions?


Most Common Control Strategies

• Buffered pressure control

• Most open valve control

• Incremental control


Buffered Pressure Control

• Modify aeration air control valves to deliversetpoint DO

• This changes the aeration header pressure

• Modify blower output to keep a constantheader pressure setpoint

9


Most Open Valve Control

• Designed to minimize pressure losses

• Adjust control valves to maintain DOsetpoints in the tanks

• Poll the valves and adjust blower capacityto keep at least one valve in the desiredopen condition (80-95% open)


Incremental Control

• Abandon PI and/or pressure control

• Vary airflow in increments based on stepchanges in DO


Effect of Water Depth

• Transfer efficiency goes up with increaseddepth

• But so does blower head

• Net wire to water difference usually small


Factors that Affect DiffuserTransfer Efficiency

• Type of diffuser

– Coarse bubble• Tray or Bubble Cap

– Fine bubble• Tube

• Disc

• Panels

• Air rate per diffuser

• Area Loading Rate

• MLSS Concentration

10


Aeration Air System

• Air Supply System

• Air Distribution System


Air Supply System

• Filters

• Silencers

• Piping and Valves

• Blowers


Flow/Pressure Table

Minimum 10,000 7.6

Average 20,000 8.2

Maximum 30,000 9.0

CONDITIONCONDITION FLOWFLOW PRESSUREPRESSURE

Flow/Pressure Graph

11


Blower Types

• Positive Displacement (PD)

• Centrifugal

– Multi-stage

– Single-stage

Positive Displacement

Multi-Stage Centrifugal Single-Stage Centrifugal

12

DO Profile


Aeration Air Demand Variation

• Minimum: 50-70 % of Average

• Maximum: 140-160 % of Average


Air Distribution Control System

• DO Probes

• Air Flow Meters

• Air Flow Control Valves

• Controller


Potential Problems with MOVSystems

• Improperly sized valves (too big)

• Pressure regulation time scale must beslower than DO change

• Integrator windup (KI e dt)

• Process dynamics

13


Potential Solutions to MOVProblems

• Properly size control valves

• Dampen control loops

• Tracking

– Keep track of integral error for all regulators

• Continuous tuning strategies

– Gain scheduling

– Self tuning

– Exact linearization


Carollo Experience with MOV

• Problems at Orange County

– Hunting of valves and blower

– Converted back to pressure control

• Working well at Clark County

– Only two basins

– Controlling air flow to zones rather thanDO

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