1

Industrial Gas TurbinePerformance Improvements Through

Advanced Controls & Modeling

Tim HealyApril, 2009

2

The Difference Between

Often Rests Heavily on The Control System

and Success,

Failure,

3

Increasing Generation Diversity Requires

Increasing Flexibility From All Sectors

Cleaner CoalGasNuclear

HydroSolarBiomass Wind

Renewables

Nuclear

Therm

al

There Exists Significant

Opportunity To Improve

Performance & Emissions

In The Thermal Sector

Through Advanced Control

& Modeling

4

Thermal Sector Remains A Very Large Part of

The Generation Portfolio

Projected World Electricity Generation by Fuel

0

5

10

15

20

25

30

35

2005 2010 2015 2020 2025 2030

Coal

Natural Gas

Liquids

Renewables

Nuclear

Source: History: Energy Information Administration (EIA), International Energy Annual 2005 (June-October 2007), Projections: EIA World Energy Projections Plus (2008)

Trillion Kilowatt-Hours

A Dramatically Revised Outlook for 09

3.2%

1.7% 1.6% 1.7%

6.4%

4.7%

8.7%8.2%

-0.5%

-2.5%-1.5%

-2.3%

2.2%

1.4%

6.5%

5.1%

World

USA Eurozone Japan

Russia MiddleEast

China India

2009 economic outlook

Source: Global Insight Outlook, April vs. December 23, 2008

World real GDP growth slowed from about 4% in 2006 and

2007 to 2.4% in 2008, expecting -0.5% in 2009

Last years outlook (April 2008)

Current outlook (January 2009)

24

6

Outline

Industrial Gas Turbines Short-Course

Legacy Control Algorithms

Model-Based Control for Fuel Flexibility

The Road Ahead

7

A Sense of Power

GE Evolution Locomotive ~5000 SHP

GE-90 Aircraft GT Engine~50,000 SHP

GE 9H Industrial GT Engine~500,000 SHP (combined-cycle)

Ford Shelby GT500~500 SHP

x 10

x 10 x 10

8

T

S

Heat Source

COMPRESSION

GT

BRAYTON GAS CYCLE

TEMPERATURE

ENTROPY

COMBUSTION

1

STACK

2

3

4

Air

Comp

Comb

TurbGen

Fuel

1

2

3

4

Stack

Gas Turbine Plant - Simple Cycle

9

Integrated Combined Cycle

1

3

4

T

S

Heat Source

Heat Sink

COMPRESSION

GT

BRAYTON GAS CYCLE

TEMPERATURE

ENTROPY

COMBUSTION

2HRSG

RANKINE STEAM CYCLESTACK

CONDENSER

ST

EXHAUST

GAS 9

105, 6

78

Gas Turbine & Steam Turbine - Combined Cycle

Pump

ST Gen

56

78

9

10

Cond

Air

Comp

Comb

GTGen

Fuel

12 3 4

HRSG

10

Industrial Gas Turbine Overview

Compressor

Combustor

Turbine

Shaft

InletFlow

FuelFlow Exhaust

Flow

11

CanAnnular Combustion Systems

Chamber Arrangement on Gas Turbine

Cross-Section Through One Chamber

Multiple Fuel Nozzles

12

Industrial Gas Turbine Operability(Also Known as Control Requirements)

Compressor

Surge

Compressor

Aero-

Mechanics

Exhaust

Frame

Durability

Hot Gas

Path

Durability

Fuel

System

Operability

Combustion

Dynamics Combustor Lean Blow-

Out (LBO)

Combustor

Emissions

(NOx, CO, UHC)

Combustor

Flashback

(Flameholding)

Auto-

Ignition

Power Output

Optimal Efficiency

13

Outline

Industrial Gas Turbines Short-Course

Legacy Control Algorithms

Model-Based Control for Fuel Flexibility

The Road Ahead

14

Typical Industrial Gas Turbine Sensor/Effector Suite

CompressorInlet Guide Vanes (IGV)

Fuel Splits

Total Fuel Flow (Wf)

Inlet Pressure Drop

Compressor Discharge Temperature

Inlet Humidity

Inlet Temperature

Exhaust Pressure Drop

Ambient Temperature

Ambient Pressure

Exhaust Temperature

Actuator stroke feedback and some fuel system pressures not shown

Fuel Temperature

Compressor Discharge Pressure

Generator Losses

Generator Power

Inlet Bleed Heat (IBH)

Sensors

Effectors

15

Sensor-Based Control Approach

Entropy

Temperature

P1=P4

Isentropic

Compression

Isentropic

Expansion

Constan

t Pressur

e

Heat Ad

dition

1

2

3

4

P2=P3

Maximum Cycle Temperature

Comp

Comb

Turb

1 2 3 4

Ideal Brayton Cycle

)T(T

)T(T)T(T

AddedHeat

OutputWork

23

1243Cycle

==

1

4

3

4

3

1

1

2

1

2

T

T

P

P

T

T

P

P

==

=

)1()1(

4

3

T

T1

=

Cycle

Higher T3 = Higher Cycle

T'

T turbine

=

( )

=

1

43

4

3

11-1

TT

P

Pturbine

Turbine Efficiency

T3 = f ( T4 , t , PRt )

T3 = f ( T4 , PRc )for assumed t and PRc ~= PRt

Problem:Desire To Control T3, But T3 is Not Measured

Solution:Correlate T3 to a Measured Variable

16

Indirect (Schedule-Based) Boundary Control

Pre-Programmed Control Schedules

Field-Tuned For Performance & OperabilityT4_max

MIN

IMU

M

T4_req

T4

P+I+

-

Wf / IGV

X ~ Tx

Split

s

X

Fuel Splits

T4

PRc

PRc

Characteristics Simple

(Easily Understood and Verified)

Approximate Boundary Protection (Accommodates Worst-Case Condition)

Poor Accommodation Of Ambient/Fuel Variation (Impact to Emissions, Combustion Dynamics, LBO Margin)

No Explicit Accommodation Of Machine Deterioration (New & Clean / Mean Machine Assumption)

Coupled Effectors Prohibit Optimization (Part-Load Exhaust Temperature & Fuel Splits)

17

Outline

Industrial Gas Turbines Short-Course

Legacy Control Algorithms

Model-Based Control for Fuel Flexibility

The Road Ahead

18

Gas Fuel Composition Variation

Composition Variation Will Increase As More LNG Is Injected Into

Pipelines

Algeria

Malaysia

Nigeria

Norway

Oman

Qatar

Trinidad

USA

Abu

Dhabi

1300

1350

1400

1450

1500

US (Typical) Abu Dhabi Algeria Malaysia Nigeria Norway Oman Qatar Trinidad

Geographic Origin

Wo

bb

e In

de

x

Algeria

Malaysia

Nigeria

Norway

Oman

Qatar

Trinidad

Abu

Dhabi

USA

80

82

84

86

88

90

92

94

96

98

US (Typical) Abu Dhabi Algeria Malaysia Nigeria Norway Oman Qatar Trinidad

Geographic Origin

Meth

an

e

Co

nte

nt

[%]

Algeria

Malaysia

NigeriaNorway

Oman

Qatar

Trinidad

Abu

Dhabi

USA

0

2

4

6

8

10

12

14

US (Typical) Abu Dhabi Algeria Malays ia Nigeria Norway Oman Qatar Trinidad

Geographic Origin

Eth

an

e

Co

nte

nt

[%]

Algeria

Malaysia

Nigeria

Norway

Oman

Qatar

Trinidad

Abu

Dhabi

USA

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

US (Typical) Abu Dhabi Algeria Malays ia Nigeria Norway Oman Qatar Trinidad

Geographic Origin

Pro

pa

ne

Co

nte

nt

[%]

gS

HHVWI =

TS

LHV

g

MWI

=

LHVHHV ,

gS

T

Wobbe Index

Modified Wobbe Index

Fuel Higher/Lower Heating Value [BTU/Scf]

Fuel Specific Gravity

Fuel Temperature [R]

19

What Is At Risk?

Gas turbine operability concerns due to composition variation:

Auto-Ignition Flashback

Emissions (NOx, CO) Combustion Dynamics Blow-out

Tuning is required to protect against fuel composition variation

Addressed by gas fuel specification

Addressed today by manual tuning

(given expected variation,

not an issue for most pre-

mixed combustion systems)

(given expected variation,

potentially a very serious issue)

20

Gas Fuel Composition Rate-of-Change

Significant & rapid shifts in Null-Point

are possible

Rate and frequency of pipeline composition changes will increase

An automatic tuning process is required to support continuous & reliable operation

LNG

LNG

LNG

NG

NG

NG NP

NP

NP

Fictitious region / pipeline

21

Legacy Solution Closed-Loop MWI With Fuel Temperature

Characteristics Costly

(Dual Gas Chromatographs)

Low-Bandwidth(GCs & Fuel Heat Exchangers)

Limited Authority(Performance Heater Capability)

Sub-Optimal Efficiency(Any Off-Nominal Fuel Temperature)

Performance Heater

IP Feedwater ControlDual GCs

22

Direct (Model-Based) Boundary Control

IGV

Fuel Splits

+_

+_

+_

+_

+_

+_

+_

+_

+_

Loop Selection

Loop Selection

Loop Selection

Wf

ARES - ParameterEstimation

Engine Model

( )

16.0

27025.1

3

*394.6

95.3

3

*

*

T

SH

eP

eW

Physics-Based Boundary Models

Qe

eNOxNOx

ref

ref

SHSH

TflTfl

refO

**

*)(5.9

)*(006.

%15@ 2

=

Lim

it S

ch

ed

ulin

g

Virtual Sensors

Characteristics Robust / Flexible / Expandable

(Additional Boundaries / Loops)

Direct Boundary Protection(Physical Space of Boundary)

Good Accommodation Of Ambient / Fuel Variation(Manages Emissions, Combustion Dynamics, LBO Margin)

Accommodation Of Machine Deterioration(Adaptive Model Ensures Accurate Virtual Sensors)

Implicitly De-Coupled Effectors(Automatic Performance Optimization)

(SISO vs. MIMO: Industrial GT System Coupling & Time Scale Does Not Demand MIMO Control, Yet)

23

Adaptive Real-time Engine Simulation (ARES)

ARES - ParameterEstimation

Engine Model

On-Line Partial Derivative Calculation

QaPaP T +=

RJPJs T +=

1= sJPK T

PJKPP =

(Covariance of

Prediction Error)

(Covariance of

Residual)

(Gain Matrix)

(Covariance of

Prediction Error)

On-Line Filter Gain Calculation

u

prtx

Ja,

+

_

+

+

u

exty

x

Z-1

MeasuredInputs

State Estimate

MeasuredOutputs

y

y

prty

Model Non-Linear Component-Level Cycle Model

Optimized for Real-Time Operation

Filter Extended Kalman Filter Formulation On-Line Jacobian & KF Gain Calculation

Re-configurable for Fault Accommodation Avoids Parallel Linear Model Process

Z-1P

K

ARES - Parameter

Estimation

Engine Model

Partial

Deriv.

Calc.

yx ,EstimatedOutputs

Extended Outputs

RQ,

24

Model-Based Control Adapts Well To Environmental / Fuel Variation

+_

Lo

op

-In

-Co

ntr

ol

Str

uc

ture

ARES - ParameterEstimation

Engine Model

Virtual Sensors

Sensors

Effectors

+_

Physics-Based

Boundary Models

Lim

it S

ch

ed

ulin

g

max

min Control

CDM

NOx @15%O2 = f ( Tflame, Humidity,

Fuel_Fraction )

NOx

(target)

NOx

eNOxx eNOx

Fuel_Fraction

e1

e2

Tflame, Tfire,

W2, etc.

+_

+_

+_

+_

+_+_

+_

+_

Environment

GT

25

Integrating Models, Sensors, & Algorithms

Fuel Temp.

Load Runback

( No Performance Impact )

( Small Performance Impact )

( Performance Impact )

Design Center

WobbeExpected LNG Range

Fuel Fraction

Closed-Loop Control

Boundary Sensor

X

X Boundary Model

RX+_

Adaptive-Model Approach

Performance optimization through hierarchical application of effectors

5

6

7

8

9

10

11

12

13

14

15

5 6 7 8 9 10 11 12 13 14 15

Measured NOx [ppm@15%O2]

Pre

dic

ted

NO

x [

pp

m@

15%

O2]

Site A

Site B

Site C

Site D

Site E (10% C2)

0.5

1.0

1.5

Measured Dynamics [psi]

Predicted Dynamics [psi]

0.5 1.0 1.5

Physics-Based Boundary Models

26

Model-Based Control Performance

-6%

-4%

-2%

0%

2%

4%

6%

-20 0 20 40 60 80 100

Time [sec]

Wo

bb

e I

nd

ex

(W

I)

Ch

an

ge

[%

]

0

20

40

60

80

100

120

-20 0 20 40 60 80 100

Time [sec]

Co

mb

usti

on

Dyn

am

ics

Am

pli

tud

e [

% O

f T

arg

et]

Frequency 1

Frequency 2

6

7

8

9

10

-20 0 20 40 60 80 100

Time [sec]

NO

x [

pp

m@

15%

O2]

80

90

100

110

120

Gas T

urb

ine O

utp

ut

[%]

NOx

Load

Closed-loop simulation of model-based control algorithm (7FA+e DLN2.6, base-load, ISO Day)

~10% WI change imposed over ~30 seconds (rate >18%/minute)

OpFlex Wide Wobbe algorithm maintains emissions & dynamics levels using fuel distribution only

0

20

40

60

80

100

120

0 200 400 600

Time [sec]

Co

mb

usti

on

Dyn

am

ics

Am

plitu

de [

% O

f T

arg

et]

Frequency 1

Frequency 2

40

42

44

46

48

50

52

54

56

0 200 400 600

Time [sec]

MW

I R

each

ing

Co

mb

usto

r

0

50

100

150

200

250

300

350

400

Fu

el T

em

pera

ture

[d

eg

F]

MWI

Fuel Temp.

6

7

8

9

10

0 200 400 600

Time [sec]

NO

x [

pp

m @

15

%O

2]

7FA+e DLN2.6 gas turbine operating in combined-cycle at base-load

~260F fuel temperature excursion imposed (~20% MWI) over five minutes (max capability of fuel heat exchanger)

OpFlex Wide Wobbe system maintains emissions & dynamics levels using fuel distribution only

Field Test Closed-Loop Simulation

27

Assessment

The Model-Based Control system provides many advantages over competing technologies with similar objectives:

Cost No additional auxiliary equipment required beyond control system sensor redundancy. No gas analyzer required

Operability Negligible change in output or efficiency as a result of changing fuel properties

Lower combustion dynamics across the operational envelope Improved output & efficiency at off-design conditionsReliability

Increased system availability due to sensor fault detection and accommodation

Emissions Tighter NOx control over a wider operational envelope

28

Fuel Flexibility

Integrated Gasification / Combined-Cycle

Plant-Level Optimization

Grid-Code Compliance

Health Management

The Road Ahead

Advanced Controls & Modeling Will Play A Greater Role In Thermal Sector Technology / Solutions

29

Fuel flex expanding the envelope

NG LNG wide wobbe High BTU hydrogen/EOR Low BTU Steel BFG/COG

Light crude Heavy crude vanadium & sulfur

Gas fuels Liquid fuels

Pet coke refining Coal syngas IGCC/SNG Biofuels ethanol

Synthetic fuels

Power producers seeking fuel diversification & flexibility

Increasing fuel prices & volatility driving substitution

Cleaner & more flexible technology lower emissions, increased turndown,

multi-fuel, durability

30

Integrated Gasification Combined-Cycle

Sulfur

SulfurRemoval

Syngas

Electricity /Steam

Combined CyclePower Block

Gasifier

Solid feed Slag

Gas/Liquid feed - Ash

Pump

ST Gen

Cond

Air

Comp

Comb

GTGen

Syngas

HRSG

Cooling

Clean-

UpGasifier

Feed Prep.

Fuel + H2O

O2

31

Plant-Level Optimization

GT loadreference

Final CC load

Control System

State estimation

Optimize GT loadingover Time Horizon

GT, HRSG, ST modelsHP & IP rotor stresses

MeasurementsSteam & metal Temperatures, Steam Pressures

MPC Controller

Measurements

HP & IP maximum rotor stresses

Physics-based models to predict stresses

real-time optimization to choose best loading profile

Handles multiple ST Stress constraints simultaneously

Handles multiple control actions simultaneously

Accommodates any initial thermal state of the plant

Model Predictive Controls for

Combined-Cycle Plant Start-Up Optimized Load Profile

Stress

constraints

Time

Time

32

Back-Up

2007

2030

35

Combustion Operability

NO

x NOx

Guarantee

Window

CO

CO

Guarantee

Fuel-Air Ratio

Dyn

am

ics Hot Tone

Dynamics

Limit

NOx

CO

Dynamics

OperabilityOperability

WindowWindowLean

Blow Out

Window

Window

Tfi

re (

Po

wer)

Lean Blow Out

Window

Cold ToneDynamics

Fuel-Air RatioFuel-Air Ratio

Fuel-Air Ratio