Industrial Gas Turbine Performance Improvements Through Advanced Controls & Modeling
Tim Healy April, 2009 1
The Difference Between… …Failure, … and Su Suc cce cess ss,,
… Of Ofte ten n Rests Rests Hea Heavil vily y on The Control System
2
Increasing Generation Diversity Requires Increasing Flexibility From All Sectors r a e l c u N
There Exists Significant Opportunity To Improve Performance & Emissions In The Thermal Sector Through Advanced Control & Modeling
l a m r e h T
Nuclear
Gas
Cleaner Coal
s e l b a w e n e R
Biomass
Wind
Solar
Hydro 3
Thermal Sector Remains A Very Large Part of The Generation Portfolio Projected World Electricity Generation by Fuel 35 30 25
Trillion Kilowatt-Hours
Coal Natural Gas Liquids Renewables Nuclear
20 15 10 5 0 2005
2010
2015
2020
2025
2030
Source: History: Energy Information Administration (EIA), International Energy Annual 2005 (June-October 2007), Projections: EIA World Energy Projections Plus (2008)
4
A Dramatically Revised Outlook for ‘09 2009 economic outlook 8.7%
Last year’s outlook (April 2008) Current outlook (January 2009)
6.4%
8.2%
6.5% 5.1%
4.7% 3.2% 1.7%
-0.5% World
1.6%
-1.5%
1.7%
2.2% 1.4%
Russia Middle East -2.3%
China
India
-2.5% USA Eurozone Japan
World real GDP growth slowed from about 4% in 2006 and 2007 to 2.4% in 2008, expecting -0.5% in 2009 Source: Global Insight Outlook, April vs. December 23, 2008
24
Outline Industrial Gas
Turbines Short-Course
• Legacy Control Algorithms • Model-Based Control for Fuel Flexibility • The Road Ahead
6
A Sense of Power x 10
Ford Shelby GT500 ~500 SHP
GE Evolution Locomotive ~5000 SHP
GE 9H Industrial GT Engine ~500,000 SHP (combined-cycle)
x 10
x 10
GE-90 Aircraft GT Engine ~50,000 SHP
7
Gas Turbine Plant - Simple Cycle Fuel Air
3
1 2 Gen
Comb
Comp
4 Turb
Heat Source
Stack
3
T COMBUSTION E R U T A R E P M E T
G T
N O I S S E R P M O C
BRAYTON GAS CYCLE
4
2 K S TA C
1 ENTROPY
S 8
Gas Turbine & Steam Turbine - Combined Cycle 9 8
G S R H
7
I n t e g r a t e d C o m b i n e d C y c l e
Gen
ST
10 Pump 6 Air 1 Gen
Cond 5
Fuel 2
Comb
3
4
Comp
GT Heat Source
3
T COMBUSTION E R U T A R E P M E T
G T
BRAYTON GAS CYCLE N O I S S E R P M O C
1
2
HRSG
S T GA A U S H X E
8 7
STACK
5, 6
4 9 S T
RANKINE STEAM CYCLE
CONDENSER
10
Heat Sink
ENTROPY
S 9
Industrial Gas Turbine Overview Inlet Flow
Fuel Flow
Combustor Turbine
Exhaust Flow
Compressor
Shaft
10
Can–Annular Combustion Systems Cross-Section Through One Chamber
Chamber Arrangement on Gas Turbine
Multiple Fuel Nozzles 11
Industrial Gas Turbine Operability (Also Known as Control Requirements) Hot Gas Path Durability
Fuel System Operability
Exhaust Frame Durability
Compressor AeroMechanics
Combustor Flashback (Flameholding)
Compressor Surge
Combustor Emissions (NOx, CO, UHC)
Power Output
Combustion Dynamics Optimal Efficiency
AutoIgnition
Combustor Lean BlowOut (LBO)
12
Outline • Industrial Gas Turbines Short-Course Legacy
Control Algorithms
• Model-Based Control for Fuel Flexibility • The Road Ahead
13
Typical Industrial Gas Turbine Sensor/Effector Suite Inlet Bleed Heat (IBH) Compressor Inlet Guide Vanes (IGV)
Actuato r stroke feedback and some fuel system pressures not shown
Ambient Pressure Ambient Temperature
Generator Power
Generator Losses
Inlet Pressure Drop
Effectors
Total Fuel Flow (Wf)
Exhaust Pressure Drop Fuel Temperature Exhaust Temperature
Inlet Temperature Inlet Humidity
Sensors
Fuel Splits
Compressor Discharge Pressure Compressor Discharge Temperature 14
Sensor-Based Control Approach e r u t a r e p m e T
u r e r e s s n t P io n a t s t C o n t A d d i H e a
2 n o c i i s p s o e r t r n p e m s I o C
P 3 P 2 =
3
Maximum Cycle Temperature
Ideal Brayton Cycle
c n i o p i s o r n t n a e p x s I E
η Cycle
Work Output Heat Added
=
(T3 − T4 ) − (T2 − T1 ) (T3 − T2 )
P 1 = P 4
T = 2 P1 T1
P2
4
1
Entropy η Cycle
Comp 1
=
Turb
2 Comb 3
γ γ −1
T = = 3 P4 T4
T = 1 − 3 T4
P3
γ γ −1
Problem: Desire To Control T3, But T3 is Not Measured
(1−γ ) ( γ −1)
Higher T3 = Higher ηCycle
4
Turbine Efficiency
Solution: Correlate T3 to a Measured Variable
η turbine
T3 =
=
∆T ∆T' T4
1 -η turbine 1 − 1γ −1 γ (P ) P
T3 = f ( T 4 , PRc )
for assumed ηt and PRc ~= PRt
3 4
T3 = f ( T4 , ηt , PRt ) 15
Indirect (Schedule-Based) Boundary Control Fuel Splits
s t i l p S
X ~ Tx
X
4 T
PRc
T4_max
M U M I N I M
T4_req +
-
P+I
Wf / IGV T4
• Pre-Programmed Control Schedules • Field-Tuned For Performance & Operability
PRc
Characteristics • Simple (Easily Understood and Verified)
• Approximate Boundary Protection (Accommodates Worst-Case Condition)
• Poor Accommodation Of Ambient/Fuel Variation
• No Explicit Accommodation Of Machine Deterioration (New & Clean / Mean Machine Assumption)
• Coupled Effectors Prohibit Optimization (Part-Load Exhaust Temperature & Fuel Splits)
(Impact to Emissions, Combustion Dynamics, LBO Margin)
16
Outline • Industrial Gas Turbines Short-Course • Legacy Control Algorithms Model-Based
Control for Fuel Flexibility
• The Road Ahead
17
Gas Fuel Composition Variation 98 Trinidad 96
] 94 e % 92 n [ a t n90 h e88 t t e n86 M o84 C
Abu Dhabi
14
USA
] [ 10 e % n t 8 a n e h t t 6 E n o 4 C2 12
Norway Nigeria Algeria
Qatar
Malaysia
Abu Dhabi
Oman
82
Algeria
Oman Qatar
Malaysia Nigeria
Norway Trinidad
USA
80 U S ( Ty pi ca l )
A bu D h ab i
A l ge ri a
M al ay si a
N i ge ri a
N or w ay
O m an
Q a at r
T ri n id a d
Geographic Origin
0
U S (T yp ic al ) Ab u D ha bi
Ag l er i a
M al ay s a i
N ig er ia
N or w ay
O ma n
Q at ar
Composition Variation Will Increase As More LNG Is Injected Into Pipelines
Tr i ni d ad
Geographic Origin 4.5
1500 Malaysia
4
] 3.5 e % 3 n [ 2.5 a t p n e 2 o r t n P o1.5 C 1
Oman Nigeria Qatar Norway Abu Dhabi
Algeria
USA
Trinidad
0.5 0
x e 1450 d n I e 1400 b b o 1350 W 1300
U S ( Ty pi ca l)
Ab u D ha bi
A lg er a i
Ma l ay s a i
N ig er ia
N or w ay
O ma n
Q at ar
Tr i ni d ad
Nigeria Algeria
Qatar
Norway Trinidad
USA
U S ( Ty pi ca l)
Ab u D ha bi
Geographic Origin
WI = HHV S
Oman
Malaysia
Abu Dhabi
A gl e ri a
Ma l ay si a
N ig er ia
N or w ay
O ma n
Q at ar
Tr i ni d ad
Geographic Origin
Wobbe Index
g
MWI =
LHV S g ⋅T
HHV , LHV S g
T
Modified Wobbe Index Fuel Higher/Lower Heating Value [BTU/Scf] Fuel Specific Gravity Fuel Temperature [°R]
18
What Is At Risk? Gas turbine operability concerns due to composition variation: Addressed by gas fuel specification (given expected variation, not an issue for most premixed combustion systems)
• Auto-Ignition • Flashback • Emissions (NOx, CO) • Combustion Dynamics • Blow-out
Addressed today by manual tuning (given expected variation, potentially a very serious issue)
Tuning is required to protect against fuel composition variation
19
Gas Fuel Composition Rate-of-Change Significant & rapid shifts in “Null-Point” are possible
NP
NG
NG NP
NG
• Rate and frequency of pipeline composition changes will increase • An automatic tuning process is required to support continuous & reliable operation
LNG
NP
LNG
LNG
Fictitious region / pipeline
20
Legacy Solution – Closed-Loop MWI With Fuel Temperature
IP Feedwater Control
Dual GCs
Performance Heater
Characteristics • Costly (Dual Gas Chromatographs)
• Low-Bandwidth (GCs & Fuel Heat Exchangers)
• Limited Authority (Performance Heater Capability)
• Sub-Optimal Efficiency (Any Off-Nominal Fuel Temperature) 21
Direct (Model-Based) Boundary Control g n i l u d e h c S t i m i L
+ _ + _ + _ + _
Loop Selection
+ _ + _ + _
Loop Selection
+ _ + _
Physics-Based 0 . 16 (6 .394 * SH ) Boundary Models W *e 3 . 95 . 006 *( Tfl −Tfl ref ) T NOxref * e 1 . NOx 25 270 @ 15% O2 = P e * 3 − 9.5( SH − SH ref ) 3
*e
IGV
Loop Selection
(SISO vs. MIMO: Industrial GT System Coupling & Time Scale Does Not Demand MIMO Control,… Yet)
Wf Fuel Splits
Virtual Sensors
ARES - Parameter Estimation
*Q
Engine Model
Characteristics • Robust / Flexible / Expandable (Additional Boundaries / Loops)
• Direct Boundary Protection (Physical Space of Boundary)
• Accommodation Of Machine Deterioration (Adaptive Model Ensures Accurate Virtual Sensors)
• Implicitly De-Coupled Effectors (Automatic Performance Optimization)
• Good Accommodation Of Ambient / Fuel Variation (Manages Emissions, Combustion Dynamics, LBO Margin)
22
Adaptive Real-time Engine Simulation (ARES) 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 Measured Inputs
Measured Outputs
On-Line Partial Derivative Calculation
y
u
xˆ , yˆ
Estimated Outputs ARES - Parameter Estimation
u
ˆ y
+
ˆ prt x
_
“State” Estimate
On-Line Filter Gain Calculation
Extended Outputs
P = a ⋅ P ⋅ aT + Q K
s = J ⋅ P ⋅ J T + R T
K = P ⋅ J ⋅ s
ˆ + +
Z-1
Partial Deriv. Calc.
yˆ prt
Engine Model
ˆ ext y Engine Model
ARES - Parameter Estimation
−1
P = P − K ⋅ J ⋅ P
P
(Covariance of Prediction Error) (Covariance of Residual)
a, J Q, R
(Gain Matrix) (Covariance of Prediction Error)
Z-1
23
Model-Based Control Adapts Well To Environmental / Fuel Variation x
NOx (target)
e1 e2 eNOx
x a m
eNOx
+ _
n i m
Control
NOx
Fuel_ Fraction
Environment g n i l u d e h c S t i m i L
+ _ + _ + _ + _ + _ + _ + _ + _ + _
l o r t n e o r u C - t c n u I r - t p S o o L CDM
Physics-Based Boundary Models NOx @15%O2 = f ( Tflame, Humidity, Fuel_Fraction )
Virtual Sensors
GT
Effectors
Sensors ARES - Parameter Estimation
Engine Model
Tflame, Tfire, W2, etc. 24
Integrating Models, Sensors, & Algorithms Physics-Based Boundary Models 5 . ] 15 ] 2 1 i O 14 s p % [ 5 13 s 1 c i @12 m m p 11 a p n [ 0 y . x 10 1 D O 9 d N e d 8 t e c t i c 7 d i d e r e r 6 P P 5 . 5 0 0.5 5
Site A Site B Site C Site D Site E (10% C2)
Adaptive-Model Approach
X R
Closed-Loop Control +_
X
6
7
8
9
ˆ X
1.0 1.5 10 11 12 13 14 15
Measured Dynamics [psi] Measured NOx [ppm@15%O2]
Design Center
( Small Performance Impact ) ( No Performance Impact )
Boundary Model
( Performance Impact ) Load Runback
Fuel Temp.
Fuel Fraction
Expected LNG Range
Boundary Sensor
Performance optimization through hierarchical application of effectors
Wobbe
25
Model-Based Control Performance Field Test r o 56 t s u 54 b m52 o C50 g 48 n i h 46 c a e 44 R I 42 W M40
MWI Fuel Temp.
0
200
400
400 350 300 250 200 150 100 50 0
•
•
9 8 7 6 200
-20
0
20
400
~260ºF fuel temperature excursion imposed (~20% MWI) over five minutes (max capability of fuel heat exchanger)
] 10 2 O % 9 5 1 @ 8 m p p [ x 7 O N 6 -20
600
120 Frequency 1 Frequency 2
80 60 40 20 0
0
200
80
400
Time [sec]
Closed-loop simulation of modelbased 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
100
0
20
40
60
80
] % 120 [ t u p 110 t u O 100 e n i b r 90 u T s 80 a G 100
Time [sec]
• 100
60
NOx Load
Time [sec]
] s t c e i g m r a a T n y f O D n % o [ i e t s d u t u b i l m p o m C A
40
•
Time [sec]
10
0
7FA+e DLN2.6 gas turbine operating in combined-cycle at base-load
6% ) I W 4% ] ( x % e [ 2% d e n g 0% I n e a b h -2% b C o -4% W -6%
600
Time [sec] ] 2 O % 5 1 @ m p p [ x O N
] F g e d [ e r u t a r e p m e T l e u F
Closed-Loop Simulation
600
OpFlex Wide Wobbe system maintains emissions & dynamics levels using fuel distribution only
] s t c e i g m r a a T n f y D O n % o [ i t e s d u t u b i l m p o m C A
120 100
Frequency 1 Frequency 2
80 60 40 20 0
-20
0
20
40
60
80
100
Time [sec]
26
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 conditions Reliability Increased system availability due to sensor fault detection and accommodation Emissions Tighter NOx control over a wider operational envelope 27
The Road Ahead Advanced Controls & Modeling Will Play A Greater Role In Thermal Sector Technology / Solutions • • • • •
Fuel Flexibility Integrated Gasification / Combined-Cycle Plant-Level Optimization Grid-Code Compliance Health Management
28
Fuel flex … expanding the envelope Power producers seeking fuel diversification & flexibility • Increasing fuel prices & volatility driving substitution • Cleaner & more flexible technology … lower emissions, increased turndown, multi-fuel, durability Gas fuels
• NG … LNG wide wobbe • High BTU … hydrogen/EOR • Low BTU … Steel BFG/COG
Liquid fuels
• Light crude … • Heavy crude … vanadium & sulfur
Synthetic fuels
• Pet coke … refining • Coal syngas … IGCC/SNG • Biofuels … ethanol
29
Integrated Gasification Combined-Cycle G S R H
Pump
Cooling
O2
Gasifier
Gen
ST
CleanUp
Feed Prep.
Cond
Syngas
Air
Comb
Gen
Comp
GT
Fuel + H2O
Electricity / Steam
a s S y n g
Gasifier Sulfur Removal
Solid feed – Slag Gas/Liquid feed - Ash
Sulfur
Combined Cycle Power Block 30
Plant-Level Optimization Model Predictive Controls for Combined-Cycle Plant Start-Up
Optimized Load Profile
•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
Time
Stress constraints
HP & IP maximum rotor stresses Final CC load
Time
MPC Controller
GT, HRSG, ST models HP & IP rotor stresses
Optimize GT loading over Time Horizon
GT load reference
Control System
State estimation Measurements Steam & metal Temperatures, Steam Pressures
Measurements
31
Back-Up 32
2007
2030
