increasing efficiency and flexibility in ccpp plants by ... · introduce the design of a master...
TRANSCRIPT
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s F. Gatti, M. Barabino,M. Rovaglio, F. Giovannini
Increasing efficiency and flexibility in CCPP plants by the use of MPC techniques
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Introduction
Introduce the design of a master controller for IGCC (Integrated Gasification Combined Cycle) and CCPP (Combined Cycle Power Production) plants based on Model Predictive Control approach
Coordinate the main process variables interacting with the basicstructure of standard controller at unit level
Demonstrate the reliability of multivariable linear MPC when adopted for non linear complex process with crucial targets
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Introduction
Design approachDetailed first principles plant model
Multivariable linear MPC instead of conventional loops based on a „pressure driven“ configuration
Comparison between the linear MPC approach and conventional controllers
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IGCC Plant
Most typical configuration is:Gasifier unit concurrently fed with refinery char (e.g. visbroken tar), steam and oxygen to produce high temperature syngas(rich in CO and H2)
Sulfur and hydrogen removal units
HRGS (Heat Recovery Steam Generator)
Gas and steam turbines
Gas and steam turbines are typically coupled on the same shaft
25-30% efficiency, 3 to 400 MW size for single group
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IGCC Plant Flowsheet
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IGCC Model
Gasifier UnitHeterogeneous plug flow reactor with mass, thermal and momentum balances
Heat ExchangersStationary heat balances
Gas and Steam TurbinesMechanical energy balance around shaft
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IGCC Model Dynamics
Gasifier has very fast response to disturbances (about 10 seconds)
Sequence of steady state conditions
HRSG and Combined Cycle have response time constants in the order of 20-40 minutes
Determined by geometry and operating conditions
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IGCC Model Dynamics
Time delay
Inverse response
Non linearity
16
16.5
17
17.5
18
18.5
19
0 20 40 60 80 100 120 140 160
kg/s
time (min)
Ste am flowrate
fre s h fe e d wate r
Me dium pre s s ure boile r
16
16.5
17
17.5
18
18.5
19
0
kg/s
S te am flowrate
fre s h fe e d wate r
Me dium pre s s ure boile r
16
16.5
17
17.5
18
18.5
19
0 20 40 60 80 100 120 140 160
kg/s
time (min)
Ste am flowrate
fre s h fe e d wate r
Me dium pre s s ure boile r
16
16.5
17
17.5
18
18.5
19
0
kg/s
S te am flowrate
fre s h fe e d wate r
Me dium pre s s ure boile r
299830003002300430063008301030123014301630183020
0 20 40 60 80 100 120
rota
ting
spee
d
time (min)
299830003002300430063008301030123014301630183020
0 20 40 60 80 100 120
rota
ting
spee
d
time (min)
(1)
(2)
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Master Control Philosophies
Two main control philosophies for two different control problems:
„Load Following“ when main objective is to satisfy the power demand
„Steam Demand“ when the main objective is to satisfy the steam steam demand
c
GASIFIER
Gas treatments
REFINERY
H R S G
oxygen plant
char MP s team
syngas
rpm
power demand
MASTER CONTROL
Tgas
External Network
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Master Control Variables
Manipulated variablesChar flow rate to Gasifier, steam flow rate to turbine
Controlled variablesShaft rotating speed turbine regime, power production, steam demand
ConstraintsTurbine combustion temperature, O2 availability
Boiler drums level, steam pressures, O2/char ratio, steam/char ratio
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MPC Design: Structure
Simplified model predictive controller is based on linear model obtained by ARX MIMO identification
Approximation of the IGCC modelFour inputs: 3 CVs + 1 constraint on output
Four outputs: 2 MVs + 2 measured disturbances
( ) ( ) ( ) ( ) ( )tetuqBtyqA +⋅=⋅
Steam flow rate to refinery
External electrical loadSteam flow rate to turbineShaft rotating speed
H2 flow rate to refineryChar inlet flow rateTurbine temperatureGenerated power
Measured DisturbancesManipulated VariablesConstraintsControlled Variables
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MPC Design: Model Identification
Define the sampling time (between 40 to 60 seconds)Open loop simulations with alternative responses as:
Identify the linear model by means step test procedure using the detailed plant model
{ }.deadtime 0.3 time, settling 0.03 min =time sampling
Model(SS,TF,ZPK)
y(k) Outputs
Manipulated Variables u(k)Measured Disturbances v(k)
DisturbanceModel
(SS,TF,ZPK)
d(k)UnmeasuredDisturbances
n(k)
x (k)
Model y(k) Outputs
u(k)v(k)
DisturbanceModel
d(k)UnmeasuredDisturbances
n(k)
Model(SS,TF,ZPK)
y(k) Outputs
Manipulated Variables u(k)Measured Disturbances v(k)
DisturbanceModel
(SS,TF,ZPK)
d(k)UnmeasuredDisturbances
n(k)
x (k)
Model y(k) Outputs
u(k)v(k)
DisturbanceModel
d(k)UnmeasuredDisturbances
n(k)
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MPC Design: Model Validation
Verify the accuracy of the identified model, not only in the operating conditions at step tests (validation data)
Enough accurate for MPC implementation
6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5x 104
-4
-2
0
2
4
6
8
10
time [s]
modeldata
rpm
6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5x 104
-4
-2
0
2
4
6
8
10
time [s]
modeldata
rpm
6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5x 104
-4
-2
0
2
4
6
8
10
time [s]
modeldata
rpm
6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5x 104
-4
-2
0
2
4
6
8
10
time [s]
modeldata
rpm
-0.5
0
0.5
1
1.5
2modeldata
6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5x 104time [s]
Kg/
s
-0.5
0
0.5
1
1.5
2modeldata
6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5x 104time [s]
-0.5
0
0.5
1
1.5
2modeldata
6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5x 104time [s]
Kg/
s
-0.5
0
0.5
1
1.5
2modeldata
6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5x 104time [s]
-0.5
0
0.5
1
1.5
2modeldata
6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5x 104time [s]
Kg/
s
-0.5
0
0.5
1
1.5
2modeldata
6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5x 104time [s]
-0.5
0
0.5
1
1.5
2modeldata
6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5x 104time [s]
Kg/
s
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MPC Design: Controller Design
MPC calculates the optimal value of MVs solving the following optimization problem:
Hard constraints on inputs and input variations and soft constraint on output to prevent optimization problems for infeasibility
( ) ( )( ) ( )[ ] ( ) ( ) ( )[ ]{ }
( )( )( )
( )
≥
==+∆
+≤++≤+−
∆≤+∆≤∆
≤+≤
+++−++++∆+−+∑−
=+
∆
++∆∆
0,..., ,0kjku
1 to subj.
11min
maxmin
maxmin
maxmin
1
0
22
1
22
target1,...,
ε
εε
ερωωω ε
pmj
ykikyy
ukikuu
ukikuu
ikrkikykikukukiku
ii
ii
ii
p
i
yi
ui
uikkmukku
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MPC Design: Controller Tuning
Prediction horizon set to about 20 sampling times
Weights imposed on inputs variations and lower/upper bounds determine control action
Turbine temperature weight is set to 0Control action only in case of potential violation of its limits
Inlet char composition is an unmeasured (and not modeled) disturbance
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MPC Performance
Power production increase of 20 MWNo constraint violation, power and steam demand are both satisfied
0 20 30 40 50 60130
135
140
145
150
155
[MW
]
time [min]
CV –Generated Power
0 10130
135
140
145
150
155
[MW
]
CV –Generated Power
0 20 30 40 50 60130
135
140
145
150
155
[MW
]
time [min]
CV –Generated Power
0 10130
135
140
145
150
155
[MW
]
CV –Generated Power
0 10 20 30 40 50 6011.1
11.2
11.3
11.4
11.5
11.6
11.7
11.8
[kg/
s]
time [min]
CV –MP Steam to the refinery
011.1
11.2
11.3
11.4
11.5
11.6
11.7
11.8CV –MP Steam to the refinery
0 10 20 30 40 50 6011.1
11.2
11.3
11.4
11.5
11.6
11.7
11.8
[kg/
s]
time [min]
CV –MP Steam to the refinery
011.1
11.2
11.3
11.4
11.5
11.6
11.7
11.8CV –MP Steam to the refinery
0 10 20 30 40 50 602999.42999.5
2999.62999.72999.8
2999.9
3000.03000.1
3000.2
3000.3
Rpm
CV –Shaft Rotating Speed
time [min] 0
2999.42999.5
2999.62999.72999.8
2999.9
3000.1
3000.2
3000.3–Shaft Rotating Speed
0 10 20 30 40 50 602999.42999.5
2999.62999.72999.8
2999.9
3000.03000.1
3000.2
3000.3
Rpm
CV –Shaft Rotating Speed
time [min] 0
2999.42999.5
2999.62999.72999.8
2999.9
3000.1
3000.2
3000.3–Shaft Rotating Speed
0 10 20 30 40 50 6012.0
12.5
13.0
13.5
[kg/
s]
time [min]
MV –Char flowrate
0
12.5
13.5
MV –Char flowrate
0 10 20 30 40 50 6012.0
12.5
13.0
13.5
[kg/
s]
time [min]
MV –Char flowrate
0
12.5
13.5
MV –Char flowrate
0 10 20 30 40 50 601030
1040
1050
1060
1070
1080
1090
1100
[°C
]
time [min]
Constraint - Turbine temperature
01030
1040
1050
1060
1070
1080
1090
1100
[°C
]
Constraint - Turbine temperature
0 10 20 30 40 50 601030
1040
1050
1060
1070
1080
1090
1100
[°C
]
time [min]
Constraint - Turbine temperature
01030
1040
1050
1060
1070
1080
1090
1100
[°C
]
Constraint - Turbine temperature
0 10 20 30 40 50 605.4
5.6
5.8
6
6.2
6.4
6.6
6.8
[kg/
s]
time [min]
MV - Steam to turbine
05.4
5.6
5.8
6
6.2
6.4
6.6
6.8
[kg/
s]
MV - Steam to turbine
0 10 20 30 40 50 605.4
5.6
5.8
6
6.2
6.4
6.6
6.8
[kg/
s]
time [min]
MV - Steam to turbine
05.4
5.6
5.8
6
6.2
6.4
6.6
6.8
[kg/
s]
MV - Steam to turbine
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MPC Performance
Power production increase of 27 MW„Load Following“ philosophy leads to steam demand penalty with respect to power generation
0 10 20 30 40 50 6010.2
10.4
10.6
10.8
11
11.2
11.4
11.6
11.8
time [min]
[kg/
s]
CV - MP Steam to the refinery
010.2
10.4
10.6
10.8
11
11.2
11.4
11.6
11.8
[kg/
s]
CV - MP Steam to the refinery
0 10 20 30 40 50 6010.2
10.4
10.6
10.8
11
11.2
11.4
11.6
11.8
time [min]
[kg/
s]
CV - MP Steam to the refinery
010.2
10.4
10.6
10.8
11
11.2
11.4
11.6
11.8
[kg/
s]
CV - MP Steam to the refinery
0 10 20 30 40 50 602999.5
2999.6
2999.7
2999.8
2999.9
3000.0
3000.1
time [min][R
pm]
CV – Shaft rotating speed
02999.5
2999.6
2999.7
2999.8
2999.9
3000.1
[Rpm
]
CV – Shaft rotating speed
0 10 20 30 40 50 602999.5
2999.6
2999.7
2999.8
2999.9
3000.0
3000.1
time [min][R
pm]
CV – Shaft rotating speed
02999.5
2999.6
2999.7
2999.8
2999.9
3000.1
[Rpm
]
CV – Shaft rotating speed
0 10 20 30 40 50 601030
1040
1050
1060
1070
1080
1090
1100
time [min]
[°C
]
Constraint – Turbine combustion temperature
01030
1040
1050
1060
1070
1080
1090
1100
Constraint – Turbine combustion temperature
0 10 20 30 40 50 601030
1040
1050
1060
1070
1080
1090
1100
time [min]
[°C
]
Constraint – Turbine combustion temperature
01030
1040
1050
1060
1070
1080
1090
1100
Constraint – Turbine combustion temperature
0 10 20 30 40 50 605.5
6
6.5
7
7.5
time [min]
[kg/
s]
MV - Steam to the turbine
05.5
6
6.5
7
7.5
[kg/
s]
MV - Steam to the turbine
0 10 20 30 40 50 605.5
6
6.5
7
7.5
time [min]
[kg/
s]
MV - Steam to the turbine
05.5
6
6.5
7
7.5
[kg/
s]
MV - Steam to the turbine
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MPC vs. Conventional Controllers
PI control configuration
AdvantagesBetter quality control
Savings (peak value of generated power)
System works more properly close to its constraints
PIValve of MP steam to refineryMP steam flow rate to refinery
PIChar flow rateSyngas manifold pressure
PISyngas flow rateShaft rotating speed
Controller TypeManipulated VariablesControlled Variables
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MPC vs. Conventional Controllers
Power production increase of 10 MW
0 10 20 30 40 50 60132
134
136
138
140
142
144
MW
Generated power
time [min] 0 10 20 30 40 50 60132
134
136
138
140
142
144
MW
Generated power
PI
0 10 20 30 40 50 602998
2998.5
2999
2999.5
3000
3000.5
rpm Shaft rotating speed
time [min] 0 10 20 30 40 50 60
2998
2998.5
2999
2999.5
3000
3000.5
rpm Shaft rotating speed
PI
0 10 20 30 40 50 604.14
4.145
4.15
4.155
4.16
4.165
4.17x 104
kg/h
time [min]
MP steam to refinery
0 10 20 30 40 50 604.14
4.145
4.15
4.155
4.16
4.165
4.17x 104
kg/h
MP steam to refinery
PI
0 10 20 30 40 50 601.98
2
2.02
2.04
2.06
2.08
2.1
2.12x 104
kg/h
Steam to turbine
time [min] 0 10 20 30 40 50 60
1.98
2
2.02
2.04
2.06
2.08
2.1
2.12x 104
kg/h
Steam to turbine
PI
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Conclusions
Although the CCPP and IGCC plants present some non linearities and control requires fast response to power demand changes, linear MPC is proven to be robust, reliable at alternative process conditions and of real value for practical purposesThe availability of rigorous simulator reduces the need for extensive tests during MPC project commissioning.Questions...
Thanks to prof. Morari, prof. Bemporad (ETH Zurich) and Ms. Rusconi for their contributions in the development of this work
