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Implementation of a MPC on a
deethanizer
Thanks to: Elvira Aske and Stig Strand, Statoil
Aug. 2004
1
MPC implementation at Kårstø gas processing
plant
• Mainly distillation columns
• In-house MPC technology (“SEPTIC”)
• Karsto: So far 9 distillation column with MPC – 11 to go, plus MPC on
some other systems, like steam production.
2
SEPTIC MPC
DV
Set point Controlled variable, optimized prediction
u
Manipulated variable,
optimized prediction
Current
v
MV
CV
Process
model
y
t
Prediction horizon
• MV blocking  size reduction
• CV evaluation points  size reduction
• CV reference specifications  tuning flexibility set point changes / disturbance
rejection
• Soft constraints and priority levels  feasibility and tuning flexibility
3
CV soft constraint:
y < ymax + RP
0 <= RP <= RPmax
w*RP2 in objective
Stepwise approach for implementation
1.
2.
3.
4.
5.
6.
7.
8.
4
Check and possible retuning of the existing controllers (PID).
Choose CV, MV and DV for the application
Logic connections to the process interface placed and tested
Develop estimators
Model identification. Step tests, (Have used: Tai-Ji ID tool)
Control specifications priorities
Tuning and model verifications
Operation under surveillance and operator training
1. Base control (PIDs)
•
•
•
Stabilize pressure: Use vapor draw-off (partial condenser)
Stabilize liquid levels: Use “LV”-configuration
Stabilize temperature profile: Control temperature at bottom
•Note: This is a multicomponent separation with non-keys in the bottom,
so temperature changes a lot towards the bottom.
•However, the sensitivity (gain) in the bottom is small, so this is against
the maximum gain rule ???
•Seems to work in practice, probably because of update from
estimator
5
2. CV, MV, DV
CV
PC
0 – 65%
65-100%
Flare
Fuel gas
to boilers
Propane
Heat ex
34
28
FC
LC
Reflux drum
23
21
FC
DV
Feed from stabilizators
20
FC
FC
16
Product pumps
10
MV
TC
MV
1
Reflux pumps
Quality estimator
PC
LC
LP Steam
6
LC
CV
Quality estimator
LP Condensate
To Depropaniser
CV
4. Composition (quality) estimators
• Quality estimators to estimate the top and bottom compositions
• Based on a combination of temperatures in the column
x = i ki Ti
Use log transformations on temperatures (T) and compositions (c)
• Coefficients ki identified using ARX model fitting of dynamic test
data.
• Typical column:
– “Binary end” (usually top) impurity needs about 2 temperatures – in
general easy to establish
– “Multicomponent end” (usually bottom) impurity needs 3-4 temperatures
and in general more difficult to identify – test period often needed to get
data with enough variation
7
Temperature sensors
Deethaniser Train 300
C1 – CO2
AR
0 – 65%
FI
65-100%
PC
A-C
Flare
TI
Propane
Fuel gas
to boilers
Heat ex
TI
34
FC
TI
28
TI
LC
TI
Reflux drum
23
FC
21
TI
Feed from stabilizators
20
TI
PD
FC
FC
16
10
TI
Reflux pumps
FI
Product pumps
1
TC
PC
LC
LP Steam
To Depropaniser
8
LP Condensate
TI
Typical temperature test data
9
Top: Binary separation in this case
Quality estimator vs. gas chromatograph
7 temperatures
2 temperatures
10
=little difference if the right temperatures are chosen
5. Step tests/Tai-Ji ID
Reflux
MV’s
TC tray 1
C3 in top
(estimator)
CV’s
C2 in bottom
(estimator)
Pressure valve
11
position
Step tests/Tai-Ji ID
MV1: Reflux
CV1
C3-top
CV2
C2-btm
CV3
z-PC
12
MV2: T-SP
Model in SEPTIC
MV
Model from
MV to CV
CV
prediction
adjustment of lower MV limit
setpoint change
13
6. Control priorities
Results: Predicts
above SP
SP Priority 2
MV1
Results: Predicts
above SP
SP Priority 2
MV2
Meet high limit
Limit Priority 1
14
DV
7. Tuning of a CV
Logarithmic
transformation of CV
Model
CV in mol %
Bias
Tuning parameters
15
Control targets
The final test: MPC in closed-loop
CV1
MV1
CV2
MV2
CV3
DV
16
Conclusion MPC
• Generally simpler than previous advanced control
• Well accepted by operators
• Use of in-house technology and expertise successful
17
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