Implementation of a MPC on a deethanizer

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Implementation of a MPC on a deethanizer

• Thanks to Elvira Aske and Stig Strand, Statoil
• Aug. 2004

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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.

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SEPTIC MPC
CV soft constraint y lt ymax RP 0 lt RP lt
RPmax wRP2 in objective

• 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

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Stepwise approach for implementation

• 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

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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

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2. CV, MV, DV
0 65
65-100
CV
Flare
Fuel gas to boilers
Propane
Feed from stabilizators
DV
Product pumps
MV
MV
Quality estimator
CV
CV
LP Steam
Quality estimator
LP Condensate
To Depropaniser
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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

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Temperature sensors
0 65
Deethaniser Train 300
65-100
Flare
Propane
Fuel gas to boilers
Feed from stabilizators
Product pumps
LP Steam
To Depropaniser
LP Condensate
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Typical temperature test data
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Top Binary separation in this caseQuality
estimator vs. gas chromatograph
7 temperatures
2 temperatures
little difference if the right temperatures are
chosen
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5. Step tests/Tai-Ji ID
Reflux
MVs
TC tray 1
C3 in top (estimator)
C2 in bottom (estimator)
CVs
Pressure valve position
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Step tests/Tai-Ji ID
MV1 Reflux
MV2 T-SP
CV1 C3-top
CV2 C2-btm
CV3 z-PC
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Model in SEPTIC
MV
Model from MV to CV
CV
prediction
adjustment of lower MV limit
setpoint change
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6. Control priorities
Results Predicts above SP
MV1
SP Priority 2
Results Predicts above SP
MV2
SP Priority 2
Meet high limit
DV
Limit Priority 1
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7. Tuning of a CV
Logarithmic transformation of CV
Model
CV in mol
Bias
Tuning parameters
Control targets
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The final test MPC in closed-loop
CV1
MV1
CV2
MV2
CV3
DV
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Conclusion MPC

• Generally simpler than previous advanced control
• Well accepted by operators
• Use of in-house technology and expertise
  successful