Implementation of a MPC on a deethanizer Thanks to: Elvira Aske and Stig Strand, Statoil Aug. 2004

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.

SEPTIC MPC CV soft constraint: y < ymax + RP Prediction horizon Current t Controlled variable, optimized prediction Manipulated variable, optimized prediction Set point Process u v y MV DV CV model CV soft constraint: y < ymax + RP 0 <= RP <= RPmax w*RP2 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

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

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

2. CV, MV, DV CV DV MV MV CV CV Quality estimator Quality estimator 0 – 65% PC 65-100% CV Flare Propane Fuel gas to boilers Heat ex 34 FC 28 LC Reflux drum 23 FC 21 DV Feed from stabilizators 20 FC FC 16 Product pumps 10 MV Reflux pumps MV One example of using MPC at the column level. What do we want to control? Product quality + avoid flaring Bias update from analyzators TC 1 Quality estimator PC CV LC CV LP Steam LC Quality estimator LP Condensate To Depropaniser

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

Temperature sensors Deethaniser Train 300 0 – 65% 65-100% Flare A - C C1 – CO2 PC 65-100% FI Flare TI Propane Fuel gas to boilers Heat ex TI 34 FC TI 28 TI LC TI Reflux drum 23 FC TI 21 PD TI Feed from stabilizators 20 FC FC 16 TI 10 Reflux pumps TC 1 Product pumps PC FI LC LP Steam To Depropaniser LP Condensate TI

Typical temperature test data

Top: Binary separation in this case Quality estimator vs Top: Binary separation in this case Quality estimator vs. gas chromatograph 7 temperatures 2 temperatures =little difference if the right temperatures are chosen

5. Step tests/Tai-Ji ID MV’s CV’s Reflux TC tray 1 C3 in top (estimator) C2 in bottom (estimator) CV’s Pressure valve position

Step tests/Tai-Ji ID MV1: Reflux MV2: T-SP CV1 C3-top CV2 C2-btm CV3 z-PC

Model in SEPTIC MV Model from MV to CV CV prediction adjustment of lower MV limit setpoint change

6. Control priorities Results: Predicts above SP MV1 SP Priority 2 Meet high limit DV Limit Priority 1

7. Tuning of a CV Logarithmic transformation of CV Model CV in mol % Bias Tuning parameters Control targets

The final test: MPC in closed-loop CV1 MV1 CV2 MV2 CV3 DV

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