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Learn how Statoil engineers Elvira Aske and Stig Strand successfully implemented MPC technology at Kårstø gas processing plant. The process involved tuning existing PIDs, logic connections, estimators, model identification, control specifications, and operator training, utilizing in-house technology ("SEPTIC"). The team applied multivariable control strategies to stabilize pressure, liquid levels, and temperature profiles, enhancing process efficiency and operator acceptance. Discover the detailed approach for implementing MPC in a distillation column system.
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Implementation of a MPC on a deethanizer Thanks to: Elvira Aske and Stig Strand, Statoil
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.
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
2. CV, MV, DV Heat ex Reflux drum Reflux pumps 34 23 28 10 1 20 21 16 LC TC LC FC PC FC LC FC PC FC 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
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 C1 – CO2 A - C Heat ex Reflux drum Reflux pumps 1 20 34 21 23 16 10 28 FI TC LC PC TI AR PD FC FC FC FI TI TI TI FC LC TI TI TI TI TI PC 0 – 65% Deethaniser Train 300 65-100% Flare Propane Fuel gas to boilers Feed from stabilizators Product pumps LP Steam To Depropaniser LP Condensate
Top: Binary separation in this caseQuality estimator vs. gas chromatograph 7 temperatures 2 temperatures =little difference if the right temperatures are chosen
5. Step tests/Tai-Ji ID Reflux MV’s 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 Results: Predicts above SP MV2 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
