Implementation of MPC in a deethanizer at the Kårstø Gas plant

1. Implementation of MPC in a deethanizer at the Kårstø Gas plant Elvira Marie B. Aske*,** Stig Strand** Sigurd Skogestad* *Department of Chemical Engineering, Norwegian University of Science and Technology, Trondheim, Norway ** Statoil R&D, Process Control, Trondheim, Norway

2. Outline • About the Kårstø gas plant • Motivation for MPC implementation • MPC implementation • Design • Estimator development • Model development • Tuning • Results • Conclusions and further work

3. The Kårstø Gas processing plant • Treats rich gas and unstabilized condensate recieved through pipelines from the Norwegian continental shelf • The products are sales gas, ethane, propane, i- and n-butane, naphta and condensate. • Sales gas is transported through pipe lines, while NGL and condensate are exported by ship • Some numbers (October 2005): • Rich gas processing capacity 88 MSm3/d • ethane production capacity 950.000 t/year • About 720 shipping arrival a year

4. Motivation for MPC implementation • Increased complexity at the plant due to extension projects • Larger degree of system integration • Systems build with different design • Crossovers between process trains • Higher demands to regularity and through-put due to new fields developments that narrows the margins • MPC is a part of the ”control revolution” at the plant

5. The control hierarchy at the Kårstø plant • Regulatory control layer – Tuning and reconfiguration 2002-2004 • Local MPC implementation Implementation 2003-2006 • Coordinator MPC – Coordinates the local MPCs, handles disturbances which effects several units - implementation in 2006 • Plant production performance model for online optimization and planning to be implemented in 2007 • Uses rigorous model to calculate the optimal operation point • Achieve full capacity utilization in integrating the systems

6. MPC implementation on a deethanizer • A typical implementation case for a distillation column at the Kårstø plant • Includes the stages in an MPC implementation: • design • estimator development • model development • tuning • Using the in-house developed MPC software SEPTIC (Statoil Estimation and Prediction Tool for Identification and Control) • Implementation done by plant engineers together with engineers from Statoil Research Centre

7. MPC implementation on a deethanizer-Design • Control variables (CV): • Product qualities • Avoid flaring • Manipulating variables (MV): • Reflux • Tray 1 temp set point • Disturbance variables (DV): • Feed rate • Column pressure as an MV in future

8. Estimator development • Estimation of the product qualities are needed – reasons: • Long dead time in the GC measurements (10- 40 min) – bad for control • GC may be out of order in periods due to service and maintenance • Bad samples in the GC may occur • Steps in boil-up and reflux to obtain variations in the product qualities if necessary • Estimator based on column temperatures and pressure • Logarithm between the ratio of the key components to reduce nonlinearity and dependence of operation point

9. Model development • Seeking the dynamic influence the MV’s and DV’s have on the CV’s • Executing steps in the process – recording the responses • Model identification: Tai-Ji ID software and built-in tools like last step response, FIR, ARX model fitting

10. Feed Tray 1 temp Reflux Model development (2) Model identification obtained by the Tai-Ji ID software Resulting CVs from the steps

11. CV MV MV CV CV DV Tuning and operation • Prioritizing • Penalty on • Deviation • Violation of constraints • MV moves • Model update parameters

12. C3 in C2 after MPC C3 in C2 before MPC C2 in C3 before MPC C2 in C3 after MPC MPC implementation - Results • 20-40% reduction in flaring frequency and has often shorter duration • General results from MPC implementations: • Overfractionation before introducing MPC • 10-12% reduction in steam consumption • Less variation in product quality  operate closer to the constraints • 10-25% reduction in reflux flow rate  increase column capacity (simulations show at least 10% increase)

13. Further work – Coordinator MPC with focus on maximizing feed rate • Each local MPC reports back on its available capacity • Coordinator MPC has a simple linear model on how its manipulated variables affect the local capacities • Based on feedback, the coordinator MPC is then able to locate the bottleneck and, by adjusting the overall feed rate, maximizing the flow rate at the bottleneck

14. Conclusions • Better quality control – with the opportunity to specify the desired product quality • Reduced flaring frequency and shorter duration of the flaring • Operator uses less time to control the column, especially under feed disturbances Acknowledgements • Personnel working at the Kårstø gas plant, Norway • Gassco for financial support