Implementation of Coordinator MPC on a Large-Scale Gas Plant

1. Implementation of Coordinator MPC on a Large-Scale Gas Plant Elvira Marie B. Aske*&, Stig Strand& and Sigurd Skogestad* *Department of Chemical Engineering Norwegian University of Science and Technology (NTNU) Trondheim, Norway &StatoilHydro R&D, Process Control, Trondheim, Norway ADCHEM 2009, Istanbul, Turkey, July 12-15 skoge@ntnu.no

2. Outline • Introduction and motivation • The Kårstø gas plant • Maximum throughput as optimal operation • Approach: Coordinator MPC*: • Maximize flow through linear network • Estimate feasible remaining capacity (R) in units using local MPCs • Application to Kårstø Gas Plant • Previous work*: Works well on simulations • Here: Actual implementation • Design • Tuning (plant runs) • Experiences • Conclusion *Aske, E.M.B., S. Strand and S. Skogestad (2008). Coordinator MPC for maximizing plant throughput. Comput. Chem. Eng. 32(1-2), 195–204.

3. Kårstø plant Gas processing area Control room

4. Snøhvit Melkøya Kristin Norne Heidrun Åsgard Tjeldbergodden Haltenpipe ÅTS Ormen Lange Statfjord Nyhamna Troll Frigg Kollsnes Vesterled Kårstø Sleipner St Fergus Europipe II Ekofisk Europipe I Langeled Norpipe Zeepipe I Franpipe Dunkerque Easington Emden Zeebrugge North Sea gas network Norwegian continental shelf • Kårstø plant: Receives gas from more than 30 offshore fields • Limited capacity at Kårstø may limit offshore production (both oil and gas) TRONDHEIM Oslo UK GERMANY

5. Kårstø plant – 20 years of development How manipulate feeds and crossovers? Condensate Ethane

6. Maximum throughput Often: Economic optimal operation = maximum throughput Operate with max feasible flow through bottlenecks No remaining unconstrained DOFs (RTO not needed) “Coordinator MPC”: Manipulate TPMs (feed valves and crossovers) presently used by operators Throughput determined at plant-wide level (not by one single unit)  coordination required Frequent changes  dynamic model for optimization TPM = Throughput Manipulator

7. Approach Objective: Max throughput, subject to feasible operation: Approach: Use Coordinator MPC to optimally adjust TPMs: Throughput manipulators (TPMs): Feeds and crossovers Remaining capacity (R) = Rs = 0 in bottleneck units Decompose the problem (decentralized). Assume Local MPCs closed when running Coordinator MPC Need flow network model (No need for a detailed model of the entire plant) Decoupling: Treat TPMs as DVs in Local MPCs Use local MPCs to estimate feasible remaining capacity (R) in each unit ?

8. ”Coordinator MPC”: Coordinates network flows, not MPCs (remaining capacity)

9. Feasible remaining feed capacity for unit k: Obtained by solving “extra” steady-state LP problem in each local MPC: subject to present state, models and constraints in the local MPC Use end predictions for the variables Recalculated at every sample (updated measurements) Very little extra effort! Remaining capacity (using local MPCs) current feed to unit k max feed to unit k within feasible operation

10. Local MPC applications • Kårstø: Most local MPC applications are on two-product distillation columns: • CVs: Distillate- and bottom products quality (estimated) + differential pressure and other constraints • MVs: Temperature setpoint (boilup) and reflux flow • DV (disturbance): Feed flow • New: Local MPCs estimate their feasible remaining capacity (R) using feed flow (DV) as degree of freedom

11. Coordinator MPC: Design Objective: Maximize plant throughput, subject to achieving feasible operation MVs: TPMs (feeds and crossovers that affect several units) CVs: total plant feed + constraints: Constraints (R > backoff > 0, etc.) at highest priority level Objective function: Total plant feed as CV with high, unreachable set point with lower priority DVs: feed composition changes, disturbance flows Model: step-response models obtained from Calculated steady-state gains (from feed composition) Plant tests (dynamic)

12. KÅRSTØ MPC COORDINATOR IMPLEMENTATION (2008) Export gas Rich gas MV CV Export gas CV CV CV MV CV CV CV Rich gas CV CV CV MV Half of the plant included: 6 MVs 22 CVs 7 DVs MV CV Condensate MV CV CV MV CV CV CV CV CV

13. Step response models in coodinator MPC Remaining capacity (R) goes down when feed increases… + more…

14. Coordinator MPC in closed loop • Test runs January and February 2008

15. TEST 07 FEB 2008 Export gas Rich gas MV CV Export gas CV CV3 CV MV1 CV CV CV2 Rich gas DV CV CV1 CV MV2 MV CV Condensate MV CV CV MV CV CV CV CV CV

16. TEST 07 FEB 2008 MV1 CV3 CV1 DV MV2 CV2 t = 0 min: Turn on t = 250 - 320 min: Change model gains (tuning) t = 500 min: Adjust back-off for R in demethanizer t = 580 – 600 min: Feed composition change (DV)

17. Experiences • Using local MPCs to estimate feasible remaining capacity leads to a plant-wide application with “reasonable” size • The estimated remaining capacity relies on • accuracy of the steady-state models • correct and reasonable CV and MV constraints • use of gain scheduling to cope with larger nonlinearities • Crucial to inspect the models and tuning of the local applications in a systematic manner • Requires follow-up work and extensive training of operators and operator managers • “New way of thinking” • New operator handle instead of feedrate: Rs (back-off)

18. Conclusions • Frequent changes in feed composition, pipeline pressures and other disturbances require a dynamic model for optimization • Coordinator MPC is promising tool for implementing maximum throughput at the Kårstø gas plant. • More focus among operator personnel on • capacity of each unit • Plant-wide perspective to decide the plant- and crossover flows

19. Acknowledgements StatoilHydro and Gassco Kjetil Meyer, Roar Sørensen Operating managers and personnel at the Statpipe and Sleipner trains. References • General approach: Aske, E.M.B., S. Strand and S. Skogestad (2008). Coordinator MPC for maximizing plant throughput. Comput. Chem. Eng. 32(1-2), 195–204. • Full paper application: E.M.B. Aske, Ph.D. thesis, NTNU, Trondheim, Norway, 2009 (Chapter 6). Available from the home page of S. Skogestad: http://www.nt.ntnu.no/users/skoge/publications/thesis/2009_aske/

21. COORDINATOR IN CLOSED LOOP DATE=05 Feb 2008

22. DATE=07 Feb 2008 Export gas Rich gas MV CV Export gas CV CV CV MV CV CV CV Rich gas CV CV CV MV MV CV Condensate MV CV CV MV CV CV CV CV CV

23. DATE=07 Feb 2008 CV: Pipeline pressure MV: Feed New constraint from pipeline network operators CV: Remaining capacity MV: Crossover Increase backoff 6 hrs 9 hrs

24. COORDINATOR IN CLOSED LOOP 07 FEB 2008

25. 07 FEB 2008 CV: Pipeline pressure MV: Feed 6 hrs 9 hrs MV: Crossover CV: Remaining capacity DV: Feed composition Composition disturbance Model adjustment