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Coordinator MPC with focus on maximizing throughput

Coordinator MPC with focus on maximizing throughput

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Coordinator MPC with focus on maximizing throughput

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  1. Coordinator MPC with focus on maximizing throughput 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 • Background • Bottleneck • Back-off • Remaining feed capacity • Coordinator MPC • Case study • Discussion • Conclusion

  3. Background • Conventional real-time optimization (RTO) offers a direct method of maximizing an economic objective function • Identifies optimal active constraints and optimal setpoints • Special case considered here (very important and common in practice): • Maximize production (active constraint) • Challenge: Implement optimal solution in real plant with dynamic changes and uncertainty • Implementation of maximum production • Identify bottleneck • Minimize “back-off” at bottleneck

  4. Often optimal: Set production rate at bottleneck! • "A bottleneck is an extensive variable that prevents an increase in the overall feed rate to the plant" • If feed is “cheap” and available: Optimal to set production rate at bottleneck • If the flow for some time is not at its maximum through the bottleneck, then this loss can never be recovered

  5. Vmax Vs Back-off • Back-off is required for robustness • Trade-off between robustness and acceptable loss • Can be placed on constraints, control targets, range changes etc. Vmax-Vs = Back-off = Loss

  6. Key idea: Remaining feed capacity • Since location of the bottleneck may change, we suggest to monitor the remaining feed capacityin eachunit (local MPC) of the plant • Estimate of the remaining feed capacity in unit k: Jk – current throughput (feed) in unit k Jk, max – found by solving a LP problem with unit constraints included • LP formulation: • Example Jk, maxfor a binary column: • x: vector of the MVs and DVs (reflux rate, boil-up, column feed) • b: vector of contraints (+back-off), (impurities, flooding limit ) • A: from the MPC model for the unit

  7. Coordinator MPC • Task: maximize an objective function • here: max feed rate and make decisions involving several local MPC applications which handle smaller process units • Feedback on minutes basis • Coordinator MPC is placed on the top of the local MPC in the control hierarchy and coordinates the underlying MPCs • Smart decomposition • Avoid one large MPC application which will be over-complex for a complete chemical plant • Can operate both with and without an RTO layer.

  8. Coordinator MPC • MVs (not used by individual MPCs) • Feed to plant Fi • Feed crossovers / split fractions • Maximize weighted overall feed subject to capacity constraints: G represents the dynamic influence from each MV to Rk • G typically obtained from step with MPCs in closed loop.

  9. Case study: Coordinator MPC on Kårstø gas plant • Task: maximize plant throughput within feasible operation (=satisfy constraints) • Selected part of Kårstø plant: 2 separation train • D-SPICE whole plant simulator used as “real plant” • Local MPCs and coordinator MPC: Implemented in SEPTIC

  10. Each column has constraints on max. pressure drop, reflux rate, boilup, etc. • Vapor capacity in the columns limited by max. pressure drop corresponding to flooding • Potential problem: Pressure drop not always a good indicator of flooding • Coordinator MPC MVs: train feed flows, feed splits and crossover • CVs: • remaining feed capacity in each column (10 in total) > Back-off >0 , • ET-100 sump level controller output in allowed range • Total plant feed (max.) • Experimental step-response model G obtained at 80-95% of the maximum throughput (typical flow rate)

  11. Case study:Simulation • The coordinator performance is illustrated with three different cases • t = 0 min: Move the plant to maximum throughput • t = 360 min: Change in feed composition in T100 • t = 600 min: Change in butane splitter T100 MPC CV limit (reducing the remaining feed capacity), which is operated at its maximum • Monitor the following: • Remaining feed capacity in each column (ET100, PT100, …..) • Sump level in ET100 (due to the crossover) • Total plant feed • MV valves

  12. Case study: CVs in the coordinator MPC

  13. Case study: MVs in the coordinator MPC Train feed Train feed Crossover Feed split Feed split

  14. Main potential improvement: Reduce back-off • Include feed forward, especially from feed composition changes • Composition measurements at the pipelines into the plant • Introduce level setpoints (buffer volume) as additional MVs for the coordinator • MV close to bottleneck, avoid loss

  15. Discussion • With existing MPCs: Little extra work to implement coordinator MPC • Max throughput: Common in practice • Column pressure drop is not always a good indicator for flooding, a more detailed remaining feed capacity model may be needed in some cases • Express flow changes as relative (%) changes • All model gains (G) are then 1 • Update nominal flows based on feed composition (“gain scheduling”) • If the feed is limited for a period, the economic optimum is not max. flow • RTO finds the optimal solution • No need to modify the coordinator MPC because we have used “trick” • Maximize feed realized through a (weighted) overall feed rate J as a CV with a high, not reachable set-point with lower priority

  16. Conclusion • Designed and implemented a coordinator MPC with experimental step response models to follow the plant optimum, under influence by disturbances of dynamic character • Implemented on Kårstø Whole plant simulator, D-SPICE® software • Performs well on the simulated challenges • Improvements by including feed forward and manipulating buffer volumes