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Self-optimizing control configurations for two-product distillation columns

Self-optimizing control configurations for two-product distillation columns Eduardo Shigueo Hori, Sigurd Skogestad Norwegian University of Science and Technology – NTNU N-7491 Trondheim, Norway Muhammad Al-Arfaj King Fahd University of Petroleum and Minerals - KFUPM. Outline.

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Self-optimizing control configurations for two-product distillation columns

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  1. Self-optimizing control configurations for two-product distillation columns Eduardo Shigueo Hori, Sigurd Skogestad Norwegian University of Science and Technology – NTNU N-7491 Trondheim, Norway Muhammad Al-Arfaj King Fahd University of Petroleum and Minerals - KFUPM

  2. Outline • Introduction. Indirect composition control • Alternative approaches for selecting controlled variables • Temperature profile heuristic 4. Self-optimizing control: Exact local method 4.1 Results for binary distillation columns 4.2 Results for multicomponent distillation columns 5. Conclusions

  3. 1. Introduction • Distillation column with given feed and pressure: Two steady-state degrees of freedom • Issue: What should we control (”fix”) to achieve indirect composition control? • Disturbances: - feed flow (F), - feed composition (zF) - feed enthalpy (qF) • Notation Stages: -top and bottom (both 0%) - feed (100%)

  4. Variables available for control: - temperatures - flows (including flow ratios L/D, L/F, etc) • 15 different binary columns - 4 multicomponent columns • No single ”best” structure for all columns • Find reasonable structure for most columns

  5. What should we control (”fix”) to achieve indirect composition control? 2. Alternative approaches • Heuristic 1: Steep temperature profile • Heuristic 2: Small optimal variation for disturbances (Luyben, 1975) • Heuristic 3: Large sensitivity, or more generally, large gain in terms of the minimum singular value (Moore, 1992) • Self-optimizing control (Skogestad et al.) a. “Maximum scaled gain rule”: Combines heuristic 2 and 3 b. “Exact” local method (main method used in this work) c. Brute-force evaluation of loss

  6. 3. Temperature profile (Heuristic method 1) • Control a temperature where the temperature slope is large • Slope rule makes sense from a dynamic point of view Initial gain → proportional to temperature difference • BUT for Indirect composition control: steady state gain (sensitivity) is more important (maximum gain rule)

  7. Binary column slope closely correlated with steady state gain TEMPERATURE PROFILE STAGE

  8. Multicomponent column Slope NOT correlated with steady-state gain TEMPERATURE PROFILE Conclusion: Temperature slope OK only for binary columns

  9. 4. Self-optimizing control: Exact local method • Evaluate ”local” steady-state composition deviation: • ec includes: - disturbances (F, zF, qF) - implementation measurement error (0.5 for T)

  10. Outline • Introduction. Indirect composition control • Alternative approaches for selecting controlled variables • Temperature profile heuristic 4. Self-optimizing control: Exact local method 4.1 Results for binary distillation columns 4.2 Results for multicomponent distillation columns 5. Conclusion

  11. Have looked at 15 binary columns • Main focus on “column A” • 40 theoretical stages • Feed in middle • 1% impurity in each product • Relative volatility: 1.5 • Boiling point difference: 10K

  12. Table: Binary mixture - Steady-state relative composition deviations ( )for binary column A

  13. Table: Binary mixture - Steady-state relative composition deviations ( )for binary column A

  14. Table: Binary mixture - Steady-state relative composition deviations ( )for binary column A

  15. Table: Binary mixture - Steady-state relative composition deviations ( )for binary column A

  16. Table: Binary mixture - Steady-state relative composition deviations ( )for binary column A

  17. Table: Binary mixture - Steady-state relative composition deviations ( )for binary column A

  18. Effect of T-location on column A Composition deviation: 1- L/F and one temperature 2- V/F and one temperature 3- Two temperatures symmetrically located Conclusion: Avoid temperature at the ends

  19. Dynamic simulation – Column A zF zF qF qF F F Conclusion: zF is the main disturbance

  20. Add composition layer on top Dynamic-ISE column A Conclusion: For large measurement delays self-optimizing variables are best

  21. MORE BINARY COLUMNS... Table: Steady state data for binary distillation column examples (Skogestad et al., 1990)

  22. Table: Binary mixtures - steady-state composition deviations. Conclusion: L/F, L and two-point control are the best choices

  23. Table: Binary mixtures - steady-state composition deviations. Conclusion: L/F, L and two-point control are the best choices

  24. Column M1 Column M2 Column M3 Tb,10%-Tt,17%* 2.29 Tb,39%–Tt,23%* 1.36 Tb,19%-Tt,27%* 1.45 Tt,17% – L/F* 4.07 Tt,23% – L/F* 8.61 Tb,50%-Tt,53%$ 2.94 Tt,50% – L/F$ 4.55 Tt,46% – L/F$ 8.67 Tb,19% – L/F* 4.67 Tt,17% - L* 4.84 Tt,23% - L* 9.25 Tb,50% – L/F$ 4.85 Tt,8% – V/F* 8.41 Tt,23% – V/F* 18.0 Tb,50% - L* 7.16 Tt,8% - V* 9.74 Tt,54% - V* 20.4 Tb,69% – V/F* 8.99 Tt,8% – V/B* 11.4 Tt,85% – L/D* 23.3 Tb,50 – L/D* 9.72 Tt,50% – L/D*$ 33.2 Tt,15% – V/B* 24.2 Tb,69% - V* 14.1 Tb,48% – L/F 150 Tb,65% – L/F** 75.1 Tb,81%– V/B* 15.3 Tb,48% – L 186 Tb,48% – L/F 76.2 Tt,53 – L/D 105 Tb,59% – L/D$ 434 Tb,48% – L 87.5 Table: Binary mixtures (Luyben 2005): steady-state composition deviations. Conclusion: L/F, L and two-point control are the best choices

  25. Column M4 Column M5 Column M6 Tb,23%–Tt,22% 1.19 Tb,25%-Tt,29% 0.96 Tb,18%-Tt,30% 1.62 Tb,46%–Tt,56%$ 1.54 Tb,25% – L/F 3.85 Tb,45% – L/F$ 2.12 Tb,15% – L/F 4.67 Tb,50% – L/F$ 3.85 Tb,9% - L 3.21 Tb,46% – L/F$ 4.71 Tb,8% – L/D 5.13 Tb,9%– L/D 3.27 Tb,23%- L 6.76 Tb,33%- L 5.62 Tb,45% – L 3.35 Tb,46% – L 6.76 Tb,50% – L 5.62 Tb,0% - V/F 8.03 Tb,8% – L/D 7.72 Tb,25% – V/F 15.4 Tb,18% - V 8.54 Tb,38% – V/F 13.5 Tb,25% – V 21.8 Tb,0% – V/B 117 Tb,77% - V 19.4 T100% – V/B 88.0 Tt,50% - L 216 Tb,38% – V/B 32.8 Tt,50% – L/F 182 Table: Binary mixtures (Luyben 2005): steady-state composition deviations. Conclusion: L/F, L and two-point control are the best choices

  26. Outline • Introduction. Indirect composition control • Alternative approaches for selecting controlled variables • Temperature profile heuristic 4. Self-optimizing control: Exact local method 4.1 Results for binary distillation columns 4.2 Results for multicomponent distillation columns 5. Conclusion

  27. Multicomponent columns • Four components: A (lightest), B, C, and D (heaviest) • Equal relative volatilities (AB=BC=CD=1.5) • The temperatures are adjusted to be compatible with relative volatility • Feed composition: 25% of each component

  28. Multicomponent columns Table: Multicomponent column data.

  29. A/B B/C C/D “Real” B/C nC5/nC6 Tt,95% - V/B 0.96 Tb,70%– Tt,75% 1.71 Tb,85% – L/D 1.38 Tb,30% – Tt,33% 1.07 Tb,80% - V/F 1.03 Tb,90% – L/F 1.77 Tb,40% – L/F 1.63 Tt,33% – V 1.74 Tb,80% – L/F 1.05 Tb,95% – L 1.88 Tb,50% – L/F 1.64 Tt,33% – L 1.78 Tb,80% – V 1.07 Tb,75% – L/D 1.91 Tb,45% – L 1.88 Tt,33% – L/F 1.85 Tb,75% – L 1.08 Tb,95% - V/F 2.03 Tb,40% - V/F 2.07 Tt,33% - V/B 2.17 Tb,80% – Tt,100% 1.86 Tb,50% – L/F 2.11 Tb,95% – Tt,75% 2.26 Tt,33% - V/F 2.19 Tb,50% – L/F 1.98 T100% – V 2.22 Tt,90% – V 2.28 Tb,50% - L 2.94 Tb,65% – L/D 2.00 Tb,50% – L 2.29 Tt,80% – V/B 4.45 Tt,33% – L/D 2.95 Tb,50% – L 2.00 Tt,90% – V/B 2.60 L/D – V/B 31.8 Tb,50% – L/F 3.08 L/F – V/B 44.7 L/D – V/B 32.0 Table: Multicomponent Column: steady-state composition deviations. Conclusion: L/F and L are the best choices

  30. 5. Conclusions • Optimal temperature location: most sensitive stage (maximize scaled steady-state gain) • Avoid temperature close to column end (especially for high purity) due to implementation errors and low sensitivity • Avoid stage with small temperature slope: For dynamic reasons • Binary and multicomponent separations: good control structure is L and a single temperature (usually in bottom section) • Two-point temperature control: good for cases with ”binary” separations and no pinch

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