Michael Baldea a,b and Prodromos Daoutidis a a University of Minnesota, Minneapolis, MN 55455

1. Integrated Process Networks:Nonlinear Control System Design for Optimality and Dynamic Performance Michael Baldeaa,b and Prodromos Daoutidisa aUniversity of Minnesota, Minneapolis, MN 55455 bPraxair, Inc., Tonawanda, NY 14150 Antonio C. Brandao Araujo and Sigurd Skogestad Norwegian University of Science and Technology NO-7491 Trondheim, Norway

2. Material recycle Heat integration Feedback interactions within the plant Chemical Plant

3. Control of Tightly Integrated Plants:Challenging and Important! • Decentralized control: inherent limitations • Fully centralized control: generally impractical • Size / complexity of dynamic models • Ill-conditioning • Efficient transient operation : critical • Moves across product slate due to frequent changes in market conditions and economics • Going beyond regulatory control… • Accounting for ‘network’ dynamics

4. Research on Integrated Process Networks Dynamic Analysis: • Slow response, high sensitivity to disturbances, instability (Gilliland et al. ’64, Denn & Lavie ’82, Skogestad & Morari ’87, Luyben ’93, Mizsey & Kalmar ’96) • Nonlinear dynamics (Morud & Skogestad ’94, ’96, Bildea et al. ’00, Kiss et al. ’06)

5. Research on Integrated Process Networks Control • Interaction of design and control for reaction-separation networks (Luyben ’93, Luyben M. & Floudas ’94, Yin & Luyben ‘97) • Plant-wide control (Price & Georgakis ’93, Luyben et al. ’97, Ng & Stephanopoulos ’98, Zheng et al. ’99) • Applications to benchmark problems (McAvoy & Ye, ’94, Ricker, ’96, Ricker and Lee, ’95, Larsson et al. ’01, Jockenhovel et al. ’03) • Self – optimizing control (Morari et al. ’80, Skogestad ’00) • Partial control (Shinnar et al. ’96, Tyreus ’99, Kothare et al. ’00) • Passivity based stabilization (Ydstie et al. ’98, ’99) • Dynamic optimization (Tosukhowong et al. ’04) • Time-scale analysis / nonlinear model reduction and control (Kumar and Daoutidis ’02, Baldea et al. ’06, Baldea and Daoutidis ’05 ’06)

6. Present Work • Combining time-scale analysis (dynamics) and self-optimizing control (steady state economics): • Control structure design • Nonlinear supervisory control • Prototype reactor-separator-recycle network

7. I. TOP-DOWN Step 1. DEGREES OF FREEDOM Step 2. OPERATIONAL OBJECTIVES Step 3. CONTROLLED VARIABLES Step 4. PRODUCTION RATE II. BOTTOM-UP Step 5. REGULATORY CONTROL LAYER (PID) Step 6. SUPERVISORY CONTROL LAYER (MPC) Step 7. OPTIMIZATION LAYER (RTO) Can we do without? Plant-wide ControlHierarchy of Decisions(Larsson andSkogestad, 2000) Planning (months - years)

8. What should we control? Optimization level: Solve Optimal solution: usually at constraints • most degrees of freedom are used to satisfy “active constraints” • Control active constraints! • Implementation usually simple • What else should we control? • Variables for remaining unconstrained degrees of freedom: acceptable losses in the presence of disturbances and implementation errors

9. Self-optimizing Control Planning (months - years) Principle: (Economically) acceptable operation (loss) should be achieved using constant set points for the controlled variables, without the need to re-optimize when disturbances occur. c=cs

10. Selection of Controlled Variables

11. Integrated Process Network:Multiple Time Scale Dynamics • Low single pass conversion - high recycle rate • Impurities present in the feed – small amount • Impurities do not separate readily -small purge stream Baldea and Daoutidis, Comp. Chem. Eng., 2006.

12. Dynamic Model : scaled inputs : largerecycle loop flowrates : scaled inputs : medium flowrates : scaled input : small purge flowrate : small parameter – ratio of throughput to recycle : small parameter – ratio of purge to throughput states terms: stiffness, multiple time scales

13. Model ReductionTime Scale Decomposition • Fast time scale (process units) • Intermediate time scale (network) • Slow time scale (impurity levels) dimensional Equilibrium manifold Manipulated inputs dimensional Equilibrium manifold Manipulated inputs 1-dimensional Manipulated input

14. Manipulated inputs Control objectives (broad) Hierarchical Controller Design

15. Self Optimizing Control economic insight selection of controlled variables Time Scale Analysis dynamic perspective selection of manipulated inputs Optimality and Dynamic Performance • Combining: • control designs with inherent optimality and good dynamic performance

16. Case StudyGeneric Reactor – Condenser Network • Slow reaction , large recycle • Product nonvolatile • Volatile impurity present in the feed • Degrees of freedom: R (W), F, P, L

17. Insights from Time-scale Analysis • Control objectives: vaporholdups (pressures) stabilization (fast), liquidholdup stabilization, X B (intermediate) impurity levels (slow) • Available degrees of freedom: R,F (fast) L, M RSP, M CSP (intermediate) P (slow)

18. Hierarchical Controller Design (I)(Baldea and Daoutidis C&ChE, 2006)

19. Control Structure I • Reactor pressure allowed to vary • Compressor/pressure constraints?

20. Insights from Self-optimizing Control • Disturbances: FO, yA,O, yI,O, xB,TR ,k1 • Cost function: J = pW*W - pL*L + pP*L • Active constraints: Reactor pressure, product purity • Self-optimizing variable: W

21. Self-optimizing Control Structure (II) • No control of impurity • Poor dynamics: small purge controls product purity

22. Hierarchical / Self-optimizing Controller Design (III)

23. 5% increase in purity setpoint

24. 5% increase in purity setpoint

25. 20% increase in production rate

26. 20% increase in production rate

27. Concluding Remarks • Self-optimizing control / time-scale analysis:complementary perspectives steady-state economics vs dynamics controlled variables vs manipulated inputs • Control configurations that are self-optimizing and have good closed - loop response characteristics • Well-conditioned nonlinear supervisory controllers based on reduced order models

28. Acknowledgements • National Science Foundation • MB partially funded by a University of Minnesota Doctoral Dissertation Fellowship

29. Integrated Process Networks:Nonlinear Control System Design for Optimality and Dynamic Performance Michael Baldeaa,b and Prodromos Daoutidisa aUniversity of Minnesota, Minneapolis, MN 55455 bPraxair, Inc., Tonawanda, NY 14150 Antonio C. Brandao Araujo and Sigurd Skogestad Norwegian University of Science and Technology NO-7491 Trondheim, Norway