Optimal dynamic operation of chemical processes: Assessment of the last 20 years and current research opportunities

Transcription

1 Optimal dynamic operation of chemical processes: Assessment of the last 2 years and current research opportunities James B. Rawlings Department of Chemical and Biological Engineering April 3, 2 Department of Chemical Engieering Carnegie Mellon University Rawlings Optimal dynamic operation of chemical processes / 4

2 Outline The last 2 years what tools have researchers developed 2 Industrial impact of these ideas 3 Have all the questions been answered? Control of large-scale systems Optimizing economics 4 Conclusions and future outlook Rawlings Optimal dynamic operation of chemical processes 2 / 4

3 The power of abstraction process dx = f (x, u) dt y = g(x, u) actuators sensors Rawlings Optimal dynamic operation of chemical processes 3 / 4

4 The model predictive control framework Measurement MH Estimate MPC control Forecast t time Reconcile the past Forecast the future sensors y actuators u Rawlings Optimal dynamic operation of chemical processes 4 / 4

5 Predictive control The future influences the present just as much as the past does. Measurement MH Estimate MPC control Forecast t time Reconcile the past Forecast the future sensors y actuators u Rawlings Optimal dynamic operation of chemical processes 5 / 4

6 Predictive control Measurement MH Estimate MPC control Forecast t time Reconcile the past Forecast the future sensors y actuators u min u(t) T y sp g(x, u) 2 Q + u sp u 2 R dt ẋ = f (x, u) x() = x (given) y = g(x, u) Rawlings Optimal dynamic operation of chemical processes 5 / 4

7 State estimation When I want to understand what is happening today or try to decide what will happen tomorrow, I look back. Measurement MH Estimate MPC control Forecast t time Reconcile the past Forecast the future sensors y actuators u Rawlings Optimal dynamic operation of chemical processes 6 / 4

8 State estimation Measurement MH Estimate MPC control Forecast t time Reconcile the past Forecast the future sensors y actuators u min x,w(t) T y g(x, u) 2 R + ẋ f (x, u) 2 Q dt ẋ = f (x, u) + w (process noise) y = g(x, u) + v (measurement noise) Rawlings Optimal dynamic operation of chemical processes 6 / 4

9 Industrial impact of the research Planning and Scheduling Steady State Optimization Validation Model Update Reconciliation Two layer structure Steady-state layer RTO optimizes steady-state model Optimal setpoints passed to dynamic layer Controller Plant Rawlings Optimal dynamic operation of chemical processes 7 / 4

10 Industrial impact of the research Planning and Scheduling Steady State Optimization Validation Controller Plant Model Update Reconciliation Two layer structure Steady-state layer RTO optimizes steady-state model Optimal setpoints passed to dynamic layer Dynamic layer Controller tracks the setpoints Linear MPC (replaces multiloop PID) Rawlings Optimal dynamic operation of chemical processes 7 / 4

11 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Rawlings Optimal dynamic operation of chemical processes 8 / 4

12 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Credits 5 to 8 M$/yr (27) Rawlings Optimal dynamic operation of chemical processes 8 / 4

13 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Credits 5 to 8 M$/yr (27) Achieved primarily by increased on-spec product, decreased energy use Rawlings Optimal dynamic operation of chemical processes 8 / 4

14 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Credits 5 to 8 M$/yr (27) Achieved primarily by increased on-spec product, decreased energy use Eastman Chemical experience with MPC First MPC implemented in 996 Rawlings Optimal dynamic operation of chemical processes 8 / 4

15 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Credits 5 to 8 M$/yr (27) Achieved primarily by increased on-spec product, decreased energy use Eastman Chemical experience with MPC First MPC implemented in 996 Currently 55-6 MPC applications of varying complexity Rawlings Optimal dynamic operation of chemical processes 8 / 4

16 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Credits 5 to 8 M$/yr (27) Achieved primarily by increased on-spec product, decreased energy use Eastman Chemical experience with MPC First MPC implemented in 996 Currently 55-6 MPC applications of varying complexity 3-5 M$/year increased profit due to increased throughput (28) Rawlings Optimal dynamic operation of chemical processes 8 / 4

17 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Credits 5 to 8 M$/yr (27) Achieved primarily by increased on-spec product, decreased energy use Eastman Chemical experience with MPC First MPC implemented in 996 Currently 55-6 MPC applications of varying complexity 3-5 M$/year increased profit due to increased throughput (28) Praxair experience with MPC Praxair currently has more than 5 MPC installations Rawlings Optimal dynamic operation of chemical processes 8 / 4

18 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Credits 5 to 8 M$/yr (27) Achieved primarily by increased on-spec product, decreased energy use Eastman Chemical experience with MPC First MPC implemented in 996 Currently 55-6 MPC applications of varying complexity 3-5 M$/year increased profit due to increased throughput (28) Praxair experience with MPC Praxair currently has more than 5 MPC installations 6 M$/year increased profit (28) Rawlings Optimal dynamic operation of chemical processes 8 / 4

19 Impact for 3 ethylene plants (Starks and Arrieta, 27) Hydrocarbons AC&O 7 We re Doing it For the Money $6,, $6,, $5,, $5,, $4,, $4,, $3,, $3,, $2,, $2,, $,, $,, $ $ Q 2 3Q 2 Q 2 3Q 2 Q 22 3Q 22 Q 23 3Q 23 Q 24 3Q 24 Q 25 3Q 25 Q26 3Q26 Cumulative Quarterly Advanced Control & Optimization Rawlings Optimal dynamic operation of chemical processes 9 / 4

20 Are all the problems solved? Some questions to consider How do we best decompose large-scale systems into manageable problems? Rawlings Optimal dynamic operation of chemical processes / 4

21 Are all the problems solved? Some questions to consider How do we best decompose large-scale systems into manageable problems? How do we optimize dynamic economic operation? Rawlings Optimal dynamic operation of chemical processes / 4

22 Electrical power distribution Rawlings Optimal dynamic operation of chemical processes / 4

23 Chemical plant integration Material flow Energy flow Rawlings Optimal dynamic operation of chemical processes 2 / 4

24 MPC at the large scale Decentralized Control Most large-scale systems consist of networks of interconnected/interacting subsystems Chemical plants, electrical power grids, water distribution networks,... Rawlings Optimal dynamic operation of chemical processes 3 / 4

25 MPC at the large scale Decentralized Control Most large-scale systems consist of networks of interconnected/interacting subsystems Chemical plants, electrical power grids, water distribution networks,... Traditional approach: Decentralized control Wealth of literature from the early 97 s on improved decentralized control a Well known that poor performance may result if the interconnections are not negligible a (Sandell Jr. et al., 978; Šiljak, 99; Lunze, 992) Rawlings Optimal dynamic operation of chemical processes 3 / 4

26 MPC at the large scale Centralized Control Steady increase in available computing power has provided the opportunity for centralized control Most practitioners view centralized control of large, networked systems as impractical and unrealistic A divide and conquer strategy is essential for control of large, networked systems (Ho, 25) Centralized control: A benchmark for comparing and assessing distributed controllers Rawlings Optimal dynamic operation of chemical processes 4 / 4

27 Nomenclature: consider two interacting units Objective functions V (u, u 2 ), V 2 (u, u 2 ) and V (u, u 2 ) = w V (u, u 2 ) + w 2 V 2 (u, u 2 ) decision variables for units u Ω, u 2 Ω 2 Rawlings Optimal dynamic operation of chemical processes 5 / 4

28 Nomenclature: consider two interacting units Objective functions V (u, u 2 ), V 2 (u, u 2 ) and V (u, u 2 ) = w V (u, u 2 ) + w 2 V 2 (u, u 2 ) decision variables for units u Ω, u 2 Ω 2 Decentralized Control min Ṽ (u ) u Ω min Ṽ 2 (u 2 ) u 2 Ω 2 Rawlings Optimal dynamic operation of chemical processes 5 / 4

29 Nomenclature: consider two interacting units Objective functions V (u, u 2 ), V 2 (u, u 2 ) and V (u, u 2 ) = w V (u, u 2 ) + w 2 V 2 (u, u 2 ) decision variables for units u Ω, u 2 Ω 2 Decentralized Control Noncooperative Control (Nash equilibrium) min Ṽ (u ) u Ω min V (u, u 2 ) u Ω min Ṽ 2 (u 2 ) u 2 Ω 2 min V 2 (u, u 2 ) u 2 Ω 2 Rawlings Optimal dynamic operation of chemical processes 5 / 4

30 Nomenclature: consider two interacting units Objective functions V (u, u 2 ), V 2 (u, u 2 ) and V (u, u 2 ) = w V (u, u 2 ) + w 2 V 2 (u, u 2 ) decision variables for units u Ω, u 2 Ω 2 Decentralized Control Noncooperative Control (Nash equilibrium) Cooperative Control (Pareto optimal) min Ṽ (u ) u Ω min V (u, u 2 ) u Ω min V (u, u 2 ) u Ω min Ṽ 2 (u 2 ) u 2 Ω 2 min V 2 (u, u 2 ) u 2 Ω 2 min V (u, u 2 ) u 2 Ω 2 Rawlings Optimal dynamic operation of chemical processes 5 / 4

31 Nomenclature: consider two interacting units Objective functions V (u, u 2 ), V 2 (u, u 2 ) and V (u, u 2 ) = w V (u, u 2 ) + w 2 V 2 (u, u 2 ) decision variables for units u Ω, u 2 Ω 2 Decentralized Control Noncooperative Control (Nash equilibrium) Cooperative Control (Pareto optimal) Centralized Control (Pareto optimal) min Ṽ (u ) u Ω min V (u, u 2 ) u Ω min V (u, u 2 ) u Ω min Ṽ 2 (u 2 ) u 2 Ω 2 min V 2 (u, u 2 ) u 2 Ω 2 min V (u, u 2 ) u 2 Ω 2 min V (u, u 2 ) u,u 2 Ω Ω 2 Rawlings Optimal dynamic operation of chemical processes 5 / 4

32 Noninteracting systems 2 b V 2 (u) n, d, p u 2 a - V (u) u Rawlings Optimal dynamic operation of chemical processes 6 / 4

33 Weakly interacting systems.5 V 2 (u) b p n, d -.5 u 2 - a V (u) u Rawlings Optimal dynamic operation of chemical processes 7 / 4

34 Moderately interacting systems 2.5 V (u) u 2.5 b V 2 (u) p a d n u Rawlings Optimal dynamic operation of chemical processes 8 / 4

35 Strongly interacting (conflicting) systems 2.5 V (u).5 V 2 (u) p a u 2 b d u Rawlings Optimal dynamic operation of chemical processes 9 / 4

36 Strongly interacting (conflicting) systems 6 4 n 2 u V 2 (u) V (u) u Rawlings Optimal dynamic operation of chemical processes 2 / 4

37 Geometry of cooperative vs. noncooperative MPC p n 5 a u 2 V (u) V 2 (u) b u Rawlings Optimal dynamic operation of chemical processes 2 / 4

38 Geometry of cooperative vs. noncooperative MPC p n 5 a u 2 V (u) V 2 (u) b u Rawlings Optimal dynamic operation of chemical processes 2 / 4

39 Two reactors with separation and recycle D, x Ad, x Bd MPC 3 MPC MPC 2 F purge F, x A F, x A H r Hm F m, x Am, x Bm H b Q F r, x Ar, x Br A B B C A B B C F b, x Ab, x Bb, T Rawlings Optimal dynamic operation of chemical processes 22 / 4

40 Two reactors with separation and recycle H m Time setpoint Cent-MPC Ncoop-MPC Coop-MPC ( iterate) H b Time setpoint Cent-MPC Ncoop-MPC Coop-MPC ( iterate) F D Time Cent-MPC Ncoop-MPC Coop-MPC ( iterate) Time Cent-MPC Ncoop-MPC Coop-MPC ( iterate) Rawlings Optimal dynamic operation of chemical processes 23 / 4

41 Two reactors with separation and recycle Performance comparison Cost ( 2 ) Performance loss Centralized MPC.75 Decentralized MPC Noncooperative MPC Cooperative MPC ( iterate) % Cooperative MPC ( iterates).84 5% Rawlings Optimal dynamic operation of chemical processes 24 / 4

42 Traditional hierarchical MPC Plantwide coordinator Setpoints Coordinator hr Coordinator Data MPC MPC min 2min MPC MPC MPC s 5s 3s s.5s Multiple dynamical time scales in plant Data and setpoints are exchanged on slower time scale Optimization performed at each layer Rawlings Optimal dynamic operation of chemical processes 25 / 4

43 Cooperative MPC data exchange Read Data storage 5s Data storage Write MPC MPC MPC MPC MPC s 5s 3s s.5s All data exchanged plantwide Slowest MPC defines rate of data exchange Rawlings Optimal dynamic operation of chemical processes 26 / 4

44 Cooperative hierarchical MPC Plantwide data storage Read hr Data storage Data storage Write MPC MPC min 2min MPC MPC MPC s 5s 3s s.5s Optimization at MPC layer only Only subset of data exchanged plantwide Data exchanged at slower time scale Rawlings Optimal dynamic operation of chemical processes 27 / 4

45 The big(ger) picture What is the goal? Rawlings Optimal dynamic operation of chemical processes 28 / 4

46 The big(ger) picture What is the goal? The goal of optimal process operations is to maximize profit. Rawlings Optimal dynamic operation of chemical processes 28 / 4

47 The big(ger) picture What is the goal? The goal of optimal process operations is to maximize profit. Helbig, Abel, and Marquardt (998)... ( years) Rawlings Optimal dynamic operation of chemical processes 28 / 4

48 The big(ger) picture What is the goal? The goal of optimal process operations is to maximize profit. Helbig, Abel, and Marquardt (998)... ( years) Thus with more powerful capabilities, the determination of steady-state setpoints may simply become an unnecessary intermediate calculation. Rawlings Optimal dynamic operation of chemical processes 28 / 4

49 The big(ger) picture What is the goal? The goal of optimal process operations is to maximize profit. Helbig, Abel, and Marquardt (998)... ( years) Thus with more powerful capabilities, the determination of steady-state setpoints may simply become an unnecessary intermediate calculation. Instead nonlinear, dynamic reference models could be used directly to optimize a profit objective. Rawlings Optimal dynamic operation of chemical processes 28 / 4

50 The big(ger) picture What is the goal? The goal of optimal process operations is to maximize profit. Helbig, Abel, and Marquardt (998)... ( years) Thus with more powerful capabilities, the determination of steady-state setpoints may simply become an unnecessary intermediate calculation. Instead nonlinear, dynamic reference models could be used directly to optimize a profit objective. Biegler and Rawlings (99)... ( 2 years) Rawlings Optimal dynamic operation of chemical processes 28 / 4

51 The big(ger) picture What is the goal? The goal of optimal process operations is to maximize profit. Helbig, Abel, and Marquardt (998)... ( years) Thus with more powerful capabilities, the determination of steady-state setpoints may simply become an unnecessary intermediate calculation. Instead nonlinear, dynamic reference models could be used directly to optimize a profit objective. Biegler and Rawlings (99)... ( 2 years) In attempting to synthesize a feedback optimizing control structure, our main objective is to translate the economic objective into process control objectives. Rawlings Optimal dynamic operation of chemical processes 28 / 4

52 The big(ger) picture What is the goal? The goal of optimal process operations is to maximize profit. Helbig, Abel, and Marquardt (998)... ( years) Thus with more powerful capabilities, the determination of steady-state setpoints may simply become an unnecessary intermediate calculation. Instead nonlinear, dynamic reference models could be used directly to optimize a profit objective. Biegler and Rawlings (99)... ( 2 years) In attempting to synthesize a feedback optimizing control structure, our main objective is to translate the economic objective into process control objectives. Morari, Arkun, and Stephanopoulos (98)... ( 3 years) Rawlings Optimal dynamic operation of chemical processes 28 / 4

53 Optimizing economics: Current industrial practice Planning and Scheduling Steady State Optimization Validation Model Update Reconciliation Two layer structure Drawbacks Controller Plant Rawlings Optimal dynamic operation of chemical processes 29 / 4

54 Optimizing economics: Current industrial practice Planning and Scheduling Steady State Optimization Validation Controller Plant Model Update Reconciliation Two layer structure Drawbacks Inconsistent models Re-identify linear model as setpoint changes Time scale separation may not hold Economics unavailable in dynamic layer Rawlings Optimal dynamic operation of chemical processes 29 / 4

55 Motivating the idea Profit -4-2 Input (u) State (x) Rawlings Optimal dynamic operation of chemical processes 3 / 4

56 Motivating the idea Profit -4-2 Input (u) State (x) Rawlings Optimal dynamic operation of chemical processes 3 / 4

57 Economics controller T min L(x, u)dt u(t) subject to: ẋ = f (x, u) y = g(x, u) Rawlings Optimal dynamic operation of chemical processes 3 / 4

58 Economics controller T min L(x, u)dt u(t) subject to: ẋ = f (x, u) y = g(x, u) Target tracking (standard) L(x, u) = y sp g(x, u) 2 Q + u sp u 2 R Rawlings Optimal dynamic operation of chemical processes 3 / 4

59 Economics controller T min L(x, u)dt u(t) subject to: ẋ = f (x, u) y = g(x, u) Target tracking (standard) L(x, u) = y sp g(x, u) 2 Q + u sp u 2 R Economic optimization (new) L is the negative of economic profit function L(x, u) = P(x, u) Rawlings Optimal dynamic operation of chemical processes 3 / 4

60 Strong duality and asymptotic stability Strong Duality If there exists a λ such that the the following problems have the same solution min L(x, u) min x,u L(x, u) λ(f (x, u)) x,u f (x, u) = h(x, u) h(x, u) Rawlings Optimal dynamic operation of chemical processes 32 / 4

61 Strong duality and asymptotic stability Strong Duality If there exists a λ such that the the following problems have the same solution min L(x, u) min x,u L(x, u) λ(f (x, u)) x,u f (x, u) = h(x, u) h(x, u) Asymptotic stability of the closed-loop economics controller with a strictly convex cost and linear dynamics (Rawlings et al., 28) Asymptotic stability of the closed-loop economics controller with strong duality in the steady-state problem (Diehl et al., 29) Rawlings Optimal dynamic operation of chemical processes 32 / 4

62 Example x k+ = [ ] [ ] x.47.5 k + u.8848 k Input constraint: u Economics L eco = α x + β u α = [ 3 2 ] β = 2 Tracking L targ = x x 2 Q + u u 2 R Q = 2I 2 R = 2 x = [ 6 ] u = Rawlings Optimal dynamic operation of chemical processes 33 / 4

63 8 6 x targ-mpc x Rawlings Optimal dynamic operation of chemical processes 34 / 4

64 8 6 x targ-mpc eco-mpc x Rawlings Optimal dynamic operation of chemical processes 34 / 4

65 State State Input 8 targ-mpc targ-mpc targ-mpc Time Rawlings Optimal dynamic operation of chemical processes 35 / 4

66 State State Input targ-mpc eco-mpc targ-mpc eco-mpc targ-mpc - eco-mpc Time Rawlings Optimal dynamic operation of chemical processes 35 / 4

67 Conclusions Optimal dynamic operation of chemical processes has undergone a total transformation in the last 2 years. Rawlings Optimal dynamic operation of chemical processes 36 / 4

68 Conclusions Optimal dynamic operation of chemical processes has undergone a total transformation in the last 2 years. Both in theory and in practice. Rawlings Optimal dynamic operation of chemical processes 36 / 4

69 Conclusions Optimal dynamic operation of chemical processes has undergone a total transformation in the last 2 years. Both in theory and in practice. The currently available theory splits the problem into state estimation and regulation. Rawlings Optimal dynamic operation of chemical processes 36 / 4

70 Conclusions Optimal dynamic operation of chemical processes has undergone a total transformation in the last 2 years. Both in theory and in practice. The currently available theory splits the problem into state estimation and regulation. Both are posed and solved as online optimization problems. Rawlings Optimal dynamic operation of chemical processes 36 / 4

71 Conclusions Optimal dynamic operation of chemical processes has undergone a total transformation in the last 2 years. Both in theory and in practice. The currently available theory splits the problem into state estimation and regulation. Both are posed and solved as online optimization problems. Basic properties have been established. Rawlings Optimal dynamic operation of chemical processes 36 / 4

72 Conclusions Optimal dynamic operation of chemical processes has undergone a total transformation in the last 2 years. Both in theory and in practice. The currently available theory splits the problem into state estimation and regulation. Both are posed and solved as online optimization problems. Basic properties have been established. Lyapunov functions are the dominant theoretical tool for analysis and design. Rawlings Optimal dynamic operation of chemical processes 36 / 4

73 Conclusions Optimal dynamic operation of chemical processes has undergone a total transformation in the last 2 years. Both in theory and in practice. The currently available theory splits the problem into state estimation and regulation. Both are posed and solved as online optimization problems. Basic properties have been established. Lyapunov functions are the dominant theoretical tool for analysis and design. Industrial implementations and vendor software are basically keeping pace with the best available theory and algorithms. Rawlings Optimal dynamic operation of chemical processes 36 / 4

74 Conclusions Optimal dynamic operation of chemical processes has undergone a total transformation in the last 2 years. Both in theory and in practice. The currently available theory splits the problem into state estimation and regulation. Both are posed and solved as online optimization problems. Basic properties have been established. Lyapunov functions are the dominant theoretical tool for analysis and design. Industrial implementations and vendor software are basically keeping pace with the best available theory and algorithms. That is a surprising and noteworthy outcome! Rawlings Optimal dynamic operation of chemical processes 36 / 4

75 Critiquing the research enterprise The abstraction level is high and barrier to entry is significant. Rawlings Optimal dynamic operation of chemical processes 37 / 4

76 Critiquing the research enterprise The abstraction level is high and barrier to entry is significant. But the barrier is no higher than any other mathematically intensive research field in chemical engineering. Rawlings Optimal dynamic operation of chemical processes 37 / 4

77 Critiquing the research enterprise The abstraction level is high and barrier to entry is significant. But the barrier is no higher than any other mathematically intensive research field in chemical engineering. Fluid mechanics, statistical mechanics, molecular dynamics,... Rawlings Optimal dynamic operation of chemical processes 37 / 4

78 Critiquing the research enterprise The abstraction level is high and barrier to entry is significant. But the barrier is no higher than any other mathematically intensive research field in chemical engineering. Fluid mechanics, statistical mechanics, molecular dynamics,... Researchers in this community have not done a good job communicating the significant advances in this field to their colleagues outside the field. Rawlings Optimal dynamic operation of chemical processes 37 / 4

79 Future directions Current research in MPC Distributed versions of MPC Rawlings Optimal dynamic operation of chemical processes 38 / 4

80 Future directions Current research in MPC Distributed versions of MPC Controlling large-scale systems composed of many small-scale MPCs Rawlings Optimal dynamic operation of chemical processes 38 / 4

81 Future directions Current research in MPC Distributed versions of MPC Controlling large-scale systems composed of many small-scale MPCs How to structure the small-scale MPCs so they cooperate on plantwide objectives Rawlings Optimal dynamic operation of chemical processes 38 / 4

82 Future directions Current research in MPC Distributed versions of MPC Controlling large-scale systems composed of many small-scale MPCs How to structure the small-scale MPCs so they cooperate on plantwide objectives Optimizing economics with MPC Rawlings Optimal dynamic operation of chemical processes 38 / 4

83 Future directions Current research in MPC Distributed versions of MPC Controlling large-scale systems composed of many small-scale MPCs How to structure the small-scale MPCs so they cooperate on plantwide objectives Optimizing economics with MPC The optimal economic point is not necessarily a steady state Rawlings Optimal dynamic operation of chemical processes 38 / 4

84 Future directions Current research in MPC Distributed versions of MPC Controlling large-scale systems composed of many small-scale MPCs How to structure the small-scale MPCs so they cooperate on plantwide objectives Optimizing economics with MPC The optimal economic point is not necessarily a steady state Allows removal of the steady-state economic optimization layer Rawlings Optimal dynamic operation of chemical processes 38 / 4

85 Future directions Current research in MPC Distributed versions of MPC Controlling large-scale systems composed of many small-scale MPCs How to structure the small-scale MPCs so they cooperate on plantwide objectives Optimizing economics with MPC The optimal economic point is not necessarily a steady state Allows removal of the steady-state economic optimization layer Dynamic economic optimization subject to settling at the optimal steady state Rawlings Optimal dynamic operation of chemical processes 38 / 4

86 New MPC graduate textbook 576 page text 24 exercises 335 page solution manual 3 appendices on web (33 pages) Rawlings Optimal dynamic operation of chemical processes 39 / 4

87 Further reading I L. T. Biegler and J. B. Rawlings. Optimization approaches to nonlinear model predictive control. In Y. Arkun and W. H. Ray, editors, Chemical Process Control CPCIV, pages CACHE, 99. M. Diehl, R. Amrit, and J. B. Rawlings. A Lyapunov function for economic optimizing model predictive control. IEEE Trans. Auto. Cont., 29. Submitted for publication. A. Helbig, O. Abel, and W. Marquardt. Structural concepts for optimization based control of transient processes. In International Symposium on Nonlinear Model Predictive Control, Ascona, Switzerland, 998. Y.-C. Ho. On Centralized Optimal Control. IEEE Trans. Auto. Cont., 5(4): , 25. J. Lunze. Feedback Control of Large Scale Systems. Prentice-Hall, London, U.K., 992. M. Morari, Y. Arkun, and G. Stephanopoulos. Studies in the synthesis of control structures for chemical processes. Part I: Formulation of the problem. Process decomposition and the classification of the control tasks. Analysis of the optimizing control structures. AIChE J., 26(2):22 232, 98. S. J. Qin and T. A. Badgwell. A survey of industrial model predictive control technology. Control Eng. Prac., (7): , 23. Rawlings Optimal dynamic operation of chemical processes 4 / 4

88 Further reading II J. B. Rawlings, D. Bonné, J. B. Jørgensen, A. N. Venkat, and S. B. Jørgensen. Unreachable setpoints in model predictive control. IEEE Trans. Auto. Cont., 53(9): , October 28. N. R. Sandell Jr., P. Varaiya, M. Athans, and M. Safonov. Survey of decentralized control methods for larger scale systems. IEEE Trans. Auto. Cont., 23(2):8 28, 978. D. D. Šiljak. Decentralized Control of Complex Systems. Academic Press, London, 99. ISBN D. M. Starks and E. Arrieta. Maintaining AC&O applications, sustaining the gain. In Proceedings of National AIChE Spring Meeting, Houston, Texas, April 27. Rawlings Optimal dynamic operation of chemical processes 4 / 4