1. Robust Methods for Control Structure Selection in Paper Making Processes Candidate: Miguel Castaño Arranz Supervisor: WolfgangBirk Oponent: Fredrik Sandin Examiner: Thomas Gustafsson

2. Outline Backgound and motivation. Basics on control structure selection. Robust methods for control structure selection. New methods for analysis of complex processes using weighted graphs. ProMoVis: a tool for Process Modeling and Visualization. Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide2

3. A complex process: Making paper From virgin and recycled fibersPaper production: 50 ton/hour Energy intensive process Some Steam Producers: Oil fired boiler 50 ton/hour Recovery boiler 150 ton/hour Some Steam Consumers: Paper Machine 90 ton/hour Evaporation plant 40 ton/hour Turbine production 18MW 1500 control loops Many storage vesselsand return flows Long process chain Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide3

4. A procedure for control design of complex processes Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide4 Subset of sensors and actuatos Model structure Model Control structure Performance specification Controller Parameters Implemented Controller

5. V1 FI1 V1 FI1 V1 FI1 V1 FI1 G11 C11 G11 C11 G11 C11 G11 C11 G21 G21 G21 G21 FF R1 FI2 FI2 FI2 FI2 V1 FI1 G22 C22 G22 C22 G22 C22 G22 C22 G11 V2 V2 V2 Consequences of loop interaction V2 C FI2 G22 R2 V2 Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide5

6. Control Structure Selection Decentralized Controller Plant Sparse Controller + r e u y - We will use norms for quantifying the importance of the input-output channels Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide6

7. Representing linear systems (with no direct term) Models for the tanks are created from balance equations State Space representation Laplace Transfer Function representation Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide7

8. Representing linear systems in frequency domain j is the standard imaginaryunit Substitutes for j  Is frequency in rad/sec G(j ) is a frequency dependant complex number Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide8

9. Quantification of system dynamics with gramians P and Q are the controllability and observabillityGramians. The eigenvalues of the product PQ quantifies the connection of the input and output spaces through the state space. The eigenvalues of PQ can be used to quantify process dynamics. PQ is positive definite and therefore the sum of its eigenvalues equals its trace. tr(PQ) has an interesting relationship with the frequency domain: Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide9

10. Nominal Analysis of The Quadruple Tank Process tr(PQ) indicates off-diagonal pairing Need for robust analysis of process interactions Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide10

11. Approximating the analytical bounds for tr(PQ) Uncertainty Description: Normal vector: Minimum area is enclosed by: Maximum area is enclosed by: Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide11

12. Example. The Quadruple Tank Process. Nominal Case Uncertain case Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide12

13. Estimation of tr(PjQi) in the frequency domain 2. Obtain at each excited frequency the value G(jk) with the estimator variance 1. Excite your process at multiple frequencies k 1 1 2 2 3 3 3. Robust estimation of process interactions Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide13

14. Obtainingtr(PQ) from the impulseresponse Miguel Castaño & Wolfgang Birk | MSC 2008| 2008-09-03 | Slide 14 The impulseresponsehij(k) can be obtainedusinglinear regression with

15. Estimation of tr(PjQi) in time domain with ImpulseResponse Estimator Biased Expectedvalue of the estimator UnbiasedEstimator Estimator distribution Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide15

17. Bark is added with a screw and air flows complete the combustionMiguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide16

18. Estimation of tr(PjQi) for a bark boiler Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide17

19. Conclusions Computing the uncertaintybounds on an Interaction Measurehelps to take robust decisions on controlstructureselection Methods for robust estimation of Interaction Measuresallow to takedecisions in the controlstructureselectionwithout the need of creatingparametricmodels. Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide18

20. New methods for interaction analysis of complex processes using weighted graphs

21. Motivation Motivation. Control structure design requires process knowledge. Requires information about how the process variables are interconnected. Goal. Provide visual and intuitive representation of the relationships between process variables. Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide20

22. Visualization Theory. New theory based in brain connectivity. Analyzing relationships between voltage signals in the brain helps to understand it’s behavior. Analyzing relationships between process signals helps to understand the process. Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide21

23. Process description based on graph theory collects the frequency description of the direct interconnections Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide22

25. The norm measures the energy transmission rate of each interconnection.Structural Analysis of a Process Structural Energy Transfer. Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide23

27. Structural VS Functional Questions arise from the structural analysis. How is the energy provided to the manipulated inputs propagated? How are process disturbances propagated? What is affecting to a measured or estimated variable? Structural Energy Transfer. SET. Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide25

28. Functional Analysis of a process Ω(s) colletsthe transfer functionsfromthemanipulated variables and processdisturbancestothemeasuredorestimated variables. Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide26

29. Functional Analysis. Effect of actuators FETc describes the effect of the manipulated inputs and process disturbances on the rest of the process (in terms of energy transmission). Conclusions u1 mainly affects h1 u1 mainly affects h1 Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide27

30. Functional Analysis. Effect of actuators FETc describes the effect of the manipulated inputs and process disturbances on the rest of the process (in terms of energy transmission). Conclusions u1 mainly affects h1 and u2 mainly affects to h2 u2 mainly affects h2 Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide28

31. FTPTr describes how the states are affected by manipulated inputs and process disturbances (in terms of energy transmission). Conclusions u1 mainly affects to h1 and u2 mainly affects to h2 h1 is mainly affected by h1 h1 is mainly affected by u1 Functional Analysis. Effect on process variables. Miguel Castaño & Wolfgang Birk | MSC 2009| 2009-07-08 | Slide 29

32. FTPTr describes how the states are affected by manipulated inputs and process disturbances (in terms of energy transmission). Conclusions u1 mainly affects to h1 and u2 mainly affects to h2 h1 is mainly affected by u1 and h2 is mainly affected by u2 Functional Analysis. Effect on process variables. h2 is mainly affected by u2 Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide30

33. Functional Analysis. Propagation of disturbances. Conclusions u1 mainly affects to h1 and u2 mainly affects to h2 h1 is mainly affected by u1 and h2 is mainly affected by u2 Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide31

34. Functional Analysis. Can Process Disturbances be rejected? Miguel Castaño & Wolfgang Birk | MSC 2008| 2008-09-03 | Slide 32

36. FDPTc describes the effect of the manipulated inputs and process disturbances on the rest of the process (in terms of power transmission).

37. FTPTr describes how the states are affected by manipulated inputs and process disturbances (in terms of power transmission).Functional Dynamic Power Transfer Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide33

38. ProMoVis A tool for Process Modeling and Visualization Process Knowledge Analyze your Process Build your process Library of Icons Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide34

40. The user chooses which information is relevant to display Build your process Analysis Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide35 Process Components Process Models Controllers

41. Analysis of a Stock Preparation Plant with ProMoVis Miguel Castaño & Wolfgang Birk | MSC 2008| 2008-09-03 | Slide 36 Numericalquantification of the effect on measurements Effect on PI represented in the frequencydomain

43. Visual understanding of industrial processes.

44. Visualization of process interconnections.

45. Visualization of process control.

46. Communicating process knowledge.

47. Implementation of the results developed in MeSTA for the communication with project partners. Miguel CastañoArranz |LicentiateSeminar| 2010-12-15 | Slide37

48. Thanks for your attention

49. Questions, suggestions and comments Miguel Castaño & Wolfgang Birk | MSC 2008| 2008-09-03 | Slide 39