1. Slide 1 Sneak Peek DeltaV Adaptive Control Coming In DeltaV v8.1 ES Terry Blevins and Darrin Kuchle DeltaV Advanced Control Team Austin, Tx.

2. Slide 2 A Fundamental Control Problem To control efficiently we need well tuned controllers To tune controllers accurately current process dynamics must be known. This requires process testing. After testing, updated tuning parameters may be calculated and then used for control. Over time process dynamics will change leading to poor control with the current tuning parameters. This means more tuning which requires more testing This entire process must be repeated again and again in an attempt to maintain acceptable control performance.

3. Slide 3 Tuning Methods First tuning method due to Ziegler & Nichols (1942) –Called Quarter-Amplitude-Damping (QAD) Most older tuning methods try for “as fast as possible” Many people still do not use any method preferring to “tune-by-feel” –Classical control skills now rare “little black books” Default tuning (gain=1.0, Reset=1 min) My Favorites - The Ever Popular SWAG method and the equally effective POMA analysis.

4. Slide 4 There Must Be A Better Way Wouldn’t it be nice to have controllers use optimal tuning parameters all the time (continually) without having to tune at all, ever?

5. Slide 5 Permitted Range Adaptive Control – Continuous Adjustment Controller Gain Starting Point Less Aggressive More Aggressive Continuous automatic adjustment of tuning parameters to changing process dynamics means better control. Easy. But don’t forget about the time constant and the dead time.

6. Slide 6 DeltaV Adapt Sneak Peak - Fully Adaptive PID Control Tuning - Learns Process Dynamics While In Automatic Control - No Bump Testing Required - Works On Feedback And Feedforward - Patents Are Now Awarded! - See It For The First Time Here No Tuning Required!

7. Slide 7 Not an overnight thing… EMERSON technology developed in Austin. Patents have now been awarded. 1997 - Dr. Wojsznis’ concept originated 1998 - Started research at Hawk Austin 2002 - Started development 2003 - Prototypes at Texas Eastman in Longview Texas with good results. 2004 - Initial release planned for DeltaV v8.1

8. Slide 8 Dr. Dale Seborg – UC Santa Barbara 1999 - Dr. Seborg started working on formal proofs of convergence for us along with his Emerson funded grad student

9. Slide 9 Patents Have Now Been Awarded! Mr. Terry Blevins Mr. Terry Blevins Dr. Wilhelm Woszjnis

10. Slide 10 It’s That Easy! Adapt x Just Check The Box

11. Slide 11 DeltaV Adaptive Control – Technology Basis Process models are automatically established for the feedback or feedforward paths. Model adaptation utilizes a data set captured after a setpoint change, or a significant change in the process input or output. Multiple models are evaluated and a new model is determined

12. Slide 12 DeltaV Adaptive Control – Technology Basis Model is internally validated by comparing the calculated and actual process response prior to its application in tuning. The user may select the tuning rule used with the feedback model to set the PID tuning.

13. Slide 13 Adaptive Control – Internal Structure Control Process Controller Redesign Supervisor Model Evaluation Model Interpolation Set of Models PID Controller w/Dyn Comp Feedforward Excitation Generator Manipulate Measured Disturbance Adaptive Control Block SP

14. Slide 14 Simple Example – Pure Gain Process Pure gain Process K Estimated Gain Multiple Model Interpolation with re-centering Changing process input For each iteration, the squared error is computed for every model I each scan Where: is the process output at the time t is i-th model output A norm is assigned to each parameter value k = 1,2,….,m in models l = 1,2,…,n. if parameter value is used in the model, otherwise is 0 For an adaptation cycle of M scans G1-Δ G1 G1+Δ Initial Model Gain = G1 G2-Δ G2 G2+Δ G3-Δ G3 G3+Δ Iteration 1 2 3 Multiple iterations per adaptation cycle

15. Slide 15 Example - First Order Plus Deadtime Process For a first order plus deadtime process, twenty seven (27) models are evaluated each sub- iteration, first gain is determined, then deadtime, and last time constant. After each iteration, the bank of models is re-centered using the new gain, time constant, and deadtime First Order Plus Deadtime Process Estimated Gain, time constant, and deadtime Multiple Model Interpolation with re-centering Changing process input Gain Time Constant Dead time G1+ Δ G1+ Δ G1+ Δ TC1 -Δ TC1–Δ TC1 -Δ DT1- Δ DT1 DT1+ Δ G1+ Δ G1+ Δ G1+ Δ TC1 +Δ TC1+Δ TC1 +Δ DT1- Δ DT1 DT1+ Δ G1+ Δ G1+ Δ G1+ Δ TC1 TC1 TC1 DT1- Δ DT1 DT1+ Δ G1 G1 G1 TC1 -Δ TC1–Δ TC1 -Δ DT1- Δ DT1 DT1+ Δ G1 G1 G1 TC1 +Δ TC1+Δ TC1 +Δ DT1- Δ DT1 DT1+ Δ G1 G1 G1 TC1 TC1 TC1 DT1- Δ DT1 DT1+ Δ G1-Δ G1- Δ G1- Δ TC1 -Δ TC1–Δ TC1 -Δ DT1- Δ DT1 DT1+ Δ G1-Δ G1- Δ G1- Δ TC1 +Δ TC1+Δ TC1 +Δ DT1- Δ DT1 DT1+ Δ G1-Δ G1- Δ G1- Δ TC1 TC1 TC1 DT1- Δ DT1 DT1+ Δ

16. Slide 16 Operating Condition Impact Process gain and dynamics may change as a function of operating condition as indicated by PV, OUT or other measured parameters e.g. plant throughput

17. Slide 17 Defining Operating Regions Adaptive control allows operating regions to be defined as a function of an input “state” parameter Define up to 5 regions When the state parameter changes from one region to another, the model values (and associated tuning) immediately change to the last model determined for the new region Limits on model parameter adjustment are defined independently for each region. Model Parameters State Parameter Value Model Parameters State Parameter Value Region 1 Region 2 Region 3 Region 4 Region 5 Region 1 Region 2

18. Slide 18 Example – Non-Linear Installed Characteristics Process gain will change as a function of valve position if the final control element has non-linear installed characteristics. Valve position is used as the state parameter.

19. Slide 19 Example – Throughput Dependent Process The process deadtime for superheater outlet temperatue control changes as a function of steam flow rate Steam flow rate is used as the state parameter

20. Slide 20 Example – Multiple Valves - Split Range The process gain and dynamic response to a change valve position may be different for each valve. Typical example is heating/cooling of batch reactor, extruder, slaker, etc. Valve position is used as the state parameter. 0 50 100 Controller Output (%) Cooling Valve Heating Valve 100 0

21. Slide 21 Example – pH Process The process gain associated with a change in reagent is highly non-linear. Extremely high gain around a pH of 7, lower gain above and below this point. The control parameter, pH, is used as the state parameter.

22. Slide 22 The sensitivity of tray temperature to changes in distillate to feed ratio is highly non-linear. Tray temperature is used as the state parameter. Example: Column Temperature Control

23. Slide 23 DeltaV Adaptive Control – Field Trials Control automatically adapts based on SP changes in Auto – Caustic loop

24. Slide 24 DeltaV Adaptive Control – Field Trials Model verification showing actual response and response calculated by the identified model.

25. Slide 25 Configuration of Adaptive Control New control block in the advanced control palette. Parameters are automatically assigned to the historian. No more difficult to use than PID. Initial for model, limits, and time to steady state are automatically defaulted based on block tuning.

26. Slide 26 Adaptive Control Application Used to view the operation of modules that include Adapt blocks. May modify adaptive operation, parameter limits, and default setup parameters from this view. Adapt blocks run independent of the DeltaV Adapt application.

27. Slide 27 Initial Overview of Adaptive Control Default system overview lists all the adaptive control blocks in service. The feedback and feedforward operation and status are summarized Status indicates if a limit has been reached in adapting the feedback /feedforward model

28. Slide 28 Adaptive Control Block – Operation View

29. Slide 29 Feedback View Adaptive Operation may be selected: –Observe –Learn –Schedule –Adapt Control Tuning rule and speed of response may be changed from defaults. Model identified for current region Calculated tuning for model and selected rule Operation Selection Model identified for current region Operation Selection

30. Slide 30 Expert Selection – Range View Parameter high and low limits may be adjusted for each region. Current parameter and state are also shown

31. Slide 31 Expert Selection - Setup Advanced features may be enabled e.g. automatic injection of change on periodic basis, integrating process.

32. Slide 32 The End Result This capability will allow DeltaV users to assign “ballpark” tuning parameters and let adaptive PID controllers tighten them up and adapt over time. Patented model switching technology means robust control over the long haul without sacrificing performance Faster startups, quicker ramp-up of production, less tuning over time, and better control over the life of the system all mean better economics.

33. Slide 33 Looking For Beta Sites Now Email to darrin.kuchle@emersonprocess.com