Utilizing DeltaV Adaptive Control

1. Utilizing DeltaV Adaptive Control

2. Presenters • Peter Wojsznis • Gregory McMillan • Willy Wojsznis • Terry Blevins

3. Outline • Introduction • Adaptive Technique Background • Adaptive PID Design • Application Examples • Field Test Results • Adaptive Control Demos • Actuator Diagnostics • Summary

4. Control Loop Performance A Never Ending Cycle Evaluate Test Degradation The more often you Tune, the better the performance. Calculate Operate Deploy

5. 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

6. 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?

7. DeltaV Adapt NoTuningRequired! • Fully Adaptive PID Control Tuning • Learns Process Dynamics While In Automatic Control • No Bump Testing Required • Works On Feedback And Feedforward • Patents Awarded! • See It Here

8. DeltaV Adapt Applicable to Most Control Loops Today

9. DeltaV Adapt – A Clear Difference

10. Not an overnight thing… • EMERSON technology developed in Austin. • Patents have been awarded. • 1997 - Dr. Willy Wojsznis’ concept originated • 1998 - Research started at Tech Center - Austin • 1999 - Dr Seborg started work on formal proofs • 2002 - Development started • 2003 - Prototypes at Eastman Chemical, Solutia and UT with good results.

11. Patents Have Been Awarded! Dr. Wilhelm Wojsznis Mr. Terry Blevins

12. Solid theoretical background 1999 - Dr. Seborg started working on formal proofs of convergence for us along with his Emerson funded grad student

13. Outline • Introduction • Adaptive Technique Background • Adaptive PID Design • Application Examples • Field Test Results • Adaptive Control Demos • Actuator Diagnostics • Summary

14. Adaptive Tuning Techniques Model Free Model Based Process Variables Evaluation Frequency Domain Time Domain Recursive Recursive PID Terms Evaluation Controller Switching Discrete Fourier Model Switching Adaptive PID Techniques

15. Model Free vs Model Based Adaptation

16. Model Switching Adaptation • Use N-models working in parallel • Evaluate model error • Select model with minimal error • Shortcoming: TOO MANY MODELS

17. Parameter Interpolation • Every parameter value of the model is evaluated independently • The weight assigned to the parameter value is inverse of the squared error • Adapted parameter value is weighted average of all evaluated values

18. Advantages of Parameter Interpolation • Sequential parameter adaptation – less models: • Example: Model with 3 parameters (Gain, Lag, Dead Time) and 3 values for every parameter has 3x3x3 model variations for model switching adaptation and 3+3+3 model variations for sequential parameter adaptation • Better convergence • Interpolation gives better model due to continuous adaptation of the model parameter value over the whole assumed range

19. Parameter Interpolation - Calculations For each iteration, the squared error is computed for every model I each scan Ei(t) = (y(t) – Yi(t))2 Where: y(t)is the process output at the time t Yi(t)is i-th model output A norm is assigned to each parameter value k = 1,2,….,m in models l = 1,2,…,n. Epkl(t) = ∑Ni=1 (γklEi(t)) γ=1if parameter value pklis used in the model, otherwise is 0 For an adaptation cycle of M scans sumEpkl = ∑Mt=1 (Epkl(t)), Fkl = 1/sumEpkl pk(a) = pk1fk1+…+pklfkl+…+pknfkn fkl = Fkl / sumFk

20. Multiple Model Interpolation with re-centering Estimated Gain K Changing process input Pure gain Process Iteration 1 2 3 Initial Model Gain = G1 G1-Δ G1 G1+Δ Multiple iterations per adaptation cycle G2-Δ G2 G2+Δ G3-Δ G3 G3+Δ Simple Example – Pure Gain Process

21. Multiple Model parameter Interpolation with re-centering Estimated Gain, time constant, and deadtime Changing process input First Order Plus Deadtime Process Gain 1 2 Time Constant 3 Dead time First Order Plus Dead Time ProcessModel Parameter Interpolation • For a first order plus deadtime process, only nine (9) 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

22. Time Constant 3rd Gain 1st G, DT, TC+∆ G+∆, DT, TC 1 2 1 1 Initial model Dead time 2nd G,DT, TC G, DT -∆, TC G, DT +∆, TC 3 Adapted model True process model G, DT, TC-∆ G-∆, DT, TC First Order Plus Dead Time Process Model Parameter Interpolation

23. Model Verification Final stage of model adaptation and verification showing actual response and response calculated by the identified models.

24. Outline • Introduction • Adaptive Technique Background • Adaptive PID Design • Application Examples • Field Test Results • Adaptive Control Demos • Actuator Diagnostics • Summary

25. DeltaV Adaptive ControlOperational Features • 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

26. DeltaV Adaptive ControlOperational Features • 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.

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

28. Region 2 Region 1 Model Parameters State Parameter Value Region 2 Region 3 Region 1 Region 4 Region 5 Model Parameters State Parameter Value 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.

29. 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 values for model, limits, and time to steady state are automatically defaulted based on block tuning.

30. 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.

31. Adaptive Control OperationAdapt Application 1. Select Window 2. Observe loop plots (PV, OUT, SP) 3. Observe Adaptation Status 4. Operate PID loop – SP, OUT, Mode

32. Feedback AdaptationAdapt Application 1. Select Window 2. Observe model parameters trends and controller tuning parameters (Gain, Reset, Rate) 3. Observe current process model and PID tuning parameters 4. Select Adaptive operation mode 5. Select tuning rules

33. Feedforward Adaptation Adapt Application • Select Window • Observe model parameters trends and controller tuning parameters (Gain, Reset, Rate) • Observe current process model and PID tuning parameters • Select Feedforward Adaptive operation mode • Select Gain FF Factor

34. Multi-range adaptation 1. Up to 5 ranges 2. The last adapted process gain, time constant and dead time is displayed for every range 3. State parameter: PV, OUT or feedforward input Range 1 State parameter Range 2

35. Adaptive Control SetupAdapt Application 1. Trigger to adapt 2. Controller Output pulse injection 3. How fast to adapt 4. Process type: Integrating – Non Integrating; Minimum time to steady state 5. Adaptive mode of operation Defaults are set for a typical operation!

36. Outline • Introduction • Adaptive Technique Background • Adaptive PID Design • Application Examples • Field Test Results • Adaptive Control Demos • Actuator Diagnostics • Summary

37. Simple 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 – if ranges are applied

38. Example Throughput Dependent Process • The process deadtime for superheater outlet temperature control changes as a function of steam flow rate • Steam flow rate is used as the state parameter

39. 100 0 Cooling Valve Heating Valve 0 50 100 Controller Output (%) ExampleMultiple 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.

40. ExampleColumn Temperature Control • The sensitivity of tray temperature to changes in distillate to feed ratio is highly non-linear. • Tray temperature is used as the state parameter.

41. Outline • Introduction • Adaptive Technique Background • Adaptive PID Design • Application Examples • Field Test Results • Adaptive Control Demos • Actuator Diagnostics • Summary

42. Adaptive Field Test • Emerson Process Management - Process Systems and Solutions Lab • University of Texas reactive distillation column • Eastman Chemical • Solutia

43. DeltaV Adaptive ControlField Trials –Eastman Chemical Control automatically adapts based on SP changes in Auto – Caustic loop

44. FC FT TT TT TC TC LC AC TT TT LT AT DeltaV Adaptive ControlField Trials – Solutia, Pensacola, FL HMD (Base) 65TC685 Acid feed from centrifuge splitter 65LC682 65TC684 65TC688 Cooling Tower Water Cooling Tower Water 65AC681 (pH) Strike Kettle Process and Instrumentation

45. DeltaV Adaptive ControlField Trials – Solutia, Pensacola, FL Kettle control – regular PID control

46. DeltaV Adaptive Control Field Trials – Solutia, Pensacola, FL Kettle control – Adapt control

47. DeltaV Adaptive Control - Field Trials - J.J.Pickle Research Campus, UT, Austin, TX Flow loop

48. DeltaV Adaptive Control - Field Trials - J.J.Pickle Research Campus, UT, Austin, TX Flow loop

49. Outline • Introduction • Adaptive Technique Background • Adaptive PID Design • Application Examples • Field Test Results • Adaptive Control Demos • Actuator Diagnostics • Summary

50. Simple Reactor Demo - Adaptation Demo with model scheduling Adaptation with process state parameter defined for two ranges