Greg McMillan is a retired automation expert who has written many books on process modeling and control. He discusses improving control loop performance for both batch and continuous processes. Some key techniques include properly tuning loops, selecting high-quality valves and flow meters, and applying model-based control strategies like model predictive control.

1. 06/06/09 Control Loop Foundation for Batch and Continuous Control GREGORY K MCMILLAN use pure black and white option for printing copies

8. 06/06/09 TS is tactical scheduler, RTO is real time optimizer, LP is linear program, QP is quadratic program Pyramid of Technologies APC is in any technology that integrates process knowledge Foundation must be large and solid enough to support upper levels. Effort and performance of upper technologies is highly dependent on the integrity and scope of the foundation (type and sensitivity of measurements and valves and tuning of loops) The greatest success has been Achieved when the technology closed the loop (automatically corrected the process without operator intervention) Basic Process Control System Loop Performance Monitoring System Process Performance Monitoring System Abnormal Situation Management System Auto Tuning (On-Demand and On-line Adaptive Loop Tuning) Fuzzy Logic Property Estimators Model Predictive Control Ramper or Pusher LP/QP RTO TS

9. Loops Behaving Badly 06/06/09 1 E i = ------------  T i  E o  K o  K c where: E i = integrated error (% seconds) E o = open loop error from a load disturbance (%) K c = controller gain K o = open loop gain (also known as process gain) (%/%) T i = controller reset time (seconds) (open loop means controller is in manual) A poorly tuned loop will behave as badly as a loop with lousy dynamics (e.g. excessive dead time)! Tune the loops before, during, and after any process control improvements You may not want to minimize the integrated error if the controller output upsets other loops. For surge tank and column distillate receiver level loops you want to minimize and maximize the transfer of variability from level to the manipulated flow, respectively.

10. Unification of Controller Tuning Settings 06/06/09 Where: K c = controller gain K o = open loop gain (also known as process gain) (%/%)  1  self-regulating process time constant (sec)  max  maximum total loop dead time (sec) All of the major tuning methods (e.g. Ziegler-Nichols ultimate oscillation and reaction curve, Simplified Internal Model Control, and Lambda) reduce to the following form for the maximum useable controller gain

11. Definition of Deadband and Stick-Slip 06/06/09 Deadband Deadband Stick-Slip Signal (%) 0 Stroke (%) Digital positioner will force valve shut at 0% signal Pneumatic positioner requires a negative signal to close valve The effect of slip is worse than stick, stick is worse than dead band, and dead band is worse than stroking time (except for surge control) Dead band is 5% - 50% without a positioner ! Stick-slip causes a limit cycle for self-regulating processes. Deadband causes a limit cycle in level loops and cascade loops with integral (reset) action. If the cycle is small enough it can get lost in the disturbances, screened out by exception reporting, or attenuated by volumes

12. 06/06/09 Controller Output (%) Saw Tooth Oscillation Controlled Flow (kpph) Square Wave Oscillation Saw Tooth Flow Controller Output Limit Cycle from Stick-Slip

13. 06/06/09 Manipulated Flow (kpph) Clipped Oscillation Controller Output (%) Rounded Oscillation Controlled Level (%) Saw Tooth Oscillation Rounded Level Controller Output Limit Cycle from Deadband

14. Identification of Stick and Slip in a Closed Loop Response 06/06/09 The limit cycle may not be discernable due to frequent disturbances and noise Time ( Seconds ) Stroke % 53 53.5 54 54.5 55 55.5 56 56.5 57 57.5 58 58.5 59 0 100 200 300 400 500 600 700 800 3.25 Percent Backlash + Stiction Controller Output Flow Dead band is peak to peak amplitude for signal reversal slip stick

15. Response Time of Various Positioners (small actuators so slewing rate is not limiting) 06/06/09 Response time increase dramatically for steps less than 1%

19. Neutralizer Control – “Before” 06/06/09 Static Mixer Neutralizer Feed Discharge AT 1-1 FT 1-1 FT 2-1 AT 2-1 FT 1-2 2 pipe diameters Reagent Stage 1 Reagent Stage 2 AC 1-1 AC 2-1 FC 1-2

20. Nonlinearity and Sensitivity of pH 06/06/09 pH Reagent Flow Influent Flow 6 8 Good valve resolution or fluid mixing does not look that much better than poor resolution or mixing due amplification of X axis (concentration) fluctuations Reagent Charge Process Volume or

21. Neutralizer Control – “After” 06/06/09 Static Mixer Neutralizer Feed Discharge AT 1-1 FT 1-1 FT 2-1 AT 2-1 FT 1-2 Reagent Stage 1 Reagent Stage 2 20 pipe diameters f(x)  Feedforward Summer RSP Signal Characterizer *1 *1 *1 - Isolation valve closes when control valve closes AC 1-1 FC 1-2 FC 2-1 AC 2-1

22. Distillation Column Control – “Before” 06/06/09 FC 3-4 FT 3-4 FC 3-3 FT 3-3 LT 3-1 LC 3-1 TE 3-2 TC 3-2 LT 3-2 LC 3-2 Distillate Receiver Column Overheads Bottoms Steam Feed Reflux PC 3-1 PT 3-1 Vent Storage Tank Feed Tank Tray 10 Thermocouple

23. Nonlinearity and Sensitivity of Tray Temperature 06/06/09 Tray 10 Tray 6 Distillate Flow Feed Flow % Impurity Temperature Operating Point Measurement Error Measurement Error Impurity Errors

24. Distillation Column Control – “After” 06/06/09 FC 3-2 FT 3-2 FC 3-4 FT 3-4 FC 3-3 FT 3-3 FC 3-1 FT 3-1 LT 3-1 LC 3-1 TT 3-2 TC 3-2 FC 3-5 FT 3-5 LT 3-2 LC 3-2 RSP RSP RSP Distillate Receiver Column Overheads Bottoms Steam Feed Reflux PC 3-1 PT 3-1 Vent Storage Tank Feed Tank Tray 6 f(x) Signal Characterizer RTD   FT3-3 FT3-3 Feedforward summer Feedforward summer

25. When Process Knowledge is Missing in Action 06/06/09 2-Sigma 2-Sigma RCAS Set Point LOCAL Set Point 2-Sigma 2-Sigma Upper Limit PV distribution for original control PV distribution for improved control Extra margin when “ war stories” or mythology rules value Benefits are not realized until the set point is moved! (may get benefits by better set point based on process knowledge even if variability has not been reduced) Good engineers can draw straight lines Great engineers can move straight lines

29. Reset Gives Them What They Want 06/06/09 Proportional and rate action see the trajectory visible in a trend! Both would work to open the water valve to prevent overshoot. Reset action integrates the numeric difference between the PV and SP seen by operator on a loop faceplate Reset works to open the steam valve Reset won’t open the water valve Until the error changes sign, PV goes above the set point. Reset has no sense of direction. Should the steam or water valve be open? SP PV Out 52 44 ? TC-101 Reactor Temperature steam valve opens water valve opens 50% set point (SP) temperature time PV

36. Batch Control 06/06/09 Reagent Optimum pH Optimum Product Feeds Concentrations pH Product Optimum Reactant Reactant Reactant Variability Transfer from Feeds to pH, and Reactant and Product Concentrations Most published cases of multivariate statistical process control (MSPC) use the process variables and this case of variations in process variables induced by sequenced flows.

37. PID Control 06/06/09 Optimum pH Optimum Product Feeds Concentrations pH Product Reagent Reactant Optimum Reactant Reactant Variability Transfer from pH and Reactant Concentration to Feeds The story is now in the controller outputs (manipulated flows) yet MSPC still focuses on the process variables for analysis

38. Model Predictive Control 06/06/09 Optimum pH Optimum Product Feeds Concentrations pH Product Reagent Optimum Reactant Reactant Reactant Time Time Variability Transfer from Product Concentration to pH, reactant Concentration, and Feeds Model Predictive Control of product concentration batch profile uses slope for CV which makes the integrating response self-regulating and enables negative besides positive corrections in CV

39. Example of Basic PID Control 06/06/09 feed A feed B coolant makeup CAS ratio control reactor vent product condenser CTW PT PC-1 TT TT TC-2 TC-1 FC-1 FT FT FC-2 TC-3 RC-1 TT CAS cascade control Conventional Control

40. Example of Advanced Regulatory Control 06/06/09 feed A feed B coolant makeup CAS ratio CAS reactor vent product maximum production rate condenser CTW PT PC-1 TT TT TC-2 TC-1 FC-1 FT FT FC-2 < TC-3 RC-1 TT ZC-1 ZC-2 CAS CAS CAS ZC-3 ZC-4 < Override Control override control ZC-1, ZC-3, and ZC-4 work to keep their respective control valves at a max throttle position with good sensitivity and room for loop to maneuver. ZC-2 will raise TC-1 SP if FC-1 feed rate is maxed out

42. Analyzer Block for Online Data Analytics 06/06/09 History Collection of Lab and Spectral Analyzer Data Controller Processing of Sample Data for Use in Analytics Module Lab Results Analyzer Block Historian Operator Station Off-line Modeling Other Data

43. Dynamic Time Warping for Online Batch Data Analytics 06/06/09 Reference trajectory Trajectory to be synchronized Synchronized trajectory

44. Virtual Plant Setup 06/06/09 Advanced Control Modules Process Models (first principal and experimental) Virtual Plant Laptop or Desktop or Control System Station This is where I hang out

45. Virtual Plant Integration 06/06/09 Dynamic Process Model Online Data Analytics Model Predictive Control Loop Monitoring And Tuning DCS batch and loop configuration, displays, and historian Virtual Plant Laptop or Desktop Personal Computer Or DCS Application Station or Controller Embedded Advanced Control Tools Embedded Modeling Tools Process Knowledge