CHE 185 – PROCESS CONTROL AND DYNAMICS

1. CHE 185 – PROCESS CONTROL AND DYNAMICS TUNING FOR PID CONTROL LOOPS

2. Controller Tuning • Involves selection of the proper values of Kc, τI, and τD. • Affects control performance. • Affects controller reliability • in many cases controller tuning is a compromise between performance and reliability.

3. Available Tuning Criteria • Specific criteria • Decay ratio • Minimize settling time • General criteria • Minimize variability • Remain stable for the worst disturbance upset (i.e., reliability) • Avoid excessive variation in the manipulated variable

4. Control Performance Assessment • Performance statistics (IAE, ISE, etc.) which can be used in simulation studies. • Standard deviation from setpoint which is a measure of the variability in the controlled variable. • SPC charts which plot product composition analysis along with its upper and lower limits.

5. Example of an SPC Chart • Reference figure 9.2.3

6. TUNING CRITERIA error • CONTROLLED VARIABLE PERFORMANCE • AVOID EXCESSIVE VARIATION • MINIMIZE THE INTEGRAL ABSOLUTE ERROR: • MINIMIZE THE INTEGRAL TIME ERROR:

7. TUNING CRITERIA error • MANIPULATED VARIABLE • AVOID EXCESSIVE SPIKES IN RESPONSE TO SYSTEM DISTURBANCES OR SETPOINT CHANGES • MAINTAIN PROCESS STABILITY WITH LARGE CHANGES • MINIMAL INTEGRAL SQUARE ERROR: • AND INTEGRAL TIME SQUARE ERROR: • OBTAIN ZERO STEADY-STATE OFFSET • MINIMAL RINGING (EXCESSIVE CYCLING)

8. SUMMARY OF GOALS FOR TUNING • DECAY RATIO APPROACHING QUARTER AMPLITUDE DAMPING, QAD

9. Decay Ratio for Non-Symmetric Oscillations • Reference figure 9.2.1 (c)

10. Classical Tuning Methods • Examples: Cohen and Coon method, Ziegler-Nichols tuning, Cianione and Marlin tuning, and many others. • Usually based on having a model of the process (e.g., a FOPDT model) and in most cases in the time that it takes to develop the model, the controller could have been tuned several times over using other techniques. • Also, they are based on a preset tuning criterion (e.g., QAD)

11. Classical Tuning Methods • Cohen and Coon method • TARGET THE VALUES SHOWN IN TABLE 9.2 • BASED ON MINIMIZING ISE, QAD AND NO OFFSET

12. Classical Tuning Methods • CIANCONE AND MARLIN • DIMENSIONLESS CORRELATIONS BASED ON A TERM CALLED FRACTIONAL DEADTIME: • RESULTING PARAMETERS ARE PLOTTED IN FIGURE 9.3.2

13. Classical Tuning Methods • CIANCONE AND MARLIN • THE SEQUENCE OF CALCULATION OF TUNING CONSTANTS: • CERTIFY THAT PERFORMANCE GOALS AND ASSUMPTIONS ARE APPROPRIATE • DETERMINE THE DYNAMIC MODEL USING AND EMPIRICAL METHOD TO OBTAIN Kp, θp AND τp • CALCULATE THE FRACTION DEADTIME • USE EITHER THE DISTURBANCE (FIGURES 9.3.2 a - c) OR SETPOINT (FIGURES 9.3.2 d - f) FOR SYSTEM PERTURBATIONS.

14. Classical Tuning Methods • CIANCONE AND MARLIN • THE SEQUENCE OF CALCULATION OF TUNING CONSTANTS: • DETERMINE THE DIMENSIONLESS TUNING PARAMETERS FROM THE GRAPHS: GAIN, INTEGRAL TIME AND DERIVATIVE TIME • CALCULATE THE ACTUAL TUNING VALUES FROM THE DIMENSIONLESS VALUES: (e.g.):

15. Classical Tuning Methods • STABILTY-BASED METHOD - ZIEGLER-NICHOLS • USES THE ACTUAL SYSTEM TO MEASURE RESPONSES TO PERTURBATIONS • AVOIDS THE LIMITS IN MODELING PROCESSES • TARGET VALUES ARE IN TABLE 9.3

16. Classical Tuning Methods • BASED ON A QAD TUNED RESPONSE • BASED ON PROPORTIONAL-ONLY VALUES • ULTIMATE VALUES • GAIN: • PERIOD

17. Controller Tuning by Pole Placement (discussed previously) • Based on model of the process • Select the closed-loop dynamic response and calculate the corresponding tuning parameters. • Application of pole placement shows that the closed-loop damping factor and time constant are not independent. • Therefore, the decay ratio is a reasonable tuning criterion. • Note eqn 9.4.5 should be

18. Controller Design by Pole Placement • A generalized controller (i.e., not PID) can be derived by using pole placement. • Generalized controllers are not generally used in industry because • Process models are not usually available • PID control is a standard function built into DCSs.

19. Internal model control (IMC)-Based Tuning • A process model is required (Table 9.4 contain the PID settings for several types of models based on IMC tuning). • Although a process model is required, IMC tuning allows for adjusting the aggressiveness of the controller online using a single tuning parameter, τf.

20. RECOMMENDED TUNING METHODS • TUNING ACTUAL CONTROL LOOPS DEPENDS ON PROCESS CHARACTERISTICS • PROCESSES CAN BE CATEGORIZED AS HAVING SLOW OR FAST RESPONSE, RELATED TO PROCESS DEAD TIME AND THE PROCESS TIME CONSTANT • SEE TABLE 9,4 FOR TYPICAL TUNING PARAMETERS FOR PROCESS TYPES.

21. LIMITATIONS ON SETTING TUNING CONSTANTS • FOR ACTUAL SYSTEMS • IT IS VERY DIFFICULT TO DEVELOP A RIGOROUS MODEL FOR A PROCESS • .THERE MAY BE MANY COMPONENTS THAT NEED TO BE INCLUDED IN THE MODEL • .NONLINEARITY IS ALSO A FACTOR • PRESENT IN ALL PROCESSES • CAN RESULT IN CHANGE IN PROCESS GAIN AND TIME CONSTANT

22. LIMITATIONS ON SETTING TUNING CONSTANTS • ACTUAL PROCESSES MAY EXPERIENCE A RANGE OF OPERATIONS, BUT CONTROL IS TYPICALLY OPTIMIZED FOR ONE SET OF CONDITIONS • TABLE 9.5 SHOWS HOW A CONTROL SYSTEMS CAN BECOME UNSTABLE DUE TO CHANGES IN FEED CONCENTRATIONS TO A REACTOR • TABLE 9.6 SHOWS THE SYSTEM REMAINS STABLE UNDER THE SAME LEVELS OF CONCENTRATION CHANGES IF A REACTION PARAMETER (ACTIVATION ENERGY) IS CHANGED

23. LIMITATIONS ON SETTING TUNING CONSTANTS • CHANGES IN CONTROL CAN ALSO AFFECT DOWNSTREAM PROCESSES • CHANGING RESIDENCE TIME IN A REACTOR CAN CHANGE THE FEED CONCENTRATIONS TO A DISTILLATION PROCESS • CHANGING FEED RATES TO DISTILLATION COLUMNS CAN ALSO IMPACT THE HEAT BALANCE AND PRODUCT CONCENTRATIONS IN THE COLUMN • IT MAY NOT BE PRACTICAL TO ACTUALLY INTRODUCE TRACERS OR PERTURBATIONS INTO OPERATING SYSTEMS IN ORDER TO OBTAIN TUNING DATA