1. Hairul Nazirah bt Abdul Halim
ERT 210/4 Process Control
CHAPTER 8
Feedback Controllers
Hairul Nazirah bt Abdul Halim
Office:

2. ExxonMobil Chemical Plant

3. Process Control in our daily life…

4. Process Control in our daily life…
Controlling the water temperature of a shower
Let’s analyze this process:
Control objective: to control the shower temp.
process: shower
sensor: person’s skin
Controlled variable (CV): shower temp.
Set point: desired shower temp.
Manipulated variable (MV): flow of cold water
Final control element: valve on cold water line
Controller: person in the shower

5. Benefits of improved process control:
1. improve product quality
2. faster
3. greater production rates
4. less expensive process validation procedure

6. Overview – Temperature Control
heat exchanger - uses steam to heat cold water.
How to control the temperature??
Manual Control
Automatic Control

7. Compare Temp. with desired value To admit more/less steam
MANUAL CONTROL
Operator
Compare Temp. with desired value
open/close valve
To admit more/less steam

8. (measure the temperature) (transmits it to electronic signal)
AUTOMATIC CONTROL
SENSOR - TRANSMITTER
(measure the temperature)
(transmits it to electronic signal)
FEEDBACK CONTROLLER
(compares the measured temp. to the set point value
& make corrective action)
I/P TRANSDUCER
(Convert electronic signal to pneumatic signal)
CONTROL VALVE
(Receive signal & adjust the manipulated variables (flow))

9. COURSE OUTCOME: CH 8, 9 & 10
CO2: Ability to identify, measure and investigate Feedback controllers, Control system instrumentation and the control system design.

10. CHAPTER 8: FEEDBACK CONTROLLERS
DEFINE and DISCUSS the standard feedback control algorithm (control laws), IDENTIFY and COMPARE Proportional-integral-derivative (PID) control and on-off control types of feedback control
Subtopic:
1. Definition
2. Basic Control Modes
3. PID Controllers
4. On-Off Controllers

11. Feedback Controllers Chapter 8
Figure 8.1 Schematic diagram for a stirred-tank blending system.

12. Chapter 8 Basic components in a feedback control loop are:
1. Process being control (blending system)
2. Sensor-transmitter combination (AT)
3. Feedback controller (AC)
4. Current-to-pressure transducer (I/P)
5. Final control element (control valve)
6. Transmission line between the various instrument
Chapter 8

13. BLOCK DIAGRAM OF A FEEDBACK CONTROL SYSTEM
Figure 11.7 Block diagram for the entire blending process composition control system.

14. INDUSTRIAL PROCESS CONTROL EXAMPLES
Flow Controller
Level Control in a tank
Aerobic Fermentation Process
Fed-Batch Bioreactor

15. FLOW CONTROLLER Control objective: to maintain the desired flow rate
The setpoint: desired flow rate
Controlled variable (CV): the outlet flow rate
Manipulated variable (MV): the inlet flow rate of the process stream
Disturbances variable (DV): changes in the upstream pressure for the process stream
Sensor: combination of an orifice plate and a device that measure a pressure drop across the orifice, which directly related to the flow rate.
Final control element: the control valve in the line
Controller – flow controller (FC) – compares the measured flow rate with the specified flow rate (flow setpoint) and opens/closes the control valve accordingly.

16. Figure 8.2 Flow control loop
Chapter 8
Figure 8.2 Flow control loop

17. LEVEL CONTROL IN A TANK
Control objective: to maintain the level in the tank
The setpoint: desired level in the tank
Controlled variable (CV): the level in the tank
Manipulated variable (MV): exit flow from the tank
Disturbances variable (DV): changes in the inlet flow rate
Sensor: level indicator on the tank (LT)
Final control element: the control valve on the outflow line
Controller – level controller (LC) – compares the measured level with the setpoint for the level in the tank and makes a change to the control valve on the exit flow from the tank.

19. AEROBIC FERMENTATION PROCESS
Control objective: to maintain a specified dissolved oxygen (DO) conc. in the fermentation reactor so that the cells in the process have adequate oxygen levels.
The setpoint: desired DO concentration
Controlled variable (CV): the DO conc. in the fermentor
Manipulated variable (MV): air flow rate to the fermentor
Disturbances variable (DV): changes in the rpm of the mixer impeller
Sensor: DO sensor-transmitter (AT)
Controller – DO controller (AC) – compares the measured DO with the setpoint value and sets the air flow rate to the fermentor.
Final control element: variable speed air compressor

21. FED-BATCH BIOREACTOR Control obj:
to maintain a specified cell concentration in the bioreactor.
What is the CV, MV, DV, sensor & final control element?

22. Control objective: to maintain a specified cell concentration in the bioreactor.
The setpoint: desired cell concentration
Controlled variable (CV): the cell conc. in the bioreactor
Manipulated variable (MV): the feed rate of glucose and nutrients mixture to the bioreactor.
Disturbances variable (DV): changes in the concentration of glucose in the feed.
Sensor: turbidity meter (AT) which provides a measurement that correlates with the cell conc. in the broth.
Controller – controller (AC) – compares the measured cell conc. with the setpoint value and sets the glucose feed rate.
Final control element: variable speed pump

23. BASIC CONTROL MODES Chapter 8 Three basic control modes:
Proportional Control
Integral Control
Derivative Control
Next, we consider :
Proportional-Integral-Derivative (PID) Control
Chapter 8

24. PROPORTIONAL CONTROL Chapter 8
Objective: To reduce the error signal to zero.
Chapter 8

25. For proportional control, the controller output is proportional to the error signal,
Chapter 8
where:

26. Chapter 8

27. Some controllers have a proportional band setting instead of a controller gain.
The proportional band PB (in %) is defined as
Chapter 8
PB correspond to Kc and vice versa

28. The transfer function for proportional-only control:
Chapter 8

29. Chapter 8 Disadvantage of proportional-only control:
Offset : Steady-state error occurs after a set-point change or a sustained disturbance.
Offset can be eliminated by manually resetting the set point, ysp or by using controller with integral action.
Applications
P-only control: for process with non-sluggish dynamic behavior & offset is not important. level)
Chapter 8

30. INTEGRAL CONTROL Chapter 8
Controller output depends on the integral of the error signal over time.
Chapter 8
= Integral Time (adjustable parameter)
- units of time (min)

31. Proportional-Integral (PI) Control
Integral control action is widely used: elimination of offset.
Integral control action is normally used in conjunction with proportional control as the proportional-integral (PI) controller:
Chapter 8

32. Transfer function for the PI controller:
Chapter 8

33. Response to unit step change in e:
Chapter 8
Figure Response of PI controller to unit step change in e(t).

34. Chapter 8 Disadvantage of integral control:
Tend to produce oscillatory responses of the controlled variable.
Oscillatory response can be eliminated by proper tuning of the controller or by including derivative action.
Applications
PI control: when offset elimination is important (Flow, Temp., Composition, DO)
Chapter 8

35. RESET WIND-UP
When a control loop is in control with a minimal error, then wind-up is not a problem.
Wind-up is only a problem when a control loop is temporarily taken out of control.
Occur during start-up of a batch process or after a large set-point change.
The integral term becomes quite large due to a sustained error
The controller output eventually saturates.
Chapter 8

36. RESET WIND-UP
Further build-up of integral term while the controller is saturated.
undesirable because the controller is already doing all it can to reduce the error.
Commercial controllers provide antireset wind-up.
Antireset wind-up reduce the windup by temporarily halting the integral control action whenever the controller output saturates.
Chapter 8

37. Reset windup
Chapter 8

38. DERIVATIVE CONTROL Chapter 8
anticipate the future behavior of the error signal by considering its rate of change.
ideal derivative action,
= derivative time, has units of time.
Chapter 8

39. “ideal” PD controller has the transfer function:
Chapter 8
Derivative control action tends to improve dynamic response of the controlled variable
The process measurement is noisy if it contains high frequency or random fluctuations.
Disadvantage: derivative action will amplify the noise unless the measurement is filter

40. Chapter 8 the transfer function for “real” PD controller,
Eq includes a derivative filter that reduces the sensitivity of the calculations to high frequency noise in the measurement.
the value of α constant : 0.05 < α <0.2,
(0.1 being a common choice)

41. Proportional-Integral-Derivative (PID) Control
Combination of P, I & D Control modes
3 most common forms of PID Control:
Parallel
Series
Expanded
Chapter 8

42. Chapter 8 Parallel Form of PID Control
Figure 8.8 Block diagram of the parallel form of PID control (without a derivative filter)

43. Chapter 8 Parallel Form of PID Control
Transfer function for “Ideal” PID control is:
Chapter 8
Transfer function for “Real” PID control, with derivative filter is:

44. Chapter 8 Series Form of PID Control
Transfer function for “Ideal” PID control is:
Chapter 8
Figure 8.9 Block diagram of the series form of PID control (without a derivative filter)

45. Chapter 8 Series Form of PID Control
Commercial versions of the series-form controller have a derivative “Real PID control”:
Chapter 8

46. Chapter 8 Expanded Form of PID Control Use in MATLAB
Control parameters are 3 gains: Kc, KI and KD
Chapter 8

47. Chapter 8 Controller Comparison P - Simplest controller to tune (Kc).
- Offset with sustained disturbance or setpoint
change.
PI - More complicated to tune (Kc, I) .
- Better performance than P
- No offset
- Most popular FB controller
Chapter 8
PID - Most complicated to tune (Kc, I, D) .
- Better performance than PI
- No offset
- Derivative action may be affected by noise

48. Chapter 8 Automatic and Manual Control Modes Automatic Mode
Controller output, p(t), depends on e(t), controller constants, and type of controller used.
( PI vs. PID etc.)
Chapter 8

49. Chapter 8 Manual Mode Controller output, p(t), is adjusted manually.
Manual Mode is very useful when unusual conditions exist:
i) plant start-up
ii) plant shut-down
iii) emergencies
Chapter 8

50. On-Off Controllers Chapter 8 Cheap
Simple type of feedback controllers
Cheap
Used in residential heating and domestic
refrigerators
Limited use in process control due to
continuous cycling of controlled variable
Less widely used than PID controllers – not
effective.
Two mode of control action:
Full control action
No control action
Chapter 8

51. On-Off Controllers Chapter 8
Consider Figure 1.6.1
The on-off heater applies heat to the system as long as the controlled temperature is below a specified upper limit.
When upper limit is reached, the addition of heat is stopped.
Heat is not added to the process until the controlled temp. becomes less than a specified lower limit
Chapter 8

52. Chapter 8 Figure 1.6.1 An example of the operation of an on-off
temperature controller.

53. Chapter 8 Typical Response of Feedback Control Systems
No control: the process slowly reaches a new steady state
P – speed up the process response & reduces the offset
PI – eliminate offset & the response more oscillatory
PID – reduces degree of oscillation and the response time
Chapter 8
Figure Typical process responses with feedback control.

54. Chapter 8
Figure Proportional control: effect of controller gain.
Increasing Kc tends to make the process response less sluggish (more faster)
Too large of Kc, results in undesirable degree of oscillation or even become unstable
Intermediate value of Kc usually results in the best control.

55. Chapter 8
Figure PI control: (a) effect of reset time (b) effect of controller gain.
Increasing τI tends to make the process response more sluggish (slower)
Too large of τI, the controlled variable will return to the set point very slowly after a disturbance set-point change occurs.

56. Chapter 8 Figure 8.15. PID control: effect of derivative time.
Increasing τD tends to improve the process response by reducing the maximum deviation, response time and degree of oscillation.
Too large of τD: measurement noise is amplified and process response more oscillatory.
The intermediate value of τD is desirable.

57. Selection of Controller
Should consider the combination of dynamic behavior of:
- final control element
- process
- sensor
P-only control: for process with non-sluggish dynamic behavior & offset is not important. level)
PI control: when offset elimination is important (Flow, Temp., Composition, DO)
PID: for sluggish process (Biomass Concentration, Temp., Composition)