UNIVERSITI TEKNOLOGI MARA

FAKULTI KEJURUTERAAN KIMIA

CHEMICAL PROCESS CONTROL

(CPE562)

Remarks:

Checked by: Rechecked by:

------------------------------- ----------------------------------(SIR MOHD AIZAD AHMAD ) ( )

Date: Date:

NAME : SHEH MUHAMMAD AFNAN BIN SEH HANAFI

STUDENT ID. : 2013210382DATE SUBMIT : 75/12/2015SEMESTER : 5PROGRAMME / CODE : EH221GROUP : EH2215A

ASSIGNMENT : CONTROL LOOP SIMULATION SUBMIT TO : SIR MOHD AIZAD AHMAD

CHAPTER 1 : INTRODUCTION

History of PID controller

PID also known as proportional–integral–derivative controller is a control feedback

mechanism. In early years, PID controller is used as automatic ship steering.It was implemented

as a mechanical device such a lever, spring and a mass and were often energized by compressed

air. The first PID controller was developed by Elmer Sperry in 1911 and theoretical analysis

first introduced by Russian American engineer Nicolas Minorsky, (Minorsky 1922). The goal is

stability, not general control, which simplified the problem significantly. Proportional control

provides stability against small disturbances while derivative term was added to improve stability

and control. In modern years, PID controllers in industry are implemented in programmable

logic controllers (PLCs) and  applied in industrial ovens, plastics injection machinery, hot

stamping machines . It used the the implementation of the PID algorithm.

PID controller theory and equation

Gc (PID )=K c ¿)

Where K c is the PID control gain, τ i (s) is the integral gain, τ d (s ) is the derivative gain

Proportional Action

Proportional (P) control has a function in determining the magnitude of the difference

between the set point and the process variable which is indicated as error. Then this proportional

control will applies appropriate proportional changes to the control variable to eliminate error. 

Many control systems will, in fact, work quite well with only Proportional control due to it fast

response time and its ability to minimize fluctuation. However, it contains large offset. It is an

instantaneous response to the control error for improving the response of a stable system.

Contrastly, it cannot control an unstable system by itself. Therefore when the frequencies

leaving the system , the gain is the same with a nonzero steady-state error.

Integral Action

Integral (I) control usually examines the offset of set point and the process variable over

time and corrects it when and if necessary. This integral control has small offset and always

return to steady state but it leads to slow response time. Integral action drives the steady-state

error towards 0 but slows the response since the error must accumulate before a significant

response is output from the controler. Since an integrator introduces a system pole at the origin,

an integrator can be detrimental to loop stability. Only controllers with integrators can wind-

up where, through actuatorsaturation, the loop is unable to comply with the control command

and the error builds until the situation is corrected.

Derivative Action

Derivative (D) control, monitored the rate of change of the process variable and consequently

makes changes to the output variable to provide unusual changes.

When there is a "process upset", meaning, when the process variable or the set point quickly

changes  - the PID controller has to quickly change the output to get the process variable back

equal to the set point. Once the PID controller has the process variable equal to the set point, a

good PID controller will not vary the output. Thus, there are two responses occur such as fast

response (fast change in output) when there is a "process upset", but slow response (steady

output).

Controller gain

The proportional gain (Kc) determines the ratio of output response to the error signal. For

instance, if the error term has a magnitude of 10, a proportional gain of 5 would produce a

proportional response of 50. In general, increasing the proportional gain will increase the speed

of the control system response. However, if the proportional gain is too large, the process

variable will begin to oscillate. If Kc is increased further, the oscillations will become larger and

the system will become unstable and may even oscillate out of control.

Deadtime

Deadtime is a delay between when a process variable changes, and when that change can

be observed. For instance, if a temperature sensor is placed far away from a cold water fluid inlet

valve, it will not measure a change in temperature immediately if the valve is opened or closed.

Deadtime can also be caused by a system or output actuator that is slow to respond to the control

command, for instance, a valve that is slow to open or close. A common source of deadtime in

chemical plants is the delay caused by the flow of fluid through pipes.

Effect of increasing and decreasing value of P,I &D toward process response

When parameters of an existing controller have to be tuned, there will be a problem in the

identification of PID controller. Controller structure has to be determined since manufacturers do

not provide data on controller structure whether serial or parallel. Manual tuning of controller

parameters had to be done if they are changed with time. Other than that, manual tuning of

controller parameters also had to be done when change in process parameters occurred. Manual

parameter tuning can be done using trial and error and if rules shown in the table below:

Parameter Speed of Response Stability Accuracy

Increasing K Increases Deteriorate Improves

Increasing Ki Decreases Deteriorate Improves

Increasing Kd increases Improves No effect

Settling time : The time at which the PV reaches ± 5% of the total change in the

process variable (ΔPV).

Overshoot : Most notably associated with P-only controllers, is the difference fromthe SP to

where the PV settles out at a steady state value.

Decay ratio : The size of the second peak above the new steady state divided by thesize of the

first peak above the same steady state level

Objective of this study is to determine the effect of PID’s parameters to the process

controllability. To study the effect of controller gain, effect of integral time, effect of derivative

time and effect of deadtime on the control loop process.

CHAPTER 2 : METHODOLOGY

LAB 1: Effect of Controller Gain to Process Controllability

Procedure

1. Open matlab software then new model is opened by selecting file button.

2. Then, untitled window will appear.

3. Click button simulink library browser, then drag clock, to workspace, constant, PID controller,

transfer fcn , sum, scope and display. Arrange and connected all simulink in the right order.

4. Process transfer function is set as

5 s

s2+10 s , process set point=1

5. PID controller`s parameter was setup as P1=0.05, I1=0.01, D1=0

6. Set simulation parameters to 600

7. Run the simulation

8. Plot PV vs time

>>plot(time,PV)

9. Run a second set of PID`s value P2=0.1, I2=0.01, D2=0

10. Plot the second process response

>>figure(2),plot(time,PV)

11. Run a third set of PID`s value P3=0.2, I3=0.01, D3=0

12. Plot the third process response

>>figure(3),plot(time,PV)

13. View all the figure in figure palette.

14. Combine response of figure(2) and figure(3) into figure(1)

15. Rename the x-axis as time and y-axis as PV and every figure as PID1, PID2, and PID3.

16. Show the SP at 1.

Figure 1 : PFD FOR EFFECT OF CONTROLLER GAIN

LAB 2: Effect of Integral Gain to Process Controllability

Procedure

1. Open mat lab software then new model is opened by selecting file button.

2. Then, untitled window will appear.

3. Click button simulink library browser, then drag clock, to workspace, constant, PID controller, transfer fcn, sum, scope and display. Arrange and connected all simulink in the right order.

4. Process transfer function is set as

5 s

s2+10 s , process set point=1

5. PID controller`s parameter was setup as P1=0.05,I1=0.01,D1=0

6. Set simulation parameters to 600

7. Run the simulation

8. Plot PV vs time

>>plot(time,PV)

9. Run a second set of PID`s value P2=0.05 I2=0.02 D2=0

10. Plot the second process response

>>figure(2),plot(time,PV)

11. Run a third set of PID`s value P3=0.05 I3= 0.04, D3=0

12. Plot the third process response

>>figure(3),plot(time,PV)

13. View the figure in figure palette

14. Combine response of figure (2) and figure (3) into figure (1)

15. Rename the x-axis as time and y-axis as PV and every figure as PID1, PID2, PID3.

16. Show the SP at 1.

Figure 2 : PFD for integral gain

LAB 3: Effect of Derivitive time to Process Controllability

Procedure

1. Open Mat lab software then new model is opened by selecting file button.

2. Then, untitled window will appear.

3. Click button simulink library browser, then drag clock, to workspace, constant, PID controller, transfer fcn , sum, scope and display. Arrange and connected all simulink in the right order.

4. Process transfer function is set as

5 s

s2+10 s , process set point=1

5. PID controller`s parameter was setup as P1=0.05,I1=0.01,D1=0

6. Set simulation parameters to 600

7. Run the simulation

8. Plot PV vs time

>>plot(time,PV)

9. Run a second set of PID`s value P2=0.05 I2=,0.01 D2=2

10. Plot the second process response

>>figure(2),plot(time,PV)

11. Run a third set of PID`s value P3=0.05 I3=0.01, D3=4

12. Plot the third process response

>>figure(3),plot(time,PV)

13. View the figure in figure palette.

14. Combine response of figure (2) and figure (3) into figure(1)

15. Rename the x-axis as time and y-axis as PV and every figure as PID1, PID2, PID3.

16. Show the SP at 1.

Figure 3 : PFD for derivitive time

LAB 4: Effect of deadtime to Process Controllability

Procedure

1. Open mat lab software then new model is opened by selecting file button.

2. Then, untitled window will appear.

3. Click button simulink library browser, then drag clock, to workspace, constant, PID controller, transfer fcn , variable time delay , sum, scope and display. Arrange and connected all simulink in the right order.

4. Process transfer function is set as

5 s

s2+10 s , process set point=1. Add “transport delay” and set

Time Delay to 5.

5. PID controller`s parameter was setup as P1=0.2, I1=0.01,D1=0

6. Set simulation parameters to 600

7. Run the simulation

8. Plot PV versus time

>>plot(time,PV)

9. Run a second set of Time delay = 7

10. Plot the second process response

>>figure(2),plot (time,PV)

11. Run a third set of Time delay = 9

12. Plot the third process response

>>figure(3),plot (time,PV)

13. View the figure in figure palette.

14. Combine response of figure (2) and figure (3) into figure (1)

15. Rename the x-axis as time and y-axis as PV and every figure as PID1, PID2, PID3.

16. Show the SP at 1.

Figure 4 : PFD for deadtime

CHAPTER 3 : RESULT AND DISCUSSION

LAB 1: Effect of Controller Gain to Process Controllability

Result

0 100 200 300 400 500 6000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

time

PV

PID1

PID2

PID3

SP

Figure 5 : Combination of 3 graph controller gain

DISSCUSSION

In the figure above shows 3 different graph plotted in order to observe the oscillations of each

graph plotted. The 3 different values of Proportional (P) are considered which are 0.05, 0.1, and

0.2. Based on the graph, it can be concluded that the high proportional value will lead the system

to become unstable and oscillate. The proportionality is given by controller gain. For a given

change in time, the amount of output process value (PV) will be determined by the controller

gain. It is the best controller gain if the peak of the graph reaches the set point. From the graph

obtained, figure 3 has the best controller gain since the peak point of the graph is nearest to the

set point (SP=1). That’s why this condition will contribute to better processes.

LAB 2: Effect of Integral Gain to Process Controllability

Result

0 100 200 300 400 500 6000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

TIME

PV

PID3

PID2

PID1

SP

Figure 6 : Combination of 3 graph Integral time

DISSCUSSION

For second experiment is to find the effect of integral time. The larger value of integral time, the

more oscillates of the graph obtained. Based on observation of the graph, there are more

oscillations for integral time, I=0.04. Thus, the integration will take part until the area under the

curve becomes zero. If there is decreasing in I, it will result in instability system. From the

graph, it can be concluded that increasing too much I will contribute the present value to

overshoot the set point value. Figure 6 has a better process since the peak point reaches nearest to

the set point. So that, we can conclude that the increasing value of I will lead the graph to more

oscillations.

LAB 3: Effect of Derivative Time to Process Controllability

0 100 200 300 400 500 6000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

TIME

PV

PID1

PID3

PID2

SP

Figure 7 : Combination of 3 graph Derivative time

DISSCUSSION:

From the the graph obtained, it can be concluded that the larger values of derivative will

decrease the overshoot. Besides that, this change will lead to instability since it will slow down

transient response. In fact, derivative control is used to reduce the magnitude of the overshoot

produced. Derivatives term is also used in slow processes such as processes with long time

constant.

LAB 4: Effect Of Deadtime to Process Controllability

Result

0 100 200 300 400 500 6000

0.5

1

1.5

2

2.5

Time

PV

PID3

PID2

PID1

SP

Figure 8 : Combination of 3 Graphs for Different deadtime

DISSCUSSION

Based on the graphs, it can be concluded that the increasing in Time Delay will produce more

oscillations on the graph. The calculation is starting at the dead time icon. The more time delay,

the instability of the system also increases. This is due to the long stopped reaction time. For

time delay = 5, there is not much oscillation occur. When we increase the time delay to 7, there is

small oscillation occur.

CHAPTER 4 : CONCLUSION AND RECOMMENDATION

The performance of each of the three types of controllers varies due to the differing

components of controller equation. In P-only control, the only adjustable tuning parameter is KC as

the proportional term is the only term in the corresponding controller equation. The advantage of

P-only control is that there is only one tuning parameter to adjust and therefore the best tuning

values are obtained rather quickly.Tthe disadvantage to P-only control is that it permits offset. To

minimize offset, KC may be increased, however this results in greater oscillatory behavior.

The advantage to PI control is that it eliminates the offset present in P-only control by

minimizing the integrated area of error over time. To assess the effect changing the two tuning

parameters has on a PI controller performance, both KC and τI were halved and doubled. In this

process, using these tuning parameters actually resulted in increased magnitude of oscillations over

time and thus an unstable system. Either lowering τI, or increasing KC from the initial value

resulted in a greater peak overshoot, larger settling time and larger decay ratio.

In PID control all three terms are utilized. The function of the derivative term is to

determine the rate of change of the error (slope) thus influence the controller output. A rapidly

changing error will have a larger derivative and therefore a larger effect on controller output. The

derivative term will therefore work to decrease the oscillatory behavior in the process variable. To

assess the effect of changing derivative time, a comparison of the tuning parameter τD was made

for the PID controller by halving and doubling the initial value.

Increasing the derivative time results in less oscillatory behavior of the process variable

however there is also an increased noise in the controller output. Increasing τD also increase rise

time, settling time, and decreases peak overshoot

RECOMMENDATION

In choosing the ‘best’ performing controller it must be noted that best performance is

subjective, meaning that some processes may desire a PV response with no overshoot, others

may be able to tolerate overshoot and prefer faster rise times. For a process that desires fast rise

time with the minimal amount of oscillatory behavior and overshoot it would be suggested to use

a moderate to moderately aggressive PI controller.

REFRENCES

1. Abdul Aziz Ishak & Zalizawati Abdullah. (2014). PID TUNING Fundamental Concepts

and Application. UITM Press.

2. H. Bischoff*, D.Hoffmann*, E.V.Terzi. (1997). Process Control System, Control of

Temperature, Flow and Filling Level. Festo Didactic GmbH & Co.

3. Basso, Christophe (2012). "Designing Control Loops for Linear and Switching Power

Supplies: A Tutorial Guide". Artech House, ISBN 978-1608075577

4. Blanke, M.; Kinnaert, M.; Lunze, J.; Staroswiecki, M. (2006), Diagnosis and

Fault-Tolerant Control (2nd ed.), Springer