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MONASH University Malaysia CHE 3162 Process Control Laboratory

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CHE 3162 Process Control

Laboratory Liquid Flow Control

Name Student ID Mah Wei-Jun How ZhongXing Yang Ge Hoa Ang Lin Yang

24640093 24554758 24686727 24133027


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Table of Contents 1.0 Summary ................................................................................................................................................................ 4 2.0 Introduction .......................................................................................................................................................... 6 3.0 Objectives .............................................................................................................................................................. 7 4.0 Experimental Procedure ................................................................................................................................. 7

4.1 Diagram of Apparatus ................................................................................................................................ 7 4.2 Description of Apparatus ........................................................................................................................... 8 4.3 Methodology .................................................................................................................................................... 8

4.3.1 Experimental Start-Up ........................................................................................................................... 8 4.3.2 Closed Loop Flow Control .................................................................................................................. 8 4.3.2.1 Proportional Controller ...................................................................................................................... 8 4.3.2.2 Proportional-Integral (PI) Controller ............................................................................................ 9 4.3.2.3 Proportional-Derivative (PD) Controller ..................................................................................... 9 4.3.2.4 Proportional-Integral-Derivative (PID) Controller .................................................................. 9 4.3.2.5 Step Test Closed Loop Tuning Method ....................................................................................... 9 4.3.2.6 Experimental Shut-Down .............................................................................................................. 10

5.0 Results and Analysis ................................................................................................................................... 11 5.1.0 Closed Loop Flow Control .................................................................................................................. 11

5.1.1 Proportional Controller .................................................................................................................. 11 5.1.2 Proportional-Integral (PI) Controller ...................................................................................... 13 5.1.3 Proportional-Derivative (PD) Controller ................................................................................ 15 5.1.4 Proportional-Integral-Derivative (PID) Controller ........................................................... 17

5.2 Step Test Closed Loop Tuning Method ............................................................................................ 19 5.3 Calculation Step .......................................................................................................................................... 20

6.0 Discussion ........................................................................................................................................................... 23 6.1 Define proportional gain, integral gain and derivative gain ................................................... 23 6.2 The effects of proportional gain applied on the proportional-only control system and the effects to response of the system. ......................................................................................................... 23 6.3 Discuss the effect of integral gain applied on the proportional-integral control system. Relate the effect to the response of the system (e.g. rise time, overshoot percentage, settling time and steady state error). ........................................................................................................ 24 6.4 The effects of derivative gain applied on the proportional-derivate control system and the effects to response of the system. ......................................................................................................... 25


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6.5 Discuss the system response curve of PID controller tuned and compare it with the system response curve obtained from proportional controller, proportional-integral controller and proportional-derivative controller. ............................................................................. 26 6.6 Discuss whether the step test closed loop tuning method is suitable for all types of process control. ................................................................................................................................................... 27

7.0 Conclusion .......................................................................................................................................................... 28 8.0 Nomenclature .................................................................................................................................................... 29 9.0 Reference ............................................................................................................................................................ 29


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1.0 Summary Throughout the conduction of this experiment, a detailed application of how the closed-loop proportional-only control (P control), closed-loop proportional-derivative control (PD control), closed-loop proportional-integral control (PI control) and a closed-loop proportional-integral-derivative control (PID control) are used in the regulations of maintaining the liquid level at a fixed level inside a reactor or a tank is seen. Basically, a proportional control will provides a fast response for adjustments made on the manipulated variable, however it will always have an offset whereas for the proportional-integral (PI) control it is able to remove the offset thus making the magnitude of error to be zero. As for the proportional-integral-derivative (PID) control, it was proven to be the best device among the other controllers as it can prevent large value overshoots by removing offset values. Besides that, it also reduces the settling time if optimum values of proportional, integral and derivative gains are entered to the controller.

Four different combinations of control system were performed in these experiments, which are primarily made up from proportional (P), integral (I) and derivative (D). These controllers are then used for different combinations of P, PI, PD and PID. Among the combinations made, the combination that was able to provide the least or shortest time to reach the steady state was chosen and used for our tuning on the step test closed-loop system.

For the proportional (P) control system experiment, the Ki and Kd values were set to zero while Kp is being varied. Results showed that when the Kp value was too small, the offset of the system will be large and when the Kp was set to a large value, the system will become unstable as it starts oscillating. However, a lower Kp value will require a shorter time to achieve steady state compared to a higher Kp value. For the proportional-integral (PI) control system, Kp was set to 5, Kd was set to zero while Ki was being altered. In the tuning process, it was concluded that when Ki = 0.01, the response curve was the best compared to the response for other values of Ki.

As for the proportional-derivative (PD) control system, values of Kp and Ki were set to 5 and zero respectively with Kd being tuned to different values. Based on results attained, it was concluded that the system was unable to achieve the set point value and the system was starting to become less stable as oscillations were increasing when Kd was changed from a smaller value to a bigger value. After series of observations and result analysis that were made, values of Ki and Kd that gave the best response graph in the PI and PD experiments was chosen for the proportional-integral-derivative (PID) control system experiment. With these optimum values, the best response curve for the PID control can be accomplished.

Lastly, series of calculations were carried out using optimum values of Kp, Ki and Kd that was determined previously for the PID control system. The process gain, Kp was calculated to be 0.06, time constant, Tp to be 0.0167 minutes while the dead time, P was determined to be 0.0333 minutes by considering calculations based on 80% and 20% valve openings as these 2 openings gave the smoothest flow in step change loop tuning.


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To conclude this experiment, the PID control system gave the best response curve as the overshoot was smaller, time required to achieve steady state was shorter and a stable system without oscillations. However, values of Kp, Ki and Kd have to be altered based on the process as different set points will have their own optimum Kp, Ki and Kd values.


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2.0 Introduction Generally, every chemical processes will have some disturbances along its operation. These processes usually deals with hazardous and dangerous chemicals and thus, process control are necessary to ensure the safety of operation and also to maintain the quality of the desired product. The simplest forms of both automatic and manual control rely on adjusting a manipulated variable (MV) in order to compensate for observed undesired variations of the process variable (PV) or output. Basic elements that make up a process control system in industries consist of a controller, process, measuring element and control element. A closed loop control exists when all these components are interlinked and information can be passed around the loop. Currently, the Proportional-Integral-Derivative (PID) algorithm is the most sort-after algorithm used in industry. Some of the most common processes that require the usage of PID are fluid flow monitoring, heating and cooling systems, flow control and temperature control. Prior to using the PID control, we need to define a set point and a process variable. The set point is the desired value of the controlled parameter while the process variable refers to the system parameter that needs to be controlled such as the pressure, temperature and flow rate. A PID controller determines a controller output value, such as the heater power or valve position and applies the controller output value to the system, which in turn drives the process variable towards the set point value. In this experiment, the main function of the process control unit (LS-33 139 Basic Flow Control Unit) is to control the water flow rate in the system. Firstly, a pump will deliver water from a storage tank through a piping system. Next, the flow rate will be measured by the flow sensor and the value will be fed back to the control valve to regulate the incoming flow rate. All the elements and units in this experiment represents a scaled down process model of a common industrial process. Experiments were conducted on various closed loop control systems as listed below.

(i) Proportional Controller a type of linear feedback control system; controller output signal is proportional to the error input signal.

(ii) Proportional-Integral Controller two-term controller; integral action eliminates offset and makes the control system less stable.

(iii) Proportional-Derivative Controller two-term controller; derivative section responds to the rate of change of the process error.

(iv) Proportional Integral-Derivative Controller a generic control loop feedback mechanism; a PID controller calculates an error value as the difference between a measured process variable


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3.0 Objectives The objective of this experiment was to accomplish and develop understanding in the basic flow control process through operation of the process control unit, which controls the water flow rate in the system. In addition, responses from different PID settings in the closed loop flow control are studied and interpreted. On top of that, the step test closed loop tuning method in the flow control process is being demonstrated and analysed.

4.0 Experimental Procedure 4.1 Diagram of Apparatus

Figure 1: LS-33 139 Basic Flow Control Unit

Water Storage Tank


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4.2 Description of Apparatus The equipment shown above is the process control unit that is designed to control the flow rate of water in the system. The pump located in water storage tank is used to transfer water through a piping system. The water flow rate is then measured by a flow sensor located along the pipe. This is followed by the transmission of the value which will be fed back to the control valve to adjust the incoming flow rate in order to achieve the required set point value. 4.3 Methodology 4.3.1 Experimental Start-Up Before the experiment began, all the equipment was checked to ensure there are in good and safe condition. Firstly, the process control unit was placed on a level surface. Also, all the pipes and fittings were checked to ensure it is well-connected to prevent any leakage of water. Next, the computer was turned on. Then lastly, it was followed by the starting up of the LS-33 139 Basic Flow Control DAQ Software in the computer. Before the main power supply and the apparatus power supply (on the control panel of the equipment) was switched on, the water level in the storage tank was observed to be approximately of the tank. 4.3.2 Closed Loop Flow Control 4.3.2.1 Proportional Controller *The following procedures were carried out through the DAQ software on the computer.* First, the initial set point was set to 4LPM in the DAQ software. Then, the experiment was continued by setting KP = 1, KI = 0 and KD = 0. To initiate the software, the Run/Execute button was clicked. In order to record the data obtained, The Logging Started button was clicked. To allow the traceability of results in future, the time the experiment started was recorded. The submersible water pump was switched on. Then, the system was allowed to run for few minutes and the response of the system from the graph was observed. Next, the Data Analysis button was clicked to access all recorded data and the response graph. Print Screen button was used to record the system response graph. The experiment was again repeated with equal increment in the Proportional Gain KP (4, 9, 13 and 17) and the system response to different Proportional Controller was observed. Each time the KP value was altered, the time was recorded. The proportional gain was increased until the response starts to oscillate or to a point where the offset between the set value and process value was unable to be further decreased. Then, the data and response graph were recorded. The water pump was turned off before pressing the Stop button at the end of this experiment.


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4.3.2.2 Proportional-Integral (PI) Controller *The following procedures were carried out through the DAQ software on the computer.* The second part of the experiment was continued by setting the desired set value flow rate to 4 LPM. Next, the proportional gain was changed to the critical gain (KP = 5) and KI was altered to 0.01. The software was then started by pressing the Run/Execute button. Subsequently, the Logging Started button was clicked on to record the data. The time the experiment started was recorded. Then the submersible water pump was switched on. After allowing the system response to stabilize, the data was recorded and the Print Screen button was again used to save the generated response graph. The submersible water pump was turned off and the flow output was allowed to drop till 0 LPM. The experiment was repeated by varying the Integral gain, KI to values of 0.001, 0.01, 0.05, 1 and 10. 4.3.2.3 Proportional-Derivative (PD) Controller *The following procedures were carried out through the DAQ software on the computer.* The third part of the experiment was started by setting the desired flow rate set value to 4 LPM. The proportional gain was set to the critical gain (KP = 5), while KI = 0 and KD = 0.01. The program was allowed to run and the response was observed. Before the data could be recorded, the system response was allowed to stabilize. Subsequently, the data was recorded and the Print Screen button was clicked to obtain the generated response graph. Then the submersible water pump was turned off and the flow output was allowed to drop till 0LPM. The experiment was repeated with increasing derivative gain, KD to values of 0.02, 0.2, and 2. 4.3.2.4 Proportional-Integral-Derivative (PID) Controller *The following procedures were carried out through the DAQ software on the computer.* By comparing the system response graphs generated previously, the KI and KD values were altered to obtain the best system response by retuning the PID controller. Several trials were carried out and the different system response graphs were compared interpreted with each other to conclude the best combination of PID controller. 4.3.2.5 Step Test Closed Loop Tuning Method *The following procedures were carried out through the DAQ software on the computer.* The initial set point was set to 4LPM in the DAQ software. The KP, KI and KD values were determined to be 5, 0.01 and 0.01 respectively as these values provide the best system response graph obtained previously. The software was again started by pressing the Run/Execute button. After that, the submersible water pump was switched on and the system was allowed to stabilize. The Open Loop button was clicked and the sliding bar was adjusted to the maximum (100%). At the same time, the Logging Started button was clicked on. The water was allowed to flow for 20 seconds for stabilization of the system to be achieved. This was followed by adjusting the sliding bar to


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approximately 90%. The steps stated were repeated with equal decreasing valve opening (approximately decrement of 10%) until the valve was fully closed. Then, the Logging Started button was clicked to stop recording the data. Next, from the system response graph obtained, two different valve opening percentage which gave the smoothest flow were selected. Subsequently, the Logging Started button was clicked followed by the Open Loop button. The sliding bar was adjusted to the higher valve opening percentage value of the two and the system was allowed to run for 20 seconds. Then, the sliding bar was changed to the lower valve opening percentage value and again the water was allowed to flow for 20 seconds. The data shown was recorded and analyzed carefully. Lastly, calculations were performed according to the lab manual to determine the P, I and D values for the closed loop control. 4.3.2.6 Experimental Shut-Down The water pump was switched off, followed by the pressing of STOP button at the end of the experiment. The DAQ software was closed and the control panel power was turned off. All the .jpg files were transferred to USB drive. Lastly, the computer and the process control unit was shut down.


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5.0 Results and Analysis 5.1.0 Closed Loop Flow Control 5.1.1 Proportional Controller Set point value = 4LPM The response graphs for proportional flow control are shown below.

Figure 2: Proportional Controller response graph for Kp = 1, KI = 0, KD = 0



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5.1.2 Proportional-Integral (PI) Controller Set point value = 4LPM The response graphs for PI flow control are shown below.

Figure 7: Proportional-Integral Controller response graph for Kp = 5, KI = 0.001, KD = 0



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Figure 10: Proportional-Integral Controller response graph for Kp = 5, KI = 1, KD = 0

Figure 11: Proportional-Integral Controller response graph for Kp = 5, KI = 10, KD = 0


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5.1.3 Proportional-Derivative (PD) Controller Set point value = 4LPM The response graphs for PD flow control are shown below.

Figure 12: Proportional-Derivative Controller response graph for Kp = 5, KI = 0, KD = 0.01



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Figure 15: Proportional-Derivative Controller response graph for Kp = 5, KI = 0, KD = 2


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5.1.4 Proportional-Integral-Derivative (PID) Controller Set point value = 4LPM After concluding findings from previous controller set up experiments, the controller is then retune by adjusting values of Kp, KI, and KD to determine the best PID value for the best system response. The response graphs for PID controller are shown below.

Figure 16: PID Controller response graph for Kp = 5, KI = 0.01, KD = 0.005



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5.2 Step Test Closed Loop Tuning Method Set point value = 4LPM

Figure 21: Step change response graph for 10% valve opening decrement from 100%

Figure 22: Response graph of 2 valve openings for smoothest flow (80% and 20%)


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5.3 Calculation Step 1) Process Gain, Kp ! = , , ! = !"#$% !"!#!$%!"#$% !"!#!$% Values for flow rate at the respective valve opening percentage are obtained from Figure 22.

! = 2.8 6.420.1754 80.2632 = . where PV is the flow rate in LPM and CO is the valve opening percentage

2) Time Constant, TP

a) = !"#$% !!"#"$% = 2.8 6.4 = . b) Initial steady state = 6.4 LPM + 0.63 = 6.4 + 0.633.6 = . c) From Figure 22, the time when PV passes through the initial steady state + 0.63(PV) is 03:42:36 PM.

d) From Figure 22, the time when PV starts a first clear response to the step change in the CO is 03:42:35 PM. e) ! = !"#$ !"#$ !"#$ ! !!"#$ !"#$ !"#$ !!" = .


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3) Dead Time, P

a) From Figure 22, the time when PV starts a first clear response to the step change in the CO is 03:42:35 PM.

b) From Figure 22, the time when CO was stepped from its original value to its new value is 03:42:37 PM

c) ! = !"#$ !" !"#$ ! !!"#$ !" !"#$ !!" = .

4) P, I and D value for Closed Loop Control

TC is the larger of (0.1TP) or (0.8P)

Since TP = 0.0167 and P = 0.0333

a) 0.1TP = 1.6710-3 b) 0.8P = 0.0267

Therefore, TC = 0.0267 PI Controller For PI controller, the KC value is calculated using the equation below,

! = !! ! + ! = 0.01670.06 0.0333 + 0.0267 = . Ti value for PI controller is the same as TP, thus = = .


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Ideal PID For the ideal PID, KC value is calculated from the equation below where,

! = ! + 0.5!! ! + 0.5! = 0.0167 + 0.50.03330.06 0.0267 + 0.50.0333 = . Ti is calculated using, ! = ! + 0.5! = 0.0167 + 0.50.0333 = .

Td is then calculated using the equation shown below,

! = !!2! + ! = 0.01670.033320.0167 + 0.0333 = .! The values calculated for both PI and Ideal PID are then tabulated in Table 1 as shown below.

Table 1: P, I and D values for Closed Loop Control KC Ti Td

PI 4.639 0.0167 - Ideal PID

12.82 0.03335 8.33710-3


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6.0 Discussion 6.1 Define proportional gain, integral gain and derivative gain Proportional controller is a type of closed-loop feedback control system in which the adjustment to the manipulated variable is proportional to the error signal; increasing the manipulated variable will cause an increase in the error. The corrections for a proportional control are based on present errors. The proportional gain, Kp is a type of control in which adjusting the output will require the constant value to be multiplied with the error. The effect of using a proportional gain is that the rise time can be reduced significantly. Adding to that, the steady state error can be reduced but it cannot be eliminated completely. In order to eliminate the effect of disturbances completely, an integral controller is introduced in which the manipulated variables can be altered until the magnitude of error is eliminated completely for a step-like input. Corrections for an integral control are based on the sum or integral of past errors. The integral gain, Ki has the ability to eliminate the steady-state error; however, the downside of it is that the transient response will behave much slower as compared to the proportional controller. The derivative gain, Kd is a variable that allows the controller to reduce the output whenever the process value (PV) overshoot the set point (SP). Some advantages of using a derivative gain would be that the stability of the system can be improved, overshoot can be reduced and also the transient response can be improved. 6.2 The effects of proportional gain applied on the proportional-only control system and the effects to response of the system. In a practical industrial system, disturbances are often present in the system as there are unavoidable. These disturbances are undesirable as they present errors within the system and thus, needs to be eliminated with a processing control system. For the first part of the experiment, the simplest controlling system which is the proportional control (P) system is used to maintain the water level in the tank by regulating the amount of water flow rate being pumped into the system, in which it is set to a desired value of 4 litres per minute (LPM). Based on theoretical explanations, the steady state error of a typical proportional control system is inversely proportional to the proportional gain of the control system, K-P. This means that when the value of the proportional gain is increased to a larger value, the steady state error of the system will be reduced.

Based on analysis and observations on the response graph of different KP values from KP = 1 to KP = 17, it can be seen that when KP value is increased with Ki and Kd values being fixed, settling time taken for the proportional control system to reach steady state is longer but with a smaller steady state error. Besides that, when the KP is increased from a smaller value to a larger value, it is very obvious that the overshoot (difference between the first peak value to the steady state value) increased dramatically. The rise time of the system varies with increasing proportional gain as there are no obvious trends for the rise time. For example, in Figure 2 where KP = 1 and Figure 6 where KP = 17, the rise time for the both system based on the graphs are having slight changes only.


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Aside from that, it is also observed that when the ! value is increased, the response of the system starts to oscillate instead of approaching the steady state value. This oscillation of the response indicates that the system is unstable. From all the proportional control response graphs above, the best proportional control system response graph was Figure 5 with a KP value of 13. This is because the output flow rate was 2.5 LPM, which is closest to the set point of 4 LPM compared to the other lower proportional gain values used, which contain a greater offset value. However, even though using a proportional gain of 13 reduces the offset value, the settling time will be increased. So, there are exact proportional gain value that can give the best graph for this system that could reach the steady state with the shortest time possible as the value of the gain increases, the control system graph will eventually oscillating and become unstable. Hence, from the analysis made, the proportional control system is expected to always have an offset value and will not reach the set point even though different values of KP are being used.

6.3 Discuss the effect of integral gain applied on the proportional-integral control system. Relate the effect to the response of the system (e.g. rise time, overshoot percentage, settling time and steady state error). For the proportional integral (PI) controller experiment, the proportional gain was fixed at Kp = 5, while the integral gain was varied accordingly with values of Ki = 0.01, 0.001, 0.05, 1, 10 respectively. Based on figure 7 to figure 11, it was observed that the value of the integral gain would result in a different control system. As what we can observe from the graph, when the value of Ki increases, the response graph will take a longer time to reach the steady state that means it is having a greater settling time. Clearly, it can be seen from the graph that when the integral gain is the lowest, the overshoot value will be the highest. When Ki = 0.001, the overshoot value is the highest and oscillations were observed in the graph. As for the settling time to converge to steady state, it was found that the value increases as the integral gain is increased. However, if the integral gain value were too small, the system would not achieve steady state and will continue to oscillate as can be seen when Ki = 0.001. Besides that, unlike proportional-only controller, the steady state errors are completely eliminated in this type of control system as the integral gain is increased. As we can see from figure 8, the system has started to achieve steady state at the step point, even when the Ki is only increased to 0.01. However, it was found that at Ki value of greater than 1, the steady state is reached at PV value greater than the set point. In this proportional integral system, the output of the controller is always proportional to the amount of time the error is present. Next, the respond of the integral controller is quite slow at first, but in a long period of time, the offsets can be eliminated completely. However, integral action tends to produce some undesired oscillations to the system. This explained well on why the time required for settling to the set-point value is longer as compared to the proportional-only controller mode.


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Based on the 5 data obtained from the experiment, it can be concluded that the best proportional integral controller would be the one with Kp value of 5 and Ki value of 0.01. This is because there is no oscillation produced and the system achieved steady state at the set point value at the shortest period of time. 6.4 The effects of derivative gain applied on the proportional-derivate control system and the effects to response of the system. From the experiment conducted previously, the proportional gain was set to 5 (! = 5) and integral gain was kept at zero (! = 0) with varying value of derivative gain, ! for the proportional-derivative control system experiment. In the experiment, Kd value was being varied from 0.01 to 2. Based the output of the graphs from the Figure 12 to Figure 15, the control system will vary according to the value of the derivative gain of the system. For the derivative control system, when the value of the derivative gain is increased, the instability of the system increases as it starts to oscillate. For this derivative mode, it is clear that when the derivative gain is low, the overshoot value is not as high as compared to the response for a higher value of ! which has a higher overshoot value and oscillations in the response. Besides that, by varying the value of the derivative gain, the offset of the system does not change which means the derivative gain will not influence the steady state error but instead will cause the output to oscillate in a high frequency mode when the KD value is increased to a bigger value. In addition, the rise time for the lower value of derivative gain is shorter compared to the rise time of the higher value of derivative gain which can be seen from Figure 12 and Figure 15. From Figure 15, due to its large derivative gain value of 2, the system is oscillating and a longer rise time is required compared to Figure 12 which has a derivative gain of 0.01. By comparing Figure 12 and Figure 15, the stability of the system is very obvious that when KD is low, the system can still be considered a stable system whereas for a larger value of KD, the system will become unstable and oscillates. Thus it can be said that the settling time of the response for a lower KD is shorter compared to the response for a higher KD value due to the instability of the system as KD is increased.


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6.5 Discuss the system response curve of PID controller tuned and compare it with the system response curve obtained from proportional controller, proportional-integral controller and proportional-derivative controller. Based on the PID controller experiment conducted, the Kp value chosen was kept constant at 5 throughout the experiment. The experiment was conducted with the Ki and Kd values that gave the best system response curve under PI and PD controllers. Ki value selected was 0.01 and Kd value chosen was 0.005. From the system response curve, overshoot is observed and it takes 15 seconds settling time to reach steady state. By keeping Ki constant and increasing Kd value to 0.01, initially overshoot is again observed, but at a relatively lower offset value compared to the previous graph. It also takes a shorter settling time of 8 seconds to achieve steady state condition. Next, by increasing the Kd value to 0.05 with Kp and Ki being kept constant, oscillation is observed and steady state cannot be achieved. Therefore with Kd value being set at optimum value of 0.01, the best system response curve among the three can be obtained.

Then, the experiment is proceeded by keeping Kd value constant at 0.01 while increasing the Ki value to 0.05 and 0.10 respectively. Hence the results obtained from this section can be compared with the second result obtained from the previous paragraph. By setting Ki value to be 0.05, overshoot can be seen at a similar peak; however it takes 52 seconds for the system to remove the offset value to achieve steady state system. Subsequently, the Ki value was altered to 0.1, in which again an overshoot can be obtained but it takes 100 seconds to reach steady state, which is relatively longer compared to the previous experiments. From this section, with the Ki value being set at 0.01, the system response curve observed is the most desired among the three different sets of values.

For the comparisons of system response curve of PID controller with other controllers, the best system response curve from each section is chosen for further analysis and comparison. By comparing PID controller to P controller, the system response curve of PID controller is much desired as the system response curve of P controller does not achieve the desired set point value of 4LPM. As for PI controller, its best system response curve obtained has an overshoot of 7.2, which is higher than that of PID controller. It also has a higher settling time of 15 seconds compared to 8 seconds as required by a PID controller. Last but not least, for a PD controller, it is impossible to obtain a system that can provide the desired flow rate of 4LPM and reaching the steady state on the same set-up of gain values. Therefore, it can be deduced that a PID controller proves to be the best device among the others controllers as it can prevent large value overshoot by removing offset values. Besides that, it also reduces the settling time if optimum values of proportional, integral and derivative gains are entered to the controller.


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6.6 Discuss whether the step test closed loop tuning method is suitable for all types of process control. For the step test closed loop tuning method we have used in our experiment, it is actually a type of method known as Zeigler-Nichols tuning method. It is a type of trial and error looping repeating technique used in the control system. After conducting the step test closed loop tuning method experiment by following the procedures as mentioned above, the gain and period could be computed. This method is an effective way that can be used in the control system to obtain the gain and period, however it is not a general tuning method that can be used for all types of process control. Furthermore, a guaranteed success rate with this method in all processing control system should not be expected. The reasons that the step test closed loop tuning method is not suitable for all types of process control is because this method requires the entire process in the system to be brought to the extreme ends of the instability state in order to determine the gain. In practical applications, industries are always trying to reduce the operational time since the operating costs are not cheap. When large adjustments or changes on the gain are made, the instability of the process increases dramatically as well. Thus, it is not suitable for applications in real industrial plants due to the oscillations that will occur when the gain is changed. These permanent oscillations in the system to determine the gain and period will put the operations of the plant at risk. Furthermore, unit operations such as distillation columns, furnaces, separators, reactors and such will be dangerous if the system approaches unstable state. As the system approaches unsteady state, it would be a very alarming proposition for the plant personnel. This Ziegler-Nichols tuning method is not suitable for all types of processes as the recommended settings are not suitable for systems that are lag-dominated, which basically means the control output has a slower respond due do delay present in the system. This method can only be used on processes where the lag-phase of the output exceeds 180 degrees at high frequencies. A clear example would be that this method does not work on a simple second order process. Lastly, this method is time consuming and is a monetarily expensive technique. The trial and error method could take few hours for the loop testing procedure. In real life industry scenarios, this method consumes a lot of time and would be a very inefficient and wasteful method. This is so because in the industrial world, minutes or seconds could mean thousands or millions of dollars. However, Zeigler-Nichols method is useful for fine and specialty chemical industries, where products require high quality and purity instead of mass volume produced.


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7.0 Conclusion Based on the experiments that were conducted, the overall characteristics of each different control systems were analysed and investigated. This is done by changing different values of gains for different control systems and analysing the response and study effect after changing certain gains.

For the proportional control system, Ki and Kd values were fixed to zero while Kp was being varied. The smallest Kp value gave the most stable response system and when the Kp value was increased, the system starts becoming unstable as oscillations are present. Also, offsets are always present in the response for all values of Kp. For proportional-derivative (PD) controller system, the Kp and Ki was set to 5 and zero respectively while Kd was set initially to 0.01 and then being varied. For the PD control, the system was unable to achieve the set point thus causing offsets. Moreover, a larger derivative gain will cause the system to oscillate and end up being unstable. Thus, since the response when Kd = 0.01 was the best, this value chosen as the optimum value to be used in the PID control system tuning process. Not to forget, a value of 0.01 was chosen as the optimum value for Ki since it was found out that the response when Ki = 0.01 gave the shortest settling time without any oscillations in the proportional-integral (PI) control although the initial Ki value was set at 0.001.

Subsequently, after the P, PI and PD experiments were conducted, the optimum gain values that is to be used for the PID control system was concluded to be Kp, = 5, Ki = 0.01 and Kd = 0.01. These values were chosen as the optimum values as they are able to provide the shortest settling time and with an zero offset value. In addition, a series of calculations was performed in the step test closed loop tuning method and the 2 valve openings that gave the smoothest flow were determined to be 80% and 20%. With consideration of values for the 2 valve openings chosen, the process gain, Kp, time constant, Tp, and dead time, P were calculated to be 0.06, 0.0167 minutes and 0.0333 minutes respectively.

As a whole to conclude this experiment, PID was concluded to be the best system that can be used for a closed-loop flow controlling system, followed by the PI controlling system and PD controlling system. The proportional-only controlling system is not suitable as a stand-alone control system because the offset cannot be eliminated with this control system. Furthermore, another downside would be that the system will start becoming unstable if the proportional gain was set at a certain large value. Similarly, the PD controlling system will become unstable as well if a high value of derivative gain is used. On the other hand, PI controlling system can achieve stability and reach the set point value as it is able to eliminate the offset. However, if the Ki value is too small, it will tend to oscillate and become unstable. When the Ki value is too big, the settling time of the system will also increase as shown in Figure 10 and the output response will reach to a value that is above the set point value and the settling time required would be very long as shown in Figure 11.


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8.0 Nomenclature

Symbol Meaning Kp Proportional Gain Ki Integral Gain Kd Derivative Gain

PID control Proportional Integral Derivative Control PI control Proportional Integral Control P Control Proportional Control

PD control Proportional Derivative Control P Dead Time Tp Time Constant

MV Manipulated Variable PV Process Value TC Critical Time SP Set Point

9.0 Reference Moodle. 2014. Monash University CHEMICAL ENGINEERING LABORATORY

MANUAL CHE3162 Process Control. [ONLINE] Available at:http://moodle.vle.monash.edu/pluginfile.php/2528762/mod_resource/content/3/Sunway%20Lab%20Manual%202014.pdf. [Accessed 24 August 14].

Seborg, Edgar and Mellichamp, Process Dynamics and Control 2nd edition,

Wiley, 2004. [Accessed 24 August 14].

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