ABSTRACT - Universiti Teknologi MARA · 2 ABSTRACT This process control laboratory is about to run open and closed loop process. An open loop is run in manual mode while closed loop

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ABSTRACT

This process control laboratory is about to run open and closed loop process. An open loop

is run in manual mode while closed loop is run in automatic mode. In this experiment, five

closed loop analysis are involved which comprises of Level Control Process (LIC 11), Air

Flow Control (FIC 91), Air Pressure Control (PIC 92), Flow Control (FIC 21) and Liquid Flow

Process (FIC 31). By using the response curve from the loop analysis, Response Rate (RR),

Time delay (Td) and Time constant (Tc) are calculated by using Reformulated Tangent

Method. Then, Ziegler-Nichols and Cohen Coon tuning rule formula are used in order to

calculate the optimum value. By using the calculated value based on Ziegler-Nichols, open

loop test is running. The process is run in automatic mode. Three main tests have been

conducted in each of the open loop which includes tuning test, set point change test and load

disturbance test. When we increase the changing in MV value, the value of controller gain, Kc

will increase and can affect the respond of the process to become more faster and the

process become more stable. By decreasing the controller gain, Kc it also reduce the

oscillatory and make the process become more stable. The integral time, I value is

decreased and it accelerates the process to the set point

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TABLE OF CONTENT

TITLES

PAGE

1.0 Introduction 4

2.0 Theory 9

3.0 Results 18

4.0 Discussion 24

5.0 Conclusion 26

6.0 References 27

7.0 Appendices 28

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1.0 INTRODUCTION

Process control keeps processes within specified boundaries and minimises

variations [5]. Nowadays, wide variety of modern process control systems have been

installed progressively in most industrial sites in order to help maintain throughput, quality,

yield and energy efficiency as well as to ensure safe and profitable operation. The

measurement and control system is the central nervous system of any plant, as it operates at

different levels that sense change and initiate actions [5]. Process control system refers to

the control of one or more process variables such as pH, level, temperature, flow and

pressure. In process control system, the process variables is compared to a set point by the

controller and the results of the comparison will be utilized to control the process in such a

manner that the process variable will be substantially equal to the set point for the process

variable (Robert W. Rutledge, 1980). Nowadays, the applications of the process control are

huge in the industrial field. For instance, advanced process control plays an important role in

chemical and process industry. PID control, conventional advanced control and

linear/nonlinear model predictive control have been used in industry in order to increase the

productivity.

The industrial PID has many options, tools and parameters for dealing with the wide

spectrum of difficulties and opportunities in manufacturing plants. The PID controller is an

essential part of every control loop in the process industry (Astrom K., Hagglund T., 2006).

Studies have shown that the PID provides an optimal solution of the regulator problem

(rejection of disturbances) and with simple enhancements, provides an optimum servo

response (setpoint response) (Bohl A., McAvoy T., 1976). Test show that the PID performs

better than Model Predictive Control (MPC) for unmeasured disturbances in terms of peak

error, integrated error or robustness (McMillan G., 2004). The PID controller in the modern

Distributed Control System (DCS) has an extensive set of features and their importance for

addressing challenging applications and control objectives for common unit operation

applications in the process industry.

In the production for most new pharmaceuticals, bioreactors are used due to proteins

too complex to be produced by chemical processes. Biopharmaceutical batch processes are

predominantly batch because of the concern for the build up of the toxins and genetically

deficient cells with continuous processes and the need to get new drugs to market quickly. In

bioreactors, pH, temperature and dissolved oxygen are the important loops to be considered.

Generally, for a well designed system the allowable controller gain is extremely limited only

by the measurement noise because of the ratio of deadtime to time constant is relatively

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small. The high controller gain in combination with the extremely slow disturbances

translates to exceptionally tight PID control (Ramon V., Antonio V., 2011). In the system, no

overshoot at the start of the batch essentially with getting to temperature setpoint as fast as

possible is being concerned. If there is no concern about batch cycle time and low

temperatures do not cause seed cell degradation, a PID structure of integral mode on error

and proportional and derivative modes on PV is used to eliminate overshoot (McMillan G.,

2010). It is usually less than ideal for the automation and bioreactor system design. In

bioreactor, pH loops and dissolved oxygen will interact whenever the piping and spurger for

the injection of oxygen or air for dissolved oxygen control and addition of carbon dioxide for

pH control are not completely separated. For this case, the interaction between pH and

dissolved oxygen loops can be reduce with the use of half decoupler and enhanced PID.

Wireless pH transmitters have been found to eliminate the spikes commonly seen in

bioreactor loops (McMillan G., Baril R., 2010). As sliding stem (globe) control stem valves are

not suitable for sanitary and sterilization-in-place (SIP), thus, most of bioreactor loops does

not use it. The alternative final control elements used may have a poor resolution or

sensitivity limit and backlash. Hence, the associated limit cycles can be extinguished with the

enhanced PID. Otherwise, enhanced PID can make sample time for a bioreactor batch that

takes 1 to 2 weeks fast enough to do closed loop control by dealing with the analyzer cycle

time and resolution limits.

Chemical reactors set the stage for the production of bulk chemicals, intermediates,

petrochemicals, polymers, pharmaceutical chemicals and speciality chemicals (Ramon V.,

Antonio V., 2011). Batch reactors are commonly used in pharmaceutical industries and in the

production of special chemicals and other products predominantly use continuous reactors.

The temperature PID is the most important controller since the reaction rate is often an

exponential function of temperature via the Arrhenius Equation (Ramon V., Antonio V.,

2011). The deadtime to time constant ratio for well mixed continuous reactors and the

deadtime to integrating process gain ratio for batch reactors are incredibly small (<0.001)

(Ramon V., Antonio V., 2011). The result is a permissible PID gain much larger than users

are accustomed to (>50) and exceptionally tight temperature control (Ramon V., Antonio V.,

2011). Since the most important task of the utility system is to satisfy the demands of the

temperature PID, the transfer of variability by the PID from the reactor temperature to its

utility system is maximised. Performance limitation is due to the accuracy of the temperature

sensor and the threshold sensitivity and resolution of the final control elements. Premium

Resistant Temperature Detectors (RTD) and sliding stem valves with digital positioners

should be used to allow the full capability of the PID to be realized [12, 13, 14].

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The pH control of neutralizers can be particularly challenging due to the extreme

nonlinearity and sensitivity of the pH measurement as a result of the exponential relationship

between pH and hydrogen ion activity [9]. The changes in controller gain and the rangeability

and threshold sensitivity of the final element needed are extraordinary. PID gain changes of

1000 to 1 and a final element rangeability requirement of 10 000 to 1 are possible with strong

acid and base system (Ramon V., Antonio V., 2011). In order to keep waste streams in

compliance with environmental regulations, it is necessary to determine the number of

neutralization stages by precision of the final element. The limit cycle amplitude from

threshold sensitivity and resolution limits can be extremely large due to amplification by the

steep slope of the titration curve (McMillan G., 2003). The 7 pH value for a broken electrode

or wire and the failure to last value of a coated electrode are insidious (McMillan G., 2003).

Some solutions that can be considered in this case are signal linearization, adaptive tuning,

split range control, valve position control (VPC) and the enhanced PID. Controlled variable

can be translated from pH to reagent demand per the titration curve by using signal

linearization. While, changes in the titration curve and process dynamics can be corrected

with adaptive tuning. Neutralization with both acids and bases can be handled with split

ranged control. Whereas, in order to keep good throttle range for pH control, a VPC can

adjust a large (coarse) valve to keep a small (fine) valve. Otherwise, PVC can also maximize

the use of waste and low cost reagents. Portable wireless pH transmitters can eliminates

spikes from EMI as well as optimize the control location. Finally, the enhanced PID can

eliminate oscillations from split range point discontinuities and from valve backlash, threshold

sensitivity, and resolution, reduce interactions between the small and large valves, prevent

overreaction to pH electrode failures, and extend wireless battery life [11, 16].

Furthermore, liquid level control plays important role in industrial application as in food

processing industry, dairy, filtration, effluent treatment, nuclear power generation plants,

pharmaceutical industries, water purification systems, industrial chemical processing and

spray coating and boilers in all the industries. The typical actuators used in liquid level control

systems include pumps, motorized valves, on-off valves and level sensors such as

displacement float, capacitance probe and pressure sensor provide liquid level measurement

for feedback control purpose so that as per the process requirements the fluids could be

controlled (Farhad A., 2011). Basically, the objective of the controller in the level control

system is to maintain a level set point at a given value and be able to accept new set point

values dynamically. In controlling the level, commonly conventional PID is used to utilize it. A

PID gain is tune automatically by auto tuning in a Simulink model containing a PID controller

block. By tuning the PID, it allows to achieve a good balance between performance and

robustness. It will automatically compute a linear model of the plant. The plant will be

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considered by PID tuner to be the combination of all blocks between the PID controller input

and output. Other than the controller itself, the plant includes all blocks in the control loop.

The main objectives of PID tuner are closed-loop stability (in which system output remains

bounded for bounded input), adequate performance (in which closed loop system tracks

reference changes and suppresses disturbance as rapidly as possible) and adequate

robustness (the loop design has enough gain margin and phase margin to allow for

modelling errors or variations in system dynamics (Farhad A., 2011). By adjusting response

time, design modes of PID tuner refine the controller design. This makes the closed loop

response of the controlled system slower or faster. While, by separately adjust loop

bandwidth and phase margin, an extended design mode of PID tuner refine the controller

design. The controllers will response faster to changes in the reference or disturbances in the

loop whenever the loop bandwidth is larger. The more robust the controller is against

modelling errors or variations in plant dynamics if the phase margin is larger.

One of the reasons that make Proportional-integral-derivative (PID) controllers is

widely used in industrial control systems is due to the reduced number of parameters to be

tuned. Zieglar-Nichols method is one of the most popular design techniques that are

commonly used. The conventional PID controller is replaced by Ziegler-Nichols tuning PID

controller to make them more general and to achieve the minimum steady state error, also to

improve the other dynamic behaviour (Rajkumar, A. Patra, Vijay, 2012). Most process

industries used PID controller to control the plant (system) for the desired set point. PID

control method is the most popular among all control method because of it is flexible and

simple. PID controller is tuned by determination of proportional (KP), derivative (KD) and

integral (KI) constants. This method is used when the system is in open loop configuration.

PID control is the proportion of error (P), integral of error (I), differential of error (D) control. Z-

N PID controller is controlling the plant or system by continuously monitoring plant output

which is known as process value with the desired process value known as set point of the

system [18]. The difference between process value and set point that manipulates by PID

controller is known as error. In the conventional controlling method the transfer function of

plant should be calculated in order to find out various parameters and the value of PID

constants. But in this method there is no necessary to derive the transfer function of the

system. Thus, Z-N PID controller is monitoring the plant depending on set point and process

value and irrespective of the nature of plant [19, 20].

The main features of PID controllers are the capacity to eliminate steady-state error of

the response to a step reference signal because of integral action and the ability to anticipate

output changes when derivative action is employed [18]. In a power plant, both active and

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reactive power demands continually vary the rising or falling trend. Power input must

therefore be continuously regulated to match the active power demand, otherwise the

machine speed will change with consequent change in frequency, which may be highly

undesirable [18]. The excitation of generators must be continuously regulated to match the

reactive power demand with reactive generation, failing which the voltage at various system

buses may go beyond the prescribed limits [18]. It is necessary to maintain the frequency of

the power system constant. The governors adjust the input to bring the frequency with in

permissible limits [21]. PID controllers is tuned as to make the plant more general and to

achieve the minimum steady state error as well as to improve the other dynamic behaviour.

The Ziegler-Nichols step response method provides systematic means to adjust the

proportional gain in order to have no overshoot on the closed-loop step-response [18]. In the

design of plant control system, the PID controllers can be designed associated with under

damped step responses. Instead of transfer function of the system, PID constants are

strongly depends on the set point and tune controller feature that can improve the system

performance. PID controllers attributed to the controllers effectiveness in a wide range of

operation conditions, its functional simplicity, and the ease with which engineers can

implement it using current computer technology. Hence, PID control algorithms are popular

and offer many benefits such ease of use, new development help to implement other PID

controller variants and control for common industry application.

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2.0 THEORY

PID controllers are capable stabilizing processes at any set point by utilizing a

mathematical function in the form of the control algorithm [1]. Process stability of a PID

control loop depends upon the proportional, integral and derivative constants used [1]. An

optimum proportional (P), integral (I) and derivative (D) values can be obtained using several

simple techniques performed on an open loop test. Open loop test can be done with several

steps, firstly by stabilizing the process in manual mode. Then, making step change (∆MV) of

5 to 20% to the controllers output and record the initial as well as the final MV value. Finally,

response of the process variable is recorded until the process reaches a new steady state

level. An open loop test might be in self regulating process or non self regulating process.

In open loop process identification, there are several quick and easy techniques that

can be approached. These techniques include tangent method, reformulated tangent method

and numerical method. Those methods extract most vital information about the process

dynamic, namely process dead time (Td) and the response rate (RR). This information then is

used in the tuning rules, such as Zieglar-Nichols in order to estimate the optimum P, I and D

for the controller.

In the tangent method, an optimum PB, I and D is found at the maximum slope. The

process dead time (Td) and the response rate (RR) are analyzed by drawing a tangent line to

the steepest point of the response curve. By the definition, the process dead time (Td) is a

period of time between starting point of step input and the intersection of old steady state

baseline and tangent line [2]. Figure 2.1 shows the step change made (∆MV), the drawn

tangent line and the estimated process dead time (Td).

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Figure 2.1: A step change of ∆MV (bottom) and the self regulating process response

curve (top). (Abdul Aziz Ishak, 2011)

The process response rate (RR) is defined [1,2] as,

RR = ∆𝑃𝑉/∆𝑡

∆𝑀𝑉 =

𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑠𝑙𝑜𝑝𝑒

∆𝑀𝑉 - - - - - (1)

Where,

RR = response rate, 1/time

∆𝑃𝑉 = change in measurement, %

∆𝑡 = change in time, time

∆𝑀𝑉 = change in controllers output, %

Td and RR are incorporated in the tuning rule for the optimum PID calculation [1].

In the reformulated tangent method, the open loop response curve is analyzed and

viewed in different perspective. This method is based on tangent method but utilized

trigonometry to estimate the gradient [2]. Figure 2.2 shows the same response curve as in

Figure 2.1 but is being analyzed in different perspective.

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Figure 2.2: Transforming process rate into trigonometric form [1]. (Abdul Aziz Ishak, 2011)

Based on the Figure 2.2, the process response rate (RR) of equation (1) is then reformulated

by,

∆𝑃𝑉/∆𝑡

∆𝑀𝑉 =

∆𝑦/∆𝑥

∆𝑀𝑉 - - - - - (2)

But the right-hand side and the left-hand side of the equations are dimensionally incorrect.

Thus, by balancing the units and place the appropriate scaling factors to the right hand side

of the equation, equation (2) will transforms into equation (4).

%

𝑡𝑖𝑚𝑒 =

𝑙𝑒𝑛𝑔𝑡 ℎ

𝑙𝑒𝑛𝑔𝑡 ℎ

(%

𝑙𝑒𝑛𝑔𝑡 ℎ)

(𝑡𝑖𝑚𝑒

𝑙𝑒𝑛𝑔𝑡 ℎ) - - - - - (3)

∆𝑃𝑉/∆𝑡

∆𝑀𝑉 =

∆𝑦

∆𝑥 𝑎

𝑏

∆𝑀𝑉 - - - - - (4)

Where,

a = scaling factor for y-axis, % / length

b = scaling factor for x-axis, time / length

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As ∆𝑦

∆𝑥 = tan 𝜃 , equation (4) transforms into,

RR = ∆𝑃𝑉/∆𝑡

∆𝑀𝑉 =

tan 𝜃

∆𝑀𝑉 𝑎

𝑏 - - - - - (5)

Td (time) = Td (length) × b - - - - - (6)

Tc (time) = Tc (length) × b - - - - - (7)

Time constant formulation based on Reformulated Tangent Method [2].

Figure 2.3: Reformulated tangent method for time constant (Abdul Aziz Ishak, 2011).

tan 𝜃 = PV nss − PV oss

Tnss −Td (𝑏

𝑎) - - - - - (8)

tan 𝜃 = PV nss −PV oss

Tc (

𝑏

𝑎) - - - - - (9)

Tc = 𝑏

𝑎 (

PV nss −PV oss

tan 𝜃 ) - - - - - (10)

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Dead time formulation based on Reformulated Tangent Method [2].

Figure 2.4: Reformulated Tangent Method for dead time (Abdul Aziz Ishak, 2011)

tan 𝛽 = PV nss −PV oss

Tnss (

𝑏

𝑎) - - - - - (11)

Tnss = PV nss −PV oss

tan 𝛽 (

𝑏

𝑎) - - - - - (12)

Td = Tnss – Tc - - - - - (13)

Td = PV nss −PV oss

tan 𝛽 (𝑏

𝑎) −

PV nss −PV oss

tan 𝜃 (𝑏

𝑎) - - - - - (14)

Td = (PVnss − PVoss) (𝑏

𝑎) {

1

tan 𝛽 −

1

tan 𝜃} - - - - - (15)

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Numerical analysis on openloop response data

Figure 2.5: Numerical formulation for response rate, RR (Abdul Aziz Ishak, 2011)

Slope = PV 1 −PV−1

2∆ℎ - - - - - (16)

Therefore, the response rate, RR becomes:

RR = 𝑠𝑙𝑜𝑝𝑒

∆𝑀𝑉 =

PV 1 −PV−1

2∆ℎ∆𝑀𝑉 - - - - - (17)

Figure 2.6: Numerical formulation for dead time, Td (Abdul Aziz Ishak, 2011)

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Slope at (Td, PVoss) = slope at (t1, PV1)

PV 1−PV oss

t1− Td =

PV 1 −PV−1

2∆ℎ - - - - - (18)

Td = t1 - 2∆h [ PV 1−PV oss

PV 1 −PV−1 ] - - - - - (19)

Figure 2.7: Numerical formulation for time constant, Tc (Abdul Aziz Ishak, 2011)

Slope at (Tnss , PVnss) = slope at (t1 , PV1)

PV nss−PV oss

Tnss − Td =

PV 1 −PV−1

t1 − t−1 - - - - - (20)

Tc = 2∆h [ PV nss −PV oss

PV 1 −PV−1 ] - - - - - (21)

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Ziegler-Nichols open loop tuning rule

(Abdul Aziz Ishak, 2011)

Takahashis openloop tuning rule


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Cohen – Coons openloop tuning rule


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3.0 RESULT

3.1 Summary of process dynamics of various control loops

Control Loop Parameters Reformulated Tangent

Method

Level Control

(LIC 11)

Response rate, RR (s-1) 0.03

Deadtime, Td (s) 3.75

Time Constant, Tc (s) 36.25

Air Mass Flowrate

(FIC 91)




Liquid Flow

(FIC 21)




Gas Pressure Control

(PIC 92)




Liquid Water Flowrate and

Level Control (FIC 31)




Table 3.1: Summary of process dynamics of various control loops

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3.2 Summary of optimum controller settings for PI controller based on Ziegler-Nichol’s

and Cohen-Coon for Reformulated Tangent Method.

Control Loop Parameters Reformulated Tangent Method

Ziegler-Nichols Cohen-Coon

Level Control

(LIC 11)

Proportional Band, PB % 12.5 11.14

Controller Gain, Kc 8.18 8.97

Integral Time, I (s) 12.49 10.27

Air Mass Flowrate

(FIC 91)




Liquid Flow

(FIC 21)





(PIC 92)




Liquid Water Flowrate

and Level Control

(FIC 31)




Table 3.2: Summary of optimum controller settings for PI controller based on Ziegler-Nichol’s

and Cohen-Coon for Reformulated Tangent Method.

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3.3 Tuning Test, Set point Test and Load Disturbance Test

Control Loop Parameters Tuning Test

Level Control

(LIC 11)

Controller Gain, Kc 8.18

Integral Time, I (s) 12.49

Air Mass Flowrate

(FIC 91)



Liquid Flow

(FIC 21)




(PIC 92)



Table 3.3: Result for Tuning Test

Control Loop Parameters Old New

Level Control (LIC 11) Set Point (mm) 500 550

Air Mass Flowrate (FIC 91) Set Point (kg/h) 25 27.5

Liquid Flow (FIC 21) Set Point (m3/h) 3.99 4.39

Gas Pressure Control (PIC 92) Set Point (psig) 9.8 11.0

Table 3.4: Result for Set Point Test

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Control Loop Parameters Old New

Level Control (LIC 11) MV, % 47.3 57.3

Air Mass Flowrate (FIC 91) MV, % 33.2 43.2

Liquid Flow (FIC 21) MV, % 64.6 74.6

Gas Pressure Control (PIC 92) MV, % 69.4 79.4

Table 3.5: Result for Load Disturbance Test

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3.4 Numerical method for FIC 31

Table 3.6: Result of FIC 31 for numerical method

Z-N Z-N Z-N C-C C-C C-C

Times PV

(m3/hr) PV (%) MV (%)

RR, 1/s Td, s Tc, s PB, % Kc I u=Td/Tc PB, % Kc I

11:30:37 0 1.0056 33.52 40

11:30:38 1 1.0056 33.52 40 0.000

11:30:39 2 1.0056 33.52 40 0.000

11:30:40 3 1.0056 33.52 45 0.203

11:30:41 4 1.0664 35.55 45 0.431 3 3.04 143.65 0.696 9.99 0.99 118.35 0.84 3.43

11:30:42 5 1.1349 37.83 45 0.348

11:30:43 6 1.1709 39.03 45 0.180

11:30:44 7 1.1889 39.63 45 0.060

11:30:45 8 1.1889 39.63 45 0.045

11:30:46 9 1.2025 40.08 45 0.045

11:30:47 10 1.2025 40.08 45 0.000

11:30:48 11 1.2025 40.08 45 0.000

11:30:49 12 1.2025 40.08 45

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3.5 Numerical Method for TIC 91

Table 3.7: Result of TIC 91 for numerical method

Z-N Z-N Z-N Z-N C-C C-C C-C C-C

Times PV (°C) PV (%) MV (%)

RR, 1/s Td, s Tc, s PB, % Kc,s I,s D,s u=Td/Tc PB, % Kc,s I,s D,s

10:12:20 0 30.0273 15.0137 5.672

10:12:10 1 30.0273 15.0137 5.672 0.000

10:12:00 2 30.0273 15.0137 5.672 0.001

10:11:50 3 30.0508 15.0254 10.67 0.007

10:11:40 4 30.1758 15.0879 10.67 0.021

10:11:30 5 30.4766 15.2383 10.67 0.030

10:11:20 6 30.7773 15.3887 10.67 0.027

10:11:10 7 31.0195 15.5098 10.67 0.031

10:11:00 8 31.3906 15.6953 10.67 0.034 4.046 5.8726 11.325 8.827 8.0922 2.0231 0.689 8.19 11.30 8.14 1.32

10:10:50 9 31.6914 15.8457 10.67 0.030

10:11:40 10 32.0000 16.0000 10.67

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4.0 DISCUSSION

Practically, this experiment has been run open loop test for Level control process (LIC

11), Air flow control (FIC 91), Air pressure control (PIC 92) and flow control (FIC 21). By

using the response curve from the loop test, Response Rate (RR), Time delay (Td) and Time

constant (Ts) are calculated by using Reformulated Tangent Method formula. Then, Ziegler-

Nichols and Cohen Coon tuning rule formula are used in order to calculate the optimum

value. As Pressure, Level and Flow is fast process, so that PI formula is used in order to

calculate the optimum value for the fast control process. From the comparison that has been

made, it’s only a small difference between the value that calculated by using Ziegler-Nichols

method and the value using Cohen Coon method. Then, values calculated using PI formula

for Ziegler-Nichols is used to running the closed loop control.

After set up and running the process control system in Auto mode, enter the value of

controller gain (Kc) and integral time (I) that has been calculated manually using Ziegler-

Nichols into the system control. During the process, the response curve is observed and

analyzed. Whenever the response curve is oscillating and active, the process is tuned until

the response curve is stable and process is closed to the set point. Tuning is the process of

setting control loop gains or other control variables to achieve optimum value and stable

performance. The value of controller gain, Kc and integral time, I are the control variables that

was considered to achieve optimum controller setting. In this experiment, there is no exact

value for the process to achieve an optimum performance. Under steady state condition,

when the graph line of set point and process (PV) variable is equal then it is considered as

optimum performance. In tuning process, whenever the response curve are oscillate very

fast, the value of the controller gain, Kc have to be reduce by divide the original value by 2 or

4 and increase the integral time, I value by multiply by 2 or 4 or with other value.

For the Level Control (LIC 11), the value of open loop test was entered at the closed

loop test. In the close loop test, the response of the graph that getting from the open loop is

oscillate. The value of Kc is divided by 4, so that the value of Kc entered now is 8.18. The

response curve now became stable. Then, set point test is made. Set point is increase from

500 to 550 and then waits until the response curve stable. Next, by performed the load

disturbance test, the value of MV is increased from 47.3 to 57.3%, and observes the

response curve as well as wait until the response curve become stable. Summary for all

control loop and all test result in table 5, 6 and 7.

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The same method is used for Air Flow Control (FIC 91), Air Pressure Control (PIC 92)

and Flow Control (FIC 21). For Air Flow Control (FIC 91), value for controller gain, Kc now is

0.134 and the value of integral time, I is 12.49s. As the response curve is fluctuation and not

stable, the Kc value is divided by 2, 0.535 and multiply the I value by 2, 16s. By decrease the

controller gain, Kc value, the process become slower and less oscillatory, but by increased

the integral time, I value, and it will accelerate the process towards the set point. The process

is waiting until it become stable. The set point test is run by increased the SP value from 25

to 27.5 kg/h and wait for the process until it became stable before proceed with the load

disturbance test. By increase the MV value from 33.2 to 43.2, it can be seen that the process

line will increase rapidly for a moment, then being decreased and become stable.

For Air Pressure Control (PIC 92), value for controller gain, Kc now is 2.71 and the

value of integral time, I is 12.49 s. The process curve is stable and reaches the set point.

Then, continue with set point test by increased the value of set point from 9.8 to 11.0 psig.

Once the process is stable, continue with load disturbance test by increase the MV value

from 69.4 to 79.4%.

For Flow Control (FIC 21), value for controller gain, Kc now is 1.075 and the value of

integral time, I 4.16 s. Due to fluctuation and the response curve is not stable, then the value

of Kc is divided by 2, 1.875 while the I value is multiply by 2, 26.70s. The response curve is

now stable and near to the set point. Then, make the set point test by increase the set point

from 3.99 to 4.39 m3/h. It can be observed the MV curve is increased rapidly before

decreased and become stable. Once the process curve is stable, continue the test with load

disturbance test by increasing the MV value from 64.6 to 74.6%.

Set point is the desired value for the operating variable. After run the steady state

experiment, the initial set point value was changed by an increment of 5%. These changes

represent the situation in which the plant operator occasionally changes the value of set point

and allows the considerable time for the control system to respond. In this experiment, all the

graphs showed that the process line has the same reaction where it moves towards the set

point and after a while it will reached stability where the process value and set point value is

almost equal.

Under load disturbance, the system will experienced disturbance that cause a large,

sustained deviation of the controlled variable from its set point. In this test, the initial load

disturbance value was also increased by 5%. Before entering the value, the system is

change in manual mode and after the value was entered, the system is quickly changed back

26

into auto mode. Through the result, it can be seen that the longer time needed for the

process line to return to the set point, the process will become more unfavourable since it

affect the process line and also the production rate. The output lines also need to be

considered because if the output line is not smooth and stable then the utilities are high.

5.0 CONCLUSION

As a conclusion, this experiment had achieved its objective which is to find the

optimum PI for Level Control Process (LIC 11), Air Flow Control (FIC 91), Air Pressure

Control (PIC 92) and Flow Control (FIC 21) by using the Reformulated Tangent Method and

by using Ziegler-Nichols tuning rules. When we increase the changing in MV value, the value

of controller gain, Kc will increase and can affect the respond of the process to become more

faster and the process become more stable. By decreasing the controller gain, Kc it also

reduce the oscillatory and make the process become more stable. The integral time, I value

is decreased and it accelerates the process to the set point.

27

6.0 References

1. Abdul Aziz Ishak & Muhammed Azlan Hussain, (2000). “Reformulation of the tangent

method for PID controller tuning”. Process Control Engineering Online, May 2013,

http://aabi.tripod.com

2. Abdul Aziz Ishak (2011), Process Control Practices note.

3. http://www.controlguru.com/pages/table.html

4. http://www.amazon.com/Principles-Practices-Automatic-Process-

Control/dp/0471431907

5. http://www.carbontrust.com/media/147554/ctv063_industrial_process_control.pdf

6. Astrom K., Hagglund T. (2006) Advanced PID Control, ISA, Research Triangle Park

(Industrial application of PID)

7. Bohl A., McAvoy T. (1976) Linear feedback vs. time optimal control. II. The regulator

problem, Ind. Eng. Chem., Process Des. Dev., Vol. 15, No. 1 (Industrial application of

PID)

8. McMillan, G. (2004) Models Unleashed, ISA, Research Triangle Park (Industrial

application of PID)

9. Ramon Vilanova, Antonio Visioli (2011), PID Control in the third Millennium: Lessons

Learned and New Approaches, Springer. (Industrial application of PID)

10. McMillan G., (Dec. 8, 2010) Exceptional opportunities in process con-trol, ISA short

course, Saint Louis

http://www.modelingandcontrol.com/2010/12/exceptional_opportunities_in_p_18.html


11. McMillan G., Baril R. (Aug. 2010) pH measurement and control, Chemi-cal

Engineering (Industrial application of PID)

12. McMillan G., (Dec. 8, 2010) Exceptional opportunities in process con-trol, ISA short

course, Saint Louis

http://www.modelingandcontrol.com/2010/12/exceptional_opportunities_in_p_18.html


13. McMillan G., (Dec. 9, 2010) Measurement and valve errors - Sources and impact,

Emerson website entry, Austin

http://www.modelingandcontrol.com/2010/12/measurement_and_valve_errors_-.html


14. McMillan G., (Dec. 16, 2010) Star performers in minimizing life cycle costs, Emerson

website entry, Austin

http://aabi.tripod.com/

http://www.controlguru.com/pages/table.html

http://www.amazon.com/Principles-Practices-Automatic-Process-Control/dp/0471431907

http://www.amazon.com/Principles-Practices-Automatic-Process-Control/dp/0471431907

http://www.carbontrust.com/media/147554/ctv063_industrial_process_control.pdf

28

http://www.modelingandcontrol.com/2010/12/star_performers_in_minimizing.html


15. McMillan, G. (2003) Advanced pH Measurement and Control, ISA, Re-search

Triangle Park

16. McMillan G., (Aug. 2010) DeltaV v11 PID enhancements for wireless, Emerson white

paper, Austin

17. Farhad Aslam, Mohd. Zeeshan Haider (2011) An implementation and Comparative

Analysis of PID Controller and their Auto Tuning Method for Three Tank Liquid Level

Control, International Journal of Computer Applications (0975-8887), Vol. 21

18. Rajkumar Bansal, A. Patra, Vijay Bhuria (2012), Design of PID Controller for Plant

Control and Comparison with Z-N PID Controller, International Journal of Emerging

Technology and Advanced Engineering, Vol. 2, Issue 4.

19. Mr. S. S. Gade, Mr. S. B. Shendageand Mr. M. D. Uplane, ―On Line Auto Tuning of

PID Controller Using Successive Approximation Method,‖ 2010 International

Conference on Recent Trends in Information, Telecommunication and Computing,

pp. 272-280

20. Gade S.S, Kanase A B, ShendageS. B. and Uplane M. D., ―Design and

Development of Universal on Line Auto Tune PID controllers,‖ in Proc. Of Int. Conf.

on Control, Communication and Power Engineering 2010, pp. 291-295..

21. Vinay Gupta, AshisPatra, ―Design of Self-Tune PID Controller for Governor Control

to Improve Dynamic Characteristics ―International Conference (ICITM-2011) to be

held on 3rdJuly2011 at Ranchi, India

7.0 APPENDICES

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7.0 APPENDICES

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Documents

ABSTRACT - Universiti Teknologi MARA · 2 ABSTRACT This process control laboratory is about to run open and closed loop process. An open loop is run in manual mode while closed loop