Final Experiment 5_1

8/6/2019 Final Experiment 5_1

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CHEMICAL ENGINEERING LABORATORY II

1.0 TITLE OF EXPERIMENT: Temperature Process Control.

2.0 OBJECTIVES OF EXPERIMENT

The objective of this experiment is to demonstrate and understand the characteristic

of proportional (P), proportional integral (PI) and proportional-integral-derivative

(PID) controller in a temperature control loop. Besides that, the objective of this

experiment is also to observe the different types of temperature responses to P, PI,

and PID controller.

3.0 INTRODUCTION

Temperature process control is a process of where the temperature of the fluid is

changed in order to be measured, as the heat energy in or out of the space is adjusted

to achieve a desired average or optimum temperature. The temperature control

system consists of a heat exchanger, a sensor, a controller and a control panel. The

controller is used for maintaining the temperature measuring the process variable at a

particular set. In this experiment, a circulation pump transfers water within a closed

circuit in order to allow water to experience heat exchange between the hot and cold

water. The water flow is controlled by an actuator, taking place automatically should

any deviation occurs from the set point with the supply of compressed air.

A proportionalintegralderivative controller (PID controller) is a control

loop feedback mechanism, which is able to minimize errors of the values by

adjusting the process control inputs. The output of a PID controller is a linear

combination of P, I, and D modes of control. The proportional controller, or P

controller, is a linear type of feedback control system and is more complex than an

on-off control system, but simpler than a proportional-integral-derivative (PID)

control system. However, the P-only controller still has some amount of offset away

from the set point. Therefore, with the addition of an extra controlling system, the


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integral controller, forming the proportional-integral (PI) control. The integral action

will attempt to avoid or minimize the offset created in the proportional control by

bringing the output closer to the set point. PID control system is a linear combination

of P, I and the derivative (D) which permits an increase in the proportional gain,

offsetting the decrease error from the integral controlling. The derivative action

reduces the period of cycling, yet producing the same speed of response as with the

proportional action but without offsets.

PI controller offers a balance of complexity and capability that makes them

popular in many process control applications due to the integral action that enables

PI controllers to eliminate offsets.

4.0 MATERIALS AND EQUIPMENT

Figure 4.1: Schematic Diagram of Temperature Control Unit / Trainer.


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5.0 RESULTS AND CALCULATIONS

Experiment 1: Closed Loop Proportional (P) Control

Variable= P Value

Constant = I Value (600 s) and D Value (0 s)

Table 5.1: Load Change (PB = 5)

Date Time TT01 (C) Set Point (C) Output (%)

23/03/2011 14:10:11 45.0 45.0 12.4

23/03/2011 14:10:13 45.1 45.0 11.8

23/03/2011 14:10:43 43.2 45.0 49.7

23/03/2011 14:11:13 43.1 45.0 50.1

23/03/2011 14:11:43 45.1 45.0 10.623/03/2011 14:12:13 45.1 45.0 11.4

23/03/2011 14:12:43 45.0 45.0 13.3

23/03/2011 14:13:13 44.9 45.0 14.4

23/03/2011 14:13:43 45.0 45.0 12.4

23/03/2011 14:14:13 44.9 45.0 14.3

Figure 5.1: Load Change (PB = 5)

Table 5.2: Set Point Change (PB = 5)


23/03/2011 17:12:58 44.9 45.0 28.7

23/03/2011 17:13:00 44.9 45.0 27.9

23/03/2011 17:13:30 47.3 50.0 79.623/03/2011 17:14:00 47.8 50.0 70.7


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23/03/2011 17:14:30 47.6 50.0 75.4

23/03/2011 17:15:00 47.7 50.0 71.7

23/03/2011 17:15:30 47.8 50.0 71.1

23/03/2011 17:16:00 47.2 50.0 83.9

23/03/2011 17:16:30 47.5 50.0 76.9

23/03/2011 17:17:00 47.7 50.0 73.523/03/2011 17:17:30 47.8 50.0 72.7

23/03/2011 17:18:00 47.9 50.0 69.5

23/03/2011 17:18:30 47.3 50.0 82.4

23/03/2011 17:19:00 47.4 50.0 81.6

23/03/2011 17:19:30 47.5 50.0 78.4

23/03/2011 17:20:00 47.7 50.0 75.4

23/03/2011 17:20:30 47.9 50.0 71.7

23/03/2011 17:21:00 47.2 50.0 85.8

23/03/2011 17:21:30 47.4 50.0 80.9

Figure 5.2: Set Point Change (PB = 5)


Date Time TT01 (C) Set Point (C) Output (%)23/03/2011 14:02:09 45.1 45.0 13.1

23/03/2011 14:02:39 43.6 45.0 20.5

23/03/2011 14:03:09 41.4 45.0 31.9

23/03/2011 14:03:39 44.0 45.0 19.1

23/03/2011 14:04:09 45.1 45.0 13.5

23/03/2011 14:04:39 45.2 45.0 12.9

23/03/2011 14:05:09 45.0 45.0 14.1

23/03/2011 14:05:39 45.0 45.0 14.0

23/03/2011 14:06:09 45.0 45.0 13.8

23/03/2011 14:06:39 44.9 45.0 14.2


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23/03/2011 16:55:30 44.7 45.0 28.1

23/03/2011 16:55:32 44.7 45.0 28.1

23/03/2011 16:56:02 46.4 50.0 44.5

23/03/2011 16:56:32 46.6 50.0 43.6

23/03/2011 16:57:02 46.4 50.0 44.7

23/03/2011 16:57:32 46.1 50.0 46.6

23/03/2011 16:58:02 46.4 50.0 45.1

23/03/2011 16:58:32 46.5 50.0 44.7

23/03/2011 16:59:02 46.6 50.0 44.4

23/03/2011 16:59:32 46.7 50.0 43.9

23/03/2011 17:00:02 46.0 50.0 47.1

23/03/2011 17:00:32 46.3 50.0 46.0

23/03/2011 17:01:02 46.4 50.0 45.5

23/03/2011 17:01:32 46.5 50.0 45.0

23/03/2011 17:02:02 46.7 50.0 44.4

23/03/2011 17:02:32 46.0 50.0 47.8

23/03/2011 17:03:02 46.3 50.0 46.523/03/2011 17:03:32 46.5 50.0 45.6

23/03/2011 17:04:02 46.6 50.0 45.3

23/03/2011 17:04:32 46.1 50.0 47.5

23/03/2011 17:05:02 46.3 50.0 46.9


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23/03/2011 13:26:15 44.9 45.0 15.1

23/03/2011 13:26:45 45.3 45.0 14.8

23/03/2011 13:27:15 45.2 45.0 14.8

23/03/2011 13:27:45 43.8 45.0 16.3

23/03/2011 13:28:15 39.8 45.0 20.3

23/03/2011 13:28:45 45.0 45.0 15.1

23/03/2011 13:29:15 44.9 45.0 15.2

23/03/2011 13:29:45 45.2 45.0 14.9

23/03/2011 13:30:15 45.0 45.0 15.1

23/03/2011 13:30:45 45.3 45.0 14.8

23/03/2011 13:31:15 45.5 45.0 14.5

23/03/2011 13:31:45 45.2 45.0 14.8

23/03/2011 13:32:15 45.3 45.0 14.8

23/03/2011 13:32:45 45.5 45.0 14.5

23/03/2011 13:33:15 45.2 45.0 14.8



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23/03/2011 13:38:47 45.2 45.0 14.8

23/03/2011 13:39:17 45.1 45.0 14.9

23/03/2011 13:39:47 44.9 45.0 15.1

23/03/2011 13:40:17 45.9 50.0 19.223/03/2011 13:40:47 45.5 50.0 19.6

23/03/2011 13:41:17 45.7 50.0 19.4

23/03/2011 13:41:47 45.9 50.0 19.2

23/03/2011 13:42:17 45.6 50.0 19.5

23/03/2011 13:42:47 45.5 50.0 19.7

23/03/2011 13:43:17 46.2 50.0 19.0

23/03/2011 13:43:47 46.0 50.0 19.2

23/03/2011 13:44:17 45.7 50.0 19.5

23/03/2011 13:44:47 46.0 50.0 19.3

23/03/2011 13:45:17 45.9 50.0 19.323/03/2011 13:45:47 45.7 50.0 19.6

23/03/2011 13:46:17 46.0 50.0 19.3

23/03/2011 13:46:47 45.9 50.0 19.4

23/03/2011 13:47:17 45.5 50.0 19.8

23/03/2011 13:47:47 45.8 50.0 19.5

23/03/2011 13:48:17 45.9 50.0 19.5

23/03/2011 13:48:47 45.5 50.0 19.9

23/03/2011 13:49:17 46.0 50.0 19.5

23/03/2011 13:49:47 45.9 50.0 19.5

23/03/2011 13:50:17 45.9 50.0 19.6

23/03/2011 13:50:47 46.2 50.0 19.3

23/03/2011 13:51:17 45.9 50.0 19.6

23/03/2011 13:51:47 45.9 50.0 19.6

23/03/2011 13:52:17 46.2 50.0 19.4

23/03/2011 13:52:47 45.8 50.0 19.7



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Table 5.7: Mean Temperatures, Settling Time for Different PB Values

PB value

Mean Temperature (C) Settling Time (s)

Load ChangeSet Point

ChangeLoad Change

Set Point

Change

5 45.0 47.6 71 127

20() 45.0 46.3 67 41100 45.2 46.0 467 1

Experiment 2: Closed Loop Proportional-Integral (PI) Control

Variable= I Value

Constant = P Value (20 s) and D Value (0 s)

Table 5.8: Load Change (I = 1)


23/03/2011 14:33:41 45.0 45.0 13.7

23/03/2011 14:33:43 45.0 45.0 13.6

23/03/2011 14:34:13 42.6 45.0 46.3

23/03/2011 14:34:43 47.3 45.0 17.4

23/03/2011 14:35:13 45.3 45.0 12.6

23/03/2011 14:35:43 45.1 45.0 11.1

23/03/2011 14:36:13 44.9 45.0 12.5

23/03/2011 14:36:43 44.9 45.0 14.8

23/03/2011 14:37:13 45.1 45.0 12.8

23/03/2011 14:37:43 44.9 45.0 13.3

23/03/2011 14:38:13 44.9 45.0 15.2

Figure 5.7: Load Change (I = 1)


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Table 5.9: Set Point Change (I = 1)


23/03/2011 16:37:29 45.1 45.0 28.1

23/03/2011 16:37:31 45.0 45.0 28.2

23/03/2011 16:38:01 46.9 50.0 92.4

23/03/2011 16:38:31 47.4 50.0 100.023/03/2011 16:39:01 47.7 50.0 100.0

23/03/2011 16:39:31 47.7 50.0 100.0

23/03/2011 16:40:01 48.0 50.0 100.0

23/03/2011 16:40:31 48.1 50.0 100.0

23/03/2011 16:41:01 47.5 50.0 100.0

23/03/2011 16:41:31 47.4 50.0 100.0

23/03/2011 16:42:01 47.7 50.0 100.0

23/03/2011 16:42:31 47.8 50.0 100.0

23/03/2011 16:43:01 47.9 50.0 100.0

23/03/2011 16:43:31 48.0 50.0 100.023/03/2011 16:44:01 47.3 50.0 100.0

23/03/2011 16:44:31 47.5 50.0 100.0

23/03/2011 16:45:01 47.7 50.0 100.0

23/03/2011 16:45:31 47.8 50.0 100.0

23/03/2011 16:46:01 48.0 50.0 100.0

23/03/2011 16:46:31 47.6 50.0 100.0

23/03/2011 16:47:01 47.6 50.0 100.0

23/03/2011 16:47:31 47.7 50.0 100.0

23/03/2011 16:48:01 47.9 50.0 100.0

Figure 5.8: Set Point Change (i = 1)



23/03/2011 14:27:14 45.0 45.0 12.7

23/03/2011 14:27:16 45.0 45.0 12.8

23/03/2011 14:27:46 44.9 45.0 13.5

23/03/2011 14:28:16 45.3 45.0 11.7


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23/03/2011 14:28:46 45.4 45.0 10.5

23/03/2011 14:29:16 45.1 45.0 11.7

23/03/2011 14:29:46 44.9 45.0 12.8

23/03/2011 14:30:16 45.2 45.0 11.3

23/03/2011 14:30:46 45.3 45.0 10.6

23/03/2011 14:31:16 44.9 45.0 12.2




23/03/2011 16:24:17 45.2 45.0 28.423/03/2011 16:24:32 45.3 45.0 28.0

23/03/2011 16:25:02 47.0 50.0 48.6

23/03/2011 16:25:32 46.6 50.0 55.5

23/03/2011 16:26:02 47.1 50.0 57.8

23/03/2011 16:26:32 47.3 50.0 61.0

23/03/2011 16:27:02 47.6 50.0 63.4

23/03/2011 16:27:32 47.7 50.0 66.1

23/03/2011 16:28:02 47.1 50.0 72.9

23/03/2011 16:28:32 47.3 50.0 76.7

23/03/2011 16:29:02 47.5 50.0 79.5

23/03/2011 16:29:32 47.6 50.0 82.5

23/03/2011 16:30:02 47.8 50.0 85.1

23/03/2011 16:30:32 48.0 50.0 87.4

23/03/2011 16:31:02 48.0 50.0 90.1

23/03/2011 16:31:32 47.5 50.0 96.2

23/03/2011 16:32:02 47.3 50.0 100.0

23/03/2011 16:32:32 47.6 50.0 100.0

23/03/2011 16:33:02 47.7 50.0 100.0

23/03/2011 16:33:32 48.0 50.0 100.0

23/03/2011 16:34:02 48.0 50.0 100.0

23/03/2011 16:34:32 48.1 50.0 100.023/03/2011 16:35:02 47.9 50.0 100.0


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Figure 5.10: Set Point Change (I = 10)



23/03/2011 14:19:49 44.8 45.0 13.1

23/03/2011 14:19:55 44.8 45.0 13.2

23/03/2011 14:20:25 41.4 45.0 30.5

23/03/2011 14:20:55 44.9 45.0 13.2

23/03/2011 14:21:25 45.1 45.0 12.0

23/03/2011 14:21:55 44.9 45.0 13.1

23/03/2011 14:22:25 45.1 45.0 12.3

23/03/2011 14:22:55 45.2 45.0 11.723/03/2011 14:23:25 45.1 45.0 12.3

23/03/2011 14:23:55 44.9 45.0 13.4



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23/03/2011 16:11:12 44.8 45.0 28.0

23/03/2011 16:11:14 44.8 45.0 28.0

23/03/2011 16:11:44 46.8 50.0 100.0

23/03/2011 16:12:14 47.8 50.0 100.023/03/2011 16:12:44 47.8 50.0 100.0

23/03/2011 16:13:14 48.0 50.0 100.0

23/03/2011 16:13:44 48.1 50.0 100.0

23/03/2011 16:14:14 47.8 50.0 100.0

23/03/2011 16:14:44 47.3 50.0 100.0

23/03/2011 16:15:14 47.6 50.0 100.0

23/03/2011 16:15:44 47.7 50.0 100.0

23/03/2011 16:16:14 47.9 50.0 100.0

23/03/2011 16:16:44 48.1 50.0 100.0

23/03/2011 16:17:14 48.1 50.0 100.023/03/2011 16:17:44 47.2 50.0 100.0

23/03/2011 16:18:14 47.5 50.0 100.0

23/03/2011 16:18:44 47.7 50.0 100.0

23/03/2011 16:19:14 47.8 50.0 100.0

23/03/2011 16:19:44 48.0 50.0 100.0

Figure 5.12: Set Point Change (I = 100)

Table 5.14: Mean Temperatures and Settling Time for Different I Values

I value



ChangeLoad Change

Set Point

Change

1() 45.0 47.7 36 14010 45.2 47.8 42 630

100 45.1 47.8 73 300


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Experiment 3: Closed Loop Proportional-Integral-Derivative (PID) Control

Variable= D Value

Constant = P Value (20 s) and I Value (1 s)

Table 5.15: Load Change (D = 1)


23/03/2011 14:41:26 45.4 45.0 13.4

23/03/2011 14:41:29 45.4 45.0 13.6

23/03/2011 14:41:59 44.3 45.0 30.1

23/03/2011 14:42:29 43.0 45.0 20.9

23/03/2011 14:42:59 44.2 45.0 23.7

23/03/2011 14:43:29 44.4 45.0 23.6

23/03/2011 14:43:59 44.5 45.0 24.8

23/03/2011 14:44:29 44.3 45.0 26.0

23/03/2011 14:44:59 44.5 45.0 25.923/03/2011 14:45:29 44.6 45.0 26.1

23/03/2011 14:45:59 44.4 45.0 28.8

23/03/2011 14:46:29 44.5 45.0 27.5

23/03/2011 14:46:59 44.7 45.0 27.6

Figure 5.13: Load Change (D = 1)

Table 5.16: Set Point Change (D = 1)


23/03/2011 14:57:49 44.8 45.0 31.7

23/03/2011 14:57:50 44.8 45.0 31.6

23/03/2011 14:58:20 46.4 50.0 49.4

23/03/2011 14:58:50 47.0 50.0 53.6

23/03/2011 14:59:20 47.2 50.0 56.8

23/03/2011 14:59:50 47.2 50.0 62.0

23/03/2011 15:00:20 47.1 50.0 65.6

23/03/2011 15:00:50 47.4 50.0 68.7


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23/03/2011 15:01:20 47.7 50.0 70.8

23/03/2011 15:01:50 47.9 50.0 73.0

23/03/2011 15:02:20 47.8 50.0 77.7

23/03/2011 15:02:50 47.2 50.0 84.0

23/03/2011 15:03:20 47.5 50.0 85.7

23/03/2011 15:03:50 47.6 50.0 89.323/03/2011 15:04:20 47.8 50.0 91.6

23/03/2011 15:04:50 48.0 50.0 93.6

23/03/2011 15:05:20 47.7 50.0 99.7

23/03/2011 15:05:50 47.4 50.0 100.0

23/03/2011 15:06:20 47.7 50.0 100.0

23/03/2011 15:06:50 47.8 50.0 100.0

23/03/2011 15:07:20 47.9 50.0 100.0

23/03/2011 15:07:50 48.1 50.0 100.0

23/03/2011 15:08:20 48.0 50.0 100.0

23/03/2011 15:08:50 47.2 50.0 100.023/03/2011 15:09:20 47.6 50.0 100.0

23/03/2011 15:09:50 47.7 50.0 100.0

23/03/2011 15:10:20 47.8 50.0 100.0

Figure 5.14: Set Point Change (D = 1)



23/03/2011 15:15:43 44.9 45.0 31.8

23/03/2011 15:15:46 44.9 45.0 30.1

23/03/2011 15:16:16 43.7 45.0 53.7

23/03/2011 15:16:46 45.2 45.0 19.0

23/03/2011 15:17:16 45.5 45.0 23.4

23/03/2011 15:17:46 45.6 45.0 26.8

23/03/2011 15:18:16 45.7 45.0 26.2

23/03/2011 15:18:46 45.8 45.0 24.4

23/03/2011 15:19:16 45.8 45.0 26.323/03/2011 15:19:46 45.6 45.0 25.8


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23/03/2011 15:20:16 45.8 45.0 22.7

23/03/2011 15:20:46 45.7 45.0 23.3

23/03/2011 15:21:16 45.6 45.0 23.0

23/03/2011 15:21:46 45.6 45.0 20.5

23/03/2011 15:22:16 45.6 45.0 19.0

23/03/2011 15:22:46 45.4 45.0 22.223/03/2011 15:23:16 45.3 45.0 19.9




23/03/2011 15:33:09 45.0 45.0 30.0

23/03/2011 15:33:11 45.2 45.0 24.8

23/03/2011 15:33:41 46.6 50.0 28.5

23/03/2011 15:34:11 47.0 50.0 36.1

23/03/2011 15:34:41 47.6 50.0 38.3

23/03/2011 15:35:11 48.1 50.0 40.2

23/03/2011 15:35:41 48.0 50.0 52.0

23/03/2011 15:36:11 48.3 50.0 49.5

23/03/2011 15:36:41 48.6 50.0 48.3

23/03/2011 15:37:11 48.6 50.0 55.9

23/03/2011 15:37:41 48.4 50.0 58.723/03/2011 15:38:11 48.7 50.0 56.4

23/03/2011 15:38:41 49.0 50.0 56.8

23/03/2011 15:39:11 48.7 50.0 67.8

23/03/2011 15:39:41 48.6 50.0 63.5

23/03/2011 15:40:11 48.9 50.0 62.9

23/03/2011 15:40:41 49.0 50.0 64.4

23/03/2011 15:41:11 49.0 50.0 69.9

23/03/2011 15:41:41 48.5 50.0 79.1

23/03/2011 15:42:11 48.9 50.0 70.1

23/03/2011 15:42:41 49.1 50.0 70.523/03/2011 15:43:11 49.3 50.0 71.1


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23/03/2011 15:48:45 45.2 45.0 22.4

23/03/2011 15:48:47 45.2 45.0 22.2

23/03/2011 15:49:17 43.8 45.0 64.8

23/03/2011 15:49:47 45.0 45.0 30.6

23/03/2011 15:50:17 45.1 45.0 28.6

23/03/2011 15:50:47 45.2 45.0 28.2

23/03/2011 15:51:17 45.1 45.0 33.0

23/03/2011 15:51:47 45.2 45.0 31.423/03/2011 15:52:17 45.3 45.0 30.4

23/03/2011 15:52:47 45.4 45.0 28.1

23/03/2011 15:53:17 45.3 45.0 32.6

23/03/2011 15:53:47 45.3 45.0 32.5



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23/03/2011 15:55:25 45.3 45.0 35.4

23/03/2011 15:55:35 45.3 45.0 35.6

23/03/2011 15:56:05 46.2 50.0 40.4

23/03/2011 15:56:35 46.4 50.0 43.023/03/2011 15:57:05 46.7 50.0 42.5

23/03/2011 15:57:35 47.0 50.0 44.5

23/03/2011 15:58:05 46.6 50.0 63.1

23/03/2011 15:58:35 46.9 50.0 61.2

23/03/2011 15:59:05 47.2 50.0 62.8

23/03/2011 15:59:35 47.3 50.0 64.4

23/03/2011 16:00:05 47.5 50.0 67.7

23/03/2011 16:00:35 47.7 50.0 67.9

23/03/2011 16:01:05 47.3 50.0 87.0

23/03/2011 16:01:35 47.4 50.0 92.623/03/2011 16:02:05 47.5 50.0 93.2

23/03/2011 16:02:35 47.7 50.0 94.8

23/03/2011 16:03:05 47.8 50.0 97.5

23/03/2011 16:03:35 48.0 50.0 97.0

23/03/2011 16:04:05 48.1 50.0 98.9

23/03/2011 16:04:35 47.4 50.0 100.0

23/03/2011 16:05:05 47.4 50.0 100.0

23/03/2011 16:05:35 47.5 50.0 100.0

23/03/2011 16:06:05 47.7 50.0 100.0

23/03/2011 16:06:35 47.8 50.0 100.0



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Table 5.21: Mean Temperatures and Settling Time for Different I Values

D value



ChangeLoad Change

Set Point

Change

1() 44.5 47.7 65 44610 45.7 48.9 208 >750100 45.2 47.7 234 550

Offset Calculation

Taking P=5 in Experiment 1 as sample calculation,

Offset = Mean Value of Operating TemperatureSet Point

= 47.6 C45 C

= 2.6 C

Refer Table 6.1 for complete offset values for all conditions.

Overshoot Calculation

Figure 5.19: Performance characteristics of an underdamped process.

Overshoot is only applicable when the system attempts to make the response of the

controlled variable to a set-point change, which exhibit a prescribed amount of

overshoot and oscillation as it settles at the new operating point.

eh

ts= settling time

tp = time to first peak


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By using Figure 5.2 for reference for calculation,

Overshoot summary,

Table 5.22: Offset value for proportional change only

P Overshoot % Remarks

100 - No overshoot

20 () 17.0212766 -

5 35.71428571 -

Table 5.23: Offset value for proportional change onlyI Overshoot % Remarks

1 - No overshoot, drifting oscillation only

10 - Drifting new set point

100 - No overshoot, drifting oscillation only

Table 5.24: Offset value for proportional change only

D Overshoot % Remarks


10 20 -


6.0 DISCUSSION

Figure 6.1: Temperature Control System Block Diagram

P

I

D

Temperature

Controller

Error

Detector

Control

Valve

Process

Temperature

Transmitter


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Figure 6.1 indicates the cycle of temperature control system. The temperature

transmitter used to measure the current temperature, . The temperature transmitter

will send an analogue signal to the error detector so that the error detector can

compare the value between the analogue of current temperature,

and the set point

temperature, . The temperature controller will then receive the error from the

Error Detector through the P,I and D. Controller will send an electrical current signal

to the Control Valve and the control valve is used to vary the liquid flow rate. The

liquid flow rate will be controlled by the openings of the control valve.

Temperature control utilizes a feedback loop that begins with a system's

measured temperature. A temperature sensor - typically a thermocouple, RTD or

thermistor - measures a process's real-time temperature and feeds the reading back to

a controller. The controller compares the measured temperature to the set point

temperature and actuates devices like heaters or valves to bring the temperature to

the desired set point. The three most common methods of control are proportional

(P), proportional-integral (PI) and proportional-integral-derivative (PID). For this

experiment we had two parts to be done, that are load change and set point change.

This both, are actually the disturbance of the process. The disturbances are the

change of the flow rate in between and also the set point change (temperature

change). When there is disturbance, there will be some fluctuating in the current

value of temperature. The current value of temperature supposed to be nearest to the

set point, but due to the disturbance the value will shoot up. To come back to the set

point value, the output (the opener of valve) will be increased automatically to have a

current value of temperature near to the set point.

For the load change experiment, the cold water flow rate is changed from 5

LPM to 10 LPM and back to 5 LPM. The increased in the amount of cold water

entering the heat exchanger will cause the reduction of water outlet temperature. This

introduced disturbance will cause the system to respond and try to return the cold

water outlet temperature to the set point by controlling the electro-pneumatic

proportional valve (adjusting the opening of hot water inlet). For the set point change

experiment, the set point is increased from 45 C to 50 C. This increase in set point

will cause the system to try to give response so that the process value (temperature)

matches the new set point.


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For proportional control, the controller output is proportional to the error

signal. A disadvantage of proportional-only control is that a steady-state error or

offset occurs after a set-point change or a sustained disturbance. In principle, offset

can be eliminated by manually resetting either the set-point or bias value after an

offset occurs. However, this approach is inconvenient because operator intervention

is required and the new value of set point must usually be found by trial and error.

Integral control action provides automatic reset of set point. It is widely used

because it provides an important practical advantage, the elimination of offset. When

integral action is used, controller output changes automatically until it attains the

value required to make the steady state error zero. Proportional-integral controller

provides immediate corrective action as soon as an error is detected without the

problem of offset. One disadvantage of using integral actions is that it tends to

produce oscillatory response of the controlled variable and reduces the stability of

the feedback control system. A limited amount of oscillation can be tolerated

because it is often associated with a faster response. The undesirable effects of too

much integral action can be avoided by proper tuning of the controller or by

including derivative action which tends to counteract the destabilizing effects.

Derivative control action is to anticipate the future behavior of the error

signal by considering its rate of change. While for proportional control, it reacts to a

deviation in temperature only, making no distinction as to the time period over which

the deviation develops. Integral control action is also ineffective for a sudden change

in temperature because corrective action occurs when the deviation persists for a

long period. Derivative control action also tends to improve the dynamic response of

the controlled variable by decreasing the process settling time, the time it takes the

process to reach steady state.

In conclusion, when there is a step change in a disturbance variable occurs,

proportional control speeds up the process response and reduces the offset. The

addition of integral control action eliminates offset but tends to make the response

more oscillatory. Adding derivative action reduced both the degree of oscillation and

the response time.


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Table 6.1: Comparison between Different Values of P, I and D.

Controller

Mode

Proportional Proportional-Integral Proportional-Integral-Derivative

P=100 P=20 P=5 I=100 I=10 I=1 D=1 D=10 D=100

Operating

Value46 46.3 47.6 47.8 47.8 47.7 47.7 48.9 46.45

Offsets 1 1.3 2.6 2.8 2.8 2.7 2.8 2.7 2.7

Response

Time

Almost Immediate

Response

Behavior

Moderate

Oscillation

Little

Oscillation

Large

Oscillation

Moderate

Oscillation

Large

Oscillation

Little

Oscillation

Little

Oscillation

Moderate

Oscillation

Large

Oscillation

Selection

Table 6.2: Comparison between P, PI and PID of Controller Mode.

Controller Mode Proportional Proportional-Integral Proportional-Integral-Derivative

Operating Value 46.63 47.77 48.1

Offsets 1.63 2.77 3.1

Response Time Almost Immediate

Response

Behavior Little Oscillation Moderate Oscillation Large Oscillation

Selection


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From the graphs and results, it can be clearly seen that the set point change

takes longer to settle and creates more cycle (oscillation) for the system to reach

steady state compared to load change.

Theoretically, the sensitivity of controller will increase as Kc increases. In

other words, when Kc increases, the controller is very sensitive and results in larger

response and more overshoot when the controller is making correction to the

disturbances in the system.

where,

PB proportional band or P-value

Kc controller gain

According to Equation 1, the P-value is inversely proportional to the

controller gain, Kc. At large P-value, the controller become less sensitive as Kc is

small. This theory applies in this experiment as the larger the P-value, the smaller the

overshoot.

Besides that, we also observed that the offset value decreases as the P-value

decreases. This phenomenon can be explained with Equations 2 and 3.

where

KOL open loop gain

Kv valve gain

Kp process gain

Km steady state gain

ffe

where,Mis the set point

From Equation 2, KOL is proportional to Kc. In Equation 1, we showed that P-value is

inversely proportional to Kc. Therefore, when P-value decreases, the Kc and KOL

increases. When KOL increases, the offset decreases. So, when P-value decreases, the


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offset decreases. A large Kc is not always desirable as it will tend to cause

oscillations.

Integral control action is widely used because it provides an important

practical advantage, the elimination of offset. When integral action is used, controller

output changes automatically until it attains the value required to make the steady

state error zero. Therefore, in this experiment, the offset value cannot be compared in

terms of integral time value as this mode of control eliminates the problem of offset.

Response time for all the three mode of control is almost immediate

whenever there is a disturbance to the system as can be seen in the results and graph.

This is because all three mode of control (P, PI and PID) include the proportional

control, where it provides immediate corrective action as soon as an error is detected.

While for the response behavior, we can see that the oscillation occur the

most in PID control mode, followed by PI and lastly P control as the addition of D-

value tends to amplify noise. This noise amplification increases as the D-value

becomes larger. A larger D-value also causes the system to exhibit larger and longer

oscillations.

Underdamped systems frequently overshoot their target value initially. This

initial surge is known as the "overshoot value". The ratio of the amount of overshoot

to the target steady-state value of the system is known as the percent overshoot.

Percent overshoot represents an overcompensation of the system, and can output

dangerously large output signals that can damage a system. Percent overshoot is

typically denoted with the term OS. No percentage overshoot is most preferable.

Based on our calculations, the proportional integral there is no overshoot percentage.

When proportional, the control process gives an overshoot percentage when P=20

and P=5. The value of P=100 there is no overshoot percentage. For proportional

integral derivate, when D=10 there is overshoot percentage. During this mode, the

settling time for set point change is very high. So we can conclude that due the

higher value of settling point, there will be an overshoot percentage. Another

situation, during the proportional mode when P=100 the settling time is just 1 s.Tha he lwe eling pin we bained hee i n any eh.


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Overall the proportional mode gives a lower operating value. And when as

the P values increases the operating value decreases and it requires less settling time

for the temperature to reach its steady temperature (nearest to its set point).

Theoretically, PID gives the best control system. However, in this

experiment, it shows that the inclusion of D gives longer settling time and higher

oscillation than PI control. Overall, by comparing all the response (operating value,

offset, overshoot, settling time, response time and response behavior), we conclude

that the best choice (a symbol of is located near all the response factors) of control

mode is PI with the setting of P = 20 and I =1 where all the factors are within the

acceptable range.

One of the recommendations is that the value of D should not be so high,

because the settling time required for the process to achieve steady state is very high.

So it require us to stay longer, hence the results we obtain might not be so accurate.

Apart from that, the pressure should be constant throughout the process and there

should be an alarm to alert us when there is no pressure being supplied. This is

because, during the experiment, the compressor may not working all of sudden, and

we are not being notified for this incident. Due to the un-working compressor, the

settling time for the process value to achieve a steady state is higher.

7.0 CONCLUSIONIn conclusion, proportional mode gives a lower operating value. The operating value

is inversely proportional with the P value. In this experiment, we found out PI

controller is the best choice with P = 20 and I = 1, in which the mean temperatures

f lad change and e pin change ae 45.0C and 47.7C, respectively.

8.0 REFERENCES

Seborg, D. E., Edgar, T. F., & Mellichamp, D. A. (2004). Process dynamics and

control (2nd Edition). New Jersey: John Wiley & Sons.

Julabo (2010). Temperature Control Solutions, Retrieved March 20, 2011 fromwww.julabo.de

Final Experiment 5_1

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