The University Of NottinghamMalaysian Campus

Department of Chemical and Environmental Engineering

Laboratory Report

Experiment number: CTR2Title: Flow controlGroup number: 6Group members: Lai Kar Chiew (013319) Lim Wei Xuen (013312) Azman Arrif Imran (013303) El Shanawany Hosam Samy (013396)Name of tutor: Dr. Ong Sze Pheng

Date of experiment: 26th March 2015Date due: 28th April 2015

SummaryThe aim of this report is to explain the conceptual basis of fluid control and to discuss the observed trends which occurred during the procedures carried out to produce the final data. In CTR2, a flow controller (G.U.N.T RT020) is used to test the means of flow control. The RT020 with the assistance of an adjustable pump asserts flowing fluid from a storage tank into a piping loop. The flow can be manipulated by changing the charge at which the electromagnetic proportional valve is set to. The integration of a rotameter and turbine wheel flow sensor helps compute the data through a software controller. A few tasks were carried out to determine open-loop behaviour, limitations of process, influence of disturbance to process, controller settings by using Ziegler-Nicholas closed-loop method and to evaluate the performance of controllers.Reliability of the overall process can be questioned when considering that external disturbances could influence the manner in which the data is processed through the software. When scrutinizing flow, it is important to consider factors of the experimentation that could affect results.

ResultsFigure 1: CTR2-1

Figure 2: CTR2-2

Figure 3: CTR2-3

Figure 4: CTR2-4

Figure 5: CTR2-5

Figure 6: CTR2-6

Figure 7: CTR2-7

Figure 8: CTR2-8

Figure 9: CTR2-9

Figure 10: CTR2-10

DiscussionControl systems are used to keep the process operating at desired conditions by manipulating certain process variables to adjust the variables of interest. A number of benefits are offered by automatic control of a process: enhanced process safety, meet product quality specification, efficient process and improved profitability. In this flow control experiment, the variable of interest (controlled variable) is flowrate, X while the manipulated variable is the setting of the electromagnetic proportional valve and the regulation ratio, Y is the percentage of opening of the electromagnetic proportional valve.Task 1: To Determine Open-Loop Behaviour and Limitation of ProcessGraph CTR 2-1 shows that the flowrate is constant at a regulation ratio of 20%. When the regulation ratio was increased from 20% to 30% the flowrate is higher then it becomes constant throughout the similar regulation ratio. The constant flowrate is an indication of steady-state. Besides, it can also be seen that when Y changed, the flowrate, X does not change immediately, instead it changes after sometime. Besides, only first order system can react immediately to the excitation as others dont. Hence, we can say that this is a first order process plus dead time (FOPDT).From graph CTR2-2, it can be observed that when Y changes, X changes accordingly. However, the increment in X is getting smaller and smaller for every constant increase in Y. For instance, when Y changes from 30% to 40%, X changes from 72 Lhr-1 to 95 Lhr-1, with an increment of 23 Lhr-1; when Y changes from 60% to 70%, X changes from 138 Lhr-1 to 145 Lhr-1, with an increment of 7 Lhr-1. Moreover, when Y reaches 100%, in which the valve is fully opened, the maximum flowrate, Xmax is 160 Lhr-1. The limitations of the process are the maximum achievable flowrate of the fluid as well as the limit of the pump performance and the maximum opening of the electromagnetic proportional valve. Task 2: To Determine Influence of Disturbance to ProcessIn graph CTR2-3, the thinner blue line represents the disturbance signal, Z. From graph CTR2-3, several observations were made. Firstly, when Z changes from 0% to 20%, X doesnt change, remains constant at 130 Lhr-1. This indicates that the disturbance signal, Z at this stage is not strong enough to implicate the process. Secondly, when Z reaches 20% and onwards, X is decreasing. This is because it is an open-loop system, there isnt any controller to bring the process back to the set point. Thirdly, the decrement in X is getting bigger as Z is stronger. For example, when Z changes from 90% to 100%, X drops from 16 Lhr-1 to 6 Lhr-1; when Z changes from 20% to 30%, X drops from 65 Lhr-1 to 62 Lhr-1. The possible disturbances that could occur in a flow process are as followed: presence of bubbles, leakage from the transportation line, pump speed, loose valve fitting and valve failure. Pump speed will probably vary from time to time which would certainly affect the fluid flowrate.The signal block diagram for the open-loop system is shown below:Flow ProcessDesired flowrate, XDisturbances: Bubbles, pump speedManipulated variable: Setting of electromagnetic proportional valve

Figure 11: Signal Block Diagram for Open-loop System.

Task 3: To Determine Controller Settings by Using Ziegler-Nichols Closed-Loop MethodFrom graph CTR2-4, a sustained oscillation in response (X) is observed at Kp = 0.6. And this Kp value is recorded as Kcrit. Furthermore, the period of sustained oscillation, Tp = 0.92s is also obtained from the graph CTR2-4. The value of the sustained oscillation, Tp is determined by measuring the time interval between two peaks of the graph. The parameters of P, PI and PID controllers are shown in Table 1.Table 1: Ziegler-Nichols Closed-Loop Optimum Controller Settings.

Type of controllerOptimum Settings

Proportional GainIntegral Action TimeDerivative Time

P0.300--

P+I0.2730.767-

P+I+D0.3530.4600.115

Task 4: To Evaluate Performance of P, PI, and PID ControllersFrom graph CTR2-5 to graph CTR2-10, the yellow line represents the set-point, W. Graph CTR2-5 and graph 2-6 are associated with a P controller. Graph CTR 2-5 shows the performance of the P controller with no disturbance. From graph CTR2-5, it can be seen that the flowrate, X is so far away from the set-point, W, results in an off-set of approximately 30 Lhr-1. Graph CTR 2-6 shows the performance of the P controller in the presence of disturbance. In graph CTR2-6, again an off-set of approximately 30 Lhr-1 is observed before introducing the disturbance. After introducing the disturbance, X first drops from 30 Lhr-1 to 0 and then is recovered back to 12 Lhr-1 by the P controller, results in an off-set of 48 Lhr-1. Graph CTR2-7 and graph CTR2-8 are associated with a PI controller. Graph CTR 2-7 shows the performance of the PI controller with no disturbance. From graph CTR2-7, the flowrate, X overlaps with the set-point, W, no off-set is observed in this case. However, the process response becomes oscillatory. Graph CTR 2-8 shows the performance of the PI controller in the presence of disturbance. In graph CTR2-8, no off-set is observed before introducing the disturbance. After introducing the disturbance, X first drops from 60 Lhr-1 to 30 Lhr-1 and is recovered back to the set-point in 8 seconds by the PI controller.The last two graphs, graph CRT2-9 and graph CTR2-10 are associated with PID controller. Graph CTR 2-9 shows the performance of the PID controller with no disturbance. In graph CTR2-9, no off-set is observed and the Y curve is subjected to abrupt changes. This is because the measured output is noisy. From graph CTR2-10, no off-set is observed until the disturbance is introduced. After introducing the disturbance, X first drops from 60 Lhr-1 to 27 Lhr-1 and is recovered back to the set-point in 4 seconds by the PID controller.After comparing the last 6 graphs (graph CTR2-5~graph CTR2-10), P controller is the least suitable controller for flow control as an off-set of 48 Lhr-1 is observed. For PI and PID controllers, both can recover the process back to set-point even though disturbance is present. PID controller responds faster than PI controller because it takes into account of now, present and future error while PI controller considers only now and present error. However, the PID controller is seldom used for flow control because the flow measurement tends to be very noisy and flow process is quite fast. From graph CTR2-9 and graph CTR2-10, it can be seen that X and Y curves are more oscillatory because the measured output is noisy. In conclusion, the PI controller is the most suitable controller for flow control as it can recover the process back to the set-point in short time and it is a lot cheaper than that of PID controller.The signal block diagram for closed-loop system is shown below:

Disturbances: Bubbles, pump speedElectromagnetic proportional valveSecondary element: Transmitter

Desired Flowrate, XPrimary element:Turbine wheel flow sensorSet-point, W

Set-point error, e

ControllerFlow Process

Figure 12: Signal Block Diagram of Closed-loop Diagram.

ConclusionThe mechanism used in CRT2 provides efficient means of controlling flow. And again, flow rate is highly dependent on the regulation ratio to which the electromagnetic proportional valve is set to. The regulation ratio increases as set points and disturbances are accounted for statistically.For task 1, the process is a first order process plus dead time (FOPDT). Furthermore, the limitations of the process are as followed: maximum achievable flowrate of the fluid, limit of the pump performance and the maximum opening of the electromagnetic proportional valve. For task 2, the possible disturbances are presence of bubbles in fluid, leakage from the transportation line, pump speed, loose valve fitting and valve failure.For task 3 and 4, the performance of P, PI and PID controllers are evaluated. We concluded that PI is the most suitable controller for flow control as it can recover the process back to the set-point in short time and it is cheap.

NotationsP ProportionalPI Proportional-IntegralPID Proportional-Integral-DerivativeW Set point, Lhr-1X Flowrate, Lhr-1Y Regulation ratioZ Disturbance singalKp Proportional gainKcrit Critical proportional gainTp Period of sustained oscillation, s

ReferencesCoughanowr, D. R. & E.LeBlanc, S., 2009. Process systems analysis and control. 3rd ed. New York: McGraw-Hill.

AppendixGiven Kp = Kcrit = 0.6, Tp = 0.92s (obtained from graph CTR2-4)The parameters of P, PI and PID controllers are calculated using the table below.Table 2: Ziegler-Nichols Closed-Loop Optimum Controller Tuning Formulae.Type of controllerOptimum Settings

Proportional GainIntegral Action TimeDerivative Time

PKcrit/2--

P+IKcrit/2.2Tp/1.2-

P+I+DKcrit/1.7Tp/2Tp/8

For P controller:Kp = Kcrit/2 = 0.6/2 = 0.300For PI controller:Kp = Kcrit/2.2 = 0.6/2.2 = 0.273Integral Action Time = Tp/1.2 = 0.92/1.2 = 0.767sFor PID controller:Kp = Kcrit/1.7 = 0.6/1.7 = 0.353Integral Action Time = Tp/2 = 0.92/2 = 0.460sDerivative Time = Tp/8 = 0.92/8 = 0.115s