EXPERIMENT G1: Dynamic Response Modelling a Temperature Controller Tuning in a Jacketed Flow Reactor Objective This experimeot involves a jacketed, mixed flow reactor in which an exochermic (heat generating) liquid phase reaction is taking place. Reactant flows into and product flows oat of the reactor, on a continoous basis. A cooling jacket, with liquid coolant flowing in and out, serves to maintaia the reactor at a constant ienperature. Reactor temperature (the control variable in this case) may be cootrolied using a controller which measures the reactor exit teaperature and manipulates the coolant flow rute in order to compensate for any process disturbances. Process disturbances are introduced as changes is the coolant inlet temperature. The purpose of this experiment is twofold: (i) to model the dynamic (time dependent) behaviour of this simple process by conducting open loop step response experiments; and, (ii) to utilise the process charncteristies data obtained to tune the reactos PID temperahure controller for optimum closed loop performance. The Control Station computer package is used for this experiment. This package allows for the real-Life simulation of a jacketed flow reactor. Step resporse experimeats are carried out in opealoop configuration, and the resultaat data are fitted to varioas quantitative theoretical response model equations in onder to deternine which of these is most appropriate for representing the process dyaamies. The best fit from this procedure gives the process chameteristic constants. Control Station is then used to calculate the optimum controller constant settings for the reactor temperalare controller using an appropriate controller tuning correlation, and the performance of the raned process control system is then examined Therectical Backaround The dynamic response of any process can be quantified by the ased of an appropriate differential equation which expresses the behaviour of the system wich respect to time. Thus for example for a process such as the liquid bolding tank shown over, the level of liquid, h, in the tank can be shown to be dependent oa the inlet flow rate, F, according to the following Ist order differenial equation: 7 dt dl t +h=KF Where 5 and K are charactenistics (constants) of the process, called the time constant and process gain tespectively. The value of the former is an indication of how rapidly the process responds to. change, waile the latier reflects the how sensitive the process is to change. Liquid Storage Tank: A First Order Process This type of process model is more generally called a first order process with the general (fins order differentiai) equation: z dt dy +y=Kx where y is the process output variable, x is the process input variable, and t is time. More complex dynamic behaviours are shown by procesies such as temperature control systems. Thas for example, the fumace control system shown below would exhibit a second order dynamic response behaviour according to the general equation: z 2 dt 2 d 2 y +2r dt dy +y=Kx where t is now callod the period of oscillation, and is an additioaal process characteristic called the dampening factor. Furnace Temperature Control: A Second Order Process Hence in this case if the set point temperature (input variable, x ) is suddealy increaned to a new: value, thee the furnace temperafure (output yariable. y) would respond aceording to equakion (3). Simple first and second order processes represent oely two of a variety of differeat peacess dymamic behavioun. For examaple. it is alse possible to have a process which exhibeits first order plus dead time bchavicar. This type of process is similar to a simple first orier one, except that the response of the process to an input change is additionally deliyed by a period of time, termed the process dead timse, fel. Since both cqaations (2) and (3) are differential equations, they must be solved by integratioe in order to get the time response of the process (ie. y as a function of t). The cxact sature of the resaltant solution will additionally depend of the nature of the input fanction (x). The latter can take the Iorm of a siep change (c.g. suddenly turm up the vet-point temperatare), ramp change (gradaally increase the set-point temperarure over time), or any number of other possibilities. The Control Station programme allows the user to fit equatioes auch as (2) and (3) abowe to data obtained from the open loop process response experiment. Hence the bect process taodel is deiermined, and values of the process constambs are obtained. In addition it is also possible use the programme wo obtain optimum controller constant settings: for conirolling this particalar process. These may be determined using the peocess constants, in Fanicus ways, depending on the exact-control objective, e.g. sct-point tracking or disturbance: Binimisation. In this experiment the open loop response process characteristies fcalculated for a first order plus deud time model, FOPDT) are used together with a selected empirical tuning correlation to give the optimal PID conltoller scltings for process disturbance rojection. There are a eumber of tuaing coctelations avalable, inclading II AE/MC (integrated time ahsolve crorfinternal model control), and MPC MDMC (madel predictive control/ynamic matris control). The foumer is useful for single controd loops, such as that used in this experiment, whilst the latter is mote saitable for multi-koop cantrol in lagger proceking units or whole phants. (Sice Control Station Help menu er a saitable process control text such as HA Real-time Approach tos Process Costrol" by Svreek et al, for more information on these tuning corrclationis). Fixperimental Note: in the text below ad a hit ENTIER key. A. Starning she Comsoi Station Prepramime 1. Doable elick oe the icoo marked Shortcur to Catation. 2. Maximase the sareea by clicking on the betton in the top rigkt Hand camer of the screen and click on the Case Studies betton. Select the Jacketed Reactor option 3. Famiiiarise yourself wich the various parts of the screen. Ask the lecturer ideimasatrator for belp if there ir iomething that you dant anderstand. B. Dyatamic Response Modelling (Open Loep) Initially the system is set up in epen loop configuration in onder to allow you ts deternine the most appropriate model for the reactor process itself (te excluding the controlier) 4. Open a file on yoar datastick to save yoar data intoc click on the SAVE, icon, choose the drive name that your datastick corresponds to, type in a file name, and click on Save. Choose OK for the 'Save simalation histary? box. 5. After the simulation has boen running for a few seconds, make a step process inpat change by inputting the new value of cooling jacket inlet temperature given to yeu by the demonstrator. This may be done by clicking oa the bor, hitting the buckspuce keyboard key ( ), entering yoar value, and hitting 1 Observe the process responic, and when a new steady state output has been achieved for a few veconds, parse the simelation and click on the stop saving data button. 6. Click on the navigate batton and choose the Design Tools option. This starts the model fitting part of the programme, where you can test out varioes peocess models to see which one beat fits your open loop response data. 7. Open the file which coatains you data by clicking on open data file button, selecting your named file and click on open. Note that your data is displayed in a number of columns labelled Time, Manipulated Variable and Process Variable. In this case you should assign the 5 columa of data (i.e. the cooling jacket inlet temperature) as your Manipulated Variable. This cun be done by clicking anywhere on the 5 column of data and hitting the letter M on the keyboard. The 5 th columin beading shoeld then apocar as 'Manipulated Variable'. Click on OK 8. Minimise the Control Station screens by clicking on the - button (top right hand corner of the sereen). Start up Microsoft Word by double clicking on the appropriate icon. This will allow you to cat and paste your fitted modelling graphs directly into Word. 9. Retum to Design Tools, click on the Seloct Model betton, and choose the FOPDT (First Order Plas Dead Time) option and click on Doee. 10. Start the fitting procedure by clicking on Surt Fitting button. Eventually a groph showing your response data (white) and the fitted fint onder repponse equatioa (yellow) will appear: Nose the degree of fit, as indicated by the value of the sum of squared errors (SSE) figure in the bottom right hand comer. The smaller this value is, the better the fit. 11. Transfer your plot to Word by clicking oe Copy, ensuring that Metafile and Clipboard options have been selected, and then click on Export. Close the Print Data graph and maximise the Word file. Paste your graph into Word, and save your Word file to your datastick. 12. Retum to Design Tools by closing the black graphical fit window. In the ease of the FOPDT fit, you should save the model parameter and tuning correlation tables to a data file on your datastick, by clicking on the 'Sive Fi' buttion, choosing the drive name that your datastick correspoods to, inputting another file tame, and elicking on save. This is necessary since Control Station utilises the fitted FOPDT process characteristics from the open loop data to calculate the optimum PID controller setlings asing the various tuning conrelation deseribed interacting), for use later- 13. Repeat seps 9. 1t for esch of the following modelsi Second Order Plus Dead Time (SOPDT). and sOPDT Uadcriamped each time cuting and pasting your rerults graphs ime your Word file and saving it. C. Tening she Unnencal Cloued Loop Performinnce Now you can assess the closed perfomince of the reactor to an input change, when it is: operating with a PID teaperature controlter. Initially we will just use the default controller settings, i.e; the controller will not be nuned (un-optimised). 14. Maximise the jacketed reactor sereen again. Click on Run and choose Restart process using program defaalts: Click on TC (the temperature controdler). choose PID, set the controlier to ON for: Propertional - direct acting, Integral with anti-feset wind-up, and, Derivative Oo idealfNon-interactiog. Click on done, Note that the process simulation diagram has changed to show that the controller is aow connected up to manipulate the cooling jacket flow rate (and bence the reactor temperature) in a fecdback control loop. 15. Pause the simulation, open another data file and re-start, running for a few scconds to obtain some initial steady state data. 16. Iaput again the value of cooling jacket inlet temperatare given to you by the demonstratoe and ran the system until it reaches a new steady state. 17. Stop the simulation, and stop saving data to the file. D. Testing the Closed Loop Perfomance with Taned (Oplinised) Controller Seltings 18. Yoa cin now try out the optimised tuned controller settings which you obtained in part B. step 12, and see if the process behaves in a opkinal way in response to cooling jacket inlet temperature changes. Click on Ras and choose 'Resart process using program defaults'. Click on TC (the temperature controller), choose PID, set the controller to ON for: Proportional - direct acting: Integral with anti-reset wind-up: and. Derivative on error. Also set K ci Fh and 0 . 10 their optimum values (as aoted in step 12 above), and click on done, 19. Pause the simulation, open another data file and re-star. rannigg for a few seconds to obrain some initial steady state data. 20. Inpat again the value of cooling jacket inlet temperature given to yoa by the demonstrator and run the system tantil it reaches a new steady state. 21. Stop the simulation, and stop saving data to the file. 22. Exit the programune by clicking on the X button in the top right hand comer of the sereen, and then clicking on the Exit Control Station button. 1. Import the data in your data files into EXCEL. You may then use the graph function in EXCEL to plot up and store the untuned and tuned closed loop response data. Make sure that your plots are large enough and properly sealed so that the variations of the process parameter (reactor temperature) can be clearly seen as a function of time. 2. You should address the following issues in the analysis/diseussion section of your write-up: - Determine the best fit model for the open loop response data for this process. Consult a text on process control/modelling and see if you can explain why this model best fitted the dynamic response characteristics of this system. - Compare the tuned and un-tuned closed loop response characteristics. Does the former show improvements in response behaviour? What features of the response profile could you quantify in order to verify any improvements or degradations in response behaviour? List and define at least three such features.

please follow step by step as i need the report with diagrams thanks