Developing a Simulink Program to Test CSTR Performance - Report Example

Developing Simulink Program to Test CSTR Performance Student’s Name: Instructor’s Name: Course: Date Developing Simulink Program to Test CSTR Performance The process of developing Simulink program to test CSTR performance remains multifaceted. From the one hand, the process has to be looked at in terms of self-tuning PID controller design for a continuously stirred tank reactor. From the other hand, there is need for succinct integration of different programming approaches including but not limited to MATLAB/Simulink programming. In such connectedness, the task provides succinct textual analysis and procedure for the process of developing Simulink program to test CSTR performance. To begin with, it is worth noting that real systems are understood to be nonlinear. This position means that to make a design for a nonlinear system, the process must model the plant with regard to MATLAB/Simulink programming. In as much as self-tuning PID controller design for a continuously stirred tank reactor can regulate a nonlinear plant, the model used within the controller need to be liner (Ponnusamy et al. 2013). Making this point differently, the controller should be designed in a manner that it can approach the process by employing a linear approximation of the nonlinear plant. However, studies and programmers have noted that the accuracy of the processes of approximation often affect the performance of the controller. The process of linearising a Simulink Model can be done through the following adopting the following: From the command line Adopting the Linear Analysis Tool The Process of Linearisation by Adopting MATLAB Code As shown below, the example provides procedure for obtaining a linear model of the plant in question when using a MATLAB script. It has to be noted that for the example provided, the CSTR model, there is CSTR_OpenLoop that is linearalised. At that point, the model inputs will act as the coolant temperatures. According to Geetha et al. (2013) they will be in charge of manipulating different variables with regard to the self-tuning PID controller design for a continuously stirred tank reactor, thus limiting reactant concentration in the feed, feed temperature and feed stream. Differently, the model states will act as the temperature as well as concentration of the limiting reactant when it comes to the product stream. However, the process of design will be measured and used for the control of the feedback. Procedure for to Obtain Steady-State Operating Point From the onset, it has to be noted that the operating point will be tasked with the responsibility of defining the nominal conditions or states at which one can linearize a model. In this case, it will be a steady-state condition. Assuming that engineers are interested in planning to operate the CSTR with the concentration of output such as C_V at approximately 2kmol/m3, in such a case, it can be assumed further that the nominal concentration of feed is 10kmol/m3 and on the same note, the nominal feed temperature of the situation is about 300K. Therefore, the process of creating a point of operation specification object that define the steady-state condition will resume the following example: opspec (=)opspec will resume (‘CSTR_OpenLoop’); opspec (=)addoutputspec will resume (opspec, ‘CSTR_OpenLoop/CSTR’); opspec.outouts (1).will be known as true or opspec.outouts (1).Known=true opspec.outouts (1).y = 2 op1 will be findop (which will be ‘CSTR_OpenLoop’ opspec). As the brief example above outlines, the process will be derived to a point of reaching at the calculated operating point that can be termed as C_A which may generate values such as 2kmol/m3 and a value of T_K = 373 K depending on the initial values. There is need to realize that the steady-state coolant temperature can also be given in different values such as 299K (Singh et al. 2016). Such values can act as the normal value of the variable manipulated of the self-tuning PID controller design for a continuously stirred tank reactor. However, there will be a need to specify the values of known inputs and the initial guesses for state values. To specify these points one will: Use the Input.u and Input.Known fields of opspec for verifying values of known inputs and Use the State.x field of opspec to verify initial guesses for sate values Generally, it has to be noted that there will be different codes that will be able to specify the coolant temperatures as or at about 305 K and there will be start or initial guess values of the C_A and T_K states which should be established before the calculation of the steady-state point of operations. Specification of Linearisation Outputs and Inputs It has to be noted that the first start point will be to establish whether the linearization of output and input signals have been defined. If for instance, the input and output signals have been defined in the model in our case it is CSTR_OpenLoop then the process will adopt a different step and this be to use io = getlinio('CSTR_OpenLoop'); in obtaining different signal set(s). However, if this is not the case then the process will have to specify the output and input signals that different considerably and one of such signals includes the following among others: io = linio('CSTR_OpenLoop/CSTR',1,'output'); io = linio('CSTR_OpenLoop/CSTR',2,'output'); io = linio('CSTR_OpenLoop/Feed Concentration',1,'input'); io = linio('CSTR_OpenLoop/Feed Temperature',1,'input'); io= linio('CSTR_OpenLoop/Coolant Temperature',1,'input') Linearisation of the Model The process will take the option of linearilising the model by using the specific or specified operating point, which in this case could take the option of io= linio('CSTR_OpenLoop/Coolant Temperature',1,'input') and input/output signals, io. This can be put as; sys = linearize('CSTR_OpenLoop',op1,io) The Process of Linearisation Using Linear Analysis Tool in Simulink Control Design The process of linearalise a Simulink model using the linear tool of analysis provides better understanding for the process especially with the Simulink Control Design product. The procedure as given below uses the CSTR model which is CSTR_OpenLoop. The first step therefore will be to open Simulink Model In the Simulink model window the second step will be to select analysis Still in the Simulink model, the third step will be opening of control design and The last step will be opening of linear analysis. These steps are as outlined below: Open Linear Analysis Tool>Analysis>Control Design>Linear Analysis Specifications of Linearisation of Inputs and Outputs It has to be noted that at this stage, the linearization outputs and inputs shall have been specified with regard to CSTR_OpenLoop. Again, there will be a case where input signals corresponds to different outputs coming from the Feed Temperature, Feed Concentration and Coolant Temperature blocks. Again, this procedure has to specify that the output signals will be acting as the inputs to the Residual Concentration blocks and CSTR Temperature so that they can make specifications of a signal as a: 1. Linearisation output; to achieve linearization out the process entails clicking right the signal in the Simulink model window and thereafter selecting Linear Analysis Points then Output Measurement. The process is as indicated below: Linear Analysis Point>Output Measurement. 2. Linearisation input; to undertake the procedure the first option is to right click the signal located at the Simulink model window and the next step will be to select Linear Analysis Points then the last option it to open Input Pertubation. The process is as indicated: Linear Analysis Point>Input Perbutation Specifications of Residual Concentration as Known Trim Constraint The next step after steps involved in specifications of linearisation of inputs and outputs is specifications of residual concentration as known trim constraint. Valarmathi et al. (2009) found that just like the previous steps, this stage will work entirely with the Simulink model window. To begin with, one needs to right-click the CA output signal and this is done from the CSTR block then a selection of Linear Analysis Points then selection on Trim Output Constraint. The process will follow steps as shown below: Simulink Model Window>CA output signal> Linear Analysis Points> Trim Output Constraint In the Linear Analysis Tool as it will be shown in the window, in the Linear Analysis option or tab, in the Operating Point (this will appear as drop down) choose or select Trip model. While at the Outputs tab the following tasks will be performed: 1. Checking the known box that corresponds to the Channel 1 (can be explained as 1 under CSTR_OpenLoop/CSTR) 2. Setting the corresponding Value at about 2 kmol/m3 The Process of Creating and Verifying the Point of Operations This process will begin by allocating Trim which is often found in the model dialog box. The next step is to click Start Trimming option. The next step will be the operating point op_trim1 which will be displayed in the Linear Analysis Workplace. Then a double click will be made on op_trim1 to allow for the viewing of the resulting operating point. A window showing ‘edit’ will be shown from which the process will select the ‘Input tab’ and this option will therefore generate the coolant temperature steadily at 299 K as recommended or desired. Linearisation of the Model In the option of Linear Analysis, located in the Operating Point (drop-down list) the process will select op_trim1. This option will therefore develop or create the linear model represented as linsys1 and will be shown in the Linear Analysis Workspace thus generating a step response for the model linsys1 uses optrim1 that acts as its point of operation. It has to be noted that when the concentration of the feed increases, there will be increase in C_A initially since more reactant will be entering thus increasing the rate of reaction. This rate also increases results located in a higher reactor temperature (known as output CSTR/1) which again, increases the rate of reaction while there is decrease in C_A. Exporting the Results of Linearisation Once the model performance is satisfactory, the next step will be dragging and dropping the result from the Linear Analysis Workspace to the MATLAB Workspace. This last step will allow the process to use the specified linear model in designing Simulink program to test CSTR performance References Geetha, M., Manikandan, P., Shanmugapriya, P., Silambarasan, V. and Naveen, R., 2013, July. Real-time implementation and performance analysis of two dimension PID fuzzy controller for continuous stirred tank reactor. In Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on (pp. 1-5). IEEE. Ponnusamy, L., Kirubagaran, R. and Atmanand, M.A., 2013, September. Design and optimization of temperature controller for high pressure rated modified CSTR system. In Signal Processing, Computing and Control (ISPCC), 2013 IEEE International Conference on (pp. 1-6). IEEE. Singh, S., Rani, A., Singh, V. and Yadav, J., 2016, November. Soft sensor for inferential control in non-isothermal CSTR. In Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on (pp. 1785-1790). IEEE. Valarmathi, K., Devaraj, D. and Radhakrishnan, T.K., 2009. Intelligent techniques for system identification and controller tuning in pH process. Brazilian Journal of Chemical Engineering, 26(1), pp.99-111. Read More

Differently, the model states will act as the temperature as well as concentration of the limiting reactant when it comes to the product stream. However, the process of design will be measured and used for the control of the feedback. Procedure for to Obtain Steady-State Operating Point From the onset, it has to be noted that the operating point will be tasked with the responsibility of defining the nominal conditions or states at which one can linearize a model. In this case, it will be a steady-state condition.

Assuming that engineers are interested in planning to operate the CSTR with the concentration of output such as C_V at approximately 2kmol/m3, in such a case, it can be assumed further that the nominal concentration of feed is 10kmol/m3 and on the same note, the nominal feed temperature of the situation is about 300K. Therefore, the process of creating a point of operation specification object that define the steady-state condition will resume the following example: opspec (=)opspec will resume (‘CSTR_OpenLoop’); opspec (=)addoutputspec will resume (opspec, ‘CSTR_OpenLoop/CSTR’); opspec.

outouts (1).will be known as true or opspec.outouts (1).Known=true opspec.outouts (1).y = 2 op1 will be findop (which will be ‘CSTR_OpenLoop’ opspec). As the brief example above outlines, the process will be derived to a point of reaching at the calculated operating point that can be termed as C_A which may generate values such as 2kmol/m3 and a value of T_K = 373 K depending on the initial values. There is need to realize that the steady-state coolant temperature can also be given in different values such as 299K (Singh et al. 2016). Such values can act as the normal value of the variable manipulated of the self-tuning PID controller design for a continuously stirred tank reactor.

However, there will be a need to specify the values of known inputs and the initial guesses for state values. To specify these points one will: Use the Input.u and Input.Known fields of opspec for verifying values of known inputs and Use the State.x field of opspec to verify initial guesses for sate values Generally, it has to be noted that there will be different codes that will be able to specify the coolant temperatures as or at about 305 K and there will be start or initial guess values of the C_A and T_K states which should be established before the calculation of the steady-state point of operations.

Specification of Linearisation Outputs and Inputs It has to be noted that the first start point will be to establish whether the linearization of output and input signals have been defined. If for instance, the input and output signals have been defined in the model in our case it is CSTR_OpenLoop then the process will adopt a different step and this be to use io = getlinio('CSTR_OpenLoop'); in obtaining different signal set(s). However, if this is not the case then the process will have to specify the output and input signals that different considerably and one of such signals includes the following among others: io = linio('CSTR_OpenLoop/CSTR',1,'output'); io = linio('CSTR_OpenLoop/CSTR',2,'output'); io = linio('CSTR_OpenLoop/Feed Concentration',1,'input'); io = linio('CSTR_OpenLoop/Feed Temperature',1,'input'); io= linio('CSTR_OpenLoop/Coolant Temperature',1,'input') Linearisation of the Model The process will take the option of linearilising the model by using the specific or specified operating point, which in this case could take the option of io= linio('CSTR_OpenLoop/Coolant Temperature',1,'input') and input/output signals, io.

This can be put as; sys = linearize('CSTR_OpenLoop',op1,io) The Process of Linearisation Using Linear Analysis Tool in Simulink Control Design The process of linearalise a Simulink model using the linear tool of analysis provides better understanding for the process especially with the Simulink Control Design product. The procedure as given below uses the CSTR model which is CSTR_OpenLoop. The first step therefore will be to open Simulink Model In the Simulink model window the second step will be to select analysis Still in the Simulink model, the third step will be opening of control design and The last step will be opening of linear analysis.

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