University of Alabama Chemical Engineering Process Dynamics and Control HW attached pic for description of the hw. the course is chemical engineering proce

University of Alabama Chemical Engineering Process Dynamics and Control HW attached pic for description of the hw. the course is chemical engineering process dynamics and control Prof. Evan K. Wujcik
Process Control & Dynamics
Fall 2019
MATLAB Project
Provide a PDF file giving your responses (plots and responses when necessary) to the following prompts.
Include all of your MATLAB “work” (aka-command window printout) as an appendix for 1-2. In your
responses, provide 2-3 sentences discussing each prompt. If something does not make sense, why does it
not make sense? What does this tell us?
1. Input the process transfer function ( ( ) =
10
)
25 +1
2. Input the process transfer function ( ( ) =
7 −4
)
9 +5
a.
b.
c.
d.
e.
a.
b.
c.
d.
e.
in the MATLAB command window, and:
calculate the inverse LaPlace Transform
plot the response to a unit step input and identify the behavior
plot the Pole-Zero Map and discuss the system’s stability & behavior
plot the Bode Plot and determine the gain margin and phase margin
plot the Nyquist Plot and discuss the system’s stability & behavior
in the MATLAB command window, and:
calculate the inverse LaPlace Transform
plot the response to a unit step input and identify the behavior
plot the Pole-Zero Map and discuss the system’s stability & behavior
plot the Bode Plot and determine the gain margin and phase margin
plot the Nyquist Plot and discuss the system’s stability & behavior
3. In MATLAB be sure that SIMULINK TOOLBOX, the CONTROL SYSTEM TOOLBOX, and
SIMULINK DESIGN OPTIMIZATION APP are installed. Run through the following tutorial
and respond to h.
a. Open the heatex_demo model using the command: open_system(‘heatex_demo’) in the
command window and run the simulation. The simulation produces an unoptimized
temperature variation of the heat exchanger and the initial data for optimization.
b. Double-click the Scope block to view the unoptimized temperature response, the
disturbance signal and the control signal.
c. Double-click the Heat Exchanger Model block to view the model details.
d. Double-click the Max Temperature Variation block to view constraints on the
temperature variation of the heat exchanger. This constraint is used to tune the controller
parameters.
e. You can launch Response Optimization Tool using the Analysis menu in Simulink, or the
sdotool command in MATLAB. You can launch a pre-configured optimization task in
Response Optimization Tool by first opening the model and by double-clicking on the
orange block at the bottom of the model. From the Response Optimization Tool, press the
Plot Model Response button to simulate the model and show how well the initial design
satisfies the design requirements.
f. The solid line represents the current response with the mean Disturbance Delay as
specified in the constraint block. The dashed lines represent the response with the
maximum and minimum Disturbance Delay. We start the optimization by pressing the
Optimize button from the Response Optimization Tool. The plots are updated to indicate
that the design requirements are now satisfied.
g. The solid curve shows the final optimized temperature variation of the heat exchanger.
h. Comment on the
Prof. Evan K. Wujcik
i.
ii.
iii.
iv.
v.
vi.
vii.
viii.
Process Control & Dynamics
Fall 2019
Process model
Process gain
Process time constant
Disturbance model
Disturbance gain
Disturbance time constant
Type of controller modes used
Types of control loops in the control block diagram (What are these for? How do
they help control the temperature?)
4. In MATLAB be sure that SIMULINK TOOLBOX, the CONTROL SYSTEM TOOLBOX, and
SIMULINK DESIGN OPTIMIZATION APP are installed. Run through the following tutorial
and respond to h.
a. Open the distillation_demo model using the command: open_system(‘distillation_demo’)
in the command window and run the simulation. The simulation produces the
unoptimized composition of methanol in the column and the initial data for optimization.
b. Double-click the Scope block to view the unoptimized methanol composition in the top
and bottom of the column.
c. Double-click the Linearized Model of Distillation Column block. Note that this is a
subsystem and shows the model for variation of methanol in the top and bottom of the
distillation column.
d. Double-click the Desired Step Response block to view constraints on the step response of
the distillation column. These constraints are used to simultaneously tune both of the
single-loop controller parameters.
e. You can launch Response Optimization Tool using the Analysis menu in Simulink, or the
sdotool command in MATLAB. You can launch a pre-configured optimization task in
Response Optimization Tool by first opening the model and by double-clicking on the
orange block at the bottom of the model. From the Response Optimization Tool, press the
Plot Model Response button to simulate the model and show how well the initial design
satisfies the design requirements.
f. There are two curves in the plot representing the methanol composition in the top and
bottom of the column. We start the optimization by pressing the Optimize button from
the Response Optimization Tool. The plots are updated to indicate that the design
requirements are now satisfied.
g. The two solid curves show the final optimized methanol composition in the top and
bottom of the distillation column.
h. Comment on the
i.
Process model
ii.
Process gain
iii.
Process time constant
iv.
Type of controller modes used
v.
Types of control loops in the control block diagram (What are these for? How do
they help control the temperature?)
Learning SIMULINK requires a lot of effort. These models are a great starting point to begin changing
model and control parameters (gains, time constants, setpoints, TFs, etc.) and seeing the effects. You
could take a semester-long course on just SIMULINK!

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