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Chap_03_Marlin_2013 - Process Control Education

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Chap_03_Marlin_2013 - Process Control Education
Process Control: Designing Process and Control
Systems for Dynamic Performance
Chapter 3. Mathematical Modelling Principles
Copyright © Thomas Marlin 2013
The copyright holder provides a royalty-free license for use of this material at non-profit
educational institutions
CHAPTER 3 : MATHEMATICAL
MODELLING PRINCIPLES
When I complete this chapter, I want to be
able to do the following.
• Formulate dynamic models based on
fundamental balances
• Solve simple first-order linear dynamic
models
• Determine how key aspects of dynamics
depend on process design and operation
CHAPTER 3 : MATHEMATICAL
MODELLING PRINCIPLES
Outline of the lesson.
• Reasons why we need dynamic models
• Six (6) - step modelling procedure
• Many examples
- mixing tank
- CSTR
- draining tank
• General conclusions about models
• Workshop
WHY WE NEED DYNAMIC MODELS
Do the Bus and bicycle have different dynamics?
•
Which can make a U-turn in 1.5 meter?
•
Which responds better when it hits a bump?
Dynamic performance
depends more on the vehicle
than the driver!
The process dynamics
are more important
than the computer
control!
WHY WE NEED DYNAMIC MODELS
Feed material is delivered periodically, but the process
requires a continuous feed flow. How large should should
the tank volume be?
Periodic Delivery flow
Continuous
Feed to process
Time
We must provide
process flexibility
for good
dynamic performance!
WHY WE NEED DYNAMIC MODELS
The cooling water pumps have failed. How long do we have
until the exothermic reactor runs away?
Temperature
F
T
Dangerous
L
A
time
Process dynamics
are important
for safety!
WHY WE DEVELOP MATHEMATICAL MODELS?
Input change,
e.g., step in
coolant flow rate
Process
Effect on
output
variable
T
A
Math models
help us answer
these questions!
How does the
process
influence the
response?
• How far?
• How fast
• “Shape”
SIX-STEP MODELLING PROCEDURE
1. Define Goals
We apply this procedure
2. Prepare
information
• to many physical systems
3. Formulate
the model
• component material balance
4. Determine
the solution
5. Analyze
Results
• overall material balance
• energy balances
T
A
6. Validate the
model
SIX-STEP MODELLING PROCEDURE
1. Define Goals
• What decision?
2. Prepare
information
• What variable?
3. Formulate
the model
4. Determine
the solution
5. Analyze
Results
6. Validate the
model
• Location
T
A
Examples of variable selection
liquid level

total mass in liquid
pressure

total moles in vapor
temperature 
energy balance
concentration 
component mass
SIX-STEP MODELLING PROCEDURE
1. Define Goals
• Sketch process
2. Prepare
information
• Collect data
3. Formulate
the model
• State
assumptions
• Define system
4. Determine
the solution
5. Analyze
Results
6. Validate the
model
Key property
of a “system”?
T
A
Variable(s) are the
same for any
location within
the system!
SIX-STEP MODELLING PROCEDURE
1. Define Goals
2. Prepare
information
3. Formulate
the model
4. Determine
the solution
5. Analyze
Results
6. Validate the
model
CONSERVATION BALANCES
Overall Material
Accumulati on of mass  mass in mass out 
Component Material
Accumulati on of  component  component 




component
mass
mass
in

 
 mass out 
generation of 


component
mass


Energy*
Accumulati on 

  H  PE  KE in  H  PE  KE out
U

PE

KE


 Q - Ws
* Assumes that the system volume does not change
SIX-STEP MODELLING PROCEDURE
1. Define Goals
• What type of equations do we use first?
2. Prepare
information
Conservation balances for key variable
3. Formulate
the model
4. Determine
the solution
5. Analyze
Results
6. Validate the
model
• How many equations do we need?
Degrees of freedom = NV - NE = 0
• What after conservation balances?
Constitutive
equations, e.g.,
Q = h A (T)
rA = k 0 e -E/RT
Not
fundamental,
based on
empirical data
SIX-STEP MODELLING PROCEDURE
1. Define Goals
2. Prepare
information
3. Formulate
the model
4. Determine
the solution
5. Analyze
Results
6. Validate the
model
Our dynamic models will involve
differential (and algebraic) equations
because of the accumulation terms.
dCA
V
 F (C A0  C A )  VkCA
dt
With initial conditions
CA = 3.2 kg-mole/m3 at t = 0
And some change to an input
variable, the “forcing function”, e.g.,
CA0 = f(t) = 2.1 t (ramp function)
SIX-STEP MODELLING PROCEDURE
1. Define Goals
2. Prepare
information
3. Formulate
the model
4. Determine
the solution
5. Analyze
Results
6. Validate the
model
We will solve simple models analytically
to provide excellent relationship between
process and dynamic response, e.g.,
C A (t )  C A (t ) t 0  ( C A 0 )K (1  e t /  )
for t  0
Many results will have the same
form! We want to know how the
process influences K and , e.g.,
F
K
F  kV
V

F  Vk
SIX-STEP MODELLING PROCEDURE
1. Define Goals
2. Prepare
information
3. Formulate
the model
4. Determine
the solution
5. Analyze
Results
6. Validate the
model
We will solve complex models
numerically, e.g.,
dCA
V
 F (C A0  C A )  VkCA2
dt
Using a difference approximation
for the derivative, we can derive the
Euler method.
C An  C An1
 F (C A0  C A )  VkCA2 
 ( t )

V

 n 1
Other methods include Runge-Kutta
and Adams.
SIX-STEP MODELLING PROCEDURE
1. Define Goals
2. Prepare
information
3. Formulate
the model
4. Determine
the solution
• Check results for correctness
- sign and shape as expected
- obeys assumptions
- negligible numerical errors
• Plot results
• Evaluate sensitivity & accuracy
5. Analyze
Results
6. Validate the
model
• Compare with empirical data
SIX-STEP MODELLING PROCEDURE
1. Define Goals
2. Prepare
information
Let’s practice modelling until we are
ready for the Modelling Olympics!
3. Formulate
the model
4. Determine
the solution
5. Analyze
Results
6. Validate the
model
Please remember that modelling is not
a spectator sport! You have to practice
(a lot)!
MODELLING EXAMPLE 3.1. MIXING TANK
Textbook Example 3.1: The mixing tank in the figure has
been operating for a long time with a feed concentration of
0.925 kg-mole/m3. The feed composition experiences a step
to 1.85 kg-mole/m3. All other variables are constant.
Determine the dynamic response.
F
CA0
(We’ll solve this in class.)
CA
V
Let’s understand this response, because we will see it
over and over!
Output is smooth, monotonic curve
tank concentration
1.8
1.6
Maximum
slope at
“t=0”
1.4
1.2
1
 63% of steady-state CA
At steady state
CA = K CA0

0.8
0
20
40
60
80
100
120
80
100
120
time
Output changes immediately
inlet concentration
2
1.5
CA0 Step in inlet variable
1
0.5
0
20
40
60
time
MODELLING EXAMPLE 3.2. CSTR
The isothermal, CSTR in the figure has been operating for
a long time with a feed concentration of 0.925 kg-mole/m3.
The feed composition experiences a step to 1.85 kgmole/m3. All other variables are constant. Determine the
dynamic response of CA. Same parameters as textbook
Example 3.2
F
CA0
A B
 rA  kCA
(We’ll solve this in class.)
CA
V
MODELLING EXAMPLE 3.2. CSTR
reactor conc. of A (mol/m3)
Annotate with key features similar to Example 1
1
0.8
Which is faster,
mixer or CSTR?
0.6
Always?
0.4
0
50
100
150
100
150
time (min)
inlet conc. of A (mol/m3)
2
1.5
1
0.5
0
50
time (min)
MODELLING EXAMPLE 3.3. TWO CSTRs
Two isothermal CSTRs are initially at steady state and
experience a step change to the feed composition to the
first tank. Formulate the model for CA2. Be especially
careful when defining the system!
F
CA0
A B
 rA  kCA
CA1
V1
CA2
(We’ll solve this in class.)
V2
MODELLING EXAMPLE 3.3. TWO CSTRs
Annotate with key features similar to Example 1
1.2
tank 2 concentration
tank 1 concentration
1.2
1
0.8
0.6
0.4
0.8
0.6
0.4
0
10
20
30
40
50
60
40
50
60
time
2
inlet concentration
1
1.5
1
0.5
0
10
20
30
time
0
10
20
30
40
50
60
SIX-STEP MODELLING PROCEDURE
1. Define Goals
2. Prepare
information
3. Formulate
the model
4. Determine
the solution
5. Analyze
Results
6. Validate the
model
We can solve only a few models
analytically - those that are linear
(except for a few exceptions).
We could solve numerically.
We want to gain the INSIGHT from
learning how K (s-s gain) and ’s
(time constants) depend on the
process design and operation.
Therefore, we linearize the models,
even though we will not achieve an
exact solution!
LINEARIZATION
Expand in Taylor Series and retain only constant and linear
terms. We have an approximation.
This is the only variable
dF
F ( x )  F ( xs ) 
dx
1 d 2F
( x  xs ) 
2
2
!
dx
xs
( x  xs ) 2  R
xs
Remember that these terms are constant
because they are evaluated at xs
We define the deviation variable: x’ = (x - xs)
LINEARIZATION
y =1.5 x2 + 3 about x = 1
We must evaluate the
approximation. It depends
on
exact
approximate
• non-linearity
• distance of x from xs
Because process control maintains variables near desired
values, the linearized analysis is often (but, not always)
valid.
MODELLING EXAMPLE 3.5. N-L CSTR
Textbook Example 3.5: The isothermal, CSTR in the figure
has been operating for a long time with a constant feed
concentration. The feed composition experiences a step.
All other variables are constant. Determine the dynamic
response of CA.
Non-linear!
F
CA0
A B
 rA 
2
k CA
(We’ll solve this in class.)
CA
V
MODELLING EXAMPLE 3.5. N-L CSTR
We solve the linearized model analytically and the non-linear
numerically.
Deviation variables
do not change the
answer, just
translate the values
In this case, the
linearized
approximation is
close to the
“exact”non-linear
solution.
MODELLING EXAMPLE 3.6. DRAINING TANK
Textbook Example 3.6: The tank with a drain has a
continuous flow in and out. It has achieved initial steady
state when a step decrease occurs to the flow in. Determine
the level as a function of time.
Solve the non-linear and linearized models.
MODELLING EXAMPLE 4. DRAINING TANK
Small flow change:
linearized
approximation is good
Large flow change:
linearized model is
poor – the answer is
physically impossible!
(Why?)
DYNAMIC MODELLING
We learned first-order systems have the same output “shape”.
dY

 Y  K[f (t ))] with f(t) the input or forcing
dt
Output is smooth, monotonic curve
1.6
Maximum
slope at
“t=0”
1.4
1.2
1
 63% of steady-state 
At steady state

= K

0.8
0
20
40
60
80
100
120
80
100
120
time
Output changes immediately
2
inlet concentration
Sample
response
to a step
input
tank concentration
1.8
1.5
 = Step in inlet variable
1
0.5
0
20
40
60
time
DYNAMIC MODELLING
The emphasis on analytical relationships is directed to
understanding the key parameters. In the examples, you
learned what affected the gain and time constant.
K: Steady-state Gain
• sign
• magnitude (don’t forget
the units)
• how depends on design
(e.g., V) and operation
(e.g., F)
:Time Constant
• sign (positive is stable)
• magnitude (don’t forget
the units)
• how depends on design
(e.g., V) and operation
(e.g., F)
DYNAMIC MODELLING: WORKSHOP 1
For each of the three processes we modelled, determine how
the gain and time constant depend on V, F, T and CA0.
• Mixing tank
• Linear CSTR
• CSTR with
second order
reaction
F
CA0
CA
V
DYNAMIC MODELLING: WORKSHOP 2
Describe three different level sensors for measuring liquid
height in the draining tank. For each, determine whether the
measurement can be converted to an electronic signal and
transmitted to a computer for display and control.
I’m getting tired of monitoring
the level. I wish this could
be automated.
L
DYNAMIC MODELLING: WORKSHOP 3
Model the dynamic response of component A (CA) for a
step change in the inlet flow rate with inlet concentration
constant. Consider two systems separately.
•
Mixing tank
• CSTR with first order reaction
F
CA0
CA
V = constant
V
DYNAMIC MODELLING: WORKSHOP 4
The parameters we use in mathematical models are never
known exactly. For several models solved in the textbook,
evaluate the effect of the solution of errors in parameters.
•
 20% in reaction rate constant k
•
 20% in heat transfer coefficient
•
 5% in flow rate and tank volume
How would you consider errors in several parameters in the
same problem?
Check your responses by simulating using the MATLAB mfiles in the Software Laboratory.
DYNAMIC MODELLING: WORKSHOP 5
Determine the equations that are solved for the Euler
numerical solution for the dynamic response of draining
tank problem. Also, give an estimate of a good initial value
for the integration time step, t, and explain your
recommendation.
DYNAMIC MODELLING: WORKSHOP 6
A. Select a topic of interest to you that can be investigated
using mathematical modelling and research
developments in modelling the topic. For some ideas, see
the following.
http://plus.maths.org/content/teacher-packagemathematical-modelling
B. Results from mathematical models contain uncertainty.
Review the following report and discuss uncertainty in
the models that you investigated in Part A above.
http://www.nap.edu/openbook.php?record_id=13395
CHAPTER 3 : MATH. MODELLING
How are we doing?
• Formulate dynamic models based on
fundamental balances
• Solve simple first-order linear dynamic
models
• Determine how key aspects of dynamics
depend on process design and operation
Lot’s of improvement, but we need some more study!
• Read the textbook
• Review the notes, especially learning goals and workshop
• Try out the self-study suggestions
• Naturally, we’ll have an assignment!
CHAPTER 3: LEARNING RESOURCES
•
SITE PC-EDUCATION WEB
- Instrumentation Notes
- Interactive Learning Module (Chapter 3)
www.pc-education.mcmaster.ca/
- Tutorials (Chapter 3)
- M-files in the Software Laboratory (Chapter 3)
•
Read the sections on dynamic modelling in previous
textbooks
- Felder and Rousseau, Fogler, Incropera & Dewitt
CHAPTER 3:
SUGGESTIONS FOR SELF-STUDY
1. Discuss why we require that the degrees of freedom for a
model must be zero. Are there exceptions?
2. Give examples of constitutive equations from prior
chemical engineering courses. For each, describe how we
determine the value for the parameter. How accurate is
the value?
3. Prepare one question of each type and share with your
study group: T/F, multiple choice, and modelling.
4. Using the MATLAB m-files in the Software Laboratory,
determine the effect of input step magnitude on linearized
model accuracy for the CSTR with second-order reaction.
CHAPTER 3:
SUGGESTIONS FOR SELF-STUDY
5. For what combination of physical parameters will a first
order dynamic model predict the following?
• an oscillatory response to a step input
• an output that increases without limit
• an output that changes very slowly
6. Prepare a fresh cup of hot coffee or tea. Measure the
temperature and record the temperature and time until
the temperature approaches ambient.
• Plot the data.
• Discuss the shape of the temperature plot.
• Can you describe it by a response by a key parameter?
• Derive a mathematical model and compare with your
experimental results
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