Ep 5512 lecture-01

Process Control Fundamentals
EP-5512
By
Goitom Tadesse (M/Tech)
Defence Engineering College
3-2-3
Feb. 2019

Course Description
Aim:
To understand the concepts of advanced industrial
process control
Description:
A Review of Fundamental Process Control; Cascade
Control; Ratio Control; Dead Time Control;
Feedforward Control; Nonlinear Compensation and
Adaptive Control; Multivariable Control; Fuzzy
Logic and Process Control Tuning; Distributed
Control Systems.

General Content
 Process Dynamics and Control
 Process modeling
 Solution of ODE
 Process Control Tuning
 Advanced Control Configurations
 Nonlinear Compensation and Adaptive Control
 Multivariable Control
 Distributed Control System In Process Control

Reference Books
1. P.W. Murrill, ―Fundamentals of Process Control
Theory‖, 3rd Edition, 2000
2. K.T. Erikson, ―Plant –Wide Process Control‖, 1st
ed., Wiley Inter-science, 1999

Prerequisites
The prerequisite knowledge required to successfully
attend this course is:
• Introduction to measurement and Instrumentation
(EL-3312)
• Control Systems (EP-3111)
• Modern Control Systems (EP-4211)
• Digital Control Systems (EP-5511)
5

Lecture One
A Review of Fundamental Process
Control
• Introduction to process dynamics and
control
• Process modeling

What does a control system do?
Why is control necessary?
Why is control possible?
How is control done?
Where is control implemented?
What does control engineering ―Engineer‖?
How is Process Control Documented?
7
Introduction to Process Dynamics
and Control

Introduction to process …
Control engineering is an engineering science that is
used in many engineering disciplines:
• Chemical, Electrical, Mechanical, Biological
engineering, …
It is applied to a wide range of physical systems from
electrical circuits to guided missiles to robots.
The field of process control encompasses the basic
principles of physicochemical systems, such as
chemical reactors, heat exchangers, and mass
transfer equipment.
8

The task of engineers is to design, construct, and
operate a physical system to behave in a desired
manner, and an essential element of this activity is
sustained maintenance of the system at the desired
conditions—which is process control engineering.
Control in process industries refers to the regulation
of all aspects of the process.
Precise control of level, temperature, pressure and
flow is important in many process applications.
9

10
controlling room temperature

Variations in proportions, temperature, flow,
turbulence, and many other factors must be carefully
and consistently controlled to produce the desired end
product with a minimum of raw materials and
energy.
Process control technology is the tool that enables
manufacturers to keep their operations running within
specified limits and to set more precise limits to
maximize profitability, ensure quality and safety.
11

Importance of Process Control
1. Safety
2. Environmental protection
3. Equipment protection
4. Smooth plant operation and production rate
5. Product quality
6. Profit optimization
7. Monitoring and diagnosis
12

Process is the methods of changing or refining raw
materials to create end products.
The raw materials, either pass through or remain in a
liquid, gaseous, or slurry state during the process, are
transferred, measured, mixed, heated or cooled,
filtered, stored, or handled in some other way to
produce the end product.
13

Process industries include:
 the chemical industry,
 the oil and gas industry,
 the food and beverage industry,
 the pharmaceutical industry,
 the water treatment industry, and
 the power industry.
Process control refers to the methods that are used to
control process variables when manufacturing a
product.
14

Manufacturers control the production process for
three reasons:
 Reduce variability
 Increase efficiency
 Ensure safety
Process control can reduce variability in the end
product, which ensures a consistently high-quality
product. Manufacturers can also save money by
reducing variability.
15

With accurate, dependable process control, the set
point can be moved
closer to the actual
product specification
and thus save the
manufacturer money.
Some processes need to be maintained at a specific
point to maximize efficiency.
For example, a control point might be the
temperature at which a chemical reaction takes place.
16

Accurate control of temperature ensures process
efficiency. Manufacturers save money by minimizing
the resources required to produce the end product.
Precise process control may also be required to
ensure safety.
For example, maintaining proper boiler pressure by
controlling the inflow of air used in combustion and
the outflow of exhaust gases is crucial in preventing
boiler implosions that can clearly threaten the safety
of workers.
17

Process Control Laws
First Law: The best control system is the simplest one
that will do the job.
Second Law: You must understand the process before
you can control it.
Third Law: The control is never possible if the
mathematical model can not be developed.
18

The dynamics of a Shower
What are the controlled or manipulated inputs?
( What part of the system can be directly changed?)
What are the set points?
( What end result is desired?)
What are the uncontrolled inputs?
(What disturbances can happen outside of the shower stall?)
Why might the set points change?
(The same way morning/night, summer/winter, …?)
What are the benefits of controlling this process?
19

20
sensor
process
control
element
controller

Design aspects of Process control systems
Manipulated Variables: Variables - adjusted by the
operator or controller
Controlled Variables: The process value that is being
manipulated by a system, (T0, P, flow rate, level, etc)
Disturbances: Uncontrolled changes, such as weather
or feed composition
Measured Variables: values-can be directly measured
Final control element: The part of a control system
that has direct influence on the process and brings it
to the set-point condition.
21

Process control objectives
Operational objectives:
 Stability of process
 Suppress influence of disturbance
 Optimize performance of plant
What variable should be measured & manipulated?
What is the best control configuration?
How should measurements be used to adjust the
manipulated variable?
22

Process Dynamics
First Order
Higher order
Dead time
23
dy
y kx
dt
   1
k
s
x y
2
2
2
2
d y dy
y kx
dt dt
    2 2
2 1
k
s s  
x y
( ) x( )y t t  
s
e x y
t0 t0
θ

Process Control
• Open-Loop Control: utilize a controller or control
actuator to obtain the desired response.
• Closed-Loop Control: utilizes feedback to compare
the actual output to the desired output response.
24

Process Modeling
Why a model is needed?
 To make quantitative predictions about system
behaviour
 To backup financial or other decisions
 To optimize a new or existing process
 To operate efficiently and safely an existing
process
 For illustration / teaching
25

Process Modeling …
Modeling is a goal-oriented task, so the proper model
depends on its application.
Modeling is a task that requires creativity and
problem-solving skills.
The models used in process control are developed to
relate each input variable (cause) to the output
variable (effect).
The modeling approach enables us to reach this goal by
(1) developing the fundamental model and
(2) deriving the linearized models for each input output
dynamic response. 26

The procedure of process modeling provides a road
map for developing, solving, and interpreting
mathematical models based on fundamental
principles.
In addition to predicting specific behavior, these
models provide considerable insight into the
relationship between the process equipment and
operating conditions and dynamic behavior.
A thorough analysis of results is recommended in all
cases so that the sensitivity of the solution to
assumptions and data can be evaluated.
27

Process Modeling…
A process model is a set of equations that allows us
to predict the behavior of a chemical process system.
Process model is developed either:
by first principle or from empirical data
The control system engineer has three options:
1. Simulate the nonlinear system and numerically compute
its solution
2. Develop a linear system model that approximates the
dynamic behavior of the system in the neighborhood of
a specified operating point
3. Transform the nonlinear in to a linear system by an
approximate transformation of variables.
28

Process Modeling
The second option involves the following steps:
1. Formulate the system differential equations based
on the first principle (conservation balance)
2. Linearize the differential equations about the
operating point
3. Laplace transform these equations
4. Express as a transfer function
29

Process models are characterized as lumped
parameter system or distributed parameter systems.
A lumped parameter system assumes that a variable
changes only with one independent variable
 Mathematically modeled by Ordinary
differential equations.
A distributed parameter system has more than one
independent variables.
 Mathematically modeled by Partial differential
equations.
30

A Modeling Procedure
1. Define goals
2. Prepare information
3. Formulate model
4. Determine solution
31
• What decisions?
• What variable?
• Location
• Sketch process • State assumptions
• Collect data • Define system
• Conservation balances
• Constitutive equations
• Check degrees of freedom
• Dimensionless form
• Analytical
• Numerical

5. Analyze results
6. Validate model
32
• Check results for correctness
- sign and shape as expected
- obeys assumptions
• Plot results
• Evaluate sensitivity & accuracy
• Compare with empirical data

Conservation balance
Overall Material Balance
{Accumulation of mass} = {mass in} - {mass out}
Component Material Balance
{Accumulation of component mass}= {component mass in} -
. {component mass out} + {generation of component mass}
Energy Balance
{Accumulation of U + PE + KE} = {H + PE + KE in due to
. convection} - {H + PE + KE out due to convection}+ Q-Ws
H= U + pv = enthalpy
33

Guidelines in selecting the proper balances:
If the variable is total liquid mass in a tank or
pressure in an enclosed gas-filled vessel, a material
balance is appropriate.
If the variable is concentration (mole/m3 or weight
fraction, etc.) of a specific component, a component
material balance is appropriate.
If the variable is temperature, an energy balance is
appropriate.
34

Constitutive equations, e.g.
Heat transfer: Q = hA(AT)
Chemical reaction rate: rA = k0e-E/RT
CA
Equation of state: PV = nRT
How many equations do we need?
Degrees of freedom = NV - NE = 0
35
Not
fundamental,
based on
empirical data

Summary of degrees-of-freedom analysis
DOF = 0 The system is exactly specified, and the
solution of the model can proceed.
DOF < 0 The system is over specified, and in general,
no solution to the model exists. This is a
symptom of an error in the formulation.
DOF > 0 The system is underspecified, & an infinite
number of solutions to the model exists.
The model must be corrected to achieve
zero degrees of freedom.
36

Example 1: The mixing tank (CST) 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. (90% composition)
Information: The system is the
liquid in the tank. the concentration
should be uniform in the liquid.
37

Assumptions:
1. Well-mixed vessel
2. Density the same for solute and solvent
3. Constant flow in (constant level)
Data:
F0 = 0.085 m3/min; V = 2.1 m3; CAinit = 0.925 mole/m3;
ΔCA0 = 0.925 mole/m3; thus, CA0 = 1.85 mole/m3 after
the step
The system is initially at steady state (CA0 = CA = CAinit)
38

Formulation: Since this problem involves
concentrations, overall and component material
balances will be prepared.
The overall material balance
{Accumulation of mass} = {mass in} - {mass out}
for constant V
(1)
39
0 1
dV
F F
dt
 
0 1
0 1
0,
dV
F F
dt
F F F
  
  

Component Material Balance
(2)
= τ time constant of mixer
40
Accumulation of component component generation
component A A in A out of A
       
       
  
 


   
0
0
0
( )
( )
1 1
A
A A A A
A
A A
A
A A
dC
MW V MW F C C
dt
dC F
C C
dt V
dC
C C
dt  
 
 
 
V
F

Variables: CA and F1
External variables: F0 and CA0
DOF = NV-NE = 2-2 = 0
Solution
Multiplying both sides of (2) by the integrating factor,
(3)
Integrating both sides
41
/1
exp( ) t
IF dt e 

 
/
/
0
( ) 1t
tA
A
d e C
C e
dt




/ /
0
/
0
1t t
A A
t
A A
C e C e I
C C Ie
 




 
 
(4)

The integration constant, I
At t=0, CAinit=CA0 – I I =CAinit – CA0
Two important aspects of the dynamic behavior:
Speed of the dynamic response, which is
characterized by the time constant, τ.
the steady-state gain, which is defined as
42
 
/
0 0
/
0 0
( )
(1 )
t
A A Ainit A
t
A Ainit A A init
C C C C e
C C C C e




   
   
(5)
0
1A
p
A
Coutput
k
input C

  
 

Results Analysis
43
Time
from step
Percent of final steady-
state change in output
0 0
T 63.2
2T 86.2
3T 95
4T 98.2
The goal statement
involves determining
the time until 90 % of
the change in outlet
concentration has
occurred.
 This time can be calculated by setting
CA = CAinit + 0.9(CA0 - CAinit) in eqn. (5) and solving
Ainit A0
Ainit A0
0.1[ ]
ln (24.7)( 2.3) 56.8min
C C
t
C C

 
      
 

Validation
The mixing tank was built, the experiment was performed, and
samples of the outlet material were analyzed.
45
 The data points are plotted
along with the model
prediction.
 By visual evaluation and
considering the accuracy of
each data point, one would
accept the model as "valid"
for most engineering
applications.
Comparison of empirical
data (squares) & model
(line)

Ep 5512 lecture-01

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