CHEMG 461 Intoduction W01.Lectures 01-02 CK 2014lpre.cperi.certh.gr/auth/files/CHEMG 461_Intoduction_W01.Lectures 01-02... · and energy) conservation equations, a system of differential

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Dr. Costas Kiparissides

CHEG 461 : Process Dynamics and Control

Subject: Introduction to Process Control

Week 01, Lectures 01‐02, Spring 2014

CHEG 461, Spring 2014 Prof. Costas Kiparissides

Content

1. Introduction to Process Dynamics and Control

2. Open‐loop Versus Closed‐loop Control Systems

3. Classification of Input and Output Variables

4. Feedback and Feedforward Control Strategies

5. Examples of Closed‐loop Control Systems

6. Block Diagrams of Controlled Processes

7. Summary and Course Objectives

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Course Outline ( 1)

The first part of the course covers the fundamentals of mathematicalmodeling and dynamic analysis of physical and chemical processes andin general of dynamic systems. Based on the fundamental (mass, molarand energy) conservation equations, a system of differential andalgebraic equations is commonly derived to describe the dynamicbehavior of a chemical/physical system. Accordingly, the dynamicresponse of a system to dynamic changes in the system input variablesis analyzed via the solution of the mathematical model describing thedynamic behavior of a given system. Solution of the mathematicalmodel for a process is effected either via analytical mathematicalmethods (i.e., solution of low order linear ODEs in the time or Laplacedomain) or numerically with the aid of available numerical solutiontools (i.e., MATLAB, etc.).

The above approach is known as open‐loop dynamic analysis of thechemical and physical systems.


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Course Outline ( 2)

The second part of the course examines the dynamic behavior ofchemical and physical processes in closed loop, that is, under the actionof a controller (e.g., feedback or feed forward, etc.). We will learn how todesign and tune a controller (i) to regulate a process output variable(e.g., temperature, concentration, etc.) to a desired value (set point)despite the presence of process disturbances (regulatory control), (ii) tostabilize and operate an open‐loop unstable process (stabilizing control),(iii) to optimize the performance of a process via the application oftrajectory/optimizing control.

In particular, we will study the fundamental principles of classical controltheory, including the different types of controllers (e.g., proportional (P),proportional/integral (PI) and proportional/integral/derivative (PID), etc.)and analyze quantitatively the dynamic behavior of closed‐loop controlsystems. We will learn how to tune a control loop (i.e., a controller), intheory and in the laboratory on real systems.


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Course Outline ( 3)

Finally, we will learn in the laboratory part of the course howto use computer simulation tools (i.e., MATLAB, SIMULING,Control Station, etc.) to design and simulate the transientbehavior of open‐loop and closed‐loop dynamic systems.



Specific Course Objectives

Specific Course Objectives

Describe the fundamentals of modern process control systems including the needs and incentives for process control and its applications.

Model the dynamic behavior of chemical processes using differential equations and transfer functions.

Solve linear ODEs in the time domain and using Laplace transforms.

Analyze the dynamic behavior of first, second and higher order systems and calculate the system’s response to changes (i.e., pulse, step, etc.) in the input variables.

Understand the differences between linear and non‐linear dynamic system behavior.

Understand the concept and use of feedback controllers (i.e., P, PI, PID).

Understand block diagram developments and closed‐loop transfer functions.

Analyze the dynamic response of systems under feedback control.

Perform stability analysis of closed‐loop systems.

Carry out Root Locus Analysis of closed‐loop systems.

Understand frequency‐response analysis of control systems.

Perform controllers tuning using fundamental and approximate process models.

Demonstrate a conceptual understanding of complex control structures (e.g., feed‐forward control, cascade control, multivariable process control, etc.).

Use software tools such as MATLAB, SIMULINK to design and evaluate open‐loop and closed‐loop dynamic systems.

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Introduction to Process Control

In the chemical industry, the design of a control system is essential to ensure:

Good Process Operation Process Safety Product Quality Minimization of Environmental Impact


Purpose of a Control System

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“Loose” Control Costs Money


“Tight” Control: Profitable Operation

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Decrease of Standard Deviation in CV


Increase of Process Profitability

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Motivation for Process Control


Process Dynamics and Control

• Process: The conversion of feed materials to products usingchemical and physical operations taking place in some unit orequipment.

• Process dynamics refers to unsteady state process behavior.Transient operation occurs during important situations such asstart‐up and shut downs, and unusual disturbances or plannedtransitions from one product to another.

• Process control refers to maintaining a process at the desiredoperating conditions, safely and efficiently, despite the presenceof process disturbances, by manipulating ,e.g., the flow of amaterial stream or the energy input into a process.

DEFINITIONS

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What Does Process Mean ?


An Example of Stirred Tank Heater

M

Tin, w

Q

T, w

Tinw TProcess

Inputs Output

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Process Dynamic Models

The dynamic behavior of a process can be in theoryanalyzed in terms of a mathematical model whichincludes a system of differential and algebraicequations.

The numerical solution of the dynamic process model(and in very limited cases, its analytical solution)provides significant information on the process outputresponses, that is, the system’s dynamic behavior interms of variations in the process input variables.


Open‐ Versus Closed‐loop Systems

• In open‐loop dynamic systems: In the first part of thecourse we will focus in open‐loop dynamic systems.We will study the transient behavior of processes. Nocontrol mechanism.

• In closed‐loop control systems: In the second part ofthe course we will focus on the closed‐loop controlof a process under the operation of an automaticcontroller.

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Open‐ Versus Closed‐loop Systems


The Elements of a Control System

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Control Instrumentation

Measurement Sensors: Temperature, pressure, pressure drop, level, flow, density, concentration, etc.

Final Control Element:Solenoid, valve, variable speed pump or compressor, heater or cooler, etc.

Types of Automatic Controllers: On/off, PID, cascade, feed forward, multivariable, model‐based Smith predictor, sampled data, parameter scheduled adaptive controllers, etc.


Control Terminology (1)

• Controlled Variables: These are the variables whichquantify the performance or quality of the finalproduct, which are also called output variables.

• Manipulated Variables: These input variables areadjusted dynamically to keep the controlled variables attheir set‐points.

• Disturbances: These are also called "load" variables andrepresent input variables that can cause the controlledvariables to deviate from their respective set points.

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Control Terminology (2)

• Set‐point Change: Implementing a change in theoperating conditions. The set‐point signal is changedand the manipulated variable is adjusted appropriatelyto achieve the new operating conditions. Also called"servo" control or trajectory control.

• Disturbance Change: When a disturbance enters aprocess , it can change the process output variablesfrom their desired values. A control system should beable to return each controlled variable back to its set‐point . This is called regulatory control.

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Controlled and Manipulated Variables


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Home Heating Control System

– Control Objective

– Measured Process Variable (PV)

– Set Point (SP)

– Controller Output (CO)

– Manipulated Variable

– Disturbances (D)


Home Heating Control System

Measurement → Computation → Action

• Is house cooler than set point? (Tsetpoint Thouse > 0)

Action Open Fuel Valve

• Is house warmer than set point? (Tsetpoint Thouse < 0)

Action Close Fuel Valve

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Control Block Diagram


General Control Block Diagram

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Control of a Stirred Tank Heater

• Continuous stirred‐tank heater

• Question: Assume that the inlet temperature, Ti(t)changes with time. How can we ensure that T(t)remains at or near the set point temperature?

(t)

(t)

(t)


Control of a Stirred Tank Heater

Feedback control of stirred tank heater

(t)

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Control Instrumentation

Measurement Element:

Thermocouple for temperature measurement and

transmitter, TT

Final Control Element:

Electric heater

Automatic Controller:

For example, PID temperature controller, TC

Electrical Transmission Lines


Classification of Input Variables

Manipulated Variables (MV or Control Variables)

Their values can be adjusted freely by an operator or a control

mechanism.

In the example of the heated tank: the amount of heat added (Q).

Disturbance Variables (DV)

They actually represent random variations in the input process variables. Their values are not the result of the adjustment by an operator or a controller. They vary in a random and non‐predictable way.

In the example of the heated tank: Inlet temperature of inlet water.

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Classification of Output Variables

The output variables can be further distinguished into:

Measured output variables or Controlled variables (CV)

Their values can be measured with the aid of a measurement element (i.e., sensor) and ,thus, can be controlled.

In the example of the heated tank: The outlet temperature.

Unmeasured output variables

They are not or cannot be measured directly.


Control Block Diagram

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Feedback Control

• Distinguishing feature: Takes corrective action after disturbance enters the process.

•Advantages:

Corrective action is taken regardless of the source ofthe disturbance.

Reduces sensitivity of the controlled variable todisturbances and changes in the process.

• Disadvantages:

No corrective action occurs until after the disturbancehas upset the process, that is, until after y(t) differsfrom ysp.

Oscillatory responses, or even instability…


Example: Heated Stirred Tank

(t)

(t)

, Q(t)

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controller sensorpump patient

glucose setpoint

u yr

measured glucose

Closed‐loop Artificial Pancreas


Feedforward Control

• Distinguishing feature: Measures a disturbance and takes corrective action before disturbance enters the process.

• Advantages:

Corrects for disturbance before it upsets the process.

• Disadvantages:

Must be able to measure the disturbance.

No corrective action for unmeasured disturbances

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Example: Heated Stirred Tank

(t)

(t)

(t)

, Q(t)


Example: Stirred Tank Heater

Possible Control Strategies

1. Measure T and adjust Q

2. Measure Ti and adjust Q

3. Measure T and adjust w

4. Measure Ti and adjust w

5. Measure T and Ti and adjust Q

6. Measure T and Ti and adjust w

7. Place a heat exchanger on the inlet stream

Classification of Control Strategies

1 & 3: Feedback control

2 & 4: Feedfoward control

5 & 6: Feedfoward‐Feedback control

7: Change of design

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Example: Blending System

Notation:

• w1, w2(t) and w are mass flow rates

• x1(t), x2 and x(t) are mass fractions

of component A

x1(t)

w2(t)

x(t)

w(t)


Example: Blending System

Assumptions:

1. w1 is constant

2. x2 = constant = 1 (stream 2 is pure A)

3. Perfect mixing in the tank

Control Objective:

Keep x at a desired value (or “set point”) xsp, despite variations

in x1(t). Flow rate w2(t) can be adjusted for this purpose.

Terminology:

• Controlled variable (or “output variable”): x(t)

• Manipulated variable (or “input variable”): w2(t)

• Disturbance variable (or “load variable”): x1(t)

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What value of is required to have 2w ?SPx x

Overall balance:

Component A balance:

1 20 (1-1)w w w

1 1 2 2 0 (1-2)w x w x wx

(The over bars denote nominal steady‐state design values.)

• At the design conditions, . Substitute in Eq. 1‐2, and , then solve Eq. 1‐2 for :

SPx x SPx x2 1x 2w

12 1 (1-3)

1SP

SP

x xw w

x

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Design Question


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Control Question

• Equation 1‐3 is the design equation for the blendingsystem.

• If our assumptions are correct, then this value of willkeep at . But what if conditions change?

Control Question. Suppose that the inlet concentration x1changes with time. How can we ensure that x remains ator near the set point ?

As a specific example, if and , then x > xSP.

2wx SPx

SPx

1 1x x 2 2w w


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• Manual control vs. automatic control

• Proportional feedback control law,

2 2 (1-4)c SPw t w K x x t

1. Where Kc is called the controller gain.

2. w2(t) and x(t) denote variables that change with time t.

3. The change in the flow rate, is proportional to

the deviation from the set point, xSP – x(t). 2 2,w t w

Method 1. Measure x and adjust w2.

• Intuitively, if x is too high, we should reduce w2;

Some Possible Control Strategies


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Blending System: Control Method 1


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Method 2. Measure x1 and adjust w2.

• Thus, if x1 is greater than , we would decrease w2 so that

• One approach: Consider Eq. (1‐3) and replace and with x1(t) and w2(t) to get a control law:

12 1 (1-5)

1SP

SP

x x tw t w

x

1x

2 2;w w

1x 2w



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Blending System: Control Method 2


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Because Eq. (1‐3) applies only at steady‐state, it is not

clear how effective the control law in Eq. (1‐5) will be for

transient conditions.

Method 3.Measure x1 and x, adjust w2.

• This approach is a combination of Methods 1 and 2.

Method 4. Use a larger tank.

•If a larger tank is used, fluctuations in x1 will tend to be

damped out due to the larger capacitance of the tank

contents.

•However, a larger tank means an increased capital cost.



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Method Measured Variable

Manipulated Variable

Category

1 x w2 FB

2 x1 w2 FF

3 x1 and x w2 FF/FB

4 - - Design change

Table. 1.1 Control Strategies for the Blending System

Control Strategies for Blending System


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Hierarchy of Control ActivitiesC

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Control System Developments

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Summary

Learn why modeling the dynamic behavior of a process isfundamental to controlling it.

Practice methods of collecting and analyzing process datato determine dynamic behavior.

Learn what "good" or "best" control performance meansfor a particular process

Understand the computational methods behind PID controland learn when and how to use each form.

Learn how controller tuning impacts performance and howto determine values for these parameters.

Robotics:Robots perform automated tasks in assembly lines, where precision is important (e.g. welding in automotive industry) and dangerous tasks physically impossible for humans (e.g. military operations, and space explorations)


Examples of Control Applications

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Aerospace Applications:Aircraft or missile guidance and controlSpace vehicles and structures



Professor Costas Kiparissides Chemical Engineering Department CHEMG 461

Intelligent Transportation & Automotive Systems:Marine, Land, Air vehiclesPlatoon of cars in Automated Highway SystemsWarning systems for trains, railroad crossings,Automatic landing systems for planes, flight control systemsAir traffic control, highway traffic control, Ship steering, etc…etc…


Documents

CHEMG 461 Intoduction W01.Lectures 01-02 CK 2014lpre.cperi.certh.gr/auth/files/CHEMG 461_Intoduction_W01.Lectures 01-02... · and energy) conservation equations, a system of differential