CHE 461: Process Dynamics and Control Course Overview ...

CHE 461: Course Overview

Daniel E. Rivera

Department of Chemical and Materials Engineeringand

Institute for Manufacturing Enterprise SystemsArizona State University, Tempe, AZ 85287-6006

[email protected]

CHE 461: Process Dynamics and Control

Course Overview Presentation


About the Instructor• Education

– B.S. ChE degree from theUniversity of Rochester (1982)

– M.S. ChE degree from theUniversity of Wisconsin (1984)

– Ph.D. from Caltech (1987)

• Engineering Employment:– Summer jobs with Eastman Kodak– Associate Research Engineer,

Shell Development Company,Houston, TX (1987-1990)

– Associate Professor, Arizona StateUniversity, (1990 - present)

• Research Interests: check out labweb page inhttp://www.eas.asu.edu/~csel


Presentation Outline

• Course Mechanics

• Course Objective and Outcomes Discussion

• What is control systems engineering?

• Wrap-up


CHE 461: Principles of Process Dynamics and ControlFall Semester 2003

Instructor : D. E. Rivera, [email protected]: Goldwater Science and Engineering Center, Room 568.Phone: (480) 965-9476FAX (480) 965-0037SCOB Lab Phone: (480) 965-3639

Official Prerequisites : MAT 274, CHE 311, ECE 384

Recommended Prerequisites : CHE 331-333, CHE 342, CHE 352; CHE 442, ECE 394A,ECE 394C

Office Hours : MW 11:40 a.m. – 12:30 p.m.; other times by appointment.


Pre-requisites (by topic)

• Conservation and Accounting Principles

• Applied Mathematics– Know how to solve first and second-order differential

equations, both analytically and numerically

• Computer Usage– Basic Matlab competency (as taught in ECE 384) expected

from everyone


Textbook: (available on reserve at Noble Library)

Process Dynamics, Modeling, and Control, B.A. Ogunnaike and W.H. Ray, 1994, OxfordUniversity Press, ISBN 0-19-509119-1.

Additional References and Materials

Internal Model Control : A Comprehensive View, D.E. Rivera and M.E. Flores.

CHE 461 Laboratory Manual, D.E. Rivera, V.E. Sater, and others.

Matlab with SIMULINK Student Version, Release 13, The Mathworks, ISBN 0-967-21959-0

If you already own the Student Version, you may consider adding the Control System,Signal Processing, and System Identification toolboxes, which can be purchased directlyfrom the MathWorks web site at http://www.mathworks.com.


Course Grading

Course Grading:

Exams (2 midterms + final;final counts as 1.5 exams) 50%

Homework 15%(failing to return more than 1 hmwk setresults in 0% credit for the entire category)

Lab 35%


Letter grades will be given at the discretion of the instructor based on overall classperformance and (in some instances) individual student behavior. The followingguidelines have historically been used to award grades in this course:

80% or greater A70-79.9 B60-69.9 C50-59.9 Dbelow 50 E

Students are expected to abide by the ASU Student Code of Conduct and AcademicIntegrity Policy (http://www.asu.edu/studentlife/judicial/integrity.html). A writtenstatement to this effect will be required from each student.

Course Grading (Continued)


Academic Integrity

Students are expected to abide by the ASU Student Code ofConduct

http://www.asu.edu/aad/manuals/sta/sta104-01.html

and the ASU Student Academic Integrity Policy

http://www.asu.edu/studentlife/judicial/integrity.html

A signed acknowledgement to this effect will be required from eachstudent.


Instructor Grade Discretion

Regardless of class average, the following actions may earn a student an “E” grade in thecourse (or at the very least, a drop in their letter grade):

• Failure to substantively participate in lab team efforts,• Failure to submit (or submitting a non-credible effort) on more than one

laboratory report or lab assignment,• Significant violations of the course Attendance Policy,• Repeated instances of inappropriate classroom behavior,• Violation of the Student Academic Integrity Policy.

To achieve an “A” in the course, students must have submitted credible efforts on all labassignments; extra credit points cannot be used to replace credit lost for an un-submitted(or non-credible) lab exercise.


Attendance Policy

Attendance to assigned lab sessions, examinations, and selected lecture sessions ismandatory. Excused absences need to be discussed and approved by me (and if theyinvolve lab, coordinated with Dr. Chen) before class. If an emergency situation occurs,you need to bring it to my attention as soon as possible. . Any lab or exam sessionsmissed must be made up, no exceptions.


Appropriate Language

• Engineers are expected to effectively communicate their ideasto managers, colleagues, and customers. Inappropriatelanguage (written or oral) fails to achieve effectivecommunication and creates a hostile learning and workingenvironment. Inappropriate language not only includes wordsthat are biased or slanted (racially, sexually, ethnically…) butalso includes profanity.


Appropriate Lab and Classroom Behavior

• In ChE 461, you are expected to participate in thevarious classroom and laboratory activities, whichinclude:

1. coming to class on time (and staying awake),2.working and contributing to team efforts on whatever

assignment has been given,3. following the instructions given by the instructors or teaching

assistants,4. avoiding disruptive side conversations,5. not working on non-course activities during class time. This

includes doing work for other courses, playing games, writingemail or surfing the web during lab hours, etc.


Appropriate Lab and Classroom Behavior(Continued)

• In addition, you also are expected to address the instructorsand teaching assistants with courtesy and respect at all times.

• Repeated occurrences of inappropriate classroom behavior ormisconduct towards the instructors or teaching assistants willresult in the loss of a letter grade.

• You will be given only one warning if your behavior merits thistype of disciplinary action.


Course Objective

• To introduce students to fundamental principles insystem dynamics and control, with emphasis onprocess systems and the problems faced by processengineers.


Course Outcomes

• At the conclusion of the course, you should be ableto:

– Apply conservation and accounting principles in order tomodel the dynamics of simple process systems from a first-principles viewpoint

– Simplify a first-principles dynamic model for a processsystem and convert it to a form amenable to solution andanalysis.


System Representations

Nonlinear Lumped Parameter System

State-SpaceModel

Linearization

{ }Step/ Impulse

Response and

FrequencyResponse

Discrete-timeStep/

Impulse Response

and FrequencyResponse

s-domainTransfer Function

Model

Discrete-time S-S Model

z-domain Transfer Function

Model

T

Sampling

T

Sampling

Laplace transforms

Realization

(difference equation)


Course Outcomes (Continued)


– Empirically model the dynamics of simple process systemsusing response testing and regression methods (i.e., systemidentification)


System Identification

“Identification is the determination, on the basis of input andoutput, of a system within a specified class of systems, to which the system under test is equivalent.” - L. Zadeh, (1962)

SystemInputs Outputs

Disturbances

System identification focuses on the modeling ofdynamical systems from experimental data


System Identification & Control Design LoopP

rior

syst

em k

now

ledg

e: p

hysi

cs, l

ingu

istic

s, fi

rst-

hand

, etc

.

Experimentdesign

Pre-treatdata

Choosemodel

structure Chooseperformance

criterion

Parameter estimation

Validatemodel Not OK revise!

OK accept model!Not OK revise prior?

Controller Design & Commissioning


Honeywell TotalPlant Solution System



AutoRegressive with eXternal Input (ARX) Models

The one-step ahead predictor for y

y(t|t−1) = −a1y(t−1)−. . .−anay(t−na)+b1u(t−nk)+. . .+bnbu(t−nk−nb+1)can be expressed as a linear regression problem via

ϕ = [−y(t− 1) . . . −y(t− na) u(t− nk) . . . u(t−nk − nb + 1) ]T

and θ, the vector of parameter estimates:

θ = [ a1 . . . ana b1 . . . bnb ]T

Rewriting the objective (“loss”) function as

minθV = min

θ

1N

N∑i=1

[y −ϕT (t)θ

]2

leads to the well-established linear least-squares solution

θ = 1N

N∑t=1

ϕ(t)ϕT (t)−1 1N

N∑t=1ϕ(t)y(t)




– Understand and classify the basic dynamic behavior of bothopen-loop and closed-loop systems based on their time-domain, transfer function, and frequency-domainrepresentations.




– Analyze a feedback control system’s stability andperformance using both time-domain and frequency-response methods. Understand fundamental limitations toachievable control performance in a process system

– Design Proportional-Integral-Derivative (PID) controllers forprocess systems using both conventional tuning rules andthe Internal Model Control design procedure




– Evaluate control system performance using standardquantitative and qualitatitive performance criteria, andsubsequently improve the design through simulation and/orexperimental testing.




– Analyze the benefits of feedforward and cascade controlstrategies in process control systems.

– Design both decentralized and decoupled control systems fora multivariable plant.




– Understand practical issues in control engineering and thebenefits of control engineering towards improvingoperations, safety, and environmental compliance in processsystems (and beyond…)


Control Engineering


Control Engineering

• Understanding and applying control engineering principles willsignificantly expand your worldview


Control Engineering


• It will impact:


Control Engineering


• It will impact:

– How you operate your process system

– How you interpret the world around you


Control Engineering


• It will impact:



• It will make you a better engineer…


Control Engineering


• It will impact:




• It will make you a smarter consumer…


Control Engineering


• It will impact:




• It will make you a smarter consumer…

• It will prolong your life…


Control Engineering (Cont.)



• Everyone applies engineering control principles as part of dailylife, without being fully aware of it.




• Control engineering is a broadly-applicable field that spans allareas of engineering:




• Control engineering is a broadly-applicable field that spans allareas of engineering:

– Chemical– Electrical– Mechanical and Aerospace– Civil / Construction– Industrial– Biomedical– Computer Science and Engineering



• Considers how to manipulate system variables inorder to transform dynamic behavior to desirablefrom undesirable

• Open-loop: refers to system behavior without acontrol policy

• Closed-loop: refers to system behavior once acontroller/decision policy is implemented.



• Many examples of control applications in society:

– Cruise control and climate control in automobiles– The “sensor reheat” feature in your microwave oven– Home heating and cooling– The insulin pump for Type-I diabetics– “Fly-by-wire” systems for high-performance jet aircraft– Many, many, more…

• The development of improved sensors and actuators, coupledwith increasing embedded computing capabilities, will continueto facilitate the application of control engineering in manydiverse application settings.


Chevy Cavalier vs. the BMW 760Li

Hundreds of feedback loops in the BMW (althoughthe Cavalier is catching up…)


An Industrial Process Control Problem

QuickTime™ and aBMP decompressor

are needed to see this picture.

Objective: Use fuel gas flow to keep outlet temperature under control, in spite ofoccasional yet significant changes in the feed flowrate.


The “Shower” Control Problem

Hot Cold

The presence of delay or“transportation lag”

makes this a difficult controlproblem


Definitions

• Controlled Variable (y): system variable that we wish to keep ata reference value or setpoint (r).

• Manipulated Variable (u): system variable whose adjustmentinfluences the response of the controlled variable; its value isdetermined by the controller/decision policy.

• Disturbance Variable (d): system variable that influences thecontrolled variable response, but cannot be manipulated by thecontroller; disturbance changes occur external to the system(hence sometimes referred to as exogeneous variables)



Hot Cold



Hot Cold

Think about what may constitutecontrolled, manipulatedand disturbance variables in this system



Hot Cold


Controlled:Temperature,Total Water Flow



Hot Cold



Manipulated: Hot and ColdWater Valve Positions



Hot Cold



Manipulated: Hot and ColdWater Valve Positions

Disturbances:Inlet Water Flows,Temperatures


Components of a Closed-Loop Control System

• Sensors (for Measurement of the Controlled andDisturbance Variables)

• Actuators (to Manipulate System Variables)

• Controllers (i.e., Decision Policies). These relatecontrol errors, previous manipulations anddisturbance measurements to current values of themanipulated variable

• Model-based control policies are desirable


Feedback and FeedforwardControl Strategies

• In feedback control strategies, a controlled variable(y) is examined and compared to a reference valueor setpoint (r). The controller issues actions(decisions on the values of a manipulated variable(u)) on the basis of the discrepancy between y and r.

• In feedforward control, changes in a disturbancevariable (d) are monitored and the manipulatedvariable (u) is chosen to counteract anticipatedchanges in y as a result of d.


Closed-Loop Feedback Control “BlockDiagram”

C = ControllerP = Plant Model/“Transfer Function”Pd = Disturbance Model/“Transfer Function”

r u

Ce = r-y y

P++

d

PP

Pd

Controlled:Measured

Temperature, TotalWater Flow

Manipulated:Hot and ColdWater Valve

Positions

Disturbances:Inlet Water Flows,

Temperatures

Reference:Desired Temperature,

Total Water Flow


Combined Feedback/Feedforward BlockDiagram

--

cF

c p

pd

r ec u

d'

d

y+++ +++

Controlled:Measured

Temperature, TotalWater Flow

Manipulated:Hot and ColdWater Valve

Positions

Measured Disturbances:Inlet Water Flows,

Temperatures

Reference:Desired Temperature,

Total Water Flow

Unmeasured disturbances


Gasoil Furnace (Under FeedbackControl)


-20

-10

0

10

20

0 500 1000 1500 2000 2500 3000 3500 4000

Measured Output

Time[Min]

-10

0

10

0 500 1000 1500 2000 2500 3000 3500 4000

Input

Time[Min]

TemperatureDeviation(ControlledVariable)

Fuel GasFlow

(ManipulatedVariable)

From Open-Loop Operation toClosed-Loop Control

The transfer of variance from an expensive resource to a cheaper one isone of the major benefits of engineering process control


-20

-10

0

10

20

0 500 1000 1500 2000 2500 3000 3500 4000

Measured Output

Time[Min]

-10

0

10

0 500 1000 1500 2000 2500 3000 3500 4000

Input

Time[Min]


Fuel GasFlow




Open-Loop(Before Control)


-20

-10

0

10

20

0 500 1000 1500 2000 2500 3000 3500 4000

Measured Output

Time[Min]

-10

0

10

0 500 1000 1500 2000 2500 3000 3500 4000

Input

Time[Min]


Fuel GasFlow




Open-Loop(Before Control)

Closed-LoopControl


Furnace example with PRBS input, PID with filter controller

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000-15

-10

-5

0

5

10

15Input

Time[Min]

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000-5

0

5

10

15

20

25Measured Output

Time[Min]

From Open-Loop Operation toClosed-Loop Feedback Control


Fuel GasFlow




0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000-15

-10

-5

0

5

10

15Input

Time[Min]

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000-5

0

5

10

15

20

25Measured Output

Time[Min]



Fuel GasFlow


Open-Loop(Uncontrolled)



0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000-15

-10

-5

0

5

10

15Input

Time[Min]

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000-5

0

5

10

15

20

25Measured Output

Time[Min]



Fuel GasFlow



Identification(Experimental Test)



0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000-15

-10

-5

0

5

10

15Input

Time[Min]

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000-5

0

5

10

15

20

25Measured Output

Time[Min]



Fuel GasFlow



Identification(Experimental Test)

Closed-LoopControl


Supply Chain Management

• A supply chain consist of interconnected entities(e.g., factories, warehouses, and retailers) whichtransform ideas and raw materials into deliveredproducts and services

F

Factory

W

Warehouse

R

Retailer


Single Node Inventory Control ProblemFeedback-Only Control

LT

CTL

Demand

In the feedback-only control problem, starts decisions are calculated basedonly on perceived changes to “level” (e.g., net stock, inventory position, orequivalent variable).

D(k) (Disturbance)

Starts O(k) (Manipulated)

Net StockI(k)

(Controlled)

θ (production time)

θd (delivery time)


Closed-Loop Feedback Control “BlockDiagram”

C = Feedback ControllerP = Process “Transfer Function”Pd = Disturbance “Transfer Function”

r u

Ce = r-y y

P++

d

PP

Pd

(DemandVariations)

(MeasuredNet Stock)

(DesiredNet Stock)

(Starts orOrders)


Single Node Inventory Problem Combined Feedback/Feedforward Control

LT

CTL

Demand

Demand Forecast(known θf daysbeforehand)

θ (production time)

θd (delivery time)

(Disturbance)

Starts (Manipulated)

Net Stock(Controlled)

In the combined feedback/feedforward problem, a demand forecast isused for feedforward compensation.


Combined Feedback/Feedforward BlockDiagram

--

cF

c p

pd

r ec u

d'

d

y+++ +++

(Starts orOrders)

(DesiredNet Stock

OrInventoryPosition)

(MeasuredNet Stock

OrInventoryPosition)

(Forecasted Demand Variations)

(UnforecastedDemand Variations)

C = Feedback ControllerCF= Feedforward Controllerp = Process “Transfer Function”pd = Disturbance “Transfer Function”


Proportional-Integral-Derivative(PID) with Filter Control

• Choice of Proportional (Kc), Integral (τI), Derivative(τD) and Filter (τF) tuning parameters influencehow control error determines the value of themanipulated variable.

• Four “adjustable” tuning knobs can represent atuning challenge for the control designer.

u t Kc

e tK

c

Ie t dt

tK

c Dde t

dt Fdu t

dt( ) ( ) ( )

( ) ( )= + ′ ′∫ + −τ

τ τ0


Model-Based PID Controller Tuning(using the Internal Model Control tuning rules)

Kc

I

=+ +

+ +

= + +

2

2 2 4 2

2

( )

( )

β λ τ

β βλ λ

τ β λ τ 22

2

42

)(2)(2

λβλββλτ

τλβλβττ

++=

+++=

F

D

β τ θ= =2

User supplies the order fulfillment time (θ) and adjustable parameter (λ)


LC

A

T

T

T

LC

LC

FEED

BOTTOMS REFLUX

INTERMEDIATE REFLUX

UPPER REFLUX

TOP DRAW

SIDE DRAW

BOTTOMS

SIDESTRIPPER

FC

FC

Q(F,T)CONTROL

F

T

PC

T

A

T

TopEndpoint

SideEndpoint

Top Draw

Side Draw

Upper Reflux Duty

Intermediate Reflux Duty

BottomsReflux Duty

Shell Heavy OilFractionator


Epsilon One Temperature Control


Semiconductor Manufacturing Basics

fabrication

sort

decision – how many wafers to start into which factory when

decision – how many of which die to put in which packages in which factory when

assembly

test 5GHz 5GHzX-Inc

decision –how many to ship, when, how to mark, from where to where

finish


Supply Chain Inventory Control

LT

ADI

SFGI

CW

LT

LT

Controller

Demand

Forecast

Real

D1D2D3t

ADI: Assembly-Die Inventory

SFGI: Semi-Finished Goods Inventory

CW: Component Warehouse

Fab/Sort starts

A/T starts

Shipments

Demand

A/T: Assembly/Test Facility


vendor1

vendor2

Fab2

P1,P2

Fab1

P1

Fab3

P2

Sort3

P2

Sort2

P1,P2

Sort1

P1

Asm3

Asm2

Asm1

vendor3 vendor4

vendor5 vendor6

Test3

Test1

Test2 Fuse2

Fuse1

BLUE = Materials In

GREEN = SubCons

BLACK = Us

RED = Products Out

= Inventory Holding

= Manufacturing

= Transport

1.1 si

1.2 si

2.1

2.2

2.3

3.1

3.2

3.4 pp3.3 pp

3.7

3.8 pp 3.9 ram

3.10 3.11

3.12

3.5

3.6

3.5

4.3

5.2

5.1

11

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

26

27

24

25

28

29

30

31

32

33

34

Box1

7.2

7.1

5

Box27.6

7.5

6

43

ven

d7 7.4

pp

45

44 vend8

46

7.3

pp

4.1

4.2

6.2

6.1

6.3

3

2

4

35

36

37

38

39

4041

42

One location

= Materials Mfg

A “mini” supply chain

Problemrepresents 10%

of Intel’s internalsupply chain,

excludinglogistics


http://www.eas.asu.edu/~csel


If PF is “Low” then Weekly Home VisitsIf PF is “Medium” then Bi-Weekly VisitsIf PF is “High” then Monthly Home VisitsIf PF is “Acceptable” then No Visits

DecisionRules

Clinical Judgment

InterventionProcess

Disturbances

++

Parental FunctionEstimation

Reliability/MeasurementError

+

+

Goal

ReviewInterval

Estimated Parental Function

Outcomes

Parental Function Feedback Loop Block Diagram(to decide on home visits for families with at risk children)


Dr. Rivera “ChE 461 Guarantee”

• Your appreciation for control engineering will go handin hand with your understanding of the topic.

• I have never met an individual who has understoodcontrol engineering and disliked it at the same time.

• If you understand control engineering, you will alsoreadily see its usefulness in technology and society.


Wrap-Up

• Please complete the ChE 461 student survey andreturn to me before leaving the lecture hall,

• Please meet for your assigned lab session this weekin SCOB 160

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CHE 461: Process Dynamics and Control Course Overview ...

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