chapter 13.process control

7/28/2019 Chapter 13.Process control

1/32

1

Overview of Control System Design

Chapter13

1. Safety. It is imperative that industrial plants operate safely

so as to promote the well-being of people and equipment

within the plant and in the nearby communities. Thus, plant

safety is always the most important control objective and is

the subject of Section 10.5.

2. Environmental Regulations. Industrial plants must comply

with environmental regulations concerning the discharge of

gases, liquids, and solids beyond the plant boundaries.

3. Product Specifications and Production Rate. In order to be

profitable, a plant must make products that meet

specifications concerning product quality and production

rate.

General Requirements


2/32

2

Chapter13

4. Economic Plant Operation. It is an economic reality that the

plant operation over long periods of time must be profitable.

Thus, the control objectives must be consistent with the

economic objectives.

5. Stable Plant Operation. The control system should facilitate

smooth, stable plant operation without excessive oscillation in

key process variables. Thus, it is desirable to have smooth,

rapid set-point changes and rapid recovery from plantdisturbances such as changes in feed composition.


3/32

3

Chapter13

Steps in Control System Design

After the control objectives have been formulated, the control

system can be designed. The design procedure consists of threemain steps:

1. Select controlled, manipulated, and measured variables.

2. Choose the control strategy (multiloop control vs.multivariable control) and the control structure

(e.g., pairing of controlled and manipulated variables).

3. Specify controller settings.


4/32

4

Control Strategies

Multiloop Control:

Each output variable is controlled using a single input

variable.

Multivariable Control:

Each output variable is controlled using more than one

input variable.

Chapter13


5/32

5

Chapter13

The degrees of freedomNF is the number or process variables

that must be specified in order to be able to determine the

remaining process variables.

If a dynamic model of the process is available,NFcan bedetermined from a relation that was introduced in Chapter 2,

(13-1)F V EN N N

whereNVis the total number of process variables, andNEis the

number of independent equations.

13.1 Degrees of Freedom for Process Control

The important concept of degrees of freedom was introduced in

Section 2.3, in connection with process modeling.


6/32

6

Chapter13

For process control applications, it is very important to determine

the maximum number of process variables that can be

independently controlled, that is, to determine the control degrees

of freedom,NFC

:

In order to make a clear distinction betweenNFandNFC, we will

refer toNFas the model degrees of freedom andNFCas the

control degrees of freedom.

Note thatNFandNFCare related by the following equation,

Definition. The control degrees of freedom,NFC, is the

number of process variables (e.g., temperatures, levels,

flow rates, compositions) that can be independently

controlled.

(13-2)F FC DN N N

whereND

is the number of disturbance variables (i.e., input

variables that cannot be manipulated.)


7/327

Chapter13

Example 13.1

General Rule. For many practical control problems, the

control degrees of freedomNFC is equal to the number of

independent material and energy streams that can be

manipulated.

DetermineNFandNFCfor the steam-heated, stirred-tank systemmodeled by Eqs. 2-50 2-52 in Chapter 2. Assume that only the

steam pressurePs can be manipulated.

Solution

In order to calculateNFfrom Eq. 13-1, we need to determineNV

andNE. The dynamic model in Eqs. 2-50 2-52contains three

equations (NE= 3) and six process variables (NV= 6): Ts,Ps, w, Ti,

T, and Tw. Thus,NF= 6 3 = 3.


8/328

Chapter13

Figure 13.1 Two examples where all three process

streams cannot be manipulated independently.


9/329

Stirred-Tank Heating Process

Figure 2.3 Stirred-tank heating process with constant holdup, V.

Chapter13


10/3210

Chapter13

If the feed temperature Ti and mass flow rate w are considered to

be disturbance variables,ND = 2 and thusNFC= 1 from

Eq. (13-2).

It would be reasonable to use this single degree of freedom tocontrol temperature Tby manipulating steam pressure,Ps.

The blending system in Fig. 13.3 has a bypass stream that allows afractionfof inlet stream w2 to bypass the stirred tank. It is

proposed that product compositionx be controlled by adjusting f

via the control valve. Analyze the feasibility of this control

scheme by considering its steady-state and dynamiccharacteristics.

In your analysis, assume thatx1 is the principal disturbance and

thatx2, w1, and w2 are constant. Variations in the volume of liquid

in the tank can be neglected because w2


11/3211

Chapter13

Figure 13.3. Blending system with bypass line.


12/3212

Chapter13

Solution

The dynamic characteristics of the proposed control scheme are

quite favorable because the product compositionx responds

rapidly to a change in the bypass flow rate. In order to evaluate the steady-state characteristics, consider a

component balance over the entire system:

Solving for the controlled variable gives,

1 1 2 2 (13-3)w x w x wx

1 1 2 2 (13-4)w x w x

xw

Thus depends on the value of the disturbance variable and

four constants (w1, w2,x2, and w).

But it does not depend on the bypass function,f.

x 1x


13/3213

Chapter13

Thus, it is notpossible to compensate for sustained disturbances

inx1 by adjustingf.

For this reason, the proposed control scheme is not feasible.

Becausefdoes not appear in (13-4), the steady-state gain

betweenx andfis zero. Thus, although the bypass flow rate can

be adjusted, it does not provide a control degree of freedom.

However, ifw2 could also be adjusted, then manipulating bothfand w2 could produce excellent control of the product

composition.


14/3214

Chapter13

Effect of Feedback Control

Next we consider the effect of feedback control on the control

degrees of freedom.

In general, adding a feedback controller (e.g., PI or PID) assigns

a control degree of freedom because a manipulated variable is

adjusted by the controller.

However, if the controller set point is continually adjusted by ahigher-level (or supervisory) control system, then neitherNFnor

NFCchange.

To illustrate this point, consider the feedback control law for a

standard PI controller:


15/3215

Chapter13

where e(t) =ysp(t)y(t) andysp is the set point. We consider twocases:

0

1

1 (10-5)

t

cu t u K e t e d

Case 1. The set point is constant, or only adjusted manually on an

infrequent basis.

For this situation,ysp is considered to be a parameter instead of a

variable.

Introduction of the control law adds one equation but no new

variables because u andy are already included in the process

model.

Thus,NEincreases by one,NVis unchanged, and Eqs. 10-1 and

10-2 indicate thatNFandNFCdecrease by one.


16/3216

Chapter13

Case 2. The set point is adjusted frequently by a higher level

controller.

Guideline 1.

All variables that are not self-regulating must be controlled.

Guideline 2.

Choose output variables that must be kept within equipment andoperating constraints (e.g., temperatures, pressures, andcompositions).

Selection of Controlled Variables

The set point is now considered to be a variable. Consequently,

the introduction of the control law adds one new equation and

one new variable,ysp.

Equations 13-1 and 13-2 indicate thatNFandNFCdo not change.

The importance of this conclusion will be more apparent when

cascade control is considered in Chapter 16.


17/3217

Chapter13

Figure 13.3 General representation of a control problem.


18/3218

Chapter13

Guideline 3.

Select output variables that are a direct measure of product

quality (e.g., composition, refractive index) or that strongly affectit (e.g., temperature or pressure).

Guideline 4.

Choose output variables that seriously interact with other

controlled variables.

Guideline 5.Choose output variables that have favorable dynamic and static

characteristics.


19/32

19

Chapt

er13

Guideline 6.

Select inputs that have large effects on controlled variables.

Guideline 7.

Choose inputs that rapidly affect the controlled variables.

Guideline 8.

The manipulated variables should affect the controlled variables

directly rather than indirectly.

Guideline 9.

Avoid recycling of disturbances.

Selection of Manipulated Variables


20/32

20

Chapt

er13

Guideline 10.

Reliable, accurate measurements are essential for good control.

Guideline 11.

Select measurement points that have an adequate degree ofsensitivity.

Guideline 12.

Select measurement points that minimize time delays and timeconstants

Selection of Measured Variables


21/32

Example 13.4: Evaporator Control

21

Chapt

er13

Figure 13.5 Schematic diagram of an evaporator.

MVs: Ps, B and D DVs: xFand F

CVs: ??


22/32

Case (a): xBcan be measured

22

Chapt

er13


23/32

23

Case (b): ): xBcannot be measured

Chapt

er13


24/32

Distillation Column Control

(a 5x5 control problem)

24

Chapt

er13


25/32

Challenges for Distillation Control

25

1. There can be significant interaction between processvariables.

2. The column behavior can be very nonlinear, especially for

high purity separations.

3. Distillation columns often have very slow dynamics.

4. Process constraints are important.

5. Product compositions are often not measured.

cf. Distillation Process Control Module (Appendix E)

Chapt

er13


26/32

Process Control Module:

Fired-Tube Furnace

26

Chapt

er13

The major gaseous combustion reactions in the furnace are:

4 2 2

2 2

3CH O CO 2H O

2

1

CO O CO2


27/32

27

Chapt

er13

Process Control Module: Fired-Tube Furnace

Table 3.1

Key Process Variables for the PCM Furnace Module

Measured Output Variables Disturbance Variables

HC outlet temperature HC inlet temperature

Furnace temperature HC flow rate

Flue gas (exhaust gas) flow rate Inlet air temperature

O2 exit concentration FG temperature

FG purity (CH4

Manipulated Variables concentration)

Air flow rate

FG flow rate


28/32

Control Objectives for the Furnace

28

Chapt

er13

1. To heat the hydrocarbon stream to a desired exit

temperature

2. To avoid unsafe conditions due to the

interruption of fuel gas or hydrocarbon feed

3. To operate the furnace economically bymaintaining an optimum air-fuel ratio.

Safety Considerations?

Control Strategies?


29/32

Catalytic Converters for Automobiles

29

Chapt

er13

Three-way catalytic converters (TWC) are designed to

reduce three types of harmful automobile emissions:1. carbon monoxide (CO),

2. unburned hydrocarbons in the fuel (HC)

3. nitrogen oxides (NOx).

Desired oxidation reactions (1000-1500 F, residence

times of ~ 0.05 s):

n 2n+2 2 2 2

2 2

3 1C H ( )O CO +(n+1)H O

2

1CO O CO2

nn

Desired reduction reaction:

x 2 22NO O +Nx


30/32

30

Chapt

er13

Figure 13.10 TWC efficiency as a function of air-

to-fuel ratio (Guzzella, 2008).


31/32

31

TWC Control Strategy

Chapt

er13


32/32

Plasma Etching in Semiconductor Manufacturing

Chapt

er13

Pre-process Measurement

Integrated

MetrologyEtch Process

Integrated

Metrology

Resist

BARC

Polysilicon

Silicon

Substrate

Oxide

Gate oxide

Resist

BARC

Polysilicon

Oxide

Gate oxide

in

CDin

CDin

Silicon

Substrate

Post-process Measurement

out

in

(center)

CDin

(center, edge)

out

(center)

CDout

(center, edge)

Figure 13.12 Inputs and outputs for polysilicon gate etch process in semiconductor manufacturing.

The measured inputs (CDin and in) in the incoming wafer can be used in feedforward control, while

the measured outputs (CDout and out) are used in feedback control. BARC is bottom anti-reflective

coating.

chapter 13.process control

Documents