1

Control Strategies

By

Dr. AA

Process Control System

Planning andScheduling

RegulatoryControl

AdvancedProcess Control

Real-TimeOptimisation

Process

Process Computer

DCS

Plantwide Computer

Regulatory Control

3

Regulatory Control

Most of the time, process variables are fixed

at some desired set point

The task is therefore to reject disturbances,

etc

Majority of the controllers are standard three-

term controllers, i.e., PID (Proportional-

Integral-Derivative) Controller

Feedback Control

Corrective Action

Measure, Decide, Correct

Robust

Process Variables

– Controlled Variables

– Manipulated Variables

– SISO Configuration

Solution to Most Control Problem

Feedback Control Block Diagram

Gd(s)

GP(s) Gv(s) GC(s)

GS(s)

D(s)

CV(s)

CVm(s)

SP(s) E(s) MV(s) +

+

+

-

Transfer functions

GC(s) = controller

Gv(s) = valve +

GP(s) = feedback process

GS(s) = sensor

Gd(s) = disturbance process

Variables

CV(s) = controlled variable

CVm(s) = measured value of CV(s)

D(s) = disturbance

E(s) = error

MV(s) = manipulated variable

SP(s) = set point

Components and Signals of a

Typical Control Loop

T

F

F2

T2

Thermocouple

millivolt signal

Transmitter4-20 mA DCS

Control

Computer

3-15 psig

4-20 mA

Operator

Console

Tsp

I/PAir

F1

T1

Thermowell

Actuator System

Controller Sensor System

D/A

A/D

PID Controller

Developed in the 1930’s, remains a workhorse

Not “optimal”, based on good properties of each mode

Extremely flexible and powerful control algorithm

when applied properly.

Most common controller in the CPI.

Preprogrammed in all digital control equipment

ONE controlled variable (CV) and ONE manipulated

variable (MV) - SISO

t

eeeKu

SS

KG

D

I

C

D

I

CC

1

)1

1(

Limitation of Feedback and

the way forward

Nonlinearity

Interactions

Constraints

Profitability

Disturbances Dead time Measurement

Feedforward

Control Inferential

Control

Cascade

Control Ratio

Control

MPC RTO

Split-range

Control

Select

Control

OVERCOMING

DISTURBANCES

10

Improving Disturbance Rejection

Single loop feedback controllers can reject disturbances to certain extent.

Cascade control – Cascade reduces the effect of specific types of

disturbances.

Feedforward control – Feedforward control is a general methodology for

compensating for measured disturbances

Ratio Control – Ratio reduces the effect of feed flow rates changes

Cascade Control

Benefits

– Useful in overcoming high frequency noise and

disturbances

– Also reduces the impact of time delay

Hot Oil System

• Without a cascade level controller, changes in upstream

fuel pressure affects outlet hot oil temperature.

• With cascade level controller, changes in upstream

pressure will be absorbed by the flow controller

Gas

(Variable Pressure)

OIL IN

FURNACE

P 7

OIL OUT

Flue Gas

P-10

TT

TC

Gas

(Variable Pressure)

OIL IN

FURNACE

P 7

OIL OUT

Flue Gas

P-10

TT

TC FC

FT

Reactor Temperature Control

Feed

Product

TT

Cooling

water

TC

• Without cascade, changes in the cooling water

temperature will create a significant upset for the

reactor temperature.

• With cascade, changes in the cooling water

temperature will be absorbed by the slave loop before

they can significantly affect the reactor temperature.

Feed

Product

TT

Cooling

water

TCTT

TC

RSP

Cascade Control

15

0 5 10 15 20 -0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Time (sec)

Ou

tpu

t (y

)

Without Cascade Control

With Cascade Control

Multiple Cascade Example

FT

AC

AT

TCTT

FC

RSP

RSP

This approach works because the flow control loop is

much faster than the temperature control loop which

is much faster than the composition control loop.

Key Features for Cascade Control to be

Successful

Secondary loop must be at least 3 times

faster than master loop.

The secondary loop should be tuned tightly.

Secondary loop often a Proportional

Controller

– Offset on inner loop normally doesn’t matter

– PI may be used in some cases, but could slow

response or cause oscillations

– Derivative seldom used

Cascade Control

Tune from INSIDE OUT

GC101 GC102 GO GF

Cascade Control

Tune inner loop for fast response

use any of the techniques covered

GC101 GC102 GO GF

Cascade Control

Tune outer loop --

use closed-loop Ziegler-Nichols, e.g.

GC101 GC102 GO GF

Example

TT

PT

Condensate

Steam

Feed

Draw schematic: A temperature controller on

the outlet stream is cascaded to a pressure

controller on the steam which is cascaded to a

control valve on the condensate.

Feedforward Control

Taking action before disturbances affecting

the process, thus a Preventive Mechanism

Feedforward and Feedback Level Control

Make-upWater

To SteamUsers

LT

LC

Make-upWater

To SteamUsers

LT

FT

FF

To Steam Users

LT

FT FF

LC +

Make-up Water

Analysis of Feedforward and

Feedback Level Control

Feedback-only must absorb the variations in

steam usage by feedback action only.

Feedforward-only handle variation in steam

usage but small errors in metering will

eventually empty or fill the tank.

Combined feedforward and feedback has best

features of both controllers.

Feedforward Control

Compensates for d’s before process is

affected

Most effective for slow processes and for

processes with significant deadtime.

Can improve reliability of the feedback

controller by reducing the deviation from

setpoint.

Since it is a linear controller, its performance

will deteriorate with nonlinearity.

Combined FF and FB Control

Cfb(s)Ysp(s)Gp(s)

Y(s)++

++

+-Gc(s)

D(s)

Gd(s)

Gff(s)Cff(s)

Example of Combined FF and FB

TTPT

PCTC

Condensate

Steam

RSP

FeedTT

FF

+

Combined FF and FB for the

CSTR

Steam

Feed

Product

TT

FT

FC

+

TC

TT

FF

RSP

Example

TT PT

Condensate

Steam

Feed

TT

Draw schematic: For a combined feedforward and feedback

controller in which the inlet feed temperature is the feedforward

variable and the outlet temperature is the feedback variable.

The combined controller output is the setpoint for a steam

pressure controller.

Ratio Control

Useful when the manipulated variable

scales directly with the feed rate to the

process.

Dynamic compensation is required when

the controlled variable responds

dynamically different to feed rate changes

than it does to a changes in the

manipulated variable.

Ratio Control for Wastewater Neutralization

NaOH

SolutionAcid

Wastewater

Effluent

FTFT

FC

pHTpHC

RSP

Analysis of Ratio Control Example

The flow rate of base scales directly with the

flow rate of the acidic wastewater.

The output of the pH controller is the ratio of

NaOH flow rate to acid wastewater flow rate;

therefore, the product of the controller output

and the measured acid wastewater flow rate

become the setpoint for the flow controller on

the NaOH addition.

Ratio Control Applied for Vent

Composition Control

Steam

Feed

Product

TT

FT

FCFT AT

AC

Vent

×

Ratio Control Requiring Dynamic

Compensation

FT

AC

AT

FC

RSP

FT

Feed

DC

Example

FT

FT TT

Flue

Gas

Process

FluidFuel

Draw schematic: For a control system that adjusts the ratio of fuel flow to the flow rate of the process fluid to control the outlet temperature of the process fluid. Use a flow controller on the fuel.

Solution: Ratio Control

FT

FC

FT TT

× TC

RSPFlue

Gas

Process

FluidFuel

Ratio

Dealing with

Constraints

37

Split Range Control

Uses more than one manipulated variables or

actuators for one control loop

Split Range Control: Another Example

FT

FT

FC

FC

Sometimes a single flow control loop cannot provide accurate flow

metering over the full range of operation.

Split range flow control uses two flow controllers

One with a small control valve and one with a large control valve

At low flow rates, the large valve is closed and the small valve provides

accurate flow control.

At large flow rates, both valve are open.

Larger Valve

Sig

nal

to C

on

tro

l V

alv

e (

%)

Smaller Valve

Total Flowrate

Application of Split Range Control:

pH Control

Acid

Wastewater

NaOH

Solution

Effluent

FTFT

FC

pHTpHC

RSP

• Strategy: control of pH using ratio of NaOH to acid waste water

• Due to dynamic behaviour, Split range is also required

Split range for this valve

Titration Curve for a Strong Acid-Strong

Base System

0

2

4

6

8

10

12

14

0 0.002 0.004 0.006 0.008 0.01

Base to Acid Ratio

pH

Therefore, for accurate pH control for a wide range

of flow rates for acid wastewater, a split range flow

controller for the NaOH is required.

Application of Split Range Control:

Temperature Control

TT

CoolingWater

Steam

Split-RangeTemperatureController

TT TC

RSP

Split Range Temperature Control

0

20

40

60

80

100

Error from Setpoint for Jacket Temperature

Sig

nal

to C

ontr

ol V

alve

(%

)

SteamCooling

Water

Exothermic Reactor: Initially steam is used to heat up reactor.

Once the desired temperature is reached, cooling water is

regulated to control temperature

Override/Select

Control

Override/Select Control

Override/Select control uses LS and HS action

to change which controller is applied to the

manipulated variable.

Override/Select control uses select action to

switch between manipulated variables using

the same control objective.

Furnace Tube Temperature Constraint Control

FT

FC

TT TT

LS TCTC

RSP

Flue

Gas

Process

FluidFuel

LC

PT

LT PC

Hot Gas

Boiler Drum

Feedwater

Steam

LS

Loop 2

Loop 1

Discharge Line

Override control to protect boiler

LS

Column Flooding Constraint Control

FT

AC

AT

LSDPC

FC

RSP

Lower value of flowrate is selected to avoid column flooding

Controlling Multiple Constraints

FT

AC

AT

LSDPC

FC

RSPTT

TC

Analysis of Tube Temperature

Constraint Controller

Under normal operation, the controller adjusts the furnace firing rate to maintain process stream at the setpoint temperature.

At higher feed rates, excessive tube temperatures can result greatly reducing the useful life of the furnace tubes.

The LS controller reduces the firing rate to ensure that the furnace tubes are not damaged.

Hot Spot Temperature Control

TT

TT

TT HS TC

FT

FC

RSP

• Detect temperature in

various places in

catalyst bed.

• Action based on

highest temperature

•Sometimes called

auctioneering system

Computed Variable

Control

Computed Manipulated Variable

Control

Used when the desired manipulated

variable is not directly controllable.

Reduces the effect of certain types of

disturbances.

Computed Reboiler Duty Control

TT

TT FT

FC

AC

AT

RSP

Computed

Reboiler

Duty Control

Qspec

Quench Water

)( outinp

spec

spTTC

QF

Internal Reflux Control

C.W.

ATFT

FC

ACRSP

Internal

Reflux

Controller

FintFex

TT

TT

vap

rohp

spec

ex

vap

rohp

ex

vaprohexp

H

TTC

FF

H

TTCFF

HFTTFC

)(1

)(1

)(

int

int

int

Inferential Control

Lack of Measurement

Control theory is quite matured – PID cascade feed forward MPC

– Without accurate measurement, good control cannot be established.

Weakness in measurement – lack of appropriate on-line instrumentation

– reliability of on-line instruments • Long delay, fouling, drifts

Indirect approaches have been used.

Effect of Deadtime on Control Performance

Time

p=1.5

p=0.5

Deadtime

-Inherent process behaviour

-Delay in sampling/or measurement

Indirect Control

Example

– Control of conversion by fixing the resident time

in a reactor

• Level and flow control

– Control of composition by fixing the tray

temperature in distillation column

• Pressure and temperature control

Indirect strategy is good for “normal

condition”

– Any upsets or serious disturbance leads to

control degradation

Traditional Practice: Indirect Control

FT

FC

RSP

TT TC

Tray 10

Composition in Distillation Columns are indirectly controlled

By controlling selected tray temperatures

Choosing a Proper Tray Temperature

Location

76

78

80

82

84

86

3 3.5 4 4.5 5

Mole Percent Propane

Tempera

ture

(ºC)

Tray 18

Tray 10

A tray temperature used for inferential control

should show strong sensitivity.

Feedback Correction for Feed Composition

Changes

FT

AC

AT

FC

RSP

TT TC RSP

Tray 10

Inferential Control

Controller is PID. Model serves as estimator (soft-sensor)

Controller

Process

Estimator

Inferred output

(delay free) secondary output

primary

output Set

Point

Reactor Conversion Control

Tout

Tin

)T(T)ΔH(C

CρX

)T(TCρ)ΔH(CX

inoutrxnA

pA

inoutprxnAA

o

in

This is based on steady state relationships.

Using Tout and Tin, knowing a and b, conversion

Can be estimated

b)T(TaX inoutA

Develop Linear Relationships Based

on Plant Data

Macroscopic Balance

Molecular Weight Control

of a Polymer

FT

DPT

TT

)]([

)](,[)(

),()(

03

20

1

TfM

TTfT

FPfT

wt

Some Empirical Models

Transfer Function )1( / e ay(t) t

Linear Regression )(tbu ay(t)

Autoregressive with external input (ARX)

1

1

1

1

)u(t-n...+ b) u(t- b

) ny(t) +...+ ay(t-y(t) + a

bn

an

b

a

(t-q),), ... , u- u(t), u(t

(t-p), ), ... , y- y(t), y(t) = f y(t+

1

11

Nonlinear Models

Dealing with

Nonlinearity

Scheduling Controller Tuning

Can be effective when either a measured

disturbance or the controlled variable

correlates with nonlinear process changes.

Tune the controller at different levels of the

scheduling parameter and combine the

results so that the controller tuning

parameters vary over the full operating range.

Heat Exchangers are Nonlinear with

Respect of Flow Rate Changes

0

1

2

3

0 20 40 60 80 100

Time (seconds)

T' (º

F)

v =10 ft/s

v =7 ft/s

v =4 ft/s

TTPT

PCTC

Condensate

SteamRSP

Feed

Effect of Scheduling Controller Tuning

• The results for a nonscheduled controller that was

tuned for v=7 ft/sec after the feed rate is changed to

v=4 ft/sec

• The results for a scheduled controller for the same

upset.

Model Predictive

Control

Recall: Process Control Hierarchy

Planning andScheduling

RegulatoryControl

AdvancedProcess Control

Real-TimeOptimisation

Process

Process Computer

DCS

Plantwide Computer

History of MPC

Reproduced from Qin and Baldgewell (2003)

Production quality can be controlled and optimized to management constraints

APC can accomplish the following: – improve product yield, quality and consistency

– reduce process variability—plants to be operated at designed capacity

– operating at true and optimal process constraints—controlled variables pushed against a limit

– reduce energy consumption

– exceed design capacity while reducing product giveaway

– increase responsiveness to desired changes (eliminate deadtime)

– improve process safety and reduce environmental emissions

Advantages and Benefits

Implementation of an APC system is time consuming, costly and complex – May require improved control hardware than currently exists

High level of technical competency required – Usually installed and maintained by vendors & consultants

Must have a very good understanding of process prior to implementation

High training requirements

Difficult to use and operate after implementation

Requires large capacity operations to justify effort and expense

New APC applications more difficult, time consuming and costly – Off-the-shelf APC products must be customized

Limitations

Model Predictive Control

1. At each time step, compute

the optimal control inputs over

the control horizon by solving

an open loop optimization

problem over the prediction

horizon taking constraints into

account

2. Apply the first value of

the computed control

input into the process

3. At the next time step,

redo the calculation

control horizon

Simple distillation column with APC – Column objective is to remove pentanes and lighter

components from bottom naphtha product

APC input: – Column top tray temperature

– Top and bottom product component laboratory analyses

– Column pressures

– Unit optimization objectives

APC controlled process variables – Temperature of column overhead by manipulating fuel

gas control valve

– Overhead reflux flow rate

– Bottom reboiler outlet temperature by manipulating steam (heat) input control valve

Note that product flow rates not controlled – Overhead product controlled by overhead drum level

– Bottoms product controlled by level in the tower bottom

APC anticipates changes in stabilized naphtha product due to input variables and adjusts relevant process variables to compensate

Distillation Tower Example

Distillation Tower APC Results

Non-exhaustive MPC Vendor List (Allgöwer, 2004)

ABB

ACT

Adaptics

Adaptive Resources

Adersa

Aspen Technology

Aurel Systems Inc.

Batch CAD

Bonner and Moore

Brainwave

C.F. Picou and Associates

Chemstations

Comdale Technologies

Control Arts Inc.

Control Consulting Inc.

Control Dynamics

Controlsoft Incorporated

Cybosoft

Cybernetica

DOT Products

Eldridge Engineering Inc.

Bailey

Envision Systems Inc.

Gensym

Enterprise Control Technologies

Fantoft Process Group

MATHWORKS

Honeywell

Inferential Control Company

IntellOpt

Knowledgescape

MDC Technology

Neuralware

Nexus Engineering

Objectspace

Optimal Control Research

Pavilion Technologies

Predictive Control Ltd.

Predictor

Process System Consultants

RSI

Simulation and Advanced

Simtech

Texas Controls Inc.

Trieber Controls

Yokogawa APC

US Process Control L.L.C.

Linear MPC Technology

Product Test Model Estimation Method

DMC-Plus Step, PRBS

VFIR, LSS MLS

RMPCT PRBS, Step FIR, ARX, BJ LS, GN, PEM

AIDA PRBS, Step

LSS, FIR, TF, MM

PEM-LS, GN

Glide Non-PRBS TF GD, GN, GM

Connoisseur PRBS, Step

FIR, ARX, MM RLS, PEM

Nonlinear MPC

Company Product Name Description

Adersa PFC Predictive Functional Control

Aspen Tech Aspen Target Nonlinear MPC Package

Continental Control MVC Multivariable Control

Dot Product NOVA-NLC NOVA Nonlinear Controller

Pavilion Technologies

Process Perfecter

Nonlinear Control

Real-Time

Optimisation

Process Control Hierarchy

Planning andScheduling

RegulatoryControl

AdvancedProcess Control

Real-TimeOptimisation

Process

Process Computer

DCS

Plantwide Computer

Real-Time Optimization

Determines optimal targets for a single

unit

More degrees of freedom than planning

model

Based on fundamental non-linear models

of a single unit (CDU, FCC, etc.)

Optimization cycles

– 4-12 executions per day

– Model calibrated to plant each run

– Uses current economics and constraints

Real-Time Optimization Benefits

Plant Optimization Hierarchy

Real-Time Optimisation

- Rigorous steady state model

- on-line tuning

- Targets automatically implemented

Planning (LP/NLP)Plant Information

System

APC

Controller

APC

Controller

APC

Controller

Operating

Conditions

and Constraints

Optimal

Targets

Plant economics

strategic Constraints

Inventory constraints

Future Model

Updates

Operating

conditions

On-line analysers

Lab Data

RTO Block Diagram

NumericalOptimizationAlgorithm

ProcessModel

EconomicParameters

EconomicFunctionEvaluation

OptimizationVariables

EconomicFunctionValue

ModelResults

Initial Estimateof Optimization

Variables

OptimumOperatingConditions

Formulation & Solution of Optimization

Problems

Identify the process variables

Select performance criteria and develop a

mathematical expression for the objective function.

Develop the models for the process and the

constraints.

Simplify the model and objective function.

Simulate and validate process model

Data reconciliation

Compute the optimum.

Perform sensitivity analysis

Ready for Implementation

RTO Interface to Control

RTO to APC – Outputs to APC controllers

– RTO limits are the same as APC limits

– RTO includes the same constraints as APC

– Operator interface via regulatory control computer

RTO to Regulatory Control

– Outputs to regulatory system

– Includes nonlinear transformations

– Operator interface via the regulatory control

computer

APC and RTO Benefits versus Project Cost

20 40 60 80 100

Inv

est

ment

%

Potential %

Advanced Regulatory Control

DCS

Advanced Process Control

Real-TimeOptimisation

20

40

60

80

100

The Principle of Optimality

– Rutherford Aris, 1964

“If you don’t do the best you can

with what you happen to have,

you’ll never do the best you

might have done with what you

should have had.”