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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.”