1
A SYSTEMATIC APPROACH
TO PLANTWIDE CONTROL
Sigurd Skogestad
Department of Chemical Engineering
Norwegian University of Science and Tecnology (NTNU)
Trondheim, Norway
Stockholm, April 2010
7
NTNU,
Trondheim
8
Main references
•
The following paper summarizes the procedure:
–
S. Skogestad, ``
Control structure design for complete chemical
plants'',
Computers and Chemical Engineering
,
28
(1

2), 219

234 (2004).
•
There are many approaches to plantwide control as discussed in the
following review paper:
–
T. Larsson and S. Skogestad,
``Plantwide control: A review and a new
design procedure''
Modeling, Identification and Control
,
21
, 209

240
(2000).
Download papers: Google ”Skogestad”
9
•
S. Skogestad
``Plantwide control: the search for the self

optimizing control structure'',
J. Proc. Control
, 10, 487

507 (2000).
•
S. Skogestad,
``Self

optimizing control: the missing link between steady

state optimization and control'',
Comp.Chem.Engng.
, 24, 569

575 (2000).
•
I.J. Halvorsen, M. Serra and S. Skogestad,
``Evaluation of self

optimising control structures for an integrated Petlyuk distillation
column'',
Hung. J. of Ind.Chem.
, 28, 11

15 (2000).
•
T. Larsson, K. Hestetun, E. Hovland, and S. Skogestad,
``Self

Optimizing Control of a Large

Scale Plant: The Tennessee Eastman
Process'',
Ind. Eng. Chem. Res.
, 40 (22), 4889

4901 (2001).
•
K.L. Wu, C.C. Yu, W.L. Luyben and S. Skogestad,
``Reactor/separator processes with recycles

2. Design for composition control'',
Comp. Chem. Engng.
, 27 (3), 401

421 (2003).
•
T. Larsson, M.S. Govatsmark, S. Skogestad, and C.C. Yu,
``Control structure selection for reactor, separator and recycle processes'',
Ind. Eng. Chem. Res.
, 42 (6), 1225

1234 (2003).
•
A. Faanes and S. Skogestad,
``Buffer Tank Design for Acceptable Control Performance'',
Ind. Eng. Chem. Res.
, 42 (10), 2198

2208
(2003).
•
I.J. Halvorsen, S. Skogestad, J.C. Morud and V. Alstad,
``Optimal selection of controlled variables'',
Ind. Eng. Chem. Res.
, 42 (14),
3273

3284 (2003).
•
A. Faanes and S. Skogestad,
``pH

neutralization: integrated process and control design'',
Computers and Chemical Engineering
, 28
(8), 1475

1487 (2004).
•
S. Skogestad,
``Near

optimal operation by self

optimizing control: From process control to marathon running and business systems'',
Computers and Chemical Engineering
, 29 (1), 127

137 (2004).
•
E.S. Hori, S. Skogestad and V. Alstad,
``Perfect steady

state indirect control'',
Ind.Eng.Chem.Res
, 44 (4), 863

867 (2005).
•
M.S. Govatsmark and S. Skogestad,
``Selection of controlled variables and robust setpoints'',
Ind.Eng.Chem.Res
, 44 (7), 2207

2217
(2005).
•
V. Alstad and S. Skogestad,
``Null Space Method for Selecting Optimal Measurement Combinations as Controlled Variables'',
Ind.Eng.Chem.Res
, 46 (3), 846

853 (2007).
•
S. Skogestad,
``The dos and don'ts of distillation columns control'',
Chemical Engineering Research and Design (Trans IChemE, Part
A)
, 85 (A1), 13

23 (2007).
•
E.S. Hori and S. Skogestad,
``Selection of control structure and temperature location for two

product distillation columns'',
Chemical
Engineering Research and Design (Trans IChemE, Part A)
, 85 (A3), 293

306 (2007).
•
A.C.B. Araujo, M. Govatsmark and S. Skogestad,
``Application of plantwide control to the HDA process. I Steady

state and self

optimizing control'',
Control Engineering Practice
, 15, 1222

1237 (2007).
•
A.C.B. Araujo, E.S. Hori and S. Skogestad,
``Application of plantwide control to the HDA process. Part II Regulatory control'',
Ind.Eng.Chem.Res
, 46 (15), 5159

5174 (2007).
•
V. Kariwala, S. Skogestad and J.F. Forbes,
``Reply to ``Further Theoretical results on Relative Gain Array for Norn

Bounded
Uncertain systems''''
Ind.Eng.Chem.Res
, 46 (24), 8290 (2007).
•
V. Lersbamrungsuk, T. Srinophakun, S. Narasimhan and S. Skogestad,
``Control structure design for optimal operation of heat
exchanger networks'',
AIChE J.
, 54 (1), 150

162 (2008). DOI 10.1002/aic.11366
•
T. Lid and S. Skogestad,
``Scaled steady state models for effective on

line applications'',
Computers and Chemical Engineering
, 32,
990

999 (2008). T. Lid and S. Skogestad,
``Data reconciliation and optimal operation of a catalytic naphtha reformer'',
Journal of
Process Control
, 18, 320

331 (2008).
•
E.M.B. Aske, S. Strand and S. Skogestad,
``Coordinator MPC for maximizing plant throughput'',
Computers and Chemical
Engineering
, 32, 195

204 (2008).
•
A. Araujo and S. Skogestad,
``Control structure design for the ammonia synthesis process'',
Computers and Chemical Engineering
, 32
(12), 2920

2932 (2008).
•
E.S. Hori and S. Skogestad,
``Selection of controlled variables: Maximum gain rule and combination of measurements'',
Ind.Eng.Chem.Res
, 47 (23), 9465

9471 (2008).
•
V. Alstad, S. Skogestad and E.S. Hori,
``Optimal measurement combinations as controlled variables'',
Journal of Process Control
, 19,
138

148 (2009)
•
E.M.B. Aske and S. Skogestad,
``Consistent inventory control'',
Ind.Eng.Chem.Res
, 48 (44), 10892

10902 (2009).
11
How we design a control system for a
complete chemical plant?
•
Where do we start?
•
What should we control? and why?
•
etc.
•
etc.
14
•
Alan Foss (“Critique of chemical process control theory”, AIChE
Journal,
1973
):
The central issue to be resolved ... is the determination of control system
structure.
Which variables should be measured, which inputs should be
manipulated and which links should be made between the two sets?
There is more than a suspicion that the work of a genius is needed here,
for without it the control configuration problem will likely remain in a
primitive, hazily stated and wholly unmanageable form. The gap is
present indeed, but contrary to the views of many, it is the theoretician
who must close it.
•
Carl Nett (
1989
):
Minimize control system complexity subject to the achievement of accuracy
specifications in the face of uncertainty.
19
c
s
= y
1s
MPC
PID
y
2s
RTO
u (valves)
Follow path
(+ look after
other variables)
CV=y
1
(+ u)
; MV=
y
2
s
Stabilize + avoid drift
CV=y
2
; MV=
u
Min J (economics);
MV=
y
1s
OBJECTIVE
Dealing with complexity
Main simplification: Hierarchical decomposition
Process control
The controlled variables (CVs)
interconnect the layers
MV = manipulated variable
CV = controlled variable
20
Example: Bicycle riding
Note: design starts from the bottom
•
Regulatory control
:
–
First need to learn to stabilize the bicycle
•
CV = y
2
= tilt of bike
•
MV = body position
•
Supervisory control
:
–
Then need to follow the road.
•
CV = y
1
= distance from right hand side
•
MV=y
2
s
–
Usually a constant setpoint policy is OK, e.g. y
1
s
=
0.5
m
•
Optimization
:
–
Which road should you follow?
–
Temporary (discrete) changes in y
1
s
Hierarchical decomposition
MV = manipulated variable
CV = controlled variable
22
Implementation
of optimal operation
•
Paradigm
1
:
On

line optimizing control
where measurements are
used to update model and states
•
Paradigm
2
:
“
Self

optimizing” control scheme
found by exploiting
properties of the solution
23
Implementation:
Paradigm 1
•
Paradigm 1
:
Online
optimizing
control
•
Measurements are primarily used
to update the model
•
The optimization problem is
resolved online to compute new
inputs.
•
Examples:
–
Conventional MPC,
–
RTO (real

time optimization)
–
Supply chain optimization (Martin
Rudberg)
•
This is the “obvious” approach (for
someone who does not know
control)
24
Implementation:
Paradigm
2
•
Paradigm
2
:
Precomputed solutions based on
off

line
optimization
•
Find properties of the solution suited for simple and robust on

line
implementation
•
Proposed method
: Turn optimization into feedback problem.
–
Control layer
: Identify good CVs
–
Find regions of active constraints and
in each region:
1.
Control active constraints
2.
Control “self

optimizing ” variables for the remaining unconstrained
degrees of freedom
•
“inherent optimal operation”
–
Higher level in hierarchy: Indentify good KPIs
CV = controlled variable
KPI = Key Performance Indicators
25
Outline
•
Control structure design (plantwide control)
•
A procedure for control structure design
I
Top Down
(main new part)
•
Step
1
: Identify degrees of freedom
•
Step
2
: Identify operational objectives (cost function and constraints)
•
Step
3
:
What to control ?
(primary CV’s)
–
Active constraints
–
Self

optimizing variables
•
Step
4
:
Where set the production rate? (
Inventory control)
II Bottom Up
•
Step
5
: Regulatory control strategy:
What more to control?
(secondary CV’s)
•
Step
6
: Supervisory control strategy: Decentralized or multivariable?
•
Step
7
: Real

time optimization: Do we need it?
•
Case studies
26
Step 1. Degrees of freedom (DOFs) for
operation
To find all operational (
dynamic
) degrees of freedom:
•
Count valves! (N
valves
)
•
“Valves” also includes adjustable compressor power, etc.
Anything we can manipulate!
27
Steady

state
degrees of freedom (DOFs)
IMPORTANT!
DETERMINES THE NUMBER OF VARIABLES TO CONTROL!
•
No. of primary CVs = No. of steady

state DOFs
CV = controlled variable (
c
)
N
ss
=
N
valves
–
N
0ss
–
N
specs
•
N
0ss
= variables with no steady

state effect
28
N
valves
=
6
,
N
0
y
=
2
,
N
specs
=
2
, N
DOF,SS
=
6

2

2
=
2
Distillation column with given feed and pressure
N
0
y
:
no. controlled variables (liquid levels) with no steady

state effect
NEED TO IDENTIFY
2
CV’s

Typical: Top and btm composition
1
2
3
4
5
6
31
Step
2
. Define optimal operation (economics)
•
What are we going to use our degrees of freedom
u
(MVs
) for?
•
Define scalar cost function J(
u
,x,d)
–
u
: degrees of freedom (usually steady

state)
–
d: disturbances
–
x: states (internal variables)
Typical cost function:
•
Optimize operation with respect to u for given d (usually steady

state):
min
u
J(
u
,x,d)
subject to:
Model equations:
f(
u
,x,d) =
0
Operational constraints:
g(
u
,x,d) <
0
J = cost feed + cost energy
–
value products
33
Optimal operation
1.
Given feed
Amount of products is then usually indirectly given and J = cost energy.
Optimal operation is then usually
unconstrained:
2.
Feed free
Products usually much more valuable than feed + energy costs small.
Optimal operation is then usually
constrained:
minimize J = cost feed + cost energy
–
value products
“maximize efficiency (energy)”
“maximize production”
Two main cases (modes) depending on marked conditions:
Control: Operate at bottleneck
(“obvious what to control”)
Control: Operate at optimal
trade

off (not obvious what to
control to achieve this)
35
Implementation of optimal operation
•
Optimal operation for given d
*
:
min
u
J(u,x,d)
subject to:
Model equations:
f(u,x,d) = 0
Operational constraints:
g(u,x,d) < 0
→
u
opt
(d
*
)
Problem:
Usally cannot keep
u
opt
constant because disturbances d change
How should we adjust the degrees of freedom
(u)?
36
Implementation (in practice): Local feedback
control!
“Self

optimizing
control:” Constant
setpoints for c gives
acceptable loss
y
Feedforward
Optimizing control
Local feedback:
Control c (CV)
d
38
Step
3
. What should we control (
c
)?
(primary controlled variables
y
1
=c
)
•
Introductory example: Runner
Issue:
What should we
control?
39
–
Cost to be minimized, J=T
–
One degree of freedom (u=power)
–
What should we control?
Optimal operation

Runner
Optimal operation of runner
40
Sprinter (
100
m)
•
1
. Optimal operation of Sprinter, J=T
–
Active constraint control:
•
Maximum speed (”no thinking required”)
Optimal operation

Runner
41
•
2
. Optimal operation of Marathon runner, J=T
•
Unconstrained optimum!
•
Any ”
self

optimizing”
variable c (to control at
constant setpoint)?
•
c
1
= distance to leader of race
•
c
2
= speed
•
c
3
= heart rate
•
c
4
= level of lactate in muscles
Optimal operation

Runner
Marathon (
40
km)
42
Conclusion Marathon runner
c = heart rate
select one measurement
•
Simple and robust implementation
•
Disturbances are indirectly handled by keeping a constant heart rate
•
May
have infrequent adjustment of setpoint (heart rate)
Optimal operation

Runner
47
Active constraints can vary!
Example: Optimal operation distillation
•
Cost to be minimized
J =

P where P= p
D
D + p
B
B
–
p
F
F
–
p
V
V
•
2
Steady

state DOFs (must find
2
CVs)
•
Product purity constraints distillation:
–
Purity spec. valuable product (
y
1
):
Always active
•
“avoid give

away of valuable product”.
–
Purity spec. “cheap” product (
y
2
): May
not
be active
•
may want to overpurify to avoid loss of valuable product
•
Many possibilities for active constraint sets (may vary from day to day!)
1.
Two active purity constraints
(
CV
1
= y
1
& CV
2
=
y
2
)
Happens when energy is relatively expensive
2.
One active purity constraint
.
CV
1
= y
1
Energy quite cheap:
Unconstrained.
Overpurify cheap product.
CV
2
=? (self

optimizing, e.g.
y
2
)
Energy really cheap: Overpurify until reach
MAX load
(active input constraint)
CV
2
= max input (max. energy)
48
a)
If constraint can be violated dynamically (only average matters)
•
Required Back

off
=
“bias” (steady

state measurement error for
c
)
b)
If constraint
cannot
be violated dynamically (“hard constraint”)
•
Required Back

off
=
“bias”
+
maximum dynamic control error
J
opt
Back

off
Loss
c
≥
c
constraint
c
J
1
. CONTROL ACTIVE CONSTRAINTS!
•
Active
input
constraints: Just set at MAX or MIN
•
Active
output
constraints: Need
back

off
Want tight control of hard output constraints to reduce the
back

off
“Squeeze and shift”
49
Example. Optimal operation = max. throughput.
Want tight bottleneck control to reduce backoff!
Time
Back

off
= Lost
production
Rule for control of hard output constraints: “Squeeze and shift”!
Reduce variance (“Squeeze”) and “shift” setpoint
c
s
to reduce backoff
51
2
. UNCONSTRAINED VARIABLES:

WHAT MORE SHOULD WE CONTROL?

WHAT ARE GOOD “SELF

OPTIMIZING” VARIABLES?
•
Intuition:
“Dominant variables” (Shinnar)
•
Is there any systematic procedure?
A. Sensitive variables: “Max. gain rule” (Gain= Minimum singular
value)
B. “Brute force” loss evaluation
C. Optimal linear combination of measurements, c = Hy
52
Optimal operation
Cost J
Controlled variable c
c
opt
J
opt
Unconstrained optimum
53
Optimal operation
Cost J
Controlled variable c
c
opt
J
opt
Two problems:
•
1
. Optimum moves because of
disturbances d: c
opt
(d)
•
2
. Implementation error, c = c
opt
+
n
d
n
Unconstrained optimum
54
Candidate controlled variables c
for self

optimizing control
Intuitive
1.
The
optimal
value
of
c
should
be
insensitive
to
disturbances
(avoid
problem
1
)
2
.
Optimum
should
be
flat
(avoid
problem
2
–
implementation
error)
.
Equivalently
:
Value
of
c
should
be
sensitive
to
degrees
of
freedom
u
.
•
“Want
large
gain”,
G
•
Or
more
generally
:
Maximize
minimum
singular
value,
Unconstrained optimum
BAD
Good
Good
56
Recycle plant: Optimal operation
m
T
1
remaining unconstrained degree of freedom
57
Control of recycle plant:
Conventional structure (“Two

point”:
x
D
)
LC
XC
LC
XC
LC
x
B
x
D
Control active constraints
(M
r
=max and x
B
=
0.015
)
+
x
D
58
Luyben rule
Luyben rule (to avoid snowballing):
“Fix a stream in the recycle loop” (
F
or
D
)
59
Luyben rule: D constant
Luyben rule (to avoid snowballing):
“Fix a stream in the recycle loop” (
F
or
D
)
LC
LC
LC
XC
60
A
. Maximum gain rule: Steady

state gain
Luyben rule:
Not promising
economically
Conventional:
Looks good
62
Outline
•
Control structure design (plantwide control)
•
A procedure for control structure design
I
Top Down
•
Step
1
: Degrees of freedom
•
Step
2
: Operational objectives (optimal operation)
•
Step
3
: What to control ? (self

optimzing control)
•
Step
4
: Where set production rate?
II Bottom Up
•
Step
5
: Regulatory control: What more to control ?
•
Step
6
: Supervisory control
•
Step
7
: Real

time optimization
•
Case studies
63
Step
4
. Where set production rate?
•
Very important!
•
Determines structure of remaining inventory (level) control system
•
Set production rate at (dynamic) bottleneck
•
Link between
Top

down
and
Bottom

up
parts
64
Consistency of inventory control
•
Consistency
(required property):
An inventory control system is said to be
consistent
if the
steady

state mass balances
(total, components and
phases)
are satisfied
for any part of the process, including
the individual units and the overall plant.
•
Local

consistency (
desired property):
A consistent inventory control system is said to be
local

consistent
if for each unit the
local
inventory control
loops by themselves
are sufficient to achieve steady

state
mass balance consistency for that unit.
66
CONSISTENT?
67
Production rate set at inlet :
Inventory control in direction of flow*
* Required to get “local

consistent” inventory control
68
Production rate set at outlet:
Inventory control opposite flow
69
Production rate set inside process
72
Where set the production rate?
•
Very important decision that determines the structure of the rest of the
control system!
•
May also have important economic implications
73
Often optimal: Set production rate at
bottleneck!
•
"A bottleneck is a unit where we reach a constraints which makes
further increase in throughput infeasible"
•
If feed is cheap and available: Optimal to set production rate at
bottleneck
•
If the flow for some time is not at its maximum through the
bottleneck, then this loss can never be recovered.
74
Reactor

recycle process:
Want to maximize feedrate: reach
bottleneck
in column
Bottleneck: max. vapor
rate in column
75
Reactor

recycle process with max. feedrate
Alt.
1
: Feedrate controls bottleneck flow
Bottleneck: max. vapor
rate in column
FC
V
max
V
V
max

V
s
=Back

off
= Loss
V
s
Get “long loop”: Need back

off in V
76
MAX
Reactor

recycle process with max. feedrate:
Alt.
2
Optimal:
Set production rate at bottleneck (MAX)
Feedrate used for lost task (xb)
Get “long loop”: May need back

off in xB instead…
Bottleneck: max. vapor
rate in column
77
Reactor

recycle process with max. feedrate:
Alt.
3
:
Optimal:
Set production rate at bottleneck (MAX)
Reconfigure upstream loops
MAX
OK, but reconfiguration undesirable…
78
Reactor

recycle process:
Alt.
3
: reconfigure (permanently)
F
0
s
For cases with given feedrate: Get “long loop” but no associated loss
79
Conclusion production rate manipulator
•
Think carefully about where to place it!
•
Difficult to undo later
•
One approach: Put MPC top that
coordinates flows
through plant
by manipulating feed rate and other ”unused” degrees of freedom:
•
E.M.B. Aske, S. Strand and S. Skogestad,
``Coordinator MPC for maximizing plant throughput'',
Computers and Chemical Engineering
,
32
,
195

204
(
2008
).
80
Outline
•
Control structure design (plantwide control)
•
A procedure for control structure design
I
Top Down
•
Step
1
: Degrees of freedom
•
Step
2
: Operational objectives (optimal operation)
•
Step
3
: What to control ? (self

optimizing control)
•
Step
4
: Where set production rate?
II Bottom Up
•
Step
5
: Regulatory control: What more to control ?
•
Step
6
: Supervisory control
•
Step
7
: Real

time optimization
•
Case studies
81
Step
5
. Regulatory control layer
•
Purpose
: “Stabilize” the plant using a
simple control configuration (usually:
local SISO PID controllers + simple
cascades)
•
Enable manual operation (by operators)
•
Main structural issues:
•
What more should we control?
y
2
=?
•
(secondary cv’s, y
2
, use of extra
measurements)
•
Pairing with manipulated variables
(mv’s u
2
)
y
1
= c
y
2
= ?
82
Example: Distillation
•
Primary controlled variable: y
1
= c = x
D
, x
B
(compositions top, bottom)
•
BUT: Delay in measurement of x + unreliable
•
Regulatory control: For “stabilization” need control of (y
2
):
–
Liquid level condenser (M
D
)
–
Liquid level reboiler (M
B
)
–
Pressure (p)
–
Holdup of light component in column
(temperature profile)
Unstable (Integrating) + No steady

state effect
Variations in p disturb other loops
Almost unstable (integrating)
TC
T
s
T

loop in bottom
86
Objectives regulatory control layer
1.
Allow for manual operation
2.
Simple decentralized (local) PID controllers that can be tuned on

line
3.
Take care of “fast” control
4.
Track setpoint changes from the layer above
5.
Local disturbance rejection
6.
Stabilization
(mathematical sense)
7.
Avoid “drift” (due to disturbances) so system stays in “linear region”
–
“stabilization”
(practical sense)
8.
Allow for “slow” control in layer above (supervisory control)
9.
Make control problem easy as seen from layer above
Implications for selection of
y
2
:
1.
Control of
y
2
“stabilizes the plant”
2.
y
2
is easy to control (favorable dynamics)
88
2
. “
y
2
is easy to control” (controllability)
1.
Statics: Want large gain
(from u
2
to y
2
)
2.
Main rule:
y
2
is easy to measure and located
close to available manipulated variable u
2
(“pairing”)
3.
Dynamics: Want small
effective delay
(from u
2
to y
2
)
•
“effective delay” includes
•
inverse response (RHP

zeros)
•
+ high

order lags
89
3
. Rules for selecting
u
2
(to be paired with
y
2
)
1.
Avoid using variable u
2
that may saturate
(especially in loops at the
bottom of the control hieararchy)
•
Alternatively: Need to use “input resetting” in higher layer (“mid

ranging”)
•
Example: Stabilize reactor with bypass flow (e.g. if bypass may saturate, then
reset in higher layer using cooling flow)
2.
“Pair close”:
The controllability, for example in terms a small
effective delay from u
2
to y
2
, should be good.
94
Outline
•
Control structure design (plantwide control)
•
A procedure for control structure design
I
Top Down
•
Step
1
: Degrees of freedom
•
Step
2
: Operational objectives (optimal operation)
•
Step
3
: What to control ? (primary CV’s) (self

optimizing control)
•
Step
4
: Where set production rate?
II Bottom Up
•
Step
5
: Regulatory control: What more to control (secondary CV’s) ?
•
Step
6
: Supervisory control
•
Step
7
: Real

time optimization
•
Case studies
95
Step
6
. Supervisory control layer
•
Purpose
: Keep primary controlled outputs c=y
1
at optimal setpoints c
s
•
Degrees of freedom: Setpoints y
2
s
in reg.control layer
•
Main structural issue:
Decentralized or multivariable?
96
Decentralized control
(single

loop controllers)
Use for
: Noninteracting process and no change in active constraints
+
Tuning may be done on

line
+
No or minimal model requirements
+
Easy to fix and change

Need to determine pairing

Performance loss compared to multivariable control

Complicated logic required for reconfiguration when active constraints
move
97
Multivariable control
(with explicit constraint handling = MPC)
Use for
: Interacting process and changes in active constraints
+
Easy handling of feedforward control
+
Easy handling of changing constraints
•
no need for logic
•
smooth transition

Requires multivariable dynamic model

Tuning may be difficult

Less transparent

“Everything goes down at the same time”
98
Outline
•
Control structure design (plantwide control)
•
A procedure for control structure design
I
Top Down
•
Step
1
: Degrees of freedom
•
Step
2
: Operational objectives (optimal operation)
•
Step
3
: What to control ? (self

optimizing control)
•
Step
4
: Where set production rate?
II Bottom Up
•
Step
5
: Regulatory control: What more to control ?
•
Step
6
: Supervisory control
•
Step
7
: Real

time optimization
•
Case studies
99
Step
7
. Optimization layer (RTO)
•
Purpose:
Identify active constraints and compute optimal setpoints (to
be implemented by supervisory control layer)
•
Main structural issue:
Do we need RTO?
(or is process self

optimizing)
•
RTO not needed when
–
Can “easily” identify change in active constraints (operating region)
–
For each operating region there exists self

optimizing variables
10
0
Conclusion I: Focus on “what to control”
•
1
. Control for economics (Top

down steady

state
arguments)
–
Primary CVs, c = y
1
:
•
Control active constraints
•
For remaining unconstrained degrees of freedom: Look for
“self

optimizing” variables
•
2
. Control for stabilization (Bottom

up; regulatory PID
control)
–
Secondary CVs, y
2
:
•
Control “drifting” variables
•
Identify pairings with MVs (u
–
Pair close and avoid MVs that saturate
•
Both cases: Control “sensitive” variables (large gain)!
c
s
y
2
s
u
MV = manipulated variable (u)
CV = controlled variable (c)
10
1
Conclusion II.
S
ystematic procedure for plantwide control
1.
Start “top

down” with economics:
–
Step
1
: Identify degrees of freeedom
–
Step
2
:
Define operational objectives and optimize steady

state operation.
–
Step
3
A
: Identify active constraints = primary CVs
c
. Should controlled to
maximize profit)
–
Step
3
B
: For remaining unconstrained degrees of freedom: Select CVs
c
based
on self

optimizing control.
–
Step
4
: Where to set the throughput (usually: feed)
2.
Regulatory control I: Decide on how to move mass through the plant:
•
Step
5
A:
Propose “local

consistent” inventory control structure (
y
2
= levels,…
).
3.
Regulatory control II: “Bottom

up” stabilization of the plant
•
Step
5
B:
Control variables to stop “drift” (
y
2
=
sensitive temperatures, pressures, ....)
–
Pair variables to avoid interaction and saturation
4.
Finally: make link between “top

down” and “bottom up”.
•
Step
6
: “Advanced control” system (MPC):
•
CVs: Active constraints and self

optimizing economic variables +
look after variables in layer below (e.g., avoid saturation)
•
MVs: Setpoints to regulatory control layer.
•
Coordinates within units and possibly between units
c
s
y
2
s
u
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