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Transcript of Integrated Process Networks: Nonlinear Control System Design for Optimality and Dynamic Performance...
Integrated Process Networks:Nonlinear Control System Design
for Optimality and Dynamic Performance
Michael Baldeaa,b and Prodromos Daoutidisa
aUniversity of Minnesota, Minneapolis, MN 55455bPraxair, Inc., Tonawanda, NY 14150
Antonio C. Brandao Araujo and Sigurd SkogestadNorwegian University of Science and Technology
NO-7491 Trondheim, Norway
Chemical Plant
• Material recycle• Heat integration
• Feedback interactions within the plant
Control of Tightly Integrated Plants:Challenging and Important!
• Decentralized control: inherent limitations
• Fully centralized control: generally impractical– Size / complexity of dynamic models– Ill-conditioning
• Efficient transient operation : critical – Moves across product slate due to frequent
changes in market conditions and economics– Going beyond regulatory control…– Accounting for ‘network’ dynamics
Research on Integrated Process Networks
Dynamic Analysis:– Slow response, high sensitivity to disturbances,
instability (Gilliland et al. ’64, Denn & Lavie ’82, Skogestad & Morari ’87,
Luyben ’93, Mizsey & Kalmar ’96)
– Nonlinear dynamics (Morud & Skogestad ’94, ’96, Bildea et al. ’00, Kiss et al. ’06)
Research on Integrated Process Networks
Control– Interaction of design and control for reaction-separation
networks (Luyben ’93, Luyben M. & Floudas ’94, Yin & Luyben ‘97)
– Plant-wide control(Price & Georgakis ’93, Luyben et al. ’97, Ng & Stephanopoulos ’98, Zheng et al. ’99)
– Applications to benchmark problems (McAvoy & Ye, ’94, Ricker, ’96, Ricker and Lee, ’95, Larsson et al. ’01, Jockenhovel et al. ’03)
– Self – optimizing control (Morari et al. ’80, Skogestad ’00)
– Partial control (Shinnar et al. ’96, Tyreus ’99, Kothare et al. ’00)
– Passivity based stabilization (Ydstie et al. ’98, ’99) – Dynamic optimization
(Tosukhowong et al. ’04)– Time-scale analysis / nonlinear model reduction and control
(Kumar and Daoutidis ’02, Baldea et al. ’06, Baldea and Daoutidis ’05 ’06)
Present Work
• Combining time-scale analysis (dynamics)
and self-optimizing control (steady state
economics):
– Control structure design– Nonlinear supervisory control
• Prototype reactor-separator-recycle network
Plant-wide ControlHierarchy of Decisions(Larsson and Skogestad, 2000)
I. TOP-DOWN
Step 1. DEGREES OF FREEDOMStep 2. OPERATIONAL OBJECTIVESStep 3. CONTROLLED VARIABLES Step 4. PRODUCTION RATE
II. BOTTOM-UP
Step 5. REGULATORY CONTROL LAYER (PID) Step 6. SUPERVISORY CONTROL LAYER (MPC) Step 7. OPTIMIZATION LAYER (RTO) Can we do without?
Planning(months - years)
What should we control?
Optimization level: Solve
Optimal solution: usually at constraints– most degrees of freedom are used to satisfy “active
constraints”
• Control active constraints!– Implementation usually simple
• What else should we control?– Variables for remaining unconstrained degrees of
freedom: acceptable losses in the presence of disturbances and implementation errors
00 0 0min ( , , )
uJ x u d
0 0
0 0
( , , ) 0
( , , ) 0
f x u d
g x u d
Self-optimizing Control
Principle:
(Economically) acceptable operation (loss) should be achieved using constant set points for the controlled variables, without the need to re-optimize when disturbances occur.
c=cs
Planning(months - years)
Identify degrees of freedom, disturbances
Define economics,operational constraints
Optimize for the various disturbances
Identify (and control) active constraints
Identify “self-optimizing” controlled variables for remaining degrees of freedom
“Bottom-up” design regulatory/stabilizing structure
Multiple possible supervisory configurations
Performance assessment: Dynamic simulation
No a-priori performance indication
Controlled Variables
Selection of Controlled Variables
Integrated Process Network:Multiple Time Scale Dynamics
•Low single pass conversion - high recycle rate
•Impurities present in the feed – small amount
•Impurities do not separate readily -small purge stream
Baldea and Daoutidis, Comp. Chem. Eng., 2006.
Dynamic Model
: scaled inputs : large recycle loop flowrates : scaled inputs : medium flowrates : scaled input : small purge flowrate : small parameter – ratio of throughput to recycle
: small parameter – ratio of purge to throughput states
terms: stiffness, multiple time scales
lusu
1pu
1
2 2
1( , ) ( )
( ) ( ) ( )
s l l
Io I p P
x f x u g x u
g x g x g x u
2
(1), ( ), (1/ )O O O
N C
Model ReductionTime Scale Decomposition
• Fast time scale (process units)
• Intermediate time scale (network)
• Slow time scale (impurity levels)
( )l ldxg x u
d 0 ( )l lg x u
lu
1/t dimensional
Equilibrium manifold
Manipulated inputs
( 1)S C
dimensional
Equilibrium manifold
Manipulated inputs
( 1) 1N S C ˆ ( , )s
df u
dt
su
0 ( , )sf u
1-dimensional
Manipulated input
2t
pu ˆ( ) ( )Io p Pdg x g x u
d
t
Hierarchical Controller Design
PRODUCT PURITYPRODUCTION RATE
HOLDUPSSTABILIZATION
HOLDUPSSTABILIZATION
LA
YE
RS
SU
PE
RV
ISO
RY
REGULATORYLAYER
IMPURITY LEVELS
SUPERVISORY CONTROL
SLOW TIME SCALESUPERVISORY CONTROL
INTERMEDIATE T.S.
OP
TIM
IZA
TIO
NL
AY
ER
FA
ST
ER
TIM
E S
CA
LE
FAST TIME SCALEDISTRIBUTED CONTROL
INTERMEDIATE T.S.SUPERVISORY CONTROL
NETWORK LEVELOPTIMIZATION
pu
,
s
L sp
uy
su
Lu
Manipulated inputsControl objectives (broad)
Optimality and Dynamic Performance
Self Optimizing Control– economic insight– selection of controlled
variables
Time Scale Analysis– dynamic perspective– selection of manipulated
inputs
Combining:
control designs with inherent optimality and good dynamic performance
Case StudyGeneric Reactor – Condenser Network
• Slow reaction , large recycle• Product nonvolatile• Volatile impurity present in the feed
• Degrees of freedom: R (W), F, P, L
A B
IB
Insights from Time-scale Analysis
• Control objectives: vapor holdups (pressures) stabilization (fast), liquid holdup stabilization, X B (intermediate) impurity levels (slow)
• Available degrees of freedom: R,F (fast) L, M RSP, M CSP (intermediate) P (slow)
Hierarchical Controller Design (I)(Baldea and Daoutidis C&ChE, 2006)
Time scale Controlled output
Manipulated
Input
Controller
Fast Reactor holdup Reactor effluent Proportional
Fast Condenser vapor holdup
Recycle rate Proportional
Intermediate Liquid holdup Liquid flowrate Proportional
Intermediate Product purity Reactor holdup
Set point
Nonlinear, model-based, cascade
Slow Inert levels in reactor
Purge flowrate Nonlinear, model-based
Control Structure I
• Reactor pressure allowed to vary • Compressor/pressure constraints?
Insights from Self-optimizing Control
• Disturbances: FO, yA,O, yI,O, xB,TR ,k1
• Cost function: J = pW*W - pL*L + pP*L• Active constraints: Reactor pressure, product purity• Self-optimizing variable: W
Self-optimizing Control Structure (II)
• No control of impurity• Poor dynamics: small purge controls product purity
Hierarchical / Self-optimizing Controller Design (III)
Time scale Controlled output
Manipulated
Input
Controller
Fast Reactor holdup Reactor effluent Proportional
Condenser vapor holdup
Recycle rate
(compressor power)
Proportional
Intermediate Liquid holdup Liquid flowrate Proportional
Product purity Condenser vapor holdup
set point
Nonlinear, model-based, cascade
Slow Compressor power
Purge flowrate Proportional Integral
5% increase in purity setpoint
5% increase in purity setpoint
20% increase in production rate
20% increase in production rate
Concluding Remarks
• Self-optimizing control / time-scale analysis: complementary perspectives
steady-state economics vs dynamics
controlled variables vs manipulated inputs
• Control configurations that are self-optimizing and have good closed - loop response characteristics
• Well-conditioned nonlinear supervisory controllers
based on reduced order models
Acknowledgements
• National Science Foundation
• MB partially funded by a University of Minnesota Doctoral Dissertation Fellowship
Integrated Process Networks:Nonlinear Control System Design
for Optimality and Dynamic Performance
Michael Baldeaa,b and Prodromos Daoutidisa
aUniversity of Minnesota, Minneapolis, MN 55455bPraxair, Inc., Tonawanda, NY 14150
Antonio C. Brandao Araujo and Sigurd SkogestadNorwegian University of Science and Technology
NO-7491 Trondheim, Norway