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Optimal control
Industrial applicationsFlavio Manenti , Dept. CMIC “Giulio Natta” ; Politecnico di Milano
Filip Logist, Jan Van Impe, Dept. of Chemical Engineering, KULeuven, University of Leuven
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Flavio Manenti, Filip Logist – KU-Leuven
2‘‘Terms and conditions’’
• Acronyms and notations
Advanced conventional process control
Advanced process control Model predictive control
• Model predictive control
Based on linear/linearized models
• Dynamic matrix control (DMC, LMPC, MPC)
• Several commercial packages
Based on nonlinear models
• Model predictive control (MPC, NMPC)
• No commercial packages
• Features of NMPC
A dynamic (convolution) model is used to foresee the future behavior of
the plant on a specific time horizon (prediction horizon, H_P) consistingof p sampling times
Receding horizon methodology (moving horizon, not rolling horizon)
Manenti, Considerations on Nonlinear Model Predictive Control Techniques, COMPUTERS & CHEMICAL
ENGINEERING, 35(11), 2491-2509, 2011.
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3Integration Pyramid
PlantManagement
Maintenance
and Production
Management
Enterprise
Management
Field
ConventionalControl
AdvancedControl
(MPC/NMPC)
Real TimeDynamic
Optimization
Scheduling
Planning
SecondsMinutes
Hours - Days
Weeks
Months - Years
1 1
2 2 2
1
min ( ) ( ) ( ) ( 1) p p pk h k h k h
SET TAR
y react react T c c u c c
j k l k i k
T j T F l F F i F i
AdvancedControl
(MPC/NMPC)
Real TimeDynamic
Optimization
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4Algorithm
• Model Predictive Control• MPC
Plant
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5Receding horizon methodology
t
u1,1
u2,1
u3,1un,1
Set-point
ManipulatedVariable
PLANT
MODEL
Controlled Variable
u1
HPHC
u2
u3
HD
1
HPHC
u1
u2
u3
2
HPHC
u1u2 u3
3
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6Industrial perspective
Outlier Detection
Robust methods
Linear/nonlinear Regressions
Performance Monitoring
Yield Accounting
Soft sensing
Data
Reconciliation
Mathematical
Modeling
Dynamic
Simulation
ModelPredictive
Control
Optimization
Model
Reduction
DCS, OTS, Plantwide control,Soft sensing, process transients,
grade/load changes
Solvers
Planning
Scheduling
Dynamic optimization
Distributed predictive control
Nonlinear Systems
Optimizers
Differential systems
Stiff systems
ODE,DAE,PDE,PDAE
Efficiency
DecisionsRaw Data
Parallel
Computing
Uncertainties
Optimal production
Optimal grade changesMulti-objective
Real-time optimization
High accuracy
Reliable process cont rol
Production improvement
Economy
Just in time
Market-driven
Logistics
Corporate
Supply Chain
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NMPCPET plant
7
Manenti, Rovaglio
Integrated multilevel optimization in large-scale poly(ethylene terephthalate) plants
Industrial & Engineering Chemistry Research
47(1), 92-104, 2008.
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8Case Study: PET Plant
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9Jacobian Matrix
• Primary esterifier
• Secondary esterifier
• Low polymerizer
• Intermediate polymerizer
• High polymerizer
• Solid state polymerizer
Resulting DAE:
1356 diff. eqs.
164 alg. eqs.
15 controls 2 controlled
16 constrained
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10Frequent Grade Changes
Grade A: PET as textile fibres ( melt process
I.V. = 0.55 ÷ 0.65 dl/g)
Grade B: PET for bottles production ( bottle
grade I.V. = 0.72 ÷ 0.85 dl/g)
Grade C: PET for special fibres ( tire-cord
resins I.V. = 0.95 ÷ 1.05 dl/g)
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11Comparison
0.440
0.445
0.450
0.455
0.460
0.465
0.470
0 500 1000 1500 2000 2500 3000
I V I P ( d l / g )
Time (min)
0.600
0.610
0.620
0.630
0.640
0.650
0.660
0.670
0.680
0 500 1000 1500 2000 2500 3000
I
V H P ( d l / g )
Time (min)
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
0 500 1000 1500 2000 2500 3000
P
H P ( m m H g )
Time (min)
0.60
0.80
1.00
1.201.40
1.60
1.80
2.00
0 500 1000 1500 2000 2500 3000
P I P ( m m
H g )
Time (min)
NMPC
NMPC
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12Comparison
0.640
0.650
0.660
0.670
0.680
0.690
0.700
0.710
0 500 1000 1500 2000 2500 3000
I V P H C R
( d l / g )
0.760
0.770
0.7800.790
0.800
0.810
0.820
0.830
0.840
0 500 1000 1500 2000 2500 3000
I V S S
P ( d l / g )
NMPC
NMPC
Time (min)
Time (min)
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NMPCEthylene splitter
Eni, Italy
13
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14The C2-splitter
Column design:
•Total tray number : 110
•Feed : tray #55 (*)
•Ethylene cut : tray #104(*)
Feed composition (**) :
•C2H4 – 79%
•C2H6 – 19%
•Others – 2% (H2, CO,
CO2, CH4, C3H8, C3H6)
(*) bottom-up numeration
(**) molar basis
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15Validation
Reflux flowrate change effect on overhead
composition and on temperature at tray #5
0,0
100,0
200,0
300,0
400,0
500,0
600,0
700,0
800,0
900,0
1000,0
89,40 89,60 89,80 90,00 90,20 90,40 90,60
O v e r h e a d
i m p u r i t i e s
[ p p m
]
Reflux flowrate [ton/h]
Reflux flowrate change effects on overhead
stream impurities
Simulation
Plant
‐41,00
‐40,00
‐39,00
‐38,00
‐37,00
‐36,00
‐35,00
89,60 89,80 90,00 90,20 90,40 90,60
T r a y
5
t e m p e r a t u r e
[ ° C ]
Reflux flowrate [ton/h]
Reflux flowrate change effects on temperature at
tray #5
Simulation
Plant
0,0
200,0
400,0
600,0
800,0
1000,0
1200,0
96,60 96,80 97,00 97,20 97,40 97,60
O v e r h e a d
i m p u r i t i e s [ p p m ]
Boil‐up flowrate [ton/h]
Boil‐up flowrate change effects on overhead
stream impurities
Smulation
Plant
‐41,00
‐40,00
‐39,00
‐38,00
‐37,00
‐36,00
‐35,00
‐34,00
96,60 96,80 97,00 97,20 97,40 97,60
T r a y
5 t e m p e r a t u r e
[ ° C ]
Boil‐up flowrate [ton/h]
Boil‐up flowrate change effects on temperature at
tray #5
Simulation
Plant
Boil-up flowrate change effect on overhead
composit ion and on temperature at tray #5
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16Servo-mechanism problem
9000
9200
9400
9600
9800
10000
10200
10400
10600
0,0 5,0 10,0 15,0 20,0 25,0 30,0
R e f l u x f l o w
r a t e [ l b m o l / h ]
Time [h]
Reflux flow rate
PI
DMC
NMPC
0,9600
0,9650
0,9700
0,9750
0,9800
0,9850
0,9900
0,0 5,0 10,0 15,0 20,0 25,0 30,0
E t h y l e n e m
o l a r f r a c t i o n [ ‐ ]
Time [h]
Ethylene molar fraction in cut stream
PI
DMCNMPC
SP Distillate
0,8600
0,8700
0,8800
0,8900
0,9000
0,9100
0,9200
0,9300
0,9400
0,9500
0,0 5,0 10,0 15,0 20,0 25,0 30,0
E t h a n e m o l a r f r a c t i o n [ ‐ ]
Time [h]
Ethane molar fraction in bottom stream
PI
DMC
NMPC
SP Bottom
43,20
43,40
43,60
43,80
44,00
44,20
44,40
44,60
44,80
45,00
0,0 10,0 20,0 30,0 40,0 50,0 60,0 R e b o i l e r t h e r m a l d u t y [ 1 . E + 0 6 B T U / h ]
Time [h]
Reboiler thermal duty
PI
DMC
NMPC
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17Servo-mechanism problem
0,8600
0,8700
0,8800
0,8900
0,9000
0,9100
0,9200
0,9300
0,9400
0,9500
0,0 5,0 10,0 15,0 20,0 25,0 30,0
E t h a n e m o l a r f r a c t i o n [ ‐ ]
Time [h]
Ethane molar fraction in bottom stream
PI
DMC
NMPC
SP Bottom
39,50
40,00
40,50
41,00
41,50
42,00
42,50
43,00
43,50
44,00
44,50
45,00
0,0 5,0 10,0 15,0 20,0 25,0 30,0 R e b o i l e r t h e r m a l d u t
y [ 1 . E + 0 6 B T U / h ]
Time [h]
Reboiler thermal duty
PI
DMC
NMPC
9000
9200
9400
9600
9800
10000
10200
10400
10600
0,0 5,0 10,0 15,0 20,0 25,0 30,0
R e f l u x f l o w
r a t e [ l b m o l / h ]
Time [h]
Reflux flow rate
PI
DMC
NMPC
0,9600
0,9650
0,9700
0,9750
0,9800
0,9850
0,9900
0,0 5,0 10,0 15,0 20,0 25,0 30,0
E t h y l e n e m
o l a r f r a c t i o n [ ‐ ]
Time [h]
Ethylene molar fraction in cut stream
PI
DMCNMPC
SP Distillate
0,9800
0,9850
0,9200
0,9250
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18Regulation problem (feedcomposition disturbance)
0,9890
0,9895
0,9900
0,9905
0,9910
0,9915
0,9920
0,0 10,0 20,0 30,0 40,0 50,0 60,0
E t h y l e n e m
o l a r f r a c t i o n [ ‐ ]
Time [h]
Ethylene molar fraction in cut stream
PI
DMCNMPC
SP Cut
0,8800
0,8900
0,9000
0,9100
0,9200
0,9300
0,9400
0,9500
0,9600
0,9700
0,0 10,0 20,0 30,0 40,0 50,0 60,0
E t h a n e m o l a r
f r a c t i o n
[ ‐ ]
Time [h]
Ethane molar fraction in bottom stream
PI
DMC
NMPC
SP Bottom
9950,00
10000,00
10050,00
10100,00
10150,00
10200,00
10250,00
10300,00
10350,00
0,0 10,0 20,0 30,0 40,0 50,0 60,0
R e f l u x f l o w
r a t e [ l b m o l / h ]
Time [h]
Reflux flow rate
PI
DMC
NMPC
43,20
43,40
43,60
43,80
44,00
44,20
44,40
44,60
44,80
45,00
0,0 10,0 20,0 30,0 40,0 50,0 60,0
R e
b o
i l e r t h e r m
a l d u t y
[ B T U / h ]
Time [h]
Reboiler thermal duty
PI
DMC
NMPC
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Flavio Manenti, Filip Logist – KU-Leuven
19Regulation problem (feedcomposition disturbance)
0,9890
0,9895
0,9900
0,9905
0,9910
0,9915
0,9920
0,0 10,0 20,0 30,0 40,0 50,0 60,0
E t h y
l e n e m
o l a r
f r a c t i o n
[ ‐ ]
Time [h]
Ethylene molar fraction in cut stream
PI
DMCNMPC
SP Cut
0,8800
0,8900
0,9000
0,9100
0,9200
0,9300
0,9400
0,9500
0,9600
0,9700
0,0 10,0 20,0 30,0 40,0 50,0 60,0
E t h a n e m o l a r
f r a c t i o n
[ ‐ ]
Time [h]
Ethane molar fraction in bottom stream
PI
DMC
NMPC
SP Bottom
9950,00
10000,00
10050,00
10100,00
10150,00
10200,00
10250,00
10300,00
10350,00
0,0 10,0 20,0 30,0 40,0 50,0 60,0
R e f l u x f l o w
r a t e [ l b m o l / h ]
Time [h]
Reflux flow rate
PI
DMC
NMPC
43,20
43,40
43,60
43,80
44,00
44,20
44,40
44,60
44,80
45,00
0,0 10,0 20,0 30,0 40,0 50,0 60,0
R e
b o
i l e r t h e r m
a l d u t y
[ B T U / h ]
Time [h]
Reboiler thermal duty
PI
DMC
NMPC
0,9900
0,9905
0,9500
0,9550
0,9500
0,9550
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Flavio Manenti, Filip Logist – KU-Leuven
Self-adaptive NMPC
Deisobutanizer
Mongstad Refinery, Norway
20
Dones, Manenti, Preisig, Buzzi-FerrarisNonlinear Model Predictive Control: a Self-
Adaptive ApproachIndustrial & Engineering Chemistry Research
49(10), 4782-4791, 2010
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Unit 21
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Spoiled Jacobian 22
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Results
• Use of compartmental models
• Model self-adaptation
To the problem
To the dynamic
To the computational effort
• Benefits
Accurate when needed
Fast when possible
(viceversa)
23
93
93.5
94
94.5
95
95.5
0 500 1000 1500 2000 2500 3000
c o n c e n t r a t i o n i C 4 t o p [ % ]
time [s]
NMPC with full dynamic model
NMPC with 5-dynamic-trays model
ANMPC
360
365
370
375
380
385
390
395
400
0 500 1000 1500 2000 2500 3000
r e f l u x s t r e a m [ m o l / s ]
time [s]
NMPC with full dynamic model
NMPC with 5-dynamic-trays model
ANMPC
4
6
8
10
12
14
16
0 500 1000 1500 2000 2500 3000
n u m b e r o f d y n a m i c t r a y s u
s e d i n t h e m o d e l
time [s]
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Flavio Manenti, Filip Logist – KU-Leuven
D-RTO
Olefins plant
Invensys, USA
24
Manenti et al.Process Dynamic Optimization Using ROMeo
Computer Aided Chemical Engineering
29, 452-456, 2011
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Flavio Manenti, Filip Logist – KU-Leuven
25Industrial perspective
Outlier Detection
Robust methodsLinear/nonlinear Regressions
Performance Monitoring
Yield Accounting
Soft sensing
Data
Reconciliation
Mathematical
Modeling
Dynamic
Simulation
ModelPredictive
Control
Optimization
Model
Reduction
DCS, OTS, Plantwide control,Soft sensing, process transients,
grade/load changes
Solvers
Planning
Scheduling
Dynamic optimization
Distributed predictive control
Nonlinear Systems
Optimizers
Differential systems
Stiff systems
ODE,DAE,PDE,PDAE
Efficiency
DecisionsRaw Data
Parallel
Computing
Uncertainties
Optimal production
Optimal grade changes
Multi-objective
Real-time optimization
High accuracy
Reliable process cont rol
Production improvement
Economy
Just in time
Market-driven
Logistics
Corporate
Supply Chain
Dynamic Optimization
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Flavio Manenti, Filip Logist – KU-Leuven
26Tools
Outlier Detection
Robust methodsLinear/nonlinear Regressions
Performance Monitoring
Yield Accounting
Soft sensing
Data
Reconciliation
MATHEMATICAL
MODELING
Dynamic
Simulation
Dynamic
Optimization
Optimization
Model
Reduction
DCS, OTS, Plantwide control,Soft sensing, process transients,grade/load changes
Solvers
Enterprise-wide
Planning
Scheduling
Nonlinear Systems
Optimizers
Differential systems
Stiff systems
ODE,DAE,PDE,PDAE
Efficiency
DecisionsRaw Data
Parallel
Computing
Supply Chain
Management
Uncertainties
Optimal production
Optimal grade changes
Multi-objective
Real-time optimization
High accuracy
Reliable process cont rol
Production improvement
Just in time
Market-driven
Conscious MGM
Mathematical
Modeling
Dynamic
Simulation
Optimization
DYNSIM
ROMeo
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Flavio Manenti, Filip Logist – KU-Leuven
27Tools
Outlier Detection
Robust methodsLinear/nonlinear Regressions
Performance Monitoring
Yield Accounting
Soft sensing
Data
Reconciliation
MATHEMATICAL
MODELING
Dynamic
Simulation
DynamicOptimization
Optimization
ModelReduction
DCS, OTS, Plantwide control,Soft sensing, process transients,grade/load changes
Solvers
Enterprise-wide
Planning
Scheduling
Nonlinear Systems
Optimizers
Differential systems
Stiff systems
ODE,DAE,PDE,PDAE
Efficiency
DecisionsRaw Data
Parallel
Computing
Supply Chain
Management
Uncertainties
Optimal production
Optimal grade changes
Multi-objective
Real-time optimization
High accuracy
Reliable process cont rol
Production improvement
Just in time
Market-driven
Conscious MGM
Is it possible?
DYNSIM
Mathematical
Modeling
Dynamic
Simulation
DynamicOptimization
Optimization
ROMeo
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28The Idea
• On-line Optimization
• Nonlinear MPC
• Data Reconciliation
T
min
. . : 0; 0
R
M R M R
i i i ii x x x x
s t f g
x W
x x
,min
. . : , 0
, 0
;n m
Z Profits Costs
s t f
g
R N
x b
x b
x b
x b
1 2, ,min ... ...
. . : 0; , 0
0; , 0
; ;
n
n p m
Z
s t f f
g g
R R N
x u b
x x x
x x x
x u b
• Dynamic Optimization
T
min
. . : , 0
, 0
R SET R SET
i i i i
i
x x x x
s t f
g
u
W
x x
x x
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29Example
• Series of three ideal CSTRs
Open-loop
Closed-loop
P-7
P-16
P-21
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30Open-loop in C++
Key-component molar flow
exiting the reactor:
• #1
• #2• #3
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 1 2 3 4 5
[ m o l / s ]
Time
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31Open-loop in DYNSIM
UAM MODELS insertedinto the ICON PALETTE
(C++ dynamic library)
32
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32Open-loop in DYNSIM
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 1 2 3 4 5
[ m o l / s ]
Time
33
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33Using BzzMath in DYNSIM
No changes at theDYNSIM’s interface
34l d l i
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34Closed-loop in C++
0.398
0.4
0.402
0.404
0.406
0.408
0.41
0.412
0.414
0.416
0.418
0 10 20 30 40 50
[ m o l / s ]
Time
0.198
0.199
0.2
0.201
0.202
0.203
0.204
0.205
0 10 20 30 40 50
[ m o l / s ]
Time
0.0982
0.0984
0.0986
0.0988
0.099
0.0992
0.0994
0.0996
0.0998
0.1
0.1002
0 10 20 30 40 50
[ m o l / s ]
Time
CV
(SP: 0.1 mol/s)
35F ll I t ti (All i )
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35Full Integration (All-in-one)
36D & D
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36Drag & Drop
DYNSIM
37D & D
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37Drag & Drop
DYNSIM
38Drag & Drop
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38Drag & Drop
ROMeo
39Drag & Drop
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39Drag & Drop
ROMeo
40Smart Dynamic Simulation with
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40Smart Dynamic Simulation withROMeo
41D-RTO with Multiple Shooting
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D-RTO with Multiple Shooting
42Friendly Interface for D-RTO
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Friendly Interface for D-RTO
Possibility to give the user to enter
any kind of data for D-RTO
Specific
D-RTOTAB
43Preliminary Comparison
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Flavio Manenti, Filip Logist – KU-Leuven
Preliminary Comparison
RTO vs D-RTO
0.095
0.1
0.105
0.11
0.115
0.12
0.125
0 10 20 30 40 50 60 70 80 90
'checkdrto.ris' u 1:4'check.ris' u 1:4
Traditional approach
Two-shooting
Multiple-shooting
44Validation Case (Olefins)
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Validation Case (Olefins)
• Cracking Furnace (SPYRO-based D-RTO)
45Stack
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Flavio Manenti, Filip Logist – KU-Leuven
SteamCrackingFurnaceFeed
Stack
Damper
Breeching
ConvectionSection
Radiant
Section
Burners / Air Blowers
Coil Outlet
Temperature
(COT)
Steam
Transfer Line
Exchanger (TLE)
High
Pressure
Steam
Main
Factionator
>800°C 400°C
COT
Olefins
TC
FC
Fuel
Air
PV
PV
OUT
OUT
SP
Temperature
Controller
Flowrate Ratio
Controller
RADIANT SECTION
PV
46Software Integration
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D-RTO- ROMeo (OPERA) -
Software Integration
All-in-one tool for SPYRO-based smart dynamic simulation and
optimization of olefins plant
SPYRO- FORTRAN -
• SPYRO (FORTRAN)
Mixed-language (FORTRAN-C++)
• Cracking furnace SPYRO-based dynamic model
(C++)
Very performing ODE/DAE solver (BzzOde,
BzzDae, BzzDaeSparse… BzzMath)
• Smart dynamic simulation (grade change,
DYNSIM)
Full integration in DYNSIM
• Dynamic real-time optimization (multiple
shooting, ROMeo)
Full integration and OPERA synchronization
BzzMath
- C++ -
Dynamic Model- DYNSIM -
47SPYRO-based (Smart) Dynamic
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( ) ySimulation
• C3H6/C2H4 Severity Change from 0.62 to 0.67
FUEL FLOWRATE[kg/h]
DUTY [kcal/h] CH4/C3H6SEVERITY
COIL OUTLETTEMPERATURE (COT)
[°C]
WALLTEMPERATURE
[°C]
C3H6/C2H4SEVERITY
Initial Severity
Arrival Severity
1.34e+007
1.35e+007
1.36e+007
1.37e+007
1.38e+007
1.39e+007
1.4e+007
0 20 40 60 80 100 120
Time [min]
0.9
0.92
0.94
0.96
0.98
1
1.02
1.04
0 20 40 60 80 100 120
Time [min]
0.61
0.62
0.63
0.64
0.65
0.66
0.67
0.68
0.69
0 20 40 60 80 100 120
Time [min]
786
788
790
792
794
796
798
800
802
0 20 40 60 80 100 120
Time [min]
1088
1090
1092
1094
1096
1098
1100
1102
1104
0 20 40 60 80 100 120
Time [min]
3450
3500
3550
3600
3650
3700
3750
3800
0 20 40 60 80 100 120
Time [min]
48SPYRO-based (Smart) Dynamic
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• Severity Change Convergence
3450
3500
3550
3600
3650
3700
3750
3800
0.9 0.92 0.94 0.96 0.98 1 1.02 1.043450
3500
3550
3600
3650
3700
3750
3800
0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69
Fuel Flowrate vs
CH4/C3H6 Severity
Fuel Flowrate vs
C3H6/C2H4 Severity
( ) ySimulation
49SPYRO-based (Smart) D-RTO
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( )
As per DYNSIM, UAM
inserted into the ICON
PALETTE (C++ dynamic
library)
508-shoots flowsheet
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5132-shoots flowsheet
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Flavio Manenti, Filip Logist – KU-Leuven
No changes at theROMeo’s interface
52Converging Path
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2000
3000
4000
5000
6000
7000
8000
9000
0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 1.05
F U E L F L O W R A T E
4 SHOOTS
16, 32 SHOOTS
TRADITIONAL
STARTINGPOINT
OPTIMUM
C3H6/C2H4 SEVERITY
No changes at the
traditional control level
53High Benefits, Few Shoots
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Flavio Manenti, Filip Logist – KU-Leuven
3200
3250
3300
3350
3400
3450
3500
3550
3600
3650
3700
3750
0.6 0.65 0.7 0.75 0.8 0.85 0.9
F U E L
F L O W R A T E
32 SHOOTS
16 SHOOTS
C3H6/C2H4 SEVERITY
54Market dynamics
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0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
1.05
0 20 40 60 80 100 120
Time [min]
0.5
0.6
0.7
0.8
0.9
1
1.1
0 20 40 60 80 100 120
Time [min]
• Market dynamics (the current market condition is a higher demand of propylene, thus higher price) imposes a severity change in ethylene/propyleneproduction:
TRADITIONAL
4 SHOOTS
TRADITIONAL
4 SHOOTS
CH4/C3H6C3H6/C2H4
16, 32 SHOOTS
16, 32 SHOOTS
55Severity change
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2000
3000
4000
5000
6000
7000
8000
9000
0 20 40 60 80 100 120
Time [min]
700
720
740
760
780
800
820
0 20 40 60 80 100 120
Time [min]
Coil outlet temperature [°C] of theradiant section of the cracking furnace
Fuel flowrate [kg/h] entering thecracking furnace
TRADITIONAL
4 SHOOTS
TIME [min]
TRADITIONAL
4 SHOOTS
Supposed practical upper bound 16, 32 SHOOTS
16, 32 SHOOTS
56Quantitative comparison
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3000
3200
3400
3600
3800
4000
0 50 100 150 200 250 300
Traditional RTO
TIME [min]
32-shoots D-RTO
2000
2500
3000
3500
4000
4500
5000
0 50 100 150 200 250 300
D-RTO off-spec time
RTO off-spec time
TIME [min]
F U E L F L O W R A T E
• To operate at the optimum conditions dictated by the market, the RTO requiresmore than 2h to accomplish the severity change, whereas the D-RTO requiresabout 1h.
• Consider that severity changes are not only imposed by market dynamics, buteven by feedstock changes, load changes… As a result, frequent severity
changes are required in each coil of each cracking furnace of each olefins plants
1-step traditional RTO
2-steps traditional RTO
32-shoots D-RTO F U E L F L O W R A T E
57Industrial feasibility9000
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D-RTO with ROMeo
• Feasible
• D-RTO is more stable than RTO
• D-RTO halves off-spec periods
• On-line feasibility for the industrial scale
• Computational times are comparable
• No visible changes to the user in ROMeo environment
• No changes to the existing control scheme
• Easy-to-use when implemented (few parameters to be defined)
SEVERITY
2000
3000
4000
5000
6000
7000
8000
0.5 0.6 0.7 0.8 0.9 1 1.1
Upper bound
F U E
L F L O W R A T
E
Corporate level 58
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Flavio Manenti, Filip Logist – KU-Leuven
• Case: Eni Versalis 17 sites, European area (large-scale)
59
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Flavio Manenti, Filip Logist – KU-Leuven
Corporate optimal control
Air separation units
Air Liquide, ItalyLinde Gas, Italy
Manenti et al.Raising the decision-making level to improve the
enterprise-wide production flexibilityAIChE J ournal
59(5), 1588-1598, 2013
Just a premise 60
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Flavio Manenti, Filip Logist – KU-Leuven
• High flexibility/operatibility is useless without corporate control The case of Linde Gas, Terni’s site:
Test preliminare (offline Munich-Arluno-Terni)
Single-site Corporate
Industrial viewpoint 61
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Flavio Manenti, Filip Logist – KU-Leuven
Outlier Detection
Robust methods
Linear/nonlinear Regressions
Performance Monitoring
Yield Accounting
Soft sensing
Data
Reconciliation
Mathematical
Modeling
Dynamic
Simulation
Model
Predictive
Control
Optimization
ModelReduction
DCS, OTS, Plantwide control,Soft sensing, process transients,
grade/load changes
Solvers
Planning
Scheduling
Dynamic optimization
Distributed predictive control
Nonlinear SystemsOptimizers
Differential systems
Stiff systems
ODE,DAE,PDE,PDAE
Efficiency
DecisionsRaw Data
ParallelComputing
Uncertainties
Optimal production
Optimal grade changes
Multi-objectiveReal-time optimization
High accuracy
Reliable process cont rol
Production improvement
Economy
Just in time
Market-driven
Logistics
Corporate
Supply Chain
62
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Flavio Manenti, Filip Logist – KU-Leuven
Available for questions: