manenti - process control.ppt [modalità compatibilità]
TRANSCRIPT
Optimal controlIndustrial applications
Flavio Manenti, Dept. CMIC “Giulio Natta”
Flavio Manenti, Politecnico di Milano
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) consisting of p sampling times
Receding horizon methodology (moving horizon, not rolling horizon)
Manenti, Considerations on Nonlinear Model Predictive Control Techniques, COMPUTERS & CHEMICALENGINEERING, 35(11), 2491-2509, 2011.
Flavio Manenti, Politecnico di Milano
3Integration Pyramid
PlantManagement
Maintenanceand Production
Management
Enterprise Management
Field
Conventional Control
Advanced Control (NMPC)
Real Time Dynamic
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 TARy 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(NMPC)
Real TimeDynamic
Optimization
Flavio Manenti, Politecnico di Milano
4Algorithm
• Model Predictive Control• MPC
Plant
Flavio Manenti, Politecnico di Milano
5Receding horizon methodology
t
u1,1
u2,1
u3,1un,1
Set-point
Manipulated Variable
PLANT
MODEL
Controlled Variable
u1
HPHC
u2
u3
HD
1
HPHC
u1
u2
u3
2
HPHC
u1u2 u3
3
Flavio Manenti, Politecnico di Milano
6Industrial viewpoint
Outlier DetectionRobust methodsLinear/nonlinear RegressionsPerformance MonitoringYield AccountingSoft sensing
DataReconciliation
MathematicalModeling
DynamicSimulation
ModelPredictive
Control
Optimization
ModelReduction
DCS, OTS, Plantwide control, Soft sensing, process transients,
grade/load changes
Solvers
PlanningScheduling
Dynamic optimizationDistributed predictive control
Nonlinear SystemsOptimizers
Differential systemsStiff systems
ODE,DAE,PDE,PDAEEfficiency
DecisionsRaw Data
ParallelComputing
UncertaintiesOptimal production
Optimal grade changesMulti-objective
Real-time optimizationHigh accuracy
Reliable process controlProduction improvement
EconomyJust in time
Market-drivenLogistics
CorporateSupply Chain
Flavio Manenti, Politecnico di Milano
NMPCPET plant
7
Manenti, RovaglioIntegrated multilevel optimization in large-scale poly(ethylene terephthalate) plants
Industrial & Engineering Chemistry Research47(1), 92-104, 2008.
Flavio Manenti, Politecnico di Milano
8Case Study: PET Plant
Flavio Manenti, Politecnico di Milano
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
Flavio Manenti, Politecnico di Milano
10Frequent Grade Changes
Grade A: PET as textile fibres (melt processI.V. = 0.55 ÷ 0.65 dl/g)
Grade B: PET for bottles production (bottlegrade I.V. = 0.72 ÷ 0.85 dl/g)
Grade C: PET for special fibres (tire-cordresins I.V. = 0.95 ÷ 1.05 dl/g)
Flavio Manenti, Politecnico di Milano
11Comparison
0.440
0.445
0.450
0.455
0.460
0.465
0.470
0 500 1000 1500 2000 2500 3000
IV I
P (
dl/g
)
Time (min)
0.6000.6100.6200.6300.6400.6500.6600.6700.680
0 500 1000 1500 2000 2500 3000
IV H
P (
dl/g
)
Time (min)
0.000.200.400.600.801.001.201.40
0 500 1000 1500 2000 2500 3000
P H
P (
mm
Hg)
Time (min)
0.600.801.001.201.401.601.802.00
0 500 1000 1500 2000 2500 3000
P I
P (
mm
Hg)
Time (min)
NMPC
NMPC
Flavio Manenti, Politecnico di Milano
12Comparison
0.6400.6500.6600.6700.6800.6900.7000.710
0 500 1000 1500 2000 2500 3000
IV P
HC
R (
dl/g
)
0.7600.7700.7800.7900.8000.8100.8200.8300.840
0 500 1000 1500 2000 2500 3000
IV S
SP (
dl/g
)
NMPC
NMPC
Time (min)
Time (min)
Flavio Manenti, Politecnico di Milano
NMPCEthylene splitter
Eni, Italy
13
Flavio Manenti, Politecnico di Milano
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
Flavio Manenti, Politecnico di Milano
15Validation
Reflux flowrate change effect on overhead composition and on temperature at tray #5
0,0100,0200,0300,0400,0500,0600,0700,0800,0900,0
1000,0
89,40 89,60 89,80 90,00 90,20 90,40 90,60
Overhead im
purities [ppm
]
Reflux flowrate [ton/h]
Reflux flowrate change effects on overhead stream impurities
SimulationPlant
‐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
Tray 5 tem
perature [°C]
Reflux flowrate [ton/h]
Reflux flowrate change effects on temperature at tray #5
SimulationPlant
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
Overhead im
purities [ppm
]
Boil‐up flowrate [ton/h]
Boil‐up flowrate change effects on overhead stream impurities
SmulationPlant
‐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
Tray 5 te
mpe
rature [°C]
Boil‐up flowrate [ton/h]
Boil‐up flowrate change effects on temperature at tray #5
SimulationPlant
Boil-up flowrate change effect on overhead composition and on temperature at tray #5
Flavio Manenti, Politecnico di Milano
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
Reflu
x flo
w ra
te [lbm
ol/h]
Time [h]
Reflux flow rate
PIDMCNMPC
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
Ethylene
molar fractio
n [‐]
Time [h]
Ethylene molar fraction in cut stream
PIDMCNMPCSP 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
Etha
ne molar fractio
n [‐]
Time [h]
Ethane molar fraction in bottom stream
PIDMCNMPCSP 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 60,0Rebo
iler the
rmal duty [1.E+06 BT
U/h]
Time [h]
Reboiler thermal duty
PIDMCNMPC
Flavio Manenti, Politecnico di Milano
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
Etha
ne molar fractio
n [‐]
Time [h]
Ethane molar fraction in bottom stream
PIDMCNMPCSP Bottom
39,5040,0040,5041,0041,5042,0042,5043,0043,5044,0044,5045,00
0,0 5,0 10,0 15,0 20,0 25,0 30,0Rebo
iler the
rmal duty [1.E+06 BT
U/h]
Time [h]
Reboiler thermal duty
PIDMCNMPC
9000
9200
9400
9600
9800
10000
10200
10400
10600
0,0 5,0 10,0 15,0 20,0 25,0 30,0
Reflu
x flo
w ra
te [lbm
ol/h]
Time [h]
Reflux flow rate
PIDMCNMPC
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
Ethylene
molar fractio
n [‐]
Time [h]
Ethylene molar fraction in cut stream
PIDMCNMPCSP Distillate
0,9800
0,9850
0,9200
0,9250
Flavio Manenti, Politecnico di Milano
18Regulation problem (feed composition 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
Ethylene
molar fractio
n [‐]
Time [h]
Ethylene molar fraction in cut stream
PIDMCNMPCSP 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
Etha
ne molar fractio
n [‐]
Time [h]
Ethane molar fraction in bottom stream
PIDMCNMPCSP 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
Reflu
x flow ra
te [lbm
ol/h]
Time [h]
Reflux flow rate
PIDMCNMPC
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
Rebo
iler the
rmal duty [BTU
/h]
Time [h]
Reboiler thermal duty
PIDMCNMPC
Flavio Manenti, Politecnico di Milano
19Regulation problem (feed composition 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
Ethylene
molar fractio
n [‐]
Time [h]
Ethylene molar fraction in cut stream
PIDMCNMPCSP 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
Etha
ne molar fractio
n [‐]
Time [h]
Ethane molar fraction in bottom stream
PIDMCNMPCSP 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
Reflu
x flow ra
te [lbm
ol/h]
Time [h]
Reflux flow rate
PIDMCNMPC
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
Rebo
iler the
rmal duty [BTU
/h]
Time [h]
Reboiler thermal duty
PIDMCNMPC
0,9900
0,9905
0,9500
0,9550
0,9500
0,9550
Flavio Manenti, Politecnico di Milano
Self-adaptive NMPCDeisobutanizer
Mongstad Refinery, Norway
20
Dones, Manenti, Preisig, Buzzi-FerrarisNonlinear Model Predictive Control: a Self-
Adaptive ApproachIndustrial & Engineering Chemistry Research
49(10), 4782-4791, 2010
Flavio Manenti, Politecnico di Milano
Unit 21
Flavio Manenti, Politecnico di Milano
Spoiled Jacobian 22
Flavio Manenti, Politecnico di Milano
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
conc
entra
tion
iC4
top
[%]
time [s]
NMPC with full dynamic modelNMPC with 5-dynamic-trays model
ANMPC
360
365
370
375
380
385
390
395
400
0 500 1000 1500 2000 2500 3000
reflu
x st
ream
[mol
/s]
time [s]
NMPC with full dynamic modelNMPC with 5-dynamic-trays model
ANMPC
4
6
8
10
12
14
16
0 500 1000 1500 2000 2500 3000
num
ber o
f dyn
amic
tray
s us
ed in
the
mod
el
time [s]
Flavio Manenti, Politecnico di Milano
D-RTOOlefins plant
Invensys, USA
24
Manenti et al.Process Dynamic Optimization Using ROMeo
Computer Aided Chemical Engineering29, 452-456, 2011
Flavio Manenti, Politecnico di Milano
25Industrial perspective
Outlier DetectionRobust methodsLinear/nonlinear RegressionsPerformance MonitoringYield AccountingSoft sensing
DataReconciliation
MathematicalModeling
DynamicSimulation
ModelPredictive
Control
Optimization
ModelReduction
DCS, OTS, Plantwide control, Soft sensing, process transients,
grade/load changes
Solvers
PlanningScheduling
Dynamic optimizationDistributed predictive control
Nonlinear SystemsOptimizers
Differential systemsStiff systems
ODE,DAE,PDE,PDAEEfficiency
DecisionsRaw Data
ParallelComputing
UncertaintiesOptimal production
Optimal grade changesMulti-objective
Real-time optimizationHigh accuracy
Reliable process controlProduction improvement
EconomyJust in time
Market-drivenLogistics
CorporateSupply Chain
Dynamic Optimization
Flavio Manenti, Politecnico di Milano
26Tools
Outlier DetectionRobust methodsLinear/nonlinear RegressionsPerformance MonitoringYield AccountingSoft sensing
DataReconciliation
MATHEMATICALMODELING
DynamicSimulation
DynamicOptimization
Optimization
ModelReduction
DCS, OTS, Plantwide control, Soft sensing, process transients, grade/load changes
Solvers
Enterprise-widePlanningScheduling
Nonlinear SystemsOptimizers
Differential systemsStiff systems
ODE,DAE,PDE,PDAEEfficiency
DecisionsRaw Data
ParallelComputing
Supply ChainManagement
UncertaintiesOptimal production
Optimal grade changesMulti-objective
Real-time optimizationHigh accuracy
Reliable process controlProduction improvement
Just in timeMarket-driven
Conscious MGM
MathematicalModeling
DynamicSimulation
Optimization
DYNSIM
ROMeo
Flavio Manenti, Politecnico di Milano
27Tools
Outlier DetectionRobust methodsLinear/nonlinear RegressionsPerformance MonitoringYield AccountingSoft sensing
DataReconciliation
MATHEMATICALMODELING
DynamicSimulation
DynamicOptimization
Optimization
ModelReduction
DCS, OTS, Plantwide control, Soft sensing, process transients, grade/load changes
Solvers
Enterprise-widePlanningScheduling
Nonlinear SystemsOptimizers
Differential systemsStiff systems
ODE,DAE,PDE,PDAEEfficiency
DecisionsRaw Data
ParallelComputing
Supply ChainManagement
UncertaintiesOptimal production
Optimal grade changesMulti-objective
Real-time optimizationHigh accuracy
Reliable process controlProduction improvement
Just in timeMarket-driven
Conscious MGM
Is it possible?
DYNSIMMathematicalModeling
DynamicSimulation
DynamicOptimization
Optimization
ROMeo
Flavio Manenti, Politecnico di Milano
28The Idea
• On-line Optimization
• Nonlinear MPC
• Data Reconciliation
Tmin
. . : 0; 0
R
M R M Ri i i i
i
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
Tmin
. . : , 0
, 0
R SET R SETi i i i
i
x x x x
s t f
g
uW
x x
x x
Flavio Manenti, Politecnico di Milano
29Example
• Series of three ideal CSTRs
Open-loop
Closed-loop
P-7
P-16
P-21
Flavio Manenti, Politecnico di Milano
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
[mol
/s]
Time
Flavio Manenti, Politecnico di Milano
31Open-loop in DYNSIM
UAM MODELS inserted into the ICON PALETTE(C++ dynamic library)
Flavio Manenti, Politecnico di Milano
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
[mol
/s]
Time
Flavio Manenti, Politecnico di Milano
33Using BzzMath in DYNSIM
No changes at the DYNSIM’s interface
Flavio Manenti, Politecnico di Milano
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
[mol
/s]
Time
0.198
0.199
0.2
0.201
0.202
0.203
0.204
0.205
0 10 20 30 40 50
[mol
/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
[mol
/s]
Time
CV
(SP: 0.1 mol/s)
Flavio Manenti, Politecnico di Milano
35Full Integration (All-in-one)
Flavio Manenti, Politecnico di Milano
36Drag & DropDYNSIM
Flavio Manenti, Politecnico di Milano
37Drag & DropDYNSIM
Flavio Manenti, Politecnico di Milano
38Drag & DropROMeo
Flavio Manenti, Politecnico di Milano
39Drag & DropROMeo
Flavio Manenti, Politecnico di Milano
40Smart Dynamic Simulation with ROMeo
Flavio Manenti, Politecnico di Milano
41D-RTO with Multiple Shooting
Flavio Manenti, Politecnico di Milano
42Friendly Interface for D-RTO
Possibility to give the user to enter any kind of data for D-RTO
Specific D-RTO
TAB
Flavio Manenti, Politecnico di Milano
43Preliminary ComparisonRTO 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
Flavio Manenti, Politecnico di Milano
44Validation Case (Olefins)
• Cracking Furnace (SPYRO-based D-RTO)
Flavio Manenti, Politecnico di Milano
45
Steam Cracking FurnaceFeed
Stack
Damper
Breeching
Convection Section
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
Flavio Manenti, Politecnico di Milano
46
D-RTO- ROMeo (OPERA) -
Software IntegrationAll-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 -
Flavio Manenti, Politecnico di Milano
47SPYRO-based (Smart) Dynamic Simulation• C3H6/C2H4 Severity Change from 0.62 to 0.67
FUEL FLOWRATE [kg/h]
DUTY [kcal/h] CH4/C3H6 SEVERITY
COIL OUTLET TEMPERATURE (COT)
[°C]
WALL TEMPERATURE
[°C]
C3H6/C2H4 SEVERITY
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]
Flavio Manenti, Politecnico di Milano
48
• Severity Change Convergence
3450
3500
3550
3600
3650
3700
3750
3800
0.9 0.92 0.94 0.96 0.98 1 1.02 1.04 3450
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 vsCH4/C3H6 Severity
Fuel Flowrate vsC3H6/C2H4 Severity
SPYRO-based (Smart) Dynamic Simulation
Flavio Manenti, Politecnico di Milano
49SPYRO-based (Smart) D-RTO
As per DYNSIM, UAM inserted into the ICON PALETTE (C++ dynamic library)
Flavio Manenti, Politecnico di Milano
508-shoots flowsheet
Flavio Manenti, Politecnico di Milano
5132-shoots flowsheet
No changes at the ROMeo’s interface
Flavio Manenti, Politecnico di Milano
52
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
Converging Path
FUEL
FLO
WR
ATE
4 SHOOTS
16, 32 SHOOTS
TRADITIONAL
STARTING POINT
OPTIMUM
C3H6/C2H4 SEVERITY
No changes at the traditional control level
Flavio Manenti, Politecnico di Milano
53
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
High Benefits, Few Shoots
FUEL
FLO
WR
ATE
32 SHOOTS
16 SHOOTS
C3H6/C2H4 SEVERITY
Flavio Manenti, Politecnico di Milano
54
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• Market dynamics (the current market condition is a higher demand of
propylene, thus higher price) imposes a severity change in ethylene/propylene production:
TRADITIONAL
4 SHOOTS
TRADITIONAL
4 SHOOTS
CH4/C3H6C3H6/C2H4
16, 32 SHOOTS
16, 32 SHOOTS
Flavio Manenti, Politecnico di Milano
55
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]
Severity change
Coil outlet temperature [°C] of the radiant section of the cracking furnace
Fuel flowrate [kg/h] entering the cracking furnace
TRADITIONAL
4 SHOOTS
TIME [min]
TRADITIONAL
4 SHOOTS
Supposed practical upper bound16, 32 SHOOTS
16, 32 SHOOTS
Flavio Manenti, Politecnico di Milano
56
3000
3200
3400
3600
3800
4000
0 50 100 150 200 250 300
Quantitative comparison
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 timeRTO off-spec time
TIME [min]
FUEL
FLO
WR
ATE
• To operate at the optimum conditions dictated by the market, the RTO requires more than 2h to accomplish the severity change, whereas the D-RTO requires about 1h.
• Consider that severity changes are not only imposed by market dynamics, but even 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-RTOFU
EL F
LOW
RA
TE
Flavio Manenti, Politecnico di Milano
57Industrial feasibility
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
9000
0.5 0.6 0.7 0.8 0.9 1 1.1
Upper bound
FUEL
FLO
WR
ATE