Secchi seminar Pasi2005 -...
Transcript of Secchi seminar Pasi2005 -...
Foz do IguaçuAugust 24th, 2005
Dynamic Simulation
GIMSCOPGroup of Integration, Modeling, Simulation,
Control, and Optimization of Processes
PASI 2005Pan American Advanced Studies Institute Program on Process Systems Engineering
Argimiro R. SecchiChemical Engineering Department
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LocationLocation
BRAZIL
Iguazu Falls Porto Alegre940 km
•
••Gramado (ADCHEM 2006)
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OutlineOutline
• When I need?
• How I use?
• What are the difficulties?
• What are the challenges?
Dynamic Simulation:
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When I need Dynamic SimulationWhen I need Dynamic Simulation
• Batch and semi-batch processes
• Dynamic real-time optimization (D-RTO)
• Process control
• Startups, shutdowns and transitions
• Process intensification
• Teaching and training
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Batch and Semi-Batch ProcessesBatch and Semi-Batch Processes
(Semi-)batch (bio)reactors
1
productreflux
Batch distillation
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Analysis (Davies et al., 2004)
Batch and Semi-Batch ProcessesBatch and Semi-Batch Processes
Recent Examples
of Dynamic Simulation
Parameter estimation (Longhi et al., 2004)
Optimal design (Zhang and Smith, 2004)
Dynamic optimization (Gao and Engell, 2005)
Control (Arpornwichanop et al., 2005)
Start-up operations (Elgue et al., 2004)
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Dynamic Real-Time OptimizationDynamic Real-Time Optimization
Process + Regulatory Control
NMPC
D-RTO / RTO
data pre-processing and dynamic data
reconciliation
model update for D-RTO
model update for NMPC
Production Planing
inferences
u(t)y(t)
Y(t)
u*(t)y*(t)
feed specification, product and market
Model server(rigorous, empiric, hybrid, reduced)
d(t)
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Plant-wide optimization (Tosukhowong et al., 2004)
Recent Examples
of Dynamic Simulation
NMPC (Jockenhövel et al., 2003)
Product transitions (BenAmor et al., 2004)
Dynamic Real-Time OptimizationDynamic Real-Time Optimization
Model update (Yip and Marlin, 2004)
Virtual Analyzers (Ferreira et al., 2003)
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Process ControlProcess Control
Process + Regulatory Control
NMPC
data pre-processing and dynamic data
reconciliation
model update for NMPC
Local Optimization
inferences
u(t)y(t)
Y(t)
targets
Model server(rigorous, empiric, hybrid, reduced)
d(t)
Nonlinear Model-Based Control
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Nonlinear model-based control (Biagiola and Figueroa, 2004)
Process ControlProcess Control
Recent Examples
of Dynamic Simulation
Nonlinear dynamics (Marquardt and Mönnigmann, 2005)
Controller tuning (Trierweiler and Farina, 2003)
Controllability and operability (Hahn et al., 2003)
Control structure design (Skogestad, 2004)
Model reduction (Lakner et al., 2005)
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Process transitions (Srinivasan et al. 2005)
Startups, Shutdowns and TransitionsStartups, Shutdowns and Transitions
Recent Examples
of Dynamic Simulation
Troubleshooting (Bhagwat et al., 2003)
Plant shutdown (Iliuta and Larachi, 2005)
Start-up of distillation column (Reepmeyer et al., 2004)
Safety studies (Molnár et al., 2004)
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Reaction/separation process (Wu et al., 2002)
Process IntensificationProcess Intensification
Recent Examples
of Dynamic Simulation
Autorefrigerated reactors (Toledo et al., 2005)
Reactive distillation (Grüner and Kienle, 2004)
Complex systems (Charpentier and McKenna, 2004)
Oscillatory motion (Gomaa and Taweel, 2005)
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Teaching and TrainingTeaching and Training
Server
Simulator ≈ Plant
Client Trainee
Client Trainee
Client Trainee
TCP/IP
Client Task Manager
Operators trainingClassroom teaching
Simulator Student
TCP/IP
Simulator
Teacher
Simulator Student
Simulator Student
Example: Operator training (Lee et al., 2000)
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How I use Dynamic SimulationHow I use Dynamic Simulation
Several things to chose! Some of them are:
Discretizers OptimizersEstimators
Finite differences
Finite volume
Finite elements
Orth. collocation
Implicit
Explicit
Low-index
High-index
Variational
Math. program.
Sequential
Simultaneous
Least square
Max. likelihood
Local minimum
Global minimum
Taylor-made
Commercial
Modular
Equation-oriented
Simulators Integrators
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Dynamic SimulatorsDynamic Simulators
Modeling and Input Data
Model Building
External ComponentsInput Data
Model Library
Code Generation
Static Library
Editable Model Text Graphic
Internal Editor
Object Oriented
Syntax Highlight
Building Blocks
Connections, ports, types
Compilation Interpretation External interfaces
CAPE-OPEN
Reject illegal data Modes
- Interactive- Batch- Automatic- Data files- Data sets- Multiple formats
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Dynamic SimulatorsDynamic Simulators
Simulation Tool
Proce ssing Simulation Strategy
NumericalPackages
Events
Optimization
Parallel Sequential DirectSolution
ModularSolution
LinearAlg ebra
Direct Iterative,precondit.
Heterog en eit y,Interfaces
CAPE-OPEN
Communication Protoco ls
An alyses
Consist en cy, stab ilit y, sen sib ilit y
TimeDepen dent
D ynamic Stead y St ate
Sparse
Var iab les and Equ ation s
Scalin g
BifurcationDiag rams
Linearizat ion
D ynamicSyst ems
DAE PDE Initialization andRe-init ialization
Índex < 2 Índex > 1
Steady St ate
Multip licit y
Stat eDepen dent
Stru ctural Produ ction Plan ing
Dat a Recon ciliatio n
Paramet er Estimation
Statistical Analysis
PDE
Differentiation
Numeric
Symbolic
Automatic
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A Very Simple Example in Different Environments
A Very Simple Example in Different Environments
Series of isothermal CSTR with first order reaction and PI controller
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MATLABMATLAB
MATLAB script file ODE file
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SIMULINKSIMULINKSIMULINK diagram S-function file
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gPROMSgPROMS
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gPROMSgPROMS
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EMSOEMSO
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Difficulties in Dynamic SimulationDifficulties in Dynamic Simulation
• Reliable models
• Truly standard interfaces
• High-Index DAE systems
• Model consistency:- Degree of Freedom (DoF)- Dynamic Degree of Freedom (DDoF)- Measurement units- Structural non-singularity- Consistent initial condition
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Standard InterfacesStandard Interfaces
CAPE-OPEN
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CAPE OPENCAPE OPEN
Example of CAPE-OPEN: DyOS (Dynamic Optimization Software) -Marquardt’s group (2000)
gPROMS
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CAPE OPENCAPE OPEN
Another example of CAPE-OPEN: EMSO (Environment for Modeling, Simulation andOptimization) - Soares and Secchi (2004)
methanol plant
CORBA Object Bus
EMSO BEMSO A
ESO ESO
EMSO ESO
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High-Index DAE SystemHigh-Index DAE System
Three general approaches:
1) Manually modify the model to obtain a lower index equivalent model
2) Integration by specifically designed high-index solvers (e.g., PSIDE, MEBDFI)
3) Apply automatic index reduction algorithms
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DAE Index ReductionDAE Index Reduction
Proposed algorithm(Soares and Secchi, 2005)
Pantelides' approachFail if the system is structurally singular
Index reduction down to index-one DAE
Search for MSS subsets with respect to ALL variable derivatives
Minimally Structurally Singular
Check MSS for singularity with respect to the entire set of variables
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DAE Index ReductionDAE Index Reduction
0),(0),',(0),,(
23
12
211
===
yxfyxxfuuxf
Example: optimal control context:
y1 and y2 are desired outputs (specified optimal profiles)
u1 and u2 are the control actions
x is the state variable
Graph for the DAE system After one analysis step One step with derivatives Singularity detected
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Consistency AnalysisConsistency Analysis
Check → Measurement units→ Structural non-singularity→ Consistent initial conditions
Degree of Freedom (DoF)
= 0 (for simulation) > 0 (for optimization)
Dynamic Degree of Freedom (DDoF)
= number of given initial conditions
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Consistency AnalysisConsistency Analysis
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Measurement UnitsMeasurement Units
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Structural SingularityStructural Singularity
Example: FLASH
• Usual case: given feed condition and heat duty (q), solve the flash → index-onesystem → simple solution.
• Optimal control problem: given feed condition and vessel temperature, find q → index-two system → not solved by available commercial simulators.
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Using AspenDynamics / ACMUsing AspenDynamics / ACM
Reports system singularity:
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Using gPROMSUsing gPROMS
Detects a high-index problem and gives the following error message:
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Using EMSOUsing EMSO
Uses the proposed index reduction algorithm and finds the optimal solution:
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It also solves the well-known index-three batch distillation problem.(Logsdson and Biegler, 1993)
1
1357910
Using EMSOUsing EMSO
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Inconsistent Initial ConditionInconsistent Initial Condition
Example: classical pendulum problem Inconsistent initial condition:
L = 1
( , , ) 0F t y y′ = (0, (0), (0)) 0F y y′ =
(1)
(2)
(3)
(4)
(5)
Differentiating (5) and using (1) and (2): 0x w y z⋅ + ⋅ = (0) (0) (0) (0) 0x w y z⋅ + ⋅ =
Differentiating (6) and using (1)–(5):
(6)
(7)
Differentiating (7) and using (2), (3), (4), (6):
2 2 2w z T L g y+ + ⋅ = ⋅ 2 2 2(0) (0) (0) (0)w z T L g y+ + ⋅ ≠ ⋅
2' 3 /T g z L= − ⋅ (8) 2'(0) 3 (0) /T g z L= − ⋅
OK!
NOT OK!
Hidden constraints:
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Example: classical pendulum problem(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
10 variables (y, y´)
8 equations
2 DDoF
(1)
(2)
(3)
(4)
(5)
(1)
(2)
(3)
(4)
(6)
(1)
(2)
(3)
(4)
(7)
(1)
(2)
(3)
(4)
(8)
Index 3 Index 2
Index 1 Index 0
Satisfies the inconsistent I.C.
But no
t any
pair!
Inconsistent Initial ConditionInconsistent Initial Condition
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Error PropagationError Propagation
x
index-0 solver vs index-3 solver Drift-off effect
L = 0.9 m , g = 9.8 m/s2 ∴ I.C.: x(0) = 0.9 m and w(0) = 0
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Dynamic Simulation ModelDynamic Simulation Model
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ChallengesChallenges
Robust strategies for on-line updating of dynamic models
Dynamic data reconciliation Parameter estimation
Related topics:• Hybrid and rigorous modeling• Order reduction of nonlinear models• Fault diagnosis• NMPC tuning strategies
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ChallengesChallenges
DAE solvers
High-index (>3) solvers
Automatic/guided selection of feasible set of variables
for initial condition
Index reduction with trajectory projection
onto hidden manifold
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ChallengesChallenges
Integrated tool for D-RTO
Multi-level dynamic simulator
Simultaneous data reconciliation and
parameter estimation tool
Dynamic optimizer with adaptive grid
Self-tuned nonlinear model predictive controller
Specialist system
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ChallengesChallenges
Systems Interoperability
Truly CAPE-OPEN Heterogeneity and multi-platform
Single communication
protocolMulti-processing
and
Shared-memory advantages
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ChallengesChallenges
Complex systems
Process simulation + CFD
Multi-scale modeling + simulation tools
Bifurcation + control system design
Hybrid modeling
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OptimizationOptimization,,
RTO, DRTORTO, DRTO
Design
Design
Adv
ance
d
Adv
ance
d
Con
trol
Con
trol
Training,
Training, SafetySafety
Decision
Decision
Making
Making
Data
Data
Reconciliation
Reconciliation
Inferences
Inferences
Hierarchical Modeling
Hierarchical Hierarchical ModelingModeling
OptimizationOptimization,,RTO, DRTORTO, DRTO
DesignDesign
Advanced
Advanced
Control
Control
Training, Training,
SafetySafety
Decisio
n
Decisio
n M
aking
Mak
ing
Dat
a D
ata
Rec
onci
liatio
nR
econ
cilia
tion
Inferences
Inferences
ProcessProcessProcess
VisionVisionIntegrated
Environment Dual Space
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ReferencesReferences
• Al-Arfaj, M. and W.L. Luyben. Comparison of Alternative Control Structures for an Ideal Two-Product Reactive Distillation Column. Ind. Eng. Chem. Res., 39, 3298–3307 (2000).
• Arpornwichanop, A., P. Kittisupakorn and I.M. Mujtaba. On-line Dynamic Optimization and Control Strategy for Improving the Performance of Batch Reactors. Chemical Engineering and Processing, 44, 101–114 (2005).
• BenAmor, Z., F.J. Doyle III and R. McFarlane. Polymer Grade Transition Control using Advanced Real-Time Optimization Software. Journal of Process Control, 14, 349–364 (2004).
• Bhagwat, A., R. Srinivasan and P.R. Krishnaswamy. Fault Detection During Process Transitions: a Model-Based Approach. Chemical Engineering Science, 58, 309–325 (2003).
• Biagiola, S.I. and J.L. Figueroa. Application of State Estimation Based NMPC to an Unstable Nonlinear Process. Chemical Engineering Science, 59, 4601–4612 (2004).
• Biegler, L.T., A.M. Cervantes and A. Wächter. Advances in Simultaneous Strategies for Dynamic Process Optimization. Chemical Engineering Science, 57, 575–593 (2002).
• Charpentier, J.C. and T.F. McKenna. Managing Complex Systems: Some Trends for the Future of Chemical and Process Engineering. Chemical Engineering Science, 59, 1617–1640 (2004).
• Costa Jr., E.F., R.C. Vieira, A.R. Secchi and E.C. Biscaia Jr. Dynamic Simulation of High-Index Models of Batch Distillation Processes. Journal of Latin American Applied Research, 32 (2) 155–160 (2003).
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ReferencesReferences
• Davies, M.L., I. Schreiber and S.K. Scott. Dynamical Behaviour of the Belousov–Zhabotinsky Reaction in a Fed-Batch Reactor. Chemical Engineering Science, 59, 139–148 (2004).
• Elgue, S., L. Prat, M. Cabassud, J.M. Le Lann and J. Cézerac. Dynamic Models for Start-up Operations of Batch Distillation Columns with Experimental Validation. Computers and Chemical Engineering, 28, 2735–2747 (2004).
• Ferreira, L.S., J.O. Trierweiler, A.R. Secchi and S.M. Marcon. Development of a Virtual Analyzer Software for Bioprocesses. AIChE Annual Meeting, San Francisco, CA, USA, p. #107ak (2003).
• Gao, W. and S. Engell. Iterative Set-point Optimization of Batch Chromatography. Computers and Chemical Engineering, 29, 1401–1409 (2005).
• Grünera, S. and A. Kienle. Equilibrium Theory and Nonlinear Waves for Reactive Distillation Columns and Chromatographic Reactors. Chemical Engineering Science, 59, 901–918 (2004).
• Hahn, J., T.F. Edgar and W. Marquardt. Controllability and Observability Covariance Matrices for the Analysis and Order Reduction of Stable Nonlinear Systems. Journal of Process Control, 13, 115–127 (2003).
• Henson, M.A. Dynamic Modeling and Control of Yeast Cell Populations in Continuous Biochemical Reactors. Computers and Chemical Engineering, 27, 1185–1199 (2003).
• Iliuta, I. and F. Larachi. Modeling Simultaneous Biological Clogging and Physical Plugging in Trickle-Bed Bioreactors for Wastewater Treatment. Chemical Engineering Science, 60, 1477–1489 (2005).
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ReferencesReferences
• Jockenhövel, T., L.T. Biegler and A.Wächter. Dynamic Optimization of the Tennessee Eastman Process using the OptControlCentre. Computers and Chemical Engineering, 27, 1513–1531 (2003).
• Kulikov, V., H. Briesen, R. Grosch, A. Yang, L. vonWedel and W. Marquardt. Modular Dynamic Simulation for Integrated Particulate Processes by Means of Tool Integration. Chemical Engineering Science, 60, 2069–2083 (2005).
• Lakner, R., K.M. Hangos and I.T. Cameron. On Minimal Models of Process Systems. Chemical Engineering Science, 60, 1127–1142 (2005).
• Lee, S., I. Jeong and I. Moon. Development of Evaluation Algorithms for Operator Training System. Computers and Chemical Engineering, 24, 1517-1522 (2000).
• Logsdon, J.S. and Biegler, L.T. Accurate Determination of Optimal Reflux Polices for the Maximum Distillate Problem in Batch Distillation. Ind. Eng. Chem. Res., 32 (4) 692-700 (1993).
• Longhi, L.G.S., D.J. Luvizetto, L.S. Ferreira, R. Rech, M.A.Z. Ayub and A.R Secchi. A Kinetic Model for the Kluyveromycesmarxianus Growth using Cheese Whey as Substrate. Journal of Industrial Microbiology, 31 (1) 35–40 (2004).
• Marquardt, W. and M. Mönnigmann. Constructive Nonlinear Dynamics in Process Systems Engineering. Computers and Chemical Engineering, 29, 1265–1275 (2005).
• Martinson, W.S. and P.I. Barton. Distributed Models in Plantwide Dynamic Simulators. AIChE Journal, 47 (6) 1372–1386 (2001).
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ReferencesReferences
• Molnár, A., M. Krajciová, J. Markos and L. Jelemensky. Use of Bifurcation Analysis for Identification of a Safe CSTR Operability. Journal of Loss Prevention in the Process Industries, 17, 489–498 (2004).
• Reepmeyer, F., J.U. Repke and G. Wozny. Time Optimal Start-up Strategies for Reactive Distillation Columns. Chemical Engineering Science, 59, 4339–4347 (2004).
• Skogestad, S. Control Structure Design for Complete Chemical Plants. Computers and Chemical Engineering, 28, 219–234 (2004).
• Soares, R.P. and A.R. Secchi. EMSO: A New Environment for Modeling, Simulation and Optimization. ESCAPE 13, Lappeenranta, Finlândia, 947 – 952 (2003).
• Soares, R.P. and A.R. Secchi. Modifications, Simplifications, and Efficiency Tests for the CAPE-OPEN Numerical Open Interfaces. Computers and Chemical Engineering, 28, 1611–1621 (2004).
• Soares, R.P. and A.R. Secchi, Direct Initialisation and Solution of High-Index DAE Systems, ESCAPE 15, Barcelona, Spain, 157–162 (2005).
• Srinivasan, R., P. Viswanathan, H. Vedam and A. Nochur. A Framework for Managing Transitions in Chemical Plants. Computers and Chemical Engineering, 29, 305–322 (2005).
• Toledo, E.C.V., R.F. Martini, M.R.W. Maciel and R. Maciel Filho. Process Intensification for High Operational Performance Target: Autorefrigerated CSTR Polymerization Reactor. Computers and Chemical Engineering, 29, 1447–1455 (2005).
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ReferencesReferences
• Tosukhowong, T., J.M. Lee, J.H. Lee and J. Lu. An Introduction to a Dynamic Plant-Wide Optimization Strategy for an Integrated Plant. Computers and Chemical Engineering, 29, 199–208 (2004).
• Trierweiler, J.O. and L.A. Farina. RPN tuning strategy for model predictive control. Journal of Process Control, 13, 591–598 (2003).
• Wu, K.L., C.C. Yu, W.L. Luyben and S. Skogestad. Reactor/Separator Processes with Recycles-2. Design for Composition Control. Computers and Chemical Engineering, 27, 401–421 (2002).
• Yip, W.S. and T.E. Marlin. The Effect of Model Fidelity on Real-Time Optimization Performance. Computers and Chemical Engineering, 28, 267–280 (2004).
• Zhang, J. and R. Smith. Design and Optimisation of Batch and Semi-Batch Reactors. Chemical Engineering Science, 59, 459–478 (2004).
DAE Solvers:DASSL or DASSLC: Petzold, l.R. (1989) or Secchi, A.R. and F.A. Pereira (1997), http://www.enq.ufrgs.br/enqlib/numeric/numeric.htmlMEBDFI: Abdulla, T.J. and J.R. Cash (1999), http://www.netlib.org/ode/mebdfi.f
PSIDE: Lioen, W.M., J.J.B. de Swart, and W.A. van der Veen (1997), http://www.cwi.nl/cwi/projects/PSIDE/SUNDIALS: R. Serban et al. (2004), http://www.llnl.gov/CASC/sundials/description/description.html
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International Symposium on Advanced Control of Chemical Processes
April 2-5, 2006http://www.adchem.org
Workshop of Solving Industrial Control and Optimization Problems
April 6-7, 2006http://www.enq.ufrgs.br/sicop2006/
ADCHEM 2006 and SICOP 2006ADCHEM 2006 and SICOP 2006
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Argimiro Resende Secchi, D.Sc.Jorge Otávio Trierweiler, D.Sc.Marla Azário Lansarin, D.Sc.Nilo Sérgio Medeiros Cardozo, D.Sc.André Bello de Oliveira, M.Sc.André Rodrigues Muniz, M.Sc.Andrey Copat, Eng.Adriano Giraldi Fisch, M.Sc.Ariel Kempf, M.Sc.Christiano Daniel Wetzel Guerra, I.C.Cristiano Sá Brito Cardoso, Eng.Débora Jung Luvizetto, Eng.Edson Cordeiro do Valle, M.ScEduardo Fontoura Birnfeld, Eng.Eduardo Guimarães de Magalhães, Eng.Euclides Almeida Neto, M.Sc.Fábio Brião de Oliveira, Eng.
Research GroupResearch GroupGIMSCOP - 2005
Gérson Balbueno Bicca, Eng.Graziela Cestari Silva Grando, Eng.Gustavo Alberto Neumann, M.Sc.Igor Rodacovski, Tec. Inf.Irma Maria Bueno, Sec.Larissa Scherer Severo, Eng.Leandro Porto Lusa, M.Sc.Letícia Caumo, Eng.Luciane da Silveira Ferreira, M.Sc.Luciano Andé Farina, M.Sc.Luís Eduardo Brose Piotrowicz, Eng.Marcelo Beninca, Eng.Marcelo Escobar, Eng.Marcelo Fank Fraga, Eng.Marcelo Farenzena, M.Sc.Marcelo Bohrer Guaritta, Eng.Márcio Ferreira, Eng.
Marcus Darci Rutsatz, Eng.Maurício Carvalho Maciel, I.C.Nina Paula Gonçalves Salau, M.Sc.Paula Betio Staudt, Eng.Rafael de Pelegrini Soares, M.Sc.Rafael Spohr, Eng.Ricardo Guilherme Duraiski, M.Sc.Rodolfo Rodrigues, Eng.Rodrigo Paliga da Rosa, I.C.Samuel Facchin, Eng.Tanise Mori Flores, Eng.Tiago da Silva Osório, I.C.Tiago Fiorenzano Finkler, M.Sc.Tito Lívio Domingues, M.Sc.Vanessa Conz, M.Sc.Vinícius Cunha Machado, M.Sc.Wagner Bertuol Casagrande, I.C.
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Process Simulation LabProcess Simulation Lab•• Chair: Prof. Dr. Chair: Prof. Dr. ArgimiroArgimiro ResendeResende SecchiSecchi•• Phone: +55Phone: +55--5151--33163316--35283528•• EE--mail: mail: [email protected]@enq.ufrgs.br•• http://http://www.enq.ufrgs.br/labs/lasim.htmlwww.enq.ufrgs.br/labs/lasim.html
Process Integration and Control LabProcess Integration and Control Lab•• Chair: Prof. Dr. Jorge Chair: Prof. Dr. Jorge OtOtááviovio TrierweilerTrierweiler•• Phone: +55Phone: +55--5151--33163316--40724072•• EE--mail: mail: [email protected]@enq.ufrgs.br•• http://http://www.enq.ufrgs.br/labs/lacip.htmlwww.enq.ufrgs.br/labs/lacip.html
... thank you for your attention!
PASI 2005Pan American Advanced Studies Institute Program on Process Systems Engineering
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Extra slidesExtra slides
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Maximum cardinality matchingMaximum cardinality matching
breadth-first search, E: equations set, V: variables set, L: lines setThere is a line Ei – Vj if the equation Ei contains the variable Vj
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Index analysis and reductionIndex analysis and reduction