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


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


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


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


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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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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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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Systems Interoperability

Truly CAPE-OPEN Heterogeneity and multi-platform

Single communication

protocolMulti-processing

and

Shared-memory advantages

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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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• 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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• 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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• 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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• 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

secchi seminar pasi2005 -...

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