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4th KoMSO Challenge Workshop
Online & Offline Optimal Control of
Chemical and Biotechnological
Processes
– Algorithms, Software and Applications –
23. January 2014
Time:
09:30 – 18:20
BASF SE Ludwigshafen
Venue: Feierabendhaus (Künstlerraum 8)
Leuschnerstraße 47, Ludwigshafen
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
About KoMSO
The Committee for Mathematical Modeling, Simulation and
Optimization (KoMSO) is a strategic alliance that was founded after
the Strategietag Mathematik 2020 (Strategy Day for Mathematics), a
component of the Strategy Dialogue for Mathematics. The latter was
founded by the German Federal Ministry of Education and Research
during the Year of Mathematics 2008. More information is available
at www.komso.org.
Organizing committee
Dr. A. Badinski (Scientific Computing, BASF SE)
Dr. S. Sauer (Scientific Computing, BASF SE, and IWR Heidelberg)
Prof. E. Kostina (University of Marburg)
Dr. A. Schreieck (Scientific Computing, BASF SE)
Contact
Dr. A. Badinski
E-Mail: [email protected]
Phone: +49 621 60-94746
Mobile: +49 174 3480690
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
Idea of the workshop
and expected outcome
Bring together academia and industry to discuss challenges
and share experiences in applying online / offline optimal
control to real world dynamic processes in chemistry and
biotechnology
Identify possible roadmap for new algorithms, software and
modeling workflows: model building – offline optimal control
– model reduction – online optimal control
Plan future activities (e.g. define test suites for different
dynamic optimization problems)
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
Program
9:00
Registration and get together
9:30
Opening
9:40
Challenges – part 1
10:00
Semi-automatic modeling and
model-based optimization and
control of bioprocesses
10:40
coffee break
11:00
Process Modelling for Online
Model-Based Applications
11:40
Design of Chemical Processes
in a Multi-level Dynamic
Optimization Framework
12:20
Closing of the morning session
Lunch break
H. G. Bock
S. Sauer
A. Badinski
A. Badinski
A. Walzenbach
R. King
C. C. Pantelides
K. Sundmacher
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
13:20
Challenges – part 2
O. Naeem
R. Jovanovic
13:35
Challenges – part 3
Modeling for Optimization
13:50
Nonlinear Model Predictive
Control (NMPC) – Recent
Advances and New Challenges
14:30
coffee break
14:50
Robustification of optimizing
control by multi-stage optimization
S. Engell
15:30
Global and Robust Optimization
for Optimal Control
A. Mitsos
16:10
coffee break
16:30
Pseudospectral methods for
solving dynamic optimization
problems, and an introduction to
TOMLAB and PROPT
P. Rutquist
17:10
Model predictive control of semibatch polymerization processes
T. S. Schei
17:50
Plenary Discussion
18:20
End of Workshop
19:00
Dinner
A. Potschka
H. G. Bock
J. P. Schlöder
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
List of Invited Speakers
Prof. Dr.-Ing. habil. Rudibert King
Deputy Director of the Institute of Process
Engineering, TU Berlin
Chair of Measurement and Control
Prof. Dr. Costas Pantelides
Managing Director at Process Systems
Enterprise Limited, London
Professor of Chemical Engineering at
Imperial College, London
Professor Dr.-Ing. Kai Sundmacher
Director of Max Planck Institute for
Dynamics of Complex Technical Systems,
Magdeburg
Full Professor for Process Systems
Engineering, Otto-von-Guericke-University
Magdeburg
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
Prof. Dr. Dres. h. c. Hans Georg Bock
Managing Director of the Interdisciplinary
Center for Scientific Computing (IWR),
Heidelberg University
Initiator and Chairman of KoMSO
Prof. Dr.-Ing. Sebastian Engell
Chair for Process Dynamics and
Operations at the Biochemical and
Chemical Engineering Department, TU
Dortmund
Prof. Ph.D. Alexander Mitsos
Professor for Process Systems
Engineering at the Chemical Engineering
Department (AVT) of RWTH Aachen
Per Rutquist
Senior developer at Tomlab Optimization
AB, Sweden
Dr.-Ing. Tor Steinar Schei
Technical director and Chairman of the
Board at Cybernetica AS, Norway
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
Semi-automatic modeling and
model-based optimization and
control of bioprocesses
Prof. Dr. Rudibert King
TU Berlin
Since a long time, model-based approaches are used to supervise,
control and optimize processes from many different disciplines. In
the talk, experimental results will be shown which highlight specific
problems seen in bioprocesses. Some of these challenges can be
addressed by the design of robust methods, e.g., a robust
optimization. The complexity of biological systems, however, cannot
be completely tackled by a combination of the often used simple
unstructured models and robust approaches. Better models,
respecting the variable responses of living cells, are needed instead.
An efficient and appropriate modeling of biological systems, on the
other side, is still an open issue. Hence, some ideas and open
questions are presented in the talk to support this step, and by this,
to promote model-based approaches in biotechnology.
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
Process Modelling for Online
Model-Based Applications
Prof. C. C. Pantelides
Process Systems Enterprise, London
Online Model-Based Applications (OMBAs) comprise a wide range
of applications used in real-time support of plant operations, from
process monitoring and forecasting to real-time optimisation and
model-predictive control. Most current OMBAs use simplified
models, often of an empirical or statistical nature. However, in
principle, there may be significant advantages in employing more
sophisticated “first-principles” models (FPMs) of the type that are nowadays increasingly used for offline applications. When properly
constructed and validated, such models are capable of providing
better representations of process behaviour over wider ranges of
operation, thereby leading to improved OMBA performance.
Moreover, re-using offline models in an online context leverages
investment in high-fidelity modelling across the process lifecycle.
In this presentation, we discuss some of the issues related to the
successful deployment of FPMs within OMBAs, and consider the
requirements that this imposes on the architecture and capabilities
of the underlying modelling tools. We note that achieving the
necessary robustness and efficiency may require the use of models
that vary in both scope and detail over time, and consider
techniques for deriving such models from a master FPM
automatically and in real time.
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
Design of Chemical Processes in
a Multi-level Dynamic
Optimization Framework
Prof. Dr. Kai Sundmacher
Max Planck Institute for Dynamics of
Complex Technical Systems, Magdeburg
University Magdeburg
Optimal process design is of paramount importance for costeffective, sustainable production processes in the chemical
industries. For this reason, during the last five years, our group has
developed a multi-level design approach called “Elementary Process Functions” which starts from the identification of optimal
process route of matter elements in the thermodynamic state space
[1-3]. The optimal route is determined as the solution of a dynamic
optimization problem where the fluxes of mass, energy and
momentum are used as manipulated variables (Level 1). In the
second step, the fluxes are expressed by kinetic laws and the
dynamic optimization problem is solved under constraints for the
rate coefficients and the conjugated thermo-dynamic driving forces
(Level 2). In the third step, the optimal process route obtained on
level 2 is translated into an optimal apparatus design by suitable
parameterization of the control functions (Level 3). This design
approach is illustrated by means of several process examples (SO2
oxidation, hydroformylation of alkenes, steam reforming). Along this
story line, the harvesting of the necessary reaction kinetic
information will be discussed and some experiences with the
numerical solution of challenging dynamic optimization problems will
be reported.
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
References
[1] Freund, H., Sundmacher, K., Towards a methodology for the
systematic analysis and design of efficient chemical processes,
Part 1. From unit operations to elementary process functions,
Chem. Eng. Process. 47 (2008) 2051-2060.
[2] Peschel, A., Freund, H., Sundmacher, K., Methodology for the
design of optimal chemical reactors based on the concept of
elementary process functions, Ind. Eng. Chem. Res. 49 (2010)
10535-10548.
[3] Hentschel, B., Peschel, A., Freund, H., Sundmacher, K.,
Simultaneous design of the optimal reaction and process
concept for multiphase systems, Chem. Eng. Sci. (2013),
available online: http://dx.doi.org/10.1016/j.ces.2013.09.046
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
Modeling for Optimization
Dr. Andreas Potschka
Interdisciplinary Center for Scientific Computing (IWR),
University of Heidelberg
Existing mathematical models for process simulation may not be
suitable for process optimization. Starting from an example of a
distillation column with vapor recompression supplied by BASF, I
point out modeling habits that become pitfalls when employed in an
optimization setting and discuss technical and social approaches to
address these issues. Finally, I highlight the methodological
challenges of a metamodeling problem for plant design under
economic criteria that incorporates complex constraints, e.g., the
behavior under load changes.
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
Nonlinear Model Predictive
Control (NMPC) – Recent
Advances and New Challenges
Prof. Dr. Hans Georg Bock and Dr. Johannes P. Schlöder
Interdisciplinary Center for Scientific Comp uting (IWR),
University of Heidelberg
One of the most promising control approaches in process
engineering is the idea to use detailed, possible first principle
differential equation models to predict the performance of a process
under perturbations over a certain horizon, and to compute an
optimal decision and control strategy subject to constraints on-line.
However, even the fastest "all-at-once" optimization BVP solvers
available today are much too slow to provide a quick enough optimal
control response for fast processes.
The lecture reports on recent progress in the development of
numerical methods for the real-time computation of constrained
closed-loop optimal controls, namely the case of nonlinear model
predictive control (NMPC) and moving horizon estimation of states
and parameters (MHE), for processes governed by large systems of
Differential Algebraic Equations (DAE).
Starting from an "all-at-once" off-line approach based on the direct
multiple shooting method, inexact SQP or Gauss-Newton methods
and a perturbation embedding, a "real-time iteration" (RTI) approach
is presented that uses the latest process data at each iteration of the
optimization process. Through precomputation of Hessians,
gradients and QP factorizations the response time to perturbations
of states and systems parameters is minimized. It is shown how this
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
approach is further drastically accelerated by special algorithmic
schemes for on-line feasibility and optimality improvement (FOI),
and a primal-dual online active set strategy (OASeS). The new
methods are capable of solving constrained optimal (closed-loop)
control problems even of very fast processes modeled by DAE online.
Practical applications of NMPC require a simultaneous state and
parameter estimation as input for the computation of the closed-loop
NMPC control in real-time. It is shown how the principles of the realtime iteration approach can be extended to the dual problem of a
moving horizon estimation of parameter and states, and how to
effectively nest the real-time iteration for both MHE and NMPC. In
order to take the statistical error of the system and parameter
estimates – and potentially other errors in the model - into account,
a robust NMPC approach is developed that is based on a
computationally efficient worst case optimization algorithm.
Furthermore it is discussed how controls can be used to excite the
system in order to allow for a targeted state and parameter
estimation and a reliable optimal overall performance of the process.
Theoretical results as well as applications to real-life problems in
chemical engineering will be presented.
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
Robustification of optimizing control
by multi-stage optimization
M. Sc. Sergio Lucia and Prof. Dr. Sebastian Engell
TU Dortmund
A key challenge in the development of model-based optimizing
control solutions is to cope with the inevitable mismatch between the
model which is used in the controller and the real behaviour of the
plant, called the robustness issue in control theory. Several
approaches to the robustification of optimizing control have been
proposed, e.g. tracking of necessary conditions of optimality and
modifier adaptation for batch processes and min-max MPC and
tube-based MPC for continuous processes. In our recent work, we
proposed a new method to compute control sequences which is
based on the representation of the uncertainties by means of a
scenario tree and employs multi-stage optimization which means
that it is taken into account that future control moves can be adapted
to the realization of the uncertainty (called recourse) and the
optimization of the first move(s) is performed under the assumption
of such an adaptation. If the model of the uncertainty is correct, this
approach provides the optimal solution to the problem of recursive
optimization under uncertainty, considering of the real-time flow of
information correctly.
We demonstrate the potential of our method for the example of a
polymerization reactor that was provided by BASF in the context of
the EU-project EMBOCON and show that realistic problems with
several uncertain parameters can be handled in real time using
advanced numerical tools, in the example CasADi from TU Leuven
(Andersson, Diehl et al.).
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
Global and Robust Optimization
for Optimal Control
Prof. Alexander Mitsos
RWTH Aachen University
In this talk we first discuss recent advances in deterministic global
optimization for nonlinear programs. Special emphasis is given to
problems with large number of equations but relatively small number
of degrees of freedom since these are often encountered in practice.
We also discuss recent advances in embedded problems: bilevel
and semi-infinite as well as problems with differential-algebraic
equation systems. We then revisit the connection of semi-infinite
and dynamic optimization programs and use a recent algorithm for
semi-infinite programs to develop an efficient algorithm that can
rigorously guarantee satisfaction of path constraints. We discuss
very challenging formulations that include both dynamic systems
and uncertainty. We finally discuss challenges in applying to
industrial case studies.
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
Pseudospectral methods for
solving dynamic optimization
problems, and an introduction to
TOMLAB and PROPT
Per Rutquist and Kenneth Holmström
Tomlab Optimization
Pseudospectral methods have been very successful in solving
trajectory optimization problems in various disciplines. This talk will
give a brief introduction to these methods, their advantages and
drawbacks and the types of systems that can be modeled.
TOMLAB is a software package for solving applied optimization
problems in Matlab. Within this software, the PROPT module
handles dynamic constraints, allowing ordinary differential equations
to be entered in straight-forward Matlab syntax, and solved using
state of the art numeric solvers. We will look at a few examples of
optimization of chemical and biotechnological processes, and
discuss potential problems and pitfalls as well as good practices.
Finally we will also talk about how PROPT can be used as a starting
point for on-line optimal control methods, such as model-predictive
control.
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
Model predictive control of
semi-batch polymerization
processes
Tor Steinar Schei and Peter Singstad
CYBERNETICA
Semi-batch polymerization processes are generally highly
exothermal and nonlinear processes which are operated over wide
ranges of operating conditions. Such processes offer great
incentives for off-line and on-line dynamic optimizations with the
purpose of minimizing batch cycle times, subject to available cooling
capacities, while fulfilling quality constraints on the final polymer or
pre-polymer. Accurate temperature control should be achieved in
spite of rapid variations in reaction heat caused by the dosing of
reactants.
Two different polymerization processes will be considered in this
talk. The first is the condensation polymerization of phenol and
formaldehyde to form phenolic resins of various grades. Cybernetica
has developed a system for nonlinear model predictive control (NMPC) of this process based on ‘first-principles’ models, which has been implemented in a number of plants. The objective of the NMPC is to achieve accurate control of reactor temperature,
according to a predefined temperature reference path, by controlling
the cooling utilities and the dosing of reactants, while simultaneously
minimizing the batch cycle time.
The second process is the suspension PVC polymerization process.
A system for ‘run-to-run’ optimization of initiator dosing and a nonisothermal temperature reference path has previously been
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
developed, in addition to a N-MPC system for temperature control.
In order to improve performance further, by allowing repetitive reoptimization of the remaining part of a batch, the N-MPC system is
planned to be extended with an additional optimization level; The
upper level will continuously re-optimize the remaining part of a
batch, including the dosing of initiators and inhibitors, as well as the
temperature reference path which will be input to the lower level NMPC controller. This new control technology is expected to be
generally applicable to semi-batch polymerization processes with
tight terminal quality constraints.
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
List of Participants
Prof. Dr. Angelika Altmann-Dieses
Hochschule Karlsruhe
Dr. Norbert Asprion
BASF SE
Dr. Alexander Badinski
BASF SE
Dr. Achim Besser
BASF SE
Prof. Dr. Christian Bischof
TU Darmstadt
Prof. Dr. Hans Georg Bock
IWR Heidelberg
Dr. Michael Bortz
Fraunhofer ITWM
Dr.-Ing. Ala Bouaswaig
BASF SE
Adrian Bürger
Hochschule Karlsruhe
Matteo Cicciotti
BASF SE
Holger Diedam
Uni Magdeburg
Prof. Dr. Sebastian Engell
TU Dortmund
Dr. Weihua Gao
TU Dortmund
Kathrin Hatz
IWR Heidelberg
Christian Hoffmann
IWR Heidelberg
Dr. Julia Hofinger
BASF SE
Alireza Hosseini
BASF SE
Prof. Oleg Iliev
Fraunhofer ITWM
Dennis Janka
IWR Heidelberg
Michael Jung
Uni Magdeburg
Prof. Dr. Rudibert King
TU Berlin
Robert Kircheis
IWR Heidelberg
Dr. Stefan Körkel
IWR Heidelberg
Manuel Kudruss
IWR Heidelberg
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
Dr. Robert Lee
BASF SE
Dr. Bo Liao
BASF SE
Sergio Lucia
TU Dortmund
Prof. Alexander Mitsos
RWTH Aachen
Fernando Moreno Leira
BASF SE
Marcus Nohr
BASF SE
Yury Orlov
BASF SE
Prof. Dr. Costas Pantelides
Process Systems Enterprise
Dr. Radoslav Paulen
TU Dortmund
Dr. Uwe Piechottka
Evonik Industries AG
Dr. Andreas Potschka
IWR Heidelberg
Dr. Michael Rieger
BASF SE
Dr.-Ing. Matthias Roth
BASF SE
Per Rutquist
Tomlab Optimization
Dr. Simeon Sauer
IWR Heidelberg / BASF SE
Dr. Ansgar Schäfer
BASF SE
Tor Steinar Schei
CYBERNETICA
Dr. Johannes P. Schlöder
IWR Heidelberg
Andreas Sommer
IWR Heidelberg
Prof. Dr. Kai Sundmacher
MPI Magdeburg
Dr. Karl-Heinz Wassmer
BASF SE
Christoph Weiler
IWR Heidelberg
Michael Willenbacher
BASF SE
Christoph Zimmer
BIOMS / IWR Heidelberg
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
Addresses
Workshop venue
BASF SE Ludwigshafen
Feierabendhaus (conference room „Künstlerraum 8“)
Leuschnerstraße 47
67063 Ludwigshafen
Dinner restaurant
„Gesellschaftshaus der BASF“ (Casino)
Wöhlerstraße 15
67063 Ludwigshafen
Conference hotel
Europa Hotel
Ludwigsplatz 5-6
67059 Ludwigshafen
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes
KoMSO Workshop - online & offline optimal control of chem. and biotechn. processes