Heinz A. Preisig
Process Systems Engineering
Methodological Approach
to
Process Operations & Design
Modelling
Control
Synthesis
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The Subject and its Components
why modelling
experiments
process identification
STMF
modelling concepts
project map
model construction
mixed
continuous & event
dynamic
model
water management
process design
container transport
state discretisation
controller design
supervisor design
DEDS research
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even-dynamic
model
fault analysis
MODELS
 Central role of models
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Models the Central Object
 Models are used for just about everything in chemical engineering
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design
control
kinetics
separations
mixing, flow patterns
etc.
 Different models for different systems and
different models for the same system but for a different purpose
 model simplification methods such as model reduction, time-scaling,
linearisation
 Model components may be re-used for different applications
Model components for the physical structure of units or plant sections only or
process units with particular reaction systems
 model libraries
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MODELLER PROJECT
 Map
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Modeller Project: Overview
transfer
Concepts
enforcing
database
editors
black-box models
database
kinetics
activity
Phys properties
encapsulation
model
Thermo state trans
Species & reactions
Concept-enforcing
model structure
editor
Subsystem selection
algebraic manipulation
ie linearisation
rapid construction,
modification,
validation and
maintenance of
consistent process
models
Library of documented consistent
process model
Assumption handling
Time scale selection
problem instantiation
Instantiated simulation
model
solver
Instantiated design| identification
model
solver
solver
applications
Instantiated optimal control
model
solver
solver
solver
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Model reduction
solver
solver
solver
reduction of
model
development time
and overall effort
by 75 to 90%
return
 was map for MATCH project
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MODELLING METHODOLOGY
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Basic components of networking approach
Components of the mathematical description
Physical and species topology example
…
Goals
 Research:
 Develop structured modelling approach
 Implementation of Model Design Tool
supporting the construction and maintenance of process models
 Key issues:
 Model consistency also under simplifying assumptions
 Support of instantiating specific (mathematical) problems
i.e. simulation, design and identification, and (optimal) control problems
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Why?
 Common experiences:
 Time spent on modelling often greater than time needed for finding solution
 Higher complexity of models
 Many different ways to model the same process
 Our experience
 tool with only implements rudimentary systematic speedup is impressive. Estimated
factor for simulations 10-100.
 Main reason:
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makes you think about time scales assumptions made
aides in model instantiation, an often tricky business
automatic code generation including splicing, thus no transcript errors
transfer of information on model structure into the solver allows for all kind of
conveniences, for example structured data analysis.
• no low-level modelling errors
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What is this all about
 Model construction
 Automatic but not constraint
 Maybe several different models each describing the process from a different point of
view and with different fidelity
 Component software (separation of problem definition, analysis and solving)
 Efficiency and correctness, thus also trust
 Handling complexity
 Generating means to gain insight
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The Modelling Process
theory
primary
model
A
secondary
model
process
assumption
s
A
A
A
A
instantiation
experiment
solution method
verification
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solved
model
Staged Approach
1:
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Physical Topology
Physical view
Network of
primitive systems
and connections
2:
Species Topology
Colour with species
add reactions
3:
Equation Topology
Transfer laws
Kinetics
Geometry
Physical properties
Equations of state
Additional variables
4:
Information Processing
Control
5:
(Simulation) Problem Definition
Instantiate consistently
Apply assumptions
Modelling Basics
plant
primary abstraction
time & length scale
assumptions
explode
simplification
&
abstraction
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Approach:
Network of communicating control volumes
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Modelling Concepts
Process Dynamics
 Control volumes on which conservation principles are applied
 conservation of component mass and energy, momentum etc.
 Transfer between control volumes
 communications between control volumes
Static constitutive equations
 Transposition of extensive quantity
 Generalized reaction concept
 Transformations between different state representations
 link between fundamental quantities and measured quantities or quantities used in
transfer or reactions
 Properties
 the grey box of property approximations.
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Example : Equations
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Model equations for a system s in its
environment e
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Complete system: Stack all systems in the
network up
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Essentials
State information (variables) used to describe transfer and reactions (transpositions) are
mapped from the basic state variables (= conserved quantities).
F
+
flow of
ext quantities
accum
primary
ex quantities
state
R
reaction
rates
transfer of extensive quantity
state variable transformations
transposition of extensive quantity
 Flow matrix F is a function of the structure (from graphical input)
 Transposition (reaction) matrix represents the ratios of the species involved
(stoichiometry)
 Equation structure is analysed on-line
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secondary
state
Basic framework of MODELLER
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Step 1 :
Structure process using control-volume concept
 network of capacities and connections
 physical topology
Step 2 :
Define species distribution using species and potential
reactions
 colouring of the physical topology
 species topology
Step 3 :
Define nature of network, the detailed mechanisms
• transfer laws
• kinetics
• state variable transformations
• properties (species, reactions and transfers)
• geometry
• assumptions: fast reaction, transfer and capacity
 equation topology
Example of Physical Topology
A
A,C
B
CH
B
R
C
A+BD+E
S
E
hardly any D,E
P
B,C,D,E
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CC
Example of Species Topology
A
A
B
B
A
C
B
A
CH
CH
R
R
C
C
E
CC
P
A
C
E
CC
P
B
A
R
A
B
CH
R
C
E
P
E
CH
R
C
CC
P
D
B
C
CC
CC
P
CH
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CH
R
E
B
E
CC
P
E
S
Completion of the Model
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Step 4 :
Adding control
 control topology
Step 5 :
Model simplification
 derived secondary models
Step 6 :
Instantiation of problem
 mathematical problem to be solved
Step 7 :
Translation into target language specific to solver plus
solver parameter instantiation
 mathematical | numerical problem to be solved
Stage 4 : Add Controller
CV
CH
B
A
 level
R
temp
CT
C
E
CC
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P
Typical assumptions
 Make late order of magnitude assumptions:
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constant volume
{unknown | fast | large} flows
fast reactions
fast process hydraulics
 Three key assumptions:
 fast process compared to flows and reactions
 negligible capacity effect, a singular perturbation problem
 fast flows with no constraints on magnitude
 first assumptions
 equilibrium
 fast reaction
 reaction equilibrium for fast parts
(discussion see ACC 2002)
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Steady state assumptions
The state of system s is solely a function of the state of the
environment
 reduces this part of the network to a connection, that is, the state
of this system can be eliminated, if this is algebraically solvable.
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Fast flows & Equilibrium
 We augment the description with the an equilibrium assumption for two systems,
that is equations of the type:
which must be solvable for the fundamental state vector x.
 This introduces an index problem, which can be resolved by first splitting the flow
term into two separating the unknown flows for which an equilibrium assumption is
made:
 Next these unknown flow term is eliminated by multiplying the whole equation with
the null matrix of the respective flow matrix:
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Current Status
 Modelling methodology that works for essentially any physical-chemical-biological
process.
 Implementation of this methodology for component mass and energy
 First serious application was a great success. Model building time was cut by one to
two order of magnitude in time
 Program has been transferred to industry together with student
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Achievements
Construct consistent algebraic models
Eliminate transcript errors
Increase turn around
Implement high-level intuitive interface
Minimal number of primitives
Maximal flexibility and coverage
Document everything transparently
Allow for applying late time-scale assumptions
Resolve all index problems
All to manipulate everything except hard facts, which must be a minimum and
as universal as possible.
 Support any level of detail and complexity
 Allow for inheritance | reuse of models components
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Things to be done
 Extension of recursive structures  approximations of distributed systems with
networks of lumped systems
 Implement more physical concepts  impose basic thermodynamic structures on
defined transformations
 Separation of equation topology definition and problem instantiation
 Implement additional model manipulation tools such as time-scaling and
linearisation
 Different target languages (currently MatLab), second one has just been added.
 Applications, applications, applications….
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return
 PhD: Mathieu Westerweele
 Collaboration: Protomation BV
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SUSTAINABLE DESIGN
 Water management in households
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Design: Water Management System for Households
 Is a distributed waste water system
 more economical
 more sustainable
 Can we get new products from human waste
 How about
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flexibility
acceptance and cultural issues
what can be said about the costs and their estimation horizon
effectiveness
problems that can be avoided or are generated
Interest in NEW and SUSTAINABLE processes
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The considered system
Collaboration with Ralf Oterphol, Hamburg
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A flow sheet
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and a Simulink Model
WASTEWATER TRANSPORT:
WATERUSE:
WASTEWATER TREATMENT:
RESULTS:
RESOURCES:
Resources
BinAmount
-KFin (g/d) 1
Biowaste
Bout
Bin B1
B2
Sustainability
indicators
Blackwater
transport
FinAmount-K-
Toilet
Uin (g/d) 2
Onsite treatment
B1
BW2
UinAmount
-K-
BW1
Fin
BW1
B1
Blackwater
treatment 1
BW3
R7
BW4
BW6
BW5
BW4
BW5
BS3
BS4
H2
R2
BS1
Y W4
C1
Disinfection
d (g/d)
D1
D2
D3
D4
D5
H2
H3
H4
H5
c (g/d)
C1
C2
C3
C4
C5
C6
R3 HD2
Kitchen
HD1
D2
C2
HD2
C6
Rin
R1
R2
R3
R4
R5
Y W6 Y Wout
Mass balance
Bout
BGout
BWout
BG2out
BSout
CSO
Y Wout
Eout
GWout
Rout
Oout
Greywater
transport
K3
Y W3
K3 GW1
W4
O2
PH4
GW2
R8
PH2
C3
PH3
Preliminairy GWT
(TS removal)
Primairy GWT
(BOD reduction)
GW2 GW3
GW3 GWout
Washing
D4
H4
W2
Water balance
W3
Outdoor
Fin (g/d)1
Y W5 Y W6
BSout
Blacksludge Blacksludge
transport
treatment 1
Evaporation
C4
FinAmount
Yellow water
treatment
BS5
K2
D3
R4
Rainwatersystem
Yellowwater
transport
BS5
H3 HD1
Personal Hygiene
h (g/d)
BG2out
BS3
Y W3
BS2
Water demand
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BW5
BS4
BS2
CSOout
GW1
D1
Blackwater
treatment 4
BW4
BW3
Uin
Blackwater
treatment 3
BGout
BW2
BS1
Blackwater
treatment 2
B
BS
YW
Rout
BW
GW
CSO
BG
Bin (g/d)
D5
H5
R5
C5
O2
O3
Rainwater
R6
R5 R7
Rout
Reuse
Potential
Achievements
 Design tool
 A simple tank of less than a 250 l fed with sieved rainwater is sufficient to supply
toilet flushing water for two people all year around in the Netherlands.
Saves in the order of 30% drinking water at very little costs.
 Socio-cultural issues are important.
 The Real Challenge:
Can I find new products being derived from (human) waste ?
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Return
 PhD: Annelies Vleuten-Balkema
 Collaboration: Ralf Otterpohl, TU Hamburg-Harburg
 part of Sustainable Technology Program TU-Eindhoven
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MODEL-BASED CONTROLLER DESIGN
 Modelling is the key
 Model reduction based on network analysis
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Storing and moving fresh products (apples, mangoes,…)
Project with ATO, the agriculture research
organisation of the Netherlands
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Abstraction of the storage and transport problem
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Key : time scale assumptions leads to controller
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Time Scale Analysis: Key to Many Problems
 Think about relative dynamics of interaction and processes involved.
 Am I interested in the fast behaviour or the slow behaviour
 Do I need one in order to get the other one
 These thinking leads very often to very significant simplifications of the models and
consequently the application in which it is used (design, control, operations,
identification, ….)
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Return
 PhD: Gerwald Verdijck
 Collaboration with ATO (Dutch Agrotechnical Research Institute, Wageningen)
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DEDS RESEARCH
 Why important for process industry
 What are they
 Some results on control
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Discrete-Event Dynamic Systems
 Natural behaviour
 overflows, switching devices, bursting pipes, unit break down, measurement problems, …
 time-scale assumptions: fast flows, reactions or small capacities (singularly perturbed systems)
 Supervisory control
 continuous plants: start-up and shut-down, change-over
level
 Fault detection
 What can I achieve with simple boundary detection?
 Observer
temp
 Can I reconstruct the continuous trajectory?
 Issues
 practical: safety, availability of models for the continuous plant
 theoretical: discrete-event dynamic models are not deterministic can thus not be inverted for the
design of the controller.
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Event-Based Control
recipe
disturbance
command
event signal
supervisor
controlled plant
state-event detector
state
state
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events
time
example
A toy problem in a toy plant, a demo
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Recursive Control-Invariant Sets
I have control available to keep process in the set of subdomains
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A possible approach to controller design
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The toy works
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Achievements
 Compute the automaton given input event space, continuous process model and
event detector
 linear plants: very simple
 nonlinear plants: mostly simple
 dimensional explosion problem: resolved, not an issue anymore
key: insight in modelling, state-space approach using also observers.
 Some ideas on controller synthesis
 Automaton tailored for fault detection == observation of not measured discrete input
in a nonlinear model, thus can also model process internal faults as being seen
triggered from the outside.
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Return
 PhD thesis : Philips, Yun-Xia Xi
 Collaboration: National University of Singapore
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IDENTIFICATION
 STMF principle
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Identification: STMF
 Kalman filtering
 Spline filters == multi-wavelets (also used for observers)
 Model mismatch of particular interest
plant
input
spline filter
output
spline filter
“derivatives”
“parameter
estimator”
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Return
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Heinz A Preisig
 Education
 Activities
 Pictures
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Education
 Sulzer, Winterthur (CH): Chemical Lab Assistant; wet analytical chemistry, material
sciences, distillation, cristallisation
 Polytechnic Institute (HTL): Chemistry, chemical engineering
 ETH Zuerich: Chemical Engineering
 @ microbiology, signal theory & ODEs & process dynamics, construction &
corrosion
 ETH Zuerich PhD with David Rippin: Identification using Spline-Type Modulating
Functions (being multi-wavelets)
 @ system theory (Kalman), stochastic systems & signal theory, advanced statistics,
linear systems (Mansour), music, history of industrialisation …
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Texas A&M University, Assistant professor; collaboration with C D Holland, R White
University of New South Wales, Sydney; Senior Lecturer
TU-Eindhoven, chair on Systems & Control, physics, chemistry & elec eng
NTNU, chair on Process Systems Engineering
Processes
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Distillation (Sulzer, CD Holland Texas A&M)
Crystallisation (Sulzer)
Membrane process (DuPont, UniLever)
Spark erosion machining (Producer in Switzerland)
Electron accelerator (TU Eindhoven)
Dryer (Dutch starch producer)
Potatoes, mangoes, apples storage and transport (ATO Netherlands)
Bio Reactor (Dutch reactor construction company)
Wastewater plants (Several Dutch organisations)
Life support system design (Texas A&M and NASA)
Catalytic bed modelling (Ford Germany)
Glass oven modelling and control (Several Dutch and German companies)
Optimizing & plant-wide control (Shell chemicals, Bayer, …)
Sugar product distribution network (CSR Australia)
Moving catalytic bet reactor (CSIRO Australia)
Control laboratories, computer networks (UNSW, TUE)
Simulator design (ETHZ, TUE, Protomation)
etc
return
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VIEWS
 Live experience: some pictures
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Other “Views”
A mix of mountains, hills and sea
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Herisau Panorama
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Cogee
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Oberdorf
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Oberdorf
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Winter outside the Village
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Wurzelmannli our Troll
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Chlausete
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Wineglass Bay
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