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Process Systems EngineeringProcess Systems Engineering

Methodological Approachto

Process Operations & Design

ModellingControl

Synthesis

Heinz A. Preisig

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The Subject and its Components

state discretisationstate discretisation

even-dynamicmodel

even-dynamicmodel

experimentsexperiments process identification process identification

process designprocess design

controller designcontroller design

supervisor designsupervisor design

mixed continuous & event

dynamicmodel

mixed continuous & event

dynamicmodel

modelling conceptsSTMF

water management

DEDS research

container transport

fault analysisfault analysis

why modelling

model constructionmodel construction

project map

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MODELS

Central role of models

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Models the Central Object

Models are used for just about everything in chemical engineering 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 applicationsModel components for the physical structure of units or plant sections only orprocess units with particular reaction systems model libraries

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MODELLER PROJECT

Map

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Modeller Project: Overview

Assumption handling

Concept-enforcing model structure

editorModel reduction

Library of documented consistent process model

problem instantiation

Instantiated optimal control model

Instantiated simulation model

Instantiated design| identification model

solver

solver

solver

solver

solver

solver

solver

solver

solver

Time scale selection

Subsystem selection

transfer

kinetics

Phys properties

Thermo state trans

Species & reactions

applications

Concepts enforcing database editors

black-box models

encapsulation

algebraic manipulation ie linearisation

database

activity

model

rapid construction, modification, validation and maintenance of consistent process models

rapid construction, modification, validation and maintenance of consistent process models

reduction of model development time and overall effort by 75 to 90%

reduction of model development time and overall effort by 75 to 90%

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was map for MATCH project

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PSEPSEMODELLING METHODOLOGY

Basic components of networking approach Components of the mathematical description Physical and species topology example …

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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:

• 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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A AA

A

The Modelling Process

process

primary model

assumptions

theory A

secondarymodel

instantiation

solution method

solved model

experiment

verification

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Staged Approach

4 : Information ProcessingControl

5 : (Simulation) Problem DefinitionInstantiate consistentlyApply assumptions

1 : Physical TopologyPhysical viewNetwork of

primitive systems and connections

2 : Species TopologyColour with speciesadd reactions

3 : Equation TopologyTransfer lawsKineticsGeometryPhysical propertiesEquations of stateAdditional variables

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Modelling Basics

plantprimary abstraction time & length scale

assumptions

explode

simplification&

abstraction

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Approach: Network of communicating control volumes

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Modelling Concepts

Control volumes on which conservation principles are applied conservation of component mass and energy, momentum etc.

Transfer between control volumes communications between control volumes

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.

Process Dynamics

Static constitutive equations

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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).

state variable transformations

transfer of extensive quantity

transposition of extensive quantity

+

F

R

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

primary

state

secondary

state

flow of

ext quantities

reaction

rates

accum

ex quantities

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Basic framework of MODELLER

Step 1 : Structure process using control-volume concept network of capacities and connections physical 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

Step 2 : Define species distribution using species and potential reactions colouring of the physical topology species topology

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Example of Physical Topology

E

R

C

A B

P

CH

CC

A,C B

A+BD+E

B,C,D,E

hardly any D,ES

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Example of Species Topology

E

R

C

A B

P

CH

CC

E

R

C

A B

P

CH

CC

E

R

C

A B

P

CH

CC

E

R

C

A B

P

CH

CC

E

R

C

A B

P

CH

CC

E

R

C

A B

P

CH

CC

A B C

D E S

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Completion of the Model

Step 4 : Adding control control topology

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

Step 5 : Model simplification derived secondary models

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Stage 4 : Add Controller

E

R

C

A B

P

CVCT

leveltemp

CH

CC

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Typical assumptions

Make late order of magnitude assumptions: 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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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

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 processesInterest 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

WATERUSE: WASTEWATER TRANSPORT: WASTEWATER TREATMENT:

RESULTS:RESOURCES:

H2H3H4H5

h (g/d)

D1D2D3D4D5

d (g/d)

C1C2C3C4C5C6

c (g/d)

YW5 YW6

Yellowwater transport

YW6 YWout

Yellow watertreatment

Water demand

Water balance

D4

H4

R4

C4

W2

W3

Washing

UinAmount

Uin (g/d)B1

Fin

Uin

D1

H2

R2

C1

BW1

YW3

YW4

Toilet

Sustainabili ty indicators

BB

SY

WR

ou

tB

WG

WC

SO

BG Reuse

Potential

Resources

C6

Rin

R1R2R3R4R5

RainwatersystemR5

R6R7

Rout

Rainwater

GW3GWout

Primairy GWT(BOD reduction)

GW2 GW3

Preliminairy GWT(TS removal)HD2

D3

C3

PH2

PH3

Personal Hygiene

D5H5R5C5

O2

O3

Outdoor

BW1BW2

BS1

Onsite treatment

BoutBGoutBWoutBG2outBSoutCSOYWoutEoutGWoutRoutOout

Mass balance

HD1D2C2

K2

K3

Kitchen

YW3K3W4O2PH4R8

GW1

GW2

Greywater transport

FinAmount

Fin (g/d)1

FinAmount

Fin (g/d)

Evaporation

H3

R3

HD1

HD2

Disinfection

BW3

BW4

BS2

Blackwater treatment 1

B1

BW2

R7

GW1

BW3

CSOout

Blackwater transport

BW5

BW6

BS4

Blackwatertreatment 4

BW4 BW5

Blackwatertreatment 3

BW4

BGout

BW5

BS3

Blackwatertreatment 2

BS5

BG2out

BSout

Blacksludgetreatment 1

BS4

BS3

BS2

BS1

BS5

Blacksludgetransport

BinBout

B1B2

Biowaste

BinAmount

Bin (g/d)

-K-

2

-K-

1

-K-

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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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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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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

Fault detection What can I achieve with simple boundary detection?

Observer 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.

level

temp

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Event-Based Control

controlled plant state-event detector

supervisor

recipe

event signalcommand

disturbance

state

time

state events

example

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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 subdomainsI 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 anymorekey: 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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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

plantinput

spline filter spline filter

“parameter

estimator”

output

“derivatives”

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PSEPSEHeinz 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 …

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

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Processes 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

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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