Process Systems Engineering

1. Heinz A. Preisig Process Systems Engineering Methodological ApproachtoProcess Operations & Design Modelling Control Synthesis

2. process identification model construction The Subject and its Components why modelling experiments STMF modelling concepts project map mixed continuous & event dynamic model water management process design container transport controller design state discretisation supervisor design DEDS research even-dynamicmodel fault analysis

3. MODELS • Central role of models PSE

4. 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

6. MODELLER PROJECT • Map PSE

7. solver solver solver solver solver solver solver solver solver Modeller Project: Overview transfer black-box models database Concepts enforcing database editors kinetics activity Phys properties encapsulation model Thermo state trans Species & reactions reduction of model development time and overall effort by 75 to 90% Concept-enforcing model structure editor Subsystem selection Model reduction algebraic manipulation ie linearisation Library of documented consistent process model Assumption handling Time scale selection rapid construction, modification, validation and maintenance of consistent process models problem instantiation Instantiated simulation model Instantiated design| identification model Instantiated optimal control model applications

8. return • was map for MATCH project

9. MODELLING METHODOLOGY • Basic components of networking approach • Components of the mathematical description • Physical and species topology example • … PSE

10. Goals • Research: • Develop structured modelling approach • Implementation of Model Design Toolsupporting 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

11. 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

12. 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

13. primary model A theory process secondarymodel A A assumptions A A instantiation experiment solution method solved model verification The Modelling Process

14. Staged Approach 1 : 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

15. plant Modelling Basics primary abstraction time & length scale assumptions explode simplification & abstraction

16. Approach: Network of communicating control volumes

17. 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 • 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. Static constitutive equations

18. Example : Equations

19. Model equations for a system s in its environment e

20. Complete system: Stack all systems in the network up

21. Essentials State information (variables) used to describe transfer and reactions (transpositions) are mapped from the basic state variables (= conserved quantities). flow of ext quantities F transfer of extensive quantity secondary state accum ex quantities primary state + state variable transformations reaction rates R 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

22. Basic framework of MODELLER 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

23. Example of Physical Topology A B A,C B CH R C A+BD+E E S CC hardly any D,E P B,C,D,E

24. Example of Species Topology A B C A B A B A B CH CH CH R R R C C C E E E CC CC CC P P P A B A B A B CH CH CH R R R C C C E E E CC CC CC P P P D E S

25. Completion of the Model 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

26. Stage 4 : Add Controller CH B A temp R  level CT CV C E CC P

27. 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)

28. 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.

29. 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:

30. 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

31. 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

32. 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….

33. return • PhD: Mathieu Westerweele • Collaboration: Protomation BV

34. SUSTAINABLE DESIGN • Water management in households PSE

35. 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 processes

36. The considered system Collaboration with Ralf Oterphol, Hamburg

37. A flow sheet

38. and a Simulink Model

39. 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 ?

40. Return • PhD: Annelies Vleuten-Balkema • Collaboration: Ralf Otterpohl, TU Hamburg-Harburg • part of Sustainable Technology Program TU-Eindhoven

41. MODEL-BASED CONTROLLER DESIGN • Modelling is the key • Model reduction based on network analysis PSE

42. Storing and moving fresh products (apples, mangoes,…) Project with ATO, the agriculture research organisation of the Netherlands

43. Abstraction of the storage and transport problem

44. Key : time scale assumptions leads to controller

45. 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, ….)

46. Return • PhD: Gerwald Verdijck • Collaboration with ATO (Dutch Agrotechnical Research Institute, Wageningen)

47. DEDS RESEARCH • Why important for process industry • What are they • Some results on control PSE

48. 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

49. recipe command disturbance event signal supervisor controlled plant state-event detector state events Event-Based Control state example time

50. A toy problem in a toy plant, a demo