Smart Adaptive Methods in Modelling and Simulation of Complex Systems

1. Smart Adaptive Methods in Modelling and Simulation of Complex Systems Esko Juuso Control Engineering Group, Faculty of Technology University of Oulu

2. EUROSIM Federation of European Simulation Societies OULU

3. EUROSIM Federation of European Simulation Societies

4. Detection of operating conditions • - system adaptation • fault diagnosis, condition monitoring, quality Control Engineering Group Competence Pyramid • Intelligent analysers • sensor fusion • software sensors • trends • Intelligent control • adaptation • model-based • Measurements • on-line analysers • DSP Intelligent actuators - model-based Dynamic simulation - controller design, prediction

5. Outline • Background • Soft computing: fuzzy set systems • Hard computing: statistical analysis • Modelling & Simulation • Data + Knowledge + Decomposition • Linguistic equation (LE) systems • Generalised moments and norms • Nonlinear scaling • Genetic tuning • Application examples • Conclusions

6. Detection of operating conditions • Symptom generation • limit values, parameter esimates • analytic, heuristic • condition monitoring • statistical process control (SPC) • Classification and reasoning • case-based reasoning (CBR), models • fault and event trees • cause-effect relationships • novelty detection • Classification and reasoning methodologies • rule-based, fuzzy, neural, support vector • artificial immune systems • qualitative models, search strategies • Soft sensors • data-collection • pre-processing • normalisation and scaling • interpolation • data quality, outliers • signal processing • feature extraction • sensor fusion • Nonlinear process control • feedback • fuzzy, neural, sliding mode • adaptation (on-line, predefined) • model-based (FF, IMC, MPC) • high-level • Nonlinear multivariable methodologies • steady-state & dynamic • decomposition, clustering, composite models • mixed models • development and tuning • statistical, fuzzy, neural, genetic

7. Steady-state modelling: Data Statistical analysis Artificial neural networks Linear networks Regression Recursive tuning Multilayer perceptron Nonlinear activation Learning Backpropagation Advanced optimisation • Interactions • Linear, quadratic & interactive Response surface methodology (RMS) • Reduce dimensions • Principal component analysis (PCA) • Partial least squares regression (PLS)

8. Steady-state modelling: Knowledge Fuzzy arithmetics Rules and relations Linguistic fuzzy Takagi-Sugeno fuzzy Singleton Fuzzy relational models • Extension principle • Interval arithmetics • Horizontal systems Type-2 fuzzysets • Uncertaintyabout the membershipfunctions

9. Fuzzy set systems Fuzzy Fuzzy Fuzzy rulebase Fuzzy relations Fuzzy inequalities Fuzzy aritmetics Defuzzification Fuzzy arithmetics Fuzzy Fuzzy reasoning Fuzzification Fuzzy Crisp Fuzzy Crisp Fuzzy

10. Steady-state modelling: Decomposition Modelling Clustering Hierarchical Partitioning: K-means Fuzzy Fuzzy c-means (FCM) Subtractive Neural: SOM Shape (Gustafson-Kessel) Robust Optimal number • Subprocesses • Hierachical • Composite models • Linear parameter varying (LPV) • Piecewise affine (PWA) • TS fuzzy models • Ensemble of redundant neural networks

11. Complex applications: Fuzzy set systems Data mining Domain expertise

12. Expert Systems • + Extracting expert knowledge • Complexity • Handling of uncertainty • Testing EXPERTISE • Chaos Theory • Risk Analysis • Economical factors Knowledge-base alternatives Rules • Fuzzy Set Systems • + Handling of uncerainty • + Natural compromises • + Easy to build (small systems) • + Explanations • Tuning (complex systems) • (Doubts about stability) Linguistic Equations + Very compact + Combining knowledge + Generalisation + Adaptive tuning + Easier testing - Structure Restrictions • Genetic Algorithms • + Large search space • + Global/local optimisation • + Design • Computer Time Consuming • - Not for Control (off-line) Neuro-fuzzy • Neural Networks • + ”Automatic” Modelling • + Black Box Modelling • + Precision (small systems) • Only for Fragments • Explanations • Safety • Precision (complex systems) NN Structures DATA

13. Fuzzy set systems  Linguistic equation systems • Smart adaptive • applications • Modelling • Control • Diagnostics Linear interactions Meaning How to define?? Hard computing??

14. Domain expertise • Nonlinear scaling • Feasible ranges • Membership definitions • Membership functions • Adaptation of scaling functions • Generalised norms and moments • Constraints • Case specific Selected variable groups • Linguistic equation alternatives • Linear regression • Case specific Selected equations Final variable groups • Data selection • Outliers • Suspicious • Adaptation • Manual • Neural • Genetic Linguistic relations - Selected and scaled data • Variable grouping • 3-5 variables • Include/exclude • Correlation • Causality Data Manually defined equations

15. Statistical analysis: norms • A generalised norm about the origin which is the lp norm • Special cases • absolute mean • rms value • Positive and negative values p is a real number

16. Generalised norms • equal sized sub-blocks  • A maximum from several samples • Increasing Recursive analysis! … …

17. Generalised moments • Normalised moments • Skewness • Positive • Symmetric • Negative • Generalised moment • Locally linear if possible • Corrections for corner points • Core • Support k = 3 Skewness k = 4 Kurtosis Central value

18. LE: nonlinear scaling linear models (interactions) Data Meaning Expertise Knowledge-based information: labels to numbers

19. Second order polynomials Tuning (1) Core (2) Ratios (3) Support • Centre point • Corner points • Calculation

20. LE models: Dynamic simulator

21. Genetic tuning • Membership definitions • Parameters • No penalties • Normalised interactions

22. Decision system Lag phase X + Exp. phase Prediction X Integration Steady state X Fuzzy weighting

23. Fuzzy LE blocks Measurements OTR forecast CO2 forecast DO forecast Submodels Volumetric mass transfer Coefficient, kLa Note: 3 phases & 3 models / phase  9 interactive dynamic models!

24. ~ 4 m Length > 100 m Slow rotation: rotation time 42-45 s LE Application examples: Control • Energy: • Solar power plant • Environment: • Water circulation & wastewater treatment • Pulp&Paper: • Lime kilns

25. Solar thermal power plant • Setpoint tracking • Cloudy conditions • Optimisation Principle: lower irradiation  lower temperatures Operator can choose the risk level: smooth … fast www.psa.es Clouds  High temperature are risky  Cloudy conditions are detected from fluctuations of irradiation Working point is limited  Further limitations for the setpoint • Constrained optimisation: • Temperature (< 300 oC) • Temperature increase (< 90 oC)

26. Solar thermal power plant • Intelligent control • Adaptation, braking, asymmetrical action • Automatic smart actions • Disturbances are handled well if the working point is on a good level • Intelligent indices • react well to disturbances (clouds, load, …) • Model-based limits for the working point • Better adaptation • Smooth adjustable operation • A good basis for optimised operation within a Smart Grid MODEL-BASED CONTROL

27. LE Application examples: Diagnostics • Stress indices • Cavitation • Condition indices • Lime kiln • Fatigue

28. Conclusions Complex systems Interactions Fuzzy set systems Linguistic equations Meaning Membership definitions  Membership functions Nonlinear scaling • Soft computing • Expertise • Fuzzy reasoning • Hard computing • Data • Statistical analysis • Generalised norms and moments

29. EUROSIM Federation of European Simulation Societies 34th Board Meeting in Vienna, February 2012, NSS became an observer member of EUROSIM

30. EUROSIM 2016September 13-16, 2016, Oulu, Finland The 9th EUROSIM Congress on Modelling and Simulation Oulu City Theatre