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Smart Adaptive Methods in Modelling and Simulation of Complex Systems. Esko Juuso Control Engineering Group, Faculty of Technology University of Oulu. EUROSIM Federation of European Simulation Societies. OULU. EUROSIM Federation of European Simulation Societies.

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smart adaptive methods in modelling and simulation of complex systems

Smart Adaptive Methods in Modelling and Simulation of Complex Systems

Esko Juuso

Control Engineering Group,

Faculty of Technology

University of Oulu

slide2

EUROSIM

Federation of European Simulation Societies

OULU

slide3

EUROSIM

Federation of European Simulation Societies

slide4

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

outline
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
slide6

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
steady state modelling data
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)
steady state modelling knowledge
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
slide9

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

steady state modelling decomposition
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
complex applications fuzzy set systems
Complex applications: Fuzzy set systems

Data

mining

Domain expertise

slide12

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

fuzzy set systems linguistic equation systems
Fuzzy set systems  Linguistic equation systems
  • Smart adaptive
  • applications
  • Modelling
  • Control
  • Diagnostics

Linear

interactions

Meaning

How to define??

Hard computing??

slide14

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

statistical analysis norms
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

generalised norms
Generalised norms
  • equal sized sub-blocks 
  • A maximum from several samples
  • Increasing

Recursive analysis!

generalised moments
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

le nonlinear scaling linear models interactions
LE: nonlinear scaling linear models (interactions)

Data

Meaning

Expertise

Knowledge-based information: labels to numbers

second order polynomials
Second order polynomials

Tuning

(1) Core

(2) Ratios

(3) Support

  • Centre point
  • Corner points
  • Calculation
genetic tuning
Genetic tuning
  • Membership definitions
    • Parameters
    • No penalties
  • Normalised interactions
fuzzy weighting

Decision system

Lag

phase

X

+

Exp.

phase

Prediction

X

Integration

Steady

state

X

Fuzzy weighting
submodels

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!

le application examples control

~ 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
solar thermal power plant
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)
solar thermal power plant1
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

le application examples diagnostics
LE Application examples: Diagnostics
  • Stress indices
    • Cavitation
  • Condition indices
    • Lime kiln
  • Fatigue
conclusions
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
slide29

EUROSIM

Federation of European Simulation Societies

34th Board Meeting in Vienna, February 2012,

NSS became an observer member of EUROSIM

slide30

EUROSIM 2016September 13-16, 2016, Oulu, Finland

The 9th EUROSIM Congress on Modelling and Simulation

Oulu City Theatre