comod 2014 st. petersburg, russia, 2-4 july 2014 smart adaptive methods in modelling and simulation...
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COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Smart Adaptive Methods in Modelling and Simulation
of Complex SystemsEsko Juuso
Control Engineering Group,
Faculty of Technology
University of Oulu
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
EUROSIM Federation of European Simulation
Societies OULU
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Detection of operating conditions- system adaptation
-fault diagnosis, condition monitoring, quality
Dynamic simulation- controller design, prediction
Intelligent analysers-sensor fusion
-software sensors-trends
Intelligent control-adaptation
-model-based
Measurements-on-line analysers
-DSP
Intelligent actuators- model-based
Control Engineering Group
Competence Pyramid
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
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
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Symptom generation
-limit values, parameter esimates-analytic, heuristic
-condition monitoring-statistical process control (SPC)
Nonlinear process control
- feedback-fuzzy, neural, sliding mode
- adaptation (on-line, predefined)- model-based (FF, IMC, MPC)
- high-level
Soft sensors
-data-collection-pre-processing
-normalisation and scaling-interpolation
-data quality, outliers-signal processing-feature extraction
-sensor fusion
Nonlinear multivariable methodologies
- steady-state & dynamic-decomposition, clustering, composite models
-mixed models-development and tuning
-statistical, fuzzy, neural, genetic
Classification and reasoning methodologies
-rule-based, fuzzy, neural, support vector-artificial immune systems
-qualitative models, search strategies
Classification and reasoning
-case-based reasoning (CBR), models-fault and event trees
-cause-effect relationships-novelty detection
Detection of operating conditions
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Steady-state modelling: Data
Statistical analysis• Interactions
– Linear, quadratic & interactive Response surface methodology (RMS)
• Reduce dimensions– Principal component
analysis (PCA)– Partial least squares
regression (PLS)
Artificial neural networks
• Linear networks– Regression– Recursive tuning
• Multilayer perceptron– Nonlinear activation
• Learning– Backpropagation– Advanced optimisation
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Steady-state modelling: Knowledge
Fuzzy arithmetics• Extension principle• Interval arithmetics• Horizontal systems
Rules and relations• Linguistic fuzzy• Takagi-Sugeno fuzzy• Singleton• Fuzzy relational
modelsType-2 fuzzy sets• Uncertainty about the
membership functions
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Fuzzification
Fuzzyreasoning
Fuzzyrulebase
Defuzzification
Fuzzy
Crisp
Fuzzy
Crisp
Fuzzy
Fuzzy relations
Fuzzy set systems
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Steady-state modelling: Decomposition
Modelling
• Subprocesses• Hierachical• Composite models
– Linear parameter varying (LPV)
– Piecewise affine (PWA)
– TS fuzzy models– Ensemble of
redundant neural networks
Clustering
• Hierarchical• Partitioning: K-means• Fuzzy
– Fuzzy c-means (FCM)– Subtractive
• Neural: SOM
• Shape (Gustafson-Kessel)
• Robust• Optimal number
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Complex applications: Fuzzy set systems
Domain expertise
Datamining
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Expert Systems+ Extracting expert knowledge
- Complexity- Handling of uncertainty- Testing
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)
Neural Networks+ ”Automatic” Modelling+ Black Box Modelling+ Precision (small systems)- Only for Fragments- Explanations- Safety- Precision (complex systems)
EXPERTISE
DATA
Neuro-fuzzyNN Structures
Knowledge-basealternatives
Rules
Chaos Theory•Risk Analysis•Economical factors
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Fuzzy set systems Linguistic equation systems
Meaning
Linear interactions
Smart adaptiveapplications- Modelling- Control- Diagnostics
How to define??
Hard computing??
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Linguistic relations- Selected and scaled data
Data Data selection- Outliers- Suspicious
Nonlinear scaling- Feasible ranges- Membership definitions- Membership functions
Adaptation of scaling functions- Generalised norms and moments- Constraints- Case specific
Variable grouping- 3-5 variables- Include/exclude- Correlation- Causality
Selected variable groups
Domain expertise
Linguistic equation alternatives- Linear regression- Case specific
Adaptation- Manual- Neural- Genetic
Selected equations Final variable groups
Manually defined equations
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Statistical analysis: norms• A generalised norm about the origin
which is the lp norm
• Special cases
– absolute mean
– rms value
• Positive and negative values
sNN
,)1
()( /1
1
)(/1 pN
i
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1
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( 2/1
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2)()(
2
)(
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iirms x
Nxx
p is a real number
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Generalised norms• equal sized sub-blocks
• A maximum from several samples
• Increasing
,)(1
)(1
/1
1
/1
1
/1
pK
ii
p
S
pK
i
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S MK
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pi
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p MMS
/1
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qp
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1
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i ix
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2)(
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(
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1
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iix
Nx
… …
Recursive analysis!
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Generalised moments• Normalised moments
• Skewness– Positive– Symmetric– Negative
• Generalised moment
• Locally linear if possible• Corrections for corner points• Core • Support
k = 3 Skewnessk = 4 Kurtosis
kX
k
k
XEXE
)(
03
03 03
k
X
k
p
p
k
MXE
)(
Central value
)](,)[( hjl cc
)]max(),[min( jj xx
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
LE: nonlinear scaling linear models (interactions)
Data
Meaning
Expertise
Knowledge-based information: labels to numbers
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Second order polynomialsTuning
(1) Core
(2) Ratios
(3) Support
• Centre point
• Corner points
• Calculation
)max(,)(,)(),min( jjhjlj xccx
jc
)](,)[( hjl cc
3,3
1j
)]max(),[min( jj xx
3,3
1j
jjj
jjj
jjj
jjj
cb
ca
cb
ca
)3(2
1
,)1(2
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,)3(2
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)min(22
)(4
)max(22
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)max(2
2
2
jj
jjjj
jjjjj
jjjj
jjjjj
jj
j
xxwith
cxxwitha
xcabb
xxcwitha
xcabb
xxwith
X
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Genetic tuning
• Membership definitions– Parameters
– No penalties
• Normalised interactions
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Lagphase
Exp.phase
Steadystate
Decision system
X
X
X +
Integration
Prediction
Fuzzy weighting
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Submodels
CO2 forecast
OTR forecast
DO forecast
Measurements
Volumetric mass transferCoefficient, kLa
Fuzzy LE blocks
Note: 3 phases & 3 models / phase 9 interactive dynamic models!
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
LE Application examples: Control
• Energy: – Solar power plant
• Environment: – Water circulation & wastewater treatment
• Pulp&Paper: – Lime kilns
Length > 100 mSlow rotation: rotation time 42-45 s
~ 4 m
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
• Setpoint tracking
• Cloudy conditions
• Optimisation
Solar thermal power plantSolar thermal power plant
www.psa.es
Principle: lower irradiation lower temperaturesOperator can choose the risk level: smooth … fast
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)
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
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 adaptationSmooth adjustable operation A good basis for optimised operation within a Smart Grid
MODEL-BASEDCONTROL
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
LE Application examples: Diagnostics
• Stress indices– Cavitation
• Condition indices– Lime kiln
• Fatigue
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
Conclusions
• Soft computing– Expertise– Fuzzy reasoning
• Hard computing– Data– Statistical analysis
• Generalised norms and moments
Complex systems• Interactions
– Fuzzy set systems– Linguistic equations
• Meaning– Membership definitions Membership functions
• Nonlinear scaling
COMOD 2014St. Petersburg, Russia, 2-4 July 2014
EUROSIM Federation of European Simulation
Societies
34th Board Meeting in Vienna, February 2012,
NSS became an observer member of EUROSIM