Esko Juuso
Control Engineering Group,
Faculty of Technology
University of Oulu
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

EUROSIM
Federation of European Simulation Societies
OULU
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

EUROSIM
Federation of European Simulation Societies
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

Control Engineering Group
Competence Pyramid
Detection of operating conditions
- system adaptation
-fault diagnosis, condition monitoring, quality
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
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

• 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 2014
St. Petersburg, Russia, 2-4 July 2014

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
Soft sensors
data-collection
pre-processing
normalisation and scaling
interpolation
data quality, outliers
signal processing
feature extraction
sensor fusion
Classification and reasoning methodologies
rule-based, fuzzy, neural, support vector
artificial immune systems
qualitative models, search strategies
Nonlinear multivariable methodologies
steady-state & dynamic
decomposition, clustering, composite models
mixed models
development and tuning
statistical, fuzzy, neural, genetic
Nonlinear process control
feedback
fuzzy, neural, sliding mode
adaptation (on-line, predefined)
model-based (FF, IMC, MPC)
high-level
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

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 2014
St. Petersburg, Russia, 2-4 July 2014

Fuzzy arithmetics
• Extension principle
• Interval arithmetics
• Horizontal systems
Rules and relations
• Linguistic fuzzy
• Takagi-Sugeno fuzzy
• Singleton
• Fuzzy relational models
Type-2 fuzzy sets
• Uncertainty about the membership functions
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

Fuzzy Fuzzy arithmetics
Fuzzy rulebase
Fuzzy relations
Fuzzy
Fuzzy
Crisp
Fuzzification
Fuzzy aritmetics
Fuzzy
Fuzzy reasoning
Defuzzification Crisp
Fuzzy
Fuzzy Fuzzy inequalities
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

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 2014
St. Petersburg, Russia, 2-4 July 2014

Data mining
Domain expertise
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

EXPERTISE
Rules
Fuzzy Set Systems
+ Handling of uncerainty
+ Natural compromises
+ Easy to build (small systems)
+ Explanations
- Tuning (complex systems)
- (Doubts about stability)
Expert Systems
+ Extracting expert knowledge
- Complexity
- Handling of uncertainty
- Testing
Linguistic Equations
+ Very compact
+ Combining knowledge
+ Generalisation
+ Adaptive tuning
+ Easier testing
- Structure Restrictions
Chaos Theory
•Risk Analysis
•Economical factors
Knowledge-base alternatives
Genetic Algorithms
+ Large search space
+ Global/local optimisation
+ Design
- Computer Time Consuming
- Not for Control (off-line)
Neuro-fuzzy
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014
Neural Networks
+ ”Automatic” Modelling
+ Black Box Modelling
+ Precision (small systems)
- Only for Fragments
- Explanations
- Safety
- Precision (complex systems)
DATA
NN Structures


Linear interactions
Smart adaptive applications
Modelling
- Control
- Diagnostics
Meaning
How to define??
Hard computing??
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

Data
Adaptation of scaling functions
- Generalised norms and moments
- Constraints
- Case specific
Data selection
- Outliers
- Suspicious
Nonlinear scaling
- Feasible ranges
- Membership definitions
- Membership functions
Adaptation
- Manual
- Neural
- Genetic
Linguistic relations
- Selected and scaled data
Linguistic equation alternatives
- Linear regression
- Case specific
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014
Selected equations
Domain expertise
Variable grouping
- 3-5 variables
- Include/exclude
- Correlation
- Causality
Selected variable groups
Manually defined equations
Final variable groups

• A generalised norm about the origin

M
 p
N p
 
N s

(

M
 p ) 1 / p 
1
(
N i
N 

1 x i
(

) p
) 1 / p , p is a real number which is the l p norm 
M
 p p
 x
(

) p
.
• Special cases
– absolute mean x
(

)
1
 x
(
 av
)

1
N i
N 

1 x i
(

)
,
– rms value x
(

)
2
 (
 x rms
) 
1
(
N i
N 

1 x i
(

)
2
)
1 / 2
,
• Positive and negative values
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

• equal sized sub-blocks 
K
S

M
 p p




1
K
S i
K  S

1

(

M
 p
) i
1 / p
 p
 1 /

 p



1
K
S i
K  S

1
(

M
 p
) i


1 / p
,
• A maximum from several samples max(

M
 p
)
 i max

1 ,..., K
S

(

M
 p
)
1 i
/ p

• Increasing
(

M
 p
)
1 / p 
(

M q

)
1 / q p
 q
Recursive analysis!
x
(

)

1
 i
N 

1
N
1 x i
(

)
,
… x
(

)
1

1
N i
N 

1 x i
(

)
,
… x
(

)
2

1
(
N i
N 

1 x i
(

)
2
)
1 / 2
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

•
Normalised moments

• Skewness k

E


X


– Positive 
3

0
– Symmetric
X
E (
  k
– Negative
3
0
X

3
)
 k

0

• Generalised moment
 
E

X
(

) k 

X
 k
M
 p p k

• Locally linear if possible
• Corrections for corner points
• Core [( c l
) j
, ( c h
)]
• Support [min( x j
), max( x j
)] k = 3 Skewness k = 4 Kurtosis
Central value
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014


Data
Meaning
Expertise
Knowledge-based information: labels to numbers
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

Tuning
(1) Core
[( c l
) j
, ( c h
)]
(2) Ratios
(3) Support
 j
 


1
3
, 3


  j



1
3
, 3


[min( x j
), max( x j
)] c j
• Centre point
• Corner points
 min( x j
), ( c l
) j
, ( c h
) j
, max( x j
)

• Calculation a
 j

1
2
( 1
  j

)
 c j

, b
 j

1
2
( 3
  j

)
 c j

, a
 j

1
2
(
 j
 
1 )
 c
 j
, b
 j

1
2
( 3
  j

)
 c
 j
X j












 b j
 b j



2 with x j
 max( x j
) b j
 2 
2 a
 j
4 a
 j
( c j
 x j
)

2 with c j
 x j
 max( x j
) b
 j
2 
4 a
 j
( c j
 x j
)

2
2 a j

2 with x j with
 min( x j
) min( x j
)
 x j
 c j
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

LE models: Dynamic simulator
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

• Membership definitions
– Parameters
– No penalties
• Normalised interactions
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

Lag phase
Exp.
phase
Decision system
X
X
Fuzzy weighting
+
Integration
Prediction
Steady state
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014
X

Submodels
Fuzzy LE blocks
Measurements
CO
2 forecast
Volumetric mass transfer
Coefficient, k
L a
OTR forecast
DO forecast
Note: 3 phases & 3 models / phase  9 interactive dynamic models!
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

• Energy:
– Solar power plant
• Environment:
– Water circulation & wastewater treatment
~ 4 m
• Pulp&Paper:
– Lime kilns
Length > 100 m
Slow rotation: rotation time 42-45 s
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

• Setpoint tracking
Principle: lower irradiation  lower temperatures
Operator can choose the risk level: smooth … fast
• Cloudy conditions 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
• Optimisation
Constrained optimisation:
-Temperature (< 300 o C)
- Temperature increase (< 90 o C)
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

• 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
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

• Stress indices
– Cavitation
• Condition indices
– Lime kiln
• Fatigue
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

• Soft computing
– Expertise
– Fuzzy reasoning
Complex systems
• Interactions
– Fuzzy set systems
– Linguistic equations
• Hard computing
– Data
– Statistical analysis
• Meaning
– Membership definitions
 Membership functions
• Generalised norms and moments
• Nonlinear scaling
COMOD 2014
St. 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
COMOD 2014
St. Petersburg, Russia, 2-4 July 2014

The 9 th EUROSIM Congress on Modelling and Simulation
Oulu City Theatre
30 COMOD 2014
St. Petersburg, Russia, 2-4 July 2014