PROCESS STATE IDENTIFICATION AND MODELING IN
A FLUIDIZED BED ENERGY PLANT BY USING
ARTIFICIAL NEURAL NETWORKS
Mika Liukkonen
Process Informatics Research Group, University of Kuopio
Finnish-Swedish Flame Days 2009, January 28 - 29, Naantali
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Process Informatics
Base:
Varkaus and Kuopio
Team:
6 researchers
Research fields: - energy production
- chemical industry
- pulp production
- waste water treatment
- electronics industry
Know-how:
Modeling
Data
processing
Software
engineering
- data processing
- modeling methods
- software engineering
Partners:
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Dynergia-project
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Partners:
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Universities: Jyväskylä, Lappeenranta, Kuopio,
Helsinki
VTT (Technical Research Centre of Finland)
Companies: Control Express, Fortum, Foster
Wheeler, Wärtsilä Biopower, Etelä-Savon
Energia, Jyväskylän Energia
Funding: Tekes, partners
2008–2011 (3 years)
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Dynergia-project
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Concentration on energy boilers
Aims:
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Development of direct and indirect (soft sensor)
measuring and data processing methods for energy
production
Methods for monitoring, control and optimization
Integrated concept for process equipment automation
Pilot scale < Small scale < Large (production) scale
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Modeling methods
Phenomenological
modeling
Data-driven
modeling
• Physics
• Chemistry
• Mathematics
• Data
• Parameter estimation
• Statistical methods
Intelligent methods
• Expertise & data
• Neural networks, Fuzzy logic
• Adaptation
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Modeling methods: SOM
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Classification of data
samples into categories
(neurons)
The input vectors
sharing common
features are projected
to the same or
neighbouring neurons
The reference vector
describes the ‘average’
of the data rows (hits)
in that neuron
SOM = Self-Organizing Map
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Modeling methods:
MLP
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Several inputs, one output
Computing in neurons
Weights strengthen or weaken the impact of each
input
Weights defined by learning from examples
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supervised learning
Output calculated from weighted inputs
Use e.g. in prediction
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MLP = Multi-Layer Perceptron
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From universal to detailed models
Pre-processing
of data
SOM
• Substitution of
missing values
• Compressing
data
• Data filtering
• Visualization
• Variance scaling
• Removing
noise
• Variable selection
Identifying
process states
Simulation
• Creating submodels within
process states
• Process lags
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Backward loop for creating
sub-models
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Case: CFB boiler
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DATA
• 10 000 data rows
• Selection of variables performed
• 18 averaged variables
• 18 standard deviations
• 5 min time resolution
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THE GOAL
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Hypothesis: the process consists of separable process
states, where the process behaves differently.
Can different states of process be discovered?
Can different states of process be identified?
Can process state -specific analysis bring extra
information on the process?
Can modeling accuracy be improved by adding the
process state information?
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PROCESS STATES
PRIMARY PROCESS
STATES
CLUSTER III
Steam flow: high
SECONDARY
WHAT
AREPROCESS
THESE
STATES
SECONDARY
STATES??
MAIN MODEL
CLUSTER II
Steam flow: medium
Start ups,
shutdowns
Idle times
CLUSTER I
Steam flow: low
Color scale = main steam flow
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Subtraction analysis identification of process states
Bed temperature:
unstable
=
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Flue gas
temperature after
separator:
unstable
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THE SUB-MODELS
Main model:
IA = 0.950
Level 1:
IA = 0.956
Level 2:
IA = 0.979
3%
improvement
in model
accuracy
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Conclusions
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Good simulation accuracy
Identification of process states can bring
extra information on the process
Modeling within process states can
improve the performance of the model
Can also be used easily to any other
process = easily applicable
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Contact
Process Informatics Research Group
University of Kuopio, Department of Environmental Science
Prof.
Yrjö Hiltunen
p. 040 7320355
yrjo.hiltunen@uku.fi
Researcher
Mika Liukkonen
p. 040 3510644
mika.liukkonen@uku.fi
More information on process informatics:
http://www.prosinfo.fi
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