System Identification of Blast Furnace Processes with Genetic Programming
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- @InProceedings{Kronberger:2009:LINDI,
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author = "Gabriel Kronberger and Christoph Feilmayr and
Michael Kommenda and Stephan Winkler and
Michael Affenzeller and Thomas Burgler",
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title = "System Identification of Blast Furnace Processes with
Genetic Programming",
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booktitle = "2nd International Symposium on Logistics and
Industrial Informatics, LINDI 2009",
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year = "2009",
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month = "10-11 " # sep,
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address = "Linz, Austria",
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pages = "1--6",
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keywords = "genetic algorithms, genetic programming, blast furnace
process, burden composition, carbon content, chemical
reaction, data-based modeling method, hot metal,
inhomogeneous burden movement, injected reducing agent,
iron ore reduction, linear model, linear regression,
melting rate, nonlinear model, oxygen per ton, physical
reaction, support vector regression, symbolic
regression, system identification, top gas composition,
blast furnaces, chemical reactions, identification,
iron, melting, metallurgical industries, regression
analysis, support vector machines",
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DOI = "doi:10.1109/LINDI.2009.5258751",
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abstract = "The blast furnace process is the most common form of
iron ore reduction. The physical and chemical reactions
in the blast furnace process are well understood on a
high level of abstraction, but many more subtle
inter-relationships between injected reducing agents,
burden composition, heat loss in defined wall areas of
the furnace, inhomogeneous burden movement,
scaffolding, top gas composition, and the effect on the
produced hot metal (molten iron) or slag are not
totally understood. This paper details the application
of data-based modeling methods: linear regression,
support vector regression, and symbolic regression with
genetic programming to create linear and non-linear
models describing different aspects of the blast
furnace process. Three variables of interest in the
blast furnace process are modeled: the melting rate of
the blast furnace (tons of produced hot metal per
hour), the specific amount of oxygen per ton of hot
metal, and the carbon content in the hot metal. The
melting rate is largely determined by the qualities of
the hot blast (in particular the amount of oxygen in
the hot blast). Melting rate can be described
accurately with linear models if data of the hot blast
are available. Prediction of the required amount of
oxygen per ton of hot metal and the carbon content in
the hot metal is more difficult and requires non-linear
models in order to achieve satisfactory accuracy.",
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notes = "http://www.fh-ooe.at/lindi2009/ Also known as
\cite{5258751}",
- }
Genetic Programming entries for
Gabriel Kronberger
Christoph Feilmayr
Michael Kommenda
Stephan M Winkler
Michael Affenzeller
Thomas Burgler
Citations