System Identification of Blast Furnace Processes with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Kronberger:2009:LINDI,
  author =       "Gabriel Kronberger and Christoph Feilmayr and 
                 Michael Kommenda and Stephan Winkler and 
                 Michael Affenzeller and Thomas Burgler",
  title =        "System Identification of Blast Furnace Processes with
                 Genetic Programming",
  booktitle =    "2nd International Symposium on Logistics and
                 Industrial Informatics, LINDI 2009",
  year =         "2009",
  month =        "10-11 " # sep,
  address =      "Linz, Austria",
  pages =        "1--6",
  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",
  DOI =          "doi:10.1109/LINDI.2009.5258751",
  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.",
  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