Automatic pyrolysis mass loss modeling from thermo-gravimetric analysis data using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Holladay:2011:GECCOcomp,
  author =       "Kenneth L. Holladay and John Marshall Sharp and 
                 Marc Janssens",
  title =        "Automatic pyrolysis mass loss modeling from
                 thermo-gravimetric analysis data using genetic
                 programming",
  booktitle =    "3rd symbolic regression and modeling workshop for
                 GECCO 2011",
  year =         "2011",
  editor =       "Steven Gustafson and Ekaterina Vladislavleva",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "655--662",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2002063",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Modelling to predict flame spread and fire growth is
                 an active area of research in Fire Safety Engineering.
                 A significant limitation to current approaches has been
                 the lack of thermophysical material properties
                 necessary for the simplified pyrolysis models ...

                 Mode ling to predict flame spread and fire growth is an
                 active area of research in Fire Safety Engineering. A
                 significant limitation to current approaches has been
                 the lack of thermophysical material properties
                 necessary for the simplified pyrolysis models embedded
                 within the models. Researchers have worked to derive
                 physical properties such as density, specific heat
                 capacity, and thermal conductivity from data obtained
                 using bench-scale fire tests such as Thermo-Gravimetric
                 Analysis (TGA). While Genetic Algorithms (GA) have been
                 successfully used to solve for constants in empirical
                 models, it has been shown that the resulting parameters
                 are not valid individually as material properties,
                 especially for complex materials such as wood. This
                 paper describes an alternate approach using Genetic
                 Programming (GP) to automatically derive a mass loss
                 model directly from TGA data.",
  notes =        "Also known as \cite{2002063} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for Kenneth L Holladay Marshall Sharp Marc Janssens

Citations