Using Genetic Programming for an Advanced Performance Assessment of Industrially Relevant Heterogeneous Catalysts

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

@Article{BBSTLCC09,
  author =       "L. A. Baumes and A. Blansche and P. Serna and 
                 A. Tchougang and N. Lachiche and P. Collet and A. Corma",
  title =        "Using Genetic Programming for an Advanced Performance
                 Assessment of Industrially Relevant Heterogeneous
                 Catalysts",
  journal =      "Materials and Manufacturing Processes",
  year =         "2009",
  volume =       "24",
  number =       "3",
  pages =        "282--292",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Data mining,
                 Heterogeneous catalysis, High-throughput, Materials
                 science",
  ISSN =         "1042-6914",
  publisher =    "Taylor and Francis",
  URL =          "http://lsiit.u-strasbg.fr/Publications/2009/BBSTLCC09",
  DOI =          "doi:10.1080/10426910802679196",
  size =         "11 pages",
  abstract =     "Beside the ease and speed brought by automated
                 synthesis stations and reactors technologies in
                 materials science, adapted informatics tools must be
                 further developed in order to handle the increase of
                 throughput and data volume, and not to slow down the
                 whole process. This article reports the use of genetic
                 programming (GP) in heterogeneous catalysis. Despite
                 the fact that GP has received only little attention in
                 this domain, it is shown how such an approach can be
                 turned into a very singular and powerful tool for solid
                 optimization, discovery, and monitoring. Jointly with
                 neural networks, the GP paradigm is employed in order
                 to accurately and automatically estimate the whole
                 curve conversion vs. time in the epoxidation of large
                 olefins using titanosilicates, Ti-MCM-41 and Ti-ITQ-2,
                 as catalysts. In contrast to previous studies in
                 combinatorial materials science and high-throughput
                 screening, it was possible to estimate the entire
                 evolution of the catalytic reaction for unsynthesized
                 catalysts. Consequently, the evaluation of the
                 performance of virtual solids is not reduced to a
                 single point (e.g., the conversion level at only one
                 given reaction time or the initial reaction rate). The
                 methodology is thoroughly detailed, while stressing on
                 the comparison between the recently proposed Context
                 Aware Crossover (CAX) and the traditional crossover
                 operator.",
  notes =        "Affiliations: Institute of Chemical Technology,
                 CSIC-UPV, Valencia, Spain

                 Louis Pasteur University, LSIIT, FDBT, Illkirch,
                 France",
}

Genetic Programming entries for Laurent Allan Baumes Alexandre Blansche Pedro Serna Ros Ariel Tchougang Nicolas Lachiche Pierre Collet Avelino Corma Canos

Citations