Data-driven Soft Sensors in the process industry

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@Article{Kadlec2009795,
  author =       "Petr Kadlec and Bogdan Gabrys and Sibylle Strandt",
  title =        "Data-driven Soft Sensors in the process industry",
  journal =      "Computers \& Chemical Engineering",
  volume =       "33",
  number =       "4",
  pages =        "795--814",
  year =         "2009",
  ISSN =         "0098-1354",
  DOI =          "doi:10.1016/j.compchemeng.2008.12.012",
  URL =          "http://www.sciencedirect.com/science/article/B6TFT-4VDS8G1-1/2/f93212bf3e52875c4130ab4c34570170",
  keywords =     "genetic algorithms, genetic programming, Soft Sensors,
                 Process industry, Data-driven models, PCA, ANN",
  abstract =     "In the last two decades Soft Sensors established
                 themselves as a valuable alternative to the traditional
                 means for the acquisition of critical process
                 variables, process monitoring and other tasks which are
                 related to process control. This paper discusses
                 characteristics of the process industry data which are
                 critical for the development of data-driven Soft
                 Sensors. These characteristics are common to a large
                 number of process industry fields, like the chemical
                 industry, bioprocess industry, steel industry, etc. The
                 focus of this work is put on the data-driven Soft
                 Sensors because of their growing popularity, already
                 demonstrated usefulness and huge, though yet not
                 completely realised, potential. A comprehensive
                 selection of case studies covering the three most
                 important Soft Sensor application fields, a general
                 introduction to the most popular Soft Sensor modelling
                 techniques as well as a discussion of some open issues
                 in the Soft Sensor development and maintenance and
                 their possible solutions are the main contributions of
                 this work.",
  notes =        "Survey",
}

Genetic Programming entries for Petr Kadlec Bogdan Gabrys Sibylle Strandt

Citations