Computational intelligence modeling of granule size distribution for oscillating milling

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@Article{Kazemi:2016:PT,
  author =       "Pezhman Kazemi and Mohammad Hassan Khalid and 
                 Jakub Szlek and Andreja Mirtic and Gavin K. Reynolds and 
                 Renata Jachowicz and Aleksander Mendyk",
  title =        "Computational intelligence modeling of granule size
                 distribution for oscillating milling",
  journal =      "Powder Technology",
  volume =       "301",
  pages =        "1252--1258",
  year =         "2016",
  ISSN =         "0032-5910",
  DOI =          "doi:10.1016/j.powtec.2016.07.046",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0032591016304387",
  abstract =     "Oscillating mills such as OscilloWitta (Frewitt) have
                 been widely used in the secondary manufacture of solid
                 dosage forms in the pharmaceutical industry. This type
                 of mill is generally used for moderate milling of
                 difficult-to-process and heat sensitive materials to a
                 particle size range of c.a. 250 micrometers. Particle
                 size distribution is the result of interaction between
                 ribbon properties and process conditions, therefore it
                 is crucial to model and optimize such a complex process
                 in order to produce more uniform particle size
                 distributions. In this work, multiple linear regression
                 (MLR), genetic programming (GP), and artificial Neural
                 Networks (ANN) assisted by 3-fold cross-validation (CV)
                 were used to present generalized models for the
                 prediction of granule size based on the experimental
                 data set. The normalized mean squared error (NRMSE) and
                 the coefficient of determination (R2) for best fit,
                 namely ANN model were obtained as follows: NRMSE =
                 2.28percent, R2 = 0.9926. MLR model was imprecise in
                 the prediction of d10 class. Due to its performance
                 similarities to ANN and its transparency and ease of
                 application, the GP model could be used widely for
                 granule size prediction. Based on the results it was
                 confirmed that the screen size has the most significant
                 effect on the granule size distribution.",
  keywords =     "genetic algorithms, genetic programming, Oscillating
                 milling, Neural network, Roll compaction, Dry
                 granulation, Modeling",
}

Genetic Programming entries for Pezhman Kazemi Mohammad Hassan Khalid Jakub Szlek Andreja Mirtic Gavin K Reynolds Renata Jachowicz Aleksander Mendyk

Citations