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",
• year = "2016",
• volume = "301",
• pages = "1252--1258",
• month = nov,
• keywords = "genetic algorithms, genetic programming, Oscillating milling, Neural network, Roll compaction, Dry granulation, Modeling",
• ISSN = "0032-5910",
• URL = "http://www.sciencedirect.com/science/article/pii/S0032591016304387",
• DOI = "doi:10.1016/j.powtec.2016.07.046",
• size = "7 pages",
• 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.",

}

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

Citations