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Developing ANN-Kriging hybrid model based on process parameters for prediction of mean residence time distribution in twin-screw wet granulation

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Show simple item record Ismail, Hamza Y. Singh, Mehakpreet Darwish, Shaza Kuhs, Manuel Shirazian, Saeed Croker, Denise M. Khraisheh, Majeda Albadarin, Ahmad B. Walker, Gavin M. 2019-05-22T15:15:50Z 2019
dc.identifier.issn 0032-5910
dc.description peer-reviewed en_US
dc.description.abstract Artificial neural network (ANN) modelling is applied to predict the mean residence time of pharmaceutical formulation in a twin-screw granulator. Process parameters including feed flow rate, screw speed, and liquid to solid ratio are correlated with the obtained values of mean residence time to build a predictive tool. In order to improve the ANN predictive capability, a kriging interpolation approach is utilised and both ANN models (before and after kriging) are compared. Experimental data is obtained for wet granulation of microcrystalline cellulose using a bench-scale 12 mm twin-screw granulator. In addition, the effect of screw configurations on mean residence time is investigated by the developed ANN. The ANN model is made of two hidden layers with 2 linear nodes in each layer, and the linear system of equations is derived for the improved ANN model. The results revealed that the developed model was capable of predicting the mean residence time in the granulator more accurately after applying kriging interpolation, with an R2 value of about 0.92 for both training and validation. ANN model after kriging shows a dramatic improvement of R2 by 4% and 22% in training and validating phases, respectively. Also, the RMSE was improved by 40% and 61.5% in training and validating phases, respectively. Furthermore, this improvement was reflected in the contour profiles of the ANN models before and after kriging interpolation, where the model that uses the interpolated data points shows a smoother contour profiles and wider prediction areas. Screw configuration has the most significant effect on the residence time of granules inside the granulator where adding more kneading zones results in a substantial increase in the mean residence time compared to other process parameters. en_US
dc.language.iso eng en_US
dc.publisher Elsevier en_US
dc.relation 13IA1980 en_US
dc.relation.ispartofseries Powder Technology;343, pp. 568-577
dc.rights This is the author’s version of a work that was accepted for publication in Powder Technology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Powder Technology, 2019, 343, pp. 568-577, en_US
dc.subject artificial neural network en_US
dc.subject continuous pharmaceutical manufacturing en_US
dc.subject Kriging en_US
dc.subject model predictive control en_US
dc.subject residence time en_US
dc.subject twin-screw granulator en_US
dc.title Developing ANN-Kriging hybrid model based on process parameters for prediction of mean residence time distribution in twin-screw wet granulation en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US 2019-05-22T15:06:08Z
dc.description.version ACCEPTED
dc.identifier.doi 10.1016/j.powtec.2018.11.060
dc.contributor.sponsor SFI en_US
dc.relation.projectid 13/IA/1980 en_US
dc.relation.projectid NPRP 10-0107-170119. en_US 2020-11-14
dc.embargo.terms 2020-11-14 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 2895297
dc.internal.copyrightchecked Yes
dc.identifier.journaltitle Powder Technology
dc.description.status peer-reviewed

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