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Predictive Modelling of Die Filling of the Pharmaceutical Granules Using the Flexible Neural Tree

Ojha, VK, Schiano, S, Wu, C, Sn´aˇsel, V and Abraham, A (2016) Predictive Modelling of Die Filling of the Pharmaceutical Granules Using the Flexible Neural Tree Neural Computing and Applications.

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Abstract

In this work, a computational intelligence (CI) technique named flexible neural tree (FNT) was developed to predict die filling performance of pharmaceutical granules and to identify significant die filling process variables. FNT resembles feedforward neural network, which creates a tree-like structure by using genetic programming. To improve accuracy, FNT parameters were optimized by using differential evolution algorithm. The performance of the FNT-based CI model was evaluated and compared with other CI techniques: multilayer perceptron, Gaussian process regression, and reduced error pruning tree. The accuracy of the CI model was evaluated experimentally using die filling as a case study. The die filling experiments were performed using a model shoe system and three different grades of microcrystalline cellulose (MCC) powders (MCC PH 101, MCC PH 102, and MCC DG). The feed powders were roll-compacted and milled into granules. The granules were then sieved into samples of various size classes. The mass of granules deposited into the die at different shoe speeds was measured. From these experiments, a dataset consisting true density, mean diameter (d50), granule size, and shoe speed as the inputs and the deposited mass as the output was generated. Cross-validation (CV) methods such as 10FCV and 5x2FCV were applied to develop and to validate the predictive models. It was found that the FNT based CI model (in the cases of both CV methods) performed much better than other CI models. Additionally, it was observed that process variables such as the granule size and the shoe speed had a higher impact on the predictability than that of the powder property such as d50. Furthermore, validation of model prediction with experimental data showed that the die filling behavior of coarse granules could be better predicted than that of fine granules.

Item Type: Article
Subjects : Chemical & Process Engineering
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
AuthorsEmailORCID
Ojha, VKUNSPECIFIEDUNSPECIFIED
Schiano, SUNSPECIFIEDUNSPECIFIED
Wu, CUNSPECIFIEDUNSPECIFIED
Sn´aˇsel, VUNSPECIFIEDUNSPECIFIED
Abraham, AUNSPECIFIEDUNSPECIFIED
Date : 24 August 2016
Identification Number : 10.1007/s00521-016-2545-8
Copyright Disclaimer : The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2545-8
Uncontrolled Keywords : predictive modeling; die filling; flowability; pharmaceutical granules; flexible neural tree; feature selection.
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 19 Aug 2016 08:32
Last Modified : 30 Nov 2016 10:36
URI: http://epubs.surrey.ac.uk/id/eprint/811739

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