Predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree
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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 (for 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.
KeywordsPredictive modeling Die filling Flowability Pharmaceutical granules Flexible neural tree Feature selection
This work was supported by the IPROCOM Marie Curie Initial Training Network, funded through the People Programme (Marie Curie Actions) of the European Unions Seventh Framework Programme.
- 20.Kohavi R, Quinlan JR (2002) Data mining tasks and methods: classification: decision-tree discovery. In: Klösgen W, Zytkow JM (eds) Handbook of data mining and knowledge discovery. Oxford University Press, Inc., pp 267–276Google Scholar
- 22.Weka (2016). http://www.cs.waikato.ac.nz/ml/weka/index.html
- 26.Poli R, Langdon WB, McPhee NF, Koza JR (2008) A field guide to genetic programming. Lulu.comGoogle Scholar
- 27.Shou-Ning Q, Zhao-lian L, Guang-qiang C, Bing Z, Su-juan W (2008) Modeling of cement decomposing furnace production process based on flexible neural tree. In: International conference on information management, innovation management and industrial engineering, 2008. ICIII’08, vol 3. IEEE, pp 128–133Google Scholar
- 28.Chen Y, Wu P, Wu Q (2008) Foreign exchange rate forecasting using higher order flexible neural tree. Artificial higher order neural networks for economics and business. IGI Global Publisher, HersheyGoogle Scholar
- 30.Chen Z, Peng L, Gao C, Yang B, Chen Y, Li J (2015) Flexible neural trees based early stage identification for ip traffic. Soft Comput 1–12Google Scholar
- 31.Ojha VK, Abraham A, Snasel V (2016) Ensemble of heterogeneous flexible neural tree for the approximation and feature-selection of poly (lactic-co-glycolic acid) micro-and nanoparticle. In: Proceedings of the second international Afro-European conference for industrial advancement AECIA 2015. Springer, pp. 155–165Google Scholar
- 33.Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the rprop algorithm. In: IEEE international conference on neural networks. IEEE, pp 586–591Google Scholar
- 35.Zhang J, Pei C, Schiano S, Heaps D, Wu CY (2016) The application of terahertz pulsed imaging in characterising density distribution of roll-compacted ribbons. Eur J Pharm Biopharm, 106(2016):20–25Google Scholar