TY - JOUR
T1 - Artificial neural network modelling of continuous wet granulation using a twin-screw extruder
AU - Shirazian, Saeed
AU - Kuhs, Manuel
AU - Darwish, Shaza
AU - Croker, Denise
AU - Walker, Gavin M.
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/4/15
Y1 - 2017/4/15
N2 - Computational modelling of twin-screw granulation was conducted by using an artificial neural network (ANN) approach. Various ANN configurations were considered with changing hidden layers, nodes and activation functions to determine the optimum model for the prediction of the process. The neural networks were trained using experimental data obtained for granulation of pure microcrystalline cellulose using a 12 mm twin-screw extruder. The experimental data were obtained for various liquid binder (water) to solid ratios, screw speeds, material throughputs, and screw configurations. The granulate particle size distribution, represented by d-values (d10, d50, d90) were considered the response in the experiments and the ANN model. Linear and non-linear activation functions were taken into account in the simulations and more accurate results were obtained for non-linear function in terms of prediction. Moreover, 2 hidden layers with 2 nodes per layer and 3-Fold cross-validation method gave the most accurate simulation. The results revealed that the developed ANN model is capable of predicting granule size distribution in high-shear twin-screw granulation with a high accuracy in different conditions, and can be used for implementation of model predictive control in continuous pharmaceutical manufacturing.
AB - Computational modelling of twin-screw granulation was conducted by using an artificial neural network (ANN) approach. Various ANN configurations were considered with changing hidden layers, nodes and activation functions to determine the optimum model for the prediction of the process. The neural networks were trained using experimental data obtained for granulation of pure microcrystalline cellulose using a 12 mm twin-screw extruder. The experimental data were obtained for various liquid binder (water) to solid ratios, screw speeds, material throughputs, and screw configurations. The granulate particle size distribution, represented by d-values (d10, d50, d90) were considered the response in the experiments and the ANN model. Linear and non-linear activation functions were taken into account in the simulations and more accurate results were obtained for non-linear function in terms of prediction. Moreover, 2 hidden layers with 2 nodes per layer and 3-Fold cross-validation method gave the most accurate simulation. The results revealed that the developed ANN model is capable of predicting granule size distribution in high-shear twin-screw granulation with a high accuracy in different conditions, and can be used for implementation of model predictive control in continuous pharmaceutical manufacturing.
KW - ANN
KW - Computational modelling
KW - Continuous pharmaceutical manufacturing
KW - Model predictive control
KW - Wet granulation
UR - http://www.scopus.com/inward/record.url?scp=85013188462&partnerID=8YFLogxK
U2 - 10.1016/j.ijpharm.2017.02.009
DO - 10.1016/j.ijpharm.2017.02.009
M3 - Article
C2 - 28163225
AN - SCOPUS:85013188462
SN - 0378-5173
VL - 521
SP - 102
EP - 109
JO - International Journal of Pharmaceutics
JF - International Journal of Pharmaceutics
IS - 1-2
ER -