Artificial neural network modelling of continuous wet granulation using a twin-screw extruder.
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 12mm 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.
Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland. Electronic address: saeed.shirazian@ul.ie.
Saeed
S
Shirazian
Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland.
Manuel
M
Kuhs
Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland.
Shaza
S
Darwish
Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland.
Denise
D
Croker
Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland.
Gavin M
GM
Walker
D002626:Chemistry, Pharmaceutical / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer
ANN; Computational modelling; Continuous pharmaceutical manufacturing; Model predictive control; Wet granulation