  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.
