Artificial neural networks (ANNs) and modeling of powder flow

Abstract

Effects of micromeritic properties (bulk, tapped and particle density, particle size and shape) on the flow rate through circular orifices are investigated, for three pharmaceutical excipients (Lactose, Emcompress and Starch) separated in four sieve fractions, and are modeled with the help of artificial neural networks (ANNs). Eight variables were selected as inputs and correlated by applying the Spearman product-moment correlation matrix and the visual component planes of trained Self-Organizing Maps (SOMs). Back-propagation feed-forward ANN with six hidden units in a single hidden layer was selected for modeling experimental data and its predictions were compared with those of the flow equation proposed by Jones and Pilpel (1966). It was found that SOMs are efficient for the identification of co-linearity in the input variables and the ANN is superior to the flow equation since it does not require separate regression for each excipient and its predictive ability is higher. Besides the orifice diameter, most influential and important variable was the difference between tapped and bulk density. From the pruned ANN an approximate non-linear model was extracted, which describes powder flow rate in terms of the four network's input variables of the greatest predictive importance or saliency (difference between tapped and bulk density ( x 2), orifice diameter ( x 3), circle equivalent particle diameter ( x 4) and particle density ( x 8)): W=b 0+{a 1[1+ exp(b 2x 2+b 3x 3+b 4x 4+b 8x 8)] −1+a 2[1+ exp(b′ 3χ 3+b′ 8x 8)] −1}.