Predicting the particle size distribution in twin screw granulation through acoustic emissions

A non-destructive process analytical technology for monitoring the complex particle size distributions inherent to twin screw granulation (TSG) was presented, based on acoustic emissions (AE). AE spectra were collected dur -ing the wet granulation of lactose monohydrate at different liquid to solid ratios from 8 to 14% and correlated with the particle size distributions (PSD) to train a neural network model. Predicted PSD for particle sizes from 44 to 7000 mu m based on the AE spectra showed the largest root mean squared error of 4.25 wt% at 2230 mu m. After transforming the AE data with a newly created digital filter based on particle impact mechanics to address auditory masking, the maximum error for predicting fractions was reduced to below 1 wt%. This technology shows great promise in predicting the complex size distributions present in TSG in real time. (c) 2021 Elsevier B.V. All rights reserved.