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 during 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 μm based on the AE spectra showed the largest root mean squared error of 4.25 wt% at 2230 μm. 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.

提出了一种基于声发射 (AE) 的非破坏性过程分析技术，用于监测双螺杆造粒 (TSG) 固有的复杂粒度分布。AE 光谱是在乳糖一水合物的湿法制粒过程中以 8% 到 14% 的不同液固比收集的，并与粒度分布 (PSD) 相关联以训练神经网络模型。基于 AE 光谱的 44 至 7000 μm 粒径的预测 PSD 在 2230 μm处显示最大均方根误差为 4.25 wt%米。在使用基于粒子冲击力学的新创建的数字滤波器转换 AE 数据以解决听觉掩蔽问题后，预测分数的最大误差降低到 1 wt% 以下。该技术在实时预测 TSG 中存在的复杂尺寸分布方面显示出巨大的希望。